Downloads
Recent publications can be downloaded here
Distillation Column
| Optimal Energy Consumption of the Distillation Process and Its Product Purity Analysis Using Ultraviolet Spectroscopy |
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| Parameter Estimation and an Extended Predictive-Based Tuning Method for a Lab-Scale Distillation Column |
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| Managing quality by design for sustainable performance in the process industry | |
| Open Loop data set of Distillation Column for modeling | |
| Non-Square Distillation Column |
UAV – Quadrotor
| Nonlinear Dynamic Modeling of a Quadrotor and Its Control Using Sliding Mode Controller |
Machine Learning
| Machine Learning Based Fault Classification in Pilot Plant Batch Reactor: Using Support Vector Machine |
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| CNN-LSTM-Based Nonlinear Model Predictive Controller for Temperature Trajectory Tracking in a Batch Reactor |
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| Q-Learning-Based Multivariate Nonlinear Model Predictive Controller: Experimental Validation on Batch Reactor for Temperature Trajectory Tracking | |
| Reinforcement Learning-Based Nonlinear Model Predictive Controller for a Jacketed Reactor: A Machine Learning Concept Validation Using Jetson Orin | |
| Online Sensor Fault Detection Using Machine Learning Algorithms on a Laboratory-Scale Batch Reactor: LSTM Approach | |
| View of Enhanced ELM Model Approach to Mitigate Multicollinearity of Lagged Independent Variables of ARMA Process | |
| Unveiling the role of artificial neural network in heavy metal detection and remediation |
Batch Reactor
| Data-Driven Modeling of a Pilot Plant Batch Reactor and Validation of a Nonlinear Model Predictive Controller for Dynamic Temperature Profile Tracking |
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| Development and Validation of Advanced Nonlinear Predictive Control Algorithms for Trajectory Tracking in Batch Polymerization |
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| Wiener-Neural-Network-Based Modeling and Validation of
Generalized Predictive Control on a Laboratory-Scale Batch Reactor |
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| Neural Network-Based Hammerstein Model Identification of a Lab-Scale Batch Reactor |
Batch Reactor Dynamics
| MATLAB codes | |
| Python Environment (SISO) | |
| Python Environment (MISO) | |
| Reference Trajectory to be used in closed loop |
Varying Fc by keeping H constant for recording Tr and Tj (Open Loop)
| Test Signal Data |
Date set with H and Fc varying for recording Tr and Tj
| Data set |
Continuous Stirred Tank Reactor (CSTR)
| Open Loop data set of CSTR Setup for modeling |
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