scholarly journals Experimental study of underground coal gasification (UCG) of a high-rank coal using atmospheric and high-pressure conditions in an ex-situ reactor

Fuel ◽  
2020 ◽  
Vol 270 ◽  
pp. 117490 ◽  
Author(s):  
Renato Zagorščak ◽  
Sivachidambaram Sadasivam ◽  
Hywel Rhys Thomas ◽  
Krzysztof Stańczyk ◽  
Krzysztof Kapusta
Keyword(s):  
Experimental Study ◽  
High Pressure ◽  
Coal Gasification ◽  
High Rank ◽  
Ex Situ ◽  
Underground Coal Gasification

scholarly journals Characterisation of the Contaminants Generated from a Large-Scale Ex-Situ Underground Coal Gasification Study Using High-Rank Coal from the South Wales Coalfield

2020 ◽  
Vol 231 (10) ◽  
Author(s):  
Sivachidambaram Sadasivam ◽  
Renato Zagorščak ◽  
Hywel Rhys Thomas ◽  
Krzysztof Kapusta ◽  
Krzysztof Stańczyk
Keyword(s):  
Iron Oxide ◽  
Large Scale ◽  
Coal Gasification ◽  
Produced Water ◽  
High Rank ◽  
Ex Situ ◽  
Underground Coal Gasification ◽  
The South ◽  
South Wales ◽  
Pressure Experiment

Abstract This paper presents an analysis of contaminants generated from large-scale, laboratory-based, underground coal gasification (UCG) experiments using a high-rank coal from the South Wales Coalfield. The experiments were performed at atmospheric and elevated pressures (30 bar) by varying the oxidants’ composition. The experiments were designed to predict the amount of produced water and contaminants generated at each stage of the operating conditions. The mass balance of water supplied and produced in the experiments was accounted for. Chemical analyses of produced water, char and ash contents were performed to quantify the inorganic and organic chemical parameters. Most of the contaminant concentrations in the produced water from the 30-bar pressure experiment were lower than the concentrations generated from the atmospheric pressure experiment. The measured concentrations of the inorganic chemical species and the inorganic parameters of the coal seam water from the South Wales Coalfield were used in theoretical calculations to predict the dominant equilibrium species concentrations in a hypothetical scenario of effluent contaminated groundwater. The biodegradation of organic contaminants such as phenol, benzene and sorbed fractions of inorganic contaminants from the produced water on iron oxide in the ash residue was predicted using existing biotransformation kinetics and surface complexation models, respectively. The biodegradation of phenol and benzene would be a slow process even at optimum conditions and the iron oxide left in the cavity can act as a sorbent for a few inorganic species. The evidence from the present study suggests future work towards (i) developing an appropriate water treatment process during gas cleaning, (ii) operational procedure (pressure and proportions of oxidant) and (iii) developing UCG-specific experimental prediction of contaminant transportation and transformation kinetics.

scholarly journals Model-Free Control of UCG Based on Continual Optimization of Operating Variables: An Experimental Study

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4323
Author(s):  
Ján Kačur ◽  
Marek Laciak ◽  
Milan Durdán ◽  
Patrik Flegner
Keyword(s):  
Experimental Study ◽  
Coal Gasification ◽  
Calorific Value ◽  
Ex Situ ◽  
Underground Coal Gasification ◽  
Water Steam ◽  
Operating Variables ◽  
Model Free ◽  
Optimization Task ◽  
Model Free Control

The underground coal gasification (UCG) represents an effective coal mining technology, where coal is transformed into syngas underground. Extracted syngas is cleaned and processed for energy production. Various gasification agents can be injected into an underground georeactor, e.g., air, technical oxygen, or water steam, to ensure necessary temperature and produce syngas with the highest possible calorific value. This paper presents an experimental study where dynamic optimization of operating variables maximizes syngas calorific value during gasification. Several experiments performed on an ex situ reactor show that the optimization algorithm increased syngas calorific value. Three operation variables, i.e., airflow, oxygen flow, and syngas exhaust, were continually optimized by an algorithm of gradient method. By optimizing the manipulation variables, the calorific value of the syngas was increased by 5 MJ/m3, both in gasification with air and additional oxygen. Furthermore, a higher average calorific value of 4.8–5.1 MJ/m3 was achieved using supplementary oxygen. The paper describes the proposed ex situ reactor, the mathematical background of the optimization task, and results obtained during optimal control of coal gasification.

scholarly journals Monitoring and evaluation of simulated underground coal gasification in an ex-situ experimental artificial coal seam system

2018 ◽  
Vol 223 ◽  
pp. 82-92 ◽  
Author(s):  
Fa-qiang Su ◽  
Akihiro Hamanaka ◽  
Ken-ichi Itakura ◽  
Wenyan Zhang ◽  
Gota Deguchi ◽  
...  
Keyword(s):  
Coal Seam ◽  
Coal Gasification ◽  
Monitoring And Evaluation ◽  
Ex Situ ◽  
Underground Coal Gasification

scholarly journals Experimental Study on the Fractal Features and Permeability Characteristics of Low Metamorphic Coal Pore Structure under Thermal Damage

Geofluids ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Zhen Liu ◽  
Mingrui Zhang ◽  
Shijian Yu ◽  
Lin Xin ◽  
Gang Wang ◽  
...  
Keyword(s):  
Experimental Study ◽  
Pore Structure ◽  
Coal Gasification ◽  
Thermal Damage ◽  
Fractal Model ◽  
Utilization Efficiency ◽  
Heating Process ◽  
Underground Coal Gasification ◽  
External Temperature ◽  
Permeability Characteristics

Underground coal gasification and exploitation of geothermal mine resources can effectively improve coal conversion and utilization efficiency, and the basic theory of the above technologies generally relies on the change law of the coal pore structure under thermal damage. Therefore, the influence mechanism of the development of the coal pore structure under thermal damage is analyzed by the nuclear magnetic resonance experiment, and the temperature-permeability fractal model is created. The results show that compared with microtransitional pores, the volume of meso-macropores in the coal body is more susceptible to an increase in temperature, which was most obvious at 200-300°C. During the heating process, the measured fractal dimension based on the T2 spectral distribution is between 2 and 3, indicating that the fractal characteristics did not disappear upon a change in external temperature. The temperature has a certain negative correlation with DmNMR, DMNMR, and DNMR, indicating that the complexity of the pore structure of the coal body decreased gradually with the increase of the temperature. Compared with the permeability calculated based on the theoretical permeability fractal model, the permeability obtained from the temperature-permeability fractal model has a similar increasing trend as the permeability measured by the NMR experiment when the temperature increases. The experimental study on pore structure and permeability characteristics of the low metamorphic coal under thermal damage provides a scientific theory for underground coal gasification and geothermal exploitation.

scholarly journals Chemometric Study of the Ex Situ Underground Coal Gasification Wastewater Experimental Data

2012 ◽  
Vol 223 (9) ◽  
pp. 5745-5758 ◽  
Author(s):  
Adam Smoliński ◽  
Krzysztof Stańczyk ◽  
Krzysztof Kapusta ◽  
Natalia Howaniec
Keyword(s):  
Experimental Data ◽  
Coal Gasification ◽  
Ex Situ ◽  
Underground Coal Gasification ◽  
Coal Gasification Wastewater

scholarly journals Regression Models Utilization to the Underground Temperature Determination at Coal Energy Conversion

Energies ◽  
2021 ◽  
Vol 14 (17) ◽  
pp. 5444
Author(s):  
Milan Durdán ◽  
Marta Benková ◽  
Marek Laciak ◽  
Ján Kačur ◽  
Patrik Flegner
Keyword(s):  
Coal Seam ◽  
Regression Model ◽  
Regression Models ◽  
Coal Gasification ◽  
Cavity Growth ◽  
Temperature Data ◽  
Ex Situ ◽  
Underground Coal Gasification ◽  
Gasification Process ◽  
Cavity Growth Rate

The underground coal gasification represents a technology capable of obtaining synthetic coal gas from hard-reached coal deposits and coal beds with tectonic faults. This technology is also less expensive than conventional coal mining. The cavity is formed in the coal seam by converting coal to synthetic gas during the underground coal gasification process. The cavity growth rate and the gasification queue’s moving velocity are affected by controllable variables, i.e., the operation pressure, the gasification agent, and the laboratory coal seam geometry. These variables can be continuously measured by standard measuring devices and techniques as opposed to the underground temperature. This paper researches the possibility of the regression models utilization for temperature data prediction for this reason. Several regression models were proposed that were differed in their structures, i.e., the number and type of selected controllable variables as independent variables. The goal was to find such a regression model structure, where the underground temperature is predicted with the greatest possible accuracy. The regression model structures’ proposal was realized on data obtained from two laboratory measurements realized in the ex situ reactor. The obtained temperature data can be used for visualization of the cavity growth in the gasified coal seam.

scholarly journals A COMPARATIVE STUDY OF DATA-DRIVEN MODELING METHODS FOR SOFT-SENSING IN UNDERGROUND COAL GASIFICATION

2019 ◽  
Vol 59 (4) ◽  
pp. 322-351
Author(s):  
Ján Kačur ◽  
Milan Durdán ◽  
Marek Laciak ◽  
Patrik Flegner
Keyword(s):  
Coal Gasification ◽  
Calorific Value ◽  
Multivariate Adaptive Regression Splines ◽  
Ex Situ ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Underground Coal Gasification ◽  
Process Data ◽  
Adaptive Regression ◽  
Data Driven Modeling

Underground coal gasification (UCG) is a technological process, which converts solid coal into a gas in the underground, using injected gasification agents. In the UCG process, a lot of process variables can be measurable with common measuring devices, but there are variables that cannot be measured so easily, e.g., the temperature deep underground. It is also necessary to know the future impact of different control variables on the syngas calorific value in order to support a predictive control. This paper examines the possibility of utilizing Neural Networks, Multivariate Adaptive Regression Splines and Support Vector Regression in order to estimate the UCG process data, i.e., syngas calorific value and underground temperature. It was found that, during the training with the UCG data, the SVR and Gaussian kernel achieved the best results, but, during the prediction, the best result was obtained by the piecewise-cubic type of the MARS model. The analysis was performed on data obtained during an experimental UCG with an ex-situ reactor.

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