Advanced decision control system for effluent violations removal in wastewater treatment plants

I. Santín; C. Pedret; R. Vilanova; M. Meneses

doi:10.1016/j.conengprac.2016.01.005

Advanced decision control system for effluent violations removal in wastewater treatment plants

I. Santín, C. Pedret, R. Vilanova, M. Meneses

Department of Telecommunications and Systems Engineering

Research output: Contribution to journal › Article › Research › peer-review

51 Citations (Scopus)

Abstract

© 2016 Elsevier Ltd. This paper presents the application of control strategies for wastewater treatment plants with the goal of effluent limits violations removal as well as achieving a simultaneous improvement of effluent quality and reduction of operational costs. The evaluation is carried out with the Benchmark Simulation Model No. 2. The automatic selection of the suitable control strategy is based on risk detection of effluent violations by Artificial Neural Networks. Fuzzy Controllers are implemented to improve the denitrification or nitrification process based on the proposed objectives. Model Predictive Control is applied for the improvement of dissolved oxygen tracking.

Original language	English
Pages (from-to)	60-75
Journal	Control Engineering Practice
Volume	49
DOIs	https://doi.org/10.1016/j.conengprac.2016.01.005
Publication status	Published - 1 Apr 2016

Keywords

Artificial neural networks
BSM2 benchmark
Fuzzy control
Model predictive control
Violations risk detection
Wastewater treatment plant

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10.1016/j.conengprac.2016.01.005

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title = "Advanced decision control system for effluent violations removal in wastewater treatment plants",

abstract = "{\textcopyright} 2016 Elsevier Ltd. This paper presents the application of control strategies for wastewater treatment plants with the goal of effluent limits violations removal as well as achieving a simultaneous improvement of effluent quality and reduction of operational costs. The evaluation is carried out with the Benchmark Simulation Model No. 2. The automatic selection of the suitable control strategy is based on risk detection of effluent violations by Artificial Neural Networks. Fuzzy Controllers are implemented to improve the denitrification or nitrification process based on the proposed objectives. Model Predictive Control is applied for the improvement of dissolved oxygen tracking.",

keywords = "Artificial neural networks, BSM2 benchmark, Fuzzy control, Model predictive control, Violations risk detection, Wastewater treatment plant",

author = "I. Sant{\'i}n and C. Pedret and R. Vilanova and M. Meneses",

year = "2016",

month = apr,

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doi = "10.1016/j.conengprac.2016.01.005",

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AU - Santín, I.

AU - Pedret, C.

AU - Vilanova, R.

AU - Meneses, M.

PY - 2016/4/1

Y1 - 2016/4/1

N2 - © 2016 Elsevier Ltd. This paper presents the application of control strategies for wastewater treatment plants with the goal of effluent limits violations removal as well as achieving a simultaneous improvement of effluent quality and reduction of operational costs. The evaluation is carried out with the Benchmark Simulation Model No. 2. The automatic selection of the suitable control strategy is based on risk detection of effluent violations by Artificial Neural Networks. Fuzzy Controllers are implemented to improve the denitrification or nitrification process based on the proposed objectives. Model Predictive Control is applied for the improvement of dissolved oxygen tracking.

AB - © 2016 Elsevier Ltd. This paper presents the application of control strategies for wastewater treatment plants with the goal of effluent limits violations removal as well as achieving a simultaneous improvement of effluent quality and reduction of operational costs. The evaluation is carried out with the Benchmark Simulation Model No. 2. The automatic selection of the suitable control strategy is based on risk detection of effluent violations by Artificial Neural Networks. Fuzzy Controllers are implemented to improve the denitrification or nitrification process based on the proposed objectives. Model Predictive Control is applied for the improvement of dissolved oxygen tracking.

KW - Artificial neural networks

KW - BSM2 benchmark

KW - Fuzzy control

KW - Model predictive control

KW - Violations risk detection

KW - Wastewater treatment plant

U2 - 10.1016/j.conengprac.2016.01.005

DO - 10.1016/j.conengprac.2016.01.005

M3 - Article

SN - 0967-0661

VL - 49

SP - 60

EP - 75

JO - Control Engineering Practice

JF - Control Engineering Practice

ER -

Advanced decision control system for effluent violations removal in wastewater treatment plants

Abstract

Keywords

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