Multivariate Data Analysis of Post Combustion CO2 Capture

Multivariate Data Analysis of Post Combustion CO2 Capture Kasturi Nagesh Pai1, Sai Gokul Subraveti1, Arvind Rajendran1, Vinay Prasad1, Zukui Li1

PARTNERS

1Dept of Chemicals and Materials Engineering , 116th st 89th Ave, Edmonton, Alberta.

FES PROJECT OVERVIEW

BACKGROUND

RESULTS

FUTURE DIRECTIONS

• Explosive growth in ability to synthesize novel solid sorbents

• Proven ability to reduce parasitic energy consumption

• Process design and optimization challenging

Adsorption for CO2 Capture

Optimizer• Multi Objective Optimization• Stochastic Output

PSA Cycle Simulator• Stiff PDEs• Cyclic Process• Convergence Complexities

≥3000 single core hours for the evaluation of 1 material

Material Characterization

& Tailoring Properties

Scale up and Industrial level simulations and

Implementation

Process Design Engineers

Reduce Parasitic Energy

Machine Learning Filters

Materials Engineers

Quick Performance

Evaluation

Process Design & Simulations

Lab Scale Demonstration

Single Column Experiments

Systems Level

Optimization

AIMS AND OBJECTIVES

Parallel Computing

Evaluation and Optimization of

One Solid Sorbent

Single Core Computing

Material Data

Machine Learning Algorithm

Evaluation and Optimization of

One Solid Sorbent

≥120 hours to evaluate one material on multiple core processors

≥0.12 hours to evaluate one material on a single processor

Previous Data about material performance

3-4 orders of magnitude computing time saved Filtering

out materials

Material Data

PSA Cycle Program

Hydrocarbons will continue to serve as an essential energy source while the world transitions toa lower-carbon energy economy, but can we prevent the use of those fuels from contributing tothe accumulation of CO2 in the atmosphere? Existing technologies can capture carbon, butthese methods can be costly and energy-intensive. Extracting energy without burning fuels,improving CO2 capture efficiencies if they are burned, and finding effective ways to store orreuse captured carbon may be essential to ensuring it does not enter the atmosphere.

Power Generation

CO2 for Storage& Usage

Air

Coal66000 TPD Flue Gas

Clean Air

CO

2C

aptu

re

Un

it

Adsorbent Material

Process Cycle

Parasitic Energy500 MW

Machine Learning Algorithm Training

Solid Adsorbent

6 variables

Isotherm

12 variables

FeedCO2:N2

15:85

Light Product

Extract Product

AD

S

Co

-BLO

Cn

-BLO

LPP

Light Product

3 variables

4 variables

Simulation based Data

Dimensional Reduction (PCA)

Supervised Machine Learning

• Support Vector Machines• Ensemble Bagged

Training

R2Adj

Purity - 0.87Recovery - 0.92

Energy - 0.98 Productivity - 0.99

70 Materials for Evaluation

Proxy Model

7 Variables

Full Model Evaluation150000 Core Hours• Accurate Purity, Recovery, Energy and Productivity values• 6 operational variables 12 material parameters

Proxy Model Evaluation 1.5 Core Hours• Accurate Filtration of Purity and Recovery• Precise Productivity Predictions • Limitations- Energy Predictions

Proxy Model Evaluation Pros:• Quick and Accurate Evaluations

of Multiple Materials• Quick Optimization of Materials• Based on only 4 Material

properties Cons:• Not as Accurate as Detailed

Model• Experimental Validation pending