Estimation of gross calorific value based on coal analysis using an explainable artificial intelligence

Prediction of gross calorific value from coal analysis using decision tree-based bagging and boosting techniques

The calorific value of any fuel is one of the crucial parameters to grade fuel's burning capability. The bomb calorimeter has historically been used to calculate coal's gross calorific value (GCV). However, for many years, engineers and scientists were trying to measure coal's GCV without a bomb calorimeter, using only laboratory-derived ultimate and/or proximate analyses to eliminate tedious and time-consuming laboratory analyses. In this study, Extra trees, Bagging, Decision tree, and Adaptive boosting are developed for the first time in coal's GCV modeling. In addition, the prediction and computational efficiency of previously applied decision tree-based algorithms, such as Random forest, Gradient boosting, and XGBoost are investigated. Well-established empirical models, namely Schuster, Mazumdar, Channiwala and Parikh, Parikh et al. and Central Fuel Research Institute of India are examined to compare their efficiency with newly developed algorithms. Proximate and ultimate analysis parameters are ranked based on their significance in GCV modeling. The studied models are tuned using an exhaustive grid search technique. Statistical indexes, such as explained variance (EV), mean absolute error (MAE), coefficient of determinant (R2), mean squared error (MSE), maximum error, minimum error, and mean absolute percentage error (MAPE) are used to critique these models. To accomplish the goals, 7430 data points containing ten coal features, such as ash, moisture, fixed carbon, volatile matter, hydrogen, carbon, sulfur, nitrogen, oxygen, and GCV are selected from the U.S. Geological Survey Coal Quality (COALQUAL) database. It has been found that, due to simplicity and location-specific constraints, empirical models could not correlate proximate and/or ultimate analyses with GCV. Bagging and boosting techniques tested here performed well with the coefficient of determinant (R 2 ) of over 0.97. The XGBoost model outperforms other tree-based algorithms with the most significant coefficient of determinant (R 2 of 0.9974) and lowest error values (MSE of 14703.3, max_error of 1027.2, MAE of 89.2, MAPE of 0.009). The studied models' ranking (highest to lowest) based on their performance are XGBoost, Extra trees, Random forest, Bagging, Gradient boosting, Decision tree, and Adaptive boosting. The correlation heatmap and scatterplots used here clearly indicate that oxygen and carbon are the utmost significant, whereas volatile matter and sulfur are the least essential rank parameters for GCV modeling. The strategy suggested in this research can aid engineers/operators in obtaining a rapid and accurate determination of the GCV with a few coal features, thus lessening complicated, tedious, expensive, and time-consuming laboratory efforts.

Modeling of energy consumption factors for an industrial cement vertical roller mill by SHAP-XGBoost: a "conscious lab" approach

Cement production is one of the most energy-intensive manufacturing industries, and the milling circuit of cement plants consumes around 4% of a year's global electrical energy production. It is well understood that modeling and digitalizing industrial-scale processes would help control production circuits better, improve efficiency, enhance personal training systems, and decrease plants' energy consumption. This tactical approach could be integrated using conscious lab (CL) as an innovative concept in the internet age. Surprisingly, no CL has been reported for the milling circuit of a cement plant. A robust CL interconnect datasets originated from monitoring operational variables in the plants and translating them to human basis information using explainable artificial intelligence (EAI) models. By initiating a CL for an industrial cement vertical roller mill (VRM), this study conducted a novel strategy to explore relationships between VRM monitored operational variables and their representative energy consumption factors (output temperature and motor power). Using SHapley Additive exPlanations (SHAP) as one of the most recent EAI models accurately helped fill the lack of information about correlations within VRM variables. SHAP analyses highlighted that working pressure and input gas rate with positive relationships are the key factors influencing energy consumption. eXtreme Gradient Boosting (XGBoost) as a powerful predictive tool could accurately model energy representative factors by R-square ever 0.80 in the testing phase. Comparison assessments indicated that SHAP-XGBoost could provide higher accuracy for VRM-CL structure than conventional modeling tools (Pearson correlation, Random Forest, and Support vector regression.

A Novel Framework Based on Deep Learning and ANOVA Feature Selection Method for Diagnosis of COVID-19 Cases from Chest X-Ray Images

Background and Objective. The new coronavirus disease (known as COVID-19) was first identified in Wuhan and quickly spread worldwide, wreaking havoc on the economy and people's everyday lives. As the number of COVID-19 cases is rapidly increasing, a reliable detection technique is needed to identify affected individuals and care for them in the early stages of COVID-19 and reduce the virus's transmission. The most accessible method for COVID-19 identification is Reverse Transcriptase-Polymerase Chain Reaction (RT-PCR); however, it is time-consuming and has false-negative results. These limitations encouraged us to propose a novel framework based on deep learning that can aid radiologists in diagnosing COVID-19 cases from chest X-ray images. Methods. In this paper, a pretrained network, DenseNet169, was employed to extract features from X-ray images. Features were chosen by a feature selection method, i.e., analysis of variance (ANOVA), to reduce computations and time complexity while overcoming the curse of dimensionality to improve accuracy. Finally, selected features were classified by the eXtreme Gradient Boosting (XGBoost). The ChestX-ray8 dataset was employed to train and evaluate the proposed method. Results and Conclusion. The proposed method reached 98.72% accuracy for two-class classification (COVID-19, No-findings) and 92% accuracy for multiclass classification (COVID-19, No-findings, and Pneumonia). The proposed method's precision, recall, and specificity rates on two-class classification were 99.21%, 93.33%, and 100%, respectively. Also, the proposed method achieved 94.07% precision, 88.46% recall, and 100% specificity for multiclass classification. The experimental results show that the proposed framework outperforms other methods and can be helpful for radiologists in the diagnosis of COVID-19 cases.