AI Application in Sustainable MSWI Process

Dear Colleagues,

Municipal solid waste incineration (MSWI) is a critical area of research within the broader field of sustainable development. As an efficient method of managing waste, MSWI has the potential to address environmental concerns while producing energy. With the integration of artificial intelligence (AI) applications, the MSWI process can be optimized for both environmental and economic sustainability. This Special Issue aims to explore the utilization of AI in MSWI to enhance its efficiency, reduce environmental impacts, and contribute to sustainable development. The selected papers fall within the journal’s scope and contribute to the field by providing innovative solutions that align with the principles of sustainability, technological advancement, and environmental conservation.

AI plays a significant role in controlling emissions and pollution within MSWI facilities. By leveraging real-time data analysis and control algorithms, AI applications can ensure that emissions—such as particulate matter, heavy metals, and dioxins—are maintained within regulatory limits. This proactive approach to controlling emissions helps MSWI plants to comply with environmental regulations and minimize their impact on surrounding communities. Furthermore, AI applications can facilitate the optimization of energy production in MSWI plants. Through advanced modeling and control algorithms, AI technologies can adjust combustion parameters to maximize energy recovery while minimizing environmental impacts. This approach not only improves the overall efficiency of the waste-to-energy process but also contributes to the more sustainable generation of energy from municipal solid waste. Further, based on AI algorithms such as deep learning, fuzzy learning, and broad learning, it can be used to improve the performance of process controllers to ensure our ability to track controlled variables and operational stability. In addition, AI-based predictive maintenance systems also contribute to the sustainability of MSWI processes by enabling proactive maintenance and the reduction of downtime. These systems utilize machine learning algorithms to analyze data concerning the performance of equipment and predict potential failures before they occur. The application of AI in the maintenance of the MSWI process is categorized into three parts in terms of recognition of flame status, qualitative detection of operational faults, and quantitative detection of operational faults. By identifying and addressing issues early on, AI-powered predictive maintenance significantly reduces maintenance costs and extends the lifespan of critical equipment as well as improving environmental sustainability. Moreover, AI technologies enable data-driven decision making throughout the entire MSWI process, allowing plant operators to leverage historical and real-time data to optimize processes and drive continuous improvement. By harnessing the power of AI for advanced analytics and decision making support, MSWI facilities can enhance their overall efficiency and sustainability while reducing operational costs. The integration of AI applications in the MSWI process also extends to waste sorting and recycling operations, thereby not only supporting sustainable waste management practices but also aligning with the principles of the circular economy by promoting the recovery and reuse of resources. Therefore, the integration of AI applications in the MSWI process serves as a catalyst for sustainable waste management and energy generation. By providing advanced modeling, control, maintenance, and optimization algorithms with their verification platform, AI technologies empower MSWI facilities to operate more efficiently, minimize environmental impact, and contribute to the transition towards a circular economy. As AI continues to advance, its potential to revolutionize the sustainability of MSWI processes remains a promising area for further innovation and development.

This Special Issue welcomes submissions on a wide range of topics related to the application of AI in MSWI. We encourage contributions that explore the use of AI for the optimization of processes, the reduction of emissions, waste-to-energy conversion, predictive maintenance, and intelligent control systems. Additionally, papers that highlight the integration of AI into numerical simulation, industrial modeling, and intelligent optimization techniques in the context of MSWI are highly encouraged. Furthermore, we invite research that explores the challenges and opportunities present in implementing AI in MSWI, as well as its potential impacts on sustainable development.

Potential themes for submissions include but are not limited to

• AI-based process optimization for MSWI plants;
• Intelligent control systems for the reduction of emissions from MSWI;
• Predictive maintenance using AI in MSWI facilities
• Integration of AI into numerical simulation for MSWI;
• Industrial modeling and AI applications in waste-to-energy conversion;
• Challenges and opportunities in implementing AI for sustainable MSWI;
• Economic and environmental impacts of AI utilization in MSWI;
• AI algorithm verification for future applications;
• Digital twins and the industrial metaverse in the context of MSWI.

Prof. Dr. Jian Tang
Dr. Heng Xia
Dr. Zhaosheng Yu
Guest Editors

Manuscript Submission Information

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• municipal solid waste incineration
• AI applications, sustainability
• waste management
• environmental conservation
• process optimization
• emission reduction
• waste-to-energy, sustainable development
• intelligent control systems