97ee3d58bf6ba905d3b047e09e41297a sensors_a2021v21n4p1237.pdf f778c588d7ca2cdd05a6976c8cfc5a4931ff25a2 sensors_a2021v21n4p1237.pdf 69387937deb26764cc333d83b491bcc40e08f42a9aed407a58b01ffd5507398e sensors_a2021v21n4p1237.pdf Title: Industrial Control under Non-Ideal Measurements: Data-Based Signal Processing as an Alternative to Controller Retuning Subject: Industrial environments are characterised by the non-lineal and highly complex processes they perform. Different control strategies are considered to assure that these processes are correctly performed. Nevertheless, these strategies are sensible to noise-corrupted and delayed measurements. For that reason, denoising techniques and delay correction methodologies should be considered but, most of these techniques require a complex design and optimisation process as a function of the scenario where they are applied. To alleviate this, a complete data-based approach devoted to denoising and correcting the delay of measurements is proposed here with a two-fold objective: simplify the solution design process and achieve its decoupling from the considered control strategy as well as from the scenario. Here it corresponds to a Wastewater Treatment Plant (WWTP). However, the proposed solution can be adopted at any industrial environment since neither an optimization nor a design focused on the scenario is required, only pairs of input and output data. Results show that a minimum Root Mean Squared Error (RMSE) improvement of a 63.87% is achieved when the new proposed data-based denoising approach is considered. In addition, the whole system performance show that similar and even better results are obtained when compared to scenario-optimised methodologies. Keywords: artificial neural networks; data-driven methods; denoising autoencoders; industrial control; wastewater treatment plants Author: Ivan Pisa, Antoni Morell, Ramón Vilanova and Jose Lopez Vicario Creator: LaTeX with hyperref Producer: pdfTeX-1.40.21 CreationDate: Wed Feb 10 07:40:49 2021 CET ModDate: Wed Feb 10 07:43:05 2021 CET Custom Metadata: no Metadata Stream: no Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 31 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 2264534 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- IRADDT+URWPalladioL-Ital Type 1 Custom yes yes no 10 0 NMFSVN+URWPalladioL-Bold Type 1 Custom yes yes no 15 0 CCPFPT+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 FNTSBS+PalatinoLinotype-Roman CID TrueType Identity-H yes yes yes 80 0 TBLJNE+PazoMath-Italic Type 1 Builtin yes yes no 91 0 SOSTRQ+CMR10 Type 1 Builtin yes yes no 95 0 QQXUPP+PazoMath Type 1 Builtin yes yes no 102 0 GMYQUS+CMSY10 Type 1 Builtin yes yes no 106 0 QDTWCG+MSBM10 Type 1 Builtin yes yes no 113 0 QIELJB+CMMI10 Type 1 Builtin yes yes no 117 0 QJUBZH+CMEX10 Type 1 Builtin yes yes no 121 0 LiberationSerif CID TrueType Identity-H yes no yes 130 0 LiberationSerif CID TrueType Identity-H yes no yes 154 0 LiberationSerif CID TrueType Identity-H yes no yes 201 0 LiberationSerif CID TrueType Identity-H yes no yes 233 0 LiberationSerif CID TrueType Identity-H yes no yes 282 0 LiberationSans CID TrueType Identity-H yes no yes 304 0 TUMWYQ+URWPalladioL-BoldItal Type 1 Custom yes yes no 342 0 Jhove (Rel. 1.28.0, 2023-05-18) Date: 2024-10-02 03:55:06 CEST RepresentationInformation: sensors_a2021v21n4p1237.pdf ReportingModule: PDF-hul, Rel. 1.12.4 (2023-03-16) LastModified: 2023-03-31 11:50:28 CEST Size: 2264534 Format: PDF Version: 1.7 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 718 FreeObjects: 1 IncrementalUpdates: 0 DocumentCatalog: PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Destination: section.1 Item: Title: Material and Methods Destination: section.2 Children: Item: Title: Benchmark Simulation Model No.1 Destination: subsection.2.1 Children: Item: Title: BSM1 Architecture Destination: subsubsection.2.1.1 Item: Title: BSM1 Simulation Protocol Destination: subsubsection.2.1.2 Item: Title: Evaluation Destination: subsubsection.2.1.3 Item: Title: Data-Based Denoising Methods Destination: subsection.2.2 Item: Title: Modelling Destination: subsection.2.3 Item: Title: Data-Based Enhanced Control Strategy Destination: section.3 Children: Item: Title: Data-Based Denoising Stage Destination: subsection.3.1 Item: Title: ANN-Based Delay Correction Stage Destination: subsection.3.2 Item: Title: Results Destination: section.4 Children: Item: Title: Data-Based Denoising Performance Destination: subsection.4.1 Item: Title: ANN-Based Delay Correction Performance Destination: subsection.4.2 Item: Title: Control Performance Destination: subsection.4.3 Children: Item: Title: Fix Set-Point Results Destination: subsubsection.4.3.1 Item: Title: Variable Set-Point Results Destination: subsubsection.4.3.2 Item: Title: Conclusions Destination: section.5 Item: Title: References Destination: section.5 Info: Title: Industrial Control under Non-Ideal Measurements: Data-Based Signal Processing as an Alternative to Controller Retuning Author: Ivan Pisa, Antoni Morell, Ramón Vilanova and Jose Lopez Vicario Subject: Industrial environments are characterised by the non-lineal and highly complex processes they perform. Different control strategies are considered to assure that these processes are correctly performed. Nevertheless, these strategies are sensible to noise-corrupted and delayed measurements. For that reason, denoising techniques and delay correction methodologies should be considered but, most of these techniques require a complex design and optimisation process as a function of the scenario where they are applied. To alleviate this, a complete data-based approach devoted to denoising and correcting the delay of measurements is proposed here with a two-fold objective: simplify the solution design process and achieve its decoupling from the considered control strategy as well as from the scenario. Here it corresponds to a Wastewater Treatment Plant (WWTP). However, the proposed solution can be adopted at any industrial environment since neither an optimization nor a design focused on the scenario is required, only pairs of input and output data. Results show that a minimum Root Mean Squared Error (RMSE) improvement of a 63.87% is achieved when the new proposed data-based denoising approach is considered. In addition, the whole system performance show that similar and even better results are obtained when compared to scenario-optimised methodologies. 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