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. ARX and NARX models have been identified using open-loop data obtained from the pilot plant batch reactor. The performance of the batch reactor with conventional linear controllers results in aggressive manipulated variable action and larger energy consumption due to its inherent nonlinearity. This issue has been addressed in the proposed work by identifying the nonlinear model and designing a nonlinear model predictive controller for a pilot plant batch reactor. The implementation of the proposed method has resulted in smooth response of the manipulated variable as well as reactor temperature on both simulation and real-time experimentation.

The paper presents the design and circuit realization of an optimized fractional-order PID controller (FOPID) for speed control of a permanent magnet brushless direct current (BLDC) motor. Here, the FOPID controller is designed using the Nelder-Mead optimization algorithm. The servo-regulatory performance of the BLDC motor using the FOPID controller is compared with integer-order PID controllers, which are designed using tuning methods like Ziegler Nichols, Astrom-Hagglund, Cohen-Coon, and Chien-Hrones-Reswick method. The FOPID controller circuit is realized using Operational Transconductance Amplifiers (OTAs) and capacitors in Cadence Virtuoso Analog Design Environment using GPDK 180 nm technology and simulated using Spectre circuit simulator. The circuit realization is based on the multiple-loop-feedback structure. The OTA-C realization facilitates advantages like electronic tunability, resistor-less, and on-chip realization capability. The time and frequency domain performance measures of the OTA-C FOPID controller with the BLDC motor are evaluated using the Cadence software and compared with the theoretical plots. Finally, a modified OTA-C circuit with reduced active element count is used, and its performance is evaluated.

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. This model offers significant advantages in NMPC by leveraging convolutional neural networks (CNNs) to capture spatial features and long short-term memory (LSTM) networks to manage temporal dependencies, thus enhancing prediction accuracy and control performance. The approach involves training the CNN-LSTM model using input and output data obtained from open-loop experimentation with the BR. This model evaluates the goal of optimizing the coolant flow rate while managing the heat generated by the exothermic reaction within the reactor. Additionally, a heuristic method incorporating a sigmoidal weighting functions are utilized to improve the computational efficiency of the model. The successful implementation of this CNN-LSTM-based NMPC model demonstrates its potential for large-scale usage in industrial applications. By providing accurate temperature predictions and optimizing control actions, this approach can enhance process efficiency, reduce energy consumption, and improve safety in various pharmaceutical industries.

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. The Wiener neural network (WNN) is employed in this work to predict and track the temperature profile of a batch reactor successfully. WNN is different from artificial neural networks in various aspects, mainly its structure. The brief methodology that was deployed to complete this work consisted of two parts. The first part is modeling the WNN-based batch reactor using the provided input-output data set. The input is feed given to the reactor, and the reactor temperature needs to be maintained in line with the optimal profile. The objective in this part is to train the neural network to efficiently track the nonlinear temperature profile that is provided from the data set. The second part is designing a generalized predictive controller (GPC) using the data obtained from modeling the reactor to successfully track any arbitrary temperature profile. Therefore, this work presents the experimental modeling of a batch reactor and validation of a WNN-based GPC for temperature profile tracking.

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. The primary focus of the paper is neural-based model identification of a complex nonlinear system, which facilitates ease of linear/nonlinear controller design with good learning speed and less computations. A future work toward the machine learning-based nonlinear model predictive controller implementation using the Jetson Orin Nano board is also described.

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. Both algorithms are implemented experimentally to track a time-varying temperature profile for an acrylamide polymerization reaction in a lab-scale polymerization reactor. It is shown that in the presence of state estimators the NMBC performs significantly better than the NMPC algorithm in real time for the batch reactor control problem.