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Coal

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Co-combustion of peanut hull and coal blends: Artificial neural networks modeling, particle swarm optimization and Monte Carlo simulation.

Bioresource technology
Co-combustion of coal and peanut hull (PH) were investigated using artificial neural networks (ANN), particle swarm optimization, and Monte Carlo simulation as a function of blend ratio, heating rate, and temperature. The best prediction was reached ...

Combination of minimum enclosing balls classifier with SVM in coal-rock recognition.

PloS one
Top-coal caving technology is a productive and efficient method in modern mechanized coal mining, the study of coal-rock recognition is key to realizing automation in comprehensive mechanized coal mining. In this paper we propose a new discriminant a...

An improved least squares SVM with adaptive PSO for the prediction of coal spontaneous combustion.

Mathematical biosciences and engineering : MBE
The problem of coal spontaneous combustion prediction is very complex, and there are many factors that affect the prediction results. In order to solve the issues of high dimension and redundancy among features and limited samples in the prediction o...

Rule-based expert system to assess caving output ratio in top coal caving.

PloS one
Coal mining professionals in coal mining have recognized that the assessment of top coal release rate can not only improve the recovery rate of top coal, but also improve the quality of coal. But the process was often performed using a manual-based o...

A Synchronous Prediction Model Based on Multi-Channel CNN with Moving Window for Coal and Electricity Consumption in Cement Calcination Process.

Sensors (Basel, Switzerland)
The precision and reliability of the synchronous prediction of multi energy consumption indicators such as electricity and coal consumption are important for the production optimization of industrial processes (e.g., in the cement industry) due to th...

Diffuse reflectance spectroscopy based rapid coal rank estimation: A machine learning enabled framework.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
This research aims at studying the ability of using diffuse reflectance spectroscopy (DRS) for discriminating or classifying coal samples into different ranks. Spectral characteristics such as the shape of the spectral profile, slope, absorption inte...

Early Warning of Gas Concentration in Coal Mines Production Based on Probability Density Machine.

Sensors (Basel, Switzerland)
Gas explosion has always been an important factor restricting coal mine production safety. The application of machine learning techniques in coal mine gas concentration prediction and early warning can effectively prevent gas explosion accidents. Nea...

Coal Mine Safety Evaluation Based on Machine Learning: A BP Neural Network Model.

Computational intelligence and neuroscience
As the core of artificial intelligence, machine learning has strong application advantages in multi-criteria intelligent evaluation and decision-making. The level of sustainable development is of great significance to the safety evaluation of coal mi...

Coal identification based on a deep network and reflectance spectroscopy.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
The rapid identification of coal types in the field is an important task. This research combines spectroscopy with deep learning algorithms and proposes a method for quickly identifying coal types in the field. First, we collect field spectral data o...

Stability Risk Assessment of Underground Rock Pillars Using Logistic Model Trees.

International journal of environmental research and public health
Pillars are important structural elements that provide temporary or permanent support in underground spaces. Unstable pillars can result in rock sloughing leading to roof collapse, and they can also cause rock burst. Hence, the prediction of undergro...