Artificial neural network model for predicting production of Spirulina platensis in outdoor culture

► Outdoor algal cultivation is influenced by many uncontrollable input variables. ► Artificial neural network model predicts outdoor Spirulina growth accurately. ► Simple to measure input parameters identified for the growth prediction model. ► Two weeks’ data sufficient to give robust model for gro...

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Bibliographic Details
Published in:Bioresource technology 2013-02, Vol.130, p.224-230
Main Authors: Sharon Mano Pappu, J., Vijayakumar, G. Karthik, Ramamurthy, V.
Format: Article
Language:English
Subjects:
Artificial neural network
Biological and medical sciences
Biomass
Culture
Culture Techniques
Fundamental and applied biological sciences. Psychology
Growth prediction
Learning theory
Mathematical analysis
Mathematical models
Models, Biological
Multiple linear regression
Neural networks
Neural Networks (Computer)
Outdoor
Outdoor culture
Regression Analysis
Spirulina
Spirulina - growth & development
Spirulina platensis
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Summary:► Outdoor algal cultivation is influenced by many uncontrollable input variables. ► Artificial neural network model predicts outdoor Spirulina growth accurately. ► Simple to measure input parameters identified for the growth prediction model. ► Two weeks’ data sufficient to give robust model for growth prediction. ► Precise output prediction will help better management of uncontrollable processes. Process variables contributing to describe the growth of Spirulina platensis in outdoor cultures were evaluated. Mathematical models of the process using inputs which were simple and easy to collect in any operating plant were developed. Multiple linear regression (MLR) and artificial neural network (ANN) modelling procedures were evaluated. The dataset contributing to the growth prediction model were biomass concentration, nitrate concentration, pH and dissolved oxygen concentration of culture fluid, light intensity and days in culture, measured once a day. Datasets of 12days were sufficient to develop a model to predict the succeeding day’s biomass concentration with a coefficient of determination of greater than 0.98 under outdoor growth conditions. Insufficient number of datasets resulted in overestimation of the predicted output value.
ISSN:0960-8524
1873-2976
DOI:10.1016/j.biortech.2012.12.082