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• W4313201852 abstract "AbstractMembrane bioreactor (MBR) is one of the most popular sewage treatment technologies. However, membrane fouling, a complicated process, has a negative effect on the membrane service life and effluent quality. A model with high accuracy, stability, generalization ability was needed to overcome this problem. Artificial neural network (ANN) stands out from numerous machine learning modeling methods with self-learning and sufficient capacity to capture the nonlinear complexity processes. In this paper, back-propagation neural network models (BPNN) with different hyper parameters were proposed using back-propagation algorithm. To improve the efficiency of learning process, batch module was introduced into training dataset. 4000 samples experimental data have been collected with the MBR pilot plant, 60% was used for training, 20% was used for validation, the rest for testing. With the simulation result, in theory a three-layer ANN have the ability to fit any mapping problem was proved with an average of 98% for R2 performance. However, with the comparison of models with different hyper parameters, two hidden layer models have a better performance with appropriate neurons, within an acceptable computational load. Over-fitting phenomenon occurs when the number of nodes is too large, resulting in larger MAE.KeywordsANNMBRBack-propagationModellingSimulation" @default.
• W4313201852 created "2023-01-06" @default.
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• W4313201852 date "2022-01-01" @default.
• W4313201852 modified "2023-09-26" @default.
• W4313201852 title "Flux Modelling of Membrane Bioreactor Process Plant Using Optimized-BPNN" @default.
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• W4313201852 doi "https://doi.org/10.1007/978-981-19-9195-0_1" @default.
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