Study on the prediction of cleaning coal ash content based on principal component analysis and machine learning

Coal slurry ash content is an important variable in the control of a flotation process. It can be concluded that coal slurry image gray feature has a certain correlation with ash content by analyzing coal slurry images at different ash values in this article. Based on image gray features, coal slurry ash content soft sensor models are developed by using BP Neural Network and simple eigenvalue-based Least Square Regression Method (LS), respectively. The simulation results of the two models indicate that the soft sensor model of coal slurry ash content based on the BP Neural Network is more optimal than that based on LS. The model based on BP Neural Network has a high accuracy when coal slurry ash content is higher than 35%. Through this investigation, coal slurry ash can be detected rapidly online.

Determination of solids weight fraction and ash content of coal in slurries of variable voidage: Laboratory measurements

Uranium can be released into the environment from various activities such as the use of phosphate fertilizers, combustion of coal in thermal power plants, mining, and depleted uranium used in the wars. One of the talked about sources for such water contamination in Bathinda city is the fly ash from the coal-fired thermal power plants. To assess the role of fly ash and Chemical toxicity risk associated with uranium from the fly ash to the water, the coal, dry fly ash, ash slurry and water samples collected from the surrounding of Guru Nanak Dev Thermal Power Plant (GNDTPP) in the Bathinda city of Punjab state, India. The samples were analyzed using the X-ray fluorescence set up. In the present work, we found that uranium concentrations in the dry fly ash and coal samples were higher than permissible limit i.e. < 2 ppm but the concentrations in the ash slurry and water samples was below the safe limit of 30 μg l−1 as recommended by World Health Organization (WHO, 2011). The 38Sr concentrations are also found to be 0.103-1.210 ppm and with average value 0.576 ppm in the various types of water in surrounding of the thermal power plant. The concentrations of 42Mo are found to be 0.002-0.050 ppm and the average value 0.022 ppm below the safe limit of 0.07 mg/l as recommended by World Health Organization (WHO, 2011) and Bureau of Indian Standards (BIS, 2012). Chemical toxicity risk calculated in the form of lifetime average daily dose (LAAD) and hazard quotient. The lifetime average daily dose (LAAD) values of ash slurry and water samples were found to be lower than WHO (2011) recommended level of 1 μg kg−1 d−1, and the values of hazard quotient of the study samples were found to be lower than unity expect dry fly ash and coal samples were higher than permissible limit. The present study is concluded that uranium contamination in water of Bathinda city is not due to the Thermal Power Plant, and there is indicating no chemical toxicity risk due to uranium from the fly ash to the water.

Convolutional neural networks (CNNs) are currently one of the most popular image classification technologies. Their excellent image classification ability enables the prediction of the ash content in clean coal using coal flotation froth images. Herein, a scheme is proposed to predict the ash content in coal flotation concentrate using coal flotation froth images and CNN architecture. According to the ash content of the concentrate, the coal flotation froth image data set is divided into seven interval categories, and data augmentation is used to expand the data set. Then, several recently trained CNNs (ResNet, EfficientNet, EfficientNetV2, ConvNeXt) are used to classify the coal flotation froth images with different concentrate ash content interval (integer ±1%). The classification performance of each network model, relationship between model performance and hyperparameters, and the abstract pixel features of the best model are visualized. The EfficientNetV2-L network achieves the highest classification accuracy (99%) after fine-tuning. The results indicate that the CNNs are effective in predicting the ash content of clean coal by classifying coal flotation froth images. The optimized CNN model is applied to froth images in the industry, resulting in high classification performance, which demonstrates that the CNN has high potential for industrial applications.

The aging and multimorbid population and health personnel shortages pose a substantial burden on primary health care. While predictive machine learning (ML) algorithms have the potential to address these challenges, concerns include transparency and insufficient reporting of model validation and effectiveness of the implementation in the clinical workflow. To systematically identify predictive ML algorithms implemented in primary care from peer-reviewed literature and US Food and Drug Administration (FDA) and Conformité Européene (CE) registration databases and to ascertain the public availability of evidence, including peer-reviewed literature, gray literature, and technical reports across the artificial intelligence (AI) life cycle. PubMed, Embase, Web of Science, Cochrane Library, Emcare, Academic Search Premier, IEEE Xplore, ACM Digital Library, MathSciNet, AAAI.org (Association for the Advancement of Artificial Intelligence), arXiv, Epistemonikos, PsycINFO, and Google Scholar were searched for studies published between January 2000 and July 2023, with search terms that were related to AI, primary care, and implementation. The search extended to CE-marked or FDA-approved predictive ML algorithms obtained from relevant registration databases. Three reviewers gathered subsequent evidence involving strategies such as product searches, exploration of references, manufacturer website visits, and direct inquiries to authors and product owners. The extent to which the evidence for each predictive ML algorithm aligned with the Dutch AI predictive algorithm (AIPA) guideline requirements was assessed per AI life cycle phase, producing evidence availability scores. The systematic search identified 43 predictive ML algorithms, of which 25 were commercially available and CE-marked or FDA-approved. The predictive ML algorithms spanned multiple clinical domains, but most (27 [63%]) focused on cardiovascular diseases and diabetes. Most (35 [81%]) were published within the past 5 years. The availability of evidence varied across different phases of the predictive ML algorithm life cycle, with evidence being reported the least for phase 1 (preparation) and phase 5 (impact assessment) (19% and 30%, respectively). Twelve (28%) predictive ML algorithms achieved approximately half of their maximum individual evidence availability score. Overall, predictive ML algorithms from peer-reviewed literature showed higher evidence availability compared with those from FDA-approved or CE-marked databases (45% vs 29%). The findings indicate an urgent need to improve the availability of evidence regarding the predictive ML algorithms' quality criteria. Adopting the Dutch AIPA guideline could facilitate transparent and consistent reporting of the quality criteria that could foster trust among end users and facilitating large-scale implementation.

Ash content is a critical coal quality indicator that determines coal grade and commercial applications, making its rapid and accurate measurement essential. Traditional slow ashing methods are operationally cumbersome, time-consuming, and pose environmental and safety concerns. Hyperspectral imaging technology offers a promising alternative with its non-contact detection and superior spatial-spectral information fusion capabilities. However, existing machine learning and deep learning approaches, while capable of modeling complex spectral-analyte relationships, often overlook spatial information and struggle to effectively capture the heterogeneous distribution characteristics of ash minerals in coal samples. Therefore, this study proposes a fast estimation method of hyperspectral coal ash based on graph neural network, using a hybrid GCN-GAT encoder and an adaptive spatial-spectral edge construction strategy. This method models the mineral distribution pattern by constructing a spatial graph, constructs a spectrogram to capture the mineral spectral characteristics, and optimizes it for different ash content ranges. In the experiments of concentrated coal (ash range from 9% to 18%) and tailings coal samples (ash range from 49% to 77 %), the average absolute errors of this method are 0.862 % and 1.18 %, respectively, which are significantly better than traditional machine learning methods and deep learning methods. The interpretability analysis shows that the absorption characteristics of hydroxyl-containing minerals corresponding to the 1200-1400 nm band contribute the most to the ash estimation, which verifies the effectiveness of the graph neural network in coal quality analysis and provides practical guidance for coal automatic characterization and industrial quality control.

The research is aimed at assessing the possibility of studying the ash content of coal seams using neutron-neutron logging in coal exploration wells drilled at the outcrops of coal seams under loose deposits using the materials of experimental work in the South Yakutsk coal basin. The prospects of using neutron-neutron logging to study coal well sections, on the one hand, is determined by the fact that hydrogen content in coal seams normally exceeds its content in the host rocks, and, on the other, by the small cross-sections of neutron capture by carbon. Within the same coal grade, an increase in its ash content both means a decrease in its hydrogen content and an increase in the content of elements with a higher capture cross section. Experimental studies were carried out at the Syllakh coal deposit. An IBN-8-1 type plutonium-beryllium source with an output of 5*104 neutrons/s was used as a fast neutron source, and a highly efficient SNM-17 type helium gas-discharge counter was used as a slow neutron detector. The logging depth provided by the equipment was 10 - 30 cm. During the processing of the obtained field data, a correlation was established between the count rate of neutron-neutron logging and coal ash content. It should be noted that the functional dependence of the count rate of neutron-neutron logging on ash content is not continuous – in the range of ash content of 45 - 55% a certain discontinuity point is observed, after which the functional dependence changes. To simplify the assessment of the dependence, the range of rock ash content above 45% is neglected since bituminous coals with an ash content of more than 45% are classified as carbonaceous rock and are not of industrial interest. A close correlation is established between the count rate of neutron-neutron logging and coal ash content. According to the results of statistical processing, the correlation coefficient is 0.97, which makes it possible to quantitatively determine the ash content according to neutron-neutron logging data. The absolute errors in ash content determination by neutron-neutron logging over the entire dataset are up to 3.625 %. The degree of analytical moisture influence on the data of neutron-neutron logging in determining coal ash content is estimated. No regular changes in the count rate of neutron-neutron logging due to a change in the analytical moisture index have been established. It is noteworthy that, with a sufficiently large dispersion value of the analytical moisture index, the trend line of this parameter regularly changes synchronously with the trend lines of ash content and count rate.

The effects of temperature and oxygen content on the PCDD/PCDFs formation in MSW fly ash

The influence of coal bottom ash on fresh properties of self-compacting concrete (SCC) were presented in this paper. Self-compacting concrete mixtures were produced by 0.40 water/powder ratio and coal bottom ash as a replacement of fine aggregates in varying percentages of 0%, 10%, 15%, 20%, 25% and 30%. The fresh concretes were tested for the key workability belongings of self-compacting concrete such as passing and filling abilities and segregation resistance. The fresh properties were investigated by slump flow; T500 spread time, sieve segregation and L-box test. It was found that the slump flow decreased whereas the T500 spread time increased with higher coal bottom ash content. The L-box blocking ratios changed from 0.92 to 0.65 and were mostly showed satisfactory blocking ratio. The presence of coal bottom ash improved the stability of SCC mixture and the segregation index obtained from sieve test reduced with greater bottom ash content. It can be concluding that the filling and passing ability of SCC decreased when the amount of coal bottom ash content increased. In addition, the segregation resistance index decreased with higher coal bottom ash content.

Investigation of oil agglomeration behaviour of Tuncbilek clean coal and separation of artificial mixture of coal–clay by oil agglomeration

Chemical characterisation of fly ash from coal-fired thermal power plants in India

ABSTRAK&#x0D; Batubara high – low volatile bituminous Formasi Tanjung di Kalimantan Tengah dikenal berpotensi sebagai coking coals. Maksimum fluiditas merupakan ssalah satu parameter penting yang sangat berkaitan dengan kualitas kokas yang dihasilkan. Penelitian ini bertujuan untuk menganalisis distribusi kandungan zat terbang dan abu serta pengaruhnya terhadap maksimum fluiditas batubara khususnya untuk batubara dari Formasi Tanjung. Batubara diambil dari lapangan di Daerah Sekako dengan channel sampling ply by ply berdasarkan litotipenya kemudian dilakukan analisis laboratorium meliputi analisis proksimat dan analisis Gieseler plastometer. Batubara yang diambil dari 2 seam Formasi Tanjung yang diproduksi tersusun dominan atas litotipe bright coals dan banded bright coals dengan memiliki kandungan abu (ash) 2,79 – 9,05 (wt%, adb), kandungan zat terbang (volatile matter) 35,14 – 39,50 (wt%, adb) dan maksimum fluiditas 22263 – 49029 (ddpm). Kandungan abu berkorelasi negatif kuat (r = -0,656; R2 = 0,431) dan tidak berpengaruh signifikan (sig. 0,055 &gt; 0,05) terhadap maksimum fluiditas batubara. Sementara itu, kandungan zat terbang berkorelasi positif sangat kuat (r = 0,794; R2 = 0,6301) dan berpengaruh signifikan (sig. 0,003 &lt; 0,05) terhadap maksimum fluiditas batubara. Semakin tinggi kandugan abu maka maksimum fluiditas batubara semakin rendah dan sebaliknya semakin tinggi kandungan zat terbang maka maksimum fluiditas batubara semakin tinggi. Hubungan kandungan zat terbang dengan maksimum fluiditas batubara dapat dinyatakan dengan persamaan y = 6327,9x - 200248.

Aiming to address the limitations of high cost, large measurement error, and low accuracy in existing online ash content detectors, this paper presents a new approach for measuring coal ash content using X-ray transmission. Through a systematic investigation of thickness attenuation characteristics, the optimal sample thickness was determined under controlled experimental conditions. Based on the theory of radiation attenuation, the fundamental principle of equal thickness X-ray transmission analysis was derived, and an online measurement model for equal thickness coal ash content was constructed. Through simulation, it is known that the model is based on X-ray attenuation and is predominantly governed by the absorption process of X-rays passing through the material. Based on experimental findings, a mathematical model for ash content measurement was developed for coal samples containing quartz and montmorillonite. The R2 values obtained after model fitting were 0.98499 and 0.999, respectively, for samples with ash content ranging from 9.00% to 18.00%. Test samples containing quartz and montmorillonite exhibited minimal errors in ash content measurement. The maximum, minimum, and average errors were all within 0.50%, demonstrating the high accuracy of the measurement method, which aligns well with the requirements of daily production.

Undernutrition among children under the age of five is a major public health concern, especially in developing countries. This study aimed to use machine learning (ML) algorithms to predict undernutrition and identify its associated factors. Secondary data analysis of the 2017 Multiple Indicator Cluster Survey (MICS) was performed using R and Python. The main outcomes of interest were undernutrition (stunting: height-for-age (HAZ) < -2 SD; wasting: weight-for-height (WHZ) < -2 SD; and underweight: weight-for-age (WAZ) < -2 SD). Seven ML algorithms were trained and tested: linear discriminant analysis (LDA), logistic model, support vector machine (SVM), random forest (RF), least absolute shrinkage and selection operator (LASSO), ridge regression, and extreme gradient boosting (XGBoost). The ML models were evaluated using the accuracy, confusion matrix, and area under the curve (AUC) receiver operating characteristics (ROC). In total, 8564 children were included in the final analysis. The average age of the children was 926 days, and the majority were females. The weighted prevalence rates of stunting, wasting, and underweight were 17%, 7%, and 12%, respectively. The accuracies of all the ML models for wasting were (LDA: 84%; Logistic: 95%; SVM: 92%; RF: 94%; LASSO: 96%; Ridge: 84%, XGBoost: 98%), stunting (LDA: 86%; Logistic: 86%; SVM: 98%; RF: 88%; LASSO: 86%; Ridge: 86%, XGBoost: 98%), and for underweight were (LDA: 90%; Logistic: 92%; SVM: 98%; RF: 89%; LASSO: 92%; Ridge: 88%, XGBoost: 98%). The AUC values of the wasting models were (LDA: 99%; Logistic: 100%; SVM: 72%; RF: 94%; LASSO: 99%; Ridge: 59%, XGBoost: 100%), for stunting were (LDA: 89%; Logistic: 90%; SVM: 100%; RF: 92%; LASSO: 90%; Ridge: 89%, XGBoost: 100%), and for underweight were (LDA: 95%; Logistic: 96%; SVM: 100%; RF: 94%; LASSO: 96%; Ridge: 82%, XGBoost: 82%). Age, weight, length/height, sex, region of residence and ethnicity were important predictors of wasting, stunting and underweight. The XGBoost model was the best model for predicting wasting, stunting, and underweight. The findings showed that different ML algorithms could be useful for predicting undernutrition and identifying important predictors for targeted interventions among children under five years in Ghana.

A total of 77 coal, slag and fly ash samples collected from six thermal power plants were measured by gamma-ray spectrometry. The average (226)Ra activity concentrations in coal, slag and fly ash were measured as 199.8±16.7, 380.3±21.8 and 431.5±29.0 Bq kg(-1), respectively. The average (232)Th activity concentrations in coal, slag and fly ash were measured as 32.0±2.4, 74.0±9.0 and 87.3±9.8 Bq kg(-1), respectively. The average (40)K activity concentrations in coal, slag and fly ash were found to be 152.8±12.1, 401.3±25.0 and 439.0±30.2 Bq kg(-1), respectively. The radium equivalent activities of samples varied from 147.6±8.5 to 1077.4±53.3 Bq kg(-1). The gamma and alpha index of one thermal power plant's fly ash were calculated to be 3.5 and 5 times higher than that of the reference values. The gamma absorbed dose rates were found to be higher than that of the average Earth's crust. The annual effective dose of residues measured in four thermal power plants were calculated higher than that of the permitted dose rate for public, i.e. 1 mSv y(-1).