Dataset Information

Modeling and optimization of tannase production with Triphala in packed bed reactor by response surface methodology, genetic algorithm, and artificial neural network.

ABSTRACT: In this research, optimization of the production medium to enhance tannase production by Bacillus gottheilii M2S2 in laboratory-scale packed bed reactor was studied. Amount of substrate Triphala, moisture content, aeration rate, and fermentation period was chosen for optimization study. During one variable at a time optimization, the highest tannase activity of 0.226 U/gds was shown with Triphala as a substrate at the fermentation period of 32 h. Furthermore, the optimum conditions predicted by response surface methodology (RSM) and genetic algorithm (GA) were found to be 11.532 g of substrate Triphala, 47.071% of the moisture content, and 1.188 L/min of an aeration rate with uppermost tannase activity of 0.262 U/gds. In addition, the single hidden layer feedforward neural network (SLFNN) and the radial basis function neural network (RBFNN) of an artificial neural network (ANN) were adopted to compare the prediction performances of the RSM and GA. It revealed that the ANN models (SLFNN, R ²?=?0.9930; and RBFNN, R ²?=?0.9949) were better predictors than the RSM (R ²?=?0.9864). Finally, the validation experiment exhibited 0.265 U/gds of tannase activity at the optimized conditions, which is an 11-fold increase compared to unoptimized media in shake flask.

SUBMITTER: Selvaraj S

PROVIDER: S-EPMC6557927 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Modeling and optimization of tannase production with Triphala in packed bed reactor by response surface methodology, genetic algorithm, and artificial neural network.

Selvaraj Subbalaxmi S Vytla Ramachandra Murty RM Vijay G S GS Natarajan Kannan K

3 Biotech 20190610 7

In this research, optimization of the production medium to enhance tannase production by Bacillus gottheilii M2S2 in laboratory-scale packed bed reactor was studied. Amount of substrate Triphala, moisture content, aeration rate, and fermentation period was chosen for optimization study. During one variable at a time optimization, the highest tannase activity of 0.226 U/gds was shown with Triphala as a substrate at the fermentation period of 32 h. Furthermore, the optimum con ...[more]

PMID: 31192084

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Project description:BACKGROUND: Olive mill wastewater (OMW) is the aqueous effluent of olive oil producing processes. Given its high COD and content of phenols, it has to be decontaminated before being discharged. Anaerobic digestion is one of the most promising treatment process for such an effluent, as it combines high decontamination efficiency with methane production. The large scale anaerobic digestion of OMWs is normally conducted in dispersed-growth reactors, where however are generally achieved unsatisfactory COD removal and methane production yields. The possibility of intensifying the performance of the process using a packed bed biofilm reactor, as anaerobic treatment alternative, was demonstrated. Even in this case, however, a post-treatment step is required to further reduce the COD. In this work, a biological post-treatment, consisting of an aerobic biological "Manville" silica bead-packed bed aerobic reactor, was developed, tested for its ability to complete COD removal from the anaerobic digestion effluents, and characterized biologically through molecular tools. RESULTS: The aerobic post-treatment was assessed through a 2 month-continuous feeding with the digested effluent at 50.42 and 2.04 gl(-1)day(-1) of COD and phenol loading rates, respectively. It was found to be a stable process, able to remove 24 and 39% of such organic loads, respectively, and to account for 1/4 of the overall decontamination efficiency displayed by the anaerobic-aerobic integrated system when fed with an amended OMW at 31.74 and 1.70 gl(-1)day(-1) of COD and phenol loading rates, respectively. Analysis of 16S rRNA gene sequences of biomass samples from the aerobic reactor biofilm revealed that it was colonized by Rhodobacterales, Bacteroidales, Pseudomonadales, Enterobacteriales, Rhodocyclales and genera incertae sedis TM7. Some taxons occurring in the influent were not detected in the biofilm, whereas others, such as Paracoccus, Pseudomonas, Acinetobacter and Enterobacter, enriched significantly in the biofilter throughout the treatment. CONCLUSION: The silica-bead packed bed biofilm reactor developed and characterized in this study was able to significantly decontaminate anaerobically digested OMWs. Therefore, the application of an integrated anaerobic-aerobic process resulted in an improved system for valorization and decontamination of OMWs.

Dataset Information

Modeling and optimization of tannase production with Triphala in packed bed reactor by response surface methodology, genetic algorithm, and artificial neural network.

Publications

Modeling and optimization of tannase production with <i>Triphala</i> in packed bed reactor by response surface methodology, genetic algorithm, and artificial neural network.

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