Advanced decision control system for effluent violations removal in wastewater treatment plants. (April 2016)

• Record Type: Journal Article
• Authors: Santín, I.; Pedret, C.; Vilanova, R.; Meneses, M.
• Is Part Of: Control engineering practice. Volume 49(2016)
• Page Start: 60
• Page End: 75
• Publication Date: 2016-04
• Subjects: Wastewater treatment plant -- BSM2 benchmark -- Model predictive control -- Fuzzy control -- Artificial neural networks -- Violations risk detection; AE aeration energy (kWh/d) -- ANN artificial neural network -- ASM1 activated sludge model No. 1 -- BOD5 5-day biological oxygen demand (mg/l) -- BSM benchmark simulation model -- CL1 first control strategy of the finalization of BSM2 plant layout in Nopens et al. (2010) -- CL2 second control strategy of the finalization of BSM2 plant layout in Nopens et al. (2010) -- COD chemical oxygen demand (mg/l) -- defCl default control strategy of the original BSM2 definition in Jeppsson et al. (2007) -- EC consumption of external carbon source (kg/d) -- EQI effluent quality index (kg/d) -- FC fuzzy controller -- HEnet net heating energy (kWh/d) -- ME mixing energy (kWh/d) -- METprod methane production in the anaerobic digester (kg/d) -- MPC model predictive control -- MPC+FF model predictive control with feedforward compensation -- OCI overall cost index -- PE pumping energy (kWh/d) -- SP sludge production (kg/d) -- TSS total suspended solids (mg/l) -- WWTP wastewater treatment plants; Automatic control -- Periodicals; 629.89
• DOI: 10.1016/j.conengprac.2016.01.005
• Languages: English
• ISSNs: 0967-0661

Abstract

Abstract: 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.