Innovation designs of industry 4.0 based solid waste management: Machinery and digital circular economy

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Cheah C.G.
Chia W.Y.
Lai S.F.
Chew K.W.
Chia S.R.
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Academic Press Inc.
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The Industrial Revolution 4.0 (IR 4.0) holds the opportunity to improve the efficiency of managing solid waste through digital and machinery applications, effectively eliminating, recovering, and repurposing waste. This research aims to discover and review the potential of current technologies encompassing innovative Industry 4.0 designs for solid waste management. Machinery and processes emphasizing on circular economy were summarized and evaluated. The application of IR 4.0 technologies shows promising opportunities in improving the management and efficiency in view of solid waste. Machine learning (ML), artificial intelligence (AI), and image recognition can be used to automate the segregation of waste, reducing the risk of exposing labour workers to harmful waste. Radio Frequency Identification (RFID) and wireless communications enable the traceability in materials to better understand the opportunities in circular economy. Additionally, the interconnectivity of systems and automatic transfer of data enable the creation of more complex system that houses a larger solution space that was previously not possible such as centralised cloud computing to reduce the cost by eliminating the need for individual computing systems. Through this comprehensive review-based work, innovative Industry 4.0 components of machinery and processes involving waste management which focuses on circular economy are identified with the critical ones evaluated briefly. It was found that the current research and work done is based on applying Industry 4.0 technologies on individual waste management systems, which lacks the coherency needed to capitalise on technologies such as cloud computing, interconnectivity, big data, etc on a larger scale. Therefore, a real world comprehensive end-to-end integration aimed to optimize every process within the solid waste management chain should be explored. � 2022 Elsevier Inc.
design; innovation; machinery; solid waste; waste management; article; artificial intelligence; big data; cloud computing; economic aspect; human; internet of things; machine learning; radiofrequency identification; solid waste; solid waste management; systematic review; waste management; wireless communication; worker; artificial intelligence; industry; machine learning; solid waste; waste management; Artificial Intelligence; Humans; Industry; Machine Learning; Solid Waste; Waste Management