sustainability-logo

Journal Browser

Journal Browser

Sustainable Green Technology for Sustainable Waste Management in Terms of Municipal Solid Waste from Recycle to Reuse Residual

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Waste and Recycling".

Deadline for manuscript submissions: 30 November 2026 | Viewed by 635

Special Issue Editors


E-Mail Website
Guest Editor
Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
Interests: municipal solid waste incineration; numerical simulation; industrial modeling; intelligent optimization; artificial intelligence; digital twin
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
School of Information and Control Engineering, Liaoning Petrochemical University, Fushun 113001, China
Interests: municipal solid waste incineration; wastewater treatment process; industrial modelling; intelligent optimization

E-Mail Website
Guest Editor
School of Information Engineering, Dalian Ocean University, Dalian 116023, China
Interests: municipal solid waste incineration; soft measurement modeling; intelligent control; time series prediction

Special Issue Information

Dear Colleagues,

Municipal solid waste (MSW) serves as a fundamental component in the implementation of the circular economy model. Within the framework of sustainable development, MSW incineration (MSWI) technology effectively achieves the dual objectives of reducing MSW volume and utilizing energy resources. This approach aligns seamlessly with the circular economy principles of "reduction, reuse, and resource recovery."

This Special Issue emphasizes technological innovation and system optimization throughout the entire MSW technology chain, examining its deep integration into sustainable development goals and the circular economy framework. Applications of artificial intelligence (AI) play a crucial role in optimizing energy production by adjusting combustion parameters to maximize energy recovery while minimizing environmental impacts.

Potential articles for submission include, but are not limited to, the following:

  • Intelligent scheduling for MSW collection and transportation;
  • MSW intelligent classification and online recognition;
  • AI-based process optimization and control for emissions reduction of the MSWI process;
  • Economic and environmental impacts of the residue secondary utilization;
  • Enhancing MSW management with large language models;
  • Collaborative optimization of the “collection–transportation–classification–incineration–purification–secondary utilization” closed-loop MSW system.

Prof. Dr. Jian Tang
Dr. Qiumei Cong
Prof. Dr. Wei Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • circular economy
  • Sustainable sustainable development
  • waste-to-energy
  • municipal solid waste (MSW)
  • solid waste collection and transportation
  • solid waste separation and classification
  • municipal solid waste incineration (MSWI)
  • residues and fly ash reuse
  • multimodal classification model
  • transportation route optimization
  • embodied intelligent control
  • prediction model
  • scheduling
  • decision-making
  • control
  • whole-process optimization
  • artificial intelligence (AI)
  • data-driven
  • emissions reduction

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

40 pages, 5766 KB  
Article
Enhancing Sustainable Waste-to-Energy: A Multi-Controlled Variable Prediction Model for Municipal Solid Waste Incineration Using Shared Features and an Improved Fuzzy Neural Network
by Qiumei Cong, Jiaying Lu and Jian Tang
Sustainability 2026, 18(5), 2616; https://doi.org/10.3390/su18052616 - 7 Mar 2026
Viewed by 423
Abstract
Municipal solid waste incineration (MSWI) is a critical technology for advancing urban sustainability, contributing to improved environmental quality, optimized energy structures, and the circular economy. However, the realization of these sustainability benefits is contingent upon the stable, efficient, and low-emission operation of the [...] Read more.
Municipal solid waste incineration (MSWI) is a critical technology for advancing urban sustainability, contributing to improved environmental quality, optimized energy structures, and the circular economy. However, the realization of these sustainability benefits is contingent upon the stable, efficient, and low-emission operation of the incineration process. This operational stability is directly governed by several key variables, such as furnace temperature, main steam flow rate, flue gas oxygen content, and burnout point temperature. The inherent complexity of controlling these interconnected variables necessitates the development of an accurate multi-variable prediction model to ensure both energy recovery efficiency and environmental compliance, which are core pillars of sustainable waste management. Existing studies have often addressed these key controlled variables in isolation, lacking a unified modeling framework. Furthermore, they have not adequately considered how dimensional differences among these variables impact the performance evaluation of predictive models, a critical oversight for ensuring holistic process sustainability. To address these gaps and support the intelligent operation of sustainable waste-to-energy systems, this study proposes a novel multi-controlled variable modeling method based on shared features and an improved fuzzy neural network. Our integrated approach begins by calculating the Pearson correlation coefficient between each manipulated variable and each controlled variable—selected based on expert knowledge—to assess the distinguishability of operating conditions within the current dataset. Subsequently, a correlation threshold, informed by expert knowledge, is applied to identify shared features that influence multiple controlled variables simultaneously. Finally, we enhance the fuzzy neural network by redefining its evaluation criterion to accommodate variable dimensional differences, leading to the development of a robust multi-controlled variable prediction model. This model is designed to provide a more comprehensive and accurate basis for process control, directly contributing to improved energy efficiency and reduced environmental impact. The effectiveness of our proposed model is validated using operational data from an actual MSWI plant, demonstrating its potential to support more sustainable and economically viable waste-to-energy operations. Full article
Show Figures

Figure 1

Back to TopTop