Intelligent air flow control in municipal solid waste incineration power plants: a novel approach integrating machine learning and physical-chemical models

Ce Cao, Qiang Zhang, Menghan Li, Shaoyang Wang, Chenghao Liu and Qingyang Chen

Energy, 2025, vol. 335, issue C

Abstract: Accurate airflow control in Municipal Solid Waste Incineration (MSWI) is essential for stable combustion, low emissions, and high operational efficiency. Despite advancements in control strategies, existing methods often fail to adapt to dynamic operational conditions and environmental fluctuations, leading to suboptimal performance. In this paper, an integrated control approach, combining physical-chemical modeling with machine learning for real-time prediction and regulation of primary and secondary air flows, was proposed. The integrated model leverages historical data and dynamically responds to operational variables, enhancing adaptive air flow regulation. Firstly, a dynamic physical-chemical model was developed to simulate mass transfer and combustion reactions. Subsequently, a Convolutional Neural Network - Long Short-Term Memory model (CNN-LSTM) optimized by the Gray Wolf Optimizer (CNN-LSTM-GWO) significantly improved airflow prediction accuracy (R2 = 0.8724, RMSE = 3.79 %). Specifically, the RMSE for primary airflow prediction was reduced by 2.21 % compared to GA and 2.16 % compared to PSO, while for secondary airflow, the reductions were 2.04 % and 1.86 %, respectively. The integrated control model adjusts grate speed, air ratio, and excess air coefficient to achieve precise airflow distribution. Results demonstrated that the proposed method accurately predicted key MSWI outputs (furnace temperature, steam flow, oxygen concentration) with less than 5 % error and effectively reduced NOx and CO emissions, highlighting its potential for widespread application in industrial settings.

Keywords: Municipal solid waste incineration; Integrated control model; Air flow control; CNN-LSTM-GWO; Emission reduction (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S036054422503960X
Full text for ScienceDirect subscribers only

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:eee:energy:v:335:y:2025:i:c:s036054422503960x

DOI: 10.1016/j.energy.2025.138318

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().