Approaches to the Gas Control in Ucg

Kačur, Ján; Kostúr, Karol

doi:10.14311/ap.2017.57.0182

Cited by 11 publications

(14 citation statements)

References 18 publications

(33 reference statements)

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“…Several experiments as trials of a real UCG were performed with the ex-situ reactor. This laboratory gasification equipment was well described in [41,42]. Similar trials of the UCG on a laboratory ex-situ reactor can be found in [43,44].…”

Section: Experimental Ucg In Ex-situ Reactormentioning

confidence: 76%

“…Lignite coal from the Slovak mine that is suitable for the UCG was gasified. The composition of the coal that was gasified and factors that affect the UCG can be found in [41]. The influence of various gasification agents (i.e., its flows and pressures) on the syngas quality was discussed in [2].…”

Section: Experimental Ucg In Ex-situ Reactormentioning

confidence: 99%

“…The SCADA/HMI system Promotic was used for the monitoring of the process and setting the controllers [46]. A detailed description of all devices that were used for the measurement and control was presented in [41]. The measured process variables (i.e., flows of gasification agents, pressures, temperatures, syngas composition and calorific value) were recorded in a database which may later be processed as a set of time series.…”

Section: Experimental Ucg In Ex-situ Reactormentioning

confidence: 99%

See 2 more Smart Citations

A Comparative Study of Data-Driven Modeling Methods for Soft-Sensing in Underground Coal Gasification

et al. 2019

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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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Section: Experimental Ucg In Ex-situ Reactormentioning

confidence: 76%

Section: Experimental Ucg In Ex-situ Reactormentioning

confidence: 99%

Section: Experimental Ucg In Ex-situ Reactormentioning

confidence: 99%

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A Comparative Study of Data-Driven Modeling Methods for Soft-Sensing in Underground Coal Gasification

et al. 2019

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“…The UCG process is complex and dynamic [45]. On the other hand, the position of active cavity moves forward continuously in the gasification process [46,47], it is not fixed like the surface gasification position. The gasified coal seam is superimposed and solidified, burning in situ, unlike surface gasification, which can break the coal body according to requirements, add feeding materials according to requirements, clean up coal ash in time, and be easily controlled.…”

Section: The Development Of Ucg Projects Worldwidementioning

confidence: 99%

Monitoring and Control in Underground Coal Gasification: Current Research Status and Future Perspective

Xiao

Yin

et al. 2019

Sustainability

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By igniting in the coal seam and injecting gas agent, underground coal gasification (UCG) causes coal to undergo thermochemical reactions in situ and, thus, to be gasified into syngas for power generation, hydrogen production, and storage. Compared with traditional mining technology, UCG has the potential sustainable advantages in energy, environment, and the economy. The paper reviewed the development of UCG projects around the world and points out that UCG faces difficulties in the field of monitoring and control in UCG. It is expounded for the current research status of monitoring and control in UCG, and clarified that monitoring and control in UCG is not perfect, remaining in the stage of exploration. To improve the problem of low coal gasification rate and gas production, and then to make full use of the potential sustainable advantages, the paper offers a perception platform of a UCG monitoring system based on the Internet-of-Things (IoT) and an optimal control model for UCG based on deep learning, and has an outlook on breakthrough directions of the key technologies related to the package structure design for moisture-proof and thermal insulation, antenna design, the strategy for energy management optimization, feature extraction and classification design for the network model, network structure design, network learning augmentation, and the control of the network model, respectively.

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“…Optimal control is the most investigated field in UCG within academic research. Much work focused on UCG control has been done on experimental ex-situ reactors (e.g., [24,30,31]) but also there are some trials on in-situ gasifier (e.g., [52]).…”

Section: Control Of Ucgmentioning

confidence: 99%

Model Predictive Control of UCG: An Experiment and Simulation Study

Kačur

Flegner

Durdán

et al. 2019

ITC

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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.

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Approaches to the Gas Control in Ucg

Cited by 11 publications

References 18 publications

A Comparative Study of Data-Driven Modeling Methods for Soft-Sensing in Underground Coal Gasification

A Comparative Study of Data-Driven Modeling Methods for Soft-Sensing in Underground Coal Gasification

Monitoring and Control in Underground Coal Gasification: Current Research Status and Future Perspective

Model Predictive Control of UCG: An Experiment and Simulation Study

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