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MACC Researchers
Abhinav  Garg Abhinav Garg
Ph.D. Candidate
Supervised by Dr. Prashant Mhaskar

Start date
January 2014

Email
garga7@mcmaster.ca

Education
B.Tech (Electronics and Instrumentation), Uttar Pradesh Technical University, India
M.S. by Research (Chemical Engineering), Indian Institute of Technology Madras, India

Topics
Optimization and Control of Batch Process Systems using Data-Driven Models

Research Title
Control and Optimization of Batch Processes Using Data-Driven Approaches

Research Objectives
The research objective is focused on modeling, control and optimization of chemical batch processes using data-driven approaches. Batch processes are indispensable constituent of chemical process industries and are universally used for manufacturing of high-quality products due to their flexibility to control different grades of products by changing the initial conditions and input trajectories. A fundamental objective in a typical batch process is to achieve the final product quality specifications. However, critical quality measurements are not available during the batch operation which renders the direct control of product quality impossible. In this era of big data revolution, this work focuses on identifying accurate, yet tractable (from optimization viewpoint), mathematical models (for prediction of terminal quality) using the historical process data. As an application of these ideas, we seek solution to optimal Hydrogen unit startup problem, a collaborative project with Praxair Inc. The objective of this project is to design a safe and optimal startup strategy for the unit as it goes from the shutdown phase to the nominal operating phase. A desired characteristic is to optimize the process in such a way that it minimizes the startup time and the cost while achieving the desired specifications of the hydrogen produced. This is expected to have immense economic advantages. Another application considered is a batch particulate process, extensively used in the chemical, pharmaceutical and various other industries to manufacture a wide variety of crystalline products.

MACC Publications

  • Garg, A.Mhaskar, P. Subspace identification and predictive control of batch particulate processes, American Control Conference,, 505-510 (2017) [ Publisher Version ]
  • Garg, A.Corbett, B.Mhaskar, P., Hu, G., Flores-Cerrillo, J. Development of a high fidelity and subspace identification model of a hydrogen plant startup dynamics, American Control Conference,, 2857-2862 (2017) [ Publisher Version ]
  • Garg, A.Corbett, B.Mhaskar, P., Hu, G., Flores-Cerrillo, J. Subspace-based model identification of a hydrogen plant startup dynamics, Computers & Chemical Engineering,, 106 183-190 (2017) [ Publisher Version ]
  • Garg, A.Mhaskar, P. Subspace Identification-Based Modeling and Control of Batch Particulate Processes, Industrial & Engineering Chemistry Research,, 56 (26) 7491-7502 (2017) [ Publisher Version ]

Other Publications

  • Garg, A.,Tangirala, A.K. Interaction assessment in multivariable control systems through causality analysis. IFAC Proceedings Volumes, 47(1), 585-592 (2014).

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