Predicting membrane fouling in a high solid AnMBR treating OFMSW leachate through a genetic algorithm and the optimization of a BP neural network model

Yao, Junqiang; Wu, Zhiyue; Liu, Yuan; Zheng, Xiaoyu; Zhang, Haibo; Dong, Renjie; Qiao, Wei

doi:10.1016/j.jenvman.2022.114585

Cited by 15 publications

(2 citation statements)

References 41 publications

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“…To overcome these challenges, hybrid optimization techniques based on genetic algorithms [ 83 , 84 ] and particle swarm optimization [ 57 , 85 ] have been implemented. Although with some advantages versus conventional ANN algorithms, these approaches suffer from issues such as a slow rate of convergence, high computational cost due to their complex structure, or the requirement of considerable data sizes [ 57 ].…”

Section: Model-based Anti-biofouling Strategiesmentioning

confidence: 99%

Prolonging the Life Span of Membrane in Submerged MBR by the Application of Different Anti-Biofouling Techniques

et al. 2023

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The membrane bioreactor (MBR) is an efficient technology for the treatment of municipal and industrial wastewater for the last two decades. It is a single stage process with smaller footprints and a higher removal efficiency of organic compounds compared with the conventional activated sludge process. However, the major drawback of the MBR is membrane biofouling which decreases the life span of the membrane and automatically increases the operational cost. This review is exploring different anti-biofouling techniques of the state-of-the-art, i.e., quorum quenching (QQ) and model-based approaches. The former is a relatively recent strategy used to mitigate biofouling. It disrupts the cell-to-cell communication of bacteria responsible for biofouling in the sludge. For example, the two strains of bacteria Rhodococcus sp. BH4 and Pseudomonas putida are very effective in the disruption of quorum sensing (QS). Thus, they are recognized as useful QQ bacteria. Furthermore, the model-based anti-fouling strategies are also very promising in preventing biofouling at very early stages of initialization. Nevertheless, biofouling is an extremely complex phenomenon and the influence of various parameters whether physical or biological on its development is not completely understood. Advancing digital technologies, combined with novel Big Data analytics and optimization techniques offer great opportunities for creating intelligent systems that can effectively address the challenges of MBR biofouling.

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Section: Model-based Anti-biofouling Strategiesmentioning

confidence: 99%

Prolonging the Life Span of Membrane in Submerged MBR by the Application of Different Anti-Biofouling Techniques

et al. 2023

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“…It is a multilayer feedforward neural network trained according to the error BP algorithm and is one of the most widely used neural network models. BP neural network is mainly composed of input layer, hidden layer and output layer (Yao et al, 2022). BP neural network adds several hidden layers (one or several layers) of neurons between the input layer and the output layer.…”

Section: Model Selectionmentioning

confidence: 99%

Prediction of municipal solid waste generation and analysis of dominant variables in rapidly developing cities based on machine learning – a case study of China

Zhao,

Tao,

et al. 2023

Waste Manag Res

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Prediction of municipal solid waste (MSW) generation plays an essential role in effective waste management. The main objectives of this study were to develop models for accurate prediction of MSW generation (MSWG) and analyze the influence of dominant variables on MSWG. To elevate the model’s prediction accuracy, more than 50 municipal variables were considered original variables, which were selected from 12 categories. According to the screening results, the dominant variables are classified into four categories: urban greening, population size and residential density, regional economic development and resident income and expenditure. Among the seven machine learning methods, back propagation (BP) neural network has the best model evaluation effect. The R2 of the BP neural network model of Jiangsu, Zhejiang and Shandong provinces were 0.969, 0.941 and 0.971 respectively. The prediction accuracy of Shandong province (93.8%) was the best, followed by Jiangsu province (92.3%) and Zhejiang province (72.7%). The correlation between dominant variables and the MSWG was mined, suggesting that regional GDP and the total retail sales of consumer goods were the most important dominant variables affecting MSWG. Moreover, the MSWG might not absolutely associate with the population size and residential density. The method used in this study is a practical tool for policymakers on regional/local waste management and MSWG control.

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A BPNN-based ecologically extended input–output model for virtual water metabolism network management of Kazakhstan

Liu

et al. 2023

Environ Sci Pollut Res

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Predicting membrane fouling in a high solid AnMBR treating OFMSW leachate through a genetic algorithm and the optimization of a BP neural network model

Cited by 15 publications

References 41 publications

Prolonging the Life Span of Membrane in Submerged MBR by the Application of Different Anti-Biofouling Techniques

Prolonging the Life Span of Membrane in Submerged MBR by the Application of Different Anti-Biofouling Techniques

Prediction of municipal solid waste generation and analysis of dominant variables in rapidly developing cities based on machine learning – a case study of China

A BPNN-based ecologically extended input–output model for virtual water metabolism network management of Kazakhstan

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