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32. Optimization of biomass and polyhydroxyalkanoate production by Cupriavidus necatorusing response surface methodology and genetic algorithm optimized artificial neural network

Author

Lhamo, Pema, Mahanty, Biswanath, and Behera, Shishir Kumar

Abstract

High polyhydroxyalkanoate (PHA) yield from selected substrates associated with minimal accumulation of residual biomass can improve the process economy. In this study, different carbon (glucose, sucrose, glycerol, and acetic acid) and nitrogen (NH4Cl and urea) sources were screened for PHA production by Cupriavidus necator. The effects of incubation time, nitrogen, and phosphate concentration on biomass growth and PHA production were co-optimized through response surface methodology (RSM) and genetic algorithm-optimized artificial neural network (GA-ANN). Sucrose and urea were found to offer significantly better (p<0.001) biomass (1.468 ± 0.007 g l-1) and PHA (0.924 ± 0.02 g l-1) yield when compared with other carbon and nitrogen sources. Though the performance of both the models remains similar for biomass (R2= 0.97–0.98), GA-ANN (with six neurones in a hidden layer) seems exceptionally better in predicting PHA yield (R2= 0.97) when compared to the polynomial model (R2= 0.92). The maximum PHA concentration of 2.69 g l-1was predicted by the ANN model at an incubation time of 62.80 h with 2.0 g l-1of nitrogen and 4.0 g l-1of phosphate concentration. The multi-composite desirability using the GA-ANN model projected a better polymer-to-biomass ratio compared to the polynomial model. The inclusion of a cost-benefit analysis framework may be warranted before recommending the optimal conditions obtained through multivariate regression and GA-ANN models.

Published
2024
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48. Modelling selenite and chemical oxygen demand removal in an inverse fluidized bed bioreactor using genetic algorithm optimized artificial neural network.

Author

Sinharoy, Arindam, Mahanty, Biswanath, Behera, Shishir Kumar, Mehta, Manan, Mantri, Shalini, Das, Raja, Saikia, Sudeshna, Rene, Eldon R., and Pakshirajan, Kanan

Subjects
CHEMICAL oxygen demand, GENETIC algorithms, BIOCHEMICAL oxygen demand, CHEMICAL models, FLUIDIZED bed reactors, PARETO optimum, ARTIFICIAL neural networks
Abstract

BACKGROUND: A genetic algorithm (GA) optimized artificial neural network (ANN) model was developed to simulate selenite removal efficiency (SRe) and chemical oxygen demand (COD) removal efficiency (CRe) in a single‐stage inverse fluidized bed reactor (IFBR) operated under different hydraulic retention time (HRT) (12–48 h), and inlet selenite concentration (12.7–635 mg L−1). Multi‐objective optimization for SRe and CRe with the ANN model was adopted to estimate the pareto optimal solutions in the bioreactor system. RESULTS: GA optimized ANN topology suggested five neurons in the hidden layer, and the optimal data partitioning between training, test, and validation subsets (in 70:15:15 ratio) using cross‐validation. The average fitness for SRe and CRe of the resulting ANN model over training (R2 = 0.885), test (R2 = 0.967) and validation (R2 = 0.972) subsets were reasonably good. Sensitivity analysis suggested an increase in SRe and CRe with HRT, while an increase in inlet selenite concentration had a negative effect on SRe. Local interpretable model‐agnostic explanations and SHapley Additive exPlanations for the developed model suggested a positive coefficient of the predictors on CRe. Pareto optimal solution for SRe (92.87%–95.26%), and CRe (91.33%–99.35%) were suggested for HRT and inlet selenite concentration varying between 35.67 h and 36 h, and 0.1 g L−1 and 0.884 g L−1, respectively. CONCLUSIONS: The GA optimized ANN model could be exploited for the prediction of contaminant removal efficiencies in the bioreactor system with a fair degree of complexity (i.e., IFBR). Multiple objective pareto optimal solutions can identify the best operating conditions relevant for wastewater treatment. © 2023 Society of Chemical Industry (SCI). [ABSTRACT FROM AUTHOR]

Published
2024
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