Seaweed Modeling for Drying and the Efficiency as Heavy Metal Removal in Kappaphycus Striatum Variety Sacol using Solar Dryer

Copyright © 2012 by Modern Scientific Press Company, Florida, USA

International Journal of Environment and Bioenergy, 2013, 8(1): 41-55

International Journal of Environment and Bioenergy

Journal homepage: www.ModernScientificPress.com/Journals/IJEE.aspx

ISSN: 2165-8951

Florida, USA

Article

Seaweed Modeling for Drying and the Efficiency as Heavy Metal

Removal in Kappaphycus Striatum Variety Sacol using Solar

Dryer

Majid Khan Majahar Ali1, Sohail Rafiq2, Mahyar Sakari2, Jumat Sulaiman1, Suhaimi Md.

Yasir3, *

1 Mathematics with Economics Programme, School of Science and Technology, Universiti Malaysia

Sabah, 88400 Kota Kinabalu, Sabah

2 Water Research Unit (WRU), School of Science and Technology, Universiti Malaysia Sabah, 88400

Kota Kinabalu, Sabah

3 Seaweed Research Unit (UPRL), Science and Technology, Universiti Malaysia Sabah, 88400 Kota

Kinabalu, Sabah

* Author to whom correspondence should be addressed; E-Mail: [email protected]; Tel.:

+6088-320000 ext : 4228.

Article history: Received 9 August 2013, Received in revised form 1 November 2013, Accepted 12

November 2013, Published 18 November 2013.

Abstract: The solar drying experiment of seaweed using Green V-Roof Hybrid Solar Drier

(GVRHSD) was conducted in Semporna, Sabah under the metrological condition in

Malaysia. Drying of sample seaweed in GVRHSD reduced the moisture content from about

92.68% to 32.06% in 4 days at average solar radiation of about 600W/m2 and mass flow

rate about 0.5 kg/s. The drying kinetics was fitted with six published exponential model

thin layer drying models. The models were fitted using the coefficient of determination

(R2), and root mean square error (RMSE). The result showed modified page was the best

model for describe the drying behavior. In addition, the dried seaweed was used to show

biosorptions of cadminium, lead, zinc and copper. Batch mode experiments were

performed to determine experimental parameters affecting sorption process such as pH,

initial metal ion concentration, shaking rate and biomass dosage. The Pb(II) showed

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Int. J. Environ. Bioener. 2013, 8(1): 41-55


42

highest sorption on pH 4, shaking rate on 250 rpm with 24.18% removal rate; at initial

concentration of 100 ppm and adsorbent dosage at 4g/l the removal percentage is 28.30%.

Overall, this report indicates that Kappaphycus Striatum Variety Sacol is an effective and

economical sorbent for removal of heavy metals from wastewaters.

Keywords: Mathematical modeling, solar drying, drying curve, biosorption, seaweed

Kappaphycus Striatum.

1. Introduction

Seaweed has become one of important agricultural crops in Malaysia. The government has

allocated RM 58.87M for year of 2011 and 2012 and more money is being invested this year to further

boost this sector (Puasa et al; 2011) of economy. In addition, there is a lot of strategies and incentives

are been taken under Third Economic Transformation Programme (3ETP) including Algae Farming

via Mini Estate System in Sabah, Seaweed Identification Grant, Seaweed Cultivation Grant and Grant

from National Key Economic Area (NKEA). Seaweed has been given much more attention because of

its high nutritional value (Wong and Cheung; 2000) and its short growth term that is only 45 days per

cycle (Mairh et al; 1995). Seaweed is mainly cultivated in Sabah due to its suitability to the

environmental and geographical factor compared to peninsular Malaysia (Fudholi et al; 2010). On

average, Malaysia receives of 4.21 to 5.56 kWh/m2 of solar radiation a year (Fudholi et al; 2011). ETP

focus is mainly on transforming the system in seaweed to make it one of the competitive, standardized

and be independent industries. This will create a lot of job opportunities to the people in Sabah. In

turn, this will increase farmer’s income and decrease the poverty of pupil especially in west coast of

Sabah. Moreover, this will also attract investor to this country (Fudholi et al; 2011). Traditionally

seaweed is dried by hanging method on open land or on wooden platform. This method depends on the

weather, requires large open space area, take long time, and produces low quality product because of

contamination (Ratti and Mujumdar, 1997). Several experimental and theoretical studies have been

reported on the development of various types of solar drying system for agricultural and marine

products (Sacilik et al; 2006). Recently, many researches on experimental studies and mathematical

modeling have been widely and effectively used for analysis (Midlli, 2001; Sharaf-Eldeen et al, 1979).

Biosorption is a term that describes the removal of heavy metals by the passive binding to non-

living biomass from an aqueous solution. This implies that the removal mechanism is not

metabolically controlled (Sheng et al; 2004). The toxic heavy metals in the aquatic world are serious

concern for fauna and flora. Most of the research is diverted to developing cost-effective technologies



43

for the removal of metal ions from aqueous solutions (Lazar et al; 1991). Common wastewater

treatment technologies include membrane separation, electrochemical precipitation, ion exchange, pre-

concentration, flotation, membrane filtration, ultra-filtration, coagulation-flocculation and adsorption

(Yang et al, 2008; Yun et al, 2001; Parado et al, 2007). In many countries the heavy metal content in

drinking waters and wastewaters often exceed the permissible standards. According to the United

States Environmental Protection Agency the discharge of Cr(VI) to surface water is regulated to <0.05

mg/L, whereas the total chromium (containing Cr(III), Cr(VI), and other forms of chromium) is

regulated to be discharged at <2 mg/L (Park et al; 2006).

The red algae are an important assemblage of plants that are classified in about 265 genera with

more than 1500 species (Sari & Tuzen; 2005). They derive their characteristic colour from the large

amounts of the carotenoid fucoxanthin (which yields a brown and green colour) contained in their

chloroplasts and the presence of various pheophycean tannins. They occur mainly in the marine

environment, where they appear as an intertidal component (Davis et al; 2003). Some marine forms

penetrate into brackish environments, and can be an important part of the salt marsh fauna (Baral et al;

2009). Brown algae flourish in temperate to subpolar regions where they exhibit the greatest diversity

in species and morphological expression (Parado et al; 2007). Rhodophyta divisions contain the largest

amount of amorphous embedding matrix polysaccharides as in fig. 1, whereas fig. 2 shows the

Morphology Kappaphycus Striatum variaty Sacol. This characteristic, combined with their well known

ability to bind metals, makes them potentially excellent heavy metal biosorbents (Murphy et al; 2008).

Figure 1: Cell wall structure in the red algae (Volesky, 2003)

Figure 2: Morphology Kappaphycus Striatum variety Sacol



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Since red algae are abundant, locally available, cost effective and readily available media

which can be utilized as biosorbent material for wastewater treatment processes (Bellu et al, 2010

Present study focuses on the thin layer drying model of seaweed (Kappaphyccus Striatum

variety Sacol) in a solar drier under climatic condition of Malaysia and the potential utilization of

locally available material to remove toxic heavy metals from waste water.

2. Materials and Methods

2.1. Seaweed Sample

The seaweed (Kappaphyccus Striatum variety Sacol) (as shown in Fig. 3) used in this study

were obtained from PELADANG association in Semporna, Sabah. The initial moisture content of

seaweed was determined by measuring its initial and final weight. Then, it was calibrated with oven

drying method at the temperature of 115°C in order to obtain constant weight. Average initial moisture

content obtained is 92.68% (AOAC, 2000)

Figure 3. Kappaphycus Striatum variaty Sacol.

2.2. Solar Drier

A Green V-Roof Hybrid Solar Drier (GVRHSD) was installed at the Selakan Island, Semporna

Sabah. The drier classified as the forced convection indirect type. A photo graph of the experimental

drier is shown in Fig. 4 and schematic diagram is shown in Fig.5. The solar drier consists of the fan

that import from Germany, drying chamber, v-aluminum roof, solar collector and trays with Teflon

screen tape. The highest recorded relative humidity (RH %) of the drier is 80% and the maximum

temperature inside the drier that was recorded is 60°C. The drier maximum capacity is 5 tons and key

parameter of the solar drier is shown in Table 1.



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Figure 4. Photographs of GVRHSD Figure 5. Schematic diagram of GVRHSD

Parameter Unit Value

Collector Area m2 2286

Drying Chamber Area m2 3630

Mass Flow Rate Average

Temperature of Drying Chamber

kg/s

°C

0.01-0.05

40-60

2.3. Drying Process

Experiment was done between 8:00am until 5:00pm from 19/1/13 until 22/1/13. On the

17/1/13, 5 tons fresh seaweed was harvested with the seaweed complete one cycle of cultivation (45

days). After determined the initial weight, all the fresh seaweed went through sauna process. The main

objective of sauna is to reduce the moisture content to 50%. The sauna process is done in Selakan

Island for 2 consecutive days. The pre-drying data also was taken for the drier for two consecutive

days. The remaining about 2.5 tones was arranged in the tray with same depth and surface area for the

seaweed.

Then, the tray arranged into the drier. Number of samples was selected with same amount and

put in the different places in the tray. The purpose was to study the drying kinetics in drying time of

samples. During the drying experiment, the weather was sunny overall except 21/1/13 rained heavily.

The data measured include air temperature (ambient temperature, air temperature inhlet and outlet of

the collector), radiation intensity, and air velocity and temperature before it enters the drying chamber,

the temperature inside drying chamber, the temperature of the air outside of the dryer chamber.

The relative humidity and temperature sensor were installed in inhlet and middle of drying

chamber. For Ambient relative humidity and temperature, we used Hygrometer. Air temperature was

measured by T-type thermocouple, and the intensity of solar radiation was measured using

pyranometer. The moisture loss was determined by means of a digital balance which having an

accuracy of 0.01g [19].

Table 1. Key parameter of GVRHSD



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2.4. Sample Preparation for Adsorption

The dried samples that collected were thoroughly washed with distilled water to remove the

impurities and salt. Then, the samples were dried in oven for 3 hours. The samples were then grounded

to obtain the 1-2 mm of particle size fraction for batch experiments. The samples were stored in

airtight glass containers without further treatment for prior analysis.

2.5. Solution Preparation

The Pb (II), Cu (II), Zn (II) and Cd (II) ionic solutions were prepared by mixing the nitrate salts

with distilled water to get 1000 mg/L ionic concentration. Working solutions were prepared by diluting

the stock solutions with distilled water to get the desired ionic concentrations.

2.6. Surface Morphology Analysis

The surface morphology of samples was determined by using Carl/Zeiss Evo MA 10 scanning

electron microscope (SEM). The sample was mounted onto 1.27 cm diameter stub and was coated with

gold using Emitech K550X Sputter Coater at 20 mA current for 1 min duration. Images were captured

under electron acceleration voltage of 15 kv.

Figure 6. SEM of Seaweed Sacol

2.7. Moisture Content and Drying Formulae

The moisture content was expressed as a percent wet basis and then converted to gram of water

per gram of dry matter. The experimental drying data for seaweed were fitted to the exponential model

thin layer models in Table 2 using non-linear regression analysis. The coefficient of determination (R2)

was one of the primary criteria to select the best model to compare the experimental data. In addition

to R2, root mean square error (RMSE) was used to compare the relative goodness of the fit. The best

model describing the drying behavior of seaweed was chosen as the one with highest coefficient of

determination and the least root mean square error. This parameter can be calculated as follow:



47

The moisture ratio (MR) can be calculated as

Where,

= Equilibrium moisture content

= Intial moisture content

The moisture content of material ( ) can be calculated by two methods on the basics using the

following equations (Sacilik et al; 2006).

The moisture content wet basis

The moisture content dry basis

Where,

w(t)= mass of wet materials at the instant t

d = mass of dry materials

Table 2. The exponential model thin layer drying models

No Model Name Model References

1 Newton (Sacilik et al;, 2006)

2 page (Sacilik et al;, 2006)

3

4

5

6

Modified page

Hendarson and Pabis

Logarithmic

Two Term

(Sacilik et al;, 2006)



(Midilli, 2001)



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3. Results and Discussion

3.1. Drying Modeling Result

During the experiments, the variation of solar radiation, collector air temperature, drying

chamber temperature and relative humidity are shown in Figs 7-9. From these figures, the daily mean

of drying chamber temperature, relative humidity and solar radiation were calculated which range from

about 30-64°C, 20-80 RH% and 150-1000 W/m2, respectively. During the 4 day drying, the daily

averages of temperature and relative humidity at the drying chamber were 50°C and 66% respectively.

The total hour taken to dry the seaweed is 36 hour. The final mass of seaweed that left is 250kg. The

final moisture content is about 32.06% which is appropriate to the industry.

Figure 7. Solar radiation versus Time

Figure 8. Ambient Temperature (°C) & Ambient Relative Humidity (RH%) versus Time



49

Figure 9. Moisture Content (db%) versus Time

The experimental data of the moisture ratio (MR) is shown in Fig. 10. From Fig 10, we can see

when MR versus time is fitted to the six selected models; it seems that modified page is the best model

that describing the seaweed drying behavior. Table 3 shows the results of the regression analysis on the

drying data. Non-linear regression analysis was performed using excel simulations. The statistical

parameter estimation showed that R2 and RMSE values ranged 0.6658-0.9989 and 0.897-3.125,

respectively.

Figure 10. Moisture Ratio versus Time

Table 3: The exponential model fitting data

No Model Name Model R2 RMSE

1 Newton k=1.654 0.9657 2.368

2 page k= 2.032 n= 0.518 0.9757 1.259

3

4

5

6

Modified page

Hendarson and Pabis

Logarithmic

Two Term

k= 2.365 n= 0.5183

k= 1.892 a=0.897

k= 2.568 a= 0.98 c= 0.025

a= 1.542 b= -5.894 k=k0=1.542

.

0.9989

0.7855

0.9658

0.6658

0.897

3.258

3.125

1.109



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3.2. Adsorption in Heavy Metal Removal Result

3.2.1. Adsorption towards initial concentration

To explore the possibility of enriching low concentrations of analyses from large volumes, the

maximum applicable sample volume must be determined. For this purpose different volumes of

sample solution, each containing 10g of selected heavy metals ions were equilibrated with seaweed of

Sacol under the optimum conditions. Concentration for metal on the biomass up to 100 ppm, but

higher concentration will cause adsorption saturated (Fig. 11). The increase in metal lead (II)

concentration increased the uptake affinity (q) and decreased the percentage removal of metals ion.

This biosorption characteristic represented that surface saturation was dependent on the initial metal

ion concentrations. At low concentrations, biosorption sites took up the available metal more quickly.

However, at higher concentrations, metal ions need to diffuse to the biomass surface by intra-particle

diffusion and greatly hydrolyzed ions will diffuse at a slower rate [18]. From Fig. 11, it can be seen

clearly that lead (II) shows a good adsorbent compared with others metals with 28.30% removal rate.

Figure 11. Removal versus initial concentration

3.2.2. Adsorption towards shake rate

Sorption of metal ions as a function of shaking speed was studied in the range of 0–250 rpm. It

was found that percentage removal increases with increasing shaking speed and attains a maximum

sorption at 250 rpm. The maximum percentage that achieved is 24.18% by lead(II). The copper

maximum removal is 20.82%. The maximum removal percentages for zinc (II) and cadminium (II) are

19.87% and 17.83% respectively.



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Figure 12. Removal versus Shake Rate (rpm)

3.2.3. Adsorption towards biomass dosage

The influence of biomass dosage on the adsorption of metal ions in multi-component systems

was examined by varying dosages from 0.5 to 5 g/L. The effect of biomass dosage of Sacol (0.5-5g/L)

on biosorption of metal ions Cu(II), Zn (II), Pb (II) and Cd (II) shown in Fig.13. It was observed that

removal efficiency increased with increase in biomass dosage. An increase in biomass concentration

generally increases the degree of biosorption of metal ions because of an increase in overall surface

area of the biosorbent, which in turn increases the number of binding sites [18]. On the contrary, the

metal uptake decreases by increasing the biosorbent dosage, this may be due to complex interactions of

several factors. The important factor is that at high sorbent dosages the available metal ions are

insufficient to cover all the exchangeable sites on the biosorbent, usually resulting in low metal uptake.

Also, the interference between binding sites due to increased biosorbent dosages cannot be overruled,

as this may result in a low specific uptake.

Figure 13: Influence of biomass dosage

From Fig. 13, it was clear that maximum removal efficiencies were observe when biomass

dosages is above 3 g/L. On the other hand, maximum biosorption capacities were observed at dosages



52

below 1 g/L. The metal uptake capacity and removal efficiency are equally important in sorption

experiments as both usually take part in deciding the sorption performance of a given biosorbent.

Taking these two factors into consideration, the biosorbent dosage of 3 g/L was selected for further

studies, as it shown relatively good removal efficiencies and uptake capacities. Results showed that the

biosorption efficiency is highly dependent on the increase in biomass dosage of the solution. The

maximum biosorption of the metal ions was attained at a biomass dosage of 43.57 % g/L and it was

almost same at higher dosages. This trend could be explained as a consequence of a partial aggregation

of biomass at higher biomass concentration, which results in a decrease in effective surface area for the

biosorption.

3.2.4. Adsorption towards pH

For all the heavy metal ions the percentage of biosorption was low at pH 2 (as shown in fig.

14), and increased to a maximum level at pH 4 (Pb2+, Cu2+, Zn2+ and Cd2+). The increase was most

noticeable for pH changes from 2 to 3. After biosorption reached maximum points, patterns were

observed at higher pH levels. In the case of positively-charged heavy metal ions (Pb2+, Cu2+, Zn2+ and

Cd2+), adsorption reached a plateau, and did not change noticeably. From the graph, we can see clearly

that biosorption was most steep between and pH 4. However the increase of biosorption stopped at pH

6. This is probably because in the green algae the number of negatively-charged binding site is limited.

The lead (II) shows the highest peak followed copper(II), zinc(II) and cadminium(II). Biosorption of

heavy metal ions by green algae can be explained by electrostatic binding of metal ions with a variety

of functional groups located in the cell wall of brown algae. Carboxyl and amino groups are commonly

found among the functional groups in green macro-algae.

Figure 14: Removal (mg/L) versus pH

The process of biosorption resembles the binding of metal ions to cation exchange resins.

Literature reported that alginate (uronic acid polymers) in the green seaweed Sacol cell walls and

intracellular spaces act as ion-exchange materials. Since protons may compete with metal cations for

the same binding sites, binding of metal ions on the functional groups generally depends on pH (i.e.,



53

proton concentration). At lower pH levels, functional groups, such as the carboxylic groups, are more

protonated (i.e., bound by hydrogen ions) due to higher concentration of protons, and negative charge

intensity of functional groups decreases. This explains why pH 2 condition was least favorable for

electrostatic attraction of metal ions. At a pH level higher than 2, biosorption of metal cations

increased presumably because functional sites became more negatively-charged due to deprotonation.

The increase in biosorption was most steep between pH levels 2 and 3. This result concurred with

literature data on a chemically modified biomass of Sacol for the biosorption of lead and copper

(Onyancha et al., 2008)

4. Conclusions

A solar drying system is tested on samples of seaweed Kappaphycus Striatum variety Sacol.

Kinetics Curve drying of seaweed is known to use in this system. From the study, non-linear

regression analyses were carried out to select the thin layer models of solar drying curve of seaweed

Kappaphycus Striatum variety Sacol. The model that best described the thin layer drying kinetics is

modified page model with R2 and RMSE values of 0.9989 and 5.34x 10-3 respectively. Besides, batch

mode experiments were performed on dried samples to determine experimental parameters affecting

sorption process such as pH, contact time, initial metal ion concentration, shaking rate and biomass

dosage. The Pb(II) sorption had highest sorption on pH 4, contact time 60 minutes, shaking rate on 250

rpm with 24.18% removal rate; and at initial concentration of 100 ppm and adsorbent dosage at 4g/l

the removal percentage was 28.30%. Overall, this report indicates that Kappaphycus Striatum Variety

Sacol is an effective and economical sorbent for removal of heavy metals from wastewaters.

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Seaweed Modeling for Drying and the Efficiency as Heavy Metal Removal in Kappaphycus Striatum Variety Sacol using Solar Dryer

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