Jones Erni Schmitz

A fuzzy–split range control system applied to a fermentation process

In this study it was proposed the application of a fuzzy-PI controller in tandem with a split range control strategy to regulate the temperature inside a fermentation vat. Simulations were carried out using different configurations of fuzzy controllers and split range combinations for regulatory control. The performance of these control systems were compared using conventional integral of error criteria, the demand of utilities and the control effort. The proposed control system proved able to adequately regulate the temperature in all the tests. Besides, considering a similar ITAE index and using the energetically most efficient split range configuration, fuzzy-PI controller provided a reduction of approximately 84.5% in the control effort and of 6.75% in total demand of utilities by comparison to a conventional PI controller.

FUZZY-PID CONTROLLER APPLIED TO A REFRIGERATION SYSTEM

A refrigeration system exhibits a dynamic behavior on which the variables are interdependent and subjected to oscillation, hence, implicating necessity of changes on operating conditions and undesirable energy expenses. These characteristics ratify the importance of adequate dimensioning and equipment selection to find pre-defined operating conditions such as, the maximum cooling capacity and the evaporating and condensing temperatures. The application of fuzzy control in industrial processes is growing fast in the last decades, mainly in processes whose first principle models require complex methods to be simulated. In these cases, the fuzzy controllers’ capacity of acting correctly based only on expert knowledge and on the capacity of inter-relating all the variables of the process are attractive features. This work presents the experimental development and evaluation of fuzzy-PID controllers for the maintenance of the evaporating temperature in a chiller. The system was submitted to load and set-point disturbances accomplishing an analysis based upon error parameters and transient response. The results showed that fuzzy controllers were adapted satisfactorily.

Development of a Predictive Control Based on Takagi-Sugeno Model Applied in a Nonlinear System of Industrial Refrigeration

Refrigeration systems exist in different branches of industry and are characterized as great energy consumers with considerable nonlinear behavior. Several studies have promoted energy costs reduction and minimization of nonlinearities effects in such systems. Model predictive control has been successfully used to stabilize processes in the presence of such nonlinearities; therefore, its application in refrigeration systems is considered promising. In the present study, Takagi–Sugeno models were developed and validated in order to predict the evaporating and secondary fluid temperatures (TE and TP) based on the ANFIS technique (Adaptive Network-based Fuzzy Inference Systems) for a vapor-compressor chiller equipment. The prediction performance of resulting models was analyzed and accessed based on the variance accounted for criteria. These models were then used as the basis for prediction models in several generalized predictive controllers (GPC) denoted here as GPC-ANFIS controllers. Different predictive controllers were designed for different local rules (Fuzzy rules) and the global control action was assumed as the weighted sum of local controllers. Experimental tests considered two distinct controllers, namely the GPC-ANFISTE (evaporating temperature control by means of compressor speed variation) and GPC-ANFISTP (propylene glycol temperature control by means of compressor speed variation), were performed. The experimental tests for setpoint tracking (±1°C) considering 3000 W of constant heat load showed satisfactory results with setpoint deviation around ±0.3°C. Therefore, the ANFIS technique demonstrated to be able to provide reliable predictive models to be used in generalized predictive control algorithms.

Identification and Online Validation of a pH Neutralization Process Using an Adaptive Network-Based Fuzzy Inference System

In this study, the application of adaptive neuro-fuzzy inference system (ANFIS) architecture to build prediction models that represent the pH neutralization process is proposed. The dataset used to identify the process was obtained experimentally in a bench scale plant. The prediction model attained was validated offline and online and demonstrated as able to precisely predict the one step-ahead value of effluent pH leaving the neutralization reactor. The input variables were the current and one past value of the acid and base flow rates and the current value of the output variable. Variance accounted for (VAF) indices greater than 99% were achieved by the model in experiments in which the disturbances in the acid and basic solutions flow rates were applied separately. For tests with simultaneous disturbances, conditions never seen in the training and suffering from reactor level oscillations, the prediction model VAF index was still approximately 96%. The validations demonstrated the capability of ANFIS to build precise fuzzy models from input–output datasets. R2 values achieved were always larger than 0.96.

Fuzzy Multivariable Control Strategy Applied to a Refrigeration System

Abstract The refrigeration process involves complex systems exhibiting nonlinearities and coupled behavior, so this paper aims to evaluate the comparative performance of a multivariable fuzzy logic-based control system and a classic multi loop PID. The process variables were the temperature of the secondary fluid (propylene glycol) outlet and the evaporating temperature. The manipulated variables were the compressor frequency speed and the pump frequency speed. Aspen Plus and Aspen Dynamics simulators were used to simulate the experimental prototype. The model was previously validated and linked with MATLAB software, where the controllers were implemented. Tuning of the fuzzy controller was performed through the membership functions and gains adjustments. The tuning of the multi loop PID controller was performed using the Ziegler-Nichols method and then a fine tuning was carried out. In order to fairly compare energy consumption and control effort, the tune of PID-based strategy was finished when similar values of Integral of Squared Error were achieved. Thus, very similar behavior for the process variables in both controllers. On the other hand, a great improvement in the control effort and energy saving was observed when the multivariable fuzzy controller was used in comparison to classic PID. The energy consumption was reduced by 25 % and the control effort by 96 % when the proposed strategy was used.

Software to Improve Control System in an Ethanol Distillation Process

This work has developed a predictive control solution based on specific models for the process of ethanol distillation. The advantages of such control are relative to the prediction of the consequences of the disturbances by the model, thus enabling the control action to be done in a previous manner, resulting in the minimization of the variables fluctuation controlled by the process. This results in, among other advantages, energy economy, in the improvement of the ethanol produced and in the increasing production capacity. Another desirable characteristic in this control mode is its capacity to act in non-linear systems as is the case of the distillation columns. Finally, it must be noted that with the application of an advanced control solution, as proposed in this study, it becomes viable, in a second moment, for the ethanol plants to operate in multiple operational conditions, such as: 1) maximum energy economy (scarcity of raw material, for example) and: 2) maximum production condition (for situations with excess of materials to be distilled). The models developed in this project will consist of purely empirical models. Several tests will be done in the different types of models to measure the precision and robustness. The proposed control strategy demonstrated be able to control selected control loops adequately. Steam savings and reduction of product losses were observed.

Utilização do protocolo de comunicação OLE for Process Control em processos industriais DOI: 10.5585/exacta.v8i3.2431

Neste artigo, propõe-se a utilização de um software matemático juntamente com o protocolo OLE for Process Control (OPC) para o desenvolvimento de sistemas de controle em processos industriais, transformando o software matemático em um sistema supervisório capaz não somente de monitorar o processo, mas também de desenvolver e implementar algoritmos de controle inteligente. Um estudo foi então realizado sobre a confiabilidade da comunicação OPC entre o software matemático, o Controlador Lógico Programável (CLP) e um sistema de refrigeração industrial. Nesta pesquisa, constatou-se que a comunicação é adequada para a aplicação, pois o software matemático utilizado na comunicação entre o CLP e o sistema de refrigeração apresentou boa confiabilidade referente à qualidade do sinal de comunicação, além de tempo real de comunicação. Conclui-se, portanto que essa forma de comunicação é uma potente ferramenta para o monitoramento, o desenvolvimento e a implementação de controladores avançados.