{"title":"Dynamic multi-objective optimization method of cement grinding process based on knowledge-guided logistic regression","authors":"Xunian Yang , Mengyu Su , Xin Shi , Xiaochen Hao","doi":"10.1016/j.powtec.2025.121142","DOIUrl":null,"url":null,"abstract":"<div><div>In the cement grinding process, the operating indicators are interdependent, and the working conditions fluctuate dynamically. Since cement quality and unit electricity consumption cannot be monitored in real time and there is a lack of coordination between the operating indicators, the quality of cement remains unstable, and energy efficiency is compromised. This paper proposes a dynamic multi-objective optimization method based on a transfer learning and evolutionary optimization framework to investigate the dynamic optimization of the cement grinding process in a variable environment. Firstly, the specific surface area (Powder fineness) and unit unit electricity consumption of cement were selected as optimization targets to construct a multi-objective optimization model. Subsequently, a multi-objective optimization algorithm based on knowledge-guided logistic regression is introduced to address the model and optimize performance under dynamic conditions. The algorithm is applied to the cement grinding process, achieving the goal of reducing unit electricity consumption while maintaining the specific surface area.</div></div>","PeriodicalId":407,"journal":{"name":"Powder Technology","volume":"463 ","pages":"Article 121142"},"PeriodicalIF":4.5000,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Powder Technology","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0032591025005376","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, CHEMICAL","Score":null,"Total":0}
引用次数: 0
Abstract
In the cement grinding process, the operating indicators are interdependent, and the working conditions fluctuate dynamically. Since cement quality and unit electricity consumption cannot be monitored in real time and there is a lack of coordination between the operating indicators, the quality of cement remains unstable, and energy efficiency is compromised. This paper proposes a dynamic multi-objective optimization method based on a transfer learning and evolutionary optimization framework to investigate the dynamic optimization of the cement grinding process in a variable environment. Firstly, the specific surface area (Powder fineness) and unit unit electricity consumption of cement were selected as optimization targets to construct a multi-objective optimization model. Subsequently, a multi-objective optimization algorithm based on knowledge-guided logistic regression is introduced to address the model and optimize performance under dynamic conditions. The algorithm is applied to the cement grinding process, achieving the goal of reducing unit electricity consumption while maintaining the specific surface area.
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.