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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 |
Files in This Item:
File | Description | Size | Format | |
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01_title page.pdf | Attached File | 157.83 kB | Adobe PDF | View/Open |
02_prelim pages.pdf | 4 MB | Adobe PDF | View/Open | |
03_content.pdf | 207.32 kB | Adobe PDF | View/Open | |
04_abstract.pdf | 121.34 kB | Adobe PDF | View/Open | |
05_chapter 1.pdf | 418.59 kB | Adobe PDF | View/Open | |
06_chapter 2.pdf | 841.8 kB | Adobe PDF | View/Open | |
07_chapter 3.pdf | 889.82 kB | Adobe PDF | View/Open | |
08_chapter 4.pdf | 935.64 kB | Adobe PDF | View/Open | |
09_chapter 5.pdf | 209.86 kB | Adobe PDF | View/Open | |
10_annexures.pdf | 623.59 kB | Adobe PDF | View/Open | |
80_recommendation.pdf | 175.52 kB | Adobe PDF | View/Open |
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