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14. 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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17. Contributors

Author

Abad, Rabia, primary, Abusafa, Abdelrahim, additional, Aghbashlo, Mortaza, additional, Ahmed, Asam, additional, Al-Jabari, Maher, additional, Alexandri, Maria, additional, Aurnob, A.K.M. Kazi, additional, Banerjee, Ayan, additional, Behera, Shishir Kumar, additional, Belard, Laurent, additional, Bezergianni, Stella, additional, Bhaskar, Thallada, additional, Bhuyan, Nilutpal, additional, Choudhury, Hanif A., additional, Chrysikou, Loukia P., additional, Coll Lozano, Caterina, additional, Das, Sutapa, additional, David-Moreno, Antonio, additional, Demichelis, Francesca, additional, Du, Chenyu, additional, Dupont, Capucine, additional, Elhamouz, Amer, additional, Fiore, Silvia, additional, García Torreiro, María, additional, Ghosh, Debashish, additional, González Granados, Inmaculada, additional, Goud, Vaibhav V., additional, Grewal, Jasneet, additional, Herrero García, Natalia, additional, Hosseinzadeh-Bandbafha, Homa, additional, Hsu, Shu-Chien, additional, Kabir, Kazi Bayzid, additional, Kataki, Rupam, additional, Kaur, Ravneet, additional, Khare, S.K., additional, Kirtania, Kawnish, additional, Lam, Chor-Man, additional, Latorre-Sánchez, Marcos, additional, Lebrero, Raquel, additional, Li, Yize, additional, Lu, Diannan, additional, Lübeck, Mette, additional, Mak, Tiffany M.W., additional, Malani, Ritesh S., additional, Manikandan, N. Arul, additional, Moholkar, Vijayanand S., additional, Mojab, Hamidreza, additional, Molto Marin, Jose L., additional, Munir, Sidra, additional, Muñoz, Raul, additional, Musinovic, Hana, additional, Nahid, Ahaduzzaman, additional, Namboodiri, M. M. Tejas, additional, Nizami, Abdul-Sattar, additional, Oliva-Dominguez, Jose Miguel, additional, Ovejero-Roncero, David, additional, Pacheco-Ruiz, Santiago, additional, Pakshirajan, Kannan, additional, Park, Hung-Suck, additional, Pascual, Celia, additional, Pascual, Andrés, additional, Pérez, Víctor, additional, Perkins, Greg, additional, Pleissner, Daniel, additional, Pugazhenthi, G., additional, Raingué, Amélie, additional, Raj, Eldon, additional, Rehan, Mohammad, additional, Rene, Eldon R., additional, Reshad, Ali S., additional, Rodrigo, Alfredo, additional, Roldan-Aguayo, Rocio, additional, Sadaf, Ayesha, additional, Sailwal, Megha, additional, Sawalha, Hassan, additional, Serna-Maza, Alba, additional, Shah, Izhar Hussain, additional, Shukla, Shailendra Kumar, additional, Singh Rathore, Pushpendra Kumar, additional, Smith, Mark, additional, Sut, Debashis, additional, Tabatabaei, Meisam, additional, Tiwari, Pankaj, additional, Tran, Khanh-Quang, additional, Tsang, Daniel C.W., additional, Van de Vossenberg, Jack, additional, van Hullebusch, Eric D., additional, Wambugu, Carol W., additional, Wang, Lei, additional, Watson, Ian, additional, Yadav, Neerja, additional, You, Siming, additional, and Yu, Iris K.M., additional

Published
2020
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23. 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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45. Bioprocesses for resource recovery from waste gases: Current trends and industrial applications

Author

Khanongnuch, Ramita, Nalakath Abubackar, Haris, Keskin Gundogdu, Tugba, Güngörmüşler, Mine, Duman, Gozde, Agrawal, Ayushi, Behera, Shishir Kumar, Li, Lu, Bayar, Büşra, Rene, Eldon R., Khanongnuch, Ramita, Nalakath Abubackar, Haris, Keskin Gundogdu, Tugba, Güngörmüşler, Mine, Duman, Gozde, Agrawal, Ayushi, Behera, Shishir Kumar, Li, Lu, Bayar, Büşra, and Rene, Eldon R.

Abstract

[Abstract] Air pollution is a topic of important global concern because it has contributed significantly to an increase in the earth's global warming potential and contributed to severe health and environmental impacts. In this review, the different bioreactor configurations commonly used for waste gas treatment, namely the biofilters, the biotrickling filters and the bioscrubbers, and their industrial applications were compared in terms of the type of inoculum, the packing material/media, removal efficiency and elimination capacity. Typically, biofilters are operated under the following range of operating conditions: gas residence time = 15–60 s; gas flow rate = 50–300,000 m3 h−1; temperature = 15–30 °C; pH = 6.0–7.5; filter area = 100–3000 m2; relative humidity >95.0%; and removal efficiencies >75.0% depending on the waste gas composition and concentration. The biotechnological approaches for resource recovery, i.e., the conversion of C1 gaseous compounds (CO, CO2 and CH4) to liquified value-added products or biofuels have been discussed. From this review, it was evident that the performances of different aerobic, anoxic and/or anaerobic lab, pilot and full-scale bioreactors for waste gas treatment and resource recovery depend on the composition, the individual concentration of pollutants present in the waste gas and the gas flow rate. Although most of the research on product recovery from waste gas is rather limited to lab/pilot-scale studies, there are some key commercialized technologies that have proven to be economical at the full-scale. Thus, this review, comprehensively presents a complete overview of the current trends and limitations of conventional waste gas treatment systems, the benefits of novel bioreactor configurations and their potential to be applied for resource recovery from waste gases.

Published
2021

49. Characterization of refuse derived fuel samples prepared from municipal solid waste in Vellore, India.

Author

Thawani, Bonny, Mahanty, Biswanath, and Behera, Shishir Kumar

Subjects
REFUSE as fuel, SOLID waste, FOAM, INCINERATION, THERMOGRAVIMETRY, ELEMENTAL analysis, SURFACE properties
Abstract

The objective of this work was to explore an alternative way to manage the non-biodegradable and non-recyclable fraction of municipal solid waste (MSW) in Vellore city, India. Refuse-derived fuel (RDF) samples with different proportions of plastic, thermocol, foam and jute straw were formulated. The RDF samples were characterized in the form of heating values (proximate and ultimate analysis), surface properties through X-ray diffraction (XRD) and thermal stability through thermogravimetric analysis (TGA). The measured higher heating values (HHV) of four RDF samples varying between 6032 and 6168 kcal/kg were effectively modelled using various empirical models for the prediction of HHV based on their elemental analysis. Higher absolute weight loss in TGA was evident in samples with jute straw while the highest rate of weight loss was noted in samples with a higher proportion of thermocol. Results from this preliminary investigation of RDF samples prepared from non-biodegradable and non-recyclable fractions of MSW warrants an exhaustive analysis of a larger pool of samples to project appropriate RDF composition for better energy recovery. [ABSTRACT FROM AUTHOR]

Published
2022
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