Systematic Approach to the Design of Operation and Control Policies in Activated Sludge Systems

Abstract

This article describes the systematic design of a control structure for a biological wastewater treatment process as given by the test-bed Benchmark Simulation Model No. 1 (BSM1) and Activated Sludge Model No. 1 (ASM1). The objective of this work was to formalize and implement a systematic and yet simple procedure for the selection of control structures in wastewater treatment plants (WWTPs) and to show that the application of the proposed methodology agrees with the "empirical"' findings regarding the operation of this process. The motivation underlying this endeavor was to search for a control configuration that leads to optimal economic operation while promptly rejecting disturbances at lower layers in the control hierarchy, thus avoiding violation of the more important regulatory constraints on effluent discharge. We started by optimizing a steady-state nonlinear model of the process for various important disturbances. The results confirmed that it is economically optimal to control the oxygen concentration in the aerobic basins and the nitrate in the second anoxic tank at their respective lower bounds, whereas the effluent ammonia from the bioreactors should be controlled at its upper limit. In addition, because it is good practice to operate with minimal manipulation, the wastage flow rate should be fixed at its nominal optimal set point. The proposed decentralized control configuration, consisting of simple PI controllers, is capable of maintaining the process well within the regulatory limits at a small cost when dynamic disturbances represented by three weather files affect the process, therefore suggesting that, according to the applied systematic methodology, more complex (multivariable) regulators are not necessary for the ASM1 process.

State estimation of wastewater treatment processes using distributed extended Kalman filters

“…To account for the different magnitudes of the estimation errors for different states, the error for each state is normalized based on its maximum estimation error from the two estimation schemes. The Euclidean norms of the normalized estimation error is defined as: e(t k ) = ∑ 78 i=1 (e i (t k )) 2 , where e(t k ) is the normalized error at time instant t k . e i (t k ) is the normalized error of state i, i = 1, 2, ..., 78, defined as:…”

“…Different process control schemes have been reported for WWTPs including proportional-integral (PI) control [1], [2], model predictive control (MPC) [3]- [6] and economic MPC [7]. While there are many results on the control system design for WWTPs, relatively less attention has been given to the state estimation of WWTPs.…”

Intelligent Control/Operational Strategies in WWTPs through an Integrated Q-Learning Algorithm with ASM2d-Guided Reward

“…Nevertheless, even for expert engineers, determining the optimal operating strategy for WWTPs remains quite difficult and laborious given the complexity of the underlying biochemical phenomena, their interaction, and the large number of operating parameters to deal with [21]. In addition, the commonly used proportional-integral and proportional-integral-derivative controllers in the context of control in WWTPs cannot predict the problematic situations nor lead back the control process toward optimal conditions [20,[22][23][24]. Therefore, given the strengthening of stringent discharge standards and highly dynamic influent loadings with variable concentration of pollutants, it is very challenging to design, and then effectively implement, real-time optimal control strategies for the existing wastewater treatment processes [7].…”

Model predictive control for the self-optimized operation in wastewater treatment plants: Analysis of dynamic issues

“…Some of them (Francisco et al, 2011;Rivas et al, 2008) also include plant design, and others are only focused on tanks aeration (Amand and Carlsson, 2012). Only Araujo et al (2011Araujo et al ( , 2013 provides a comprehensive approach, performing a sensitivity analysis of optimal operation for the selection of the best control structure in term of costs and effluent quality. The work of Cadet et al (2004) is similar but without considering the economics of the system.…”