Energy Optimisation of Industrial Limestone Grinding Using ANN
Source abstract
Abstract
This paper presents methods for modelling and optimising the industrial limestone grinding process carried out using a real limestone plant. Two key process evaluation indicators were developed: specific electric energy consumption and an extended indicator that also includes gas usage. Using process data collected from the SCADA system and results from industrial factorial experiments, regression artificial neural network models were developed, with controllable process parameters used as inputs. In the next phase, black-box optimisation was performed using Bayesian and genetic algorithms to identify optimal mill operating settings. The results demonstrate significant improvements in energy efficiency, with energy savings reaching up to 48% in selected cases. The proposed methodology can be effectively applied to enhance energy performance in similar industrial grinding processes.
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Research context
OpenAlex topics: Advanced machining processes and optimization · Advanced Machining and Optimization Techniques · Mineral Processing and Grinding
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Environmental Engineering, Faculty of Civil Engineering and Resource Management, AGH University of Krakow, 30059 Krakow, Poland
Department of Process Control, Faculty of Mechanical Engineering and Robotics, AGH University of Krakow, 30059 Krakow, Poland
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