e1267b3791c130ca235536d191a3ea9e pro_a2023m8v11n8p2269.pdf 6c98b5e9e80319a6f1e77558935857575ed3308c pro_a2023m8v11n8p2269.pdf 63aef03aafb617e2c847853386a5a64e77604f41c36f7cc447d2977f3c863e60 pro_a2023m8v11n8p2269.pdf Title: Intelligent Control of Wastewater Treatment Plants Based on Model-Free Deep Reinforcement Learning Subject: In this work, deep reinforcement learning methodology takes advantage of transfer learning methodology to achieve a reasonable trade-off between environmental impact and operating costs in the activated sludge process of Wastewater treatment plants (WWTPs). WWTPs include complex nonlinear biological processes, high uncertainty, and climatic disturbances, among others. The dynamics of complex real processes are difficult to accurately approximate by mathematical models due to the complexity of the process itself. Consequently, model-based control can fail in practical application due to the mismatch between the mathematical model and the real process. Control based on the model-free reinforcement deep learning (RL) methodology emerges as an advantageous method to arrive at suboptimal solutions without the need for mathematical models of the real process. However, convergence of the RL method to a reasonable control for complex processes is data-intensive and time-consuming. For this reason, the RL method can use the transfer learning approach to cope with this inefficient and slow data-driven learning. In fact, the transfer learning method takes advantage of what has been learned so far so that the learning process to solve a new objective does not require so much data and time. The results demonstrate that cumulatively achieving conflicting objectives can efficiently be used to approach the control of complex real processes without relying on mathematical models. Keywords: intelligent control; model-free deep reinforcement learning; reusing policy; waste water treatment plant Author: Oscar Aponte-Rengifo, Mario Francisco, Ramón Vilanova, Pastora Vega, Silvana Revollar Creator: LaTeX with hyperref Producer: pdfTeX-1.40.21 CreationDate: Fri Jul 28 05:14:46 2023 CEST ModDate: Fri Jul 28 05:23:10 2023 CEST Custom Metadata: no Metadata Stream: no Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 25 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 6706273 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- CIBAZF+URWPalladioL-Bold Type 1 Custom yes yes no 10 0 MKNVUI+URWPalladioL-Roma Type 1 Custom yes yes no 15 0 ROQTYF+URWPalladioL-Ital Type 1 Custom yes yes no 19 0 BHUVWR+TimesNewRomanPS-BoldItalicMT Type 1C WinAnsi yes yes no 37 0 SOSTRQ+CMR10 Type 1 Builtin yes yes no 62 0 PLMWZL+CMSY10 Type 1 Builtin yes yes no 66 0 YNVNNA+CMEX10 Type 1 Builtin yes yes no 88 0 QQXUPP+PazoMath Type 1 Builtin yes yes no 92 0 QCVCWF+PazoMath-Italic Type 1 Builtin yes yes no 99 0 SYFPBV+CMMI10 Type 1 Builtin yes yes no 103 0 QDTWCG+MSBM10 Type 1 Builtin yes yes no 107 0 DNJKBS+URWPalladioL-BoldItal Type 1 Custom yes yes no 163 0 Jhove (Rel. 1.28.0, 2023-05-18) Date: 2024-04-20 02:36:08 CEST RepresentationInformation: pro_a2023m8v11n8p2269.pdf ReportingModule: PDF-hul, Rel. 1.12.4 (2023-03-16) LastModified: 2024-04-19 18:17:10 CEST Size: 6706273 Format: PDF Version: 1.7 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 352 FreeObjects: 1 IncrementalUpdates: 0 DocumentCatalog: PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Destination: section.1 Item: Title: Plant Description Destination: section.2 Children: Item: Title: Benchmark Simulation Model nº1 Destination: subsection.2.1 Item: Title: Benchmark Simulation Model nº2 Destination: subsection.2.2 Item: Title: Performance Indices Destination: subsection.2.3 Item: Title: Problem Statement Destination: section.3 Item: Title: Methodology Destination: section.4 Children: Item: Title: Policy Gradient Algorithm Destination: subsection.4.1 Item: Title: Transfer Reinforcement Learning approach Destination: subsection.4.2 Item: Title: Controlling Agents Destination: subsection.4.3 Item: Title: Deep Neural Network as Policy Destination: subsection.4.4 Item: Title: Performance during Training Destination: subsection.4.5 Children: Item: Title: Objectives Evolution Destination: subsubsection.4.5.1 Item: Title: Oxygen Set-Point Evolution Destination: subsubsection.4.5.2 Item: Title: Results and Discussion Destination: section.5 Item: Title: Conclusions Destination: section.6 Item: Title: References Destination: section.7 Info: Title: Intelligent Control of Wastewater Treatment Plants Based on Model-Free Deep Reinforcement Learning Author: Oscar Aponte-Rengifo, Mario Francisco, Ramón Vilanova, Pastora Vega, Silvana Revollar Subject: In this work, deep reinforcement learning methodology takes advantage of transfer learning methodology to achieve a reasonable trade-off between environmental impact and operating costs in the activated sludge process of Wastewater treatment plants (WWTPs). WWTPs include complex nonlinear biological processes, high uncertainty, and climatic disturbances, among others. The dynamics of complex real processes are difficult to accurately approximate by mathematical models due to the complexity of the process itself. Consequently, model-based control can fail in practical application due to the mismatch between the mathematical model and the real process. Control based on the model-free reinforcement deep learning (RL) methodology emerges as an advantageous method to arrive at suboptimal solutions without the need for mathematical models of the real process. However, convergence of the RL method to a reasonable control for complex processes is data-intensive and time-consuming. For this reason, the RL method can use the transfer learning approach to cope with this inefficient and slow data-driven learning. In fact, the transfer learning method takes advantage of what has been learned so far so that the learning process to solve a new objective does not require so much data and time. The results demonstrate that cumulatively achieving conflicting objectives can efficiently be used to approach the control of complex real processes without relying on mathematical models. 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