Mario Francisco

Integrated design and control of chemical processes – Part I: Revision and classification

Abstract This work presents a comprehensive classification of the different methods and procedures for integrated synthesis, design and control of chemical processes, based on a wide revision of recent literature. This classification fundamentally differentiates between “projecting methods”, where controllability is monitored during the process design to predict the trade-offs between design and control, and the “integrated-optimization methods” which solve the process design and the control-systems design at once within an optimization framework. The latter are revised categorizing them according to the methods to evaluate controllability and other related properties, the scope of the design problem, the treatment of uncertainties and perturbations, and finally, the type the optimization problem formulation and the methods for its resolution.

Integration of set point optimization techniques into nonlinear MPC for improving the operation of WWTPs

Abstract Optimization and control strategies are necessary to keep wastewater treatment plants (WWTPs) operating in the best possible conditions, maximizing effluent quality with the minimum consumption of energy. In this work, a benchmarking of different hierarchical control structures for WWTPs that combines static and dynamic real time optimization (RTO) and nonlinear model predictive control (NMPC) is presented. The objective is to evaluate the enhancement of the operation in terms of economics and effluent quality that can be achieved when introducing NMPC technologies in the distinct levels of the multilayer structure. Three multilayer hierarchical structures are evaluated and compared for the N-Removal process considering the short term and long term operation in a rain weather scenario. A reduction in the operation costs of approximately 20% with a satisfactory compromise to effluent quality is achieved with the application of these control schemes.

Integrated design and control of chemical processes : Part II: an illustrative example

Abstract In this paper, several methodologies of integrated design are proposed and applied to the design of wastewater treatment plants and their control system, focusing on the activated sludge process, within a novel multiobjective framework. The scope of the problem considers both fixed plant layout and plant structure selection by defining a simple superstructure. The control strategy chosen is a linear Model Predictive Controller (MPC) with terminal penalty. The evaluation of the controllability has been performed using norm based indexes, and the robustness conditions for different uncertainty sources have been considered, in the frequency and time domains. The optimization strategies used are based on the integration of stochastic and deterministic methods, as well as genetic algorithms. The presented methodologies and their application to wastewater treatment plants can be considered as an illustrative example in the universe of integrated design techniques presented in the Part I article of this series.

Multiobjective Optimization for Automatic Tuning of Robust Model Based Predictive Controllers

In this paper a general procedure for tuning multivariable model predictive controllers (MPC) with constraints is presented. It has been applied to tune the control system of an activated sludge process control in a wastewater treatment plant. Control system parameters are obtained by solving a mixed sensitivity optimization problem, defined in terms of the H∞ norms of different weighted closed loop transfer functions matrices of the system, and a set of constraints, some of them expressed using the l1 norm. The use of multiple linearized models for the control allows for the specification of many robust performance criteria. The mathematical optimization for tuning all controller parameters is tackled in two iterative steps due to the existence of integer and real numbers. First, integer parameters are obtained using a special type of random search, and secondly a sequential programming method is used to tune the real parameters.

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

Abstract This paper describes a procedure to find the best controlled variables in an economic sense for the activated sludge process in a wastewater treatment plant, despite the large load disturbances. A novel dynamic analysis of the closed loop control of these variables has been performed, considering a nonlinear model predictive controller (NMPC) and a particular distributed NMPC-PI control structure where the PI is devoted to control the process active constraints and the NMPC the self-optimizing variables. The well-known self-optimizing control methodology has been applied, considering the most important measurements of the process. This methodology provides the optimum combination of measurements to keep constant with minimum economic loss. In order to avoid nonfeasible dynamic operation, a preselection of the measurements has been performed, based on the nonlinear model of the process and evaluating the possibility of keeping their values constant in the presence of typical disturbances.

Model Predictive Control of BSM1 benchmark of wastewater treatment process: A tuning procedure

The BSM1 (Benchmark Simulation Model N° 1) has become the standard simulation tool for performance assessment of control techniques applied to wastewater treatment plants (WWTP). Control of WWTP is not trivial because of large disturbances in the influent, nonlinearities, delays, and interaction between variables. In this work a multivariable Model Predictive Controller (MPC) is implemented and optimally tuned. The paper presents results for different tuning situations without the use of long time consuming simulations and considering uncertainty by means of multiple linearized models. Control of the dissolved oxygen in the last aerated reactor and nitrate level in the last anoxic compartment has been tested in simulations using the MPC control strategy. The obtained results show the MPC benefits when it is properly tuned.

People detection and stereoscopic analysis using MAS

This paper presents a multiagent system that can process stereoscopic images and detect people with a stereo camera. In the first of two phases, the system creates a model of the environment using a disparity map. It can be constructed in real time, even if there are moving objects present in the area (such as people passing by). In the second phase, the system is able to detect people by combining a series of novel techniques. A multi-agent system (MAS) is used to deal with the problem. The system is based on cooperative and distributed mechanisms and was tested under different conditions and environments.

Genetic Algorithms for the Synthesis and Integrated Design of Processes Using Advanced Control Strategies

This work presents a real-coded genetic algorithm to perform the synthesis and integrated design of an activated sludge process using and advanced Multivariable Model-based Predictive Controller (MPC). The process synthesis and design are carried out simultaneously with the MPC tuning to obtain the most economical plant which satisfies the controllability indices that measure the control performance (H∞ and l1 norms of different sensitivity functions of the system). The mathematical formulation results into a mixed-integer optimization problem with non-linear constraints. The quality of the solutions obtained evidence that real-coded genetic algorithms are a valid and practical alternative to deterministic optimization methods for such complex problems.

PROCESS INTEGRATED DESIGN WITHIN A MODEL PREDICTIVE CONTROL FRAMEWORK

Abstract In this work the Integrated Design of the activated sludge process in a wastewater treatment plant has been performed, including a linear multivariable predictive controller with constraints. In the Integrated Design procedure, the process parameters are obtained simultaneously with the parameters of the control system by solving a multiobjective constrained non-linear optimization problem, taking into account investment and operation costs. The mathematical optimization for tuning all parameters is tackled in two iterative steps. First, plant parameters are obtained using a sequential quadratic programming (SQP) method, and secondly, a type of random search method is used to tune the controller parameters (horizons and weights). Due to the difficulty to measure some variables, there has been also developed a Kalman Filter for state estimation.

An activated-sludge-process application of integrated design and predictive control with instantaneous linearization

In this work the integrated design of the activated sludge process is addressed. The objective is to determine the best plant parameters and working point that minimize the operation costs related to the Effluent Quality and the Aeration Energy, while imposing constraints on the plant condition number (γ) and the perturbation condition number (γp), ensuring open-loop controllability. A linear multivariable predictive control (MPC) is used for the closed loop design, and it is used also a very practical non-linear version of the MPC, based on the instantaneous linearization of non linear models of the plant (the phenomenological model as well as a neural network model obtained by identification). The results are analyzed based on two aspects: the improved performance of the controlled system when using the integrated design instead of a classical economic design, and the convenience of the instantaneous linearization to realize a non-linear MPC.