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Author：

Wang, D. (Wang, D..) | Li, X. (Li, X..) | Xin, P. (Xin, P..) | Liu, A. (Liu, A..) | Qiao, J. (Qiao, J..)

Indexed by：

EI Scopus SCIE

Abstract：

With　the　rapid　development　of　industry,　the　amount　of　wastewater　discharge　is　increasing.　In　order　to　improve　the　efficiency　of　the　wastewater　treatment　process　(WWTP),　we　often　desire　that　the　dissolved　oxygen　(DO)　concentration　and　the　nitrate　nitrogen　(NO)　concentration　can　be　controlled　to　track　set　values.　However,　the　wastewater　treatment　system　is　a　type　of　unknown　nonlinear　plant　with　time-varying　dynamics　and　strong　disturbances.　Some　traditional　control　methods　are　difficult　to　achieve　this　goal.　To　overcome　these　challenges,　a　supplementary　heuristic　dynamic　programming　(SUP-HDP)　control　scheme　is　established　by　combining　the　traditional　control　method　and　heuristic　dynamic　programming　(HDP).　A　parallel　control　structure　is　constructed　in　the　SUP-HDP　control　scheme,　which　not　only　complements　the　shortcomings　of　traditional　control　schemes　in　learning　and　adaptive　abilities　but　also　improves　the　convergence　speed　and　the　stability　of　the　learning　process　of　HDP.　Besides,　the　convergence　proof　of　the　designed　control　scheme　is　provided.　The　SUP-HDP　control　scheme　is　implemented　utilizing　neural　networks.　Finally,　we　validate　the　effectiveness　of　the　SUP-HDP　control　method　through　a　benchmark　simulation　platform　for　the　WWTP.　Compared　with　other　control　methods,　SUP-HDP　has　better　control　performance.　©　2024　Elsevier　Ltd

Keyword：

Tracking control Neural networks Wastewater treatment process control Reinforcement learning Supplementary heuristic dynamic programming

Author Community：

  • [ 1 ] [Wang D.]Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
  • [ 2 ] [Wang D.]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, 100124, China
  • [ 3 ] [Wang D.]Beijing Laboratory of Smart Environmental Protection, Beijing University of Technology, Beijing, 100124, China
  • [ 4 ] [Wang D.]Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing, 100124, China
  • [ 5 ] [Li X.]Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
  • [ 6 ] [Li X.]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, 100124, China
  • [ 7 ] [Li X.]Beijing Laboratory of Smart Environmental Protection, Beijing University of Technology, Beijing, 100124, China
  • [ 8 ] [Li X.]Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing, 100124, China
  • [ 9 ] [Xin P.]Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
  • [ 10 ] [Xin P.]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, 100124, China
  • [ 11 ] [Xin P.]Beijing Laboratory of Smart Environmental Protection, Beijing University of Technology, Beijing, 100124, China
  • [ 12 ] [Xin P.]Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing, 100124, China
  • [ 13 ] [Liu A.]Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
  • [ 14 ] [Liu A.]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, 100124, China
  • [ 15 ] [Liu A.]Beijing Laboratory of Smart Environmental Protection, Beijing University of Technology, Beijing, 100124, China
  • [ 16 ] [Liu A.]Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing, 100124, China
  • [ 17 ] [Qiao J.]Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
  • [ 18 ] [Qiao J.]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, 100124, China
  • [ 19 ] [Qiao J.]Beijing Laboratory of Smart Environmental Protection, Beijing University of Technology, Beijing, 100124, China
  • [ 20 ] [Qiao J.]Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing, 100124, China

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Source ：

Expert Systems with Applications

ISSN： 0957-4174

Year： 2024

Volume： 247

8 . 5 0 0

JCR@2022

Cited Count：

WoS CC Cited Count： 0

SCOPUS Cited Count： 3

ESI Highly Cited Papers on the List： 0 Unfold All

WanFang Cited Count：

Chinese Cited Count：

30 Days PV： 7

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