Optimization of Wastewater Anaerobic Digestion Using Mechanistic and Meta-heuristic Methods: Current Limitations and Future Opportunities

Poh, Phaik Eong; Gouwanda, Darwin; Mohan, Y. Krishna; Gopalai, Alpha Agape; Tan, Hui

doi:10.1007/s41101-016-0001-3

Cited by 29 publications

(10 citation statements)

References 119 publications

(184 reference statements)

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“…Traditional anaerobic digestion has long been used to breakdown organic compounds and pathogens in wastewater collected in ponds [59]. Because conventional AD methods necessitate a long retention period and large treatment areas, more advanced anaerobic reactors, such as the Upflow anaerobic sludge blanket reactor (UASB), with short contact time between bacteria and wastewater, have been developed [59,60].…”

Section: Liquid Waste Conversionmentioning

confidence: 99%

Integration of Waste to Bioenergy Conversion Systems: A Critical Review

2022

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Sustainable biofuel production is the most effective way to mitigate greenhouse gas emissions associated with fossil fuels while preserving food security and land use. In addition to producing bioenergy, waste biorefineries can be incorporated into the waste management system to solve the future challenges of waste disposal. Biomass waste, on the other hand, is regarded as a low-quality biorefinery feedstock with a wide range of compositions and seasonal variability. In light of these factors, biomass waste presents limitations on the conversion technologies available for value addition, and therefore more research is needed to enhance the profitability of waste biorefineries. Perhaps, to keep waste biorefineries economically and environmentally sustainable, bioprocesses need to be integrated to process a wide range of biomass resources and yield a diverse range of bioenergy products. To achieve optimal integration, the classification of biomass wastes to match the available bioprocesses is vital, as it minimizes unnecessary processes that may increase the production costs of the biorefinery. Based on biomass classification, this study discusses the suitability of the commonly used waste-to-energy conversion methods and the creation of integrated biorefineries. In this study, the integration of waste biorefineries is discussed through the integration of feedstocks, processes, platforms, and the symbiosis of wastes and byproducts. This review seeks to conceptualize a framework for identifying and integrating waste-to-energy technologies for the varioussets of biomass wastes.

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Section: Liquid Waste Conversionmentioning

confidence: 99%

Integration of Waste to Bioenergy Conversion Systems: A Critical Review

2022

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“…These anaerobic technologies are not duly explored; thus, this work aimed to search and evaluate the behaviour of an obtained SRB community that displayed the ability to degrade FLX, which is a recalcitrant compound with a strong and extremely difficult to break C-F bond. Poh et al [36] claimed that the application of an appropriate model and process control can maximize the anaerobic process efficiency, improve sustainability of plant operation, and stimulate more groups to adopt anaerobic wastewater treatment systems, leading to a reduction in greenhouse gas emissions.…”

Section: Fluoxetinementioning

confidence: 99%

Anaerobic biodegradation of fluoxetine using a high-performance bacterial community

Palma

Costa

2021

Anaerobe

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Fluoxetine (FLX), an antidepressant extensively used worldwide is considered an emerging pollutant. The present work intends to investigate for the first time the capacity of a bacterial community containing sulphate-reducing bacteria (SRB) enriched from an anaerobic sludge to biodegrade and use FLX as sole carbon source, since current literature suggests that this drug is poorly biodegraded being mainly removed by adsorption to sediments, where it persists. FLX was biodegraded under sulphate reducing conditions until reaching its lowest and reliably detectable concentration, when 20 mg/L of the drug was used as sole carbon source, while 66 ± 9% of 50 mg/L FLX was removed, after 31 days. The initial bacterial population was mainly constituted by Desulfomicrobium and Desulfovibrio whereas during the experiments using FLX as unique carbon source a clear shift occurred with the increase of vadinBC27 wastewater-sludge group, Macellibacteroidetes, Dethiosulfovibrio, Bacteroides, Tolumonas, Sulfuricurvum, f_Enterobacteriaceae_OTU_18 that are assumed for the first time as FLX degrading bacteria. Although the main mechanism of FLX removal described in literature is by adsorption, in the results herein presented anaerobic biodegradation appears to play the main role in the removal of the FLX, thus demonstrating the potentialities that the anaerobic processes can play in wastewater treatment aiming the removal of new emerging compounds.

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“…Khajeh et al [19] validated the hybrid model, ANN-PSO, which was robust in modelling Mn(II) and Co(II) removal efficiency in adsorption (R 2 was 0.942 and 0.944 for Mn(II) and Co(II)alt, respectively). GA is a metaheuristic algorithm inspired from the natural selection process [20]. It is suitable to search for a single and exclusive target and obtain satisfying performance with reduced complexity of ANN [15].…”

Section: Application Of Hybrid Ann On Modelling the Watermentioning

confidence: 99%

Employing Artificial Neural Networks to Predict the Performance of Domestic Sewage Treatment Terminals in the Rural Region

Lin

Luo

Zhang

et al. 2021

Mathematical Problems in Engineering

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Domestic sewage in rural regions is mainly treated by small-scale treatment terminals in China. The large quantities and high dispersion of these terminals render the chemical measurement of effluent to be a time and energy intensive work and further hinder the efficient surveillance of terminals’ performance. After a thorough investigation of 136 operating terminals, this study successfully employs two artificial neural network (ANN) models to predict effluent total nitrogen (TN) and COD (R2 both higher than 0.8) by setting some easily detectable parameters, e.g., pH and conductivity, as inputs. To prevent ANN models getting stuck on local optima and enhance the model performance, genetic algorithm (GA) and particle swarm optimization (PSO) are introduced into ANN, respectively. By comparison, ANN-PSO excels in modelling both TN and COD. The root mean square error (RMSE) and R2 of ANN-PSO in modelling TN are 9.14 and 0.90, respectively, in the training stage, and 11.54 and 0.90, respectively, in the validation stage. The RMSE and R2 of ANN-PSO in modelling COD are 22.10 and 0.90, respectively, in the training stage, and 26.57 and 0.85, respectively, in the validation stage. This is the first study to provide performance prediction models that are available for different terminals. Two established ANN-PSO models show great practical significance in monitoring huge amounts of terminals despite the slight sacrifice of models’ accuracy caused by the great heterogeneity of different terminals.

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Optimization of Wastewater Anaerobic Digestion Using Mechanistic and Meta-heuristic Methods: Current Limitations and Future Opportunities

Cited by 29 publications

References 119 publications

Integration of Waste to Bioenergy Conversion Systems: A Critical Review

Integration of Waste to Bioenergy Conversion Systems: A Critical Review

Anaerobic biodegradation of fluoxetine using a high-performance bacterial community

Employing Artificial Neural Networks to Predict the Performance of Domestic Sewage Treatment Terminals in the Rural Region

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