{"title":"利用机器学习预测工业系统积灰:提高维护和运行效率","authors":"Seyed Hamed Godasiaei","doi":"10.1016/j.partic.2025.05.008","DOIUrl":null,"url":null,"abstract":"<div><div>Predicting ash accumulation in industrial environments is crucial for improving operational efficiency, enabling proactive maintenance, reducing downtime, and optimizing plant performance. Understanding of these processes requires the analysis of key parameters, including time, heat flux, particle size, velocity, excess air ratio, furnace temperature, heat load, and oxide concentrations, with a particular focus on deposition thickness. Traditional methods often fail to capture the complexity of these interactions, necessitating innovative approaches for accurate prediction and analysis. The experimental data, along with four algorithms, i.e. Support Vector Regression (SVR), Random Forest (RF), Deep Neural Network (DNN), and Extreme Gradient Boosting (XGBoost), were employed to analyze 20 features, providing a robust evaluation of their predictive capabilities. Furthermore, the use of SHAP (SHapley Additive Explanations) values introduces a novel dimension to the study, enabling interpretability and transparency in understanding the contribution of each feature to the model's predictions. The results demonstrate exceptional predictive accuracy for the RF and XGBoost models, achieving an R<sup>2</sup> value of 0.99 and minimal mean absolute errors (MAE). A novel comparison of training times reveals that SVR outperforms the other algorithms in speed due to its simpler structure, making it highly efficient for real-time applications. Correlation analysis identifies strong relationships between deposition thickness and key parameters such as time, heat flux, and deposition probability at varying surface temperatures. Time directly influences deposition thickness, as particles accumulate and sinter over prolonged operation. Heat flux drives particle movement through thermophoresis, affecting surface adhesion and increasing deposition probability. Surface temperature modulates particle adhesion and slag viscosity, with optimal temperatures maximizing stickiness and deposition probability.</div></div>","PeriodicalId":401,"journal":{"name":"Particuology","volume":"103 ","pages":"Pages 41-54"},"PeriodicalIF":4.3000,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Predicting ash accumulation in industrial systems using machine learning: Enhancing maintenance and operational efficiency\",\"authors\":\"Seyed Hamed Godasiaei\",\"doi\":\"10.1016/j.partic.2025.05.008\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Predicting ash accumulation in industrial environments is crucial for improving operational efficiency, enabling proactive maintenance, reducing downtime, and optimizing plant performance. Understanding of these processes requires the analysis of key parameters, including time, heat flux, particle size, velocity, excess air ratio, furnace temperature, heat load, and oxide concentrations, with a particular focus on deposition thickness. Traditional methods often fail to capture the complexity of these interactions, necessitating innovative approaches for accurate prediction and analysis. The experimental data, along with four algorithms, i.e. Support Vector Regression (SVR), Random Forest (RF), Deep Neural Network (DNN), and Extreme Gradient Boosting (XGBoost), were employed to analyze 20 features, providing a robust evaluation of their predictive capabilities. Furthermore, the use of SHAP (SHapley Additive Explanations) values introduces a novel dimension to the study, enabling interpretability and transparency in understanding the contribution of each feature to the model's predictions. The results demonstrate exceptional predictive accuracy for the RF and XGBoost models, achieving an R<sup>2</sup> value of 0.99 and minimal mean absolute errors (MAE). A novel comparison of training times reveals that SVR outperforms the other algorithms in speed due to its simpler structure, making it highly efficient for real-time applications. Correlation analysis identifies strong relationships between deposition thickness and key parameters such as time, heat flux, and deposition probability at varying surface temperatures. Time directly influences deposition thickness, as particles accumulate and sinter over prolonged operation. Heat flux drives particle movement through thermophoresis, affecting surface adhesion and increasing deposition probability. Surface temperature modulates particle adhesion and slag viscosity, with optimal temperatures maximizing stickiness and deposition probability.</div></div>\",\"PeriodicalId\":401,\"journal\":{\"name\":\"Particuology\",\"volume\":\"103 \",\"pages\":\"Pages 41-54\"},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2025-05-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Particuology\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1674200125001336\",\"RegionNum\":2,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, CHEMICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Particuology","FirstCategoryId":"88","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1674200125001336","RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, CHEMICAL","Score":null,"Total":0}
Predicting ash accumulation in industrial systems using machine learning: Enhancing maintenance and operational efficiency
Predicting ash accumulation in industrial environments is crucial for improving operational efficiency, enabling proactive maintenance, reducing downtime, and optimizing plant performance. Understanding of these processes requires the analysis of key parameters, including time, heat flux, particle size, velocity, excess air ratio, furnace temperature, heat load, and oxide concentrations, with a particular focus on deposition thickness. Traditional methods often fail to capture the complexity of these interactions, necessitating innovative approaches for accurate prediction and analysis. The experimental data, along with four algorithms, i.e. Support Vector Regression (SVR), Random Forest (RF), Deep Neural Network (DNN), and Extreme Gradient Boosting (XGBoost), were employed to analyze 20 features, providing a robust evaluation of their predictive capabilities. Furthermore, the use of SHAP (SHapley Additive Explanations) values introduces a novel dimension to the study, enabling interpretability and transparency in understanding the contribution of each feature to the model's predictions. The results demonstrate exceptional predictive accuracy for the RF and XGBoost models, achieving an R2 value of 0.99 and minimal mean absolute errors (MAE). A novel comparison of training times reveals that SVR outperforms the other algorithms in speed due to its simpler structure, making it highly efficient for real-time applications. Correlation analysis identifies strong relationships between deposition thickness and key parameters such as time, heat flux, and deposition probability at varying surface temperatures. Time directly influences deposition thickness, as particles accumulate and sinter over prolonged operation. Heat flux drives particle movement through thermophoresis, affecting surface adhesion and increasing deposition probability. Surface temperature modulates particle adhesion and slag viscosity, with optimal temperatures maximizing stickiness and deposition probability.
期刊介绍:
The word ‘particuology’ was coined to parallel the discipline for the science and technology of particles.
Particuology is an interdisciplinary journal that publishes frontier research articles and critical reviews on the discovery, formulation and engineering of particulate materials, processes and systems. It especially welcomes contributions utilising advanced theoretical, modelling and measurement methods to enable the discovery and creation of new particulate materials, and the manufacturing of functional particulate-based products, such as sensors.
Papers are handled by Thematic Editors who oversee contributions from specific subject fields. These fields are classified into: Particle Synthesis and Modification; Particle Characterization and Measurement; Granular Systems and Bulk Solids Technology; Fluidization and Particle-Fluid Systems; Aerosols; and Applications of Particle Technology.
Key topics concerning the creation and processing of particulates include:
-Modelling and simulation of particle formation, collective behaviour of particles and systems for particle production over a broad spectrum of length scales
-Mining of experimental data for particle synthesis and surface properties to facilitate the creation of new materials and processes
-Particle design and preparation including controlled response and sensing functionalities in formation, delivery systems and biological systems, etc.
-Experimental and computational methods for visualization and analysis of particulate system.
These topics are broadly relevant to the production of materials, pharmaceuticals and food, and to the conversion of energy resources to fuels and protection of the environment.