Experimental Study and Neural Network Modeling of Enzymatic Hydrolysis of Microalgal Biomass for Bioethanol Production

Document Type : Research Article

Authors

1 Biotechnology Research Centre, Faculty of Chemical Engineering, Sahand University of Technology, Tabriz, I.R. IRAN

2 Biotechnology Research Centrer, Faculty of Chemical Engineering, Sahand University of Technology, Tabriz, I.R. IRAN

Abstract

Some key factors such as high growth rate, low demand for arable land and fresh water and high carbohydrate content causes microalgae to introduce a new sourceof bioethanol.In this study,mixed microalgae culture was cultivated in the plate photo-reactors. Afterwards, nitrogen starvation strategy was used to accumulate storage carbohydrates. After harvesting of microalgae, enzymatic hydrolysis was applied to extract the sugars in biomass. The concentrations of extracted reducing sugar using hydrolysis process were investigated at four different substrate concentrations and three different temperatures over time. The process simulation by neural networks was performed using MATLAB software program. The networkinput consisted of substrate concentration, temperature, hydrolysis time and the networkoutput consisted of reducing sugar concentration. Artificial neural network with one hidden layer of 8 neurons in conditions (70-15-15) has minimum Mean Square Error (MSE).

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References

[۱] گلزاری، ابوعلی؛ عبدلی، محمد علی؛ خدادادی، عباسعلی؛ کرباسی، عبدالرضا؛ ایمانیان، سجاد؛ بررسی فرایندهای انعقاد الکتریکی و شیمیایی برای جداسازی میکروجلبک­های آب شور، نشریه شیمی و مهندسی شیمی ایران، (۱)۳۵: 39 تا 52 (۱۳۹۵).
[۲] احمدی اسبچین، سلمان؛ پور بابایی ،احمد علی؛ آندوره، ایو ؛ بررسی فرایند جذب زیستی همزمان دو فلز روی/نیکل به وسیله جلبک قهوه ای فوکوس سراتوس. نشریه شیمی و مهندسی شیمی ایران، (۱)۳۲: 85 تا 92 (۱۳۹۲).
[3] Markou G., Angelidaki I., Georgakakis D., Microalgal Carbohydrates: an Overview of the Factors Influencing Carbohydrates Production, and of Main Bioconversion Technologies for Production of Biofuels, Applied Microbiology and Biotechnology, 96: 631-645 (2012).
[4] Chen C-Y., Zhao X-Q., Yen H-W., Ho S-H., Cheng C-L., Lee D-J., Bai FW, Chang JS., Microalgae-Based Carbohydrates for Biofuel Production, Biochemical Engineering Journal, 78:1-10  (2013).
[5] Mata T.M., Martins A.A., Caetano N.S., Microalgae for Biodiesel Production and other Applications: A Review, Renewable and Sustainable Energy Reviews, 14:217- 232 (2010).
[6] Ahadian S., Moradian S., Sharif F., Amani Tehran M., Mohseni M., Prediction of Time of Capillary Rise in Porous Media Using Artificial Neural Network (ANN)., Iranian Journal of Chemistry and Chemical Engineering (IJCCE) 26(1):71-83 (2007).
[7] Ameri F., Moradian S., Amani Tehran M., Faez K., The use of Fundamental Color Stimulus to Improve the Performance of Artificial Neural Network Color Match Prediction Systems, Iranian Journal of Chemistry and Chemical Engineering (IJCCE), 24(4): 53-61 (2005).
[8] Fu R-Q., Xu T-W., Pan Z-X., Modelling of the Adsorption of Bovine Serum Albumin on Porous Polyethylene Membrane by Back-Propagation Artificial Neural Network, Journal of Membrane Science, 251:137-144 (2005).
[9] Ahadian S., Moradian S., Mohseni M., Amani Tehran M., Sharif F., Determination of Surface Tension and Viscosity of Liquids by the Aid of the Capillary Rise Procedure Using Artificial Neural Network (ANN), Iranian Journal of Chemistry and Chemical Engineering (IJCCE), 27(1):7-15 (2008).
[10] Chang C-W., Yu W-C., Chen W-J., Chang R-F., Kao W-S., A Study on the Enzymatic Hydrolysis of Steam Exploded Napiergrass with Alkaline Treatment Using Artificial Neural Networks and Regression Analysis, Journal of the Taiwan Institute of Chemical Engineers, 42: 889- 894 (2011).
[11] Nikzad M., Movagharnejad K., Talebnia F., Comparative Study between Neural Network Model and Mathematical Models for Prediction of Glucose Concentration During Enzymatic Hydrolysis, International Journal of Computer Applications, 56(1):  43-48(2012).
[12] Jaya E.M.J., Norhalim N.A., Ahmad Z., Artificial Neural Network Model Prediction of Glucose by Enzymatic Hydrolysis of Rice Straw, Journal of Engineering Science, 10: 85-94 (2014).
[13] Cataldo D., Maroon M., Schrader L., Youngs V., Rapid Colorimetric Determination of Nitrate in Plant Tissue by Nitration of Salicylic Acid 1, Communications in Soil Science & Plant Analysis, 6: 71-80 (1975).
[14] Hedge J., Hofreiter B., Methods of Estimating Starch and Carbohydrates, Carbohydrate Chemistry, 17:163-201 (1962).
[15] Miller G.L., Use of Dinitrosalicylic Acid Reagent for Determination of Reducing Sugar, Analytical Chemistry, 31:426-428 (1959).
[16] Shokrkar H., Salahi A., Kasiri N., Mohammadi T., Mullite Ceramic Membranes for Industrial Oily Wastewater Treatment: Experimental and Neural Network Modeling, Water Science & Technology, 64: 670-676 (2011).
[17] Yao C., Ai J., Cao X., Xue S., Zhang W., Enhancing Starch Production of a Marine Green Microalga Tetraselmis Subcordiformis Through Nutrient Limitation, Bioresource Technology, 118: 438-444 (2012).
[18] Brányiková I., Maršálková B., Doucha J., Brányik T., Bišová K., Zachleder V., Vítová M., Microalgae—Novel Highly Efficient Starch Producers, Biotechnology and Bioengineering, 108: 766- 776 (2011).
[19] Harun R., Danquah M.K., Enzymatic Hydrolysis of Microalgal Biomass for Bioethanol Production, Chemical Engineering Journal, 168:1079- 1084  (2011).
[20] Khataee A., Mirzajani O., UV/Peroxydisulfate Oxidation of CI Basic Blue 3: Modeling of Key Factors by Artificial Neural Network, Desalination, 251:64- 69 (2010).
[21] Aghaeinejad-Meybodi A., Ebadi A., Shafiei S., Khataee A., Rostampour M., Modeling and Optimization of Antidepressant Drug Fluoxetine Removal in Aqueous Media by ozone/H2O2 Process: Comparison of Central Composite Design and Artificial Neural Network Approaches, Journal of the Taiwan Institute of Chemical Engineers; 48:40-48 (2015).

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