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Prashant K.

@Prashantk13

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Data Scientist

Over 9 years’ experience in predictive maintenance and process improvement projects in the oil/gas and petrochemicals industries, covering conceptual process design, heat integration, process simulation/optimisation, MES and combining predictive maintenance with Artificial Intelligence and Machine Learning. Prashant has an MSc from Manchester University. He was first in the batch (Gold Medallist) 2005-2009 of Chemical Engineering and received 3 awards for highest academic grades during undergraduate studies. He has written papers on predictive maintenance, process integration, utility systems and operational excellence and has patents granted in areas of hydrocracking and gas-to-liquids. He has a keen interest in using new technology to meet client challenges and in matching on-line solutions with visualisation technologies.

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教育

Advanced Chemical Process Design

The University of Manchester, United Kingdom 2010 - 2011
(1 年)

资质

Deep Learning Essentials

IBM
2020
core knowledge of how the Deep Learning class of machine learning algorithms can be harnessed for more powerful and insightful data processing and pattern creation used in decision-making processes.

Developing Artificial Intelligence Applications using Python and TensorFlow

University of Oxford
2020
Deep Learning with TensorFlow and Keras: Regression, ConvNets, GANs, RNNs, NLP

出版物

Optimal Selection of Steam Mains in Total Site Utility Systems

The Italian Association of Chemical Engineering - AIDIC
This paper examines the optimal location (pressure) and the number of steam levels required to meet the external heating and cooling demands of individual site processes. The model developed makes use of Mixed Integer Linear Programming (MILP) techniques, implemented in a Visual Basic/Excel environment and linked to existing simulation software in order to extract the appropriate data for the total site.

Smart Plant Applications in Crude Distillation Units

AIChE Spring Meeting and Global Congress on Process Safety
https://aiche.confex.com/aiche/s17/webprogram/Paper480299.html

Refinery Preheat Train Fouling Management for Enhanced Energy Efficiency

AIChE Spring Meeting and Global Congress on Process Safety
This paper describes a method for managing fouling to minimize its effect on cost, the environment, reliability and maintenance. The approach combines advanced process modelling and optimization techniques with the latest data visualization techniques in an Operational Excellence (OE) platform.

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