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Desarrollo de un sistema de control predictivo de la temperatura en un reactor de transesterificación
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... Si bien el proceso de producción de biocombustibles es bastante extenso, y abarca múltiples fases entre las que se incluyen la selección y tratamiento de la materia prima, y la obtención del aceite base, la transesterificación es la etapa central de dicho proceso (Rodríguez, 2017), y por consiguiente resulta conveniente detenerse en la misma, para revisar sus características. ...
As Ciências Agrárias são um campo de estudo multidisciplinar por excelência, e um dos mais profícuos em termos de pesquisas e aprimoramento técnico. A demanda mundial por alimentos e a crescente degradação ambiental impulsionam a busca constante por soluções sustentáveis de produção e por medidas visando à preservação e recuperação dos recursos naturais. A obra Agrárias: Pesquisa e Inovação nas Ciências que Alimentam o Mundo compila pesquisas atuais e extremamente relevantes, apresentadas em linguagem científica de fácil entendimento. Na coletânea, o leitor encontrará textos que tratam dos sistemas produtivos em seus diversos aspectos, além de estudos que exploram diferentes perspectivas ou abordagens sobre a planta, o meio ambiente, o animal, o homem e a sociedade no ambiente rural. É uma obra que fornece dados, informações e resultados de pesquisas tanto para pesquisadores e atuantes nas diversas áreas das Ciências Agrárias, como para o leitor que tenha a curiosidade de entender e expandir seus conhecimentos. Este Volume VIII traz 25 artigos de estudiosos de diversos países, divididos em quatro eixos temáticos: Cultura e Sociedade no Contexto Rural; Produção Sustentável; Produção Vegetal e Solos e Aquacultura, Produção Animal e Veterinária. Desejo a todos uma proveitosa leitura! Eduardo Eugênio Spers
... Dynamic matrix control (DMC) is an MPC technique developed in Shell [17]. It is widely introduced in the industry since it provides good control of unconstrained multivariable systems [18]. Although this method is derived from linear systems, over the years, some modifications have been made to allow its implementation in the control of nonlinear processes [19] and its inclusion in power electronic systems such as high-efficiency battery chargers [20] and output power regulation in a distributed system with a group of PV generators [21]. ...
In recent years, the model predictive control (MPC) has become a powerful control technique capable of dealing with the nonlinearities of complex and non-complex systems. This paper presents a Dynamic Matrix Control algorithm applied to a single-phase photovoltaic inverter for its application in isolated areas. The proposed algorithm is evaluated in Matlab/Simulink for reference signal tracking and disturbance rejection. The experimental validation is performed at the Federal University of Rio de Janeiro through Hardware-In-the-Loop testing, using a Typhoon HIL402 real-time emulator machine and a dSPACE DS1103 controller board. The results show that this control algorithm is robust to load variations and has a fast response to reference change and disturbance rejection.
This paper presents the development of a predictive dynamic matrix controller (DMC) on a Stewart platform with 6 degrees of freedom for the ball and plate application that consists of controlling the position of a ball on a rotating plane surface. Prior to implementation, the construction characteristics of the system and its kinematic analysis are established. Obtaining the position of the ball on the platform is carried out by means of digital signal processing of the images from a camera by means of algorithms implemented in a Raspberry Pi 3B microcomputer. It also includes the description of the identification process of the mathematical model of the system, which starts from an excitation of the closed-loop system with a pseudo-random binary signal (PRBS). The qualities of this type of controllers are detailed, as well as the considerations necessary for their application and design. The device that runs the control algorithm is a STM32F4 Discovery development board. The results show that the proposed controller stabilizes the ball in an average time of less than 6 s and reacts to disturbances by re-stabilizing the system in a similar time with an expected error of 10%.KeywordsNonlinear controlDynamic matrix controlBall and plate systemStewart platform
A fractional-order robust controller to control a reverse osmosis desalination plant is proposed. The dynamics of this plant was experimentally identified. A multivariable mathematical model was obtained which showed large plant parameter variations. The proposed control system is composed of a decoupler, a diagonal compensator of the less variable dynamics, and a fractional order PI controller in each of the two loops. The fractional orders of these last controllers were optimized in order to improve the phase margin robustness. It was shown that these controllers outperformed the robustness achieved using PI controllers.
Se desarrolló un controlador predictivo generalizado basado en modelo (GPC) para el control efectivo de la temperatura de calcinación en un horno rotatorio de cemento.
In this paper a fractional order model for an irrigation main canal is proposed. This system has two pools that present a strong interaction between them. Then a multivariable model with two inputs: the pump flow and the opening of an intermediate gate, and two outputs: the water levels in the two pools, is derived. The identification of the model parameters is based on the experiments developed in a laboratory prototype of a hydraulic canal and the application of a direct system identification methodology. The accuracy of the proposed fractional order models is compared with the standard integer-order models of the canal. The parameters of the mathematical models have been identified by minimizing the Integral Square Error Index (ISE) existing between the time responses of the models and the real-time experimental data obtained from the canal prototype. A comparison of the performances of the integer-order and fractional-order models shows that the fractional-order model has significantly lower error: about 30%, and, therefore, higher accuracy in capturing the canal dynamics.
In this paper a modified Smith predictor, using an adaptive block based on fuzzy logic and a feedforward compensator, for e�ective control of mixture concentration in the injectable drugs manufacturing process of a pharmaceutical industry is designed. A new mathematical model with varying time delay that adequately characterized the dynamic behavior of the plant under study is obtained. It is shown that the designed controller allows controlling this plant with a high accuracy considering di�erent real industrial operation scenarios.
In this paper an adaptive expert generalized predictive multivariable controller (AEGPMC) is developed for control of a seawater reverse osmosis desalination plant, which supplies freshwater for a mineral processing facility. Based on the use of subspace system identification algorithms a dynamic multivariable model, which considered the permeate flow rate and the permeate conductivity as the controlled variables, is derived and its parameters are computed from experimental data. This model is used in the design of the AEGPMC. The obtained simulation results demonstrate that the performance of the control system with the designed AEGPMC is much more appropriate and robust than with the other multivariable GPC.
p align="left">El control predictivo basado en modelo ( Model Based Predictive Control – MBPC o simplemente MPC ) es una metodologia de control que hace uso del modelo del proceso para predecir las salidas futuras de la planta y con base en ello optimizar las acciones de control futuras. De hecho, el control predictivo no se puede considerar como una estrategia de control independiente sino, que por el contrario, integra toda una familia de metodos de control tales como, el control optimo, el control de procesos con tiempos muertos, el control de procesos multivariables, etc. Esto ha permitido que el control predictivo haya tenido un desarrollo importante tanto en la comunidad cientifica y academica, como en el sector industrial.</p
The steel industry is among the companies with highest energy consumption due to the heating processes in their production. Furnaces used for slabs reheating in this industry require a good regulator to control temperature in each zone optimizing combustible consumption. Considering the complex and variable dynamic behavior of furnace temperature, this paper proposes a modification of the Smith predictor scheme, combined with a gain scheduled fuzzy block idea, for controlling the soaking zone temperature in a steel slab-reheating furnace. For this purpose, a soaking zone temperature dynamic model is obtained from an identification procedure resulting in a second order plus time delay transfer function, where the dominant time delay varies with respect to the slab thickness. The performance of the proposed method is compared against the filtered Smith predictor and the classic one. The best results were obtained using the fuzzy gain-scheduled Smith predictor achieving a smooth transition in each operation point.
Mediante la aplicación de las herramientas de modelado se obtiene un modelo matemático multivariable de un generador de vapor de tubos de fuego. El modelo se representa mediante una ecuación de estado con retardo múltiple de tiempo en el control
