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Increase in the global competition, stringent environmental regulations and availability of less expensive computational power has motivated process industries to operate the processes closer to the economical optimal conditions in the presence of unknown disturbances or process abnormalities by using integrated control and optimization. Focus of our research group is in developing these integrated schemes, using advanced soft-sensing algorithms, control and optimization techniques, where first principles models, machine learning models and their integration will be of interest.
Increase in the global competition, stringent environmental regulations and availability of less expensive computational power has motivated process industries to operate the processes closer to the economical optimal conditions in the presence of unknown disturbances or process abnormalities by using integrated control and optimization. Focus of our research group is in developing these integrated schemes, using advanced soft-sensing algorithms, control and optimization techniques, where first principles models, machine learning models and their integration will be of interest.
Real-time integrated online optimizing control of Reactive Distillation System
Valluru, J., Patwardhan, S.C., An Integrated Frequent RTO and Adaptive Nonlinear MPC Scheme based on Simultaneous Bayesian State and Parameter Estimation. Industrial Engineering & Chemistry Research, 58 (18) (2019) 7561-7578.
Valluru, J., Patwardhan, S.C., Adaptive Optimizing Control using Robust State and Parameter Estimators. Proc. of the SICE Annual Conference, Hiroshima, Japan, 2019, September 10-13.
Valluru, J., Purohit, J.L., Patwardhan, S.C., Mahajani, S.M., Adaptive Optimizing Control of an Ideal Reactive Distillation Column. Proc. of 9th IFAC Symposium on Advanced Control of Chemical Processess (ADCHEM-2015), Whistler, Canada, 7-10 June, 2015.
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