Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1
kingsun zhang
 -- 1385 2024-01-27 12:58:49 |
2 layout
Camila Xu
 Meta information modification 1385 2024-01-29 01:12:22 |

Video Upload Options

Do you have a full video?
Send video materials Upload full video

Confirm

Are you sure to Delete?
Yes No
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Zhang, J.; Yang, H.; Xu, X. Service Design of Garbage Classification Driven by AI. Encyclopedia. Available online: https://encyclopedia.pub/entry/54437 (accessed on 08 May 2024).
Zhang J, Yang H, Xu X. Service Design of Garbage Classification Driven by AI. Encyclopedia. Available at: https://encyclopedia.pub/entry/54437. Accessed May 08, 2024.
Zhang, Jingsong, Hai Yang, Xinguo Xu. "Service Design of Garbage Classification Driven by AI" Encyclopedia, https://encyclopedia.pub/entry/54437 (accessed May 08, 2024).
Zhang, J., Yang, H., & Xu, X. (2024, January 27). Service Design of Garbage Classification Driven by AI. In Encyclopedia. https://encyclopedia.pub/entry/54437
Zhang, Jingsong, et al. "Service Design of Garbage Classification Driven by AI." Encyclopedia. Web. 27 January, 2024.
Service Design of Garbage Classification Driven by AI
Edit
This entry is adapted from the peer-reviewed paper 10.3390/su152316454

Compared with other countries, there are big differences in the treatment modes and actual results of garbage classification due to different economic strengths, resource demands, technical levels, and legal policies.

AI municipal solid waste classification garbage classification service design

1. Introduction

Since 2019, Chinese provinces and prefecture-level cities have advocated solving problems of “garbage siege” and carrying out the environmental protection policies like the “Ban on Free Plastic bags”, both of which have put forward the overall requirements of “reduce, reuse, and recycle” for municipal waste classification [1][2]. Compared with other countries, there are big differences in the treatment modes and actual results of garbage classification due to different economic strengths, resource demands, technical levels, and legal policies. With the reuse, superimposition, and crossover collaboration of “Internet plus”, “AI plus”, big data, Internet of Things, and other technologies in recent years, the exploration of the technology and service changes in garbage classification, a systematic project, is particularly prominent and important in the round of the comprehensive management of garbage classification in major cities of China [3]. At the same time, it introduces the case of the ZRR2 waste classification robot used in the waste classification processing plant in Barcelona [4]. By comparing the two cases, it more prominently highlights the huge potential of AI in waste management strategies.

2. Research on Service Design of Garbage Classification Driven by Artificial Intelligence

Municipal solid waste (MSW) classification management comprises the activities associated with the collection, transfer, treatment, recycling, resource recovery, and disposal of solid waste generated within urban locales [5]. The process extends from the point of waste generation to its ultimate disposal, with efficiency and productivity being critical considerations. Artificial intelligence (AI) technology has emerged as a promising tool in bolstering the efficiency and precision of MSW classification. Artificial intelligence-based technologies like smart garbage bins, garbage sorting robots, predictive models for waste production, and optimizing the performance of waste processing facilities. The details are shown in (Table 1). Notably, Finland, Japan, and the United States have pioneered the R&D of automatic garbage-sorting robots. ZenRobotics Recycle system (ZRR) from Finland, the first garbage classification robot globally, can efficiently differentiate mixed waste, useful, and non-useful waste within MSW. Japan’s FANUC garbage sorting robots utilize the AI vision analysis system to analyze wood quality and discriminate polymer from plastic. The Computer Science and AI Laboratory at the Massachusetts Institute of Technology has developed ‘Rocycle’, a garbage recycling and sorting robot that can distinguish paper, metal, and plastic by touch [4].
The application of AI in waste management transcends waste classification. AI modeling methods can accurately predict waste generation quantities, facilitating the design and operation of effective waste management systems [6]. AI has also been instrumental in forecasting waste generation, managing construction waste, and optimizing landfill site selection [7]. Other studies have employed non-parametric models to track the temporal productivity changes in waste management services, considering both economic and environmental aspects [8].
In essence, AI technology, in conjunction with the Internet of Things (IoT), plays a vital role in the classification and management of MSW. These technologies enable precise waste classification, waste collection and transportation optimization, and the establishment of efficient waste management systems. AI modeling and DEA-based models are employed to measure productivity from various perspectives, thus providing comprehensive insights into the performance of waste management services in terms of productivity and eco-productivity [8].
In China, the government initiated a new MSW classification strategy in 2017, with Hangzhou being one of the first pilot cities. The policy aims to ensure effective MSW classification implementation via measures like AI- and computer vision (CV)-based approaches [9]. The implementation of IoT technology in MSW classification and management can enhance the classification level and optimize waste collection and transportation, thereby reducing costs and environmental pollution. The COVID-19 pandemic has amplified the challenges of MSW management due to the surge in medical and household waste [10]. This scenario has highlighted the criticality of waste collection, recycling, treatment, and disposal services. Concurrently, the pandemic has exposed the complexities of waste management, emphasizing the importance of efficiency, health considerations, and customer satisfaction. In this context, factors such as waste collection frequency, age, educational status, and family size play a crucial role in customer satisfaction [11].
Table 1. The main application of artificial intelligence to waste management [12].
Type of AI Technology Types of Waste Measures of AI Key Information Results/Benefits References
Smart garbage bin Solid waste Sensor network 1. Garbage bin monitoring
2. Collect data
3. Analyze information		 Used to collect municipal waste Khan et al. (2021) [13];
Ghahramani et al. (2022)
[14]
Solid waste Ultrasonic sensors 1. Garbage will not overflow
2. The lid will open automatically
3. Automatic detection of garbage		 Digital garbage bin Wijaya et al. (2017) [15];
Praveen et al. (2020a) [16]
Solid waste Ultrasonic sensors
Red external sensor		 1. Identify garbage
2. tracking the vehicle and IR sensors
3. Garbage level monitoring		 Instantly detection of the status of Bins: Filled or Empty Pawar et al. (2018); [17]
Garbage-sorting robot Reusable garbage Computer vision
Robotic framework		 1. Gripping
2. Motion control
3. Material categorization		 Success rates:
glass: 79%
plastic: 91%		 Wilts et al. (2021) [6];
Kshirsagar et al. (2022) [18]
Solid waste Computer vision simultaneous localization and mapping 1. Automatic navigation
2. Garbage recognition
3. Pick up automatically		 Recognition accuracy is 94%, even without path planning Bai et al. (2018) [19];
Lee, K.-F. (2023) [4]
Seven types of garbage Skin-Inspired Tactile Sensor 1. Quadruple tactile sensing
2. Object recognition
3. Garbage classification		 Recognizing 7 types of garbage, accuracy of 94% Li et al. (2020) [20];
Lee, K.-F. (2023) [4]
Predictive model for waste production Hazardous waste, construction site waste Genetic algorithm-adaptive neuro-fuzzy inference system 1. Defining targets for waste production
2. Optimizing resources
3. Reporting and conducting inspections
4. Compared with different AI prediction models		 Raised proposed measures for waste reduction prediction Haque, M.S. et al. (2021) [10];
Bang et al. (2022) [8]
Solid waste Proximate analysis 1. Generation rate and waste composition
2. Quantified, characterized, and evaluated energy potential and nutrient value of solid waste		 Reduce tons of carbon dioxide equivalent greenhouse gas emissions. Fetene et al. (2018) [11]
Solid waste Eco-Productivity Analysis 1. DEA-based models
2. Sampling and characterization
3. Carbon emissions evaluation of MSW disposal system		 Decline of daily carbon emission in MSW disposal system after waste sorting. Lo Storto, C. (2017) [7]
Wang, Y. et al. (2021) [9]
Therefore, it is imperative to conduct further research to unravel the multiple roles of AI technology or AI agents in waste classification management systems. These technologies function not only as technical components of the service system but also as collaborative agents alongside human operators [21]. The aim is to explore how AI influences service design and management practices for MSW from different perspectives. This investigation would provide constructive insights for future service ecosystem innovation, preparing us to better handle unforeseen events and navigate the multifaceted challenges of our evolving social environment.
Service design models the social, material, and relational elements that support the customer experience [22][23], integrating the various silos of the organization into a coordinated service offering [24]. It applies human-centered and collaborative methods to explore the experiences of different stakeholders. This ability to integrate stakeholders enables service design to develop solutions that are relevant to customers while considering the structural context of the organization [25][26]. However, the relationship between artificial intelligence and service systems is a complex and evolving new one [27]. Some believe a key aspect of the relationship between AI and service systems is that AI has the potential to complement rather than completely replace human labor [28]. While AI can automate certain tasks, it also amplifies the comparative advantages of human workers in areas such as problem solving, adaptability, and creativity. To fully leverage the potential of AI in service systems, it is crucial to study and implement strategic frameworks [29]. This framework should consider the nature of service activities, service processes, and other specifics [30].
Consistent with all service-dominant logic premises, the AI-driven service system guides the description of the situation at three levels: Value constellation, whole picture view of the system, service activities, and other specifics. According to Alter [30], there are three frameworks for service systems, namely the Work System Framework, the Service Value Chain Framework, and the Service Lifecycle Framework. The Work System Framework provides a systematic perspective for understanding and analyzing any system that performs work within or between organizations. The Service Value Chain Framework extends the Work System Framework by introducing functions that are particularly relevant to services. The Service Lifecycle Framework emphasizes the evolution of service systems, including the creation, operation, and planned and unplanned changes to services. Therefore, AI technologies or AI agents are set to become vital focal points in the construction of new service systems, interactive experiences, and value co-creation [30][31].

References

  1. Li, X.; Zhang, C.; Li, Y.; Zhi, Q. The Status of Municipal Solid Waste Incineration (MSWI) in China and its Clean Development. Energy Procedia 2016, 104, 498–503.
  2. Xiao, S.J.; Dong, H.; Yong, G.; Brander, M. An overview of China’s recyclable waste recycling and recommendations for integrated solutions. Resour. Conserv. Recycl. 2019, 134, 112–120.
  3. Zhou, M.-H.; Shen, S.-L.; Xu, Y.-S.; Zhou, A.-N. New Policy and Implementation of Municipal Solid Waste Classification in Shanghai, China. Int. J. Environ. Res. Public Health 2019, 16, 3099.
  4. Lee, K.-F. Touch Recognition, Intelligent Sorting: How AI Empowers Waste Classification. Kai-Fu Lee’s Caixin Blog. Caixin. Available online: https://likaifu.blog.caixin.com/archives/208221 (accessed on 31 August 2023). (In Chinese).
  5. Bernstein, J. Social Assessment and Public Participation in Municipal Solid Waste Management—Toolkit; Worldbank: Washington, DC, USA, 2004; Available online: https://documents.worldbank.org/en/publication/documents-reports/documentdetail/741051468340748098/-Social-assessment-and-public-participation-in-municipal-solid-waste-management-toolkit (accessed on 31 August 2023).
  6. Wilts, H.; Garcia, B.R.; Garlito, R.G.; Gómez, L.S.; Prieto, E.G. Artificial Intelligence in the Sorting of Municipal Waste as an Enabler of the Circular Economy. Resources 2021, 10, 28.
  7. Lo Storto, C. Eco-Productivity Analysis of the Municipal Solid Waste Service in the Apulia Region from 2010 to 2017. Sustainability 2021, 13, 12008.
  8. Bang, S.; Andersen, B. Utilising Artificial Intelligence in Construction Site Waste Reduction. EPPM—J. 2022, 12, 239–249.
  9. Wang, Y.; Shi, Y.; Zhou, J.; Zhao, J.; Maraseni, T.; Qian, G. Implementation Effect of Municipal Solid Waste Mandatory Sorting Policy in Shanghai. J. Environ. Manag. 2021, 298, 113512.
  10. Haque, M.d.S.; Uddin, S.; Sayem, S.M.d.; Mohib, K.M. Coronavirus Disease 2019 (COVID-19) Induced Waste Scenario: A Short Overview. J. Environ. Chem. Eng. 2021, 9, 104660.
  11. Fetene, Y.; Addis, T.; Beyene, A.; Kloos, H. Valorisation of Solid Waste as Key Opportunity for Green City Development in the Growing Urban Areas of the Developing World. J. Environ. Chem. Eng. 2018, 6, 7144–7151.
  12. Fang, B.; Yu, J.; Chen, Z.; Osman, A.I.; Farghali, M.; Ihara, I.; Hamza, E.H.; Rooney, D.W.; Yap, P.-S. Artificial Intelligence for Waste Management in Smart Cities: A Review. Environ. Chem. Lett. 2023, 21, 1959–1989.
  13. Khan, A.A.; Sajib, A.A.; Shetu, F.; Bari, S.; Zishan, M.d.S.R.; Shikder, K. Smart Waste Management System for Bangladesh. In Proceedings of the 2021 2nd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST), Dhaka, Bangladesh, 5–7 January 2021; pp. 659–663.
  14. Ghahramani, M.; Zhou, M.; Molter, A.; Pilla, F. IoT-Based Route Recommendation for an Intelligent Waste Management System. IEEE Internet Things J. 2022, 9, 11883–11892.
  15. Wijaya, A.S.; Zainuddin, Z.; Niswar, M. Design a Smart Waste Bin for Smart Waste Management. In Proceedings of the 2017 5th International Conference on Instrumentation, Control, and Automation (ICA), Yogyakarta, Indonesia, 9–11 August 2017; pp. 62–66.
  16. Praveen, A.; Radhika, R.; Rammohan, M.U.; Sidharth, D.; Ambat, S.; Anjali, T. IoT Based Smart Bin: A Swachh-Bharat Initiative. In Proceedings of the 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC), Coimbatore, India, 2–4 July 2020; pp. 783–786.
  17. Pawar, S.S.; Pise, S.; Walke, K.; Mohite, R. Smart Garbage Monitoring System Using AVR Microcontroller. In Proceedings of the 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), Pune, India, 16–18 August 2018; pp. 1–4.
  18. Kshirsagar, P.R.; Kumar, N.; Almulihi, A.H.; Alassery, F.; Khan, A.I.; Islam, S.; Rothe, J.P.; Jagannadham, D.B.V.; Dekeba, K. Artificial Intelligence-Based Robotic Technique for Reusable Waste Materials. Comput. Intell. Neurosci. 2022, 2022, 2073482.
  19. Bai, J.; Lian, S.; Liu, Z.; Wang, K.; Liu, D. Deep Learning Based Robot for Automatically Picking Up Garbage on the Grass. IEEE Trans. Consum. Electron. 2018, 64, 382–389.
  20. Li, G.; Liu, S.; Wang, L.; Zhu, R. Skin-Inspired Quadruple Tactile Sensors Integrated on a Robot Hand Enable Object Recognition. Sci. Robot. 2020, 5, eabc8134.
  21. Huang, M.-H.; Rust, R.T. Artificial Intelligence in Service. J. Serv. Res. 2018, 21, 155–172.
  22. Zomerdijk, L.G.; Voss, C.A. Service Design for Experience-Centric Services. J. Serv. Res. 2010, 13, 67–82.
  23. Wetter-Edman, K.; Sangiorgi, D.; Edvardsson, B.; Holmlid, S.; Grönroos, C.; Mattelmäki, T. Design for Value Co-Creation: Exploring Synergies between Design for Service and Service Logic. Serv. Sci. 2014, 6, 106–121.
  24. Kimbell, L. Beyond Design Thinking: Design-as-Practice and Designs-in-Practice. In Proceedings of the CRESC Conference, Manchester, UK, 1–4 September 2009.
  25. Miettinen, S. An Introduction to Industrial Service Design; Taylor & Francis: Oxfordshire, UK, 2016.
  26. Stickdorn, M. Service Design. In The Routledge Handbook of Tourism Marketing; Routledge: London, UK, 2008; ISBN 978-1-315-85826-5/978-1-317-93620-6/978-0-415-59703-6.
  27. Bock, D.E.; Wolter, J.S.; Ferrell, O.C. Artificial Intelligence: Disrupting What We Know about Services. JSM 2020, 34, 317–334.
  28. Autor, D.H. Why Are There Still So Many Jobs? The History and Future of Workplace Automation. J. Econ. Perspect. 2015, 29, 3–30.
  29. Huang, M.-H.; Rust, R.T. Engaged to a Robot? The Role of AI in Service. J. Serv. Res. 2021, 24, 30–41.
  30. Alter, S. Service System Fundamentals: Work System, Value Chain, and Life Cycle. IBM Syst. J. 2008, 47, 71–85.
  31. Alter, S. Metamodel for Service Analysis and Design Based on an Operational View of Service and Service Systems. Serv. Sci. 2012, 4, 218–235.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
Upload a video for this entry
Information
Subjects: Environmental Sciences
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Jingsong Zhang
,
Hai Yang
,
Xinguo Xu
View Times: 218
Entry Collection: Environmental Sciences
Revisions: 2 times (View History)
Update Date: 29 Jan 2024
Table of Contents
    1000/1000
    Hot Most Recent
    Feedback