Rosalba Lamanna

Metodología para la Obtención de Modelos Semifísicos de Base Fenomenológica Aplicada a una Sulfitadora de Jugo de Caña de Azúcar

This work presents the Phenomenological Based Semi-physical Model (PBSM) as a useful tool for the design, control and optimization of chemical and bio-technology processes. First, a detailed description of a methodology to obtain a PBSM based on the knowledge of the basic principles of the process: mass, energy and momentum conservation, as well as gradient principle for obtaining model constitutive equations. The described methodology is applied to the clarification stage of a sugar cane refining plant. Specifically, the sulfitation tower is modelled step by step. The model is simulated and validated by comparing its behaviour with data taken from a real sulfitation tower.

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.

Algorithmic synthesis and integrated design for activated sludge processes using genetic algorithms

Abstract This work presents an approach for the Synthesis and Integrated Design of an activated sludge process. The mathematical formulation translates a superstructure that contains all the design alternatives into a mixed-integer dynamical optimization problem with non-linear constraints. A real-coded genetic algorithm is proposed for the solution of such complex problem as an alternative to classical optimisation techniques. Thus, the process synthesis considering open-loop-dynamical-performance indexes and also the closed loop integrated design for the plant are carried out. The results are encouraging for future application of these techniques to solve process synthesis problems

Diseño Simultáneo de Proceso y Control de una Torre Sulfitadora de Jugo de Caña de Azúcar

This work focuses in the design and control of a plant used in the clarification stage of the sugar cane juice refining process. An innovative approach to the simultaneous design and control problem is presented, which considers the state controllability (based on practical controllability metrics) and the output controllability (based on dynamical performance indices) using as example the sulphitation tower. This statement translates into a non linear optimization problem where constraints are imposed over the plant operating conditions, the state controllability metrics and some closed loop performance indices while the investment, operating and control costs are minimized. The optimization problem was solved successfully using genetic algorithms.

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.

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.

Control Neuronal Predictivo por Linealización Instantánea

A Generalized Predictive Control scheme (GPC) is developed, based on a neural model of the process, and then applied on a laboratory neutralization reactor. The neural model, which is obtained previously by identification, is linearized at each iteration of the control algorithm. Hence, the learning capacity of the neural networks and the computer efficiency of the GPC are combined, producing a system with the advantages of a predictive controller extended to non-linear systems, that shows good precision and transient response performance.

A COMPARATIVE STUDY OF DETERMINISTIC AND STOCHASTIC OPTIMIZATION METHODS FOR INTEGRATED DESIGN OF PROCESSES

Abstract This paper focuses on the application of stochastic (genetic algorithms, simulated annealing) and deterministic (sequential quadratic programming) optimization methods for the Integrated Design of processes considering dynamical non-linear models. Moreover, a hybrid methodology that combines both types of methods is proposed, showing an improvement on performance. Controllability indexes such as disturbance sensitivity gains, the H∞ norm, and the ISE were considered to obtain optimum disturbance rejection. In order to illustrate and validate our proposal, an activated sludge process with PI schemes is taken. The problem is stated as a multiobjective non-linear optimization problem with non-linear constraints. The application of the mentioned strategies is discussed. The results are encouraging for future application of these techniques to solve synthesis MINLP problems.

Simultaneous Synthesis, Design and Control of Processes Using Model Predictive Control

Abstract Abstract This work presents the simultaneous synthesis, design and control of an activated sludge process using a 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 11 norms of different sensitivity functions of the system). The mathematical formulation results into a mixed-integer optimization problem with non-linear constraints that is solved using a real coded genetic algorithm. The solutions reflects the effect of applying different bounds over the controllability norms. The results are encouraging for the development of integrated design approaches with advanced control schemes which usually results in complex optimization problems difficult to solve with conventional techniques.

Neural-MPC for N-removal in activated-sludge plants

A nonlinear model predictive controller based on neural networks is designed in this paper to regulate the nitrogen removal in the activated-sludge process. The benchmark simulation model (BSM1) is used to implement the predictive controller and to study its behavior in different situations. Also input-output data are gathered from the benchmark for the neural networks training. Control results under dry-weather perturbations are satisfactory when compared to other types of NLMPC.