Environment & Safety Gas Processing/LNG Maintenance & Reliability Petrochemicals Process Control Process Optimization Project Management Refining

Lahiri, S. K.

National Institute of Technology, Durgapur, India

Development of support vector regression-based soft sensor

National Institute of Technology: Khalfe, N.  |  Lahiri, S. K.

Application was used in a commercial ethylene glycol plant

A support vector classification method for regime identification of slurry transport in pipelines

National Institute of Technology: Ghanta, K. C.  |  Lahiri, S. K.

Statistical analysis showed the proposed solution has an average misclassification error of only 1.5%

Computational fluid dynamics simulation of solid–liquid slurry flow

National Institute of Technology: Ghanta, K. C.  |  Lahiri, S. K.

The resulting model's predictions showed reasonably good agreement with the experimental data

Genetic algorithm tuning improves artificial neural network models

National Institute of Technology: Ghanta, K. C.  |  Lahiri, S. K.

The technique is illustrated by predicting hold-up of slurry flow in pipelines

Minimize power consumption in slurry transport

National Institute of Technology: Ghanta, K. C.  |  Lahiri, S. K.

Accurately predict critical velocity

Process modeling and optimization strategies integrating neural networks and differential evolution

National Institute of Technology: Garawi, M. A.  |  Khalfe, N.  |  Lahiri, S. K.

The technology was applied to an ethylene oxide reactor

Novel approach for process plant monitoring

Jubail United Petrochemical Co., Sabic: Al-Baiyaa, M.  |  Lenka, C.
National Institute of Technology: Khalfe, N.  |  Lahiri, S. K.

Using statistical data compression important process changes can be quickly detected and identified

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