《1 Engineering research fronts》

1 Engineering research fronts

《1.1 Trends in top 12 engineering research fronts》

1.1 Trends in top 12 engineering research fronts

The top 12 engineering research fronts assessed by the Energy and Mining Engineering Group are shown in Table 1.1.1. These fronts involves the fields of energy and electrical science, technology, and engineering; nuclear science, technology, and engineering; geology resources science, technology, and engineering; and mining science, technology, and engineering.

Among these top 12 research fronts, emerging research fronts include “multivariate and high-density energy storage methods coupled with renewable energy,” “research on photoelectrochemical-photocatalytic hydrogen production from water splitting,” “research on methods of coupling big data and artificial intelligence (AI) in the smart grid,” “all-solid-state lithium ion batteries with high energy density and fast charging,” and “application of big data technologies and methods in oil-gas field geology–engineering–surface integration.” “Characteristics, prevention, and mitigation of severe accidents in nuclear power stations,” “critical metal mineralization and enrichment mechanisms,” “ultra- high-temperature and high-pressure drilling fluids,” and “multiphysics-coupling disaster-causing mechanisms in deep metal mining processes” are further developments of traditional research fields. “AI applied to reservoir prediction mechanisms,” and “concepts of safe, highly efficient, and intelligent extraction of coal, oil, and gas” are the fronts of interdisciplinary integration. “Research on damage mechanisms and verification of advanced nuclear fuel and related materials” is both an emerging front and a subversive front.

The number of core papers published annually from 2013 to 2018 for the top 12 engineering research fronts is listed in Table 1.1.2.

《Table 1.1.1》

Table 1.1.1 Top 12 engineering research fronts in energy and mining engineering

《Table 1.1.2》

Table 1.1.2 Annual number of core papers published for the top 12 engineering research fronts in energy and mining engineering

(1)   Multivariate and high-density energy storage methods coupled with renewable energy

Efficient and large-scale utilization of renewable energies, such as hydro, wind, solar, biomass, and geothermal energies, is the inevitable choice and important guarantee for sustainable development of global energy and environment. As far as resources and technology development are concerned, wind, solar, and hydro energies have the greatest prospects for development, and power generation is the most effective way to use them. However, a notable disadvantage of wind and solar power generation is that the output power is directly affected by environmental and climatic factors, resulting in features of randomness, intermittence, and fluctuation. Ensuring the continuous and stable output of qualified power from the power generation system is the basis and key for large-scale connection of renewable energy to the power grid. Constructing a renewable energy system that combines wind, solar, hydro, and thermal energies with energy storage units is an essential way to solve this problem, and is the trend of global power development.

Energy storage technology is an energy utilization technology that absorbs and stores energy for a period of time and then releases it in a controlled manner. It can be divided into five types: mechanical energy storage, electrical energy storage, electrochemical energy storage, thermal energy storage, and chemical energy storage. High-density energy storage, in terms of both high energy density and high power density, is the key to large-scale and efficient utilization of renewable energy, which is also a research hotspot of energy storage technology.

The emerging frontiers include low-cost, high-performance, single-type, and high-density energy storage methods; multivariate and high-density energy storage methods that are complementary to each other and coupled with renewable energy; multivariate, high-density energy storage methods that meet the different needs of renewable energy generation, transmission, distribution, and use; and renewable energy power supply systems based on the complementary coupling of wind, solar, hydro, and thermal energies.

(2)   Research on damage mechanisms and verification of advanced nuclear fuel and related materials

Nuclear fuel contains most of the radioactive materials and its containment is the first line of defense against the release of radioactive materials in nuclear power plants. Therefore, while the international community is developing advanced nuclear fuels, it is also vigorously conducting research on the damage mechanisms of nuclear fuels and related materials. At present, the research on the damage mechanisms of the current pressurized water reactor (PWR) fuel is primarily focused on the interaction between the fuel and the cladding during power transients; the instability caused by oxidation of the cladding, the fuel fragmentation, dispersal and relocation behavior in a large water loss accident; and the performance degradation of spent fuel cladding in dry storage. To explain the fuel and material behavior from a macroscopic viewpoint, numerous research institutions have conducted various microscopic studies in materials science to master the material behavior of different materials under a variety of corrosive environments, neutron irradiation, temperature fields, and stress fields. Combined with molecular dynamics methods, the damage mechanisms of advanced nuclear fuel and related materials are predicted, evaluated, and verified.

(3) AI applied to reservoir prediction mechanisms

AI has begun to enter into the petroleum field. Applying AI to reservoir prediction can solve various bottlenecks and problems in oilfield exploration and development. However, the application of AI in the petroleum industry is still in its infancy. The forecasting mechanism aims to store and analyze the massive data obtained during reservoir prediction, such as geological data, geomechanical data, reservoir data, engineering data, and economic data, among others, and use big data analysis techniques for high-precision geological modeling and high-efficiency numerical simulation to accurately describe the underground reservoir. Ultimately, all data is integrated on a unified platform, and reservoir prediction is performed from both data and model levels to facilitate reasonable scientific decisions. In short, with the innovation and technological advances of the times, only by combining the traditional petroleum engineering technical knowledge with AI, and realizing the strategic integration of creativity and creative thinking, can we actively adapt to and promote the future development of the petroleum industry.

(4)  Concepts of safe, highly efficient, and intelligent extraction of coal, oil, and gas

Intelligent perception, intelligent decision-making, and automatic control (execution) are the three elements of intelligent mining. Intelligent mining differs from general automatic mining in that the equipment has the functions of autonomous learning and decision-making, and has the abilities of self-perception, self-control, and self-correction. Only the intelligent and fully-mechanized mining system of this kind can completely respond to the changes of production environment, realize the real sense of intelligent mining, and realize the goal of unattended remote mining under constrained conditions.

The key technologies of intelligent coal mining include intelligent mining of thin seams and extremely thin seams, intelligent mining of large and extremely-large mining heights of thick seams, and intelligent mining of fully mechanized top coal caving in extra-thick seams. The development trend is to comprehensively promote the intelligent technology of fully mechanized mining in the future, realize the goal of limited unmanned mining, and achieve the goal of unattended robotic fluidized mining.

As the oil and gas exploration and development industry gradually turns its attention to the complex oil and gas resources (including unconventional, low-permeability, ultra- deep, deep-water oil and gas), there appears a series of new problems and challenges in the extraction of oil and gas in terms of safety, economy, and efficiency. Therefore, there is an urgent need to accelerate the cross-border integration of big data, AI, information engineering, underground control engineering, and establishment of a sound theoretical system of safe, efficient, and intelligent development. The eventual aim is to achieve advanced detection, closed-loop regulation, real-time warning, and intelligent decision-making, further promote the safe and economical extraction of oil and gas resources, and provide important support for achieving the major breakthroughs of complex oil and gas extraction in China.

The concepts of safe, highly efficient, and intelligent extraction of oil and gas involve reservoir characterization, downhole condition perception, closed-loop parameter control, and intelligent decision-making for choice of extraction scheme.

The reservoir characterization describes and evaluates the oil and gas flow characteristics in the reservoir during the entire production process and provides a basic reference for oil and gas exploitation regulation. The principal research trends of reservoir characterization include the detailed characterization of reservoir elements, dynamic three-dimensional (3D) geological modeling, and real-time correction of geological models and reconstruction theory.

The downhole condition perception identifies and diagnoses downhole conditions and offers early warning of risks, which then provides important support for safe extraction of oil and gas. The research trends related to downhole condition perception include the response mechanisms of underground monitoring equipment, automatic diagnosis of downhole risks, and early warning concepts.

The closed-loop parameter control is primarily intended to transmit downhole data to the ground, and then issue control instructions to the downhole equipment to adjust the parameters based on the dynamic analysis by a ground-based expert system. In this way, the bidirectional transmission of information is established, and the closed- loop real-time control is achieved, which can establish an important foundation for the economical and efficient production of oil and gas. The foremost research trends of closed-loop parameter control include the bidirectional high-efficiency transmission of massive datasets and closed- loop optimization theory applied to the downhole control parameters.

The intelligent decision-making necessary to devise extraction schemes utilizes the big data from oil and gas fields and then optimizes the extraction scheme for the oil and gas fields by using AI. The key research trends of intelligent decision-making include the dynamic optimization of oil and gas extraction schemes using big data and AI techniques, combined with the concepts of intelligent flow, integration, and self-purification of massive data during long-term extraction.

(5)    Research on photoelectrochemical-photocatalytic hydrogen production from water splitting

When a semiconductor photocatalyst is irradiated with light, the electrons located on the valence band undergo a transition due to the external light energy. Photogenerated electrons and holes will be generated simultaneously in the semiconductor. Water could then be split into hydrogen and oxygen by the photogenerated electrons and holes, respectively. This catalytic reaction is the so-called photocatalytic hydrogen production reaction. The efficiency of hydrogen production could also be promoted by applying an external bias, whose reaction is called the photoelectrochemical or photoelectrocatalytic hydrogen production reaction. Photocatalysis and photoelectrocatalysis have a great potential for future sustainable solar hydrogen production.

The fundamental limitations to improving the photocatalytic and photoelectrochemical hydrogen production efficiency include the limitation on the efficiency of light absorption, photo-generated carrier separation efficiency, and catalytic reaction kinetics. At present, the research mainly focuses on development of the strategy for regulating the energy bandgap, construction of heterogeneous structures and crystal planet, and modification of photo cathode/anode semiconductor material. The search for high-efficiency photocatalysts and the development of new low-cost and stable catalytic material and reaction systems have also become a research hotspot. Recently, selection of matched catalysts for photocatalytic hydrogen production and degradation of contaminants in water have also attracted wide attention.

(6)   Research on methods of coupling big data and AI in the smart grid

Big data is a dataset consisting of extremely large volumes of numerous data types having complex structures. Big data uses data analysis as the core, covering data preprocessing, fusion, storage, and processing. It has the characteristics of high volume, quick response, wide variety, and low value density. Broadly defined, AI is a smart machine that can react in a way that mimics human intelligence. Deep learning, a form of machine learning, is a method to realize AI. The coupling of big data and AI to the smart grid is a deep integration of data, autonomous knowledge learning, and application scenarios, which is an effective way to cope with the increasing complexity and uncertainty of the grid.

The current research directions include renewable energy generation power forecasting considering time-space correlation; automatic mining and extracting key features of grid stability assessment and emergency control; intelligent analysis and decision making of the power grid; intelligent diagnosis and identification of power grid faults; massive load data classification and prediction; and analysis of the power consumers’ behavior.

The emerging fronts are the development and construction of more advanced and intelligent analysis methods and platforms, and the application of the methods and platforms to deeper and more core fields. In the development of analysis methods and construction of platforms, stress should be placed on the introduction of mature, efficient, and advanced methods of big data analysis and AI, and the integration of existing analytics platforms to gradually build an integrated AI system platform for processing tasks in the power grid field. In field applications, emphasis should be placed on appropriate modification of AI methods in combination with the requirements of the power grid, enhancement of the applicability of the algorithm to increase the coupling depth, and the extension of the application to core areas such as grid security and stability analysis and control, and grid dispatching operations, to increase coupling breadth.

(7)   All-solid-state lithium ion batteries with high energy density and fast charging

All-solid-state lithium ion batteries (ASSLIBs) are considered to be one of the most important next generation technologies for energy storage, especially for portable electronic devices and electric vehicle (EV) applications. ASSLIBs have many advantages over commercial lithium ion batteries having liquid organic electrolytes; such advantages include improved safety and reliability, higher volumetric energy densities, wider operating temperatures, higher charging/discharging rates, and simpler battery design. Therefore, ASSLIBs have attracted increasing attention in recent years and are being pursued by many world-leading EV manufacturers such as Toyota, Renault, Nissan, BMW, and Tesla.

Solid electrolyte materials are the key to ASSLIBs. The major problem that hinders the development of ASSLIBs to date has been associated to the relatively low ionic conductivity of the solid electrolyte as well as the poor solid/solid contact at the electrolyte/electrode interface. The properties of a good solid electrolyte material must meet several requirements apart from high Li-ion conductivity. These include low activation energy for Li+ migration, negligible electronic conductivity (to avoid an internal short circuit), good chemical and electrochemical stability against electrode materials, good thermal stability, excellent mechanical properties, simple fabrication processes, and low cost. To date, there are a variety of material choices for solid electrolyte materials such as oxides, sulfides, hydrides, halides, borates or phosphates, thin films, and polymers. However, none of them can meet all of the essential requirements. Breakthrough discoveries of solid-state electrolyte materials with superior properties are in urgent demand.

ASSLIBs are the ultimate solution to achieving a high energy density and fast charging rate for lithium ion batteries. For its commercialization, great efforts are still to be made in key materials, structure designs, and fabrication techniques.

(8)   Characteristics, prevention, and mitigation of severe accidents in nuclear power stations

A serious accident at a nuclear power plant refers to an accident in which the severity of the accident exceeds the design-basis accident (DBA) circumstances and causes the core to deteriorate significantly or even melt, and the failure of corresponding safety facilities may cause a large amount of radioactive material to be released, which is part of the nuclear power plant’s beyond-design-basis accident. The accident process is divided into core melt disintegration, pressure vessel failure, and containment failure.

The PWR nuclear power plant depends on the three barriers to prevent nuclear fission products from leaking, namely, the fuel cladding, the primary circuit pressure boundary, and the containment. When serious accidents such as core melting occur, the mitigation and treatment of accidents primarily focus on lessening the core damage and ensuring the function of the third barrier (i.e., the containment) to reduce the release of radioactive materials into the environment.

In the design of comprehensive accident mitigation measures for nuclear power plants, it is necessary to consider the serious core damage that is actually eliminated. Accident conditions typically include direct containment heating, large- scale steam explosion, hydrogen explosion, containment heat loss, and melt–concrete interaction.

Study should first be conducted on the core melting mechanism. Accident prevention and mitigation engineering techniques, including the in-throw melt retention technology, core melt traps, and hydrogen elimination technology, should be optimized and improved by conducting research on the processes and phenomena of core melt migration in the reactor and migration outside the reactor. Research should then be conducted on the integrity of containment, including containment failure probability calculation, source removal and other preventive and mitigation measures, as well as response to containment isolation failure, containment bypass, early failure of containment, and other accident sequences that lead to failure of containment. Finally, the elimination of large-scale releases should be verified by deterministic and probabilistic methods and the development of severe accident analysis methods and software.

(9)  Application of big data technologies and methods in oil- gas field geology–engineering–surface integration

In oil and gas exploration and development, the “sweet spots” have been gradually reduced, forcing major oil companies to reduce costs and increase efficiency. However, big data, to some extent, can compensate for the theoretical defects. The big data application technologies and methods in oil-gas field geology–engineering–surface integration are thus proposed in this context. Based on the big data methods combined with pattern recognition technology, multi-scale data mining is performed on a large number of actual operational and historical data such as drilling, logging, oil testing, fracturing acidification, and ground testing to improve economic and social benefits. Through research and integration of geological, engineering, ground, and other full-process data; big data collection, processing, and storage; as well as cloud computing, exchange, and sharing, the big data application technology will ultimately be applied to strategic analysis and decision-making. That is to say, through the establishment of an integrated database, the existing mature data mining methods are used for regular analysis, dimensional analysis, correlation analysis, and empirical correlation statistical regression to guide the actual oilfield production practice. At the same time, the applications require a new management model to have a greater decision-making power, insight, and process optimization capabilities. In short, this is an essential technology for scientific exploration and development of oil and gas fields. It can transform data into information faster and more efficiently, quickly discover oil and gas, and reduce production costs. It will bring new vitality to major oil and gas fields and promote the informationization and intelligent development of oil and gas field development.

(10)  Critical metal mineralization and enrichment mechanisms

Critical metals, namely strategic metals, including rare trace elements, rare earth elements, dispersed rare elements, and platinum group elements, are the raw materials essential for development of modern advanced manufacturing, low-carbon clean energy technologies and other growing industries. China is well-endowed with a variety of critical metals. For example, China has supplied 95% of the total rare earth elements, 84% of the total tungsten, and 53% of the total scandium resource to satisfy global demands in the past years. However, other critical metals, such as manganese, niobium, beryllium, nickel, cobalt, chromium, and platinum group elements, are relatively scarce but crucially needed, which may increase the risks of bottlenecks in the establishment of advanced manufacturing and growing industries in China.

Most developed western countries have recently been dedicated to unraveling significant factors that lead to enrichment mechanisms and mineralized characterization of strategically critical metals, and encouraging their domestic mining companies to launch exploration projects targeted at critical metal resources. The mineralization of critical metals is characterized by dispersion, low-grade, small tonnage, and challenge in geometallurgy. Some critical metals can be produced on a relatively large scale, but they commonly occur as by-products in extraordinarily small quantities from base metal refining. The mineralization of critical metals is typically uncontinuous and it is difficult to delimit their orebodies. Furthermore, the ore genesis and accumulation mechanism of critical metals remain unclear. Therefore, new theories and deposit models for mineralization of critical metals are urgently needed. It is recommended that scientific research be strengthened to reveal the enrichment processes and mineralization mechanisms related to low-abundance critical metals, to elucidate the microscale occurrence modes and macroscale distribution patterns of critical metal elements, and to define the vital, coupled geological and physiochemical (-biological) factors controlling the mineralization of critical metals. All of the proposed research works aim to discover new types of critical metal ore deposits and increase the resource reservoirs, to maintain a stable supply of critical raw materials that are needed to achieve the strategic plan of “Made in China 2025.”

(11)  Ultra-high-temperature and high-pressure drilling fluids

Drilling fluid is the lifeblood of drilling engineering, providing important support for removing bottom cuttings, cooling drilling tools, and controlling formation pressure during drilling. Its performance is the key to successful drilling. At present, as energy demand increases year-by-year, deep exploration and development, especially deep and ultra- deep oil and gas, marine oil and gas, and new energy sources such as dry hot rock geothermal resources, have become inevitable. The ultra-high-temperature and high- pressure environment in the well poses severe challenges to the performance of the drilling fluid. This environment may even lead to complete destruction of the drilling fluid system and rapid deterioration of performance, resulting in various complex downhole accidents. Therefore, it is urgent to study ultra-high-temperature and high-pressure drilling fluids to optimize the high-temperature and high-pressure resistance of drilling fluids. As a result, it is necessary to establish ultra-high-temperature and high-pressure water- based drilling fluids, ultra-high-temperature and high- pressure oil-based drilling fluids, and ultra-high-temperature and high-pressure synthetic-based drilling fluid systems to ensure safe and efficient drilling of deep formations. Among them, the research of ultra-high-temperature and high- pressure water-based drilling fluids includes the development of new monomer synthesis and conversion techniques; development of salt-resistant high-temperature and high- pressure fluid loss reducers, viscosity reducers, inhibitors, lubricants, plugging agents, and well-wall stabilizers; and the development of anti-high-temperature thickeners and high- temperature and high-pressure fluid loss reducers. Research on ultra-high-temperature and high-pressure oil-based drilling fluids includes development of oil-based drilling fluid treatment agents such as ultra-high-temperature and high-efficiency emulsifiers, tackifiers, fluid loss additive, and flocculants; development of high-performance adhesives and surfactants; development of new reversible emulsification drilling fluid systems; and rheology control methods for forming a system. Research on ultra-high-temperature and high-pressure synthetic-based drilling fluid includes research and development of new low-cost synthetic base materials, development of emulsifiers, flow modifiers, tackifiers, and flocculants for synthetic-based drilling fluids.

(12)  Multiphysics-coupling disaster-causing mechanisms in deep metal mining processes

Deep metal mining faces the complex environment of the “three highs” and “one disturbance,” i.e., high ground stress, high osmotic pressure, high temperature, and strong mining disturbance. This research front aims to explore the multi-field coupling regulation of stress field, seepage field, temperature field, and chemistry field of deep metal mining, revealing gestation, evolution, and occurrence mechanisms of deep metal explosions such as deep rock burst and soft rock deformation under the multi-field coupling condition to lay the foundation for the advanced prediction and regulation of deep metal mine disasters.

Research and development areas include deep high-efficiency stress, seepage, temperature, chemical, and other multi-field environmental identification sensors and rapid detection methods to accurately identify the multi-field coupling environment of deep mining; multi-field coupling mechanics test systems, and discovery of the multi-field coupling rock mechanics behaviors; multi-field environmental intelligence inversion and dynamic monitoring analysis technology, deduction of the multi-field environment in the mining area by limited test data; investigation of the mechanisms of multi- field coupling disasters in deep mining; and development of numerical simulation software for mining disasters under multi-field coupling to develop the rock mass control concepts and the technology of multi-field coupled environmental conditions.

Based on existing research bases, the development in the future will focus on key research fields, i.e., “accurate intelligent identification technology under deep multi-field coupled environment,” “deep multi-field coupling mechanics test system,” “multi-field environmental intelligent deduction and dynamic monitoring analysis technology,” and “multi- field coupling disaster mechanism and rock mass control technology in deep mining,” to achieve innovative theoretical development and equipment-level breakthrough.

《1.2 Interpretations for four key engineering research fronts》

1.2 Interpretations for four key engineering research fronts

1.2.1 Multivariate and high-density energy storage methods coupled with renewable energy

(1) Conceptual explanation and key technologies

The efficient and large-scale utilization of renewable energies include hydro, wind, solar, biomass, and geothermal energies is the inevitable choice and important guarantee for the sustainable development of global energy and environment. As far as resources and technology development are concerned, wind, solar, and hydro energies have the best prospects for development, and power generation is the most realistic and prospective way to use them. However, a notable disadvantage of wind and solar power generation is that the output power is directly affected by environmental and climatic factors and, thus, exhibits great fluctuation, randomness, and intermittent behaviors. Such unqualified power cannot be directly connected to the grid on a large scale.

Energy storage technology is an energy utilization technology that absorbs and stores energy for a period of time and then releases it in a controlled way. According to the different storage media, energy storage technologies can be divided into five categories of mechanical, electrical, electrochemical, thermal, and chemical energy storage. Mechanical energy storage includes pump water, compressed air, and flywheel energy storage. Electrical energy storage generally includes supercapacitor and superconductor energy storage. Electrochemical energy storage refers to the energy storage using all kinds of batteries. Thermal energy storage stores electricity in the form of sensible, phase change, or chemical heat in the medium in a thermal insulation container, and then converts the stored heat into power or uses it as a thermal source. Chemical energy storage refers to the use of electricity to produce hydrogen or synthetic gas, ammonia, and other secondary energy carriers. Energy storage density is used to measure the energy storage capacity of energy storage equipment per unit mass or volume, which is also divided into energy density and power density. The former corresponds to the amount of energy stored, while the latter corresponds to the speed of energy storage and release. High-density energy storage with high energy density and high power density is the key to large-scale and efficient utilization of renewable energy. The associated research directions include the development of a single type of energy storage method with a high-density energy performance, and the development of multivariate energy storage systems using a combination of multiple energy storage methods.

(2)  Development status and future development trends

The technologies of renewable energy power generation and energy storage are developing rapidly worldwide. By the end of 2018, the total installed capacity of renewable energy power generation in the world had reached 2351 gigawatts (GW), including 1172 GW of hydropower, 564 GW of wind, 480 GW of solar energy, 121 GW of biomass, and 539 GW of geothermal energy. The cumulative in-service installed capacity of energy storage worldwide was 180.9 GW, an increase of 3% over the previous year, of which the pump water storage was 170.7 GW, accounting for 94%. The electrochemistry energy storage reached 4.89 GW, an increase of 66.3% over 2017. Among the electrochemistry energy storage, lithium ion batteries accounted for 86% of the total installed capacity, followed by sodium-sulfur batteries and lead-acid batteries, both accounting for 6%. In 2018, the installed capacity of renewable energy in the world was amounted to 171 GW, a year-on-year growth of 7.9%, of which 94 GW was solar energy, accounting for the largest proportion, while 49 GW and 21 GW was wind power and hydropower, respectively. Two-thirds of the world’s new electricity generation came from renewable energies. The newly-added in-service installed capacity of energy storage in the world was 5.5 GW, of which the largest was electrochemical energy storage, with a growth rate of 288% compared with the previous year.

China is the country with the fastest development of renewable energy power generation and energy storage technologies. By the end of 2018, the cumulative installed capacity of renewable energies had reached 729 GW, accounting for 38.4% of the total installed capacity of the country, of which hydropower reached 352 GW, accounting for 18.5% of the total installed capacity, wind power reached 184 GW, accounting for 9.7%, and photovoltaic power reached 174 GW, accounting for 9%. The cumulative installed capacity of in-service energy storage projects in China was 31.2 GW, an increase of 8% over the previous year, of which pump water energy storage was approximately 30 GW, accounting for 96% of the total installed capacity. The next largest two were electrochemical storage and molten salt storage, with a capacity of 1.01 GW and 0.22 GW and an increase of 159% and 1000%, respectively. In 2018, the newly-installed capacity of photovoltaic power was 44.26 GW, while the newly-added in- service installed capacity of the energy storage was 2.3 GW, of which electrochemical energy storage was the largest, with a number of 0.6 GW, an increase of 414% over 2017.

Constructing a power generation system that couples wind, solar, hydro, and thermal energies with energy storage units is an important way to solve the problem of discontinuity and instability of renewable energy generation. In recent years, China has successfully launched several demonstration applications of centralized renewable energy grid connection technologies. In Qinghai Province, the world’s largest water– solar complementary photovoltaic power station was established. An 850 megawatt (MW) photovoltaic power station was connected to a hydropower station as a “virtual hydropower unit.” The photovoltaic power is smoothly and steadily transferred to the power grid through the rapid regulation of the hydro-turbine units. In Jilin Province, one wind farm realized the fast tracking of the changes of curtailed wind power by means of an energy storage system equipped with regenerative electric boilers, and another wind farm demonstrated the integrated operation of wind power and energy storage systems.

Generally speaking, up to the present, only pump water energy storage and compressed air energy storage are relatively mature among the large-scale energy storage technologies in China and worldwide, but they are restricted by geographical conditions. In-service compressed air storage also burns fossil fuels to provide the heat source. Other energy storage technologies are still at the stage of demonstration and laboratory research. Breakthroughs are needed in their reliability, service life, cost, and application adaptability. These technologies are still not qualified for wide popularization and application in the power grid, restricting the efficient and large-scale utilization of renewable energy. Coupling the renewable energy generation technology with multivariate high-density energy storage can not only make possible the large-scale access of renewable energy to power grid, but also meet the urgent demand of flexible peak shaving required by the smart grid.

The emerging fronts include low-cost, high-performance, single- type, high-density energy storage methods; multivariate and high-density energy storage methods that are complementary to each other and coupled with renewable energy; multivariate and high-density energy storage methods that meet the different needs of power generation, transmission, distribution, and usage stages of renewable energy; and renewable energy power supply systems based on complementary coupling of wind, solar, hydro, and thermal energies.

(3)   Comparison and cooperation analysis based on major countries/regions and institutions

As shown in Table 1.2.1, China, the USA, India, and the UK are the countries with the largest number of core papers published in this research direction, with China and the USA ranking first and second, publishing 39.05% and 20.95% of the core papers, respectively. The proportion of core papers published by India and the UK is 9.05% each.

As shown in Table 1.2.2, the Chinese Academy of Sciences, Shanghai Jiao Tong University, the University of Chinese Academy of Sciences, the Universiti Malaysia Pahang, and Tsinghua University are the organizations with the largest number of core papers published in this research direction, the number of core papers being 11, 7, 6, 5, and 5, respectively.

As shown in Figure 1.2.1, China, the USA, the UK, Australia, and Spain are the countries that pay more attention to this engineering research front. China has the largest number of core papers published, and the major countries that cooperate with China in publishing core papers are the USA, the UK, Australia, and Japan. The USA has the second largest number of core papers published, and it cooperates with China, Spain, Australia, and Canada in publishing core papers.

As depicted in Figure 1.2.2, in this research direction the Chinese Academy of Sciences has close cooperation with the University

《Table 1.2.1》

Table 1.2.1 Countries or regions with the greatest output of core papers on “multivariate and high-density energy storage methods coupled with renewable energy”

《Table 1.2.2》

Table 1.2.2 Institutions with the greatest output of core papers on “multivariate and high-density energy storage methods coupled with renewable energy”

《Figure 1.2.1》

Figure 1.2.1 Collaboration network among major countries or regions in the engineering research front of “multivariate and high-density energy storage methods coupled with renewable energy”

《Figure 1.2.2》

Figure 1.2.2 Collaboration network among major institutions in the engineering research front of “multivariate and high-density energy storage methods coupled with renewable energy”

of the Chinese Academy of Sciences, and has cooperation with Tsinghua University. Massachusetts Institute of Technology (MIT) and the Pacific NW National Laboratory also have some cooperation.

Table 1.2.3 shows that the country with the greatest number of citing papers is China, with a percentage of 45.38%, followed by the USA, with a percentage of 17.98%. Germany and South Korea also cite more than 5% of core papers.

In Table 1.2.4, the institution with the greatest number of citing papers is the Chinese Academy of Sciences, with a percentage of 27.65%, followed by the University of the Chinese Academy of Sciences, with a percentage of 10.28%, Tsinghua University and China University of Science and Technology, with a percentage of 9.51% and 8.11%, respectively.

The above data analysis indicates that China and the USA are at the forefront in the number and citation of core papers in the engineering research front of multivariate high-density energy storage methods coupled with renewable energy. The number and citation of the core papers of the Chinese Academy of Sciences and several Chinese universities are of the highest in the world.

1.2.2 Research on damage mechanisms and verification of advanced nuclear fuel and related materials

Nuclear fuel contains most of the radioactive materials and is the first line of defense against the release of radioactive materials. Therefore, while the international community is developing advanced nuclear fuels, it is also vigorously

《Table 1.2.3》

Table 1.2.3 Countries or regions with the greatest output of citing papers on “multivariate and high-density energy storage methods coupled with renewable energy”

《Table 1.2.4》

Table 1.2.4 Institutions with the greatest output of citing papers on “multivariate and high-density energy storage methods coupled with renewable energy”

conducting research on the damage mechanisms of nuclear fuels and related materials.

At present, according to the existing engineering experience and material irradiation behavior, it is possible to better predict and evaluate the behavior of cladding corrosion, stress strain, and fission gas release of existing PWR fuel under stable operating conditions. The current research primarily focuses on the material damage and failure mechanism in transient and accident scenarios of nuclear power plants. Among them, the pellet-cladding interaction (PCI) in power transients involves the initial microcrack of cladding under the action of the corrosive fission gas atmosphere, which continuously expands due to the extrusion of fuel and causes the cladding to fail. In addition to the special concern on the instability oxidation of the cladding in a loss of coolant accident, attention should also be paid to the fragmentation, dispersal, and relocation of the fuel itself during the accident, which will have a profound impact on the consequences of the accident. In addition to the above-mentioned damage mechanisms and failure behaviors in the reactor, the material performance degradation mechanisms of spent fuel cladding during dry storage of PWRs, including hydride reorientation and delayed hydrogenation cracking, are closely related to safety of dry storage of spent fuel. Therefore, it is also the focus of research of the international community at present.

The analysis and mechanism of damage and failure behavior of advanced nuclear fuels and related materials are conducted in combination with macro-scale and micro-scale multi-scale research and analysis. Macroscopically, various types of experiments are performed, including various material performance tests, ion irradiation tests, and re-fabricated rod tests to obtain the behavior of materials in specific scenarios. Microscopically, various types of microstructure, morphology, and composition analysis tools, such as metallographic microscopes, scanning electron microscopes, and fluoroscopy electron microscopes, are used to study the internal characterization of defects, dislocations, phase transitions, and segregation, to explain the macroscopic material behavior.

To better carry out the unification of material behavior and mechanism at multi-scales, many countries have conducted research on the damage mechanisms of advanced nuclear fuel and related materials under irradiation conditions in the molecular dynamics field, and have established related models to predict the material irradiation behavior.

According to Table 1.2.5, the countries or regions with the largest number of core papers published in this research direction are the USA, China, and South Korea. Among them, the USA ranked first, having a proportion of core papers reaching 64.15%. The proportion of core papers of China exceeded 10%.

According to Table 1.2.6, the largest number of core papers published in this research direction are from Oak Ridge National Laboratory and Idaho National Laboratory, whose numbers of core papers published are both more than 10.

《Table 1.2.5》

Table 1.2.5 Countries or regions with the greatest output of core papers on “research on damage mechanisms and verification of advanced nuclear fuel and related materials”

《Table 1.2.6》

Table 1.2.6 Institutions with the greatest output of core papers on “research on damage mechanisms and verification of advanced nuclear fuel and related materials”

According to Figure 1.2.3, the USA, China, the UK, Germany, the Czech Republic, France, South Korea, and Sweden are more concerned about the cooperation between countries or regions in this field. China has many published papers, mainly in cooperation with the USA and Sweden.

According to Figure 1.2.4, the Oak Ridge National Laboratory, the Idaho National Laboratory, Pennsylvania State University, the Los Alamos National Laboratory, Westinghouse Electric Company, and the University Wisconsin have collaborated.

As given in Table 1.2.7, the country with the largest number of citing papers is the USA, with a proportion of 37.44%. China has a citing proportion of 26.99%, while South Korea has a citing proportion of 10.92%.

As shown in Table 1.2.8, Oak Ridge National Laboratory is the organization that produces the most of core papers, having a proportion of citing papers of 29.03%. The proportion of citing papers of the Korea Atomic Energy Research Institute is 10.26%.

The above data analysis indicates that the USA and China are leading producers of core papers in the field of nuclear fuels and related materials in terms of damage mechanisms and verification techniques. The number of citing papers of US research institutions is comparatively large.

1.2.3 AI applied to reservoir prediction mechanisms

The combination of AI and oilfield technologies may solve various bottlenecks and new problems in oilfield exploration and development. It is imperative to promote the transformation and application of the AI technology in old oilfields and oilfield industries. The greatest revelation of AI on reservoir prediction is that it can still produce significant prediction results by relying on massive data and deep

《Figure 1.2.3》

Figure 1.2.3 Collaboration network among major countries or regions in the engineering research front of “research on damage mechanisms and verification of advanced nuclear fuel and related materials”

《Figure 1.2.4》

Figure 1.2.4 Collaboration network among major institutions  in the engineering research front of “research on damage mechanisms and verification of advanced nuclear fuel and related materials”

《Table 1.2.7》

Table 1.2.7 Countries or regions with the greatest output of citing papers on “research on damage mechanisms and verification of advanced nuclear fuel and related materials”

《Table 1.2.8》

Table 1.2.8 Institutions with the greatest output of citing papers on “research on damage mechanisms and verification of advanced nuclear fuel and related materials”

learning algorithms without clear reservoir patterns and laws. The reservoir prediction process involves a large amount of data, including geological data, geomechanical data, reservoir data, engineering data, economic data, and so on. However, the data scale and resolution are not uniform, the spatial density is large, the time and frequency are different, and most of the explanatory data are obtained indirectly with great uncertainty.

Big data techniques should be used for high-precision geological modeling and high-efficiency reservoir numerical simulation, while techniques such as unstructured grids and dynamic simulations should be used to describe the underground reservoirs more accurately. Ultimately, all the data is integrated on a unified platform to facilitate decision- making based on both data and models. Based on the quantitative and visual 3D model, all measurement data and constraints for the specific oil and gas field exploration and development life cycle are presented.

Based on the AI algorithms, the uncertainties of the model are reduced by continuously updating the model, driven by the abundant data, thus automatically facilitating making the most scientific and reasonable predictions and decisions based on full consideration of all data and laws. Based on a series of intelligent tools, equipment, and methods, deep learning and cognitive analysis of various data in the reservoir can help find oil and gas enrichment areas. However, the AI technology is still in its theoretical infancy stage for reservoir prediction applications. It is necessary to clarify the direction in strategic research and implement breakthroughs in key technologies. This is the key to realizing the enhancement of oil and gas exploration and development technology in China. It is hoped that this application will allow the big data and AI technologies to go hand-in-hand with the evolution of oil and gas exploration and development, thereby extracting more oil and gas resources underground to meet the country’s growing energy needs.

Relevant papers in this area are published and cited primarily by Iran, Saudi Arabia, the USA, China, and other countries, as given in Tables 1.2.9 and 1.2.11. The foremost institutions include Petroleum University of Technology, King Fahd University of Petroleum and Minerals, Islamic Azad University, and the Technical College of Amirkabir University, as given in Tables 1.2.10 and 1.2.12. Australia has cooperated with the USA and Iran. Saudi Arabia has cooperated with Egypt and Malaysia; the Islamic Azad University, the Petroleum University of Technology, and the Southern Cross University have cooperated, as shown in Figures 1.2.5 and 1.2.6.

1.2.4 Concepts of safe, highly efficient, and intelligent extraction of coal, oil, and gas

(1)  Concepts of safe, highly efficient, and intelligent extraction of coal

At present, the principals of safe, highly efficient, and intelligent extraction of coal are still in their infancy; they are entering the key stage of technological innovation and development. Therefore, it is necessary to research and develop key technologies such as integration of adaptive control and support systems for intelligent control of mining

《Table 1.2.9》

Table 1.2.9 Countries or regions with the greatest output of core papers on “AI applied to reservoir prediction mechanisms”

《Table 1.2.10》

Table 1.2.10 Institutions with the greatest output of core papers on “AI applied to reservoir prediction mechanisms”

《Figure 1.2.5》

Figure 1.2.5 Collaboration network among major countries  or regions in the engineering research front of “AI applied to reservoir prediction mechanisms”

height in surrounding rock, intelligent navigation for working face straightness, multi-information fusion and coordination of systems, intelligent advance support, and auxiliary operation. By realizing self-perception of environmental parameters for comprehensive mechanized equipment and self-regulation of mining behavior, the intelligent mining technology can be upgraded.

With the intelligent development of fully mechanized mining, its strategic position is becoming increasingly prominent. Intelligent mining is one of the core systems of an intelligent coal mine. Precise mining also requires few people or applies unattended remotely-controllable intelligent mining as

《Figure 1.2.6》

Figure 1.2.6 Collaboration network among major institutions in the engineering research front of “AI applied to reservoir prediction mechanisms”

《Table 1.2.11》

Table 1.2.11 Countries or regions with the greatest output of citing papers on “AI applied to reservoir prediction mechanisms”

《Table 1.2.12》

Table 1.2.12 Institutions with the greatest output of citing papers on “AI applied to reservoir prediction mechanisms”

an important support. At present, many coal enterprises grasp the new trend of technological development and vigorously upgrade mining technologies and equipment, which provides an important opportunity for the intelligent and fully mechanized mining technology. However, there also exist some practical problems such as an insufficient understanding of intelligent mining. The key research directions of safe, highly efficient, and intelligent extraction of coal include the “transparent mining” technology, intelligent coupling and adaptive control of a hydraulic support control group and surrounding rocks, intelligent height control of the shearer, the cooperative control technology based on multi-information fusion systems, and intelligent control of advanced support and auxiliary operation.

(2)  Concepts of safe, highly efficient, and intelligent extraction of oil and gas

With the development of oil and gas exploration and development towards unconventional, low-permeability, ultra-deep, deep-water oil and gas, future oil and gas exploitation faces a series of difficulties and challenges in terms of safety, economy, and efficiency. Therefore, it is urgent to accelerate the cross-border integration of big data, AI, information engineering, and underground control engineering, and establish a sound theoretical system of safe, efficient, and intelligent development. The eventual aim is to achieve advanced detection, closed-loop regulation, real-time warning, and intelligent decision-making, further promote the safe and economical extraction of oil and gas resources, and provide an important support for achieving the major breakthroughs in complex oil and gas extraction in China.

The concepts of safe, highly efficient, and intelligent extraction of oil and gas primarily involve reservoir characterization, downhole condition perception, closed-loop parameter control, and intelligent decision-making for selection of extraction schemes. The reservoir characterization describes and evaluates the oil and gas flow characteristics in the reservoirs during the entire production process and provides a basic reference for oil and gas exploitation regulation. Currently, Texas A&M University has conducted research on multi-scale interpretation of oil and gas reservoirs based on geophysical data. Shell Oil Co. has built a 3D transparent reservoir that can be used for real-time interactive analysis. The downhole condition perception identifies and diagnoses downhole conditions, and offers early warning of risks, which can then provides crucial support for safe extraction of oil and gas. At present, British Petroleum (BP) has conducted a study on integrated risk control for safety of underground production. China’s SINOPEC Corp. has established a relatively complete risk identification and hierarchical management mechanism.

The closed-loop parameter control is designed to transmit downhole data to the ground, and then issue control instructions to the downhole equipment to adjust the parameters based on the dynamic analysis by a ground-based expert system. In this way, the bidirectional transmission of information is established, and the closed-loop real-time control is achieved, which can lay an important foundation for the economical and efficient production of oil and gas. At present, Schlumberger, PetroChina, and other companies have proposed a relatively complete automatic regulation mechanism for oil and gas production equipment based on monitoring of real-time data such as downhole temperature, pressure, and flow velocity. The intelligent decision-making of extraction scheme for oil and gas fields utilizes the big data of oil and gas fields, and then optimizes the extraction scheme using AI, which provides key support for China’s progress toward the smart oilfield. Currently, based on analysis of multi-dimensional massive data, Shell has realized the intelligent management of extraction schemes in hundreds of production and injection wells. However, China is still in its early stage of development in this research field.

The predominant research trends of reservoir characterization include the fine-grained characterization of reservoir elements, 3D dynamic geological modeling, real-time correction of the geological models, and the reconstruction theory. The key research trends of downhole condition perception include the response mechanisms of underground monitoring equipment, automatic diagnosis of downhole risks, and the associated early warning concepts. The primary research trends of closed-loop parameter control include the bidirectional high-efficiency transmission of massive data and the closed-loop optimization of downhole control parameters. The foremost research trends of intelligent decision-making include the dynamic optimization of extraction schemes of oil and gas based on big data and AI, and the concepts of intelligent flow, integration, and self-purification of massive data during long-term extraction.

(3)  Comparison and cooperation between countries/regions and institutions

As seen in Table 1.2.13, the countries or regions with the largest number of core papers addressing intelligent extraction concepts are China, the USA, and Saudi Arabia. The proportion of core papers published in other countries is less than 10% while the proportion of core papers in China exceeds 50%. It is observed that the largest number of core papers published are from Xi’an Shiyou University with a ratio of more than 10%, while the output ratio of core papers of other institutions is approximately 6% as seen in Table 1.2.14.

Based on Figure 1.2.7, the USA and Saudi Arabia focus more on cooperative research in this field. The USA has cooperated with China and France. Saudi Arabia has cooperated with Pakistan and Malaysia. No collaborative research has yet been conducted between other countries or regions. According to Figure 1.2.8, the Ocean University of China, National Laboratory for Marine Science & Technology (Qingdao), the SINOPEC Research Institute of Petroleum Exploration and Development, Shandong University, Shandong University of Science & Technology, and Shandong Zhengyuan Construction Engineering Co., Ltd. perform cooperative studies. Each institution has cooperated with the other four institutions. However, Xi’an Shiyou University, which has the largest number of core papers published, has not cooperated with any other institution.

According to Table 1.2.15, the countries or regions with the largest number of citing papers published are China, Saudi Arabia, Canada, and the UK. The proportion of citing papers in China is more than 30%, and the proportion of citing papers in

《Table 1.2.13》

Table 1.2.13 Countries or regions with the greatest output of core papers on “concepts of safe, highly efficient, and intelligent extraction of coal, oil, and gas”