General Information
    • ISSN: 2010-0221
    • Frequency: Bimonthly
    • DOI: 10.18178/IJCEA
    • Editor-in-Chief: Prof. Dr. Shen-Ming Chen
    • Executive Editor: Mr. Ron C. Wu
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Editor-in-chief
Prof. Dr. Shen-Ming Chen
National Taipei University of Technology, Taiwan
 

IJCEA 2011 Vol.2(4): 288-293 ISSN: 2010-0221
DOI: 10.7763/IJCEA.2011.V2.119

Identification of Algal Biomass Production with Partial Least Squares & Neural Network

Diamond Das and Madhusree Kundu

Abstract—Parameters like pH, nitrate concentration, depth of liquid column and temperature influenced the yield of Algal biomass in a batch culture. For 64 combinations of the aforesaid parameters, the experimentation was done and each run was observed for 10 days. The Taguchi method of design of experiments was used for creating the 64 combinations. The yield of Algal biomass was modeled as a function of pH, nitrate concentration, depth of liquid column and temperature using partial least squares (PLS) and neural networks. The predictions using both the techniques were in excellent agreement with the experimental data generated in this work.

Index Terms—Algae, Bio-mass, Batch culture,Partial Least Square, Artificial Neural Network, Modeling.

Diamond Das and Madhusree Kundu are with Department of Chemical Engineering, National Institute of Technology, Rourkela, P.O. Box 769008,orissa, India (corresponding author to provide :e-mail: mkundu@nitrkl.ac.in)

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Cite: Diamond Das and Madhusree Kundu, "Identification of Algal Biomass Production with Partial Least Squares & Neural Network," International Journal of Chemical Engineering and Applications vol. 2, no. 4, pp. 288-293, 2011.

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