Dhrumil Gandhi
Dharmsinh Desai University, Birla Institute of Technology and Science, Pilani
0009-0008-6245-0468
About
I am an Assistant Professor of Chemical Engineering at Dharmsinh Desai University, India. My research broadly focuses on process control, monitoring, and optimization of complex chemical and industrial processes using integrated first-principles and data-driven methodologies. My primary research interests include state-space modeling, dynamic and nonlinear system identification, soft sensor development, fault detection, fault diagnosis, and state estimation for large-scale, multivariable, and nonlinear systems. I actively work on hybrid modeling frameworks that combine mechanistic models with machine learning and deep learning techniques such as neural networks, recurrent neural networks, and time-series learning for improved prediction, estimation, and process monitoring. My research also covers data-driven fault estimation, statistical process monitoring, model-based and data-based diagnostics, filtering and observer design, process analytics, and real-time decision support. I apply optimization, estimation, and control strategies for performance improvement, robustness enhancement, and fault-tolerant operation in chemical processes. My academic activities emphasize bridging classical chemical engineering principles with modern artificial intelligence, system theory, and industrial process automation.
Employment
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Dharmsinh Desai University Assistant Professor2017 - Present
Education
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Dharmsinh Desai University Ph.D.2019 - 2025
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Birla Institute of Technology and Science, Pilani M.E.2014 - 2016
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Dharmsinh Desai University B.Tech2010 - 2014
Projects & Funding
Projects & funding information is unavailable.
Publications (8)
- Fault detection and diagnosis for multi-rate descriptor systems using a combination of extended Kalman filter and support vector machines Save
- Fault estimation for multi‐rate descriptor systems using bi‐directional long short‐term memory neural network Save
- State estimation of Two-Phase Reactor Condenser System with Recycle using multi-rate Unscented Kalman Filter Save
- Enhancing Carbon Capture Efficiency in Chemical Looping Combustion Using Fe2O3 and Mn2O3 Oxygen Carriers: Insights from ASPEN Plus Modeling Save
- Machine learning-based fault estimation of nonlinear descriptor systems Save
- Qualitative Analysis of Process Hazard Over Quadrupole System Using HAZOP, Fault Tree and Directed Acyclic Graph Save
- Fault Detection of Multi-Rate Two Phase Reactor Condenser System with Recycle Using Multiple Probabilistic Principal Component Analysis Save
- Machine Learning based Fault Estimation of NonLinear Descriptor Systems Save