Oscar A.Z. Sotomayor

Multivariable identification of an activated sludge process with subspace-based algorithms

Abstract This paper is aimed at identifying a linear time-invariant dynamical model (LTI model with lumped parameters) of an activated sludge process. Such a system is characterized by stiff dynamics, nonlinearities, time-variant parameters, recycles, multivariability with many cross-couplings and wide variations in the inflow and the composition of the incoming wastewater. In this simulation study, a discrete-time identification approach based on subspace methods is applied in order to estimate a nominal MIMO state-space model around a given operating point, by probing the system in open-loop with multi-level random signals. Six subspace algorithms are used and their performances are compared based on adequate quality criteria, taking into account identification/validation data. As a result, the selected model is a very low-order one and it describes the complex dynamics of the process well. Important issues concerning the generation of the data set and the estimation of the model order are discussed.

Analysis of activated sludge process using multivariate statistical tools—a PCA approach

Abstract Twelve original physical variables of an activated sludge wastewater treatment system are considered. These cross-correlated variables are transformed in new ones that are not correlated by the use of PCA (principal component analysis), a powerful tool for analysis, monitoring and diagnostics of wastewater treatment processes. Just three principal components explain most of the system total variability (78% of total variance). Thus, the ability to describe the overall characteristics of the process using only three principal components will make the analysis, monitoring and diagnostic of the system easier. Three groups of variables characterizing the system are detected. The first group identifies variables that represent micro-organisms and inert particulate matter arising from cellular decay, while the second group refers to substrates and flow rate. The third group is related to the pH. Based on these results, the present paper shows how to enlarge the ways of interpreting the characteristics of activated sludge wastewater treatment system.

Software sensor for on-line estimation of the microbial activity in activated sludge systems

This paper considers the design of a software sensor (or soft-sensor) for the on-line estimation of the biological activities of a colony of aerobic micro-organisms acting on activated sludge processes, where the carbonaceous waste degradation and nitrification processes are taken into account. These bioactivities are intimately related to the dissolved oxygen concentration. Two factors that affect the dynamics of the dissolved oxygen are the respiration rate or the oxygen uptake rate (OUR) and the oxygen transfer function (K(l)a). These items are challenging topics for the application of recursive identification due the nonlinear characteristic of the oxygen transfer function, and to the time-varying feature of the respiration rate. In this work, OUR and the oxygen transfer function are estimated through a software sensor, which is based on a modified version of the discrete extended Kalman filter. Numerical simulations are carried out in a predenitrifying activated sludge process benchmark and the obtained results demonstrate the applicability and efficiency of the proposed methodology, which should provide a valuable tool to supervise and control activated sludge processes.

A simulation benchmark to evaluate the performance of advanced control techniques in biological wastewater treatment plants

Wastewater treatment plants (WWTP) are complex systems that incorporate a large number of biological, physicochemical and biochemical processes. They are large and nonlinear systems subject to great disturbances in incoming loads. The primary goal of a WWTP is to reduce pollutants and the second goal is disturbance rejection, in order to obtain good effluent quality. Modeling and computer simulations are key tools in the achievement of these two goals. They are essential to describe, predict and control the complicated interactions of the processes. Numerous control techniques (algorithms) and control strategies (structures) have been suggested to regulate WWTP; however, it is difficult to make a discerning performance evaluation due to the nonuniformity of the simulated plants used. The main objective of this paper is to present a benchmark of an entire biological wastewater treatment plant in order to evaluate, through simulations, different control techniques. This benchmark plays the role of an activated sludge process used for removal of organic matter and nitrogen from domestic effluents. The development of this simulator is based on models widely accepted by the international community and is implemented in Matlab/Simulink (The MathWorks, Inc.) platform. The benchmark considers plant layout and the effects of influent characteristics. It also includes a test protocol for analyzing the open and closed-loop responses of the plant. Examples of control applications in the benchmark are implemented employing conventional PI controllers. The following common control strategies are tested: dissolved oxygen (DO) concentration-based control, respirometry-based control and nitrate concentration-based control.

MODEL-BASED PREDICTIVE CONTROL OF A PRE-DENITRIFICATION PLANT: A LINEAR STATE-SPACE MODEL APPROACH

Abstract This paper focuses on the design of a model-based predictive control (MPC or MBPC) technique to regulate the concentration levels of nitrate in both anoxic and aerobic zones of a pre-denitrifying activated sludge plant, aiming to improve the nitrogen (N)-removal from wastewater. The synthesis of the MPC controller is based on a linear extended state-space model of the process, where an identification horizon is added to include a sequence of past inputs/outputs. This sequence can be used to estimate the model or the updated state of the process, thus eliminating the need for a state observer. The linear state-space model was obtained through subspace identification methods. The controller performance is tested by simulation and the results show the efficiency of the proposed strategy.

Observer-Based Supervision and Fault Detection of a FCC Unit Model Predictive Control System

Abstract This work is concerned with the design of a fault detection and isolation (FDI) system to monitor malfunctions in sensors and actuators of a fluid catalytic cracking (FCC) unit model predictive control (MPC) system. The control system is based on an infinite-horizon MPC algorithm. The fault detection scheme is developed based on two banks of robust observers, while the fault isolation task is done employing a structured residual approach. The models used in the control and in the FDI of the FCC unit are obtained by using subspace identification methods. The effectiveness of the proposed strategy is verified through numerical simulations carried out on a dynamical model of an industrial FCC unit under abrupt fault in its control devices.

Nitrate Concentration-Based Control of a Pre-Denitrifying Activated Sludge System

Abstract The strong variation in the flow and composition of the incoming wastewater generates a demand for on line control of the denitrification process in order to improve nitrogen removal. In this paper the nitrate concentration is controlled by manipulation of the nitrate recycle flow rate in an activated sludge process. The control strategy is based on the Reference System Synthesis/Generic Model Control (RSS/GMC) technique, using a reduced order model whose parameters were updated through an extended Kalman filter. Results are evaluated in simulations, demonstrating the robustness of the algorithm to variations in the influent flow and concentration, and dynamics not considered in the reduced order model.

PERFORMANCE ASSESSMENT OF MODEL PREDICTIVE CONTROL SYSTEMS

Abstract This paper aims at to propose a benchmark MPC controller to be used in the performance assessment of existing industrial MPC systems. The basic questions are how the performance could be evaluated in a realistic basis and how to judge the performance of a controller that is already in operation by comparing it with another controller that could be really implemented in the same system. Here, it is assumed that the ideal controller will inherit the structure, input constraints and tuning parameters of the controller whose performance is to be evaluated. This means that the design of the ideal controller is standard and there is no need to tune the performance assessment algorithm. It is proposed a controller that preserves closed loop stability for any adopted tuning parameters. This is requisite for any performance evaluation procedure that is expected to operate in an on-line scheme. The proposed controller is compared by simulation with other benchmark controllers proposed in the control literature.

MPC Control of a Predenitrification Plant Using Linear Subspace Models

Abstract In this paper, a model-based predictive control (MPC) technique is designed aiming to control the nitrogen (N)-removal from domestic sewage in an activated sludge wastewater treatment plant. The objective is to control the nitrate concentrations in both, anoxic and aerobic zones of the bioreactor and, therefore, to inferentially to control the effluent inorganic nitrogen concentration. The synthesis of the MPC controller is based on a linear subspace (state-space) model of the process, where an identification horizon is added to include a sequence of past inputs/outputs. This sequence can be used to estimate the model or the updated state of the process, thus eliminating the need for a state observer. Two different MPC control configurations are compared and the result shows the successful of the control application. The linear state-space model was obtained using subspace identification methods.

Stable IHMPC for Unstable Systems

An alternative method to formulate the stable Model Predictive Control (MPC) optimization problem, which allows controlling unstable systems with a large domain of attraction, is presented in this work. Usually, stability is guaranteed by means of an appropriate selection of a terminal cost, a terminal constraint, and a local unconstrained controller for predictions beyond the control horizon. This is the case, for instance, of the infinite horizon MPC (IHMPC) with a null local controller, and the dual MPC with a local Linear Quadratic Regulator (LQR). In the last case, the MPC formulation also allows a local optimality. However, its domain of attraction is limited (small, in most of the cases) and depends on the size of the terminal set and the length of the control horizon. Here we propose the inclusion of an appropriate set of slacked terminal constraints into the optimization problem as a way to enlarge the domain of attraction of the MPC that uses the null local controller. In addition, this slack allows a simple offset-free operation in the proximities of the input saturation. Despite the proposed controller does not achieve local optimality, simulations show that its performance is similar to the one obtained with the dual MPC that uses a LQR local controller.