Chemical Waste Management And Recycling Optimization Using Iot-Enabled Systems

Authors

  • Anirudh Gupta
  • Mahimna Shukla

DOI:

https://doi.org/10.63682/jns.v15i1.9909

Keywords:

Waste Management, IoT Intelligent Waste Management, Waste Monitoring, Eco-friendly Waste Management, Hazardous Waste Management, Landfill Management, Environmental Safety, Environmental Regulatory Compliance

Abstract

Background: This requires substantial support and resources in chemical waste management and recycling, such as automated technology, chemical data management systems, advanced materials, etc. The growing need for efficiency and sustainability in waste management systems is leading industries to adopt IoT-based applications in monitoring and optimizing chemical waste disposal and recycling systems. For instance, IoT-based techniques are adding real-time tracking, automated segregation, and predictive analytics, which can help make waste management much more efficient. Despite their benefits, myriad challenges remain to the adoption of IoT-enabled systems, including high implementation costs, regulatory inconsistencies, technical expertise gaps, and data security concerns. Tackling these issues is essential to improving waste management practices, ensuring compliance with regulations, and promoting environmental sustainability.

Aim: The current paper investigates the trends, problems, and future directions in chemical waste management and recycling optimization with IoT-based approaches. It specifically explores the knowledge, level of use, and perceived effectiveness of IoT-based waste management systems, where it also aims to determine whether there are barriers and factors that influence the widespread implementation of IoT-based waste management systems. The findings of the study will inform the industries, policymakers, and the environmental agencies to encourage or accelerate the adoption of the IoT in waste management and propel the sustainable waste disposed of solutions.

Methodology: Based on structured survey interviews with 110 respondents, including representatives from chemical industries, waste management companies, environmental non-government organizations, government agencies, and academic researchers. The study employed a cross-sectional mixed-methods design, integrating both quantitative and qualitative data for a comprehensive evaluation of IoT implementation in chemical waste management. Descriptive and inferential statistical analyses were used to assess awareness levels, adoption rates, challenges, and perceived benefits of IoT-assisted waste management. In order to position the findings in the context of existing literature, a systematic literature review was also conducted focusing on smart waste tracking, hazardous waste management, and IoT use cases for sustainability.

Findings: The paper provides important insights to characterize the status of IoT-enabled waste management by finding out the awareness level about the IoT-based waste management along with its adoption barriers. While 58% of the respondents were aware of IoT-enabled waste tracking, only 30% had implemented IoT within their organizations. According to the report, regulatory compliance requirements (50%), cost efficiency (45%), and sustainability goals (40%) were the primary factors driving IoT adoption in waste management. The most important barriers identified were high implementation costs (50%), lack of technical expertise (40%), and concerns around data privacy and security (35%). Respondents believed that 55% of respondents said government incentives like grants and standard regulatory frameworks (48%) would greatly increase adoption rates.

Conclusion: But to facilitate broader adoption, we need to solve for the cost, technical and regulatory challenges. The results underscore the role of tailored financial motivations, supportive government laws, and sectoraliszied training initiatives in adopting the use of IoT in waste management. Further work can address cost-benefit analysis, case studies in paradigm shift in implementing IoT-based systems and frameworks on secure, scalable and smart waste management. Transforming chemical waste management into a smart and eco-friendly system will require collaboration across sectors, between industries, policymakers, and technology providers

 

Downloads

Download data is not yet available.

References

Sosunova, I. and J. Porras, IoT-enabled smart waste management systems for smart cities: A systematic review. IEEE Access, 2022. 10: p. 73326-73363.

2. Farjana, M., et al., An iot-and cloud-based e-waste management system for resource reclamation with a data-driven decision-making process. IoT, 2023. 4(3): p. 202-220.

3. Lakhouit, A., Revolutionizing Urban Solid Waste Management with AI and IoT: A review of smart solutions for waste collection, sorting, and recycling. Results in Engineering, 2025: p. 104018.

4. Arputharaj, J.V., et al., Leveraging Intelligent Systems and the AIoT/IIoT for Enhanced Waste Management and Recycling Efficiency, in Intelligent Systems and Industrial Internet of Things for Sustainable Development. 2024, Chapman and Hall/CRC. p. 266-291.

5. Sah, R.R., IoT-EnabledAI Solutions for Efficient Smart City Waste Management. Annals of Process Engineering and Management, 2024. 1(1): p. 26-32.

6. Al Duhayyim, M., et al., Artificial ecosystem-based optimization with an improved deep learning model for IoT-assisted sustainable waste management. Sustainability, 2022. 14(18): p. 11704.

7. Salehi-Amiri, A., et al., Designing an effective two-stage, sustainable, and IoT based waste management system. Renewable and Sustainable Energy Reviews, 2022. 157: p. 112031.

8. Anagnostopoulos, T., et al., Challenges and opportunities of waste management in IoT-enabled smart cities: A survey. IEEE Transactions on Sustainable Computing, 2017. 2(3): p. 275-289.

9. Hussain, I., et al., Smart city solutions: Comparative analysis of waste management models in IoT-enabled environments using multiagent simulation. Sustainable Cities and Society, 2024. 103: p. 105247.

10. Sharma, R. Leveraging AI and IoT for sustainable waste management. in International Conference on Sustainable Development through Machine Learning, AI and IoT. 2023. Springer.

11. Kumar, A., et al., IoT-Enabled Systems for E-Waste Monitoring and Recycling, in Integrated Waste Management: A Sustainable Approach from Waste to Wealth. 2024, Springer. p. 375-394.

12. Al-Masri, E., et al. Recycle. io: An IoT-enabled framework for urban waste management. in 2018 IEEE international conference on big data (big data). 2018. IEEE.

13. Shafik, W., IoT-Enabled Model and Waste Management Technologies for Sustainable Agriculture, in IoT-Based Models for Sustainable Environmental Management: Sustainable Environmental Management. 2024, Springer. p. 137-163.

14. Srikantha, N., et al., Waste management in IoT-enabled smart cities: a survey. Int. J. Eng. Comput. Sci, 2017. 6(6): p. 2319-7242.

15. Poonkuzhali, R., et al. Recycling as a Service: IoT Enabled Smart Waste Management System with Machine Learning. in 2024 International Conference on Knowledge Engineering and Communication Systems (ICKECS). 2024. IEEE.

16. Vishnu, S., et al., IoT-enabled solid waste management in smart cities. Smart Cities, 2021. 4(3): p. 1004-1017.

17. Arun, M., Investigation of a deep learning-based waste recovery framework for sustainability and a clean environment using IoT. Sustainable Food Technology, 2025.

18. Choubey, A., et al., Smart e-waste management: a revolutionary incentive-driven IoT solution with LPWAN and edge-AI integration for environmental sustainability. Environmental Monitoring and Assessment, 2024. 196(8): p. 720.

19. Murugesan, S., S. Ramalingam, and P. Kanimozhi, Theoretical modelling and fabrication of smart waste management system for clean environment using WSN and IOT. Materials Today: Proceedings, 2021. 45: p. 1908-1913.

20. Li, Y., et al., Leveraging Municipal Solid Waste Management with Plasma Pyrolysis and IoT: Strategies for Energy Byproducts and Resource Recovery. Processes, 2025. 13(2): p. 321.

21. Mosallanezhad, B., et al., The IoT-enabled sustainable reverse supply chain for COVID-19 Pandemic Wastes (CPW). Engineering Applications of Artificial Intelligence, 2023. 120: p. 105903.

22. Saha, H.N., et al. Waste management using internet of things (iot). in 2017 8th annual industrial automation and electromechanical engineering conference (IEMECON). 2017. IEEE.

23. Rahmanifar, G., et al., Heuristic approaches to address vehicle routing problem in the Iot-based waste management system. Expert Systems with Applications, 2023. 220: p. 119708.

24. Rahman, M.A., et al., IoT-Enabled Intelligent Garbage Management System for Smart City: A Fairness Perspective. IEEE access, 2024.

25. Montasir, F. and A.I. Riad, LEVERAGING AI AND IOT FOR SUSTAINABLE WASTE MANAGEMENT: A FRAMEWORK FOR BANGLADESH. 2024.

26. Ketineni, S., et al., IoT-based waste management: hybrid optimal routing and waste classification model. Environmental Science and Pollution Research, 2024: p. 1-24.

27. Channi, H.K. and S. Kaur, Smart Waste Management Systems Integrating IoT for Real-Time Monitoring and Optimization, in AI Technologies for Enhancing Recycling Processes. 2025, IGI Global Scientific Publishing. p. 319-348.

28. Ahmed, K., et al., Artificial intelligence and IoT driven system architecture for municipality waste management in smart cities: A review. Measurement: Sensors, 2024: p. 101395.

29. Middha, R., N. Srivastava, and N. Saxena, Chemical Principles in Waste Segregation and Recycling, in Waste Management for Smart Cities. 2024, Springer. p. 1-46.

30. Thaseen Ikram, S., et al., An intelligent waste management application using IoT and a genetic algorithm–fuzzy inference system. Applied Sciences, 2023. 13(6): p. 3943

Downloads

Published

2026-01-24

How to Cite

1.
Gupta A, Shukla M. Chemical Waste Management And Recycling Optimization Using Iot-Enabled Systems. J Neonatal Surg [Internet]. 2026 Jan. 24 [cited 2026 May 24];15(1):34-4. Available from: https://jneonatalsurg.com/index.php/jns/article/view/9909