Abstract
Bioethanol production from microalgal biomass is an attractive concept, and theoretical methods by which bioenergy can be produced indicate saving in both time and efficiency. The aim of the present study was to investigate the efficiencies of carbohydrate and bioethanol production by Chlorella saccharophila CCALA 258 using experimental, semiempirical, and theoretical methods, such as response surface methods (RSMs) and an artificial neural network (ANN) through sequential modeling. In addition, the interactive response surface modeling for determining the optimum conditions for the variables was assessed. The results indicated that the maximum bioethanol concentration was 11.20 g/L using the RSM model and 11.17 g/L using the ANN model under optimum conditions of 6% (v/v %) substrate and 4% (v/v %) inoculum at 96-h fermentation, pH 6, and 40 °C. In addition, the value of the experimental data for carbohydrate concentration was 0.2510 g/g biomass at ANN with the maximums of 50% (v/v) wastewater concentration, 4% (m/m) hydrogen peroxide concentration, and 6000 U/mL enzyme activity. Finally, although the RSM model was more effective than the ANN model for predicting bioethanol concentration, the ANN model yielded more precise values than the RSM model for carbohydrate concentration.
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The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
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MO performed all experiments, such as the determining carbohydrate and bioethanol concentrations, and applied all theoretical methods, such as RSM, ANN, and interactive RSM. In addition, MO interpreted all the data in the manuscript and prepared the original draft of the manuscript. The author read and approved the final manuscript.
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Onay, M. Sequential modelling for carbohydrate and bioethanol production from Chlorella saccharophila CCALA 258: a complementary experimental and theoretical approach for microalgal bioethanol production. Environ Sci Pollut Res 29, 14316–14332 (2022). https://doi.org/10.1007/s11356-021-16831-w
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DOI: https://doi.org/10.1007/s11356-021-16831-w