App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 10

Large language model-driven multimodal prediction of clean coal ash content

Lu Faming1a
Li Zichen1a
Lin Zedong1a
Zeng Qingtian1a
Bao Yunxia1b
Huang Wanpeng1c
1. a. College of Computer Science and Engineering, b. College of Mathematics and Systems Science, c. College of Energy and Mining Engineering, Shandong University of Science and Technology, Qingdao Shandong 266590, China
DOI: 10.19734/j.issn.1001-3695.2026.02.0048

Abstract

During coal preparation, numerous process parameters exhibit irregular sampling and severe missing values. Traditional prediction models not only struggle to capture the complex correlations among these parameters, but also fail to fully utilize domain knowledge. To address these issues, this study proposes a large language model-driven multimodal method for clean coal ash content prediction. First, the method combines the large language model with domain knowledge to convert coal preparation data into structured text and extract text embeddings. Second, it uses a message passing mechanism to fill missing values and then generates multi-channel line charts from the completed time-series data to extract image embeddings. Finally, it applies a multi-head attention mechanism to align and fuse the text embeddings and image embeddings, and introduces a physics-constrained loss to enhance the reliability of the prediction results. Experimental results on a real heavy-medium coal preparation dataset show that, compared with the suboptimal method, this approach reduced the root mean square error (RMSE) of clean coal ash content prediction by 3.1%, providing a new methodological reference for research on clean coal ash content prediction.

Foundation Support

新一代人工智能国家科技重大专项(2022ZD0119501)
国家自然科学基金项目(52374221)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2026.02.0048
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 10

Publish History

[2026-07-29] Accepted Paper

Cite This Article

鲁法明, 李子辰, 林泽东, 等. 一种大语言模型驱动的多模态精煤灰分预测方法 [J]. 计算机应用研究, 2026, 43 (10). (2026-07-30). https://doi.org/10.19734/j.issn.1001-3695.2026.02.0048. (Lu Faming, Li Zichen, Lin Zedong, et al. Large language model-driven multimodal prediction of clean coal ash content [J]. Application Research of Computers, 2026, 43 (10). (2026-07-30). https://doi.org/10.19734/j.issn.1001-3695.2026.02.0048. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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