New paper published on Journal of Cleaner Production
Paper Title: Data-driven modelling of the flocculation process on mineral processing tailings treatment
Authors: Chongchong Qi, Andy Fourie, Qiusong Chen, Xiaolin Tang, Qinli Zhang & Rugao Gao
Abstract: The clarification of tailings slurry using
polymer flocculants has been widely used in the mining industry to promote the
cleaner production of mineral resources. In this
paper, a data-driven prediction model was proposed using gradient
boosting machine (GBM) for the non-linear relationship modelling and firefly
algorithm (FA) for GBM hyper-parameters tuning. Two studies were
performed, among which the main study omitted the influence of chemical
characteristics of mineral processing tailings (MPT) while the supplementary
study considered. For the main study, 27 types of MPT and 4 types of anionic flocculants were used to
prepare the dataset. The flocculation performance was represented by the
initial settling rate (ISR) and its influencing variables were selected to be the
particle size distribution (PSD) of MPT, the solids content of tailings slurry,
the flocculants type, and the flocculants dosage. For the
supplementary study, the chemical characteristics of 7 types of MPT were also
considered as influencing variables and its influence on the predictive
performance of GBM was investigated. The main study shows that the optimum GBM
model achieved a correlation coefficient of 0.841 between the predicted and
experimental ISR values on the testing set, denoting it was robust in
predicting the ISR of the flocculation. Compared with the
solids content, the flocculants dosage and the flocculants type, the PSD of MPT
was found to be the most significant influencing variable for the flocculation
with an importance score of 0.420 out of 1. The supplementary study
shows that the predictive performance of GBM could be improved considering
chemical compositions of MPT, which were also important influencing variables
for the flocculation process.
Keywords: Mineral processing tailings; recycling; flocculation; gradient boosting machine; firefly algorithm.
Reviewer 1: The Topic is really interesting and good for the journal. The paper needs major revision before publication. My comments are within the manuscript.
Reviewer 2: This paper proposes a new model to optimize the flocculation process on mineral processing tailings treatment. In fact, the issue studied is major when considering the clarification and settlement of tailings slurry in a thickener and its ultimate design factors for mining owners and operators. The paper is well written and presents some practical information for mine operators. The introduction and the following sections provide useful information for the readers. Nevertheless some information presented is not accurate and need to be further clarified. Experiments are well designed and executed with the pertinent findings. This reviewer has few comments or corrections that the authors need to improve the paper's quality. These are given below for due attention.
Representative figure from this paper:
Performance of the optimum GBM model on the testing set of the main dataset(a) comparison between experimental and predicted ISR values, (b) residual ISR values, (c) distribution of the absolute residual ISR values.
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