CNN-LSTM-Based Nonlinear Model Predictive Controller for Temperature Trajectory Tracking in a Batch Reactor.

Batch reactors are type of chemical reactors, where the reactants are loaded to process for a defined batch time and the products are removed after the polymerization reaction completion. Specialty chemicals and food processing industries widely use BRs due to their versatility and suitability for handling small- to medium-scale production, complex reactions, and varying reaction conditions. This article employs a CNN-LSTM-based nonlinear model predictive controller (NMPC) to effectively track the temperature profile of a BR.

Machine Learning Based Fault Classification in Pilot Plant Batch Reactor: Using Support Vector Machine.

Identifying and diagnosing faults is a critical task in process industries to maintain effective monitoring of process and plant safety. Minimizing process downtime is critical for enhancing the quality of the product and minimizing production costs. Real-time categorization of issues across several levels is essential for the monitoring of processes.

Neural Network-Based Hammerstein Model Identification of a Lab-Scale Batch Reactor.

This paper focuses on two types of neural network-based Hammerstein model identification methods for the acrylamide polymerization reaction of a batch reactor process. The first neural-based identification type formulates the weights of the multilayer network directly as parameters of the nonlinear static and linear dynamic blocks of the Hammerstein model and trains the weights using a gradient-based backpropagation algorithm. In the second identification type, the nonlinear static block of the Hammerstein model is framed as a single hidden-layer feedforward network and both nonlinear and linear block parameters are trained using an extreme learning machine, where the training procedure is exempted from gradient calculation.

Development of a Nonlinear Model Predictive Control-Based Nonlinear Three-Mode Controller for a Nonlinear System.

This paper presents the novelty on a nonlinear proportional integral derivative (NPID) controller developed from the gain values obtained using the Lyapunov-based nonlinear model predictive controller (LyNMPC). The tuning parameters of the proposed controller are taken from the dynamics of the nonlinear system, and these parmeters are dynamic with their value varying according to the error in the system. In this article, the authors have considered two highly nonlinear systems, namely, batch polymerization reactor and quadrotor unmanned aerial vehicle systems.

Wiener-Neural-Network-Based Modeling and Validation of Generalized Predictive Control on a Laboratory-Scale Batch Reactor.

Batch reactors are large vessels in which chemical reactions take place. They are mostly found to be used in process control industries for processes such as reactant mixing, waste treatment of leather byproducts, and liquid extraction. Modeling and controlling of these systems are complex due to their highly nonlinear nature.

Development and Validation of Advanced Nonlinear Predictive Control Algorithms for Trajectory Tracking in Batch Polymerization.

In this work, a computationally efficient nonlinear model-based control (NMBC) strategy is developed for a trajectory-tracking problem in an acrylamide polymerization batch reactor. The performance of NMBC is compared with that of nonlinear model predictive control (NMPC). To estimate the reaction states, a nonlinear state estimator, an unscented Kalman filter (UKF), is employed.

Data-Driven Modeling of a Pilot Plant Batch Reactor and Validation of a Nonlinear Model Predictive Controller for Dynamic Temperature Profile Tracking.

Batch process plays a very crucial and important role in process industries. The increased operational flexibility and trend toward high-quality, low-volume chemical production has put more emphasis on batch processing. In this work, nonlinearities associated with the batch reactor process have been studied.

Optimal Energy Consumption of the Distillation Process and Its Product Purity Analysis Using Ultraviolet Spectroscopy.

This paper addresses the energy consumption of distillation process via an actuator, which is a challenging problem in process industries. Precise control action would enhance energy consumption and improve the productivity. This paper is an experimental validation of EPC-PI control algorithm and analysis of distillate purity of a lab-scale distillation column.

Parameter Estimation and an Extended Predictive-Based Tuning Method for a Lab-Scale Distillation Column.

The present study proposes a new PI controller tuning method using extended predictive control (EPC). The PI controller parameter values are calculated using the EPC controller output and its closed-loop response. This provides a simple and an effective tuning strategy which results in an improved closed-loop response compared to conventional tuning methods.