Neural Network Based Modeling and Control for a Batch Heating/Cooling Evaporative Crystallization Process

Crystallization processes have been widely used for separation in many fields to provide a high purity product. In this work, dynamic optimization and neural network (NN) have been applied to improve the quality of the product: citric acid. In the dynamic optimization, optimization problems maximizing both crystal yield and crystal size have been formulated. The neural networks have been developed to provide NN models to be used in the formulation of not only neural network inverse control (NNDIC) but also neural network model predictive control (NNMPC) strategies. The Levenberg Marquadt algorithm has been used to train the network and optimal neural network architectures have been determined by a mean squared error (MSE) minimization technique. In addition, a neural network model has been designed to provide estimates of the temperature and the concentration of the crystallizer. These estimates have been incorporated into the NNMPC controller. In the NNDIC controller, another neural network model has been applied to predict the set point of jacket temperature. The simulation results have shown that the obtained crystal size is increased by 19% and 30% compared to that by cooling and evaporation methods respectively and the obtained yield is increased more than 50%. The robustness of the proposed controller is investigated with respect to parameters mismatches. The results have shown that the NNMPC controller provides superior control performances in all case studies

Hybrid Neural Network Controller Design for a Batch Reactor to Produce Methyl Methacrylate

Methyl methacrylate (MMA) production in an exothermic batch reactor provides a challenging problem for studying its dynamics behavior and temperature control. This work presents a neural network forward model (NN) to predict a concentration of methyl methacrylate, a jacket temperature and temperature profile in the reactor. An optimal NN model has been employed to predict state variables incorporating into a model predictive control (MPC) algorithm to determine optimal control actions. To control the temperature, neural network based control approaches: a neural network direct inverse control (NNDIC) and a neural network based model predictive control (NNMPC) have been formulated. In addition, a dynamic optimization approach has been applied to find out an optimal operating temperature to achieve maximizing the MMA product at specified final time. Simulation results have indicated that the NNMPC is robust and gives the best control results among the PID and NNDIC in all cases

Neural Network-based Hybrid Estimator for Estimating Concentration in Ethylene Polymerization Process: An Applicable Approach

Estimation of a monomer concentration of an ethylene polymerization process has been a challenging problem due to its highly nonlinear behavior and interaction among state variables.  Applying of an extended Kalman filter (EKF) to provide the estimates of the concentration based on measured bed temperatures has usually been prone to errors. Here, alternatively, neural network-based hybrid estimators have been developed and classified into three structures which integrating of either EKF or Kalman filter (KF) to neural network (NN) to provide the estimates. The NNs are integrated to provide the estimates’ error or concentration’s estimates corresponding to individual structure for reducing the estimation error. Simulation results have shown that the hybrid estimators can provide good estimates under nominal condition and disturbance cases. However, in dealing with noises, the NN-KF hybrid estimator gives superior robustness with smooth and accurate estimated values

Neural Network Based Model Predictive Control of Batch Extractive Distillation Process for Improving Purity of Acetone

In a pharmaceutical industry, batch extractive distillation (BED), a combination process between extraction and distillation processes, has been widely implemented to separate waste solvent mixture of acetone-methanol because of minimum-boiling azeotrope properties. Normally, water is used as solvent and semi-continues mode is proposed to improve purity of acetone. The solvent is charged into the BED column until the purity of a desired product is achieved. After the total reflux start-up period is ended, a dynamic optimization strategy is applied to determine an acetone distillate composition profile maximizing the weight of the distillate product (acetone). The acetone distillate composition profile is used as the set point of neural network model-based controllers: the neural network direct inverse model control (NNDIC) and neural network based model predictive control (NNMPC) in order to provide the acetone composition with the purity of 94.0% by mole within 9.5 hours. It has been found that although both NNDIC and proportional integral derivative (PID) control can maintain the distillate purity on its specification for the set point tracking and in presence of plant uncertainties, the NNMPC provides much more satisfactory control performance and gives the smoothest controller action without any fluctuation when compared to the NNDIC and PID

Improving of Crystal Size Distribution Control Based on Neural Network-Based Hybrid Model for Purified Terephthalic Acid Batch Crystallizer

A main difficult task in batch crystallization is to control the size distribution of crystal products. Complexity and highly nonlinear dynamic behavior directly affect to model-based control strategies which heavily depend on the rigorous knowledge of crystallization. In this work, neural network-based model predictive control and inverse neural network control strategies are proposed and integrated with an optimization based on neural network-based hybrid model to control temperatures of a purified terephthalic acid batch crystallizer. A neural network-based hybrid model of the batch crystallizer is developed to provide nonlinear dynamic responses used in optimization algorithm for finding an optimal temperature profile related to the quality of a crystal product. Then, the obtained optimal profile is used as set points of the proposed control strategies for improving the crystal product quality. The performances and robustness of the proposed controllers are evaluated in several cases such as for set point tracking and plant/model mismatches. Simulation results show that the neural network-based model predictive control gives the best control performance among the inverse neural network control and a conventional PID controller in all cases.A main difficult task in batch crystallization is to control the size distribution of crystal products. Complexity and highly nonlinear dynamic behavior directly affect to model-based control strategies which heavily depend on the rigorous knowledge of crystallization. In this work, neural network-based model predictive control and inverse neural network control strategies are proposed and integrated with an optimization based on neural network-based hybrid model to control temperatures of a purified terephthalic acid batch crystallizer. A neural network-based hybrid model of the batch crystallizer is developed to provide nonlinear dynamic responses used in optimization algorithm for finding an optimal temperature profile related to the quality of a crystal product. Then, the obtained optimal profile is used as set points of the proposed control strategies for improving the crystal product quality. The performances and robustness of the proposed controllers are evaluated in several cases such as for set point tracking and plant/model mismatches. Simulation results show that the neural network-based model predictive control gives the best control performance among the inverse neural network control and a conventional PID controller in all cases

Neural network inverse model-based controller for the control of a steel pickling process

The present work investigates the use of neural network direct inverse model-based control strategy (NNDIC) to control a steel pickling process. The process is challenging due to the fact that the pH of effluent streams must be regulated accurately to protect aquatic and human welfare, and to comply with limits imposed by legislation. At the same time, the concentration of acid solution in the pickling step needs to be maintained at the optimum value in order to obtain the maximum reaction rate. Various changes in the open-loop dynamics are performed before implementation of the inverse neural network modeling technique. The optimal neural network architectures are determined by the mean squared error (MSE) minimization technique. The robustness of the proposed inverse model neural network control strategy is investigated with respect to changes in disturbances, model mismatch and noise effects. Simulation results show the superiority of the NNDIC controller in the cases involving disturbance, model mismatch and noise while the conventional controller gives better results in the nominal case