Transactions Template JOURNAL OF ENGINEERING RESEARCH AND TECHNOLOGY, VOLUME 1, ISSUE 3, SEPTEMBER 2014 95 Optimization of Calcium Alginate Preparation in Aqueous by Response Surface Methodology Kamaruddin, M.A.1, *Yusoff, M.S.1, Aziz, H.A.1 and Alrozi, R.2, Zawawi, M.H.3 1School of Civil Engineering, Universiti Sains Malaysia, 14300 Nibong Tebal, Penang, Malaysia, email: suffian@usm.my 2Faculty of Chemical Engineering, Universiti Teknologi MARA Pulau Pinang, 13000 Penang, Malaysia 3Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional, 43000 Kajang, Selangor Malaysia Abstract— In this study, a statistical software package and design of experiment were applied for the preparation of calcium alginate in aqueous. Alginate which is originates from polysaccharide brown algae with different type of uranic and ma- nuronic chains were used as an intermediate, blended with calcium carbonate powder for macro size adsorbent preparation. Though adsorption has been an ideal choice in waste water purification, the needs to find an alternative source of adsorbent has received considerable interests recently. In this study, a central composite design was used to develop a model to predict and optimize the preparation condition of calcium alginate. Mathematical model equations were obtained from simulation programming. Analysis of variance (ANOVA) for viscosity and pH (responses) indicated that the model was adequate to fit the experimental data (p values, lack of fit, R2adj). From the statistical parameters, it showed that the quadratic effects for both calcium carbonate and alginate powder were the most significant. Meanwhile, the correlation coefficient, R2 for both independent variables (calcium carbonate and alginate) of 0.9974 and 0.9008 implied that the developed models wer ead- equate to navigate the design space. The optimum preparation condition was carried out by compromising the independent factors and responses at different criteria. Finally, the optimum preparation condition for calcium alginate was obtained wit h 2.00 g of calcium carbonate and 10 % (w/v) that result in 38 cP of viscosity at pH 10, respectively. Index Terms— Adsorbent, Alginate, pH, Statistical analysis, Viscosity. I INTRODUCTION Managing water pollution is one of the crucial challeng- es in current world due to rapid changes of product manu- facturing and technological advancement that result in wide variation in industrial effluent. For instance, heavy metals byproducts from electroplating, paint, textile, mining and steel making activities carries significant amount of copper, leads, manganese and cadmium that poses a threat to human and environment. Generally, the discharge of industrial waste waters can be vary in terms of quantity and quality of heavy metals, organic and non organic matter, suspended particulates, to name a few. Therefore, discharge of untreat- ed waste water has been a major concern to many stakehold- ers in order to safe guard environment and human health, particularly. If not properly and safely treated, waste water from industrial activities can be an impendent source that is very costly to remediate. One of the available methods in physico-chemical is ad- sorption which offers better removal of heavy metals, high efficiency, high resistance, plentiful source of material and cost effectiveness [1]. Until now, various types of adsorbents have been discovered and tested for their efficiency in ad- sorption process including mineral deposits [2], agriculture wastes [3-5] and industrial byproducts [6, 7] The selection of these types of material mostly depending on the insoluble porous matrix and some available active groups that capable to reacts with polar and non polar pollutants [8]. Alginate based polysaccharides have been widely em- ployed in biomedical and pharmaceutical fields mostly in drug delivery, wound dressings, and dental implants. In nano particles application, alginate has found their application in biological devices [9]. In environmental field, due to its gel forming ability, biocompatibility, non toxicity and biodegra- dability [10, 11], alginate has been used as catalysts and ad- sorbents for separation and purification in waste water treatment. However, the utilization of alginate in aqueous exposed their solubility which limits the adsorption proper- ties for ionic pollutants specifically for the case of heavy metals ions. Therefore, an experimental work was carried out to modify alginate in aqueous blended with calcium car- bonate powder to improve the adsorption properties. The preparation condition of the calcium alginate was tested to- wards viscosity and pH based on the mathematical equation developed from statistical software. “ Kamaruddin, M.A., Yusoff, M.S., Aziz, H.A and Alrozi, R. Application of Response Surface Methodology for Calcium Alginate Preparation in Aqueous (2014) 96 II EXPRIMENTAL SET UP A MATERIALS Principally, alginate has been recognized as an excellent polysaccharide in gelling system because of its unique phys- icochemical, thermal and rheological properties. It im- portance relies in hydrocolloid properties including their viscosity, pH, solubility and mechanical properties. In this work, sodium alginate (C6H7O6Na) powder was obtained locally and supplied by R&M Chemicals (Malaysia) with low molecular weight was preferred because it is widely used in many encapsulation processes [12]. Meanwhile, cal- cium carbonate powder was obtained from a limestone quar- ry wastes which is considered as a byproduct from the quar- rying activity. The composition of alginate and calcium car- bonate is listed in Table 1. TABLE 1: Alginate and Calcium Carbonate Composition Alginate (C6H7O6Na) Specification Content Assay 91-106% Moisture Max 15% Matter insoluble in water Max 1% Lost of ignition, LOI at 1100 °c Max 25% Molecular weight 85000 Viscosity; max at 2 g/L (used spindle no. 4), mPas 65 Calcium carbonate powder (CaCO3) Elements Content C 20 CaO 75 SiO2 3.4 Al2O3 1.1 Particle size Passing 75 µm sieve aper- ture B Calcium alginate preparation The aqueous solution was prepared by first adding known volume of distilled water in 250 mL beaker and stirred at 85 °C for 15 min. Then, a known weight of algi- nate was added slowly with a constant stirring rate of 150 rpm for 10 minutes until homogeneous mixture was ob- served. Prior to the addition of calcium carbonate powder, the alginate solution was cool down to 50 °C to prevent thermal shock from occurring that would initiate lump for- mation between alginate and calcium carbonate. Subse- quently, the mixture of alginate and calcium carbonate pow- der was stirred for 15 minutes and the measurement of vis- cosity and pH was carried out, respectively. C Viscosity and pH measurement The viscosity of the calcium alginate in aqueous was measured by using laboratory viscometer model DV II+Pro (Brookfield, USA). The spindle used during measurement was based on the manufacturer recommendation of SC4-27. During the measurement, the aqueous temperature was in- creased to 80 °C for 15 minutes interval and the spindle speed was maintained at 20 rpm throughout the measure- ment process. Next, pH was measured by using Eutech 2700 pH meter (Thermo-Scientific, USA). The entire measure- ments were done in triplicates and the average values were used further in statistical analysis. D Design of experiment To achieve adequate and reliable measurements of inter- ests, the response surface methodology (RSM) was used. RSM is a collection of mathematical and statistical tech- nique for developing, improving and optimizing independent and dependent variables (5). It is normally used to identify the relative of several affecting factors in the presence of complex relationship. Above all, the application of RSM will increase product yields, reduce process variability, clos- er confirmation of the output response to nominal and target requirements and reduces trial and overall cost [13]. In this work, a central composite design (CCD) which is an efficient tool for sequential experiment was used to in- corporate information from a properly planned factorial ex- periment. The CCD consists of 2k factorial or “cube” points where ‘k’ is the number of factors. 2k axial points fixed axi- ally at a distance α, from the center to generate quadratic terms, and replicate tests at the center of experimental region (14). In addition, replicates of the tests are important as they provide an independent estimate of the experimental error. In this work, a CCD for 2 factors (alginate and calcium car- bonate), with 5 replicates at the center resulting in total 22 + 22 + 5 = 13 runs. A value of α = [2k]1/4 assures rotation of the CCD and equivalent to 1.414. In order to narrower the independent variables ranges, a pre- liminary experiment was conducted prior to design of exper- imental runs to minimize the uncontrolled factors effects. It was found that the effective ranges of calcium lies between 2 to 10 g. Meanwhile, the effective alginate amount in aque- ous preparation was found between 5 to 10 % (w/v). Gener- ally, the viscosity and pH of calcium alginate relies on these preparation conditions knowing that increasing calcium car- bonate dosage led to increase of pH due the precipitation of calcium ion. In addition, viscose solution tends to retard the formation of alginate beads when ejecting from injector nozzle. Therefore, these two independent factors have been identified as the key variables in the preparation of calcium alginate in aqueous. Simplified design summary for inde- pendent variables and responses in terms of coded factors is listed in Table 2. A complete CCD with 4 factorial points, 4 axial points and 5 replicates of the center point are given in Table 3. Kamaruddin, M.A., Yusoff, M.S., Aziz, H.A and Alrozi, R. Application of Response Surface Methodology for Calcium Alginate Preparation in Aqueous (2014) 97 TABLE 2: Independent Variables and Responses In Coded Term Independent factors Code Unit Range CaCO3 x1 g 2 – 10 Alginate x2 % (w/v) 5 – 10 Responses Code Unit Range Viscosity Y1 cP 24 – 49 pH Y2 - 9.7 – 11.8 TABLE 3: Experimental Design and Results Run Design order Results CaCO3, x1 Alginate, x2 Viscosity, Y1 (cP) pH, Y2 Experiment Predicted Experiment Predicted 1 -1 -1 24.00 24.37 10.40 10.20 2 +1 -1 40.00 40.63 9.70 10.03 3 -1 +1 38.00 37.87 11.30 11.67 4 +1 +1 45.00 45.13 10.40 10.37 5 -1.414 0 27.00 26.93 11.80 11.71 6 +1.414 0 44.00 43.57 9.00 9.34 7 0 -1.414 33.00 32.39 10.90 10.43 8 0 +1.414 45.00 45.11 11.30 11.54 9 0 0 49.00 48.60 11.40 11.54 10 0 0 49.00 48.60 11.80 11.54 11 0 0 49.00 48.60 11.80 11.54 12 0 0 48.00 48.60 11.40 11.54 13 0 0 48.00 48.60 10.40 10.20 III RESULTS AND DISCUSSIONS A Model fitting The most important parameters which affect the viscosi- ty and pH of the calcium alginate are calcium carbonate and alginate dosage. In order to investigate the combine effects of these factors, experiments were conducted at different combination. Thirteen runs of experiments were carried out to evaluate the effects of these combinations and correlated based on the second-order polynomial model. Also, the sug- gested models for both responses were found to be quadratic as shown in Equations 1 and 2: 𝑉𝑖𝑠𝑐𝑜𝑠𝑖𝑡𝑦, 𝑌1 = +48.60 + 5.88𝑥1 + 4.50𝑥2 − 6.67𝑥12 − 4.92𝑥22 − 2.25𝑥1𝑥2 (1) 𝑝𝐻, 𝑌2 = +11.51 + 0.47𝑥1 + 0.39𝑥2 − 0.25𝑥12 − 0.8𝑥22 + 0.35𝑥1𝑥2 (2) where x1 and x2 are the calcium carbonate and alginate dos- age. The coefficient with one factor is known as the effect of that particular factor, meanwhile the coefficient of two fac- tors and the other with second-order terms known as the interaction between the two factors and quadratic effects. In addition, the positive and negative sign represents and syn- ergistic and antagonistic effects, respectively (15). B Models validation To ensure satisfactory and adequate prediction to the re- al system of the fitted data, model validation was carried out. Also, the fitted model was validated for precise judg- ment as to avoid misleading conclusions. In this work, we used graphical and numerical methods as an ideal tool for model explanatory. A residual is defined as difference be- tween an observed value and estimated value. Figure 1 shows residuals against the fitted values. Meanwhile, Figure 2 shows the residuals against observation data. The plot was drawn to evaluate any inconsistency or any drift for each observation of the residuals. It can be assumed that both residuals (viscosity and pH) of the models were randomly distributed and no obvious drift of the data models were found. Next, normal probability plots of the models data were plotted to check for the normality. Additionally, if the resid- ual plot lies approximately along a straight line, the normali- ty assumption is satisfied. Any departure from a straight line indicates that a departure from a normal distribution of the residuals. In this study, it was observed that the residuals for both viscosity and pH were normally distributed and there- fore the normality assumptions are satisfied and response variables are normally distributed. Plots of normal probabil- ity against residuals for viscosity and pH are shown in Fig- ure 3. Kamaruddin, M.A., Yusoff, M.S., Aziz, H.A and Alrozi, R. Application of Response Surface Methodology for Calcium Alginate Preparation in Aqueous (2014) 98 (a) (b) Figure 1: Plot of residuals against fitted values for a) Vis- cosity and b) pH (a) (b) Figure 2: Plot of residuals against order of observation for a) Viscosity and b) pH (a) (b) -2 -1 0 1 2 20 30 40 50 60 R e si d u a ls Fitted values -2 -1 0 1 2 3 8 9 10 11 12R e si d u a ls Fitted values -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 0 5 10 15 R e si d u a ls Order of observation -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0 5 10 15 R e si d u a ls Observation order 0 20 40 60 80 100 120 -2 -1 0 1 2 N o r m a l % p r o b a b il it y Residuals 0 20 40 60 80 100 120 -2 -1 0 1 2 3 N o r m a l % p r o b a b il it y Residuals Kamaruddin, M.A., Yusoff, M.S., Aziz, H.A and Alrozi, R. Application of Response Surface Methodology for Calcium Alginate Preparation in Aqueous (2014) 99 For numerical analysis, the developed models were then checked by employing the coefficient of determination (R2) and adjusted R2 (R2adj). R2 indicates how fit a set of data points distribute in a statistical model. In contrast, R2adj modifies the R2 by taking into account extra explanatory variable in the model. To obtain this, the sum of squares (SS), number of experiment (n) and the number of predictor or terms (p) were used and calculated as follows: 𝑅2 = 1 − SSresidual SSmodel + SSresidual (3) Radj 2 = 1 − n − 1 n − p (1 − R2) (4) The calculated R2 values for both viscosity and pH for respective models were higher than 90%, corresponds to 0.9974 and 0.9008. In addition, the Radj2 for both responses were found close to R2 (0.9955 and 0.8299) and conforms satisfactory adjustment of the quadratic models towards the experimental data. Analysis of variance (ANOVA) was then carried out to analyze the differences between group means and variation. As demonstrates in Table 4, low probability values of less than 0.0001 and 0.0021 for viscosity and pH explained that the regression were significant to the quadrat- ic models. To further investigate the model adequacy, lack of fit for each of the models was carried out. Principally, the lack of fit describes the variation in the data to the fitted model. In the case that the model does not fit the data sufficiently, the lack of fit will be significant. As can be seen from Table 4, the lack of fit of 0.395 and 0.1084 for viscosity and pH indi- cates that the lack of fit was not significant which suggests that the model was capable to describe the data well. More- over, adequate precision measures the signal to noise ratio for both viscosity and pH of 61.540 and 9.592 (data not shown) which more than 4 implies that the model can be used to navigate the design space. C Optimization analysis To obtain an optimum preparation condition for calcium alginate, response optimizer was employed. However, it is crucial to analyze the relationship between predictors and responses prior to optimization for each model and carried out first. The analyses were carried out by means of Fisher ‘s ‘F’ and Student ‘t’ tests. Generally, an F-test is used to test for more than one coefficient or joint hypotheses, whereby, t-test is used whenever the hypotheses test is concern to one coefficient at a time. The p values associated with t- test explain the significance for each factor and the interaction between them. As the magnitude of t increases, the values of p become smaller which corresponds to more significance of the coefficient term. As can be seen from Table 5, linear, quadratic and interaction terms for predictors were found to be significant for viscosity measurement with p values less than 0.000. It can be considered that all the coefficients gave ultimate effect in determining the optimum condition for viscosity. Meanwhile, quadratic effect of calcium carbonate gave least significant effect for determining pH with p values of 0.110 followed by interaction between calcium carbonate and algi- nate (0.095). In addition, linear effect of calcium carbonate (0.008), alginate (0.020) and quadratic (alginate, 0.001) were found to have significant impact from the coefficient of regression for pH, respectively. TABLE 4: ANOVA for Viscosity and pH Figure 4 shows the three dimensional mesh wire plots for viscosity and pH. The plots enable graphical visualiza- tion to understand the relationship between independent variables and responses. From the figure, increasing amount of alginate and calcium carbonate led to increase in viscosi- ty. In contrast, little dosing of alginate and calcium car- bonate reduced viscosity from 37 to 21 cP due to the leach- ing of calcium carbonate and alginate compared to water’s density. This condition is undesirable because low viscosity will only retard the formation of calcium alginate in cross- link solution. Therefore, the interest region for viscosity was fixed at 37 cP to ensure pourability of calcigum alginate mixture. For the case of pH, its proven that lower dosing of calcium carbonate and alginate results in low pH. Generally, precipitation of alkaline ions from calcium will increase hydroxide ions. Therefore, increasing the amount of calcium Statistical parameter Degree of free- dom Sum of square Mean square Prob.>F Remarks Viscosity Model 5 884.88 176.98 < 0.0001 Significant x1 1 276.61 276.61 < 0.0001 x2 1 161.74 161.74 < 0.0001 x12 1 309.95 309.95 < 0.0001 x22 1 168.73 168.73 < 0.0001 x1x2 1 20.25 20.25 0.0001 Lack of fit 1.15 0.34 0.3953 Not signif- icant pH Model 5 8.36 1.67 0.0021 Significant x1 1 1.79 1.79 0.0078 x2 1 1.19 1.19 0.0197 x12 1 0.44 0.44 0.1105 x22 1 4.75 4.75 0.0005 x1x2 1 0.49 0.49 0.0950 Lack of fit 0.1084 Not signif- icant Kamaruddin, M.A., Yusoff, M.S., Aziz, H.A and Alrozi, R. Application of Response Surface Methodology for Calcium Alginate Preparation in Aqueous (2014) 100 carbonate results in increase the pH values. Meanwhile, it is worth to mention that alginate plays least effect to the pH of the aqueous because dissolution of uranic and manuranic acid in alginate was limited to the amount of hydrophobic agents presence in the aqueous. In this case, water, as the main dissolving mediator was capable to overcome the acid- ic condi tion of alginate and produces bases aqueous condition. TABLE 5: Estimated Regression Coefficient for Viscosity and pH Figure 4: 3 dimensional wire plots for a) viscosity and b) pH An optimum preparation condition for calcium alginate in aqueous was determined based on the numerical optimi- zation process. To obtain this, combination of two responses were compromised subject to optimization parameters. Prin- cipally, a viscous condition and relatively high pH are de- sired to obtain an optimum preparation condition. This pro- cess is crucial in order to reduce the number of experimental runs when the original designs contain more points. In addi- tion, the target goal for independent variables (calcium car- bonate and alginate) were fixed in the range while the re- sponses (viscosity and pH) were fixed at 37 cP and 10, re- spectively. The software searches for a combination of input variables levels that would jointly optimize a set of respons- es by satsifying the requirements for each response in the set. Finally, after obtaining composite desirability for each response, the globla solution for each of the preparation conditions was obtained sucssfully at 2 g of calcium car- bonate and 10% of alginate that results in 37 cP of viscosity and pH 10. To conforms with suggested preparation condi- tions from the software, three replicates of experiments were carried out for Calcium carbonate and alginate. As shown in Table 6, the viscosity and pH obtained from the additional experiments are close to those predicted from the model which indicates that the RSM was the ideal tool for optimiz- ing the preparation conditions of the calcium alginate. TABLE 6: Replication of Suggested Preparation Condition Viscosity pH Predicted values 37 10 Replicate 1 35 10 Replicate 2 36 11 Replicate 3 35 10 Error (%ave) 4.5 3.3 V CONCLUSION This study has demonstrated that the utilization of RSM and design of experiment has successfully obtained the optimum preparation conditions of calcium carbonate and alginate for calcium alginate. A statisical modelling with CCD with fixed values of independent variables managed to produce a high correlation coefficient from two quadratic models. Normal probability plots for both responses were found normally distributed and successfully explained by the Parameter Viscosity, Y1 pH, Y2 Estimated coefficient Standard error T-value Prob.>T Estimated coefficient Standard error T-value Prob.>T Constant 48.600 0.2591 187.540 0.00 11.5400 0.1622 71.136 0.000 x1 5.880 0.2049 28.702 0.00 0.4725 0.1283 3.684 0.008 x2 4.496 0.2049 21.947 0.00 0.3859 0.1283 3.009 0.020 x12 -6.675 0.2197 -30.382 0.00 -0.2513 0.1375 -1.827 0.110 x22 -4.925 0.2197 -22.417 0.00 -0.8263 0.1375 -6.008 0.001 x1x2 -2.250 0.2897 -7.766 0.00 0.3500 0.1814 1.930 0.095 Kamaruddin, M.A., Yusoff, M.S., Aziz, H.A and Alrozi, R. Application of Response Surface Methodology for Calcium Alginate Preparation in Aqueous (2014) 101 ANOVA. F inally, additional experiments have shown that relatively small error arised from the replicates indicates that RSM and CCD can be used for modelling and optimizaing the calcium alginate preparation conditions. 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Mohamad Anuar Kamaruddin is a Ph.D candidate in waste wa- ter engineering at Universiti Sains Malaysia and a recipient of Min- istry of Higher Education Malaysia scholarship. He received his first degree from Universiti Sains Malaysia in 2009 awarded with Bachelor of Science in civil engineering. He obtained degree in Master of Science in civil engineering from Universiti Sains Ma- laysia in 2011 with major in landfill technology. His current re- search is focuses on alleviating problems associated with waste water and solid waste management. To date, he has published sev- eral scientific articles related to environmental engineering field. Associate Professor Dr Mohd Suffian Yusoff obtained his first degree from Universiti Putra Malaysia in agricultural science in 1995. He later pursued master degree in mineral resources engi- neering in Universiti Sains Malaysia and graduated in 2000. Dr. Yusoff received his doctorate from Universiti Sains Malaysia in 2006 with major in solid waste management. Currently, Dr Yusoff serves School of Civil Engineering Universiti Sains Malaysia as anacademic programme chairperson (environmental and sustaina- bility). He has published numerous refereed articles in professional journals. Dr Yusoff’s field of expertise’s are solid waste manage- ment, landfill technology and leachate treatment. Dr Yusoff also has conducted numerous consultancies and research works at na- tional and international level. His vast experience in landfill opera- tion and management has enabled him to conduct numerous talks and seminars at national and international level. Hamidi Abdul Aziz is a Professor in environmental engineering at the School of Civil Engineering, Universiti Sains Malaysia. Dr. Aziz received his Ph.D in civil engineering (environmental engi- neering) from University of Strathclyde, Scotland in 1992. He is the Editor-in-chief of CJASR, IJSES and the Managing Editor of Kamaruddin, M.A., Yusoff, M.S., Aziz, H.A and Alrozi, R. Application of Response Surface Methodology for Calcium Alginate Preparation in Aqueous (2014) 102 IJEWM, IJEE. He has published over 200 refereed articles in pro- fessional journals/proceedings and currently sits as the Editorial Board Member for 8 International journals. Dr Aziz's research has focused on alleviating problems associated with water pollution issues from industrial wastewater discharge and solid waste man- agement via landfilling, especially on leachate pollution. He also interests in biodegradation and bioremediation of oil spills. Rasyidah. Alrozi received her first degree from Universiti Sains Malaysia in 2009 awarded with Bachelor of Science in Chemical Engineering. She obtained degree in Master of Science in Chemical Engineering from Universiti Sains Malaysia in 2010. Currently, she serves at Faculty of Chemical Engineering Universiti Teknologi Mara, Pulau Pinang as a lecturer. Her research interest lies in acti- vated carbon, adsorption and wastewater treatment. Mohd Hafiz, Zawawi received his Ph.D in civil engineering from Universiti Sains Malaysia. His major is on groundwater study, iso- tope, hydrochemical as well as landfill mangament. He has pub- lished numerous scientific journals and involved in various consul- tancy works in the field of environmental engineering. Currently he serves as a senior lecturer in well reputable private university in Malaysia. .