Study Of Industrial Heat Exchangers And Membrane System Using Ml-based Algorithm (Record no. 16615)

MARC details
000 -LEADER
fixed length control field 02602nam a22001937a 4500
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 665.5
Item number M678S
100 ## - MAIN ENTRY--AUTHOR NAME
Personal name Mishra, Sandeep
245 ## - TITLE STATEMENT
Title Study Of Industrial Heat Exchangers And Membrane System Using Ml-based Algorithm
Statement of responsibility, etc by Sandeep Mishra
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication IIT Jodhpur
Name of publisher Department of Chemical Engineering
Year of publication 2023
300 ## - PHYSICAL DESCRIPTION
Number of Pages viii, 60p.
Other physical details HB
500 ## - GENERAL NOTE
General note The 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.<br/><br/>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.<br/><br/>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.<br/><br/>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 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Department of Chemical Engineering
Topical Term Heat Exchanger Performance
Topical Term Fouling Resistance Prediction
Topical Term Energy and Mass Balance Optimization
Topical Term MTech Theses
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Sengupta, Angan
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Thesis
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Withdrawn status Lost status Damaged status Not for loan Collection code Permanent Location Current Location Shelving location Date acquired Source of acquisition Full call number Accession Number Price effective from Koha item type
        Theses S. R. Ranganathan Learning Hub S. R. Ranganathan Learning Hub Reference 2024-04-01 Office of Academics 665.5 M678S TM00550 2024-07-02 Thesis