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The research conducted covers concepts in the evolution of process automation and control using Artificial Intelligence (AI) and Machine Learning (ML) in the Fourth Industrial Revolution (4IR), leading to discussions on the practical implementation concepts. Part of the literature study allows the reader, apart from the technical aspects, to overview on national and international considerations, for effective implementation of 4IR, AI and ML, in the interest of the benefits. Process automation and control comprises four fundamental components, i.e. the measuring instrument, the automation/control system, the final correcting instrument, and the process that needs to be measured and controlled. AI has introduced a concept of integrating measurement and control instruments to optimise processes by incorporating rules to reach approximated or definite results. ML is a subset of AI through the development of algorithms to teach a software algorithm to learn from generated data. Traditional algorithms are confined to limitations, which may be mathematically based allowing concepts like 0.25 wave damping to determine how the final correcting element regulates the final process. However, an AI-based algorithm is knowledge-based, facilitating a controller to respond to different sets of rules, for different process conditions. An overview of the Fuzzy Logic Controller (FLC) will be discussed. The evolution of process automation and control is evident by the technological advancements in the field. The benefits of 4IR, AI and ML, in both industry and education will be discussed. These technologies also facilitate machines to learn from archived data to optimise process operations in real time. The Industrial Internet of Things (IIoT) establishes a platform where Information Technology (IT) of business process automation integrates with Operational Technology (OT) of industrial process automation. This creates a totally integrated system, linking production, human resources, finance, automation, and all factors to detail, that is required for data analysis in a manufacturing environment.

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The Evolution in Process Automation and Control Using Artificial Intelligence (AI) and Machine Learning (ML) in Fourth Industrial Revolution (4IR)

  • 章节

  • 发表日期：

    2025年

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  • Puramanathan Naidoo

摘要

The research conducted covers concepts in the evolution of process automation and control using Artificial Intelligence (AI) and Machine Learning (ML) in the Fourth Industrial Revolution (4IR), leading to discussions on the practical implementation concepts. Part of the literature study allows the reader, apart from the technical aspects, to overview on national and international considerations, for effective implementation of 4IR, AI and ML, in the interest of the benefits. Process automation and control comprises four fundamental components, i.e. the measuring instrument, the automation/control system, the final correcting instrument, and the process that needs to be measured and controlled. AI has introduced a concept of integrating measurement and control instruments to optimise processes by incorporating rules to reach approximated or definite results. ML is a subset of AI through the development of algorithms to teach a software algorithm to learn from generated data. Traditional algorithms are confined to limitations, which may be mathematically based allowing concepts like 0.25 wave damping to determine how the final correcting element regulates the final process. However, an AI-based algorithm is knowledge-based, facilitating a controller to respond to different sets of rules, for different process conditions. An overview of the Fuzzy Logic Controller (FLC) will be discussed. The evolution of process automation and control is evident by the technological advancements in the field. The benefits of 4IR, AI and ML, in both industry and education will be discussed. These technologies also facilitate machines to learn from archived data to optimise process operations in real time. The Industrial Internet of Things (IIoT) establishes a platform where Information Technology (IT) of business process automation integrates with Operational Technology (OT) of industrial process automation. This creates a totally integrated system, linking production, human resources, finance, automation, and all factors to detail, that is required for data analysis in a manufacturing environment.