Results 71 to 80 of about 164 (117)

Forecasting Potential Resources of Humic Substances in the Ukrainian Lignite

Resources
The present research deals with forecasting the potential content of humic acids (HA) in Ukrainian lignite based on the coal’s physicochemical characteristics.
Serhiy Pyshyev, Denis Miroshnichenko, Mariia Shved, Volodymyr Riznyk, Halyna Bilushchak, Olexandr Borisenko, Mikhailo Miroshnychenko, Yurii Lypko   +7 more
doaj   +1 more source

A Physics-Informed Combinatorial Digital Twin for Value-Optimized Production of Petroleum Coke

Energies
Petroleum coke quality strongly influences refinery economics and downstream energy use, yet real-time control is constrained by slow quality assays and a 24–48 h lag in laboratory results.
Vladimir V. Bukhtoyarov, Alexey A. Gorodov, Natalia A. Shepeta, Ivan S. Nekrasov, Oleg A. Kolenchukov, Svetlana S. Kositsyna, Artem Y. Mikhaylov   +6 more
doaj   +1 more source

Interpretable Optimized Extreme Gradient Boosting for Prediction of Higher Heating Value from Elemental Composition of Coal Resource to Energy Conversion

Big Data and Cognitive Computing
The higher heating value (HHV), sometimes referred to as the gross calorific value, is a crucial metric for determining a fuel’s primary energy potential in energy production systems. By combining extreme gradient boosting (XGBoost) with the differential
Paulino José García-Nieto, Esperanza García-Gonzalo, José Pablo Paredes-Sánchez, Luis Alfonso Menéndez-García   +3 more
doaj   +1 more source

Prediction method of blasting vibration velocity of high slope based on bayes

Scientific Reports
Accurate prediction of blasting vibration velocity and effective control of vibration-induced hazards remain key challenges in blasting engineering. Using monitoring data from a high-slope blasting project in the Pan Nan 2 × 660 MW low-calorific-value ...
Xiangzhao Deng, Junli Nie, Zhen Lei, Beibei Zhang, Junhao Chen, Xijiang Zhang   +5 more
doaj   +1 more source

Indirect Gross Calorific Value prediction using Random Forest

International Journal of Engineering Works, 2020
openaire   +1 more source

Prediction of Calorific Value for Coal Gangue Based on the Machine Learning Algorithm

openaire   +1 more source

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Calorific Value of Coke. 1. Prediction

Coke and Chemistry, 2019
There is great scope for energy conservation in iron production. It is shown that blast furnaces and coke plants are the greatest consumers of energy and carbon in the steel industry. There are not even any optional guidelines for the calorific value of blast-furnace coke produced in Ukraine or elsewhere.
D V Miroshnichenko, I V Shulga, Miroshnichenko D V   +2 more
exaly   +2 more sources

Prediction models of calorific value of coal based on wavelet neural networks

Fuel, 2017
Abstract New prediction models based on wavelet neural networks (WNNs) have been proposed to estimate the gross calorific value (GCV) of coals. The input sets for the prediction models are involved of the proximate and ultimate analysis components of coal and the oxide analyses of ash.
Shuguang Jian
exaly   +2 more sources

Research on New Nonlinear Method Applied on Coal Calorific Value Prediction

Applied Mechanics and Materials, 2013
Based on research of the relationship between the industrial analysis of coal composition and the calorific value, a multiple linear regression - support vector machine model for predicting calorific value of coal is put forward. The training sample set is made up of the original industrial analysis data and calorific value.
Ke Lei Sun, Xiao Juan Zhu, Hua Ping Zhou
exaly   +2 more sources

Optimal use of condensed parameters of ultimate analysis to predict the calorific value of biomass

Fuel, 2018
Abstract Higher heating value (HHV) and lower heating value (LHV) of 39 biomass species that include woody samples, herbaceous materials, agricultural residues, juice pulps, nut shells, etc. were predicted based on elemental analysis results.
Yaman S, Ayse Özyuguran, Aysen Aktürk   +2 more
exaly   +2 more sources