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.

Ex Situ UCG Model Experiments with Oxygen Enriched Air in an Artificial Coal Seam

2017 ◽  
Vol 737 ◽  
pp. 379-384
Author(s):  
Fa Qiang Su ◽  
Ken-ichi Itakura ◽  
Akihiro Hamanaka ◽  
Gota Deguchi ◽  
Kohki Sato ◽  
...  
Keyword(s):  
Coal Seam ◽  
Coal Gasification ◽  
Calorific Value ◽  
Close Correlation ◽  
Gas Production ◽  
Ex Situ ◽  
Underground Coal Gasification ◽  
Coal Consumption ◽  
Gas Leakage ◽  
Gasification Reaction

Underground Coal Gasification (UCG) demands precise evaluation of the combustion area in the coal seam. Especially, the monitoring of fracture activity in the coal seam and around rock is important not only for efficient gas production but also for estimation of subsidence and gas leakage to the surface. For this objective, laboratory experiments were conducted using the simulated UCG models. This paper also investigated gas energy for coal consumption, the production gas quantity and heat value, the application of oxygen element balance in the gasification reaction process, and the gas composition obtained in this study. During burning of the coal, temperatures inside the coal, contents of product gases and acoustic emission (AE) activities were monitored successively under the control of feeding gas (air/oxygen and steam) flow rate. Comparison of the temperature variation and accumulated AE event curves revealed a close correlation between them. The local change of temperature inside the coal induced fractures with AE. The AE activity was related closely to the local changes of temperature inside the model. The evaluation of gas energy recovery calculated from the obtained product gas provided a fair evaluation for the coal consumed, and the quantity of gas product and calorific value obtained from the UCG process.

scholarly journals Evaluation of a Compact Coaxial Underground Coal Gasification System Inside an Artificial Coal Seam

Energies ◽  
2018 ◽  
Vol 11 (4) ◽  
pp. 898 ◽  
Author(s):  
Fa-qiang Su ◽  
Akihiro Hamanaka ◽  
Ken-ichi Itakura ◽  
Gota Deguchi ◽  
Wenyan Zhang ◽  
...  
Keyword(s):  
Coal Seam ◽  
Large Scale ◽  
Horizontal Well ◽  
Coal Gasification ◽  
Calorific Value ◽  
Ex Situ ◽  
Underground Coal Gasification ◽  
Study Results ◽  
Geological Conditions ◽  
Feasible Option

The Underground Coal Gasification (UCG) system is a clean technology for obtaining energy from coal. The coaxial UCG system is supposed to be compact and flexible in order to adapt to complicated geological conditions caused by the existence of faults and folds in the ground. In this study, the application of a coaxial UCG system with a horizontal well is discussed, by means of an ex situ model UCG experiment in a large-scale simulated coal seam with dimensions of 550 × 600 × 2740 mm. A horizontal well with a 45-mm diameter and a 2600-mm length was used as an injection/production well. During the experiment, changes in temperature field and product gas compositions were observed when changing the outlet position of the injection pipe. It was found that the UCG reactor is unstable and expands continuously due to fracturing activity caused by coal crack initiation and extension under the influence of thermal stress. Therefore, acoustic emission (AE) is considered an effective tool to monitor fracturing activities and visualize the gasification zone of coal. The results gathered from monitoring of AEs agree with the measured data of temperatures; the source location of AE was detected around the region where temperature increased. The average calorific value of the produced gas was 6.85 MJ/Nm3, and the gasification efficiency, defined as the conversion efficiency of the gasified coal to syngas, was 65.43%, in the whole experimental process. The study results suggest that the recovered coal energy from a coaxial UCG system is comparable to that of a conventional UCG system. Therefore, a coaxial UCG system may be a feasible option to utilize abandoned underground coal resources without mining.

scholarly journals Model Predictive Control of UCG: An Experiment and Simulation Study

2019 ◽  
Vol 48 (4) ◽  
pp. 557-578
Author(s):  
Jan Kacur ◽  
Patrik Flegner ◽  
Milan Durdan ◽  
Marek Laciak
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Coal Gasification ◽  
Technical Problem ◽  
Calorific Value ◽  
Multivariate Adaptive Regression Splines ◽  
Underground Coal Gasification ◽  
Mining Equipment ◽  
Real Process

Underground coal gasification (UCG) is a potential technology that enables to mine coal without traditional mining equipment. The coal is gasified deep in underground and produced syngas is processed on the surface. The most important technical problem in UCG is unstable quality of syngas and control. This paper proposes advanced control based on an adaptive predictive controller. The maintaining of desired calorific value depends on flow rates of gasification agents injected to the underground geo-reactor and controlled exhaust. The paper proposes a physical model of UCG technology and applies a method of multivariate adaptive regression splines (MARS) to model the gasification process. This method satisfactorily approximates nonlinearity in the process variables. The paper proposes adaptive model predictive control (MPC) using online model estimation and applied it on the MARS model of UCG that imitates the real process. The results have shown that optimization of manipulation variables can replace manual control in UCG. Getting better quality of syngas depends on setpoints, optimized manipulation variables, and constraints used in MPC. In simulations, the adaptive MPC has shown better performance in comparison with manual and PI control.

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 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 Effect of Lignite Properties on Its Suitability for the Implementation of Underground Coal Gasification (UCG) in Selected Deposits

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5816
Author(s):  
Krzysztof Kapusta
Keyword(s):  
Moisture Content ◽  
Coal Gasification ◽  
Calorific Value ◽  
Process Efficiency ◽  
Average Moisture Content ◽  
Underground Coal Gasification ◽  
Gasification Process ◽  
Laboratory Setup ◽  
Large Bulk

Two experimental simulations of underground coal gasification (UCG) processes, using large bulk samples of lignites, were conducted in a surface laboratory setup. Two different lignite samples were used for the oxygen-blown experiments, i.e., “Velenje” meta-lignite (Slovenia) and “Oltenia” ortho-lignite (Romania). The average moisture content of the samples was 31.6wt.% and 45.6wt.% for the Velenje and Oltenia samples, respectively. The main aim of the study was to assess the suitability of the tested lignites for the underground coal gasification process. The gas composition and its production rates, as well as the temperatures in the artificial seams, were continuously monitored during the experiments. The average calorific value of gas produced during the Velenje lignite experiment (6.4 MJ/Nm3) was much higher compared to the result obtained for the experiment with Oltenia lignite (4.8 MJ/Nm3). The Velenje lignite test was also characterized by significantly higher energy efficiency, i.e., 44.6%, compared to the gasification of Oltenia lignite (33.4%). The gasification experiments carried out showed that the physicochemical properties of the lignite used considerably affect the in situ gasification process. Research also indicates that UCG can be considered as a viable option for the extraction of lignite deposits; however, lignites with a lower moisture content and higher energy density are preferred, due to their much higher process efficiency.

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.

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