51af56bc17f0237ced486b16427bffe1 pmc_34577522.pdf 8483584a9ebc7746f92e2604bf326d6490c96e7d pmc_34577522.pdf e06c957375cae1d61ccbb918e78f359b58f310a3ffeabaf05891f83215b630e5 pmc_34577522.pdf Title: Transfer Learning in Wastewater Treatment Plant Control Design: From Conventional to Long Short-Term Memory-Based Controllers Subject: In the last decade, industrial environments have been experiencing a change in their control processes. It is more frequent that control strategies adopt Artificial Neural Networks (ANNs) to support control operations, or even as the main control structure. Thus, control structures can be directly obtained from input and output measurements without requiring a huge knowledge of the processes under control. However, ANNs have to be designed, implemented, and trained, which can become complex and time-demanding processes. This can be alleviated by means of Transfer Learning (TL) methodologies, where the knowledge obtained from a unique ANN is transferred to the remaining nets reducing the ANN design time. From the control viewpoint, the first ANN can be easily obtained and then transferred to the remaining control loops. In this manuscript, the application of TL methodologies to design and implement the control loops of a Wastewater Treatment Plant (WWTP) is analysed. Results show that the adoption of this TL-based methodology allows the development of new control loops without requiring a huge knowledge of the processes under control. Besides, a wide improvement in terms of the control performance with respect to conventional control structures is also obtained. For instance, results have shown that less oscillations in the tracking of desired set-points are produced by achieving improvements in the Integrated Absolute Error and Integrated Square Error which go from 40.17% to 94.29% and from 34.27% to 99.71%, respectively. Keywords: control design; industrial control; transfer learning; WWTP Author: Ivan Pisa, Antoni Morell, Ramón Vilanova and Jose Lopez Vicario Creator: LaTeX with hyperref Producer: pdfTeX-1.40.21 CreationDate: Thu Sep 23 05:24:45 2021 CEST ModDate: Thu Sep 23 05:58:33 2021 CEST Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 26 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 1383567 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- PFFNRC+URWPalladioL-Ital Type 1 Custom yes yes no 10 0 MELOBT+URWPalladioL-Bold Type 1 Custom yes yes no 15 0 DMWGEP+URWPalladioL-Roma Type 1 Custom yes yes no 19 0 RVSEMT+TimesNewRomanPS-BoldItalicMT Type 1C WinAnsi yes yes no 27 0 MCBCCI+SourceSansRoman-Regular Type 1C Custom yes yes no 49 0 MCBCCM+SourceSansRoman.613wght Type 1C Custom yes yes no 53 0 MCBCCO+SourceSansRoman-Bold Type 1C Custom yes yes no 57 0 LiberationSerif CID TrueType Identity-H yes no yes 76 0 SOSTRQ+CMR10 Type 1 Builtin yes yes no 123 0 UYVLXV+CMEX10 Type 1 Builtin yes yes no 127 0 NWBGJJ+CMSY10 Type 1 Builtin yes yes no 131 0 SUZJEW+PazoMath-Italic Type 1 Builtin yes yes no 173 0 MBXBYR+URWPalladioL-BoldItal Type 1 Custom yes yes no 290 0 MPUDFL+PazoMath Type 1 Builtin yes yes no 325 0 Jhove (Rel. 1.22.1, 2019-04-17) Date: 2022-03-22 02:44:21 CET RepresentationInformation: pmc_34577522.pdf ReportingModule: PDF-hul, Rel. 1.12.1 (2019-04-17) LastModified: 2021-10-04 03:35:50 CEST Size: 1383567 Format: PDF Version: 1.7 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 530 FreeObjects: 1 IncrementalUpdates: 0 DocumentCatalog: PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@7b69c6ba Item: Title: Materials and Methods Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@46daef40 Children: Item: Title: Benchmark Simulation Model No. 1 Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@12f41634 Children: Item: Title: BSM1 Layout Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@13c27452 Item: Title: BSM1 Simulation and Evaluation Protocols Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@262b2c86 Item: Title: Long Short-Term Memory Cells Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@371a67ec Item: Title: Transfer Learning Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@5ed828d Item: Title: Modelling Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@50d0686 Item: Title: TL-Based Control Design Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@7a3d45bd Children: Item: Title: LSTM-Based PI Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@1e7c7811 Item: Title: Control Knowledge Transfer Approach Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@77ec78b9 Item: Title: Results Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@1a3869f4 Children: Item: Title: TL-Based Control Design Results Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@a38d7a3 Item: Title: Transfer Learning from DO to NO Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@77f99a05 Item: Title: Transfer Learning from NO to DO Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@63440df3 Item: Title: LSTM-Based Controller Fine-Tuning & Transfer Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@3aeaafa6 Item: Title: Conclusions Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@76a3e297 Item: Title: References Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@4d3167f4 Info: Title: Transfer Learning in Wastewater Treatment Plant Control Design: From Conventional to Long Short-Term Memory-Based Controllers Author: Ivan Pisa, Antoni Morell, Ramón Vilanova and Jose Lopez Vicario Subject: In the last decade, industrial environments have been experiencing a change in their control processes. It is more frequent that control strategies adopt Artificial Neural Networks (ANNs) to support control operations, or even as the main control structure. Thus, control structures can be directly obtained from input and output measurements without requiring a huge knowledge of the processes under control. However, ANNs have to be designed, implemented, and trained, which can become complex and time-demanding processes. This can be alleviated by means of Transfer Learning (TL) methodologies, where the knowledge obtained from a unique ANN is transferred to the remaining nets reducing the ANN design time. From the control viewpoint, the first ANN can be easily obtained and then transferred to the remaining control loops. In this manuscript, the application of TL methodologies to design and implement the control loops of a Wastewater Treatment Plant (WWTP) is analysed. Results show that the adoption of this TL-based methodology allows the development of new control loops without requiring a huge knowledge of the processes under control. Besides, a wide improvement in terms of the control performance with respect to conventional control structures is also obtained. For instance, results have shown that less oscillations in the tracking of desired set-points are produced by achieving improvements in the Integrated Absolute Error and Integrated Square Error which go from 40.17% to 94.29% and from 34.27% to 99.71%, respectively. Keywords: control design; industrial control; transfer learning; WWTP Creator: LaTeX with hyperref Producer: pdfTeX-1.40.21 CreationDate: Thu Sep 23 05:24:45 CEST 2021 ModDate: Thu Sep 23 05:58:33 CEST 2021 ID: 0xef8159f80b0b8cc538b3dd5c6fb30ea9, 0xef8159f80b0b8cc538b3dd5c6fb30ea9 Filters: FilterPipeline: FlateDecode Fonts: Type1: Font: BaseFont: MBXBYR+URWPalladioL-BoldItal FontSubset: true FirstChar: 82 LastChar: 82 FontDescriptor: FontName: MBXBYR+URWPalladioL-BoldItal Flags: Symbolic FontBBox: -170, -300, 1073, 935 FontFile: true EncodingDictionary: Differences: true Font: BaseFont: DMWGEP+URWPalladioL-Roma FontSubset: true FirstChar: 2 LastChar: 250 FontDescriptor: FontName: DMWGEP+URWPalladioL-Roma Flags: Symbolic FontBBox: -166, -283, 1021, 943 FontFile: true EncodingDictionary: Differences: true Font: BaseFont: NWBGJJ+CMSY10 FontSubset: true FirstChar: 0 LastChar: 106 FontDescriptor: FontName: NWBGJJ+CMSY10 Flags: Symbolic FontBBox: -29, -960, 1116, 775 FontFile: true Font: 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