CO emission predictions in municipal solid waste incineration based on reduced depth features and long short-term memory optimization

Zhang, Runyu; Tang, Jian; Xia, Heng; Pan, Xiaotong; Yu, Wen; Qiao, Junfei

doi:10.1007/s00521-023-09329-8

Cited by 6 publications

(1 citation statement)

References 39 publications

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“…The standard of a half-hour average is generally adopted due to the noticeable spike phenomenon in CO emission concentration [106]. Zhang et al [107] proposed a CO emission prediction method based on reduced-depth features and a long short-term memory (LSTM) optimization strategy. This method comprises three parts: a particle design for the reduced-depth feature and LSTM optimization, a fitness function design for the reduced-depth feature and LSTM optimization, and optimization based on PSO.…”

Section: Environmental Indices Modelingmentioning

confidence: 99%

An Overview of Artificial Intelligence Application for Optimal Control of Municipal Solid Waste Incineration Process

Tang,

Wang,

Xia

et al. 2024

Sustainability

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Artificial intelligence (AI) has found widespread application across diverse domains, including residential life and product manufacturing. Municipal solid waste incineration (MSWI) represents a significant avenue for realizing waste-to-energy (WTE) objectives, emphasizing resource reuse and sustainability. Theoretically, AI holds the potential to facilitate optimal control of the MSWI process in terms of achieving minimal pollution emissions and maximal energy efficiency. However, a noticeable shortage exists in the current research of the review literature concerning AI in the field of WTE, particularly MSWI, hindering a focused understanding of future development directions. Consequently, this study conducts an exhaustive survey of AI applications for optimal control, categorizing them into four fundamental aspects: modeling, control, optimization, and maintenance. Timeline diagrams depicting the evolution of AI technologies in the MSWI process are presented to offer an intuitive visual representation. Each category undergoes meticulous classification and description, elucidating the shortcomings and challenges inherent in current research. Furthermore, the study articulates the future development trajectory of AI applications within the four fundamental categories, underscoring the contribution it makes to the field of MSWI and WTE.

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Section: Environmental Indices Modelingmentioning

confidence: 99%