Intelligent Control of the Main Steam Flow Rate for the Municipal Solid Waste Incineration Process

Jinxiang Pian (), Jianyong Liu, Jian Tang and Jing Hou
Additional contact information
Jinxiang Pian: School of Electrical and Control Engineering, Shenyang Jianzhu University, Shenyang 110168, China
Jianyong Liu: School of Electrical and Control Engineering, Shenyang Jianzhu University, Shenyang 110168, China
Jian Tang: School of Information Science and Technology, Beijing University of Technology, Beijing 100124, China
Jing Hou: School of Electrical and Control Engineering, Shenyang Jianzhu University, Shenyang 110168, China

Sustainability, 2025, vol. 17, issue 13, 1-30

Abstract: The stable control of the main steam flow rate (MSFR) can effectively improve the waste combustion efficiency and energy utilization, reduce environmental pollution, and is crucial for promoting the sustainable development of municipal solid waste incineration (MSWI). Developed countries benefit from stable municipal solid waste (MSW) composition, enabling advanced automated combustion control. However, in developing countries, fluctuating waste composition and calorific value cause frequent disturbances, limiting the use of foreign control methods. Therefore, MSFR control technologies suited to developing countries are crucial. This study proposes a two-layer intelligent control method, consisting of an optimization setting layer and a loop control layer. The optimization layer uses a steam flow prediction model (OPTICS and RBF) and an improved antlion optimizer (IALO) for manipulated variable setpoints. The control layer applies reinforcement learning (actor–critic) to fine-tune PI controller parameters. Experimental results show that the proposed method adaptively adjusts manipulated variables, ensuring MSFR control within the target range and maintaining efficient, stable MSWI operation.

Keywords: municipal solid waste incineration (MSWI); two-layer intelligent control method; main steam flow rate (MSFR); intelligent control; reinforcement learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/17/13/6036/pdf (application/pdf)
https://www.mdpi.com/2071-1050/17/13/6036/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:17:y:2025:i:13:p:6036-:d:1692446

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().