ADVANCES IN MINE TAILINGS AND WATER MANAGEMENT IN THE MINING INDUSTRY FOR A CIRCULAR ECONOMY Natalia Andrea Araya Gómez Thesis presented in accordance to the requirements to obtain the degree of Ph.D. in Mineral Process Engineering Supervisors Luis Cisternas Ph.D. - Andrzej Kraslawski Ph.D. Laboratory of Optimization and Modelling Department of Chemical and Mineral Process Engineering Universidad de Antofagasta Antofagasta, Chile 2020
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ADVANCES IN MINE TAILINGS AND WATER
MANAGEMENT IN THE MINING INDUSTRY
FOR A CIRCULAR ECONOMY
Natalia Andrea Araya Gómez
Thesis presented in accordance to the requirements to obtain the degree of Ph.D. in
Mineral Process Engineering
Supervisors
Luis Cisternas Ph.D. - Andrzej Kraslawski Ph.D.
Laboratory of Optimization and Modelling
Department of Chemical and Mineral Process Engineering Universidad de Antofagasta
Antofagasta, Chile
2020
AVANCES EN EL MANEJO DE RELAVES Y DE
AGUA EN LA INDUSTRIA MINERA PARA UNA
ECONOMÍA CIRCULAR
Natalia Andrea Araya Gómez
Tesis para optar al grado de Doctor en Ciencias de la Ingeniería de Procesos de
Minerales
Profesor Patrocinante Luis Cisternas Ph.D.
Profesor copatrocinante
Andrzej Kraslawski Ph.D.
Laboratorio de Optimización y Modelamiento Departamento de Ingeniería Química y Procesos de Minerales
Universidad de Antofagasta Antofagasta, Chile
2020
Supervisors
Professor Luis Cisternas Arapio Departamento de Ingeniería Química y Procesamiento de Minerales Universidad de Antofagasta Chile
Professor Andrzej Kraslawski
Industrial Engineering and Management
LUT School of Engineering Science
Lappeenranta-Lahti University of Technology
Finland
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Abstract
Natalia Araya Gómez
The mining industry consumes vast quantities of water and energy to produce a metal
or mineral product from mineral resources on the earth’s crust, leaving an enormous
amount of mining waste.
The linear thinking of the economy needs to be replaced with the circular economy to
achieve sustainable development goals. In this context, the mining industry needs to
improve in several aspects, such as reducing primary resource consumption and
improve the efficiency of its processes. Strategies to reduce water and energy
consumption, recycle water and wastes, and to recover energy are needed.
The objective of this thesis is to develop methodologies to advance towards the
circular economy in mining, with a focus on mine tailings and water management.
The strategies and tools proposed are meant to be applied in mining processes to
mitigate environmental impacts.
The strategies proposed in water management include an integrated water
distribution network to supply water to several mine plants while recovering energy
using energy recovery devices. The method used is mathematical optimization to
design the water distribution network. The methodology is validated with a case
study that corresponds to an area of the Antofagasta Region. Results show that the
optimal solution is an integrated system with energy recovery devices in areas with a
complex topography where energy production is feasible.
Reducing waste is a key component of the circular economy. Mine tailings are the
main waste produced by the mining industries. Re-processing of mine tailings to
obtain critical materials is proposed, the methodology is an economic assessment
validated with a case study of tailing deposits of the Antofagasta Region. The
economic evaluation considers the discounted cash flow method, sensitivity analysis,
and real options analysis. Results show that an investment based on re-processing
mine tailings to obtain critical materials is feasible in some cases.
The contribution of this thesis is a collection of methodologies to improve mine
tailings and water management to improve mining processes. About mine tailings
management, the strategy proposed is re-processing mine tailings, which will reduce
the amount of waste produced by mining plants and will reduce the need for mining
primary ores. In the case of water management, the mining industry needs to reduce
the demand for freshwater. To supply seawater is still an expensive option but having
an integrated water distribution network with energy recovery devices will reduce
the total cost of the network.
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Keywords: Water Distribution Networks, water management, mine tailings, mining,
Mixed Integer Linear Programming (MILP), dynamic programming, and heuristic-
based optimization methods (Amit and Ramachandran, 2009; D’Ambrosio et al.,
2015).
3.1.2. Problem Statement
To achieve objective number 1, a methodology was designed to represent the WDN,
including the selection of desalination plants, specifically RO plants, to supply mining
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sites. The model includes energy recovery devices for energy production located in
areas where due to the difference of altitude between 2 points is possible to produce
energy like in the hydropower industry, these energy recovery devices are known as
PATs. Additionally, parallel pipelines are considered to split the water flow when
needed.
Mine sites are located away from the coast; these sites have a water requirement that
needs to be fulfilled with desalinated water coming from RO plants located on the
coast. The area features a complex topography, which means several changes in
altitude from the coast until the mine site. The only option to fulfill the water
requirement needed is desalinated water from RO plants. The methodology proposed
by Herrera-León et al., 2019 was used as a reference for this methodology.
3.1.3. Mathematical Formulation
A superstructure was used to represent the WDN, which includes RO plants,
pipelines, pump stations, PATs, and the mine sites as nodes. Distances between nodes
and altitude of each node were designed base on topography data obtained in Google
Earth. The superstructure also includes the option to use parallel pipelines.
The objective function minimized the total annualized cost of RO plants, pipes, pump
stations, and PATs required to supply several mine sites located in an isolated area far
away from the coast. The objective function is not linear, so the problem results in an
MINLP problem, which is transformed into a MILP problem using a piecewise
methodology extracted from Lin et al., 2013. This methodology allows converting a
non-linear programming problem into a linear programming problem or a mixed-
integer convex programming problem for obtaining an approximated global optimum
solution.
3.2. An economic evaluation of re-processing of mine tailings to
obtain critical materials
This section is intended to explain the methodology designed to complete objectives
2 and 3. It also presents an overview of the methods applied in publications 2 and 3.
3.2.1. Project valuation Tools
Project valuation is probably the most crucial part of the selection process because it
assigns a monetary value to the project. Broadly defined, the project value is the net
difference between the project revenues and costs over its entire life cycle. If the net
revenues of the project during the production phase are higher than the investment
costs, the project is considered worthy of investment (Kodukula and Papudesu, 2006).
The quality of the project valuation will depend on how they effectively include these
three factors:
Cash flow streams through the entire life cycle of the project
The discount rate used to discount future cash flows to account for their
uncertainty
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Availability of management’s contingent decisions to change the course of the
project
Project cash flows represent the revenues and the costs of the project through the
entire life cycle of the project. The costs should include the initial investment for the
project and the production phase.
Valuation methods assume that money today is worth more than in the future, so to
acknowledge that a discount rate is used. The discount rate is the rate that is utilized
to convert the future value of the project cash flows to today’s money. The discount
rate is adjusted to the risk perceived to be associated with the project, so the higher
the risk, the higher the discount rate (Kodukula and Papudesu, 2006).
3.2.2. Discounted Cash Flow (DCF)
Discounted Cash Flow (DCF) is a valuation method that is based on calculating future
cash flows of a project to decide whether it is feasible or not to invest. DCF is based on
the estimation of the Net Present Value (NPV) of the project in its entire project life
(Kodukula and Papudesu, 2006).
NPV is the preferred metric for project valuation under most circumstances (Arnold,
2014). To calculate NPV, cash outflows, which are the costs of the project, are deducted
from the cash inflows, which represents the revenues of the project. Additionally, all
cash flows are discounted to generate NPV. If the NPV is positive, then the project is
feasible because the revenues exceed the expenses of the project. On the contrary, if
the NPV is negative, then the project is not feasible because the costs are higher than
the revenues. If the NPV is zero, then the return of the project is equal to the expenses.
Traditional valuation tools, including the DCF method, have been employed for many
decades in the economic evaluation of projects. Although these methods have been
effective in many cases, under specific conditions, they have certain challenges. One
of the biggest dilemmas when using DCF is to choose a discount rate. The main factors
that determine the discount rate for a given cash flow stream is the magnitude and the
type of risk (Kodukula and Papudesu, 2006). An important consideration to determine
the discount rate is whether there is uncertainty associated with the cash flow streams.
There are several methods designed to consider uncertainty when calculating the
NPV, such as: to increase the discount rate, to apply sensitivity analysis, to compare
pessimistic and optimistic cash flows or, to estimate the expected cash flows through
scenario planning and the probability distribution (Gaspars-Wieloch, 2019).
3.2.3. Real Options Analysis (ROA)
When there is considerable uncertainty related to the project cash flows, and
contingent decisions are involved, traditional tools don’t include the flexibility to
change the course of the project and its possible outcome (Kodukula and Papudesu,
2006). ROA addresses the issue of choices a manager may have throughout the life of
the project and how those choices can enhance the value of a project (Arnold, 2014).
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ROA is a complement to traditional methods as the DCF method, which seeks to
analyze different possible choices for an investment project to give flexibility to the
project, unlike conventional valuation methods. A real option is a right, but not an
obligation, to undertake business initiatives that are connected to and exist on real
assets or within real assets (Trigeorgis, 1993).
ROA adds value to an economic analysis like the DCF method by considering different
options such as wait to invest, abandon an investment, or expand a business. Then,
more opportunities and flexibility are available when a project is surrounded by
considerable uncertainty.
ROA can be applied using different methods such as Black & Scholes Equation,
Decision trees or binomial trees, Datar-Mathews model, which uses cash flow
scenarios combined with Monte Carlo simulation, and Fuzzy Pay-off Method (Collan,
2011).
3.2.4. Sensitivity Analysis
As a complement to the DCF method, sensitivity analysis (SA) can be performed on
the NPV to explore the sensitivity of key input parameters. Sensitivity analysis is the
study of how the uncertainty in the inputs of a mathematical model or system can be
distributed into different sources of uncertainty in its inputs. Alongside sensitivity
analysis, uncertainty analysis is usually performed. Uncertainty analysis assesses the
uncertainty in model outputs that derives from the uncertainty in the inputs. Methods
to perform SA include simulation and scenario techniques. Global sensitivity analysis
methods consider the changes in model outputs as input factors change all together
over specified ranges. These methods have the ability to manage non-linear and non-
additive responses and to observe relationships between multiple factors.
Monte Carlo Simulation is a method that consists of the simulation of thousands of
possible project scenarios, calculation of the NPV for each scenario using the DFC
method, and analyzing the probability distribution of the NPV results (Kodukula and
Papudesu, 2006).
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4. Publications and Results Reviews An overview of the publications is included in chapter 4, which includes the
objectives, findings, and contributions of each publication of this doctoral dissertation.
The overall purpose of the dissertation is to…
4.1. Publication I: Design of desalinated water distribution networks:
Complex topography, energy production, and parallel pipelines
4.1.1. Research Objective
The objective of this publication is to provide a methodology to design the water
distribution network (WDN) and reverse osmosis (RO) plants to supply mining
companies with desalinated water, the mathematical model considers the following
elements to design the WDN: energy recovery devices, parallel pipelines, and complex
topography. Energy recovery devices considered in the model are PATs for producing
energy in the network using hydropower principles. Parallel pipelines are considered
when the water requirement is large and cannot be supplied with one pipeline. In this
study complex topography means
4.1.2. Contributions
PATs are often used in urban water distribution networks to reduce pressure, and
there are some applications to produce energy. The main contribution of this study is
proposing the use of energy recovery devices in areas with the mining industry where
they are yet not used. These devices can be used in areas with complex topography,
meaning isolated areas and presents changes in elevation as the pipelines go from the
seashore to the mining site. Mining sites are usually located in areas suffering from
water scarcity; thus, the use of desalinated water is a must.
A methodology to simultaneously design the WDN to supply several mining sites
located in an area with complex topography, additionally to find the location and sizes
of desalination plants, pipelines, pump stations, and energy recovery devices, was
addressed. A case study based on an area in northern Chile was used to validate the
model. A superstructure was used to represent the WDN, which includes RO plants,
pipelines, pump stations, and energy recovery devices. The objective of the model is
to minimize the total annual cost by finding the optimal WDN.
Results show the use of PATs in a region with complex topography is indeed feasible
for producing energy in the WDN in areas like the Coastal Cordillera, where larger
elevations in altitude are often avoided by mining companies. The use of parallel
pipelines is a feasible option to supply with desalinated water to several mining sites,
instead of having their own RO plant and own pipeline system for each mining site.
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4.2. Publication II: Towards mine tailings valorization: Recovery of critical
materials from Chilean mine tailings
4.2.1. Research Objective
This study aims to conduct a technical and economic assessment of the valorization of
mine tailings of Chile as a source of critical materials. In recent years, the use of
secondary sources, such as mining waste and electrical waste, has gained importance.
This research adds knowledge to that field of study by adding a novel techno-
economic assessment for producing critical materials by re-processing mine tailings.
4.2.2. Contributions
Results show that mine tailings facilities of the copper industry in Chile store valuable
elements such as critical materials. Therefore, the evaluation of geochemical content
alongside the identification of suitable technologies, and an economic analysis will
help to find alternative sources of critical materials, as is the case of re-processing mine
tailings with such purpose.
The DCF method is a method widely used in economic assessment but is not a decisive
metric for a final decision on real investment. To ensure the robustness of the
assessment, a sensitivity analysis was performed on the results of the NPV, by
analyzing the effect of the market prices of critical materials, capital, and operating
costs on the options assessed for producing critical materials. The options analyzed
were producing rare earths concentrate or producing vanadium pentoxide. Results
show that under certain conditions the production of vanadium pentoxide is feasible.
The main contribution of this study is to show the economic potential of Chilean mine
tailings by performing a techno-economic assessment of the valorization of tailings.
4.3. Publication III: Feasibility of re-processing mine tailings to obtain critical
materials using real options analysis (submitted in Sustainable
Development)
4.3.1. Research objective
This study aims to propose a framework to assess the feasibility of re-processing mine
tailings to obtain critical materials, considering flexibility aspects, such as waiting for
investment in the case of a negative outcome in the present.
The NPV estimated with the DCF is used as a starting point; then, with ROA, different
outcomes for an investment project are assessed. ROA is applied using binomial tree
analysis. The binomial tree is built by using the method of risk-neutral probabilities.
For re-processing mine tailings, technology development and business models are still
needed. To study the influence of several variables on the NPV outcome, Monte Carlo
simulation is applied to perform sensitivity and uncertainty analyses.
The framework developed in this study is applied to a case study based on active mine
tailings located in the Antofagasta Region in Northern Chile. This region features
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several mining projects based on copper resources, leaving tremendous volumes of
mine tailings that are stored without any economic purpose.
4.3.2. Contributions
The novelty of this study is to consider ROA and sensitivity analysis to provide
flexibility to the economic assessment of re-processing mine tailings, acknowledging
the uncertainties involved. Traditional valuation tools such as the DCF method are
statistic methods that do not consider the uncertainty of the variables used to estimate
the profitability of an investment.
ROA gives additional value, so decision-makers can explore alternatives while
waiting to uncertainty to clear off to invest, to re-estimate the project payoff, or they
can decide to abandon a project.
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5. Conclusions Reducing primary ore extraction and reducing waste are part of the strategies that the
mining industry needs to apply for implementing sustainable manufacturing.
Reducing approaches in every manufacturing and processing industry is the core of
the circular economy. Re-processing mine tailings will reduce the amount of waste
that a mining plant left behind, and it will reduce the need for primary mining ores.
Another challenge for the mining industry is to reduce and optimize the demand for
freshwater, especially in arid zones where water is a scarce resource. The purpose of
this thesis is to develop methodologies to improve water and mine tailings
management in mining processes.
The conclusions, related to the research questions, are presented below:
RQ 1.1. Is an integrated water supply system that includes energy recovery devices a
feasible option in areas with complex topography?
A methodology was developed to simultaneously design a water distribution network
to supply mining plants located in an area with complex topography with desalinated
water, as well as the location and size of desalination plants, pipelines, pump stations,
and PATs stations. The methodology was validated with a case study that
corresponds to a geographic area located in the Antofagasta region, which includes
four mining plants (Araya et al., 2018). This area features several changes in altitude,
as is located in the Atacama Desert, which is cross by the Coastal Cordillera.
The optimal solution resulted in a WDN that considers one desalination plant to
supply the four mining plants, as well as PATs located in locations with altitude.
Additionally, the optimal solution included the use of parallel pipelines. A ranking of
solutions was presented, which showed that an integrated system prevails to the
traditional water supply system, which consists of a desalination plant and a set of
pumping stations and pipelines to each mining plant (Araya et al., 2018).
RQ 1.2. Is it economically and technically feasible to use energy recovery devices in the
water supply system?
The use of energy recovery devices, in this case, PATs is feasible in the presence of a
great difference in altitude between two nodes. Having an integrated system to supply
desalinated water to several mining plants reduces the costs of the water supply
system and enhances the industrial symbiosis between different companies.
Moreover, the use of PATs can reduce the costs by producing energy, which can be
used in the water distribution network (Araya et al., 2018, 2017).
The difference of altitude between two nodes, in which the option of using PATS was
considered, was varied to understand in which situations PATs are a feasible option,
from both technical and economic aspects. This analysis showed that there is a
difference in altitude where the option of PATs is no technically possible(Araya et al.,
2018).
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RQ 1.3. When using parallel pipes is a feasible option in the water supply system?
Parallel pipes are needed when the water flow is larger than the maximum water flow
allowed in industrial pipelines, so in the case of an integrated water supply system
using parallel pipes is a feasible option to supply water to several mining companies.
The optimal solution features parallel pipes, as a set of three pipes and two pipes.
Furthermore, the three first solutions include parallel pipes, demonstrating that
parallel pipes are needed when having to supply several mines (Araya et al., 2018).
RQ 2.1. What critical materials can be recovered from mine tailings?
Mine tailings contain several elements, including some critical materials. A
methodology to assess the valorization of mine tailings was developed, with the focus
on the recovery of critical materials. The methodology was validated with a case study
that corresponds to inactive mine tailings deposits located in the Antofagasta region
(Araya et al., 2020). Mine tailings of copper mining in Chile contains several critical
materials, including rare earths, vanadium, cobalt, and scandium. A ranking of critical
materials that could be extracted from mine tailings was presented, the critical
materials were evaluated according to the quantity present in tailings deposits and
the price of the critical material. This analysis showed that rare earths, cobalt,
scandium, and vanadium could be extracted from inactive mine tailings in the
Antofagasta Region, due to their price and quantity (Araya et al., 2020).
RQ 2.2. What are the challenges in the production of critical materials using mine tailings
as a source?
Emerging technologies for recovering critical materials from mine tailings were
reviewed, showing that Technology development is still much needed to make the
recovery of critical materials from mine tailings at an industrial scale. At this time,
most of the research is still in the laboratory and pilot-scale (Araya et al., 2020). Further
research should address new strategies to anticipate the future use of material beyond
the closing of a mine (Lèbre et al., 2017).
RQ 2.3. Is it economically feasible to invest in a project developed around the idea of re-
processing mine tailings to obtain critical materials?
The economic assessment developed to assess the production of critical materials from
mine tailings showed that, in certain conditions, the recovery of critical materials is
feasible. DCF method was used to calculate the NPV of two projects, one based on the
production of rare earths concentrate and another one based on the production of
vanadium pentoxide. Results showed that a project based on the recovery of
vanadium pentoxide is feasible (Araya et al., 2020).
Alongside the DCF method, a sensitivity analysis was performed on the inputs of
NPV, to understand which variables have a more significant effect on the NPV. The
inputs variables studied were capital expenditures, operational expenditures, price of
the critical material, and discount rate (Araya et al., 2020).
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RQ 3.1. How can real options improve the decision to invest in a project using mine tailings
as a source of critical materials?
A project based on the recovery of critical materials from mine tailings can have great
uncertainty in their inputs. ROA complements traditional valuation methods such as
the DCF, by including other options for investment. ROA provides flexibility to a
project based on the re-processing of mine tailings, acknowledging the uncertainties
involved.
RQ 3.2. How can the net present value variables influence real options performance?
The inputs of the NPV can have uncertainties that affect the outcome. Using the NPV
as a decisive metric for investments can lead to errors in the decision-making process.
The NPV is used as a starting point to ROA since it is a complement to traditional tools
rather than a method by itself.
5.1.Theoretical Contribution of the Study
This thesis contributes with a collection of methodologies to assess water management
and mine tailings management in mining. The mining industry is facing many
challenges as industries need to fulfill sustainable development goals to achieve a
circular economy.
The first contribution of this thesis is to provide a methodology to design integrated
water supply systems to provide desalinated water to several mining plants. The
novel contribution of this methodology is to include the option of placing energy
recovery devices in locations where the difference of altitude can be advantageous to
produce energy as it is done in hydropower plants. Additionally, the methodology
includes parallel pipes to fulfill the demand of several mine plants at the same time.
The other contributions of the thesis are embedded in the field of mine tailings
management. These contributions are a methodology to assess the feasibility of re-
processing mine tailings to obtain critical materials and a methodology to assess the
feasibility of re-processing mine tailings with a real options approach. The novelty of
theses methodologies is to assess the feasibility of projects based on the idea of
obtaining critical materials from a secondary source such as mine tailings.
5.2.Limitation and Future Research Suggestions
A limitation of this study is the difficulty to find data about technologies suitable for
recovering critical materials from mine tailings. Data about capital expenses,
operational expenses, and prices are not easy to find.
Another limitation is that all the methodologies presented in this thesis are validated
with case studies of the copper mining industry in the Antofagasta Region. These
methodologies could be applied to other case studies, but some considerations and
changes should be made.
Future research suggestions will be focused on implementing the circular economy in
mining processes with a focus on mine tailings management. Mine tailings are the
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biggest sink of water of the mineral processing, so efforts should be made to create a
framework that integrates water management for mine tailings with the re-processing
of mine tailings. Future studies should also incorporate an environmental assessment
of projects based on the re-processing of mine tailings.
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Design of Desalinated Water Distribution Networks: ComplexTopography, Energy Production, and Parallel PipelinesNatalia Araya,† Freddy A. Lucay,† Luis A. Cisternas,† and Edelmira D. Galvez*,‡
†Depto. de Ing. Química y Procesos de Minerales, Universidad de Antofagasta, Antofagasta, Chile‡Depto. de Ing. Metalurgia y Minas, Universidad Catolica del Norte, Antofagasta, Chile
ABSTRACT: A methodology was developed to determinate thelocation and size of desalination plants, the water distributionnetwork, and the location and size of energy recovery devices toprovide desalinated water in regions with complex topography. Thenovelty of this methodology is that energy recovery devices such aspumps as turbines are incorporated to produce energy. Anothernovelty is the consideration of using multiple pipelines. Themethodology proposed uses a superstructure with a set ofalternatives in which the optimal solution is found. A mathematicalmodel is generated that corresponds to a mixed integer nonlinearprogramming (MINLP) problem, which is linearized to become amixed integer linear programming (MILP) problem solved usingthe CPLEX solver in GAMS. A case study is presented todemonstrate the applicability of the methodology to real sizeproblems.
1. INTRODUCTION
Water and energy are vital elements to the well being ofsociety. At a basic level, electricity generation requires water,and water treatment and transport require electricity. World-wide energy consumption destined for water supply represents7% of global energy consumption.1 Hamiche et al.2 reviewedthe nexus between water and energy comprehensively andpresented a classification system; their work suggested that thisnexus should be explored widely.Nowadays, fresh water sources are scarce, and nontraditional
water sources are utilized more every day to fulfill theincreasing demand for both human and industrial consump-tion. The advance in desalination processes, reduction ofmembrane cost, and lower energy consumption have madeseawater a viable source of freshwater.3
Reverse osmosis (RO) is a feasible option to provide waterfor isolated and desert areas where there is access to seawater.RO is the desalination process most utilized worldwide; morethan 50% of desalination plants correspond to RO plants dueto its simplicity and because its energy requirements are minorwhen compared to distillation-based thermal processes.4
The transport of desalinated water in areas with complextopology, e.g., areas with mountains, is a major challenge forseveral reasons. On the one hand, the transport costs due tothe elevations are significant, which can be several times thecost of desalting, and on the other hand, finding only upwarddistribution systems is difficult. An example corresponds to thenorth of Chile (Antofagasta Region) where there are severalmining plants located at high altitude in a desert area. Fromthis area, Chile produces more than half of the Chilean copper
production (35% of the world’s copper production) and othermetallic elements like silver, gold, and molybdenum andnonmetallic minerals like potassium nitrate, lithium carbonate,and boron. The Antofagasta Region is located in the AtacamaDesert, the driest nonpolar desert of the world, and water isneeded to process these ore resources. Water is a limitedresource due to the overexploitation of groundwater sources.On the other hand, the Antofagasta Region has a large seacoast; therefore, seawater has become the main source of freshwater for the mining industry and urban populations.The mines are often located far from the coast and, more
importantly, at high altitudes. These changes in elevation arethe result of the presence in the coast of the Coastal Cordillera,which is a mountain range, and the presence of the AndesMountains. Mine elevations vary between 1000 and 4000 mabove sea level. In the region, there are several mining plantsalready using desalinated water obtained through RO. Eachmining company has a desalination plant with a waterdistribution network (WDN) to provide fresh water for itsprocesses without any integration between companies. Theneed to develop a more efficient desalination and WDNsystem, for example, through the integration of several WDNs,motivates this work.
Special Issue: 2017 European Symposium on Computer-AidedProcess Engineering
Received: December 22, 2017Revised: June 5, 2018Accepted: June 7, 2018Published: June 7, 2018
There have been several works regarding the design of ROplants and water supply systems. The methodologies to designwater supplies systems usually result in mixed integer nonlinearproblem (MINLP) models. Amit and Ramachandran5 realizeda review of the current status of optimization models fordesigning water distribution networks and presented recom-mendations for future research; Coehlo and Andrade-Campos1
provided a review about methods to achieve water supplysystem efficiency, which also included design optimization.Khor et al.6 provided state of the art water networks synthesisfocusing on a single site and continuous process problems,where major modeling and computational challenges werediscussed exploring issues such as nonconvexity and non-linearity.El-Halwagi7 introduced the novel notion of synthesizing RO
networks applied to waste reduction. The synthesis wasformulated as MINLP, and the objective of this work was tominimize the total annualized cost of RO networks. Since then,considerable research has been made regarding the design ofRO plants and water distribution systems.Liu et al.8 considered using desalinated water, wastewater,
and reclaimed water to supply water deficient areas using amixed integer linear programming (MILP) model taking intoaccount geographical aspects of the region; however, this workdid not consider using energy production. Ahmetovic andGrossman9 proposed a general superstructure and a model tooptimize integrated process water networks using MINLP andNLP models, considering multiple sources of water anddifferent qualities of water; the methodology was applied indifferent case studies. Atilhan et al.10 proposed a novelapproach for the design of desalination and water distributionnetworks considering first a source-interception sink repre-sentation; then, an optimization problem was formulatedresulting in a NLP problem whose objective was to meet therequirements for the sinks at the minimum cost while satisfyingconstraints for the sinks. The methodology was applied to acase study. Lira-Barragan et al.11 proposed that mathematicalprogramming models for synthetizing WDN associated withshale gas production take into account uncertainties, and theproposed superstructure for water integration allows themanagement of fresh water consumption and wastewaterstreams. Liang et al.12 proposed a convex model, whichcorresponded to a MINLP problem, to obtain the optimaldesign of water distribution systems using the Hanoi network,which is a known problem of the looped water distributionnetwork as a case study.13 Gonzalez-Bravo et al.14 proposed amultiobjective optimization approach for synthesizing waterdistribution networks considering domestic, agricultural, andindustrial users involving dual purpose plants consideringenvironmental, economic, and social aspects. Gonzalez-Bravoet al.15 presented an approach to design water and energydistribution networks based on a multistakeholder environ-ment where a multiobjective model considering economic,environmental, and social impacts was applied in a stressedscheme, and the proposed method identified the optimalsolution to minimize the dissatisfaction level of the involvedstakeholders.The WDN including RO selection in complex topography
has not been studied in depth. Herrera et al.16 developed amodel to design water distribution networks considering ROplants to supply mining plants located in high areas that areisolated and arid, up to 4000 m above sea level. They used asuperstructure to design the whole system using a MINLP
model. The methodology was demonstrated with a case study,and its application was adaptable to similar problems.However, they did not consider WDN design including siteswith changes in the elevations resulting from the presence ofmountains.The use of pumps as turbines (PATs) can be an option of
producing energy in the network that can be used to supplyenergy for water pumping. Pumps operating as turbines can beused in water distribution systems to reduce pressure instead ofusing pressure reducing valves. Additionally, they can produceelectricity.17
There are some reviews on energy efficiency in water supplysystems that include hydropower generation and PATsimplementation. Vilanova and Balestieri18 presented modelsof hydropower recovery in water supply systems that could beapplied in a water distribution network design. Zakkour et al.19
reviewed some emerging technologies and practices forsustainable water utility. McNabola et al.20 presented a reviewof energy use in the water industry and opportunities formicrohydropower (MHP) energy recovery.Vilanova and Balestieri21 evaluated the possibilities and
benefits of recovering and producing energy in water supplysystems. They illustrate technical, economic, and environ-mental aspects of hydropower recovery in water supply systemsusing a case study. Corcoran et al.22 developed a methodologyto find the optimal location of turbines in a water distributionnetwork using a MINLP approach and an evolutionaryapproach as a comparison. Tricarico et al.17 presented amethodology to implement PATs combined with pumpscheduling to recover energy and pressure water regime in awater distribution network, showing clear economic benefits.De Marchis et al.23 analyzed the application of PATs in a waterdistribution network using a hydrodynamic model. The modelwas demonstrated to correctly represent the impact of energyrecovery on water supply distribution. Carravetta et al.24
presented the implementation of microhydroelectric plantswhich included PATs in urban water networks. The project ofa small hydroelectric plant was performed at the inlet node of areal network. The case study showed that the installation ofsmall hydroelectric plants could provide interesting economicbenefits for the manager of pipe networks in urban areas. Noneof the works reviewed analyzed the implementation of PATs ina WDN on a larger scale; furthermore, none consideredproducing energy like that in the hydropower industry fromthe downfall of water in a WDN that included RO plants andindustrial sites with a large requirement of water.Parallel pipelines are arranged to divide flow and to keep the
head loss at the same rate, meaning that head loss in all parallelpipes remains the same.25 The reliability of a water distributionnetwork can be improved by adding a parallel pipe to anexisting one.26 In actual industrial operations, more than onepipeline is often utilized to provide higher flows, mainly inmining operations which require a great amount of water intheir operations, so one pipeline cannot satisfy the waterrequirements. Parallel pipes must be included in the WDNdesign.The objective of this work is to provide a methodology to
design the WDN and selection of RO plants to supplyindustrial plants with desalinated water. The developed modelfor this methodology considers using energy recovery devicessuch as pumps as turbines for energy production in thenetwork in places where there is a considerable difference ofheight that allows producing energy like in the hydropower
Industrial & Engineering Chemistry Research Article
industry. Another novelty of the work is to considerer morethan one pipeline in cases where flows are high.Pumps as turbines are usually used in urban water
distribution networks to reduce pressure, and there are someapplications of energy production. The novelty of this workrelies in proposing the use of energy recovery devices in areaswhere they are usually not used, meaning areas that have acomplex topography that are also isolated and suffering fromwater scarcity, so the only source of fresh water is desalinatedwater.
2. PROBLEM STATEMENTIndustrial sites such as mining plants are water consumers thatare located far away from the coast, in areas with a complextopography; by complex, this refers to an area with changes inelevation between the RO plants and water consumers. Waterdemand of industrial sites can only be satisfied with desalinatedwater from RO plants located along the coast; no other optionwas taken into account as the industrial sites are located inisolated areas, which means that water is a scarce resource. Themethodology used by Herrera et al.16,27 was used as a referencein this work.A superstructure was used to represent the WDN, which
included RO plants; pipelines; nodes with pump stations,pumps as turbines, or just a junction; and industrial sites. Somenodes of the WDN are located in mountains where there is apronounced altitude, so pipelines have to ascend and thendescend, so the downfall of water is used to produce energywith the passage of water through a pumping station withpumps as turbines (PATs) like in Figure 1. The possibility of
producing energy with water passing through the downstreamwas evaluated and compared to water ascending through theWDN until reaching industrial sites in the traditional way ofavoiding the descend of water.Another consideration in the superstructure is to use more
than one pipeline until three pipelines are allowed; the choicebetween using one, two, or three pipelines depends mainly onthe requirements of water of each consumer’s site. There is amaximum velocity allowed in a pipeline, which is used as arestriction in the model. The diameters of the pipelines aredetermined by the model by choosing between a set of discretediameters that are commercial sizes.The objective of the model was to minimize total annual
cost and find the locations and sizes of RO plants, locationsand sizes of pumping stations, locations of PATs, pipediameters, operational conditions of pumping stations, andnumber of pipelines in the WDN in order to providedesalinated water to industrial sites.
3. MATHEMATICAL FORMULATIONFour sets were used to represent the superstructure: RO plantsSO = {so| so is an RO plant}; nodes which can be pumpingstations, pumping stations working as turbines, or just ajunction without a pumping stations, so N = {n|, n is a node};industrial sites or water consumers SI = {si|, si is industrialsite}; and the set of diameters D = {d|, d is a diameter}.Distances and elevations of each point were designated basedon the topography data obtained using Google Earth, anddistances were defined as Li,j and elevations as ΔZi,j.Water flow is only in one direction, from RO plants located
along the shore to the user that are industrial sites, which areusually mining plants. Bidirectional flow makes no sense giventhe high costs of transporting water from a low altitude to ahigh altitude location. Only feasible connections betweennodes were considered in the WDN.Known parameters are the water requirements of industrial
sites, according to the needs of its facilities, water capacities ofRO plants, altitude of each node, and distances between eachnode.The model has equations of continuity for RO plants, nodes,
and mining plants; these equations are presented as eqs 1−3
∑* = ∀ ∈∈
Q Q so SOson N
so n,(1)
∑ ∑= ∀ ∈∈ ∈
Q Q n Ni input
i nj output
n j, ,(2)
∑* = ∀ ∈∈
Q Q si SIsin N
n si,(3)
where Qi,j is the volumetric flow pumped from i to j;constraints associated with the maximum production capacityof RO plants and desalinated water demands of the miningplants were also included in the model.
3.1. Pipe Diameter. For selecting the pipe diameter frompoints i to j, a disjunction expressed using the Convex Hullmethod28,29 was used (eq 4).
∨=
∀ ∈∈
Ä
Ç
ÅÅÅÅÅÅÅÅÅÅÅ
É
Ö
ÑÑÑÑÑÑÑÑÑÑÑ
y
D Di j FIJ( , )
d D
i j d
i j d
, ,
, (4)
The friction factor is assumed to be a function of the pipediameter and the roughness. The Reynolds number isconsidered big enough to not represent a contribution to thefriction factor.30 A set of discrete values were considered tochoose diameter. The values used for the diameter were 0.7,0.8, 0.9, 1, and 1.1 m.
3.2. Objective Function. The objective function mini-mizes the total annualized cost of desalination plants, pipes,pumping stations, and pumps as turbines required to supplyindustrial sites located in a geography with complex top-ography. This function includes four terms that are costs: (1)cost of producing desalinated water, (2) costs of pumpingstations, (3) costs of pumps as turbines stations, and (4) costsof pipelines. The fifth term is the valorization of energyproduced by PATs that can be used in the grid.
∑ ∑ ∑ ∑
∑
= + + +
−
∈ ∈ ∈ ∈
∈
TC C C C C
E
so SOSO
i j FI Ji j
n Nn
n NPAT
n NPATs
, ,,
(5)
Figure 1. Mountain scheme: Point A represents a node with a pumpstation. Point B represents a node that is only a junction betweenpipes. Point C represents a PATs station and a traditional pumpstation.
Industrial & Engineering Chemistry Research Article
The cost of producing desalinated water was extracted fromthe database cost raised by Wittholz et al.31 The function usedincluded UPC, which is the unit cost of producing desalinatedwater.
∨ = × × *
≤ * ≤
∀ ∈∈
Ä
Ç
ÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅ
É
Ö
ÑÑÑÑÑÑÑÑÑÑÑÑÑÑÑÑÑÑÑ
y
C PA UPC Q
Q Q Q
so SOc C
so c
so so c so
cLO
so cUP
,
,
(6)
The cost of water transport is represented by a functionproposed by Swamee;25 this function includes two functions:annualized capital cost of pipelines and operational and capitalcost of pumping stations. So, the function that described theannualized capital cost of pipelines proposed by Swamee25 isdescribed below
=+
∀ ∈( )
Ck L D
Pi j FIJ
1( , )i j
PH
H i j i jm
L,
, ,i j
b
,
(7)
where kP is a proportionality constant; Hi,j is the pressure head;Hb is the length parameter; Li,j is the pipe length; Di,j is the pipediameter; and m is the exponent.The function to estimate pumping station costs is
∑
ρη
ρη
=+
+ ×
× ∀ ∈∈
ikjjjjj
y{zzzzzC
k s gP
F F E g
Q H n N
(1 ) 8.76n
N b
L
D A C
j outputn j n j, ,
(8)
where kN is a proportionality constant; sb is the standbyfraction; ρ is the mass density of water; g is the gravitationalacceleration; η is the combined efficiency of pump and primemover; FD is the daily averaging factor; FA is the annualaveraging factor; EC is the unit electrical cost ($/kW h); and PLis the plant life years.The function includes the pumping head Hn,j, which is
calculated with the Darcy−Weisbach equation, which is
π= + Δ +H H z
fL Q
gD
8n j n j
n j n j
n j, 0 ,
, ,2
2,
5(9)
where Δzn,j is the elevation difference from n to j; H0 is theterminal head; f is the friction factor; and Ln,j is the length ofthe pipe from n to j.3.3. Pumps as Turbines (PATs) for Energy Production.
For pumps as turbines stations, the net head for producingenergy was also calculated with the Darcy−Weisbach equationextracted from ref 32. In turbines, the net head is the actualhead used to produce energy. Net head is the gross head whichis the difference in height between two points minus the headloss. So, in the case of pumps as turbines, the head loss wouldhave a minus sign in the Darcy−Weisbach equation. Thevalorization of the energy produced in a year by PATs iscalculated using the next equation:
∑ ρ η=ikjjjjj
y{zzzzzE g Q H t E
USDyearPATs
n
N
n n C
PATS
(10)
where NPATS is the number of PATs; Qn,k is the hourly flowthrough the nth PATs (m3/s); Hn,k is the net head (m) acrossthe same PATs at the same time; η is the efficiency; t is
operating hours in a year (h); and EC is the electricity cost($/kW h).33
To obtain the cost of pumps as turbines CPAT, the price ofthese devices was estimated using catalogs from the pumpscompany’s using the net head and the flow as a reference. Civilwork and maintenance work of PATs was estimated usingvalues from ref 33.
3.4. Parallel Pipelines. In order to distribute thedesalinated water flow, the superstructure has the option ofchoosing up to three parallel pipelines depending on the waterflow. The velocity of water has a maximum value of 1.5 m/s;34
this condition is fulfilled using the next equation
π≤Q v
D
4n j n ji j
, ,,
2
(11)
The diameter of pipelines is the same for all pipelines ifmore than one is chosen. So, the next conditions must befulfilled
∑= ∀ ∈=
Q Q n Nn jp
n jp
,1
3
,(12)
= = =H H H Hn j n j n j n j, ,1
,2
,3
(13)
The total water flow is the sum of the water flow of eachpipeline where p is the pipeline, and the head drop is the samefor every pipeline and corresponds to the total head drop.
3.5. Piecewise Linearization. The objective function isnot linear because UPC and Q3 are not linear, Q3 appears whenthe Darcy−Weisbach equation is multiplied for the water flow,soQ2 is multiplied for Q. Since the objective function is notlinear, the model is a MINLP model.UPC is the amortized capital cost and the operating cost of
obtaining desalinated water and is included in the cost ofproducing desalinated water; this cost is a compound of thenext form
= × ×C Q UPC cte2SO SO (14)
where QSO is the desalinated water flow, and cte2 is a constantwhich includes desalination plant availability and time andmoney conversions to obtain the requirements units.To obtain the global optimum in short time, UPC and Q3
were linearized using a piecewise methodology extracted fromLin et al.35 Piecewise methodologies allowed us to convert anonlinear programming problem into a linear programmingproblem or a mixed-integer convex programming problem forobtaining an approximated global optimal solution.
4. CASE STUDYAn area of the Antofagasta region was used as a case study; thisarea is mainly desert and isolated and features significantchanges in elevation due to the presence of the CoastalCordillera that crosses this area. The case study contained fourmining plants that can be supplied water by three potential ROplants. Between the RO plants and the mining plants, there arepotential pumping stations, and since there are mountains inthe area, PATs were considered in some points. Connectionsthat were unfeasible were not considered as an option.Locations of RO plants and industrial sites were determined
on assumptions based on actual places where RO plants andmining plants could be located. Pipeline lengths weredetermined based on real data about the water supplies of
Industrial & Engineering Chemistry Research Article
different mining companies. The area was chosen using GoogleEarth; distances between points and elevations weredetermined using this software. Figure 2 shows the chosenarea as a case study.RO plants are located on the coast, so their elevation is zero.
There is a zone where the Coastal Cordillera goes throughabout 40 km of extension; this zone is where the use of PATswas considered and compared to the possibility of avoidingthese mountains and to only go up until reaching the miningsites. Elevation of pumping stations varied from 0 to 2.860 kmabove sea level, and mining plants were located between 3.046and 3.875 km above sea level. The elevation of each node isshown in Table 1, and the distance between each node isshown in Table 2.
5. RESULTSThe model was solved as a MILP problem using the Cplexsolver in a GAMS environment. GAMS stands for GeneralAlgebraic Modeling System; it is a high-level modelingsoftware for mathematical programming and optimization.
GAMS is designed for modeling linear programming (LP),mixed integer linear programming (MILP), and mixed integernonlinear programming (MINLP) problems.36 The Cplexsolver in GAMS is designed to solve large and difficultproblems quickly and works to solve the majority of linearproblems (LP).37
The global optimum was found in a short time aslinearization allows a model to be obtained that can be solvedquickly using Cplex, which is a desirable attribute for complexmodels like this one.
5.1. Global Optimum. The requirements of desalinatedwater of the WDN was 3.2 m3/s in total; the requirements foreach mining plant was 0.8 m3/s. These values were similar tothe requirements of the mining plants in the area.
Figure 2. Superstructure. SO are RO plants with a pumping station; n are pumping stations; P are PATs stations, and Si are industrial sites.
The global optimum solution considers using only one ROplant to supply all mining sites; in n9, there were PATs sincethe previous node, n6, is located at a high altitude. In node n9,a PATs station was followed by a traditional pump station. Allpipelines going between n2 and n11 considered using threepipelines to fulfill the water requirements of the three industrialsites (continuous line in Figure 3). Between n2 and n13, therewere two pipes to supply si4. When the requirements of waterof the industrial sites were elevated, parallel pipes that camefrom one single RO plant seemed to be a suitable solution tosupply more than one industrial site instead of using differentRO plants with a single or double pipeline to supply eachindustrial site, which is the conventional way. The optimalsolution generated 168,593 MW-h/year, which considering0.12 USD/kW-h represents a saving of 20.231 million USD/
year. Furthermore, considering that energy is produced fromfossil fuels, this energy production can contribute to reducinggreenhouse gas emissions.A ranking of the first three optimal solutions was made to
compare them economically and operationally (Table 3). Thethree first solutions considered using PATs in the same area;the first two solutions considered only the SO2 RO plant tosupply desalinated water. Instead, the third solution consideredusing desalinated water from SO2 and SO3. Additionally, theWDN of solutions 1 and 2 used seven pumping stations,whereas the WDN of solution 3 used eight pumping stations.The differences in costs between these optimal solutions wereless than one million USD/year. More details are present inTable 3, and solutions 2 and 3 are illustrated in Figures 4 and5, respectively.
Figure 3. Optimal solution. Solid line represents three parallel pipes, and segmented line represents two parallel pipes.
Table 3. Ranking of Solutions
Solution RO plants No. of pumping stations No. of PATs stations Energy generated by PATs (MW-h/year) Total cost (million USD/year)
By way of comparison, a network was designed withoutconsidering the PATs. For this purpose, the pipes in the areawhere the PATs could be installed were not considered in thesuperstructure. The optimal solution without PATs is shown inFigure 6, which considered the SO1 RO plant and fivepumping stations. The cost was 258.52 million USD/year; thisis 7.81 million USD/year more expensive than the option withPATs. This difference was due to a higher cost in the pipes, areduction in costs for not considering the equipment for PATs,and an increase in energy costs.Another comparison was made to compare the optimal
solution with the water supply strategy currently used bymining companies, where each company had its own RO plantand water distribution network. Since there is no energyproduction, and mining companies usually avoid puttingpipelines in places with pronounced changes in elevations,the pipelines were located where there was not a big differencein altitude. The cost of this system (Figure 7) was 258.56million USD/year.The optimal solution was considered going through n6,
which was 2.231 km above sea level; the next pump stationcoupled to a PATs station was n9, which was 1.375 km abovesea level, so 0.856 km was the difference in height and 22.47km the distance between each point. In order to analyze theeffect of height in the optimal solution, several differences ofheight between n6 and n9 were used. Results are shown inTable 4.The optimal solution generated 168,593 MW-h/year, which
is 20.231 million USD/year that can be utilized in the network.If the height of the mountains was 200 m less high, the optimalsolution still considered energy generation with an importantamount of energy generated, 127,950 MW-h/year.With a height of 1.831 km, the optimal solution considered
two RO plants, SO1 and SO3, which were located at theextremes of the network, instead of choosing SO2 as theoptimal solutions as in the cases of 2.231 and 2.031 km ofheight solutions. Energy production was low in comparisonwith the solutions obtained when elevations were higher; this isdue to the water flow sent to n6 was 0.2 m3/s, which was verylow in comparison to 2.4 m3/s which is the water flow sent ton6 where the elevation was 2.231 km. The reason for such lowwater flow is that the net head was lower as the gross head,which is the actual elevation of the mountains, was lower.Head loss was high due to the distance between each point andwater velocity, so the gross head had to be high to compensatefor the head loss.
6. CONCLUSIONS
A methodology to simultaneously design a WDN to supplyindustrial sites located in regions with complex topography,find the location and sizes of desalination plants, pipelines,pump stations, and PATs stations, was addressed. A case studywas used to validate the model.
Results showed that using PATs in a region with complextopography was feasible for producing energy in the WDN bypassing through a region with changes in elevation like theCoastal Cordillera where big changes in altitude are oftenavoided by mining companies.Linearization of two functions allowed us to find the global
optimum with low computational cost, which is a desiredattribute for complex models.Using parallel pipes is a feasible option to supply several
industrial sites using water from one RO plant when therequirements of water are elevated, instead of having a differentRO plant supply every industrial site with its respectivepipeline.In the future, more elements will be added to the model
such as considering more than one water quality to supplymultiple users and multiple sources of water to broaden thewater offer. Another element that can be included is usingrenewable energies like solar energy.
■ AUTHOR INFORMATIONCorresponding Author*E-mail: [email protected] D. Galvez: 0000-0001-9558-443XNotesThe authors declare no competing financial interest.
■ ACKNOWLEDGMENTSThe financial support from CONICYT (Fondecyt 1171341) isgratefully acknowledged. N.A. thanks CONICYT for thenational Ph.D. scholarship.
■ NOMENCLATURECSO = Annualized cost of RO plants (million USD/year)Ci,j = Annualized cost of pipelines (million USD/year)Cn = Annualized cost of pumping stations (million USD/year)CPAT = Annualized cost of PATs stations (million USD/year)Di,j = Diameter of pipeline between i and j (m)EC = Unit electrical cost ($/(kW-h))EPAT = Valorization of energy generated by PATs in oneyear (million USD/year)f = Friction factor (dimensionless)FA = Annual average factor (dimensionless)FD = Daily average factor (dimensionless)g = Gravitational acceleration (m/s2)Hi,j = Pressure head (m)Hb = Length parameter (m)Hn,j = Pumping head (m)H0 = Terminal head (m)Hn,k = Net head (m)
Table 4. Comparison between Different Heights of Nodes
kN = Proportionality constant for pumping stations(dimensionless)kP = Proportionality constant for pipelines (dimensionless)Li,j = Pipe length (m)PA = Desalination plant availability (dimensionless)PL = Desalination plant life (years)Qi,j = Volumetric flow pumped from i to j (m3/s)Qi,n = Volumetric flow pumped from i to n (m3/s)Qn,j = Volumetric flow pumped from n to j (m3/s)Qn,si = Volumetric flow pumped from n to an industrial site(m3/s)Qsi = Volumetric flow required in an industrial site (m3/s)Qso = Volumetric flow pumped from RO plant (m3/s)Qso,n = Volumetric flow pumped from a RO plant to n(m3/s)Qn,k = Volumetric flow through PATs (m3/h)sb = Standby fraction (dimensionless)t = Operating hours in a year (h)TC = Total annualized cost (million USD/year)UPC = Unit cost of producing desalinated water (USD)vn,j = Water velocity (m/s)yi,j,d = Disjunction to choose diameterρ = Water density (kg/m3)Δzn,j = Elevation difference from n to j (m)η = Combined efficiency of pump and prime mover
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Industrial & Engineering Chemistry Research Article
Towards mine tailings valorization: Recovery of critical materials from Chilean mine
tailings.
Journal of Cleaner Production
Vol 263, 2020
https://doi.org/10.1016/j.jclepro.2020.121555
Towards mine tailings valorization: Recovery of critical materials fromChilean mine tailings
Natalia Araya a, b, *, Andrzej Kraslawski b, Luis A. Cisternas a
a Departamento de Ingeniería Química y Procesos de Minerales, Universidad de Antofagasta, Antofagasta, 1240000, Chileb School of Engineering Science, Lappeenranta-Lahti University of Technology (LUT University), Lappeenranta, FI-53851, Finland
a r t i c l e i n f o
Article history:Received 16 June 2019Received in revised form6 March 2020Accepted 5 April 2020Available online 9 April 2020
Handling editor:Yutao Wang
Keywords:Mine tailingsCritical raw materialsTechno-economic assessmentDiscounted cash flowSensitivity analysis
a b s t r a c t
The mining industry produces large volumes of mine tailings e a mix of crushed rocks and process ef-fluents from the processing of mineral ores. Mine tailings are a major environmental issue due to im-plications related to their handling and storage. Depending on the mined ore and the process used, itmay be possible to recover valuable elements from mine tailings, among them critical raw materials(CRMs) like rare earths, vanadium, and antimony.
The aim of this study was to investigate the techno-economic feasibility of producing critical rawmaterials from mine tailings. Data from 477 Chilean tailings facilities were analyzed and used in thetechno-economic assessment of the valorization of mine tailings in the form of CRMs recovery. A reviewof applicable technologies was performed to identify suitable technologies for mine tailings processing.To assess the economic feasibility of CRMs production, net present value (NPV) was calculated using thediscounted cash flow (DCF) method. Sensitivity analysis and design of experiments were performed toanalyze the influence of independent variables on NPV. Two options were assessed, rare earth oxides(REOs) production and vanadium pentoxide (V2O5) production. The results show that it is possible toproduce V2O5 with an NPV of 76 million US$. In the case of REOs, NPV is positive but rather low, whichindicates that the investment is risky. Sensitivity analysis and the ANOVA run using the design of ex-periments indicated that the NPV of REOs is highly sensitive to the price of REOs and to the discount rate.
Mine tailings are waste from the processing of mineral ores.They are a mixture of ground rocks and process effluents generatedduring processing of the ores, and their composition depends onthe nature of the mined rock and the recovery process used. Incopper mining, tailings can account for 95e99% of crushed andground ores (Edraki et al., 2014). Worldwide, mine tailings areproduced at a rate of anywhere from five to fourteen billion tonsper year (Adiansyah et al., 2015; Edraki et al., 2014; Schoenberger,2016).
In view of the volumes of mine waste produced and the natureof the chemicals involved, the storage and handling of mine tailingsis a significant environmental problem. Mine tailings are a source of
serious contamination of soils and groundwater with nearbycommunities particularly badly affected by the results of eolian andwater erosion of tailing disposal sites (Mendez and Maier, 2008).Another cause of environmental pollution frommine tailings is acidmine drainage (AMD) (Larsson et al., 2018; Moodley et al., 2017).AMD is formed from the exposure of sulfide ores and minerals towater and oxygen, once the ore is exposed, sulfate and heavymetals are released into the water (Moodley et al., 2017). AMD isconsidered one of the most significant forms of water pollution andthe USA Environmental Protection Agency (US-EPA) considers it tobe the second only to global warming and ozone depletion in termsof ecological risk (Moodley et al., 2017).
Tailing storage facilities (TSF), also called tailing deposits, are thesource of most mining-related disasters (Schoenberger, 2016). Ap-proaches to the handling and storage of mine tailings includeriverine disposal, wetland retention, backfilling, dry stacking andstorage behind damned impoundments (Kossoff et al., 2014). Minetailings dam failures can have catastrophic consequences. 237 casesof significant tailings accidents were reported for the period 1971 to2009 (Adiansyah et al., 2015). More recently, in January 2019, an
* Corresponding author. School of Engineering Science, LUT University, FI-53851,Lappeenranta, Finland.
accident at the C�orrego do Feij~ao mine in Brumadinho in themetropolitan region of Belo Horizonte in southeastern Brazil killedat least 65 people with about 280 people were missing (De S�a,2019).
To achieve a circular economy model, the valorization of minetailings is crucial for the mining industry, which needs to improveits processes to minimize its environmental impact and close theloops (Kinnunen and Kaksonen, 2019). Different approaches totailings valorization can be taken, such as reprocessing to extractmetals and minerals, tailings as backfill material, tailings as con-struction material, energy recovery and carbon dioxide sequestra-tion (Lottermoser, 2011).
Challenges that the mining industry needs to face to achieve thevalorization of tailings aligned with circular economy principlesinclude improving the rather limited knowledge about mineralogy,impurities concentration, and the quantity of tailings; developingnew business models that take account of price development,lower disposal costs, and market demand; providing institutionalimpulse indispensable to encourage the transformation from alinear to a circular economy; technology development to makeprocesses economically feasible since most mine tailings have lowgrades of different elements mixed with residues of previous pro-cesses (Kinnunen and Kaksonen, 2019; Lottermoser, 2011).
Due to the geological heterogeneity of the rocks mined and thecontinuous flow processes used in mineral processing, tailingsdeposits contain large quantities of valuable elements whose re-covery could bring potential economic benefits. A number ofstudies have investigated the recovery of valuable elements frommine tailings (Ahmadi et al., 2015; Alcalde et al., 2018; Anderssonet al., 2018; Ceniceros-G�omez et al., 2018; Falag�an et al., 2017;Figueiredo et al., 2018; Khalil et al., 2019; Khorasanipour, 2015;Mohamed et al., 2017; Sracek et al., 2010).
As shown by recent studies (Ceniceros-G�omez et al., 2018;Markovaara-Koivisto et al., 2018; Moran-Palacios et al., 2019; Tunsuet al., 2019), among elements contained in mine tailings, there aremany critical raw materials (CRMs). Raw materials have significanteconomic importance and are utilized in the manufacture of a widerange of goods. In particular, critical rawmaterials can be applied inareas such as alternative energy production and communicationsdevices, and they play a significant role in the development ofglobally competitive and eco-friendly innovations. Securing accessto a stable supply of many raw materials has become a majorchallenge for national and regional economies with a limited pro-duction, which relies on imports of numerous minerals and metals(European Commission, 2017a).
Many studies have examined the criticality of raw materials.This study utilizes the list compiled by the European Commission(EC), where raw materials are considered critical when they areboth of high economic importance for the European Union (EU) andvulnerable to supply disruptions (European Commission, 2017b).The term “vulnerable to supply disruption”means that their supplyis associated with a high risk of not meeting the demand of the EUindustry. High economic importancemeans that the rawmaterial isof fundamental importance to industry sectors that create addedvalue and jobs, which may be lost in the case of inadequate supplyand if adequate substitutes cannot be found (Blengini et al., 2017).The most critical metals are those for which supply constraintsresult from the fact that they are largely or entirely mined as by-products, generate environmental impacts during production,have no effective substitutes, and are mined in areas prone togeopolitical conflict (Graedel et al., 2015).
In 2011, the European Commission (EC) published a list of 14 rawmaterials that are critical for emerging technologies of Europeanindustries, so-called critical raw materials (CRMs) (EuropeanCommission, 2017a, 2014, 2011). The list has been updated twice
since 2011, the last update was in 2017, and it currently containstwenty-seven CRMs including 3 element groups: light rare earthelements (LREEs), heavy rare earth elements (HREES) and platinumgroup elements.
According to the International Union for Pure and AppliedChemistry (IUPAC), rare earth elements (REEs) are a group of 17elements that includes lanthanides, composed of 15 elements, andyttrium and scandium, which are included in this group due to thesimilarity in chemical characteristics. REEs can be found in over 250different minerals (Jordens et al., 2013; Sadri et al., 2017). REEs havean important role in the transition to green technologies because oftheir use in crucial components such as permanent magnets andrechargeable batteries, and their use as catalysts (Binnemans et al.,2013a). China is responsible for almost 80% of the global supply ofREEs, such monopoly has raised concerns about a possible shortageof supply, (Hornby and Sanderson, 2019; Vekasi and Hunnewell,2019).
Other elements on the list of CRMs are platinum group elements(PGEs), which include ruthenium (Ru), rhodium (Rh), palladium(Pd), osmium (Os), iridium (Ir) and platinum (Pt). These metals arevery rare in the Earth’s continental crust, ranging from 0.022 ppbfor iridium to 0.52 to Pd (Mudd et al., 2018).
Nowadays, due to the increasing demand for CRMs, new sourcesare being sought, and secondary sources such as metal scrap andindustrial waste are attracting more attention. The use of thehitherto unexploited secondary sources can reduce demand forvirgin materials and, in consequence, contribute to a decrease inmining production. One of the core principles of the circulareconomy is the reduction and minimization of resource use, andways to achieve that goal include recycling and reuse of wastes(Kirchherr et al., 2017). Mine tailings from mineral processing of acertain branch of the metal industry could be used as a source in aprocess designed to obtain one or more critical raw materials, asimplified flowsheet of this idea is shown in Fig. 1.
Chile has a long history of mining and large-scale mining startedin the first decade of the twentieth century. In 2016, Chileanminingexports were valued at 30,379 million USD according to the Na-tional Service of Geology and Mining (SERNAGEOMIN), 90% ofwhich came from copper mining (SERNAGEOMIN, 2017). Chile isthe world’s leading producer of copper. Currently, a decrease in thegrade of mined copper ores is being observed, which increases theamount of processed ore and, consequently, leads to greater tailingsdeposits for the same level of copper production. Currently, Chileproduces 1,400,000 tons of mine tailings daily and there are 696tailings storage facilities (TSF) (SERNAGEOMIN, 2018).
The objective of this study is to conduct a technical and eco-nomic assessment of the valorization of mine tailings of Chile as asource of CRMs. Therefore, the research questions addressed in thispaper are:
What critical materials can be recovered from mine tailings?What are the challenges in the production of critical materials
using mine tailings as a source?In recent years, the use of secondary sources for obtaining raw
materials has gained growing importance. This research supple-ments these works with a techno-economic feasibility study forproducing critical raw materials from mine tailings.
The data used in the study refer to mine tailings samples of 477Chilean copper mining industrial deposits. These data have notbeen previously used to assess the economic potential of the re-covery of critical materials.
2. Methodology
The first step to evaluate the recovery of CRMs from mine tail-ings is the calculation of the amount of each CRM present in
N. Araya et al. / Journal of Cleaner Production 263 (2020) 1215552
tailings. The feasibility of recovery is next assessed for criticalmaterials found in larger quantities.
In the technological assessment, technologies for processingmine tailings are first examined. If no technologies are available,technologies for processing ore, as an analogous process, areconsidered taking into account differences between the processingof ore and processing of waste.
In the economic assessment, the discounted cash flow (DCF)method is used to assess the feasibility of the options for the re-covery CRMs frommine tailings. This method has beenwidely usedfor valuation projects (De Reyck et al., 2008; Kodukula andPapudesu, 2006; �Zi�zlavský, 2014). DCF is a commonly adoptedeconomic valuation technique and consists of discounting expectedcash flow of a future project at a given discount rate and thensumming all the cash flows of a determined period of time (Ib�a~nez-For�es et al., 2014; �Zi�zlavský, 2014).
Sensitivity analysis is performed to assess the impact of variousparameters on the NPV of CRMs recovery from mining tailings.Sensitivity analysis is a tool used to analyze how different values ofa set of independent variables affect a dependent variable. The saleprice of critical materials, operating costs, capital costs, and dis-count rate are the main inputs in the DCF method, then thesevariables are studied in the sensitivity analysis. These variables andinteractions among them were also tested using a design of ex-periments with response surface methodology.
3. Mine tailings assessment
Mining is one of the main economic activities in Chile due to thecountry’s favorable geochemical and mineralogical characteristics.Chile is the world’s leading producer of copper, producing 5,552.6thousand tons of copper in 2016 (SERNAGEOMIN, 2017), theworld’ssecond supplier of molybdenum, producing 62,746.1 tons in 2017,and the second producer of lithium, producing 77,284 tons oflithium carbonate in 2017 (SERNAGEOMIN, 2017). For some regionsin Chile, mining is the main economic activity; most mining activityis found in the Atacama Desert in northern Chile.
The Atacama Desert is the driest non-polar desert on the earth,and its copper ore deposits are world-class porphyry copper de-posits (Oyarzún et al., 2016; Tapia et al., 2018). Porphyry depositsare the principal sources of copper and molybdenum(Khorasanipour and Jafari, 2017). Porphyry deposits consist ofdistributed and stockwork sulfide mineralization located in varioushost rocks that have been altered by hydrothermal solutions intoroughly concentric zonal patterns (Dold and Fontbot�e, 2001).
Chilean mining processing plants produce large quantities ofwaste every year. Tailings dams are the most common type of
tailing deposit in the country (Ghorbani and Kuan, 2017). Previ-ously, prior to the adoption of appropriate regulations, tailings wereabandoned in deposits and no efforts were made to ensure thesafety of the nearby communities but nowadays the handling andstorage of tailings are strictly regulated. In 2011, the Law 22.551waspromulgated. It regulates the closing of mining facilities andspecifies that tailings must be physically and chemically stabilized(Ministerio de Minería, 2011; SERNAGEOMIN, 2011).
In Chile, there are 696 mine tailings deposits registered in anational registry, compiled between 2016 and 2018. The registry isexpected to be updated as new mine tailings facilities are openedand old abandoned tailing deposits are discovered. AntofagastaRegion hosts larger mine tailings deposits (SERNAGEOMIN, 2018)because of the size of the mining sector in this region, which ac-counts for 47% of the contribution to Chilean mining activity. Themost serious problems associated with tailings and handling andstorage of tailings are related to the seismic nature of the country,and risks associated with tailings dam failure include fatalities,serious water contamination, and destruction of the land.
3.1. Characterization of mine tailings
The chemical composition of tailings in 477 mine tailings de-posits is available on the website of the National Service of Geologyand Mining of Chile (SERNAGEOMIN) (SERNAGEOMIN, 2018). Thisdatabase contains values for concentrations of 56 elements,including 22 CRMs featuring on the latest EC list. The CRMsanalyzed in the SERNAGEOMIN database are vanadium, cobalt,yttrium, niobium, scandium, hafnium, tantalum, antimony, bis-muth, tungsten, lanthanum, cerium, praseodymium, neodymium,samarium, europium, gadolinium, terbium, dysprosium, holmium,erbium, thulium, ytterbium and lutetium (SERNAGEOMIN, 2018).
Chemical composition in each mine tailings deposit is differentand it depends on the type of mineral rocks mined and the pro-cesses used in the plant. In the geochemical characterization ofChilean tailings, it can be noticed that most tailings deposits have ahigh percentage of silicon oxide or ferric oxide due to the type ofminerals processed (SERNAGEOMIN, 2018).
Data in the SERNAGEOMIN database are classified by the currentstatus of the tailings deposits: active, inactive, and abandoned. In themethodology used in this study, only inactive and abandoned tailingswere analyzed, because their volume and chemical composition donot change over time. In the case of active tailings, although theirvolume is greatest, their chemical composition may change over thecourse of years, which is why they have not been considered in thisstudy. Mine tailings of the Antofagasta Region are examined becausethe tailing volume storage is greater in this region than in other
Fig. 1. Simplified mining processes flowsheet featuring conventional processes to obtain metal and the re-processing of tailings to obtain CRMs.
N. Araya et al. / Journal of Cleaner Production 263 (2020) 121555 3
regions. The TSFs analyzed cover 16 inactive deposits. The location ofmine tailings of the Antofagasta Region can be seen in Fig. 2.
CRMs found in larger quantities are given in Table 1. The sum ofREEs was also calculated, to produce REE concentrate or mis-chmetal, which is an alloy of REEs. The sum of REEs does notconsider scandium because it is separated in a different process.
3.2. Technology assessment
A literature review was conducted to investigate the availabletechnologies for the recovery of critical raw materials from minetailings. If no technologies are available for tailings processing, thenthose used for processing of primary ores are considered as areference. It is important to notice that mine tailings are already inthe form of slurry or paste, depending on the percentage of waterpresent, so there are no mining costs, which represent approxi-mately 43% of operating cost in a mine (Curry et al., 2014).
Existing technologies for CRMs production are briefly describedin Table 2. Most of these technologies are for primary ores. Someapplications for secondary sources such as industrial waste andmine tailings exist (Abisheva et al., 2017; Binnemans et al., 2015;Figueiredo et al., 2018; Innocenzi et al., 2014; Jorjani and Shahbazi,2016; Peelman et al., 2016), but they should be treated as emergingtechnologies. Significant further development of these new tech-nologies is required before they are suitable for industrial-scaleusage (Kinnunen and Kaksonen, 2019).
In spite of the low concentration of REEs in comparison to end-of-life consumer goods, mine tailings are a potential source of REEsbecause of the large volumes of mine tailings, which mean that thetotal amount of recoverable REEs could be high (Binnemans et al.,2015). Several processes have been proposed for the recovery ofREEs from mine tailings. Peelman et al. (2018) have proposed amethod for the recovery of REEs from mine tailings from apatitemineral with an REE content of 1200e1500 ppm using acidicleaching followed by cryogenic crystallization and solvent extrac-tion. They achieved a 70e100% recovery of REE.
There are no processing plants using copper mine tailings as asource of CRMs. Therefore, technologies used for primary sources
are assumed to be also applicable to the processing of mine tailings.Based on the content of the mine tailings analyzed, two feasibilitystudies are conducted; the first for producing rare earth oxides andthe second for vanadium recovery, using mine tailings as a source.
The extraction process for REEs, in a general form, includes threesteps: mining and comminution; ore beneficiation processes con-sisting of flotation, gravity and magnetic techniques to generateREE concentrate; and hydrometallurgical methods to extract REEcompounds (Sadri et al., 2017). Hydrometallurgical methodsinclude cracking of REE concentrate; leaching, neutralization andprecipitation processes; and separation and purification techniquessuch as solvent extraction. Solvent extraction allows recoveringREEs with a high degree of purity, moreover, a variety of solventextraction reagents is available. For secondary waste, selectiveextraction of REEs is required from solutions with a high content ofother species (Tunsu et al., 2019).
A life cycle inventory and impact assessment of the productionof RE oxides from primary bastnasite and monazite has been pre-sented for the Bayan Obo mine in Inner Mongolia, China, in (Koltunand Tharumarajah, 2014). The study found out the mining andbeneficiation stage accounts for 6.98% of energy consumption and6.51% of water consumption. When processing mine tailings, thereis no mining stage, so the values were adapted. Adapted values ofenergy and water consumption to obtain RE oxides from wastematerial are included in the supplementary material.
Primary ores of REEs are usually treated with alkaline pressureleaching or sulfuric acid roasting. However, mine tailings are a low-grade source of REEs, so these technologiesmaynot be economicallyfeasible. Chloride-based hydrometallurgical processes may be apotential alternative to traditional capital intensive hydrometallur-gical processes based on high temperature and pressure (Onyedikaet al., 2012) and they could be a suitable option for REE recoveryfrom tailings at economically viable capital and operating cost.
In the case of vanadium, it is mainly produced as a co-productfrom the vanadium slag before the steel converter. The main va-nadium products are vanadium pentoxide (V2O5) and ferrovana-dium (FeV) (European Commission, 2017b). Other sources ofvanadium are stone coal, steel scrap, and fossil fuels.
The mine tailings analyzed in this study have a CRMs contentthat varies between 80 and 214,000 g per ton of tailing. In Chile,there are currently no projects providing for the use ofmine tailingsas a source of CRMs, nor approved initiatives for the production ofCRMs from primary ores.
3.3. Economic assessment
The economic assessment is done in two main steps. The firststep focuses on the economic potential of CRMs found in inactivemine tailings as an in-situ value, considering the monetary value ofthe CRMs to assess the feasibility of CRMs production. The secondstage concentrates on the analysis of the feasibility of CRMs pro-duction using mine tailings as a source.
Prices of critical materials may differ fromone source to another.In addition, the prices of some critical materials are not publiclyavailable as they are traded privately. To calculate the economicpotential of inactive mine tailings deposits, the following priceswere used, see Table 3.
The economic potential of CRMs recovery was calculated as thefraction of each CRM in the tailings multiplied by the mass of eachTSF for the 16 TSFs studied. The economic potential is a referencevalue for the total REE value of the mine tailings. The economicpotential of these TSFs is shown as supplementary material.
To assess the feasibility of CRMs recovery, the DFC method wasused to calculate the NPV and IRR for REOs production and V2O5productionusingmine tailings. TheNPV is thedifferencebetween the
Fig. 2. Tailings storage facilities in Antofagasta Region, blue represents inactive orabandoned deposits and red is for active deposits. (For interpretation of the referencesto colour in this figure legend, the reader is referred to the Web version of this article.)
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presentvalueof cash inflowsand thepresentvalueof cashoutflows inaparticularperiodof time. IRR is thediscount rate atwhich theNPVoffuture cash flows is equal to the initial investment. NPV and IRR aremetrics used in capital budgeting and decision-making. The calcula-tion does not include external factors such as inflation. To obtain theNPVand IRR for theoptionsassessed, capital costs andoperating costsof projects with similar characteristics were used.
Capital costs, also referred to as capital expenses or CAPEX,
represent the investment made for the project, which includescosts of the development phase which, among other costs, com-prises the purchase of the equipment, building a manufacturingplant and the cost of product launch. The investment represents thefirst cash flow in the DFC method.
Operating costs, operating expenses or OPEX, are expensesincurred during the lifetime of the project. In the case of a miningproject, these would include the cost of labor, water, and energy,
Table 1Total tonnage and uses of CRMs present in inactive tailings deposits of the Antofagasta Region (16 deposits).
CRMs Tons Uses
Vanadium (V) 46,110 Most of the vanadium produced is used in ferrovanadium or as a steel additive. Another use is as vanadium pentoxide.Cerium (Ce) 22,886 Cerium is used as a catalyst converter for carbon monoxide emissions, as an additive in glass for reducing UV transmission, and in carbon-arc
lighting.Cobalt (Co) 16,940 The main uses of cobalt are in battery chemicals for NieCd, Ni-metal hydride and Li-ion battery types, superalloys, hard materials, catalysts, and
magnets.Yttrium (Y) 16,039 Yttrium is used for energy-efficient fluorescent lamps, in the treatment of various cancers, in aerospace surface and barriers, as a superconductor,
in aluminum and magnesium alloys, and in-camera lenses.Neodymium
(Nd)14,880 Neodymium is used to create high-strength magnets for computers, cell phones, medical equipment, electric cars, wind turbines, and audio
systems. It is also used in the glass and ceramic industries.Lanthanum (La) 10,253 Lanthanum is used in nickel metal hydride rechargeable batteries for hybrid automobiles, in high-quality camera and telescope lenses, and in
petroleum cracking catalysts in oil refineries.Scandium (Sc) 9,359 Scandium is used to increase strength and corrosion resistance in aluminum alloys, in high-intensity discharge lamps, and in fuel cells to increase
efficiency at lower temperatures.Niobium (Nb) 4,823 Niobium is used in high strength low alloy (HSLA) steels as ferroniobium and in superconducting magnets.Antimony (Sb) 3,751 Principal uses for antimony are in alloys with lead and tin, and in lead-acid batteries.Samarium (Sm) 3,456 The main use of samarium is in cobalt-samarium alloy magnets for small motors, quartz watches, and camera shutters. Samarium is also used in
lasers.Gadolinium
(Gd)3,357 Gadolinium is mainly used for NdFeB permanent magnets, lightning applications and in metallurgy.
Praseodymium(Pr)
3,245 Praseodymium is used in NdFeB magnets, ceramics, batteries, catalysts, glass polishing and fiber amplifiers.
Dysprosium(Dy)
2,705 Dysprosium is used mainly and almost inclusively in NdFeB magnets.
REEs (total) 82,254
Table 2Available and emerging technologies for CRMs processing.
CRMs Production process
Rare earthelements
- Acidic leaching-cryogenic crystallization-solvent extraction from mine tailings with apatite and monazite. (Peelman et al., 2016).- Bioleaching for REEs extraction from low-grade sources. (Peelman et al., 2014).- Solvent extraction to recover REEs from mine tailings of gold and tellurium mining (Tunsu et al., 2019).-Use of solvent impregnated resins (SIR) to recover REEs from low concentration solutions (Onishi et al., 2010; Sun et al., 2009; Yoon et al., 2016)
Antimony - Crushing and pyrometallurgical methods for primary ores (Anderson, 2012).-Crushing and hydrometallurgical methods like leaching and electrodeposition (Anderson, 2012)
Cobalt - Bioleaching of sulfidic tailings of iron mines. (Ahmadi et al., 2015).- Mineral beneficiation, comminution, flotation, smelting, leaching or refining for sulfide ores (European Commission, 2017b).-Calcination, pyrometallurgical process, hydrometallurgical methods for lanthanides ores (European Commission, 2017b)
Niobium -Gravity separation, froth flotation, magnetic and electrostatic separation, and acid leaching depending on the ore (European Commission, 2017b)Vanadium - Extraction of vanadium as a co-product to iron from vanadium slag includes bearing, roasting, acid leaching solvent extraction, ion exchange, and
precipitation (Xiang et al., 2018).- Desliming-flotation from low-grade stone coal (European Commission, 2017b).-Preform reduction process (PRP) based on a metallothermic reduction of vanadium pentoxide (V2O5). (Miyauchi and Okabe, 2010).
Table 3CRMs prices in July 2018.
Critical material Price ($US/kg)a Critical material Price (US$/kg)a
Antimony 8.51 Neodymium metal � 99.5% 68.0Cerium metal � 99.5% 7.00 Neodymium oxide � 99.5% 66.7Cerium oxide � 99.5% 5.59 Praseodymium metal � 99% 125.00Cobalt 87.5 Praseodymium oxide � 99.5% 81.6Dysprosium metal � 99% 268.57 Samarium metal � 99.9% 15Dysprosium oxide � 99.5% 226.80 Scandium metal � 99.9% 3,458Gadolinium metal � 99.9% 44.00 Scandium oxide � 99.95% 1,079Gadolinium oxide � 99.5% 20.94 Vanadium (as V2O5 80%) 40.00Lanthanum metal � 99% 7.00 Yttrium metal � 99.9% 36.5Lanthanum oxide � 99.5% 7.80 Yttrium oxide � 99.99% 4.60
a Sources: (Mineralprices.com, 2018), (Thenorthernminer.com, 2018), (LME, 2018).
N. Araya et al. / Journal of Cleaner Production 263 (2020) 121555 5
maintenance, spare parts, and indirect costs (Bhojwani et al., 2019).The first option assessed is the production, using mine tailings
as a source, of the following rare earth oxides (REOs): cerium,lanthanum, neodymium, yttrium, samarium, gadolinium, praseo-dymium and dysprosium. Scandium is also considered as REE but ithas different properties and a different production process, whichis why it was not assessed togetherwith the abovementioned REEs.
The second option assessed is vanadium as the production ofvanadium pentoxide (V2O5). It is due to the fact that vanadium isthe main CRM found TSFs in the Antofagasta Region (see Table 1).
3.3.1. Feasibility of producing rare earth elements using minetailings as a source
For REOs production, we have considered only REEs found inlarger quantities. Due to the lack of data about similar projects thatuse mine tailings or industrial waste as source material, we useddata from a Canadian project that produces rare earth oxides(Hudson Resources Inc, 2013) from primary sources to produce ofneodymium, praseodymium, lanthanum, and cerium. Data used forNVP calculation are shown in Table 4.
The price used to calculate NPV corresponds to the weightedaverage for REOs; cerium, lanthanum, samarium, gadolinium,praseodymium, dysprosium, and yttrium oxide, which is 37 USD/kgof REOs produced, 40% was discounted to reflect the differencebetween REO concentrate and separated individual rare earth oxideprices, so the price used for NPV calculations is 22 USD/kg, as in thereport it was used as a reference price. The grade of REEs corre-sponds to the average REEs grade in all the deposits analyzed. In themine tailings covered by the analysis, the average grade is lowerthan in most primary ore processing projects, so the productionwas reduced accordingly.
It is important to note that operating costs and capital costs arereferential values. In the case of mine tailings, costs related toextracting mineral ores should not be considered since tailings arematerials that have already been mined and processed.
The NPV is 672,987 USD which means that the projected earn-ings generated for this proposed REOs production exceed theanticipated costs and the overall value for the project is positive.However, even though the NPV is positive, its value is too low toinvest in a project of such a magnitude. The IRR is 10.03% which isalmost the same as the discount rate chosen for the project, thisconfirms that the project is not highly profitable. Cash inflows andoutflows are included as supplementary material.
3.3.2. Feasibility of producing vanadium using mine tailings as asource
Vanadium is the main CRM found in mine tailings in the Anto-fagasta Region. There are 46,110 tons of vanadium in inactive TSFs,but active tailings in this area have the potential for ca. 900, 000tons of vanadium.
Capital and operating costs for vanadium production are takenfrom a preliminary economic assessment study for the Gibellini
vanadium project (Lee, 2018). This project has been designed as anopen pit heap leaching operation to obtain vanadium pentoxide(V2O5). The Gibellini project is designed for processing of low-grademinerals, so it is suitable for mine tailings, but in this study, pro-duction is reduced because the grade in mine tailings is lower inmine tailings. The values used for the calculation of NPV and IRR aregiven in Table 5. The values of NPV and IRR for vanadium produc-tion from Chilean mine tailings are shown in the supplementarymaterial.
The NPV is 76 million US$ and the IRR is 21%, these valuesindicate that the project is profitable as the NPV is positive and theIRR is higher than the discount rate. Cash inflows and outflows areshown as supplementary material.
3.4. Sensitivity analysis
In this study, a sensitivity analysis was performed on four pa-rameters: capital cost, operating cost, critical materials price, andthe effect of the discount rate on NPV for the examined options. Theobjective of the sensitivity analysis is to understand the uncertaintyin the NPV for the examined parameters. These parameters werechosen because they are the key components in the DCF method.
Sensitivity analysis determines how different values of one ormore independent variables affect a dependent variable under agiven set of assumptions. Sensitivity analysis is the last stage of theprocess of assessing and selecting a technological alternative(Ib�a~nez-For�es et al., 2014). Sensitivity analysis studies how severalsources of uncertainty contribute to the entire uncertainty of amathematical model.
In the DCF method, the discount rate is the rate used to convertthe future value of a project cash flows to today’s value. The dis-count rate is adjusted to the risk associated with a project. There-fore, the higher the risk, the higher the discount rate (Kodukula andPapudesu, 2006). Risk is associated with the uncertainty of aproject. In business, risks may have a positive or negative effect. Thediscount rate was varied to acknowledge that mining projects dealwith uncertainties that can be included in the model by choosing ahigher discount rate.
Mining commodity prices always show greater volatility thanthose of any other primary products (Foo et al., 2018). Prices ofcritical materials may experience price spikes due to their insta-bility caused by the risk of supply disruption. Critical materials haveinelasticity element in their prices, this means that the demand forthese materials is not highly affected by the price (Binnemans et al.,2013b; Leader et al., 2019). Critical materials are needed in tech-nologies, such as clean energy technologies, in which there are notsubstitutes for the critical materials needed (Leader et al., 2019).
The price of each critical material assessed was considered as animportant parameter that contributes to the overall uncertainty ofthe project.
Since capital costs and operating costs used in this study arereferential values, and they are further used as inputs in the DCF
N. Araya et al. / Journal of Cleaner Production 263 (2020) 1215556
method, it was necessary to address the variability of the real valuesof these parameters vis-a-vis the values used here.
Capital cost, operating costs, and prices varied between�30 and30% of the original value. The discount rate varied between 0.05 and0.3.
The results of the sensitivity analysis for the REOs price areshown in Fig. 3. It can be seen that for every 5% increase in the priceof the REOs, the NPV increases by 38 million US$. NPV is highlysensitive to changes in REO prices. NPV becomes negativewhen theprice of REOs is below 22 US$/kg, making the project financiallyunviable.
The NPV is less sensitive to changes in operating costs thanprice; NPV decreases to 21 million US$ with an increase of 5% inoperating costs. The results of the sensitivity analysis of the NPV tothe capital cost show that as the investment cost increases by 5%,the NPV decreases by ca. 15 million US$.
The discount rate varied between 0.05 and 0.3. The NPV is not alinear function of the discount rate, the value considered was 0.1.When the discount rate is 0.11, NPV decreases by approximately 21million US$. With a discount rate higher than 0.1, NPV becomesnegative, making the project unviable.
Results of sensitivity analysis of NPV for vanadium pentoxideproduction are shown in Fig. 3. When the price increases by 5%,NPV increased by ca. 14 million US$. When the price drops by 26%,NPV becomes negative and the project unviable.
Results of the sensitivity analysis of NPV to operating costs showthat NPV is slightly sensitive to changes in operating costs. Whenoperating costs increase by 5%, the NPV decreases by ca. 5 millionUS$. Sensitivity analysis of the NPV to changes in capital cost showsthat with an increase of 5% in the capital cost, the NPV decreases byca. 5 million US$. The values of NPV are very similar for bothoperating costs and capital costs.
The sensitivity analysis of NPV to changes in the discount rate
shows that if the discount rate increases by 0.01 from the value of0.1 used to 0.11, the NPV decreases by 10million US$ approximately.When the discount rate is higher than 0.21, NPV becomes negative.
Results show that under certain prices, operating costs andcapital costs, it is possible to invest in producing CRMs using asecondary source such as mine tailings.
The parameters analyzed in the sensitivity study may changesimultaneously. Therefore, their interactions were analyzed usingdesign-of-experiments together with response surface methodol-ogy. In the analysis of the NPV of both projects, REOs productionand V2O5 production, four factors and three levels were considered.The factors are: the price, capital costs (CAPEX), operating costs(OPEX), and the discount rate (iÞ. The levels correspond to the valueused in the economic assessment, then low and high levels for thesame value were multiplied by 0.85 and 1.15, respectively, whichmeans the experimental design results are valid in the rangebetween�15% andþ15%. A percentage of 15%was chosen to ensurea good adjustment. The values tested for the discount rate are 0.05,0.1, and 0.15. ANOVA results show which parameters and in-teractions influence the NPV by analyzing the p-value. For the p-value < 0.01 all linear parameters and the interaction with thediscount rate were significant. Also, the statistical analysis confirmsthat price and the discount rate are the parameters exerting greaterinfluence. Regression models obtained have the following form:
NPV ¼ aþ b CAPEX þ c OPEX þ d priceþ e iþ f i2 þ g CAPEX i
þ h OPEX iþ j price i
The values for a; b; c; d; e; f ; g; h and j are �17.6, �0.9103,�59.55, 67.1, �1766, 14323, 0.0, 242.9, and �299.5 for REOs project,and 47.64, �0.9932, �12.572, 12.572, �1143.3, 5717, 0.828,49.63, �49.625 for V2O5 project, respectively. The units for NPV and
Fig. 3. Sensitivity analysis, a) Sensitivity of the NPV (REOs project) to the price of REOs, operating costs and capital costs; b) Sensitivity of NPV to discount rate in REOs project; c)sensitivity of NPV (vanadium project) to the price of V2O5, operating costs and capital costs; d) Sensitivity of NPV to discount rate in vanadium project.
N. Araya et al. / Journal of Cleaner Production 263 (2020) 121555 7
CAPEXaremillionUS$,OPEXandprice arekUS$/ton, and thediscountrate is dimensionless. The R-squared values or the coefficient of theregressions were R2 ¼ 98:17%, R2adj ¼ 97:99%, and R2pred ¼ 97:79%for REOs project, and R2 ¼ 99:95%, R2adj ¼ 99:95%, and R2pred ¼99:94% for the V2O5 project. The R2 for both projects are over 98%which means that at least 98% of the variation of the NPV can beexplained by the model. Also, excellent values of adjusted R2 andpredicted R2 were observed which suggests that the number of pa-rameters is themodel is correct and that themodel is able to producehigh quality predictions. The ANOVA results and Pareto graphics areincluded in the supplementary material. Also, supplementary mate-rial gives the results of the design-of-experiment and response sur-facemethodology for the IRRwhichbehavesdifferently fromtheNPV.
4. Discussion
Mine tailings are waste obtained from the processing of a rockwith a view to obtain one or more products that will be refined tofinally get a metal(s) that is needed. Tailings should be stored infacilities where they are disposed in accordance with the regula-tions binding in each region, otherwise, the consequences to theenvironment can be devastating.
The lack of a long-term consideration of the entire life-cycle of amine and the instability of mine projects contribute to irreversiblemineral losses and resource sterilization. With this knowledge inmind, further research should address new strategies to anticipatethe future use of material beyond the closing of a mine (L�ebre et al.,2017). Mine waste hierarchy goes from prevention as the mostfavorable option to treatment and disposal as the least favorableoptions; if waste cannot be prevented then reuse and recycling areneeded (Lottermoser, 2011). Nowadays most mine tailings go to thetreatment and disposal phase. In the Sustainable DevelopmentGoals, the World Economic Forum suggests the re-use of tailings,these goals are meant to be achieved by 2030 (World EconomicForum, 2016). The reprocessing of mine tailings is also anelement of the transformation from a linear to a circular economythat the mining industry must face. Reprocessing mine tailings toobtain critical materials reduces the dependency on reserveextraction (El Wali et al., 2019).
Other approaches to mine tailings management from a circulareconomy point of view include recovering water from mine tail-ings, which helps to reduce the reliance on seawater (Cisternas andG�alvez, 2018). Recovering water or reducing the amount of water intailing diminish the need to pump water, which decreases energyconsumption and greenhouse gas emissions involved in pumpingwater to high altitudes, where mines are usually located in Chile(Araya et al., 2018; Herrera-Le�on et al., 2019; Ramírez et al., 2019).Another approach is to use mine tailings as cementitious materialsand pigment for sustainable paints (Barros et al., 2018; Vargas andLopez, 2018).
There have been conducted several studies on new technologiesor processes to recover CRMs from secondary sources such as minewaste (Alcalde et al., 2018; Andersson et al., 2018; Figueiredo et al.,2018; Khalil et al., 2019; Markovaara-Koivisto et al., 2018; Peelmanet al., 2018). Most of these studies are carried out at laboratory andpilot plant scale. Nevertheless, the literature on the recovery ofCRMs from mine tailings is constantly growing. It is due to the factthat new sources of CRMs are urgently needed as their importancein the global economy is constantly growing. Moreover, the utili-zation of wastes such as mine tailings, instead of mineral deposits,is essential from a circular economy point of view. Therefore,extrapolation of the potential of these technologies is immenselyneeded.
Results show that mine tailings facilities of the copper industryin Chile store valuable elements such as CRMs. Therefore, the early
evaluation of geochemical content, identification of suitable tech-nologies, and an economic analysis will help to find more sus-tainable alternatives to CRMs production.
The DCF is a widely used method of financial assessment, but itis not a decisive metrics for making a final decision on real in-vestment. In order to ensure the robustness of assessment, sensi-tivity analysis was performed to analyze the effect of the possiblefluctuations of market prices, capital and operating costs on theanalyzed options of CRMs production. It has been found out thatthe discount rate and both capital and operating costs play criticalroles in economic decisions in different areas (Choi et al., 2018;Cisternas et al., 2014; Santander et al., 2014).
Reprocessing mine tailings will also have an impact on theenvironment. Due to the nature of chemical and physical processes,mineral processing is water and energy intensive, some quantitiesof solvents and reagents are used and at the end of the process,therewill still bewaste that should be stored in a tailing facility. Themining waste obtained after the reprocessing of tailings should bestored in a tailing facility complying with the regulations designedto protect people and the environment.
5. Conclusions
There are 696 tailings storage facilities in Chile, mainly fromcopper mining, which is the biggest mining industry in the country.The biggest TSF has the capacity to store 4,500,000,000 tons oftailings. Currently, there are some initiatives for recovering metalsof interest frommine tailings, but such initiatives are all in the earlystages of feasibility assessment. This study provides valuable in-formation for the assessment of the techno-economic feasibility ofindustrial-scale critical materials recovery from copper industrytailings.
Copper production will continue to grow as the copper gradedecrease. Therefore, the volume of mine tailings that are producedevery year will increase as well. Mine tailings are a worldwideenvironmental problem as they can generate acid drainage, andcause air pollution and soil contamination. Yet, mine tailingscontain several valuable elements, among them critical raw mate-rials. Therefore, the use of mine tailings as a secondary sourcewould help mitigate shortages in critical raw materials by mini-mizing the reliance on primary sources.
Chilean copper mine tailings have substantial economic poten-tial as a source of critical materials such as vanadium, cobalt, rareearth elements and antimony. Minerals contained in Chilean minetailings from copper production are mostly silicates with a lowgrade of CRMs; currently, no approved projects exist that considermine tailings as a source of CRMs. Although mine tailings have alow grade of CRMs, their already stored quantity is enormous. Inaddition, prices of critical rawmaterials can be very high, and thesefactors could make a future production of CRMs from mine tailingsfeasible.
Two options of producing CRMs using mine tailings wereassessed; production of rare earth oxides (REOs) and production ofvanadium pentoxide (V2O5). The DFC method was used to evaluatethe economic feasibility of both operations. The NPV and IRR for theproduction of REOs are positive, which means that the project isfeasible. Nevertheless, the NPV is low for an investment of this scaleand the IRR is close to the discount rate value. The sensitivityanalysis of the NPV of REOs production from mine tailings showedthat NPV is highly sensitive to the discount rate and REO prices.Results of the ANOVA confirm that the discount rate and price arethe most significant variables influencing the NPV behavior.
Vanadium pentoxide production is feasible for an investment of14 years, as the NPV is 76 million US$ and the IRR IS 21% for V2O5production. Vanadium is the main CRMs found in tailings in the
N. Araya et al. / Journal of Cleaner Production 263 (2020) 1215558
Second Region in Chile. It is concluded that producing CRMs usinginactive tailings and later tailings from the active mining processesmay be a feasible option to ensure profitable use of mine tailingsand to diversify CRMs supply.
Declaration of competing interest
The authors declare that they have no known competingfinancial interests or personal relationships that could haveappeared to influence the work reported in this paper.
CRediT authorship contribution statement
Natalia Araya: Conceptualization, Methodology, Validation,Formal analysis, Investigation, Writing - original draft, Visualiza-tion. Andrzej Kraslawski: Conceptualization, Methodology,Writing - review & editing, Supervision. Luis A. Cisternas:Conceptualization, Validation, Formal analysis, Writing - review &editing, Visualization, Supervision, Funding acquisition.
Acknowledgments
This publication was supported by Agencia Nacional de Inves-tigaci�on y Desarrollo de Chile (ANID), AnilloeGrant ACM 170005. N.Araya thanks the ANID (2017, No. 21170815) for a scholarship insupport of her doctoral studies. L.A.C. thanks the supported ofMINEDUCUA project, code ANT1856 and Fondecyt program grantnumber 1180826. The authors are grateful to Peter Jones for his helpin editing the paper.
Appendix A. Supplementary data
Supplementary data to this article can be found online athttps://doi.org/10.1016/j.jclepro.2020.121555.
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