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This paper explores the potentials of Artificial intelligence in the transition to a circular
economy. A circular economy is an emerging economic model that is restorative or regenerative by intention
and design .The model emphasizes that by keeping materials at their optimal use and value continually, the
system can be optimized. The CE model op...
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... include: applying real time traffic data to reduce traffic congestion, optimization of energy usage for cooling servers in different data centers and enhancing collaboration between car sharing companies and automotive companies.AI is promising, the potential value unraveled by AI in assisting to remove waste in a circular economy for food is estimated to be USD 127 billion a year in 2030.This feat is accomplished through various opportunities at the farming,processing,logistics and consumption levels. Some of the applications include: using image recognition to determine when fruit is ready to pick; matching food supply and demand more effectively; and enhancing the valorization of food by-products (Ellen MacArthur Foundation,2019).The fourth industrial revolution (AI&IOT) can accelerate the of the transition from linear economy to circular economy as depicted in figure 3. (Ellen MacArthur Foundation, 2019) argued that the potential to drive the use of AI in circular economy is substantial and presently largely untapped. ...
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... The recycling of plastic garbage could be done more accurately and efficiently with the use of blockchain technology and multisensory artificial intelligence interfaces. According to Akinode and Oloruntoba (2020) 9 , the potential value of trash that artificial intelligence (AI) might unlock in a circular economy is projected to be around 127 billion annually by 2030. A mathematical approach to optimize net profit by combining remanufacturing to meet SDGs was also presented by Rajak et al. in 2021. ...
... The recycling of plastic garbage could be done more accurately and efficiently with the use of blockchain technology and multisensory artificial intelligence interfaces. According to Akinode and Oloruntoba (2020) 9 , the potential value of trash that artificial intelligence (AI) might unlock in a circular economy is projected to be around 127 billion annually by 2030. A mathematical approach to optimize net profit by combining remanufacturing to meet SDGs was also presented by Rajak et al. in 2021. ...
Academic institutions' dedication to environmental care is shown in their waste management practices, which are crucial to sustainable campus operations. Encouraging sustainable behaviors in educational institutions requires effective waste management. Higher education institutions are acknowledged as miniature towns with sizable daily populations, which calls for all-encompassing waste management plans. These organizations are essential to sustainability. According to a comprehensive analysis of waste management in HEIs, involving the academic community in teaching and research is one of the most successful strategies. The identification of sustainability indicators that support decision-making and the ongoing enhancement of campus sustainability are complementary to these initiatives. Case studies from Indian higher education institutions show how the incorporation of environmental management systems can improve these programs even more.which highlights the advantages of these kinds of technologies in learning environments. In general, creating a sustainable culture that is appealing to all parties concerned is just as important to the development of sustainable waste management at educational institutions as adhering to environmental laws. In this paper, we give a comprehensive solution for a smart waste management system, aiming to improve the campus's overall waste cycle management and boost recycling rates. Based on a smart waste bin prototype, the system can autonomously separate different waste types and categorize trash that is commonly created on campus using an image classification algorithm combined with a hybrid sensor. From the requirements analysis to the implementation details, we cover the full system prototype design and assess its performance across many situations. We conclude by going over sophisticated application functionalities, such as optimal maintenance scheduling, that are developed around the smart waste bin.
... Smarter extensions of specialist robots, which combine ever-increasing AI advancements, can accomplish the corresponding gains. In a recent article, Akinode and Oloruntoba (2020) highlighted some other uses of the artificial intelligence in occurrence and consolidation of business models, which are compatible to the principles of circular economy, such as: data analysis (historical and real time), learning techniques for the product design, extended use of classification algorithms and chat bots. ...
The circular economy (CE) is a key component in achieving SDG-12. A number of features couldgreatly enhance and quicken the circular economy. Artificial intelligence (AI) and eco-investmentare two of the most important ones. The primary objective of this article is to assess the impact ofartificial intelligence and eco-investment on the development of circular economy in the contextof Sustainable Development Goal (SDG) - 12. The authors designed a panel regression model toascertain the influence on CE. In this model, CE is the dependent variable, whereas AI and eco-investment are the independent variables. Over a ten-year period, data and information from27 EU member states were employed in the modelling. The strong results demonstrate that, albeithaving a lesser impact than eco-investment, AI has a substantial impact on CE. The model createdexhibits flexibility that permits the estimation of distinct equations for any country underconsideration. One crucial finding is that there are slow processes that may take many years tocomplete in order to meet deadlines and require a substantial sum of money. One more point ofattention is the replacement that occurs between the two independent variables. Up to a certainpoint, it is feasible to replace one variable with another while accounting for unique features ofevery country. The paper highlights the significance of AI and how it can help us get closer toSDG-12 and accelerate the CE.
... By analyzing data on individual customer behavior, using AI, companies can design products that meet specific customer needs and preferences and develop customized reuse and sharing models that encourage greater product use and engagement. By analyzing data on consumer behavior using AI, companies can identify the most effective marketing channels and messaging that resonate with target audiences, thereby increasing awareness and adoption of circular products and services [25]. ...
The aim of this study is to draw attention to Circular Economy (CE) approach, to address a future trend and to create a compilation of some important CE studies. In this context, we provide information to guide businesses in the transition to CE and to provide ideas for future studies. This study examines some studies about the concept, structure, principles, business models, benefits, and barriers of CE implementation, moreover differences between Linear Economy (LE) and CE. The failure to solve problems such as climate change, loss of biodiversity, overpopulation, and resource scarcity in the current LE Model has shown that sustainable economy models can be a successful solution. Artificial intelligence (AI), on the other hand, is an emerging technology with enormous potential to impact CE. The use of AI in integrating the Circular Economy into product lifecycle processes was investigated. This study collects many studies examining CE from different perspectives in a single study and also shows the usability areas of AI in applying CE in production. Another contribution of this study to the literature is, proposing the use of AI regarding CE’s the 9R framework. This study has shown that, through artificial intelligence, the stated barriers to the transition to the circular economy can be removed or reduced. By using the business models outlined in the study, using Artificial Intelligence and implementing CE strategies, the stated benefits of the circular economy can begin to be achieved. The study guides the implementation of the CE concept.
... Thus, attracting interest from academia, companies, and policymakers is a promising approach promoting sustainability and competitiveness (Bressanelli et al., 2018). Furthermore, Big Data have been proposed as one the driving force of the new circular economy (Awan et al., 2021) with technological innovation as a key factor for instance to connect assets that enable predictive maintenance to prolong the asset life; blockchain technology that can create traceability and transparency in supply chains to reduce waste and 3D printing spare parts that make repairing easier (Akinode et al., 2020& Charnley et al., 2022. It can help in overcoming barriers to CBMs, facilitate the operationalization of circular material, components, and product flows, offer the far-reaching potential for comprehensive networking of "smart" circular economy strategies from analysis to artificial intelligence (AI) supported prediction of data, and can be seen as "glue" between value chain partners (Baumgartner et al., 2022& Kristoffersen et al., 2020. ...
This article discusses the role of data sources in data-driven circular business models (CBMs) that support sustainability. The circular economy (CE) is a response to the challenge of meeting sustainability targets, and CBMs have gained traction in recent years. Big data is seen as a driving force for the new circular economy, and can help overcome barriers to CBMs. Data sources are key elements of data-driven solutions, and this paper collects use cases that contain data-driven business models that contribute to CE, analyzes those use cases, identifies circular strategies in businesses, and displays the connection of them with data sources. The most frequently used circular strategies in the analyzed use cases are Reduce, Rethink, and Recycle, and the most frequently used data sources are Product-generated Data, Process Data, Open Data, Geographic Data, and User-generated Data. The paper recommends businesses focus on collecting product- and user-generated data for implementing circular strategies in their business model.
... Moreover, AI, blockchain, geographical information systems (GIS), and mobile apps acted as catalysts in CE transitions. Another study stressed food and plastic waste removal for the well-being of the environment (Akinode & Oloruntoba, 2020). It portrayed the cradle-to-cradle CE model as a productive and sustainable model that is socially and economically feasible. ...
... Some other essential features are disassembly, recycled content, and upgradability. AI magnifies the R-principles for the foundation of a circular economy (Akinode & Oloruntoba, 2020). The following points illustrate how AI expands R-principles, ...
Circular economy (CE) regenerates nature by introducing circularity in economic systems. It tackles various global challenges of sustainability, specifically in the agriculture and food sectors. Digital technologies support its implementations; especially, artificial intelligence (AI) is acquiring momentum. In this regard through algorithms, remote sensors, drones etc must be incorporated to achieve the desired target. The current studies provide bounded investigations of AI-driven CE exclusively in Pakistan. The main purpose of this paper is to elaborate on AI support in the implementation of CE practices and to explore the current waste situation in Pakistan and the implementations of CE and AI-driven CE practices in it. Inductive research is conducted in two stages. On the one hand, the theory is developed to evaluate the CE concept and AI techniques to strengthen its practices. On the other hand, a framework is proposed for multi-purpose case studies in the agriculture and food industries of Pakistan to integrate capabilities of CE and CE driven by AI. The outcomes of this research reveal that the true value of AI lies in the transition of CE and recommends that Pakistan must take some crucial measures to boost these practices to achieve sustainable development goals. Some limitations and future research proposals are also provided. The study helps researchers, companies and institues to participate positively towards the Circular Economy goal achievement by imlementing the AI.
... CE is a transition and shift from the traditional principle of make-use-dispose to a new era based on artificial intelligence [3]. There are several strategies used for circular the product database and knowledge between design engineers based on AI through the network is called collaborative design. ...
... Hybrid AI techniques are a combination of some individual techniques that benefit from their advantages and overcome the weaknesses of each stand-alone technique. This will develop a significant feature and add important values that lead to more motivation for the overall AM process [3,77]. Composing functional hybrid techniques for optimization purposes depends on the strong features embedded in each one. ...
The concept of repair and restoration using additive manufacturing (AM) is to build new metal layers on a broken part. It is beneficial for complex parts that are no longer available in the market. Optimization methods are used to solve product design problems to produce efficient and highly sustainable products. Design optimization can improve the design of parts to improve the efficiency of the repair and restoration process using additive manufacturing during the end-of-life (EoL) phase. In this paper, the objective is to review the strategies for remanufacturing and restoration of products during or at the EoL phase and facilitate the process using AM. Design optimization for remanufacturing is important to reduce repair and restoration time. This review paper focuses on the main challenges and constraints of AM for repair and restoration. Various AI techniques, including the hybrid method that can be integrated into the design of AM, are analyzed and presented. This paper highlights the research gap and provides recommendations for future research directions. In conclusion, the combination of artificial neural network (ANN) algorithms with genetic algorithms as a hybrid method is a key solution in solving limitations and is the future for repair and restoration using additive manufacturing.
... According to Macarthur (2019), technology such as AI can unlock CE opportunities by improving design to empower cycles of reuse, repair, refurbishing and recycling of technical materials. According to Akinode & Oloruntoba (2020) AI is a key driver of IR 4.0 that can bring dreams of CE vision to fruition. Ghoreishi & Happonen (2020) stated that AI enables the transfer of precise data and information on materials and product availability, condition and accessibility that will lead to ease of monitoring. ...
The conventional component repair in remanufacturing involves human decision making that is influenced by several factors such as conditions of incoming cores, modes of failure, severity of damage, features and geometric complexities of cores and types of reparation required. Repair can be enhanced through automation using additive manufacturing (AM) technology. Advancements in AM have led to the development of directed energy deposition and laser cladding technology for repair of damaged parts and components. The objective of this systematic literature review is to ascertain how intelligent systems can be integrated into AM-based repair, through artificial intelligence (AI) approaches capable of supporting the nature and process of decision making during repair. The integration of intelligent systems in AM repair is expected to enhance resource utilization and repair efficiency during remanufacturing. Based on a systematic literature review of articles published during 2005–2021, the study analyses the activities of conventional repair in remanufacturing, trends in the applications of AM for repair using the current state-of-the-art technology and how AI has been deployed to facilitate repair. The study concludes with suggestions on research areas and opportunities that will further enhance the automation of component repair during remanufacturing using intelligent AM systems.
The digital transformation of manufacturing operations represents an opportunity for sustainable development. Industry 4.0 is implementing innovations that bridge the gap between the physical and digital worlds, enabling intelligent and autonomous systems with great potential when used in conjunction and in conjunction with circular economy practices. Assessing the importance of Industry 4.0 to optimize available resources with practices such as recycling, the development of new circular business models such as remanufacturing, as well as the sustainability of the supply chain, will be the objective of this research. Through exploratory documentary methodology, it was confirmed that industrial development is limited to sustainable development and that Industry 4.0 can be decisive in responding to the challenge of sustainability.
The notion of a circular economy, which prioritizes the prolonged utilization of resources and the minimization of
waste, is progressively garnering heightened recognition within the business community. Leveraging technology,
notably artificial intelligence, enables businesses and various organizations to enhance resource utilization,
diminish waste and emissions, and streamline the effectiveness of resource recovery and recycling procedures. The
primary objective of this manuscript was to delineate the scope of artificial intelligence's capacity in facilitating the
transition towards a circular economy. This review will begin by providing an overview of the circular economy
concept and its potential benefits. It will then discuss the current challenges faced in realizing circular practices and
how artificial intelligence can help overcome these challenges. Anticipated outcomes of this study include providing
businesses and policymakers with valuable insights and recommendations on the efficient deployment of artificial
intelligence in the context of transitioning towards a circular economy
The concept of a circular economy, where resources are kept in use for as long as possible and waste is minimized, is gaining increasing attention in the business world. With the help of technology, particularly artificial intelligence (AI), businesses and other organizations can optimize resource use, reduce waste and emissions, and improve the efficiency of resource recovery and recycling processes. This manuscript aimed to draw the boundaries of the potential of AI in facilitating the transition to a circular economy. This review will begin by providing an overview of the circular economy concept and its potential benefits. It will then discuss the current challenges faced in realizing circular practices and how AI can help overcome these challenges. The paper will also highlight potential risks and limitations of using AI in the circular economy. The study is expected to offer guidance for businesses and policymakers on effectively utilizing artificial intelligence in the transition to a circular economy.
