Prediction of froth flotation responses based on various conditioning parameters by Random Forest method

Shahbazi, Behzad; Chelgani, S. Chehreh; Matin, S.S.

doi:10.1016/j.colsurfa.2017.07.013

Cited by 48 publications

(21 citation statements)

References 21 publications

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“…As a tree-based statistical model, random forest (RF) was constructed and developed by Breiman et al (1993) [23]. A powerful AI machine learning (ML) model can provide low-bias and low-variation outcomes, with highly accurate predictions [24][25][26][27]. In this system, an estimated value generates according to the average of overall trees through the bagging system; different bootstrap data L(θ) with size n would be selected from the training set (L) with size N. Each tree "T L(θ) " would be related to the random vector θ, which is given for the bagged samples from the main training set L. The final predictor "f " is the average over the forest (with y' η the estimated response for sample x η where K is the size of the ensemble) [28][29][30], as follows:…”

Section: Random Forestmentioning

confidence: 99%

Ventilation Prediction for an Industrial Cement Raw Ball Mill by BNN—A “Conscious Lab” Approach

et al. 2021

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In cement mills, ventilation is a critical key for maintaining temperature and material transportation. However, relationships between operational variables and ventilation factors for an industrial cement ball mill were not addressed until today. This investigation is going to fill this gap based on a newly developed concept named “conscious laboratory (CL)”. For constructing the CL, a boosted neural network (BNN), as a recently developed comprehensive artificial intelligence model, was applied through over 35 different variables, with more than 2000 records monitored for an industrial cement ball mill. BNN could assess multivariable nonlinear relationships among this vast dataset, and indicated mill outlet pressure and the ampere of the separator fan had the highest rank for the ventilation prediction. BNN could accurately model ventilation factors based on the operational variables with a root mean square error (RMSE) of 0.6. BNN showed a lower error than other traditional machine learning models (RMSE: random forest 0.71, support vector regression: 0.76). Since improving the milling efficiency has an essential role in machine development and energy utilization, these results can open a new window to the optimal designing of comminution units for the material technologies.

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Section: Random Forestmentioning

confidence: 99%

Ventilation Prediction for an Industrial Cement Raw Ball Mill by BNN—A “Conscious Lab” Approach

et al. 2021

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“…In addition, in most implementations, so-called out-of-bag or generalization errors are automatically calculated and their performance is not particularly sensitive to the few hyperparameters that are required to tune the models. As a consequence, the popularity of these models in the process industries is growing rapidly, with applications, for example, in predictive modeling [11,23], fault diagnosis and root cause analysis [24,25], and change point detection [26], as well as diagnostic modeling [27,28]. Random forests consist of ensembles of decision trees, as briefly summarized below.…”

Section: Random Forestsmentioning

confidence: 99%

Process Variable Importance Analysis by Use of Random Forests in a Shapley Regression Framework

Aldrich

2020

Minerals

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Linear regression is often used as a diagnostic tool to understand the relative contributions of operational variables to some key performance indicator or response variable. However, owing to the nature of plant operations, predictor variables tend to be correlated, often highly so, and this can lead to significant complications in assessing the importance of these variables. Shapley regression is seen as the only axiomatic approach to deal with this problem but has almost exclusively been used with linear models to date. In this paper, the approach is extended to random forests, and the results are compared with some of the empirical variable importance measures widely used with these models, i.e., permutation and Gini variable importance measures. Four case studies are considered, of which two are based on simulated data and two on real world data from the mineral process industries. These case studies suggest that the random forest Shapley variable importance measure may be a more reliable indicator of the influence of predictor variables than the other measures that were considered. Moreover, the results obtained with the Gini variable importance measure was as reliable or better than that obtained with the permutation measure of the random forest.

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“…Afghanistan mainly acquires its required electricity from neighbors. For more than a century, one of the most significant impediments in Afghanistan has been the lack of adequate fuel for power generation (Hare et al 2008;SanFilipo 2005;Wnuk 2016) and around 70% of Afghans do not have a reliable access to electricity (Doebrich et al 2006;Hackley et al 2010;Hare et al 2008;Tewalt et al 2010;Wnuk 2016). However, some studies have been conducted to find the potential coal deposits, and it was reported that Afghanistan has potentially abundant coal resources (Jacob 1961;Siebdrat and Weippert 1963;Wood et al 1983).…”

Section: Introductionmentioning

confidence: 99%

Prediction of specific gravity of Afghan coal based on conventional coal properties by stepwise regression and random forest

Chelgani

2019

Energy Sources, Part A: Recovery, Utilization, and Environmenta

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Coal can be considered as the main fuel for electricity generation in Afghanistan. However, there is a quite limited data available about the overall quality, distribution, and character of coals in Afghanistan. Specific gravity (S.G) of coal as a key factor can be used for the estimation of potential tonnage production and be a fundamental parameter for the selection of coal washery process method. However, there is no investigation which comprehensively explores relationships between S.G and coal properties. In this investigation, the potential of S.G prediction based on conventional properties for Afghan coal samples was explored by stepwise regression and random forest. Pearson correlation (r) and variable importance measurement (VIM) of random forest (RF) were applied to select the most effective variables among conventional parameters for the S.G prediction. Results of VIM indicated that ash and carbon content of coal samples had the highest importance for the S.G prediction.Stepwise regression and RF models were developed based on these two coal variables. Testing the generated models indicated that S.G of Afghan coals can quite accurately predict by these models (R 2 > 0.90). Modeling outcomes showed that the highest S.G (S.G > 2) for Afghan coal occurred when ash was higher than 40% and carbon was lower than 30%.

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Prediction of froth flotation responses based on various conditioning parameters by Random Forest method

Cited by 48 publications

References 21 publications

Ventilation Prediction for an Industrial Cement Raw Ball Mill by BNN—A “Conscious Lab” Approach

Ventilation Prediction for an Industrial Cement Raw Ball Mill by BNN—A “Conscious Lab” Approach

Process Variable Importance Analysis by Use of Random Forests in a Shapley Regression Framework

Prediction of specific gravity of Afghan coal based on conventional coal properties by stepwise regression and random forest

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