首页|Recent Findings from China University of Petroleum Provides New Insights into Ma chine Learning (On the Evaluation of Coal Strength Alteration Induced By Co2 Inj ection Using Advanced Black- Box and White- Box Machine Learning ...)
Recent Findings from China University of Petroleum Provides New Insights into Ma chine Learning (On the Evaluation of Coal Strength Alteration Induced By Co2 Inj ection Using Advanced Black- Box and White- Box Machine Learning ...)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Current study results on Machine Learning have be en published. According to news reporting out of Beijing, People's Republic of C hina, by NewsRx editors, research stated, "The injection of carbon dioxide (CO2) into coal seams is a prominent technique that can provide carbon sequestration in addition to enhancing coalbed methane extraction. However, CO2 injection into the coal seams can alter the coal strength properties and their long-term integ rity." Our news journalists obtained a quote from the research from the China Universit y of Petroleum, "In this work, the strength alteration of coals induced by CO2 e xposure was modeled using 147 laboratorymeasured unconfined compressive strengt h (UCS) data points and considering CO2 saturation pressure, CO2 interaction tem perature, CO2 interaction time, and coal rank as input variables. Advanced white -box and black-box machine learning algorithms including Gaussian process regres sion (GPR) with rational quadratic kernel, extreme gradient boosting (XGBoost), categorical boosting (CatBoost), adaptive boosting decision tree (AdaBoost-DT), multivariate adaptive regression splines (MARS), K-nearest neighbor (KNN), gene expression programming (GEP), and group method of data handling (GMDH) were used in the modeling process. The results demonstrated that GPR-Rational Quadratic p rovided the most accurate estimates of UCS of coals having 3.53%, 3 .62%, and 3.55% for the average absolute percent rela tive error (AAPRE) values of the train, test, and total data sets, respectively. Also, the overall determination coefficient (R-2) value of 0.9979 was additiona l proof of the excellent accuracy of this model compared with other models. More over, the first mathematical correlations to estimate the change in coal strengt h induced by CO2 exposure were established in this work by the GMDH and GEP algo rithms with acceptable accuracy. Sensitivity analysis revealed that the Spearman correlation coefficient shows the relative importance of the input parameters o n the coal strength better than the Pearson correlation coefficient. Among the i nputs, coal rank had the greatest influence on the coal strength (strong nonline ar relationship) based on the Spearman correlation coefficient. After that, CO2 interaction time and CO2 saturation pressure have shown relatively strong nonlin ear relationships with model output, respectively. The CO2 interaction temperatu re had the smallest impact on coal strength alteration induced by CO2 exposure b ased on both Pearson and Spearman correlation coefficients."
BeijingPeople's Republic of ChinaAsi aAlgorithmsCyborgsEmerging TechnologiesMachine LearningChina Universit y of Petroleum
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