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A homeostatic approach to adaptive ambient control in smart factories

This project aims to develop a system using Artificial Intelligence (AI) and Internet of Things (IoT) to regulate and adjust environmental conditions in smart factories, focusing on the production of feed pellets, particularly during the drying stage. This paper addresses this gap by exploring exist...

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Bibliographic Details
Main Author: Chong, Shao Yang
Format: Final Year Project / Dissertation / Thesis
Published: 2024
Subjects:
T Technology (General)
TD Environmental technology. Sanitary engineering
Online Access:http://eprints.utar.edu.my/7024/1/fyp_IB_2024_CSY.pdf
http://eprints.utar.edu.my/7024/
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spelling my-utar-eprints.70242025-02-27T07:27:28Z A homeostatic approach to adaptive ambient control in smart factories Chong, Shao Yang T Technology (General) TD Environmental technology. Sanitary engineering This project aims to develop a system using Artificial Intelligence (AI) and Internet of Things (IoT) to regulate and adjust environmental conditions in smart factories, focusing on the production of feed pellets, particularly during the drying stage. This paper addresses this gap by exploring existing technologies within the framework of Industrial Revolution 4.0. We've examined various factors affecting feed pellet quality by examining moisture level and climate setting using near-infrared (NIR), microwave, and capacitance sensors to detect ambient conditions. These data are visualized on a Grafana Dashboard for real-time data monitoring, a predictive model for dryer setup, and a computer vision system for quality control assessment. Testing will be conducted in a simulated environment to achieve a minimal working product (MVP). Lastly, the project has successfully implemented both a LSTM predictive modelling and a Siamese Network with CNN as base model for computer vision task. The LSTM predictive model is used to identify the optimal ambient setting to produce the highest quality pellet based on parametric optimisation. It is then integrated with the machine to manipulate and scale the environment conditions to best fit the pellet requirements. With the implemented Siamese Network with CNN has also successfully classify the pellets quality based on the appearance and colour of the given pellet. 2024-06 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/7024/1/fyp_IB_2024_CSY.pdf Chong, Shao Yang (2024) A homeostatic approach to adaptive ambient control in smart factories. Final Year Project, UTAR. http://eprints.utar.edu.my/7024/
institution Universiti Tunku Abdul Rahman
building UTAR Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tunku Abdul Rahman
content_source UTAR Institutional Repository
url_provider http://eprints.utar.edu.my
topic T Technology (General)
TD Environmental technology. Sanitary engineering
spellingShingle T Technology (General)
TD Environmental technology. Sanitary engineering
Chong, Shao Yang
A homeostatic approach to adaptive ambient control in smart factories
description This project aims to develop a system using Artificial Intelligence (AI) and Internet of Things (IoT) to regulate and adjust environmental conditions in smart factories, focusing on the production of feed pellets, particularly during the drying stage. This paper addresses this gap by exploring existing technologies within the framework of Industrial Revolution 4.0. We've examined various factors affecting feed pellet quality by examining moisture level and climate setting using near-infrared (NIR), microwave, and capacitance sensors to detect ambient conditions. These data are visualized on a Grafana Dashboard for real-time data monitoring, a predictive model for dryer setup, and a computer vision system for quality control assessment. Testing will be conducted in a simulated environment to achieve a minimal working product (MVP). Lastly, the project has successfully implemented both a LSTM predictive modelling and a Siamese Network with CNN as base model for computer vision task. The LSTM predictive model is used to identify the optimal ambient setting to produce the highest quality pellet based on parametric optimisation. It is then integrated with the machine to manipulate and scale the environment conditions to best fit the pellet requirements. With the implemented Siamese Network with CNN has also successfully classify the pellets quality based on the appearance and colour of the given pellet.
format Final Year Project / Dissertation / Thesis
author Chong, Shao Yang
author_facet Chong, Shao Yang
author_sort Chong, Shao Yang
title A homeostatic approach to adaptive ambient control in smart factories
title_short A homeostatic approach to adaptive ambient control in smart factories
title_full A homeostatic approach to adaptive ambient control in smart factories
title_fullStr A homeostatic approach to adaptive ambient control in smart factories
title_full_unstemmed A homeostatic approach to adaptive ambient control in smart factories
title_sort homeostatic approach to adaptive ambient control in smart factories
publishDate 2024
url http://eprints.utar.edu.my/7024/1/fyp_IB_2024_CSY.pdf
http://eprints.utar.edu.my/7024/
_version_ 1825817830635864064
score 13.244413

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