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Membrane Fouling Prediction Based on Tent-SSA-BP

In view of the difficulty in obtaining the membrane bioreactor (MBR) membrane flux in real time, considering the disadvantage of the back propagation (BP) network in predicting MBR membrane flux, such as the local minimum value and poor generalization ability of the model, this article introduces te...

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Autores principales: Ling, Guobi, Wang, Zhiwen, Shi, Yaoke, Wang, Jieying, Lu, Yanrong, Li, Long
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9318055/
https://www.ncbi.nlm.nih.gov/pubmed/35877894
http://dx.doi.org/10.3390/membranes12070691
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author Ling, Guobi
Wang, Zhiwen
Shi, Yaoke
Wang, Jieying
Lu, Yanrong
Li, Long
author_facet Ling, Guobi
Wang, Zhiwen
Shi, Yaoke
Wang, Jieying
Lu, Yanrong
Li, Long
author_sort Ling, Guobi
collection PubMed
description In view of the difficulty in obtaining the membrane bioreactor (MBR) membrane flux in real time, considering the disadvantage of the back propagation (BP) network in predicting MBR membrane flux, such as the local minimum value and poor generalization ability of the model, this article introduces tent chaotic mapping in the standard sparrow search algorithm (SSA), which improves the uniformity of population distribution and the searching ability of the algorithm (used to optimize the key parameters of the BP network). The tent sparrow search algorithm back propagation network (Tent-SSA-BP) membrane fouling prediction model is established to achieve accurate prediction of membrane flux; compared to the BP, genetic algorithm back propagation network (GA-BP), particle swarm optimization back propagation network (PSO-BP), sparrow search algorithm extreme learning machine(SSA-ELM), sparrow search algorithm back propagation network (SSA-BP), and Tent particle swarm optimization back propagation network (Tent–PSO-BP) models, it has unique advantages. Compared with the BP model before improvement, the improved soft sensing model reduces MAPE by 96.76%, RMSE by 99.78% and MAE by 95.61%. The prediction accuracy of the algorithm proposed in this article reaches 97.4%, which is much higher than the 48.52% of BP. It is also higher than other prediction models, and the prediction accuracy has been greatly improved, which has some engineering reference value.
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spelling pubmed-93180552022-07-27 Membrane Fouling Prediction Based on Tent-SSA-BP Ling, Guobi Wang, Zhiwen Shi, Yaoke Wang, Jieying Lu, Yanrong Li, Long Membranes (Basel) Article In view of the difficulty in obtaining the membrane bioreactor (MBR) membrane flux in real time, considering the disadvantage of the back propagation (BP) network in predicting MBR membrane flux, such as the local minimum value and poor generalization ability of the model, this article introduces tent chaotic mapping in the standard sparrow search algorithm (SSA), which improves the uniformity of population distribution and the searching ability of the algorithm (used to optimize the key parameters of the BP network). The tent sparrow search algorithm back propagation network (Tent-SSA-BP) membrane fouling prediction model is established to achieve accurate prediction of membrane flux; compared to the BP, genetic algorithm back propagation network (GA-BP), particle swarm optimization back propagation network (PSO-BP), sparrow search algorithm extreme learning machine(SSA-ELM), sparrow search algorithm back propagation network (SSA-BP), and Tent particle swarm optimization back propagation network (Tent–PSO-BP) models, it has unique advantages. Compared with the BP model before improvement, the improved soft sensing model reduces MAPE by 96.76%, RMSE by 99.78% and MAE by 95.61%. The prediction accuracy of the algorithm proposed in this article reaches 97.4%, which is much higher than the 48.52% of BP. It is also higher than other prediction models, and the prediction accuracy has been greatly improved, which has some engineering reference value. MDPI 2022-07-04 /pmc/articles/PMC9318055/ /pubmed/35877894 http://dx.doi.org/10.3390/membranes12070691 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ling, Guobi
Wang, Zhiwen
Shi, Yaoke
Wang, Jieying
Lu, Yanrong
Li, Long
Membrane Fouling Prediction Based on Tent-SSA-BP
title Membrane Fouling Prediction Based on Tent-SSA-BP
title_full Membrane Fouling Prediction Based on Tent-SSA-BP
title_fullStr Membrane Fouling Prediction Based on Tent-SSA-BP
title_full_unstemmed Membrane Fouling Prediction Based on Tent-SSA-BP
title_short Membrane Fouling Prediction Based on Tent-SSA-BP
title_sort membrane fouling prediction based on tent-ssa-bp
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9318055/
https://www.ncbi.nlm.nih.gov/pubmed/35877894
http://dx.doi.org/10.3390/membranes12070691
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