A PSO-Based Internal Model Control Approach for Stability Enhancement in Cascade Processes Raju Yerolla, Suhailam Pullanikkattil, Chandra Shekar Besta Advanced Control for Applications Engineering and Industrial Systems, 2025 This study presents a robust control strategy for unstable cascade processes with time delays, integrating Particle Swarm Optimization (PSO) and the Internal Model Control (IMC) framework. The proposed methodology employs a dual‐controller architecture, where the secondary controller is systematically tuned using the IMC approach, while the primary controller is optimized via PSO to enhance closed‐loop stability and performance. Extensive simulation studies were conducted across various unstable cascade processes to evaluate the effectiveness of the proposed approach in both servo and regulatory tasks. Comparative analysis with state‐of‐the‐art control methodologies demonstrates that the proposed strategy achieves superior closed‐loop performance, particularly in handling system uncertainties. A comprehensive numerical assessment using multiple performance indices indicates a substantial reduction in total error by 65% and control effort by 43%. The evaluation metrics include error minimization, total variation, rise time, and overshoot percentage, affirming the efficacy and robustness of the proposed control scheme.
Enhanced cryogenic distillation column identification for methane separation: a hybrid artificial neural network approach Suhailam Pullanikkattil, Raju Yerolla, Chandra Shekar Besta Chemical Product and Process Modeling, 2025 Modelling the dynamics of cryogenic distillation columns is challenging due to their complex, nonlinear behaviour. This study introduces a novel identification approach using a hybrid Artificial Neural Network (ANN) optimized with Particle Swarm Optimization (PSO), applied to cryogenic distillation as a case study. The NARX-PSO-ANN model effectively captures the nonlinear dynamics of the distillation process by optimizing model parameters and avoiding local optima. The novelty of this work lies in integrating the NARX (Nonlinear Autoregressive with Exogenous Inputs) architecture with PSO, which enhances robustness and performance. To validate the model’s efficacy, realistic simulations of the cryogenic distillation column were conducted using Aspen Plus Dynamics, generated 2,000 data samples-1,400 training and 600 for validation. The NARX-PSO-ANN model was evaluated against established methods like BP-ANN and NARX-based BP-ANN, consistently outperforming them in identifying cryogenic distillation column dynamics and demonstrating superior effectiveness for complex separation processes. A user-friendly Python-based graphical user interface (GUI) was developed for real-time methane composition prediction, making the model accessible for practical applications. This innovative approach offers a reliable solution for optimizing complex, nonlinear systems in the process industry.
Artificial Intelligence Based Personalized Learning for Chemical Engineering Education Blockchain and AI in Shaping the Modern Education System, 2025
Advanced temperature control in ethanol fermentation using a PSO-PID controller with split-range control strategy Raju Yerolla, Suhailam P, Chandra Shekar Besta Preparative Biochemistry and Biotechnology, 2025 Global energy demand is experiencing a notable surge due to growing energy security. Renewable energy sources, like ethanol, are becoming more viable. In the present study, the application of a PSO-PID (Particle Swarm Optimization - Proportional Integral Derivative) controller with a split-range control strategy was suggested for the regulation of temperature within the fermentation system. To optimize performance, a POS-PID controller with a split-range arrangement utilizing two control valves for hot and cold utilities was constructed. The study began by examining the open-loop dynamic response of the system to inlet temperature and concentration disturbances during ethanol production fermentation. Subsequently, a transfer function model was developed through linearization at the steady-state operating point. The split-range controller structure, implemented by optimizing the PSO-PID controller parameters using PSO, effectively demonstrated temperature control in simulations of a nonlinear model. In this investigation, the ethanol fermentation system was modeled as a CSTR using a modified Monod equation for microbial growth kinetics. Various dynamic behavioral disturbances were explored and verified in the model with plant data in this study. The simulation model results were validated through plant data. The proposed method showed superior closed-loop performance with respect to errors, with the actuators proving to be effective than other reported methods for temperature control.
A new analytical method for designing centralised PI controllers for unstable systems using a direct synthesis approach Raju Yerolla, P. Suhailam, Chandra Shekar Besta International Journal of Systems Science, 2024 This paper introduces a new analytical technique using a direct synthesis strategy to develop centralised proportional–integral (PI) controllers for multivariable processes. The current method design controller is focused on attaining the desired closed-loop response for multi input multi output (MIMO) processes that involve multiple time delays. The conventional multivariable PI controller is obtained by approximating the ideal multivariable controller by the Maclaurin series expansion. This is accomplished by choosing the desired closed-loop response order as the system order plus two. Subsequent analysis investigates the proposed method’s efficacy in designing multivariable PI controllers. Servo and regulatory problems studies were conducted to find the effectiveness of the proposed method and compared it with other methods recently reported in the literature, as well as assessment of performance indices like integral absolute error (IAE) and integral square error (ISE). The proposed controller's robustness is assessed by plotting the inverse maximum singular value against frequency, both input and output multiplicative uncertainties.
The Identification of Various Materials in Process Industries Using Advanced Intelligent Techniques Sustainability in Chemical Processes Through Digitalization and Green Chemistry Approaches, 2024
Filtration Process Raju Yerolla, P Suhailam, Praveen Kumar Ghodke, Chandra Shekar Bestha Advanced Computational Approaches for Water Treatment Applications in Food and Chemical Engineering, 2023
Interpretable machine learning model for temperature prediction in coal pulverizer of thermal power plants S Pullanikkattil, R Yerolla, R Vilanova, CS Besta International Journal of Coal Preparation and Utilization 46 (3), 679-703 , 2026 2026 Citations: 4
A PSO‐Based Internal Model Control Approach for Stability Enhancement in Cascade Processes R Yerolla, S Pullanikkattil, C Shekar Besta Advanced Control for Applications: Engineering and Industrial Systems 7 (4 … , 2025 2025
Identification of Time-Delayed Second-Order Unstable systems with Two RHP Poles and no zeros P Suhailam, R Yerolla, CS Besta 2025 6th International Conference on Control, Communication and Computing … , 2025 2025
Enhancing Efficiency in Piping & Instrumentation Diagrams (P&IDs) through AI-Driven Digitization P Suhailam, R Yerolla, CS Besta 2025 6th International Conference on Control, Communication and Computing … , 2025 2025
Artificial Intelligence Based Personalized Learning for Chemical Engineering Education S Pullanikkattil, R Yerolla, CS Besta Blockchain and AI in Shaping the Modern Education System, 232-256 , 2025 2025
Enhanced cryogenic distillation column identification for methane separation: A hybrid artificial neural network approach S Pullanikkattil, R Yerolla, CS Besta Chemical Product and Process Modeling 20 (1), 111-128 , 2025 2025 Citations: 3
Advanced temperature control in ethanol fermentation using a PSO-PID controller with split-range control strategy R Yerolla, S P, CS Besta Preparative Biochemistry & Biotechnology 55 (2), 196-209 , 2025 2025 Citations: 8
Interpretable Machine learning model for predicting Ethane-Ethylene composition in binary distillation process S Pullanikkattil, R Yerolla, CS Besta Thermal Science and Engineering Progress 58, 103236 , 2025 2025 Citations: 11
A new analytical method for designing centralised PI controllers for unstable systems using a direct synthesis approach R Yerolla, P Suhailam, CS Besta International Journal of Systems Science 55 (14), 2857-2873 , 2024 2024 Citations: 2
Cloud of Things: Architecture and Industrial Applications P Suhailam, R Yerolla, CS Besta Cloud of Things, 1-17 , 2024 2024 Citations: 1
The Identification of Various Materials in Process Industries Using Advanced Intelligent Techniques RYCSB Pawar Ajay Abhaysing, Nilesh Shriram Vishwakarma, Patil Sharvari ... Sustainability in Chemical Processes through Digitalization and Green … , 2024 2024
Filtration Process R Yerolla, P Suhailam, PK Ghodke, CS Bestha Advanced Computational Approaches for Water Treatment, 169-182 , 2023 2023
Modeling and control of beer fermentation for optimal flavor and performance R Yerolla, SH Nekkanti, S Pullanikkattil, CS Besta Chemical Engineering & Technology 46 (8), 1554-1565 , 2023 2023 Citations: 13
Application of Artificial Intelligence (AI) and the Internet of Things (IoT) in process industries toward Industry 4.0 R Verma, R Yerolla, P Suhailam, CS Besta Internet of Things in Modern Computing, 13-36 , 2023 2023 Citations: 8
Modeling and advanced control strategies for the beer fermentation process P Suhailam, R Yerolla, CS Besta 2023 International Conference on Control, Communication and Computing (ICCC … , 2023 2023 Citations: 2
Fault diagnosis of centrifugal pump using parameter estimation and parity equation R Verma, R Yellora, TR Vakamalla, CS Besta Advanced Engineering Optimization Through Intelligent Techniques: Select … , 2023 2023 Citations: 4
Molecular dynamics simulations and theoretical modeling studies of fluoropolymer nanocomposites S Varun, R Yerolla, AM Chandran, CS Besta, LA Varghese, PKS Mural Advanced Fluoropolymer Nanocomposites, 787-807 , 2023 2023 Citations: 3
Optimization based control strategy for second order unstable processes with time delay CS Besta, R Yerolla Authorea Preprints , 2022 2022
Deep learning-based fault detection in the Tennessee Eastman process R Verma, R Yerolla, CS Besta 2022 Second International Conference on Artificial Intelligence and Smart … , 2022 2022 Citations: 11
Computational Study of Retort Processing R Yerolla, PK Ghodke, CS Bestha Advanced Computational Techniques for Heat and Mass Transfer in Food … , 2022 2022
MOST CITED SCHOLAR PUBLICATIONS
Development of tuning free SISO PID controllers for First Order Plus Time Delay (FOPTD) and First Order Lag Plus Integral Plus Time Delay model (FOLIPD) systems based on … R Yerolla, CS Besta Results in Control and Optimization 5, 100070 , 2021 2021.0 Citations: 18
Modeling and control of beer fermentation for optimal flavor and performance R Yerolla, SH Nekkanti, S Pullanikkattil, CS Besta Chemical Engineering & Technology 46 (8), 1554-1565 , 2023 2023.0 Citations: 13
Interpretable Machine learning model for predicting Ethane-Ethylene composition in binary distillation process S Pullanikkattil, R Yerolla, CS Besta Thermal Science and Engineering Progress 58, 103236 , 2025 2025.0 Citations: 11
Deep learning-based fault detection in the Tennessee Eastman process R Verma, R Yerolla, CS Besta 2022 Second International Conference on Artificial Intelligence and Smart … , 2022 2022.0 Citations: 11
Beer fermentation modeling for optimum flavor and performance R Yerolla, MM KM, N Roy, NS Harsha, MPP Ganesh, CS Besta IFAC-PapersOnLine 55 (1), 381-386 , 2022 2022.0 Citations: 11
Advanced temperature control in ethanol fermentation using a PSO-PID controller with split-range control strategy R Yerolla, S P, CS Besta Preparative Biochemistry & Biotechnology 55 (2), 196-209 , 2025 2025.0 Citations: 8
Application of Artificial Intelligence (AI) and the Internet of Things (IoT) in process industries toward Industry 4.0 R Verma, R Yerolla, P Suhailam, CS Besta Internet of Things in Modern Computing, 13-36 , 2023 2023.0 Citations: 8
Simulation of cryogenic distillation of atmospheric air using aspen hysys R Yerolla, RCA Muhammed, Y Naseef, CS Besta IFAC-PapersOnLine 55 (1), 860-865 , 2022 2022.0 Citations: 6
Interpretable machine learning model for temperature prediction in coal pulverizer of thermal power plants S Pullanikkattil, R Yerolla, R Vilanova, CS Besta International Journal of Coal Preparation and Utilization 46 (3), 679-703 , 2026 2026.0 Citations: 4
Fault diagnosis of centrifugal pump using parameter estimation and parity equation R Verma, R Yellora, TR Vakamalla, CS Besta Advanced Engineering Optimization Through Intelligent Techniques: Select … , 2023 2023.0 Citations: 4
PI/PID controller design for critically damped SOPTD system and experimental validation R Yerolla, CS Bestha 2021 5th International Conference on Intelligent Computing and Control … , 2021 2021.0 Citations: 4
Enhanced cryogenic distillation column identification for methane separation: A hybrid artificial neural network approach S Pullanikkattil, R Yerolla, CS Besta Chemical Product and Process Modeling 20 (1), 111-128 , 2025 2025.0 Citations: 3
Molecular dynamics simulations and theoretical modeling studies of fluoropolymer nanocomposites S Varun, R Yerolla, AM Chandran, CS Besta, LA Varghese, PKS Mural Advanced Fluoropolymer Nanocomposites, 787-807 , 2023 2023.0 Citations: 3
A new analytical method for designing centralised PI controllers for unstable systems using a direct synthesis approach R Yerolla, P Suhailam, CS Besta International Journal of Systems Science 55 (14), 2857-2873 , 2024 2024.0 Citations: 2
Modeling and advanced control strategies for the beer fermentation process P Suhailam, R Yerolla, CS Besta 2023 International Conference on Control, Communication and Computing (ICCC … , 2023 2023.0 Citations: 2
Cloud of Things: Architecture and Industrial Applications P Suhailam, R Yerolla, CS Besta Cloud of Things, 1-17 , 2024 2024.0 Citations: 1
Methane Rich Synthetic Natural Gas Separation Using Cryogenic Distillation Column with Robust Control Structure S Haridas, R Yerolla, CS BESTA Citations: 1
A PSO‐Based Internal Model Control Approach for Stability Enhancement in Cascade Processes R Yerolla, S Pullanikkattil, C Shekar Besta Advanced Control for Applications: Engineering and Industrial Systems 7 (4 … , 2025 2025.0
Identification of Time-Delayed Second-Order Unstable systems with Two RHP Poles and no zeros P Suhailam, R Yerolla, CS Besta 2025 6th International Conference on Control, Communication and Computing … , 2025 2025.0
Enhancing Efficiency in Piping & Instrumentation Diagrams (P&IDs) through AI-Driven Digitization P Suhailam, R Yerolla, CS Besta 2025 6th International Conference on Control, Communication and Computing … , 2025 2025.0
