Márcio Martins

A PSO-based optimal tuning strategy for constrained multivariable predictive controllers with model uncertainty

This paper describes the development of a method to optimally tune constrained MPC algorithms with model uncertainty. The proposed method is formulated by using the worst-case control scenario, which is characterized by the Morari resiliency index and the condition number, and a given nonlinear multi-objective performance criterion. The resulting constrained mixed-integer nonlinear optimization problem is solved on the basis of a modified version of the particle swarm optimization technique, because of its effectiveness in dealing with this kind of problem. The performance of this PSO-based tuning method is evaluated through its application to the well-known Shell heavy oil fractionator process.

A robustly stabilizing model predictive control strategy of stable and unstable processes

This paper deals with the development of a robust model predictive control strategy with guarantee of stability, applicable to the stable and unstable processes. The model uncertainty is assumed to be described by a discrete set of linear models (multi-plant uncertainty), and the robustness is achieved by assembling cost-contracting constraints for all the possible models in the uncertainty domain. On the basis of a suitable state-space model description, an offset free control law is obtained by means of a one-step optimization formulation. The usefulness of the method proposed here is illustrated with control simulations of an unstable reactor system taken from the literature.

Dynamics of a True Moving Bed separation process: Linear model identification and advanced process control

The control of Simulated Moving Bed (SMB) units is challenging due to their complex dynamic behaviour and the difficulty of measuring their main properties. Furthermore, for the SMB units, the transfer function identification when the unit is operating at its optimal point is not easy to be done through the usual way. This work presents the development of a novel strategy to identify transfer functions of TMB/SMB and its application on classical linear model predictive controllers (MPC). However, for the process in study, due its unique dynamics, only the identification of the linear model is not enough to solve its control problem. Therefore, it is proposed a modification in the MPC prediction, that consists in a strategy based on a switching system where the most adequate transfer function is employed in the controller to overcome the problems related with the process dynamic behaviour. The results show that the used methodology enables the easy identification of transfer functions at the process optimal operating point and that the MPC can control the process in both the servo and regulator problem cases. It is also showed that the transfer function identified can be applied in the control of a SMB unit with four columns, under its optimal conditions.

COMPARAÇÃO ENTRE OS MÉTODOS LINEAR E NÃO LINEAR PARA A AVALIAÇÃO DA INCERTEZA DE MEDIÇÃO

The main method recognized by the metrologists for the evaluation of measurement uncertainty is de facto the Guide to the Expression of Uncertainty in Measurement (ISO Guide). Due to some limitations of the proposed method by ISO Guide however, ISO has developed a supplementary method for evaluating the measurement uncertainty based on the propagation of probability density functions using the Monte Carlo method (ISO-S1). The present paper discusses these methods for the quantification of measurement uncertainty. We review the literature, in particular the main papers presenting these modern approaches. We also discuss the merits and the limitations of the ISO Guide and ISO-S1 approaches. Furthermore, a comparative study between these two methods was carried out in two case studies. The obtained results show that it is necessary to evaluate the influence of the degree of non linearity in order to estimate the measurement uncertainty before either method is chosen

Chromatographic studies of n-Propyl Propionate: Adsorption equilibrium, modelling and uncertainties determination

Abstract The n-Propyl Propionate (ProPro) is a compound that has several possible industrial applications. However, the current production route of this component presents several problems, such as the downstream purification. In this way, chromatographic separation could be an alternative solution to the downstream purification. In this work experimental studies of the ProPro reaction system separation in a chromatographic fixed bed unit packed with Amberlyst 46 were performed. The adsorption equilibrium isotherms and the corresponding Langmuir model parameters were determined. A phenomenological model to represent the process was developed and validated through the experimental data. Meanwhile, it is proposed the characterization of the uncertainties of all steps and its extension to the model prediction, which allowed to estimate the model parameters with a reduced number of experiments, when compared with other reports in the literature; nevertheless, the final results lead to a statistically more reliable model.

CONSTRUÇÃO DE UM BALANÇO HÍDRICO RECONCILIADO EM PLANTA DE METANOL COM EXIGUIDADE DE INFORMAÇÃO

As previsões incertas em relação à oferta e demanda de água no futuro, juntamente com a cobrança de captação e liberação de efluentes nas bacias hidrográficas brasileiras, vem forçando o setor industrial a adotarem estratégias de melhor gestão dos seus recursos hídricos. O balanço hídrico reconciliado é uma ferramenta que permite mapear as correntes aquosas para um melhor conhecimento do fluxo de água em uma unidade industrial. Entretanto, no geral, os processos de produção carecem de medições de água para a sua construção. O conceito de qualidade de informação (QI) foi criado pela Rede de Tecnologia Limpas, da Universidade Federal da Bahia, com intuito de permitir utilizar diversas formas para obtenção de dados, mas levando em consideração as incertezas associadas pela falta de informação precisa das vazões de entrada e saída. O objetivo deste artigo é criar XXXVII ENCONTRO NACIONAL DE ENGENHARIA DE PRODUCAO “A Engenharia de Produção e as novas tecnologias produtivas: indústria 4.0, manufatura aditiva e outras abordagens avançadas de produção”

Métodos clássicos para a avaliação da incerteza de medição em sistemas multivariáveis

The aim of the present work is to give a brief review of two classical methods for the evaluation of measurement uncertainty in multivariate systems. The theoretical and practical issues of the method based on the law of propagation of uncertainties (MLPU) and of the method based on the law of propagation of probability density functions using Monte Carlo method (MLPP) are presented. In order to elucidate an application of both the MLPU and MLPP methods an illustrative example is also presented.

Uncertainty evaluation for multivariate industrial processes

Abstract The paper presents a supplement for the equation-oriented process simulator EMSO - Environment for Modeling Simulation and Optimization - which automatically evaluates the uncertainty of process output variables. Two methods for the evaluation of process uncertainty in multivariate systems have been implemented, namely: MLPU (Multivariate Law of Propagation of Uncertainties) and MLPP (Multivariate Law of Propagation of Probability Density Functions). For illustrative purposes, a comparative analysis of both approaches is carried out in a typical nonlinear industrial process system: a continuous stirred-tank reactor (CSTR). The paper finally closes with a discussion which raises some issues for future work.