Investigating the impact of key variables on differential pressure in cement vertical roller Mills, using the GBM algorithm

IF 4.5 2区工程技术 Q2 ENGINEERING, CHEMICAL

Powder Technology Pub Date : 2025-05-26 DOI:10.1016/j.powtec.2025.121168

Rasoul Fatahi, Hadi Abdollahi, Mohammad Noaparast, Mehdi Hadizadeh

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引用次数: 0

Abstract

Vertical Roller Mills (VRMs) are critical grinding equipment in cement production, with differential pressure (DP) being a key operational parameter affecting stability and efficiency. This research investigates DP prediction in cement VRMs using machine learning algorithms. Addressing the gap in understanding operational variable relationships with DP, Random Forest, Gradient Boosting Machine (GBM), and LightGBM algorithms were compared using 1026 h of operational data from a cement plant VRM. Shapley Additive Explanations (SHAP) identified mill fan speed, working pressure, and feed rate as key influencers of DP, with working pressure being the most dominant factor. K-fold cross-validation validated model performance, with GBM achieving superior results (R² = 0.9684, RMSE = 0.1637). Marginal plots revealed nonlinear relationships between operational variables and DP. The stability of VRM operation significantly depends on mill fan speed, working pressure, and feed rate, with working pressure having the most substantial impact on system performance, according to SHAP analysis. Stabilized DP ensures a stable material bed under the rollers and efficient VRM operation. This research aligns with the Conscious Lab concept, utilizing explainable AI algorithms based on plant control room data to optimize operational parameters and improve energy efficiency in cement production.

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利用GBM算法研究了水泥立式辊磨机关键变量对压差的影响

立辊磨是水泥生产中的关键磨矿设备，压差是影响稳定性和效率的关键操作参数。本研究使用机器学习算法研究水泥vrm的DP预测。利用来自水泥厂VRM的1026小时运行数据，比较了DP、随机森林、梯度增强机（GBM）和LightGBM算法在理解操作变量关系方面的差距。Shapley添加剂解释（SHAP）确定轧机风扇转速、工作压力和进给量是影响DP的关键因素，其中工作压力是最主要的因素。K-fold交叉验证验证了模型的性能，GBM取得了较好的结果（R2 = 0.9684, RMSE = 0.1637）。边际图揭示了操作变量与DP之间的非线性关系。根据SHAP分析，VRM运行的稳定性很大程度上取决于磨机风扇转速、工作压力和进料速率，其中工作压力对系统性能的影响最大。稳定的DP确保了辊下稳定的物料床和高效的VRM操作。这项研究与Conscious Lab的概念一致，利用基于工厂控制室数据的可解释的人工智能算法来优化操作参数，提高水泥生产的能源效率。

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来源期刊

Powder Technology 工程技术-工程：化工

CiteScore

9.90

自引率

15.40%

发文量

1047

审稿时长

46 days

期刊介绍： Powder Technology is an International Journal on the Science and Technology of Wet and Dry Particulate Systems. Powder Technology publishes papers on all aspects of the formation of particles and their characterisation and on the study of systems containing particulate solids. No limitation is imposed on the size of the particles, which may range from nanometre scale, as in pigments or aerosols, to that of mined or quarried materials. The following list of topics is not intended to be comprehensive, but rather to indicate typical subjects which fall within the scope of the journal's interests: Formation and synthesis of particles by precipitation and other methods. Modification of particles by agglomeration, coating, comminution and attrition. Characterisation of the size, shape, surface area, pore structure and strength of particles and agglomerates (including the origins and effects of inter particle forces). Packing, failure, flow and permeability of assemblies of particles. Particle-particle interactions and suspension rheology. Handling and processing operations such as slurry flow, fluidization, pneumatic conveying. Interactions between particles and their environment, including delivery of particulate products to the body. Applications of particle technology in production of pharmaceuticals, chemicals, foods, pigments, structural, and functional materials and in environmental and energy related matters. For materials-oriented contributions we are looking for articles revealing the effect of particle/powder characteristics (size, morphology and composition, in that order) on material performance or functionality and, ideally, comparison to any industrial standard.