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Coal and Coke Industries Research
Research Guide
What is Coal and Coke Industries Research?
Coal and Coke Industries Research is the quantitative analysis and future development of China's coal industry, encompassing oxidative desulphurisation, coke production, neural network applications, coking conditions, coal quality parameters, sulfur content, mineral components' influence on coke quality, and prediction of coke yield based on coal characteristics.
The field contains 48,928 published works focused on coal industry analysis and coke production techniques. Research addresses coal quality parameters, sulfur content reduction via oxidative desulphurisation, and neural network models for predicting coke yield. Studies examine how mineral components in coal affect coke quality under varying coking conditions.
Topic Hierarchy
Research Sub-Topics
Oxidative Desulphurisation of Coal
This sub-topic covers chemical oxidation processes using peroxides, ozone, and catalytic systems to selectively remove organic and pyritic sulfur from coal. Researchers optimize reaction conditions and kinetics for industrial scalability.
Coke Quality Prediction Models
This sub-topic develops empirical and machine learning models correlating coal petrography, rank, and mineralogy with coke strength, reactivity, and yield. Researchers validate predictions against pilot oven trials.
Neural Networks in Coal Analysis
This sub-topic applies artificial neural networks and deep learning for predicting coal properties, coke yield, and process outcomes from spectroscopic and petrographic data. Researchers address overfitting and interpretability challenges.
Influence of Coal Minerals on Coke Quality
This sub-topic investigates catalytic effects of alkali, alumina, and silica minerals on coke graphitization, porosity, and mechanical strength during carbonization. Researchers use mineral beneficiation and additive trials.
Coking Conditions Optimization
This sub-topic optimizes carbonization temperature profiles, heating rates, and pressure to maximize coke yield and quality from specific coal blends. Researchers employ pilot-scale ovens and thermal modeling.
Why It Matters
Coal and Coke Industries Research supports optimization of China's coal sector, which relies on precise predictions of coke yield and quality for steel production. For instance, analysis of mineral components enables better control of coke quality parameters, reducing defects in industrial coking processes. Investigations into oxidative desulphurisation lower sulfur content in coal, aiding compliance with emission standards in fuel technology applications. Neural network applications improve forecasting of coal characteristics, enhancing efficiency in coke production facilities.
Reading Guide
Where to Start
"CHEMISTRY of coal utilization" by H. H. Lowry (1963) provides foundational knowledge on coal properties essential for understanding coke production and quality parameters.
Key Papers Explained
"Stach's Textbook of Coal Petrology" by P A Hacquebard (1976) and "Stach's Textbook of coal petrology" by Erich Stach (1975) detail coal origin, petrographic constitution, and applications to fuel technology, building toward "FTIR study of the evolution of coal structure during the coalification process" by JoséV. Ibarra, Edgar Muñoz, R. Moliner (1996), which analyzes structural changes relevant to coke quality. "Graphical-statistical method for the study of structure and reaction processes of coal" by Van Krevelen (1950) introduces methods for coal reaction analysis that connect to these petrology works.
Paper Timeline
Most-cited paper highlighted in red. Papers ordered chronologically.
Advanced Directions
Current work emphasizes neural network applications for coke yield prediction and oxidative desulphurisation in China's coal industry, with focus on sulfur content reduction and mineral influences on quality.
Papers at a Glance
| # | Paper | Year | Venue | Citations | Open Access |
|---|---|---|---|---|---|
| 1 | Statistical Analysis of Finite Mixture Distributions. | 1987 | Journal of the America... | 2.9K | ✕ |
| 2 | Methods of soil analysis. Part 2 — Microbiological and biochem... | 1995 | Scientia Horticulturae | 2.6K | ✕ |
| 3 | Volatile Fatty Acid Analyses of Blood and Rumen Fluid by Gas C... | 1961 | Journal of Dairy Science | 1.8K | ✓ |
| 4 | CHEMISTRY of coal utilization | 1963 | John Wiley & Sons eBooks | 1.5K | ✕ |
| 5 | Methods of soil analysis. Part 2: Chemical and microbiological... | 1965 | — | 1.4K | ✕ |
| 6 | Stach's Textbook of Coal Petrology | 1976 | Geoscience Canada | 1.3K | ✕ |
| 7 | Stach's Textbook of coal petrology | 1975 | Medical Entomology and... | 1.0K | ✕ |
| 8 | Methods for Statistical Data Analysis of Multivariate Observat... | 1978 | Journal of the America... | 900 | ✕ |
| 9 | FTIR study of the evolution of coal structure during the coali... | 1996 | Organic Geochemistry | 879 | ✕ |
| 10 | Graphical-statistical method for the study of structure and re... | 1950 | Fuel | 799 | ✕ |
Frequently Asked Questions
What topics are covered in Coal and Coke Industries Research?
The field covers quantitative analysis of China's coal industry, oxidative desulphurisation, coke production, neural network applications, coking conditions, coal quality parameters, sulfur content, mineral components' influence on coke quality, and coke yield prediction. It totals 48,928 works. These topics address future development needs in fuel technology.
How do mineral components affect coke quality?
Mineral components in coal influence coke quality parameters during coking. Research examines their role in determining final coke properties. This analysis supports predictions of coke yield based on coal characteristics.
What role do neural networks play in this research?
Neural networks are applied to predict coke yield from coal characteristics and model coking conditions. They enable quantitative analysis of coal quality parameters. Such methods improve accuracy in China's coal industry development.
What is oxidative desulphurisation in coal research?
Oxidative desulphurisation reduces sulfur content in coal through chemical processes. It is a key method studied for cleaner coal utilization. This technique supports fuel technology advancements in the coal industry.
How is coke yield predicted?
Coke yield is predicted based on coal characteristics, including quality parameters and mineral components. Neural networks and statistical models facilitate these predictions. Research focuses on applications in China's coal sector.
Open Research Questions
- ? How can neural networks be optimized to improve coke yield predictions under varying coking conditions?
- ? What specific mineral components most strongly influence coke quality parameters in Chinese coals?
- ? Which combinations of oxidative desulphurisation methods most effectively reduce sulfur content without degrading coal quality?
- ? How do coal quality parameters interact to affect future scalability of coke production in China?
Recent Trends
The field maintains 48,928 works with no specified 5-year growth rate, centering on China's coal industry quantitative analysis, coke production optimization, and neural network predictions of coke yield from coal characteristics.
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