Engenharia Química
URI permanente desta comunidadehttps://repositorio.fei.edu.br/handle/FEI/25
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8 resultados
Resultados da Pesquisa
- Turbidity control on dissolved air flotation process using fuzzy logic(2018) Fonseca R.R.; Franco I.C.; Thompson J.P.; da Silva F.V.© IWA Publishing 2018. This study intends to explore fuzzy logic control on clean water turbidity process with dissolved air flotation (DAF). Three different strategies were tested to regulate clean water turbidity by manipulating the saturator vessel flow output aiming for low actuators control effort. Saturator pressure was the manipulated variable (MV) in the first control loop named SISO I. The second control loop, SISO II, used recycle stream valve opening as MV. The third control loop (MISO) was developed applying fuzzy logic instead PID control. Several performance criteria were used to analyze the process control performance such as integral absolute error (IAE), recycle stream valve and saturator vessel pressure regulating valve control efforts, ECV 01 and ECV 02 respectively. Results from SISO I and SISO II strategies showed that recycle stream is a better MV than saturator vessel pressure for clean water turbidity control in the DAF process. Only SISO II and MISO strategies proved to be capable of regulating the process variable. However, MISO control showed better performance based on IAE value against SISO II, with a reduction of 11.24% on its value, even the recycle stream valve control effort for MISO control being fairly higher than that for SISO II. Nevertheless, fuzzy logic control application gave rise to better turbidity control, and consequently prevented the excessive use of clean water in the recycling stream.
- Plantwide control systems design and evaluation applied to biodiesel production(2019) da Silva B.F.; Schmitz J.E.; Franco I.C.; da Silva F.V.© 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.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 value.
- Dynamic linear modeling of a refrigeration process with electronic expansion valve actuator Modélisation dynamique linéaire d'un procédé frigorifique à actionneur de détendeur électronique(2017) Siqueira Dantas T.S.S.; Franco I.C.; Fileti A.M.F.; Silva F.V.© 2017 Elsevier Ltd and IIRUsually, commercial control solutions for superheat control still use PID controllers as a standard. Although there are several applications of advanced control in refrigeration processes in the literature, there isn't a consensus about the optimal control solution for each system. The implementation of advanced control algorithms ultimately depends on accurate process knowledge in the form of dynamic mathematical models. This study aims to take a first step toward the designing an adaptive stochastic MPC controller for superheat control in an R404 refrigeration cycle with electronic expansion valve by developing stochastic dynamic models of the process. Both time-varying and time-invariant versions of the models are identified. Statistical validation results show whitening of the residuals of the time-invariant models, creating a basis for comparison. The recursive estimation of the time-varying parameters was realized with the Kalman Filter and the Forgetting Factor algorithms. Results of validation tests by simulation show good results, with average output errors between 0.05 and 1.39°C, indicating that the ARMAX with time-varying parameters may be a good presentation for this system.
- Analysis of the effects of neuro-fuzzy control configuration parameters on PH neutralization process(2018) Mazzali D.G.Z.; Franco I.C.; Silva F.V.© 2018 Walter de Gruyter GmbH, Berlin/Boston.The pH neutralization process is typical in chemical, biological and petrochemical industries. One of the major challenges to control it is to understand its nonlinearities and that requires several fine adjustments from conventional controls. Artificial Intelligence has been used to study these nonlinearities; one of them is Neuro-Fuzzy Logic, which was investigated in this work to develop controls dedicated to this process. These controls are formed by logical structures and may be adjusted to different configurations. In practical applications, it is highly important to adapt control parameters based on artificial intelligence to obtain better performance. The present work studied the effect of different configurations of a neuro-fuzzy control on the performance of a regulatory control to pH neutralization process by means of a virtual plant developed in both Indusoft and Matlab environments. For both variables, pH and reactor level control, membership function (MF) = [Gaussian], method "OR" = [probabilistic], method "E" = [product], type of MF output = [linear] and the optimization method = [hybrid], have improved control performance, which confirms the importance of configuration choices in neuro-fuzzy control adjustments. Moreover, the most determining factor in NFC performance is the types of membership functions.
- Automation and Control of a Dissolved Air Flotation Pilot Plant(2017) Fonseca R.R.; Thompson J.P.; Franco I.C.; da Silva F.V.© 2017Turbidity is an important process variable on water treatment processes for safeguard health requirements, and dissolved air flotation (DAF) is a physical treatment that allows water turbidity reduction by floating particles dispersed on water. Hence, a DAF pilot plant was assembled and automated using supervisory control and data acquisition (SCADA), open platform of communication protocol (OPC), signal filters and Ethernet to test two possible strategies for water turbidity control. The results indicated that manipulating recycle ratio by needle valve opening has advantages on DAF control.
- An adaptive fuzzy feedforward-feedback control system applied to a saccharification process(2018) Fonseca R.R.; Sencio R.R.; Franco I.C.; Da Silva F.V.© 2018 Walter de Gruyter GmbH, Berlin/Boston.In industrial bioprocess control, disturbance sources typically influences process variable regulation. These disturbances may reduce a system control performance or even affect the final bioproduct quality. Therefore, feedforward control is desired because it anticipates the effects caused by these disturbances in an attempt to keep the process variable at the setpoint value. However, designing a feedforward control law requires process modeling, which can be a tough task when dealing with bioprocesses that are intrinsically nonlinear and multivariable systems. Thus, an adaptive feedforward control law or other advanced control system is needed for satisfactory disturbance rejection. For this reason, a general fuzzy feedforward control system is proposed in this paper to replace the classical feedforward control, making it easier to implement the feedforward control action by avoiding nonlinear and multivariable process modeling. The adaptive fuzzy feedforward-feedback (A4FB) system was applied to a product concentration control loop in an enzymatic reactor, to reject disturbances caused by variations in the substrate and enzymatic solutions feed concentration. The results showed that the A4FB controller rejected much more disturbance effects than classical feedforward control law, demonstrating its advantage, supported by not only its simple implementation, but also its improved disturbance rejection.
- Development of a Predictive Control Based on Takagi-Sugeno Model Applied in a Nonlinear System of Industrial Refrigeration(2017) Franco I.C.; Schmitz J.E.; Costa T.V.; Fileti A.M.F.; Silva F.V.© 2017, Copyright © Taylor & Francis Group, LLC.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(2016) Mota A.S.; Menezes M.R.; Schmitz J.E.; Da Costa T.V.; Da Silva F.V.; Franco I.C.© Taylor & Francis Group, LLC.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.