Browsing by Author "Chowdhury, S."

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    Performance enhancement of commercial ethylene oxide reactor by artificial intelligence approach
    (De Gruyter Open Ltd, 2022) Chowdhury, S.; Lahiri, S.K.; Hens, A.; Katiyar, S.
    The present work emphasizes the development of a generic methodology that addresses the core issue of any running chemical plant, i.e., how to maintain a delicate balance between profit and environmental impact. Here, an ethylene oxide (EO) production plant has been taken as a case study. The production of EO takes place in a multiphase catalytic reactor, the reliable first principle-based model of which is still not available in the literature. Artificial neural network (ANN) was therefore applied to develop a data-driven model of the complex reactor with the help of actual industrial data. The model successfully built up a correlation between the catalyst selectivity and temperature with other operational parameters. A hybrid multi-objective metaheuristic optimization technique, namely ANN-multi-objective genetic algorithm (MOGA) algorithm was used to develop a Pareto diagram of selectivity versus reactor temperature. The Pareto diagram will help the plant engineers to make a strategy on what operating conditions to be maintained to make a delicate balance between profit and environmental impact. It was also found that by applying this hybrid ANN-MOGA modeling and optimization technique, for a 720 KTA ethylene glycol plant, approximately 32,345 ton/year of carbon-di-oxide emission into the atmosphere can be reduced. Along with the reduction of environmental impact, this hybrid approach enables the plant to reduce raw material cost of nine million USD per annum simultaneously. © 2021 Walter de Gruyter GmbH, Berlin/Boston.
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    Reliability Analysis Using Bayesian Belief Network on Drone System: A Case Study
    (Institute of Electrical and Electronics Engineers Inc., 2024) Das, M.; Mohan, B.R.; Ram Mohana Reddy, G.; Chhaparwal, E.; Krishna Kumar, K.; Chowdhury, S.; Sharma, S.
    Ensuring the reliability of software components is of paramount importance in safety-critical systems. Grave consequences might occur if software failures in such systems. Hence, predicting software reliability is important in these systems. This research uses Bayesian Belief Network(BBN) and leverages historical failure data to find fault interdependencies, giving much more insights than methodologies like Fault Tree Analysis (FTA) and Reliability Block Diagrams (RBD). By comparing BBNs with these traditional methods, the research shows the dynamic capabilities of BBNs. BBN also shows the capability of using real-time data and machine learning together to increase the software reliability of the software components, making this system much safer. © 2024 IEEE.