Claudio Garcia

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.

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.

KARNOPP FRICTION MODEL IDENTIFICATION FOR A REAL CONTROL VALVE

Abstract This paper presents an algorithm to estimate friction parameters of a real control valve. Data are collected from a valve installed in a bench, submitted to different input signals and subject to different friction forces. Two different parameter sets are obtained, based on distinct methods. These parameter sets are applied in the Karnopp friction model, generating two versions of the same model. These models are validated with different input signals and distinct friction forces. The validation tests have revealed that both models described quite well the behavior of the control valve.

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.

Obstacle Detection and Tracking Using Laser 2D

An obstacle detection and tracking system using a 2D laser sensor and the Kalman filter is presented. This filter is not very efficient in case of severe disturbances in the measured position of the obstacle, as for instance, when an object being tracked is behind a barrier, thus interrupting the laser beam, making it impossible to receive the sensor information about its position. This work suggests a method to minimize this problem by using an algorithm called Corrector of Discrepancies.

Enhancement in performance and stability of MRI methods

Abstract Two representative approaches for MRI (MPC Relevant Identification) methods are reported in the literature. The first one is based on the solution of an optimal problem, while the second is based on the prefiltering of the system input and output signals. Each method has advantages and disadvantages in accordance with the process to identify, the length of the prediction horizon or its mathematical implementation. A new MRI method is proposed herein, based on the advantages of both algorithms. A comparison is performed among some MRI methods and the new proposed one. The results indicate that in the studied case, the performance of the new method is better.

Detection of no-model input/output combination in transfer matrix in closed-loop MIMO systems

Abstract A method to detect input/output (IO) combinations with no-model or poor model in the transfer matrix of a closed-loop MIMO system is proposed. Traditional approaches to IO selection are not adequate when used to detect no-model IO combination of a closed-loop identification process. The feedback effect, controller action and the characteristics of the excitation signal employed during the pre-identification stage cause this limitation. In this proposal the detection of no-model or poor model IO combinations is made based on regularity of low values of polynomial coefficients of parametric identification models. This information is gathered during the pre-identification stage. Improvement in model estimation is obtained once these “null” combinations are zeroed, before the identification process takes place. A study case involving identification of a 2 x 2 MIMO system is discussed.

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.

Multivariable system identification using an output-injection based parameterization

The challenge of identifying multivariable models from input/output data is a subject of great interest, either in scientific works or in industrial plants. The parameterization of multi-output models is considered to be the most crucial task in a MIMO system identification procedure. In this work, a pioneering multivariable identification method is proposed, implemented and evaluated using a linear simulated plant. It is compared to other traditional MIMO identification methods and its results outperformed the other analyzed methods. It was also tested the situation of over-dimensionality of the estimated models, through the use of Hankel singular values and again the proposed method surpassed the other ones in estimating the correct model order.