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...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-07-01
|
| Series: | Membranes |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-0375/12/7/691 |
| _version_ | 1797445274944143360 |
|---|---|
| author | Guobi Ling Zhiwen Wang Yaoke Shi Jieying Wang Yanrong Lu Long Li |
| author_facet | Guobi Ling Zhiwen Wang Yaoke Shi Jieying Wang Yanrong Lu Long Li |
| author_sort | Guobi Ling |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-09T13:24:25Z |
| format | Article |
| id | doaj.art-7c4469c9168f40e6be875706478121f0 |
| institution | Directory Open Access Journal |
| issn | 2077-0375 |
| language | English |
| last_indexed | 2024-03-09T13:24:25Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Membranes |
| spelling | doaj.art-7c4469c9168f40e6be875706478121f02023-11-30T21:26:09ZengMDPI AGMembranes2077-03752022-07-0112769110.3390/membranes12070691Membrane Fouling Prediction Based on Tent-SSA-BPGuobi Ling0Zhiwen Wang1Yaoke Shi2Jieying Wang3Yanrong Lu4Long Li5College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, ChinaCollege of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, ChinaCollege of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, ChinaCollege of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, ChinaCollege of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, ChinaCollege of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, ChinaIn 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.https://www.mdpi.com/2077-0375/12/7/691MBRmembrane flux predictiontent chaotic mappingSSATent-SSA-BP model |
| spellingShingle | Guobi Ling Zhiwen Wang Yaoke Shi Jieying Wang Yanrong Lu Long Li Membrane Fouling Prediction Based on Tent-SSA-BP Membranes MBR membrane flux prediction tent chaotic mapping SSA Tent-SSA-BP model |
| 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 | MBR membrane flux prediction tent chaotic mapping SSA Tent-SSA-BP model |
| url | https://www.mdpi.com/2077-0375/12/7/691 |
| work_keys_str_mv | AT guobiling membranefoulingpredictionbasedontentssabp AT zhiwenwang membranefoulingpredictionbasedontentssabp AT yaokeshi membranefoulingpredictionbasedontentssabp AT jieyingwang membranefoulingpredictionbasedontentssabp AT yanronglu membranefoulingpredictionbasedontentssabp AT longli membranefoulingpredictionbasedontentssabp |