Mohammad Anwar Hosen

Optimization and control of polystyrene batch reactor using hybrid based model

Abstract The effects of operating conditions such as initiator and monomer concentration as well as reactor temperature of polymerization reactors have been studied in this work. A recently developed hybrid model for polystyrene batch reactor was utilized in simulation study. The simulation results revealed the sensitivity of polymer properties and conversion to variation of these operating conditions. Furthermore, the study deals with the optimization of batch polymerization reactors. The optimization problem involving minimum time optimal temperature policy has been formulated and solved. Different numerical techniques have been tested and compared. The online control works were performed to validate the optimal temperature profiles. The experimental studies reveal that the calculated optimal policies were able to reduce the batch time keeping the same polymer quality.

Prediction interval-based ANFIS controller for nonlinear processes.

Prediction interval (PI) has been appeared as a promising tool to quantify the uncertainties and disturbances associated with point forecasts. Despite of its numerous applications in prediction problems, the use of PIs in control application is still limited. In this paper, a PI-based ANFIS controller is proposed and designed for nonlinear systems. In the proposed algorithm, a PI-based neural network model (PI-NN) is developed to construct the PIs, and this model is used as an online estimator of PIs for the controller. The PIs along with other traditional inputs are used to train the inverse ANFIS model. The developed PI-based ANFIS model is then used as a nonlinear PI-based controller (PIC). The performance of the proposed PIC is examined for a nonlinear numerical plant. Simulation results revealed that the proposed PIC performance is superior over the traditional ANFIS-based controller.

Prediction interval-based neural network controller for nonlinear processes

Prediction interval (PI) has been extensively used to predict the forecasts for nonlinear systems as PI-based forecast is superior over point-forecast to quantify the uncertainties and disturbances associated with the real processes. In addition, PIs bear more information than point-forecasts, such as forecast accuracy. The aim of this paper is to integrate the concept of informative PIs in the control applications to improve the tracking performance of the nonlinear controllers. In the present work, a PI-based controller (PIC) is proposed to control the nonlinear processes. Neural network (NN) inverse model is used as a controller in the proposed method. Firstly, a PI-based model is developed to construct PIs for every sample or time instance. The PIs are then fed to the NN inverse model along with other effective process inputs and outputs. The PI-based NN inverse model predicts the plant input to get the desired plant output. The performance of the proposed PIC controller is examined for a nonlinear process. Simulation results indicate that the tracking performance of the PIC is highly acceptable and better than the traditional NN inverse model-based controller.

Aggregation of Pi-based forecast to enhance prediction accuracy

In contrast to point forecast, prediction interval-based neural network offers itself as an effective tool to quantify the uncertainty and disturbances that associated with process data. However, single best neural network (NN) does not always guarantee to predict better quality of forecast for different data sets or a whole range of data set. Literature reported that ensemble of NNs using forecast combination produces stable and consistence forecast than single best NN. In this work, a NNs ensemble procedure is introduced to construct better quality of Pis. Weighted averaging forecasts combination mechanism is employed to combine the Pi-based forecast. As the key contribution of this paper, a new Pi-based cost function is proposed to optimize the individual weights for NN in combination process. An optimization algorithm, named simulated annealing (SA) is used to minimize the PI-based cost function. Finally, the proposed method is examined in two different case studies and compared the results with the individual best NNs and available simple averaging Pis aggregating method. Simulation results demonstrated that the proposed method improved the quality of Pis than individual best NNs and simple averaging ensemble method.

Control of Polystyrene Batch Reactor Using Fuzzy Logic Controller

Control of polymerization reactors is a challenging issue for researchers due to the complex reaction mechanisms. A lot of reactions occur simultaneously during polymerization. This leads to a polymerization system that is highly nonlinear in nature. In this work, a nonlinear advanced controller, named fuzzy logic controller (FLC), is developed for monitoring the batch free radical polymerization of polystyrene (PS) reactor. Temperature is used as an intermediate control variable to control polymer quality, because the products quality and quantity of polymer are directly depends on temperature. Different FLCs are developed through changing the number of fuzzy membership functions (MFs) for inputs and output. The final tuned FLC results are compared with the results of another advanced controller, named neural network based model predictive controller (NN-MPC). The simulation results reveal that the FLC performance is better than NN-MPC in terms of quantitative and qualitative performance criterion.

Prediction interval-based control of nonlinear systems using neural networks

Prediction interval (PI) is a promising tool for quantifying uncertainties associated with point predictions. Despite its informativeness, the design and deployment of PI-based controller for complex systems is very rare. As a pioneering work, this paper proposes a framework for design and implementation of PI-based controller (PIC) for nonlinear systems. Neural network (NN)-based inverse model within internal model control structure is used to develop the PIC. Firstly, a PI-based model is developed to construct PIs for the system output. This model is then used as an online estimator for PIs. The PIs from this model are fed to the NN inverse model along with other traditional inputs to generate the control signal. The performance of the proposed PIC is examined for two case studies. This includes a nonlinear batch polymerization reactor and a numerical nonlinear plant. Simulation results demonstrated that the proposed PIC tracking performance is better than the traditional NN-based controller.

Forecasting Bike Sharing Demand Using Fuzzy Inference Mechanism

Forecasting bike sharing demand is of paramount importance for management of fleet in city level. Rapidly changing demand in this service is due to a number of factors including workday, weekend, holiday and weather condition. These nonlinear dependencies make the prediction a difficult task. This work shows that type-1 and type-2 fuzzy inference-based prediction mechanisms can capture this highly variable trend with good accuracy. Wang-Mendel rule generation method is utilized to generate rulebase and then only current information like date related information and weather condition is used to forecast bike share demand at any given point in future. Simulation results reveal that fuzzy inference predictors can potentially outperform traditional feedforward neural network in terms of prediction accuracy.

Adaptive neuro-fuzzy interface system (ZNFIS) controller for polymerization reactor

It is a challenging task to control polymerization reactor due to the complex reactions mechanism. Moreover, the dynamic behaviour of the polymerization reactor is highly nonlinear. Thousand of reactions involed during polymerization that make the system complex in nature. Artificial intelligent appeared as promising tool to control such kind of nonlinear and complex processes. In the present work, a advanced nonlinear controller, namely adaptive neuro-fuzzy interface system (ANFIS) is proposed and designed for polymerization reactor. Sugeno type fuzzy interface system is used in ANFIS. Hybrid optimization algorithm, a combination of least-square estimation and backpropagation methods is used to optimize the neural network-based fuzzy output model. Styrene free radical polymerisation batch reactor is used as a case study. Simulation results demonstrated that the tracking performance of the ANFIS-based controller is better than the traditional neural network (NN)-based controller.

Hybrid Controller with the Combination of FLC and Neural Network-Based IMC for Nonlinear Processes

This work presents a hybrid controller based on the combination of fuzzy logic control (FLC) mechanism and internal model-based control (IMC). Neural network-based inverse and forward models are developed for IMC. After designing the FLC and IMC independently, they are combined in parallel to produce a single control signal. Mean averaging mechanism is used to combine the prediction of both controllers. Finally, performance of the proposed hybrid controller is studied for a nonlinear numerical plant model (NNPM). Simulation result shows the proposed hybrid controller outperforms both FLC and IMC.