Prediction of Particle Size Distribution of Mill Products Using Artificial Neural Networks
نویسندگان
چکیده
High energy consumption in size reduction operations is one of the most significant issues concerning sustainability raw material beneficiation. Thus, process optimization should be done to reduce consumption. This study aimed investigate applicability artificial neural networks (ANNs) predict particle distributions (PSDs) mill products. PSD key sources information after milling since it significantly affects subsequent beneficiation processes. precise prediction can contribute and by avoiding over-grinding. In this study, coal particles (−2 mm) were ground with a rod under different conditions, their PSDs measured. The variables studied included volume% (vol.%) feed (coal particle), vol.% load, grinding time. Our supervised ANN models developed trained experimental data sets. verified other results showed that predicted fitted very well training. Root mean squared error (RMSE) was calculated for each condition, between 0.165 0.965. Also, products conditions (i.e., feed, time). confirmed ANNs and, thus potential contribution reducing optimizing conditions.
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ژورنال
عنوان ژورنال: ChemEngineering
سال: 2022
ISSN: ['2305-7084']
DOI: https://doi.org/10.3390/chemengineering6060092