000			02602nam a22001937a 4500
082			_a665.5 _bM678S
100			_aMishra, Sandeep _945623
245			_aStudy Of Industrial Heat Exchangers And Membrane System Using Ml-based Algorithm _cby Sandeep Mishra
260			_aIIT Jodhpur _bDepartment of Chemical Engineering _c2023
300			_aviii, 60p. _bHB
500			_aThe petroleum refining industry's crude oil fouling of heat exchangers has been a challenge, resulting in inefficient heating of crudes in the upstream process. It has been observed that fouling due to various reasons, such as crude composition, process parameter effects, working conditions, etc., has been the main cause of degradation of these heat exchangers' performances. We have therefore proposed Machine Learning (ML)-based models and algorithms development that can take essential steps to prevent fouling and thus apprehend proper maintenance schedules of these unit operations in refineries. This report describes in detail the various forms of fouling that typically affect a heat exchanger in the refinery and proposes an ML-based algorithm that can predict the effectiveness of each heat exchanger in the network through energy and mass balance error estimations. The present algorithm takes into account a network factor on energy and mass balance calculations, which optimizes the flow rates and temperatures of various flow streams to enhance the overall effectiveness of the heat exchanger network. This study also investigates the application of machine learning techniques to estimate fouling resistance in water filtration systems. Two methods are analyzed in this work to study the performance of ML models on process industrial problems. In the first methodology, a linear regression model using various parameters to predict fouling resistance has been proposed, while in the second study, the performance of different regression models has been examined regarding an industrial problem. The results show that random forest regression outperforms linear regression. The results of this study suggest that machine learning models can effectively predict fouling resistance in water filtration systems, enabling optimization of the systems to reduce fouling, improve efficiency, and reduce costs.
650			_aDepartment of Chemical Engineering _945624
650			_aHeat Exchanger Performance _945625
650			_aFouling Resistance Prediction _945626
650			_aEnergy and Mass Balance Optimization _945627
650			_aMTech Theses _945628
700			_aSengupta, Angan _945629
942			_cTH
999			_c16615 _d16615