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Review

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

by
Yuteng Xiao
1,
Jihang Yin
1,
Yifan Hu
1,
Junzhe Wang
2,
Hongsheng Yin
1,* and
Honggang Qi
3
1
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
2
Bell Honors School, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
3
School of Computer and Control Engineering, University of Chinese Academy of Sciences, Beijing 101408, China
*
Author to whom correspondence should be addressed.
Sustainability 2019, 11(1), 217; https://doi.org/10.3390/su11010217
Submission received: 23 October 2018 / Revised: 20 December 2018 / Accepted: 29 December 2018 / Published: 4 January 2019
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Abstract

:
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.

1. Introduction

Coal is one of the most important fossil fuels in the world, playing a vital role in human production, especially in industrial heating, urban gas production, power generation, and other fields. However, the use of coal, especially the emission of sulfides and nitrogen oxides during coal combustion, causing a series of environmental problems, has seriously affected the physical and psychological health and living quality of human beings all over the world [1]. One way to solve these problems is UCG. It converts coal into gas, followed by the operation of removing sulfides and nitrogen oxides, bypassing the traditional coal-burning process, and has the characteristics of low pollution emission [2]. In addition, UCG is also characterized by a high resource utilization rate [3] (it can exploit the unmineable coal compared with the traditional mining technology, such as coal that is too deep, of low grade, and in thin seams) and low cost [4] (water and coal in situ can be used directly during UCG process, reducing operating costs). Therefore, UCG is one of the most promising sustainable technologies in energy, environment, and the economy, having broad application prospects [5].
Figure 1 illustrates the schematic diagram for UCG. With the compressor injecting a gas agent (air, oxygen, oxygen enrichment with different concentration, carbon dioxide, or steam) through an injection well, the coal in the coal seam, deeply buried under overburden, is gasified into syngas by igniting in the active cavity. Then the harmful gases, such as sulfides and nitrogen oxides, in syngas are in the operation of gas cleaning and the residue is left underground. Finally, the syngas with H2, CO, and CH4, which is output through the production well, becomes a clean feed gas for power generation, hydrogen production, and storage [6,7].
In the nearly 100 years of research and practice in the world, many experiences and achievements have been accumulated in the methods and technologies of UCG. However, in practice the gasification process needs to be monitored and controlled to ensure that the underground gasification has a stable combustible gas composition and calorific value, as well as a higher gas yield and gasification rate [1]. UCG is a very complex physical and chemical process, and many factors affect the composition and quality of syngas. Considering high temperature humidity and closed environment, it is difficult to effectively monitor and control the UCG process to improve the quality of the syngas. Therefore, scholars in various countries have carried out a series of meaningful research on the monitoring and control in UCG, such as the GPR (Ground Penetrating Radar) technique for the monitoring [8,9], and study on controlling the combustion state in the gasification process [10,11,12,13,14]. There are few related studies, technical means remaining on paper, or at the experimental teaching level, and basic research is almost blank. Therefore, there are still some obstacles standing in the way to actual application at present. In recent years, with the rapid development of the Internet-of-Things (IoT) and deep learning research [15,16,17,18,19], great success has been achieved in solving complex monitoring and control problems [20,21,22,23,24,25]. This undoubtedly provides a new idea for the monitoring and control of UCG. Based on this, the paper puts forward a new idea about monitoring and control of UCG based on IoT and deep learning to improve the problem of the low coal gasification rate and gas production, and then makes full use of the potential sustainable advantages.
This paper reviews the development of UCG projects worldwide and the research progress related to monitoring and control for UCG. Taking the development of IoT and deep learning as an opportunity, it is a new idea of applying the IoT and deep learning into monitoring and control in UCG to be put forward, and the key technical breakthrough directions related to them are also discussed.

2. The Development of UCG Projects Worldwide

In 1868, William Siemens creatively proposed the technical idea of coal gasification in the underground, and then the Russian chemist Dimitri I. Mendeleev proposed the process conception of UCG in 1888 [26]. By the beginning of the 20th century, as many cities were covered with smoke from the side effects of the Industrial Revolution, William Ramsay opened the first UCG test in Durham, England to try to solve the problem [27]. Since then, the world’s major coal powers have been competing for nearly a hundred years of research and engineering experiments. Table 1 shows the primary projects of overseas UCG in the 20th century [2]. Since the beginning of this century, UCG has been identified as an important source of energy in the context of a low-carbon economy [28], and UCG projects have set off a new upsurge [29]. Table 2 shows the primary UCG projects in the 21st century. Due to space limitations, the paper only briefly introduces the relevant gasification experiments in countries around the world since the 21st century.
In order to reduce the pollution of coal-fired power plants, the Chinese government decided to gradually develop the UCG project. There are about 15 underground coal gasification test centers in China. The UCG Research Center of China University of Mining and Technology (Beijing) has conducted theoretical research, model tests, and field tests. At the same time, the coal industry has led the development of UCG in China. In 2011, Seamwell International Limited (UK) and China Energy Conservation and Environmental Protection Corporation launched a $1.5 billion business partnership to gasify 6 × 106 tons of coal buried in the Yihe coalfield in Inner Mongolia, with a plan to produce 1000 MW of electricity over 25 years. In 2013, Zhengzhou Coal Industry Group Co., Ltd. as the sole technical cooperation partner of Carbon Energy’s UCG project in China, owned the technology licensing agreement for UCG [26,30,31].
In 2005, Neyveli Lignite Company, funded by the Ministry of Education of India, launched a new UCG project in India to study and evaluate the calorific value of the gases produced by the UCG test using suitable lignite blocks. Subsequently, the Central Mine Planning and Design Institute Limited (CMPDI) prepared five UCG coalfields and appointed the Russian Skochinsky Institute of Mining (SIM) as a consultant. The Raniganj coalfield, one of the five coalfields, has completed the UCG trial and drilling. In July 2014, two other UCG coalfields under the responsibility of Central Coalfields Ltd. and Western Coalfields Ltd. were identified by Coal India Limited (CIL) for commercial use [29,32,33].
In December 2009, Thar Coal and Energy Board partnered with Chinese, German, and South African companies to build a 6.5 × 106 ton/a coal mine in Pakistan that is expected to generate 1200 MW of electricity. A UCG project was launched in Thar Coal deposits in 2010, and the plant is still under construction, generating electricity in 2018 [4].
The United States and Canada have carried out field trials and theoretical studies on UCG for decades. The major developments of UCG in North America took place in 2005, mainly in Lawrence Livermore National Laboratories, which received Department of Energy (DOE) funding in 2006 to summarize the best test methods for UCG. Subsequently, Lawrence Livermore National Laboratories continued to study the UCG project, focusing on the development of a complete three-dimensional cavity growth simulator (reaction, geothermal, hydrological, hydrodynamics). The commercial development of UCG in the United States is mainly undertaken by Australian and Canadian companies (Linc Energy, Carbon Energy, Ergo Energy, and Laurus). In September 2014, Linc Energy was granted permission to develop UCG projects in Wyoming and to develop UCG-related gas to liquids from natural gas liquefaction. In the same year, Linc Energy also carried out UCG project exploration with the relevant state departments in Cooks Bay Alaska. Canada Company Laurus plans to develop a series of UCG projects. Currently, Canada’s most advanced UCG project is a Swan Hills Synfuels pilot project supported by the Alberta Energy Research Institute. Its mining depth is 1400 m thick, the coal seam is 4 to 5.2 m, and it used to be the world’s deepest UCG project [34].
Australia’s UCG project, the world leader in 2000–2012, has made considerable progress and development. In 1999, Linc Energy Company set up a UCG-IGCC (Integrated Gasification Combined Cycle) pilot project in Chinchilla, Queensland. Five underground coal gasification reactors were tested. After running for 21 months, the total output power is 67 MWh. In addition, Australia also actively conducts UCG syngas conversion into fuel research experiments. In 2007, Australia built a coal underground gasification and liquefaction plant. At present, the Australian Government has indicated its willingness to undertake carbon capture and storage (CCS) projects involving power generation and is interested in cooperating with European research institutions in UCG-CCS projects [4,26,31,35,36,37].
The Polish government believes that its vast coal resources can be exploited by UCG technology and generate electricity. Since 2007, Poland has assessed the feasibility of implementing UCG projects in the country through new exploration and field tests. The Central Mining Institute of Poland has undertaken the European Union project, Hydrogen Oriented Underground Coal Gasification (2007–2010), funded by the Research Fund for Coal and Steel. Its main focus is the production of hydrogen-rich gases in situ by underground gasification, with a total gasification of 22 tons of anthracite, an average gasification rate of 6.2 × 10−2 ton/h, and a total gas production of 71,764 m3. The follow-up project HUGE2 (2011–2014) funded by the Research Fund for Coal and Steel focuses on environmental and safety issues related to UCG processes. A total of 5.36 tons of anthracite was gasified with a gasification rate of 3.78 × 10−2 ton/h and a total gas production of 11,043 m3 [38].
In addition, underground coal gasification projects in Britain, Bulgaria, South Africa, and other countries are also gradually developing [39,40,41,42].
To summarize, many valuable practices have been carried out in the field of UCG all over the world, which proves that UCG is one of the important ways of coal production technology revolution. However, UCG has not been able to form a large-scale commodity development compared with the surface gasification [43]. The main reasons are: (1) difficulty in monitoring. (2) hard to control. It is difficult to perceive and monitor the combustion state of the gasification face because gasification is carried out in a closed underground and the underground conditions are complex and changeable as well as high temperature and humidity [44]. 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.

3. The Research Progress Related to Monitoring and Control for UCG

The coal gasification rate is low and the gas production as well as calorific value is unstable due to the difficulty in monitoring and controlling in UCG process. In order to solve these two kinds of problems, and then make full use of the potential sustainable advantages, scholars in various countries have worked out a series of meaningful research. The following discusses research status of UCG monitoring and control in two aspects.

3.1. Research Status of UCG Monitoring

Early UCG tests used thermocouples, flowmeters and gas analysis equipment to monitor underground temperature and combustion status [48]. Although these measurements effectively track combustion status and related underground and ground subsidence, they only have good effect on shallow seams and poor resolution for deeper seams. However, facing the high temperature and humidity environment of UCG, such sensors are not very accurate and may not work properly. In addition, Wang [49] studied and designed monitoring and control system of UCG based on wireless senor self-organizing network. However, the system has been tested in the laboratory and has not considered the high temperature and humidity environmental factors in the design. Li [50] designed a real-time monitoring and control system for UCG by using Siemens S7-300 PLC and Fame View configuration software. However, the system is used in the teaching experiment, and the high temperature and humidity environment factors are also not considered in the design. Barnwa et al. [51] proposed a UCG monitoring system based on wireless sensor networks to monitor groundwater pollution in the process of gasification. However, its sensor nodes are not suitable for working in typical high temperature and humidity UCG environments. Kostúr et al. [44] proposed a new method for the low-calorific coal with a higher humidity to improve the UCG technology from the point of syngas quality. However, these methods are costly or temporarily impossible to scale. Based on this, scholars from various countries explore other effective methods to monitor the UCG reaction process: Su et al. [43,52] monitored the temperature distribution, gas generation rate and gas content in the active cavity by the acoustic emission technique. Mellors et al. [48] applied electrical resistivity tomography and interferometric synthetic aperture radar techniques into UCG monitoring. Kotyrba et al. [8] used ground penetrating radar to remote monitor the image of mass loss (porosity, crack) area in coal during and after UCG process. Unfortunately, such methods are in the simulation stage. On the other hand, Nurzynska et al. [5] developed a UCG visualization information system to visualize the combustion state of UCG. On this basis, Kotyrba [9] monitored UCG data by GPR and visualized them in 3D. However, the standard commercial radar system is used to test the in situ and non-in situ combustion conditions of coal. The results show that the method can be used to monitor and visualize the coal gasification process under the non-in situ combustion conditions. It is not applicable in other cases, that is, it cannot monitor the in situ combustion state of coal in the actual gasification process.

3.2. Research Status of UCG Control

The control for UCG is an emerging field with rare references and limited to theoretical exploration and laboratory simulation. Based on model test, according to mass conservation, energy conservation, and chemical thermodynamics theory, Yang et al. [53,54,55,56] established and deduced the mathematical expression of moving velocity of gasification face, discussing the relationship between moving velocity of gasification face and oxygen supply as well as coal particle size. The calculated value is basically consistent with the model test value. On the basis of mathematical models, some scholars have studied the combustion state in gasifier [10,11,12,57]. These studies are effective for predicting combustion state. However, it is needed for real-time and accurate evaluation of combustion state in practice. Daggupati et al. [13] gave the relevant characteristics that affect the combustion state of UCG through laboratory simulation experiments. Subsequently, the team determined the optimal operating conditions for syngas conversion through experiments, and studied the effects of different operating variables on the evolution of the gasification face [14]. However, it did not determine an accurate mathematical model to characterize such processes. Therefore, Perkins et al. [58] developed a one-dimensional numerical model to investigate the effects of operating conditions (e.g., temperature, pressure, water influx, gas composition) and coal properties (e.g., thermo-mechanical spalling behavior, reactivity, composition) on the rate of local cavity growth and the effectiveness of energy utilization. The thermo-mechanical spalling behavior of coal, the behavior of the ash, and the amount of fixed carbon in coal were found to mostly affect the combustion rate. Uppal et al. [59] proposed a control-oriented UCG one-dimensional packed bed model that maintains the expected heating value of the outlet gas mixture by controlling the flow rate of the injected gas. The model can also predict important data parameters such as gas composition and combustion rate. However, in these control models, it is necessary to assume that the total concentration of all gases remains constant throughout the active cavity. Subsequently, Uppal made improvements to design a sliding mode control algorithm in the UCG simplified model to ensure the stability of the output heat value of the entire system [60]. However, the proposed method has simplified the modelling of the UCG process. In reality the chemical reactions, heat, and mass transfer, pore pressure, moisture content variations, evaporation, reactor void pressure, water influx, and development of cracks also affect the total behavior of a system. A three-dimensional thermal–mechanical modelling around UCG cavities using the computational software ABAQUS has been performed. The model was able to simulate the heat propagation, stress distribution, and surface subsidence in the UCG process [61]. However, this method only studied the thermo-mechanical behavior. The control of UCG process on site scale is a difficult task, especially considering process nonlinearity and ground disturbance [62]. Some other factors also make UCG control a challenging task.
In summary, these two kinds of research results will undoubtedly provide strong technical support for the research UCG monitoring and control. However, the research is relatively rare and in the theoretical exploration stage. To ensure the stability of the gasification process and to improve the composition and calorific value of the combustible gas, new technologies and methods are urgently needed to solve the problems of monitoring and control in the UCG process.

4. Future Perspectives

The IoT is an important part of the new generation of information technology. The related technology based on the IoT in the field of monitoring are developing rapidly. Saravanan et al. [21] put forward a real-time water quality monitoring SCADA system based on IoT. The real-time system generates, collects, transacts, and stores sensor data through the GSM module in the Web server. Its data is analyzed, and then instant reports are generated and displayed at any time on the Web browser. The system is designed to reduce manpower and costs, improve water distribution and monitoring efficiency. Marques et al. [22] proposed a real-time PM exposure monitoring system based on the IoT architecture. By monitoring indoor air quality, the system can accurately perceive the air quality status and, if necessary, plan interventions to reduce the level of PM exposure. It is an effective tool for monitoring indoor air particulate matter, aiming to ensure the permanent classification of particulate matter. There are other success stories, and this article does not list them one by one. Successful cases have shown that the IoT has a more thorough perceptive capability, i.e., it can use any device, system, or process that can sense, measure, capture, and transmit information at anytime, anywhere to monitor the environment in real-time. There is no doubt that the new impetus and imagination are brought to monitoring in the field of UCG.
On the other hand, with the rise of artificial intelligence, deep learning has developed rapidly in the field of control in recent years. Wang et al. [24], who transformed flame images into multi-layer DNN (depth neural network) or CNN (convolution neural network) by a deep learning model, predicted and identified the combustion state and heat release rate of a furnace. At the same time, the technology of stabilization and adjustment can balance the stability and sensitivity to ensure the accurate prediction of combustion state. The experimental results show that this method can predict the combustion state of the image. The recognition speed of each image is less than 1 ms, and the average accuracy is 99.91%. Therefore, it has great potential in industrial applications. Zhang et al. [25] proposed a new method to identify caving coal and rock accurately and quickly by means of bimodal deep learning and the Hilbert-Huang transform for mechanical vibration and acoustic signals of a hydraulic support tail beam. In this study, the bimodal learning method is used to make a DNN fully characterize the characteristics of coal and rock. It is used for transfer learning to solve the problem that training a DNN needs a large number of samples, and the extracted acceleration and sound pressure signal characteristics are combined to extract the most effective features. Experimental results show that the method has obvious advantages in recognition accuracy. Successful application cases show that the deep learning method can fully utilize the autonomy of the model, actively learn the surrounding environment information, constantly adjust its own parameters and network structure, and does not need to design specific algorithms for the data. It can explore its expression in low-dimensional space by high-dimensional learning, and obtain the self-learning features which are totally different from manual features, so as to guide the physical model to make correct action choices. This also brings new impetus and imagination to control research in the field of UCG.
In conclusion, we intend to apply the IoT into monitoring in UCG in order to accurately perceive the site information of UCG. We intend to apply deep learning into the control in UCG in order to accurately control the combustion speed and combustion state of UCG. Next, we will focus on the two aspects that we should study in the future.

4.1. Perception Platform of UCG Monitoring System Based on IoT

Figure 2 shows the perception platform of UCG monitoring system based on IoT. The wireless nodes are distributed reasonably to monitor the input gasifier, the output syngas, the three-zone gas component of the combustion furnace, the gasification channel, the pressure, the temperature, and the flow rate. The wireless infrared cameras are installed reasonably for flame monitoring and the wireless AP nodes are placed reasonably. These three devices transmit perceptual information to the ground database server for unified storage. Based on perceptive information, the factor analysis serves to construct an analysis model of influencing factors in the UCG process and dynamically analyzes the influencing factors on the stable combustion of coal gasification. It is also used to guide the optimized control server to rapidly utilize the deep learning method. A guidance model for coal gasification, which is constructed based on the perceptive information, can precisely adjust the injection of gasifier components and the output of coal gas, so as to achieve precise control of the combustion speed and combustion state for UCG.
However, with the complexity and dynamic of UCG process and the high temperature and humidity environment, the mature ground IoT technology and products will not work well under UCG environment. Based on this, the three key directions are proposed to solve the problem of reliable information monitoring in the UCG process for the perceptive information and communication nodes.

4.1.1. Package Structure Design for Moisture Proof and Thermal Insulation

The traditional wireless sensors for mine monitoring products will not work because of the high temperature and humidity environment of UCG. Therefore, perceptive information based on the IoT of UCG is facing the problem of the package structure design for moisture and thermal insulation. It is found for suitable high-temperature sensitive monitoring element and low-power semiconductor chips to design the intrinsic safety circuit of a WSN perceptive information and communication node, so as to achieve the purpose of active high-temperature resistance. It is designed for packaging internal cooling pipe networks to ensure the isothermal temperature inside the node, so as to achieve the purpose of resisting high temperature and high humidity. It is searched for suitable high-temperature resistant materials and surface thermal control coatings to study special packaging technology of nodes, so as to achieve the purpose of moisture and heat insulation.

4.1.2. Antenna Design

After the perceptive information and communication nodes are packaged by the intrinsic safety design of moisture and thermal insulation, it is difficult to communicate for nodes due to shielding of packaged metal, and it is intricate for the physical and chemical changes of the UCG process because of the restricted physical space and many changed dynamic factors affecting the gasification process, such as gasifier properties, gasification channel pressure, temperature, and flow rate injection. Therefore, the theoretical model of radio wave propagation is explored under high temperature and high humidity environments, and it is designed for the node antenna that meets the requirements of UCG communication to solve such problems. In addition, due to the small size and limited battery capacity of the wireless sensor nodes, it is necessary to reduce the antenna size and improve the radiation resistance and antenna radiation efficiency. All of these lays a foundation for node energy optimization.

4.1.3. The Strategy for Energy Management Optimization

In the UCG process, it is difficult for people to reach the perceptive information and communication nodes so that most of the nodes will be powered by unreplaceable batteries. Therefore, based on the theory of radio wave propagation in high temperature and high humidity environments, the strategy for energy management optimization of nodes are studied to ensure their stable operation and reliable transmission and, at the same time, energy is used efficiently to maximize the network life cycle. On the other hand, to save energy and consumption, UCG uses dynamic voltage regulation technology, which refers to reducing the processing power by reducing the operating voltage and frequency of the microprocessor when the load is light.

4.2. Optimal Control Model for UCG Based on Deep Learning

Figure 3 shows the optimal control model for UCG based on deep learning. There are two kinds of input in deep learning model, which are monitored by the perception platform of UCG monitoring system based on the IoT. One is the perceptive factors affecting gasification result, such as the gasifying agent component, the gasification channel pressure, the gasification channel temperature, the gasification channel velocity, and so on, the other is the perception of gasification result, which is divided into video perception and syngas perception. The complex high-dimensional nonlinear function between the input and output is characterized by deep learning model, which accurately predicts the gasification combustion and combustion speed. Subsequently, the proportion of the syngas component is precisely controlled by the controller, so as to achieve the purpose of controlling the velocity and output of the gas, and guiding the development of gasification in a good direction.
The key directions of optimal control model for UCG based on deep learning are mainly composed of feature extraction and classification design for network model, network structure design, network learning augmentation, and the control of the network model.

4.2.1. Feature Extraction and Classification Design for Network Model

The input of deep learning network used in video analysis is usually the original video image, and the convolutional neural networks are not always ideal for non-image signal processing. However, non-video perceptive information is also obtained by the perception platform of UCG monitoring system based on IoT. In order to realize the fusion of video and other non-video perceptual signal features of coal gasification, it is necessary to construct a hybrid deep learning network with video convolution-signal local connection by extracting the input data features of different attributes in one network and fusing the features through full connection in the later stage of the deep network. Thus, it can be determined for the classification of combustion state and the fitting of combustion speed characterized by the output node. Then the control parameters of coal gasification are also determined.

4.2.2. Network Structure Design

The accuracy for the training and control of deep neural networks depends on the deep of network structure to a certain extent. Theoretically, the deeper the structure is, the more complex the nonlinear mapping relation of the function can be expressed, that is, the more sufficient feature extraction is, the stronger the classification and fitting ability of the network is. However, in practical applications, the training algorithm of existing deep learning networks will expand rapidly with the increase of network depth, and may cause the gradient disappearance, which will lead to the convergence of the training process and is prone to over-fitting. Therefore, while improving the network training algorithm, it is also adjusted the structure of the deep learning network to determine the most suitable network structure and the depth of the network hierarchy, as well as the nonlinear activation function and the output objective function.

4.2.3. Network Learning Augmentation

Based on the past UCG test experience, the risk of failure is high and the deployed perceptive nodes may disappear after combustion so that the number of effective learning samples may be very limited. The collected data of video, gasification component, temperature, and pressure are even more precious. It is necessary to expand and augment the learning samples for deep neural network training through some means. Therefore, it is particularly important for the study of the augmentation methods of learning samples. The deep learning network can be quickly converged in the case of limited learning samples, reducing the need for precious learning samples.

4.2.4. The Control for Network Model

After obtaining a good deep learning model, it is also necessary to study the control strategy in the UCG process to obtain more accurate classification results. The control for network model is used to realize the control prediction and feedback iterative correction by deep learning. In this way, feedback function is added to a classification network, so that the classification results are fed back to correct input and the output classification results are obtained again. It is precisely adjusted for the injection of gasification component and the output of gas to achieve the purpose of precise controlling of the combustion state and combustion speed.

5. Conclusions

Compared with traditional coal mining technology, UCG has the potential sustainable advantages of high energy efficiency and low operating cost, and also has the sustainable potential to improve global air pollution problems. Among them, monitoring and control, which help to improve the quality of UCG syngas and maintain the stability of the calorific value and then make full use of the potential sustainable advantages, is a very important technology in the field of UCG. Researchers have conducted a series of meaningful studies in the field of monitoring and control for UCG, but this kind of research is relatively rare and in the stage of theoretical exploration, which prompted us to think about and explore new methods of monitoring and control in the field of UCG.
The paper had on outlook on the new monitoring and control in UCG, which can be realized from two models: the perception platform of a UCG monitoring system based on IoT and the optimal control model for UCG based on deep learning. These methods are expected to enable the injection of a UCG gas agent component and the output of gas to be accurately adjusted, thereby achieving the purpose of accurately controlling the combustion state and combustion speed of UCG, and finally make full use of the potential sustainable advantages. It should be pointed out that the new monitoring and control methods still face many problems, such as the package structure design for moisture-proof and thermal insulation, antenna design, the strategy for energy management optimization, and feature extraction and classification design for the network model, network structure design, network learning augmentation, and the control for network model. These are also the direction that the authors continue to explore.

Author Contributions

Conceptualization: Y.X. and H.Y.; data curation: J.Y.; writing—original draft preparation: Y.H.; writing—review and editing: J.W.; supervision: H.Q.

Funding

This research was funded by the National Natural Science Foundation of China 61379100, 61472388, and 51574232.

Conflicts of Interest

The authors declare no conflict of interest.

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Sustainability 11 00217 g001 550
Figure 1. Schematic diagram for UCG.
Figure 1. Schematic diagram for UCG.
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Figure 2. Perception platform of UCG monitoring system based on the IoT.
Figure 2. Perception platform of UCG monitoring system based on the IoT.
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Figure 3. Optimal control model for UCG based on deep learning.
Figure 3. Optimal control model for UCG based on deep learning.
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Table
Table 1. Primary projects of overseas UCG in the 20th century.
Table 1. Primary projects of overseas UCG in the 20th century.
Test SiteCountryYearSeam Thickness (m)Seam Depth (m)Coal Gasified (ton)Syngas cv (mj/m3)
LisichanskRussia1934–19360.7524 3–4
LisichanskUkraine1943–19630.4400 3.2
GorlovkaRussia1935–19411.940 6–10
PodmoskovaRussia1940–1962240 6 with O2
Bois-la-DameBelgium19481
Newman SpinneyUK1949–19591751802.6
Yuzhno-AbinskRussia1955–19892-Sep1382 × 1069–12.1
AngrenUzbekistan1965–now4110>1 × 1073.6
Hanna 1USA1973–19749.11203130
Hanna 2USA1975–19769.18475805.3
Hoe Creek 1USA19767.51001123.6
Hanna 3USA19779.18423704.1
Hoe Creek 2AUSA19777.510018203.4
Hoe Creek 2BUSA19777.5100609
Hanna 4USA1977–19799.110047004.1
Hoe Creek 3AUSA19797.51002903.9
Hoe Creek 3BUSA19797.510031906.9
PricetownUSA19791.82703506.1
Rawlins 1AUSA19791810513305.6
Rawlins 1BUSA1979181051698.1
Rawlins 2USA197918130–180776011.8
Brauy-en-ArtoisFrance19811200
ThulinBelgium1982–1984860
Centralia Tono AUSA1984–19856751909.7
Centralia Tono BUSA1984–19856753908.4
Haute-DueteFrance1985–19862880
ThulinBelgium1986–19876860157
Rocky Mountain 1AUSA1987–1988711011,2009.5
Rocky Mountain 1BUSA1987–1988711044408.8
EI TremedalSpain19972600
Table
Table 2. Primary UCG projects in the 21st century.
Table 2. Primary UCG projects in the 21st century.
CountriesYear of Project CommencementCompany OrganizationObjective
China2011UCG Research Centers (Beijing)
Seamwell, China Energy Conservation and Environmental Protection Corporation
Zhengzhou Coal Industry Co., Ltd.		Power Generation
H2 for fuel cells.
India2005Neyvell Lignite Corporation Limited
Central Mine Planning and Design Institute Limited
Central Coalfields Ltd., Western Coalfields Ltd.		Power Generation
Study and evaluate the calorific value of the gas generated.
Pakistan2009Thar Coal & Energy BoardPower Generation.
the U.S.2005Lawrence Livermore National Laboratories
Linc Energy, Carbon Energy and Ergo Energy		Natural gas liquefaction,
Developing 3D cavity growth simulators.
Australia2007Linc Energy companyUCG-CCS, UCG-IGCC
Power Generation.
Poland2007Central Mining Institute of Polandenvironmental and safety issues related to UCG processes.

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Xiao, Y.; Yin, J.; Hu, Y.; Wang, J.; Yin, H.; Qi, H. Monitoring and Control in Underground Coal Gasification: Current Research Status and Future Perspective. Sustainability 2019, 11, 217. https://doi.org/10.3390/su11010217

AMA Style

Xiao Y, Yin J, Hu Y, Wang J, Yin H, Qi H. Monitoring and Control in Underground Coal Gasification: Current Research Status and Future Perspective. Sustainability. 2019; 11(1):217. https://doi.org/10.3390/su11010217

Chicago/Turabian Style

Xiao, Yuteng, Jihang Yin, Yifan Hu, Junzhe Wang, Hongsheng Yin, and Honggang Qi. 2019. "Monitoring and Control in Underground Coal Gasification: Current Research Status and Future Perspective" Sustainability 11, no. 1: 217. https://doi.org/10.3390/su11010217

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