Intelligent Automation Across Sectors: Real-Time Waste Segregation and Engineering Innovations in Oil–Gas–Chemical Industries
DOI:
https://doi.org/10.62486/978-9915-9851-0-7_202638Keywords:
Intelligent automation, real-time waste segregation, machine vision, smart monitoring, industrial sensing systems, oil–gas–chemical engineeringAbstract
The swelling urban population and fast-paced industrialization have, literally, put the squeeze on the world's solid waste management systems, which has, in turn, triggered various environmental and economic problems that are increasing day by day. A rapidly growing concern about the environmental impact of industrial activities has led to a significant interest in the use of intelligent automation that can efficiently work in different areas. The article explains how the fusion of the evolution of real-time waste segregation technologies with advanced engineering systems in the oil-gas-chemical industries is taking place. First of all, the improvements in the machine-vision-based classification, embedded sensing, and edge-level decision architectures have, by their very nature, changed waste management to a process that is responsive, data-driven, and, therefore, capable of increasing material recovery and reducing environmental burdens, from one that was predominantly manual. At the same time, the oil-gas-chemical industries are undergoing a significant change to automation architectures that integrate distributed sensors, robotics, and predictive analytics for enhanced operational reliability and safety. In this situation, real-time waste segregation mechanisms that bring together sensors, robotics, machine learning, and Internet of Things (IoT)–based monitoring are viewed as a possible solution to the problem and have the potential to change the waste management methods that are currently in use. This survey moves through the landscape occupied by available technologies for the real-time segregation of waste led by algorithmic enhancements, automation machinery, and adaptive structures. It weighs the design compromise between accuracy, cost, and power draw, conversely discussing the implementation constraints and performance indicators. Machine learning algorithm, sensor installation, and robotic control systems forms of comparison are addressed in the five figures and tables of the dissertation, thus facilitating the understanding of the reader. Besides these, the authors have analyzed the scenarios and experiments of the pilot program to emphasize not only the prospects but also the limitations of such technology in various geographic and local economy contexts.
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Copyright (c) 2026 J. Maniraj, P. Prabhu, J. Samuel Jenishraj, A. Abeeth Ali, R. Roshan, Qodirov Asliddin Asomiddin (Author)
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