Modeling of free swelling index based on variable importance measurements of parent coal properties by random forest method

Chelgani, S. Chehreh; Matin, S.S.; Makaremi, S.

doi:10.1016/j.measurement.2016.07.070

Cited by 57 publications

(12 citation statements)

References 54 publications

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“…It reduced the issue of overfitting, and is especially suitable for processing high-dimensional data. In addition, RF model provides the relative importance of each variable, by measuring the increased mean square error (MSE) when that variable is changed [72]. The increased MSE represents the effect of a variable change on the prediction accuracy of the RF model: the larger the MSE value, the more important the corresponding input variable [38].…”

Section: Rf Modelmentioning

confidence: 99%

Soil Moisture Retrievals by Combining Passive Microwave and Optical Data

et al. 2020

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This paper aims to retrieve the temporal dynamics of soil moisture from 2015 to 2019 over an agricultural site in Southeast Australia using the Soil Moisture Active Passive (SMAP) brightness temperature. To meet this objective, two machine learning approaches, Random Forest (RF), Support Vector Machine (SVM), as well as a statistical Ordinary Least Squares (OLS) model were established, with the auxiliary data including the 16-day composite MODIS NDVI (MOD13Q1) and Surface Temperature (ST). The entire data were divided into two parts corresponding to ascending (6:00 p.m. local time) and descending (6:00 a.m. local time) orbits of SMAP overpasses. Thus, the three models were trained using the descending data acquired during the five years (2015 to 2019), and validated using the ascending product of the same period. Consequently, three different temporal variations of the soil moisture were obtained based on the three models. To evaluate their accuracies, the retrieved soil moisture was compared against the SMAP level-2 soil moisture product, as well as to in-situ ground station data. The comparative results show that the soil moisture obtained using the OLS, RF and SVM algorithms are highly correlated to the SMAP level-2 product, with high coefficients of determination (R2OLS = 0.981, R2SVM = 0.943, R2RF = 0.983) and low RMSE (RMSEOLS = 0.016 cm3/cm3, RMSESVM = 0.047 cm3/cm3, RMSERF = 0.016 cm3/cm3). Meanwhile, the estimated soil moistures agree with in-situ station data across different years (R2OLS = 0.376~0.85, R2SVM = 0.376~0.814, R2RF = 0.39~0.854; RMSEOLS = 0.049~0.105 cm3/cm3, RMSESVM = 0.073~0.1 cm3/cm3, RMSERF = 0.047~0.102 cm3/cm3), but an overestimation issue is observed for high vegetation conditions. The RF algorithm outperformed the SVM and OLS, in terms of the agreement with the ground measurements. This study suggests an alternative soil moisture retrieval scheme, in complementary to the SMAP baseline algorithm, for a fast soil moisture retrieval.

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Section: Rf Modelmentioning

confidence: 99%

Soil Moisture Retrievals by Combining Passive Microwave and Optical Data

et al. 2020

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“…These outcomes could be a useful key for the industry to predict AFT interactions of coal feed before use in a boiler. Furthermore, results indicated that RBIS can be used to model and predict other complicated problems in engineering and energy areas (Hardgrove grindability index, gross calorific value, free swelling index, etc). − …”

Section: Resultsmentioning

confidence: 99%

Rule-Based Intelligent System for Variable Importance Measurement and Prediction of Ash Fusion Indexes

2017

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Ash fusion temperatures [AFTs: initial deformation temperature (IDT), softening temperature (ST), and fluid temperature (FT)] are standard keys to estimate behavior of ash oxide for using coal and controlling the slag making at boilers. In this study, the modeling of AFTs based on ash oxide contents for 6537 U.S. coal samples have been investigated by a rule-based intelligent system (RBIS). Variable importance measurements (VIMs) of RBIS through the database indicated that Al2O3 contents in coal samples have the highest importance for prediction of AFTs. The RBIS model based on various rules was generated for predictions of IDT, ST, and FT. A comparison between RBIS and other typical predictive models [linear regression, genetic algorithm–neural network (GA–NN), and multilayer perceptron trained by back-propagation algorithm (MLP-BP)] was implemented to assess the capability of this purposed predictive model. Results indicated that RBIS can quite satisfactory predict AFTs, where R 2 for IDT, ST, and FT for the testing stage of models was over 0.82 and differences between actual and RBIS-predicted values for over 80% of data were less than 100 °C. These comprehensive results indicated that the RIBS method can be used for the industry sector to model AFT of coal samples and predict their fouling behavior before feeding them to boilers. Moreover, outcomes of this investigation are introducing RBIS as a powerful method for modeling other complicated problems in coal geology, fuel, and energy sectors.

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“…Additional testing proved a reverse flotation addition would produce an even better quality final product and enable eliminating the roasting step. Chelgani et al 2016aChelgani et al , 2016bShahbazi et al 2017;Chelgani and Matin 2018;Matin et al 2018;Nazari et al 2019;Tohry et al 2019;Jafari et al 2019aJafari et al , 2019b 2018; Matin et al 2018;Nazari et al 2019;Tohry et al 2019;Jafari et al 2019aJafari et al , 2019b.…”

Section: Random Forestmentioning

confidence: 99%

Variable importance assessments of an innovative industrial-scale magnetic separator for processing of iron ore tailings

Tohry

Jafari

Farahani

et al. 2020

Mineral Processing and Extractive Metallurgy

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Reprocessing of iron ore tailings (IOTs) and extracting recoverable valuable iron oxides will become increasingly financially attractive for mining companies and also may reduce environmental problems. Using databases built based on long term monitoring of units installed on plants to control the operational conditions to generate artificial intelligence models can decrease the cost of reprocessing operations Although some investigations have been focused on the reprocessing of IOTs, several challenges still remain which need to be addressed, especially for fine particles. SLon®, has developed a pulsating high gradient magnetic separator for the processing of fine iron oxides. However, there has been no systematic optimisation and variable assessments for SLon® operating variables to examine their effects on metallurgical responses (separation efficiency) on the industrial scale. This study addressed these drawbacks by linear (Pearson correlation) and non-linear (random forest) variable importance measurements (VIM) through an industrial SLon® installation.

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Modeling of free swelling index based on variable importance measurements of parent coal properties by random forest method

Cited by 57 publications

References 54 publications

Soil Moisture Retrievals by Combining Passive Microwave and Optical Data

Soil Moisture Retrievals by Combining Passive Microwave and Optical Data

Rule-Based Intelligent System for Variable Importance Measurement and Prediction of Ash Fusion Indexes

Variable importance assessments of an innovative industrial-scale magnetic separator for processing of iron ore tailings

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