Artificial Intelligence in enhancing sustainable practices for infectious municipal waste classification

Rapeepan Pitakaso; Thanatkij Srichok; Surajet Khonjun; Paulina Golinska-Dawson; Sarayut Gonwirat; Natthapong Nanthasamroeng; Chawis Boonmee; Ganokgarn Jirasirilerd; Peerawat Luesak

doi:10.1016/j.wasman.2024.05.002

Artificial Intelligence in enhancing sustainable practices for infectious municipal waste classification

Waste Manag. 2024 May 11:183:87-100. doi: 10.1016/j.wasman.2024.05.002. Online ahead of print.

Authors

Rapeepan Pitakaso¹, Thanatkij Srichok², Surajet Khonjun³, Paulina Golinska-Dawson⁴, Sarayut Gonwirat⁵, Natthapong Nanthasamroeng⁶, Chawis Boonmee⁷, Ganokgarn Jirasirilerd⁸, Peerawat Luesak⁹

Affiliations

¹ Artificial Intelligence Optimization SMART Laboratory, Industrial Engineering Department, Faculty of Engineering, Ubon Ratchathani University, Ubon Ratchathani 34190, Thailand. Electronic address: rapeepan.p@ubu.ac.th.
² Artificial Intelligence Optimization SMART Laboratory, Industrial Engineering Department, Faculty of Engineering, Ubon Ratchathani University, Ubon Ratchathani 34190, Thailand. Electronic address: thanatkij.s@ubu.ac.th.
³ Artificial Intelligence Optimization SMART Laboratory, Industrial Engineering Department, Faculty of Engineering, Ubon Ratchathani University, Ubon Ratchathani 34190, Thailand. Electronic address: surajet.k@ubu.ac.th.
⁴ Institute of Logistics, Poznan University of Technology, Jacka Rychlewskiego 2 Street, 60-965 Poznan, Poland. Electronic address: paulina.golinska@put.poznan.pl.
⁵ Department of Computer Engineering and Automation Kalasin University, Kalasin 46000, Thailand. Electronic address: sarayut.go@ksu.ac.th.
⁶ Artificial Intelligence Optimization SMART Laboratory, Engineering Technology Department, Faculty of Industrial Technology, Ubon Ratchathani Rajabhat University, Ubon Ratchathani 34000, Thailand. Electronic address: natthapong.n@ubru.ac.th.
⁷ Department of Industrial Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, Thailand. Electronic address: chawis.boonmee@cmu.ac.th.
⁸ Department of Industrial and Environmental Management Engineering, Faculty of Liberal Arts and Sciences, Sisaket Rajabhat University, Sisaket 33000, Thailand. Electronic address: ganokgarn.j@sskru.ac.th.
⁹ Department of Industrial Engineering, Faculty of Engineering, Rajamangala University of Technology Lanna, Chiang Rai 57120, Thailand. Electronic address: peerawat@rmutl.ac.th.

PMID: 38735094
DOI: 10.1016/j.wasman.2024.05.002

Abstract

This research paper focuses on effective infectious municipal waste management in urban settings, highlighting a dearth of dedicated research in this domain. Unlike general or specific waste types, infectious waste poses distinct health and environmental risks. Leveraging advanced artificial intelligence techniques, we prioritize infectious waste categorization and optimization, integrating metaheuristics into optimization methods to create a robust dual-ensemble framework. Our model, the "Enhanced Artificial Intelligence for Infectious Municipal Waste Classification System," combines ensemble image segmentation methods and diverse convolutional neural network models. Innovative geometric image augmentation enhances model robustness, diversifies training data, and improves accuracy across waste types. A pivotal aspect is the integration of a reinforcement learning-differential evolution algorithm as a decision fusion strategy, optimizing classification by harmonizing outputs from ensemble methods and convolutional neural network models. Computational results, using a newly collected dataset, demonstrate our model's accuracy exceeding 96.54% while using the existing solid waste dataset we achieve the accuracy of 97.81%, outperforming advanced approaches by margins ranging from 2.02% to 8.80%. This research significantly advances sustainable waste management, showcasing artificial intelligence's transformative potential in addressing intricate waste challenges. It establishes a foundational framework prioritizing efficiency, effectiveness, and sustainability for future waste management solutions. Acknowledging the importance of diverse datasets, customization for urban contexts, and practical integration into existing infrastructures, our work contributes to the broader discourse on the role of artificial intelligence in evolving waste management practices.

Keywords: Artificial intelligence; Convolutional neural networks; Infectious municipal waste; Reinforcement learning-differential evolution; Waste management.