Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/575129
Title: Gas Consumption Analysis and Optimization in Casting Industry Using IOT
Researcher: PATHAK, AMISHA YASHODHAR
Guide(s): BHATT, MANGAL G
Keywords: Engineering
Engineering and Technology
Engineering Mechanical
University: Gujarat Technological University
Completed Date: 2024
Abstract: With Industrial Revolution, there has been continuous growth in the industrial sector. Any Industries weather it is a process industry or manufacturing industry has several operations to do and needs continuous monitoring to make it work smoothly. With the increasing trend of computing and Information technology and advancements in connectivity, there are several opportunities opened that can get this technology into the industrial segment. The Internet of Things is a concept that is widely researched for its implementation in Industrial processes to make all the processes and operations in the industry smooth and error-free. This concept makes it easier for industries to implement different production lines and make flexible operations to cope with the market demand dynamics. Here in the proposed study, IoT was used in the casting industry for monitoring the Gas Consumption in heat treatment furnaces. The study was not limited to monitoring but it also gave the forecasting for gas consumption in heat treatment processes with respect to different heat treatment phases. Two heat treatment furnaces were considered for study and data fetching hardware was developed for monitoring data and further using the algorithm with statistical models. Forecasting of gas consumption was done for both furnaces based on heat treatment phases. Further, the prediction of gas consumption was done using five statistical models namely, Simple Linear Regression, Polynomial Linear Regression, Support vector Regression, Decision Tree Regression, and Random Forest Regression. It was observed from the study that best performing tool for prediction was Random Forest Regression followed by Decision Tree Regression. newline newline
Pagination: 
URI: http://hdl.handle.net/10603/575129
Appears in Departments:Mechanical Engineering

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01_title page.pdfAttached File157.83 kBAdobe PDFView/Open
02_prelim pages.pdf4 MBAdobe PDFView/Open
03_content.pdf207.32 kBAdobe PDFView/Open
04_abstract.pdf121.34 kBAdobe PDFView/Open
05_chapter 1.pdf418.59 kBAdobe PDFView/Open
06_chapter 2.pdf841.8 kBAdobe PDFView/Open
07_chapter 3.pdf889.82 kBAdobe PDFView/Open
08_chapter 4.pdf935.64 kBAdobe PDFView/Open
09_chapter 5.pdf209.86 kBAdobe PDFView/Open
10_annexures.pdf623.59 kBAdobe PDFView/Open
80_recommendation.pdf175.52 kBAdobe PDFView/Open
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