Prediction of Particle Size Distribution of Mill Products Using Artificial Neural Networks

Abstract

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

Prediction of energy consumption in grinding using artificial neural networks to improve the distribution of fragmentation size [Predicción del consumo de energía en la molienda utilizando redes neuronales artificiales para mejorar la distribución del tamaño de la fragmentación]

“…Table 6 shows the effectiveness of the implemented ANN, which is reflected in the results obtained, which guarantee high performance in the optimization of the The adjustment and prediction capacity of the model to estimate the Wi accurately is mainly reflected in the critical evaluation metrics; RMSE and R2, which have reached a value of 1.396 and 0.984 respectively. Otsuki and Jang (2022), in their research, trained six ANN models separately and obtained strong positive correlation coefficients (R2 > 0.90) in all models, which confirmed the statistical reliability of the models, they also obtained an RMSE for each grinding condition, and was between 0.165 and 0.965, which was lower than that found in the prediction of the crystallite size, with an RMSE of 3.34). The results indicated the applicability of ANN to predict particle size distributions (PSD), concluding that the proposed approach can be used not only for coal particles but also for other minerals/materials to predict their PSD in order to reduce consumption.…”

“…The division of data for training and validation varies depending on the amount of data in each study. Otsuki and Jang (2022), in their study collected a total of 56 data sets and randomly divided into training data sets (40 data sets: 70%), validation (8 data sets: 15%) and training (8 data sets: 15%), for their grinding study, 56 experimental data sets were considered more than enough. Saldaña, et al (2023), in their research divided the historical data into two groups, that is, the training set (70%) and the validation set (30%), while the fitted model was used to estimate the production after the application of the M2M strategy and simulate production, at different values of the mill rotation speed and lining age factors.…”

“…Furthermore, these networks can identify patterns and non-linear relationships between input variables and energy consumption, which can help discover new control and optimization strategies (López et al, 2023). Otsuki and Jang (2022) in their research use predictive neural networks to predict particle size distributions (PSD) in size reduction operations. The importance of accurately predicting PSDs is highlighted to avoid over-grinding and reduce energy consumption.…”