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. (C) 2013 Elsevier B.V. All rights reserved.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Univ Estadual Campinas, Sch Chem Engn, BR-13083970 Campinas, SP, BrazilFed Univ São Paulo UNIFESP, Dept Exact & Earth Sci, BR-09972270 Diadema, SP, BrazilFed Univ São Paulo UNIFESP, Dept Exact & Earth Sci, BR-09972270 Diadema, SP, BrazilWeb of Scienc

Plantwide control systems design and evaluation applied to biodiesel production

Chemical processes have complex dynamic behaviors due to the presence of recycle streams, heat integration and several unit operations being interconnected, leading to interaction problems among variables and difficulties in completing an effective process control. Plantwide control methodologies aim to establish control systems for entire chemical plants; however, it is possible to obtain different control solutions. This work proposes that the evaluation of different plantwide control structures for a specific process could be carried out using key performance indicators (KPIs) to determine which control method best meets the industry strategic goals. In order to evaluate the proposed approach a typical biodiesel process model was implemented in Aspen Plus Dynamics; the selected KPIs were the dynamic disturbance sensitivity and an economic indicator that represents the variation of the ratio between the product’s selling price and the raw material cost over time. It was observed that both KPIs allowed a view of the plantwide control system performance and aided in choosing a set of designed controllers. However, the economic indicator enables one to choose a set of designed controllers that reduce the variability of the economic indicator by around 74% while providing a slight increase in the indicator mean valueCOORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESNão te

Fuzzy multivariable control strategy applied to a refrigeration system

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 used122CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQsem informaçã

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. R-2 values achieved were always larger than 0.96.University Center of FEIUniversity Center FEI, Department of Chemical Engineering, Av Humberto Alencar Castelo Branco, BR-09850901 Sao Bernardo Do Campo, SP, BrazilFederal University of Sao Paulo UNIFESP, Department of Exact & Earth Science, Diadema, SP, BrazilFed Univ Itajuba UNIFEI, Sch Chem Engn, Itajuba, MG, BrazilUniv Estadual Campinas, Sch Chem Engn, Campinas, SP, BrazilFederal University of Sao Paulo UNIFESP, Department of Exact & Earth Science, Diadema, SP, BrazilWeb of Scienc