Coal Demand Research Articles

Coal consumption prediction based on least squares support vector machine

At present, China’s economic construction continues to move forward, and the demand for energy is increasing day by day, and the problem of energy shortage is showing. All industries are actively conducting research on energy saving and emission reduction. However, thermal power plants have large demand for coal and heavy pollution from flue gas. Therefore, technological upgrading, energy saving and operation control of coal-fired power plants need to be optimized. Support vector machine (SVM) algorithm is applied in the aspect of function fitting based on the strict statistics basis, and the coal consumption prediction model based on the least squares support vector machine is the product of the informatization application of power plant and the competition requirement of power market. Firstly, the research status of the optimization algorithm of thermal power unit is described. Secondly, the principle of support vector machine and least squares support vector machine is introduced, so as to do the basic work for establishing the prediction model of coal consumption later. Finally, the key problems to be further studied and solved in the field of coal consumption prediction in thermal power plants are discussed.

Can energy-price regulations smooth price fluctuations? Evidence from China’s coal sector

Due to the dominance of coal in China’s energy mix, coal prices have always been a challenging part of pricing reform. The recent frequent government interventions raise the key research questions: what is the actual impact of price policies on coal price fluctuations, and how can forward-looking pricing policies be made. By proposing a novel classification of coal pricing policies and introducing an expectation and forward-looking coefficient, the paper examines the relationship between coal price fluctuations and pricing policies using the generalized method of moments (GMM) method. It shows that the lagging coal price and coal demand play a positive role in regulating coal prices, while coal supply and marketization have significantly negative effects on coal price fluctuations. The heterogeneous impacts of price policies are due to differences in market players’ expectations, policy instruments and the methods of policy release. In addition, China’s coal pricing policy portfolio from 2013 to 2016 exerted synergy effects on the restraint of coal price fluctuations. As the forward-looking coefficient was considerably low, the government's intervention behaviors were obviously biased towards ex post facto responses. The paper suggests short run and long run policies to advance marketization of coal prices amid the energy transition.

Whither the Regulatory "War on Coal"? Scapegoats, Saviors, and Stock Market Reactions

Complaints about excessive economic burdens associated with regulation abound in contemporary political and legal rhetoric. In recent years, perhaps nowhere have these complaints been heard as loudly as in the context of U.S. regulations targeting the use of coal to supply power to the nation’s electricity system, as production levels in the coal industry dropped by nearly half between 2008 and 2016. The coal industry and its political supporters, including the president of the United States, have argued that a suite of air pollution regulations imposed by the U.S. Environmental Protection Agency during the Obama administration seriously undermined coal companies’ bottom lines, presenting an existential threat to the industry. Under the Trump administration, industry players have lobbied hard for (and sometimes received) financial subsidies and regulatory changes, with the president seemingly all too happy to play the role of the industry’s savior. Stepping back, we consider the extent to which regulations have really led to the decline in demand for coal and how much the coal industry can actually expect to gain from the deregulatory policies of the current administration. To illuminate these questions, we statistically analyze stock market reactions to important events in what critics called the regulatory “war on coal” during the Obama administration. Using an event-study framework that measures abnormal market activity in the immediate wake of these events, we are able to isolate any potential impact of regulatory developments above and beyond market factors, such as secular trends in natural gas prices and market performance as a whole. Surprisingly, we find no systemic evidence consistent with a “war on coal” based on investor assessments of the industry’s financial prospects in the wake of new regulatory developments, even though our methods do find evidence of stock market reactions to other events, such as bankruptcies of other companies. Coal firms’ investors—the very actors with financial stakes in understanding the impact of regulation on the industry—appear to have behaved as if they never actually bought into the regulatory “war on coal” narrative. Our findings are consistent both with broader evidence about the effects of regulation and with an underlying political economy of regulatory scapegoating, according to which actors in a declining industry prefer to blame regulation rather than competitive factors for their businesses’ decline. By calling attention to the pervasive incentives for scapegoating and cheap talk by politicians seeking to be saviors, we offer an account that can explain the mismatch between our findings and the rhetoric of the “war on coal.” Along the way, our account reinforces how important it is for courts, elected officials, and the public to demand that government agencies base their regulatory decisions on evidence instead of relying on political rhetoric.

Empirical Analysis of Gross Domestic Product and Coal Import Based on VAR Model

The speed of China’s economic development is gradually accelerating, and the demand for energy is also constantly increasing, especially the demand for coal. In order to reveal whether the coal imports have an impact on China’s economic development, this paper constructs the VAR(6) model by selecting the quarterly data of coal imports (CIV) and gross domestic product (GDP) from 2002 to 2017, performing ADF (Augmented Dickey-Fuller) stationarity test and Johansen cointegration test. It shows that there is a long-term stable equilibrium relationship between coal imports and GDP. Then the impulse response function is used to obtain the relationship between coal imports and GDP. It is found that the impact of coal imports on GDP is greater than the impact of GDP on coal imports.

Determination of Most Suitable Working Height of Powered Roof Support Considering Roof Stresses

In order to meet the increasing demand for coal, longwalls having large working heights up to 7.3 m are being operated worlwide. As the working heights increase, the load-bearing capacities of powered roof supports used at such longwalls are to be raised too. From a powered roof support it is expected that it safely bears the roof loads and transmits them to the footwall at different working heights. This article presents the results of numerical analyses on roof stresses at different longwall working heights. In order to determine the most suitable longwall working height, average stress distributions that occur on roof and gob zones are calculated for different longwall working heights. For this purpose, numerical models are built up and the procedure is applied to a real-life underground coal mine. Six distinct longwalls are modeled considering rock mass properties and working heights. In conclusion, the most suitable longwall working height for the mine under study is determined considering stresses occurring on roof strata and the gob zone.

Трансформація енергетичної системи: оцінка соціально-економічних наслідків для старопромислових шахтарських регіонів

The article defines the socio-economic effect for the old industrial mining regions as a result of the implementation of: measures to optimize production at domestic TPPs (optimal load distribution among TPPs) and the Energy Strategy of Ukraine for the period up to 2035 «Safety, energy efficiency, competitiveness». The socioeconomic consequences of reducing the demand for coal due to the optimization of the load on thermal power plants are given. The socio-economic implications for old industrial mining regions in the framework of the implementation of the Energy Strategy of Ukraine for the period up to 2035 under two scenarios are analyzed.

ESTIMASI PASOKAN BATUBARA UNTUK PLTU RENCANA DI PROVINSI KALIMANTAN BARAT

Abstrat - Coal is a cheap of fossil energy and encouraged in the framework of national energy diversification. Based on the Handbook of Energy and Economic Statistics of Indonesia 2016, Indonesia's coal reserves amounted to 32 billion tons which are spread on the islands of Sumatra and Borneo. Most of the domestic coal demands are currently used as fuel for PLTU to produce electricity. Based on the RUPTL PLN, to increase the reliability of the electricity system in West Borneo, the construction of non-fuel power plants such as the Parit Baru Power Plant (FTP1 and FTP2) and the Pantai Kura-Kura PLTU (FTP1). However, in West Borneo coal has not been exploited and coal reserves have also not been identified. To ensure the continuity of electricity supply in West Borneo Province, its need to estimated coal supply for the planned PLTU in West Borneo Province. which require data such as the coal specification data that will be used in the PLTU plan. After the data is collected, then the coal demands are calculated for each planne PLTU.

Will Trump's coal revival plan work? - Comparison of results based on the optimal combined forecasting technique and an extended IPAT forecasting technique

Discussions about United States President Trump's coal revival plan are dominated by qualitative analyses, few quantitative analyses have been conducted. To fill the research gap, this study analyzes the future coal demand of the United States from a market perspective. Both time-series and econometric forecasting techniques are developed to quantify the change of total coal consumption and coal consumption of electricity (sharing over 90% of total coal consumption) in the United States. The proposed time-series forecasting techniques are based on metabolic grey model, Autoregressive Integrated Moving Average Model-grey model, and induced ordered weighted geometric averaging operator. The mean absolute percent error of the proposed technique is less than 1%, indicating the proposed forecasting technique provides reliable information. The forecasting results obtained by time-series model show coal consumption and coal demand for electricity sector in U.S. will continue to decline in the next decade. The proposed econometric forecasting technique is based on the IPAT identity, grey model and Vector Auto-Regression. The combination econometric forecasting technique is used simultaneously to analyze the impact of various internal factors on coal consumption. The results from the proposed econometric technique also show the decline trend of coal demand in the U.S. Thus, the results from the time-series and econometric forecasting technique are consistent. Based on the quantitative analyses, this study contend that Trump's policy is unlikely to revive the coal industry.

Hydraulic Technology Design of Mine Fire Extinguishing Robot Mobile Platform Based on FPGA

China’s coal resources are very rich, and it is the world’s largest coal producer and consumer. In China’s energy industry, coal accounts for about 70% of China’s primary energy production and consumption structure, and is expected to account for more than 50% by 2050. Therefore, coal will remain China’s main energy source for a long period of time. At present, the rapid growth of China’s economy has put forward higher requirements for the development of the coal industry. With the rapid economic growth, the increase in coal demand has also brought a series of problems. In recent years, China’s coal mine accidents have occurred frequently. Mobile robots involve various high-end technologies such as information processing, mechanical engineering, and automation control. Mobile robots can move freely, so they can replace workers in hazardous or harsh environments. Because of its self-organizing, running, and planning functions, it has greater flexibility and flexibility than general robots.

Reducing Overcapacity in China’s Coal Industry: A Real Option Approach

Coal accounts for more than 60% of China’s primary energy consumption. Due to the demand decline since 2013, the coal industry was facing the dilemmas of falling prices, overcapacity, and high debt ratios. Reduction of overcapacity of the coal industry has become a crucial task in China’s supply-side structural reform. This paper attempts to explain several issues related to overcapacity reduction in the coal industry. First, we analyze the characteristics of China’s coal market and the causes of over-capacity in the coal industry. It is revealed that the aggregate coal demand of China is price inelastic, and the coal enterprises own market power. In addition, we illustrate that current overcapacity is the result of enterprises’ rational expansion in the context of rapid growth in demand in the previous period. Second, different capacity reduction schemes are compared. The results suggest that some of the inefficient production capacity should be temporarily withdrawn from the market, rather than ordering all coal mine to limit production capacity in the same proportion. Third, we conduct a regression model to describe the long-term price trend of coal and establish a mean-reverting model to simulate the motion path of the coal price. According to the Monte Carlo simulation, we estimate the value of the real option of coal capacity and find it is higher than the capacity replacement cost. This demonstrates that the real option is economically feasible in application.

Sales Forecasting and Coal Mining Planning in the Context of Hard Coal Production Strategies

Abstract Polish energy security is based mainly on a coal. Such dependency will be still maintained for several years. It is therefore necessary to improve the economic situation of polish mining companies. The mining companies in Poland are operating under tremendous uncertainty of turbulent environment. The article presents used by the authors methods to facilitate the creation and selection of optimal production strategy. These are scenario planning, forecasting and linear programming, which allow the simulation of company operations in the future. The authors suggest the most successful and optimal forecasting models for the Polish mining industry, as well as factors affecting the demand for hard coal in Poland. The manuscript presents also possible to apply coal production strategies and the method of evaluation and selection of optimal strategy in the difficult conditions of Polish mining companies environment. This makes it possible to take a decision burdened with minor errors and less risk.

Factors affecting industrial coal demand in Turkey

The aim of this paper is to investigate the factors affecting industrial coal demand by using an econometric model. Industrial coal demand, both short term and long term, was examined with Autoregr...

Has China’s coal consumption already peaked? A demand-side analysis based on hybrid prediction models

To deal simultaneously with the environmental problems caused by the current high-intensity exploitation and extensive use of coal resources, it is necessary to perform a scientific prediction of the trend and, especially, the peak of China’s coal demand. Based on the historical data on coal consumption and four primary factors (economic growth, energy structure, investment, and industrial structure) during the period of 1981–2015, this study established a hybrid model for coal demand prediction, using particle swarm optimization and cointegration methods. According to the prediction results combined with the actual statistics, in the business-as-usual scenario, China’s coal demand had peaked in 2014, then a downward trend started with an average annual decline rate of 5.85% for 2016 to 2020. However, future coal demand will keep increasing in the pessimism scenario. And in the optimism scenario, coal demand will decline much faster than the business-as-usual scenario. Sensitive analysis on four influential factors shows that coal demand is more sensitive to changes in investment and industrial structure, and more emphasis should be put on the supply and the demand side of coal industry.

Predicting Coal Consumption in South Africa Based on Linear (Metabolic Grey Model), Nonlinear (Non-Linear Grey Model), and Combined (Metabolic Grey Model-Autoregressive Integrated Moving Average Model) Models

South Africa’s coal consumption accounts for 69.6% of the total energy consumption of South Africa, and this represents more than 88% of African coal consumption, taking the first place in Africa. Thus, predicting the coal demand is necessary, in order to ensure the supply and demand balance of energy, reduce carbon emissions and promote a sustainable development of economy and society. In this study, the linear (Metabolic Grey Model), nonlinear (Non-linear Grey Model), and combined (Metabolic Grey Model-Autoregressive Integrated Moving Average Model) models have been applied to forecast South Africa’s coal consumption for the period of 2017–2030, based on the coal consumption in 2000–2016. The mean absolute percentage errors of the three models are respectively 4.9%, 3.8%, and 3.4%. The forecasting results indicate that the future coal consumption of South Africa appears a downward trend in 2017–2030, dropping by 1.9% per year. Analysis results can provide the data support for the formulation of carbon emission and energy policy.

Rare-earth recovery

Cover story The U.S. coal industry is facing tough times. Demand for coal is dwindling, and the industry is under fire for polluting streams and rivers with coal ash and acid mine drainage. But in that waste, and in some cases in the coal itself, are valuable rare-earth elements that may be readily recovered. If researchers can figure out how to extract them economically, sales of rare earths could help pay for some of the cleanup costs now borne by the coal industry and several coal-mining states. Demand for rare-earth elements, which include the lanthanides plus scandium and yttrium, has ticked upward over the past few decades. The long list of high-tech products that rely on rare earths include cell phones, flat-screen TVs, electric cars, wind turbines, satellites, defense aircraft, and high-performance magnets. In 2015, global demand for rare-earth elements was 149,000 metric tons per year, according to Mary Anne

Research on Coal Demand Problem Based on System Dynamics

Coal is the main energy resource in China. Predicting the dynamic trend of coal demand accurately plays an important role in energy consumption. Four scenarios that include weak industrialization scenario, strong service scenario, weak industrialization scenario, and strong service scenario that are based on the progress of science and technology, have been set to regulate five key factors, consisting of natural population growth rate, GDP growth rate, industrial proportion, tertiary industry proportion, and proportion of investment in science and technology. Based on system dynamics method and Vensim-PLE software, the system dynamics model of coal demand forecasting is built to simulate the total coal demand during 2011–2016 and forecast the total coal demand from 2017 to 2030. The constructed model can better predict the future coal demand through the test. In terms of total amount of coal demand, China’s coal demand has a trend of decreasing, which is lower than the 2016 level setting. The total coal demand of scenario 2 is higher than the rest of the scenarios and the total coal demand of scenario 4 is the smallest. Coal demand slows down under strong service sector, and inversely weak industrialization scenario, which is based on the progress of science and technology, can better control the total coal demand.

Analyses of Different Modifications Proposed for the Sponge Iron Process for Best Utilization of Waste Heat

In the present work, an effort is made for best utilization of waste heat to reduce energy requirements in sponge iron plants. For this purpose, based on the principles of process integration, three design modifications, scheme-1, scheme-2, and scheme-3, are proposed and applied on actual data of two sponge iron plants being operated in India. Scheme-1 accounts for preheating kiln inlet streams using waste gas exiting ESP and cooling kiln outlet using same ESP exit gas. However, for scheme-2, initially the kiln outlet is cooled using kiln air and further kiln inlet streams are preheated using waste gas exiting ABC. Scheme-2 with power generation is considered as scheme-3. To compute the coal demand of the proposed schemes, a modified model is developed based on heat of reactions, feed preheating, sensible heat, radiation losses, etc. Efficacies of schemes are computed while comparing results of these based on economic analysis. Savings in coal and water and hence reduction in waste gas generation, obtained in scheme-2, are found greater than that of scheme-1 as well as various design modifications reported in literature. Hence, scheme-2 is selected as the best possible design for sponge iron plants, proving process integration to be an important tool to save energy in these plants. However, for plants without a power generation unit, scheme-3 can be considered as best design.

Evaluation of initiating system by measurement of seismic energy dissipation in surface blasting

Enhanced demand for coal and minerals in the country has forced mine operators for mass production through large opencast mines. Heavy blasting and a large amount of explosive use have led to increased environmental problems, which may have potential harm and causes a disturbance. Ground vibrations generated due to blasting operations in mines and quarries are a very important environmental aspect. It is clear that a small amount of total explosive energy is being utilized in blasting for breakage of rock mass, while the rest is being wasted. The amount of energy which is wasted causes various environmental issues such as ground vibrations, air overpressure, and fly rock. Ground vibrations caused by blasting cannot be eliminated entirely, yet they can be minimized as far as possible through a suitable blasting methodology. A considerable amount of work has been done to identify ground vibrations and assess the blast performance regarding the intensity of ground vibrations, i.e., peak particle velocity and frequency spectrum. However, not much research has done into reducing the seismic energy wasted during blasting leading to ground vibrations. In this paper, the blast-induced ground vibrations in three orthogonal directions, i.e., transverse, vertical, and longitudinal, were recorded at different distances using seismographs. An attempt has been made for the estimation of the percentage of explosive energy dissipated in the form of seismic energy with electronic and non-electric (NONEL) initiation system. signal processing techniques with the help of DADiSP software is used to study the same.

Climate change and sustainability as drivers for the next mining and metals boom: The need for climate-smart mining and recycling

To avoid passing the Paris Agreement's proposed 1.5–2 °C global temperature rise and achieve the United Nations Sustainable Development Goals (UN SDGs) to reduce poverty, a rapid and sizeable investment in decarbonisation of energy systems, infrastructure and the built environment is needed. Many low-carbon sustainable technologies are more metal-intensive than current high carbon equivalents. Analysis of the increased demands of the transition to the low-carbon economy shows it could lead to the next mining and metals boom increasing metals demand by up to 10-fold by 2050. This major opportunity for the sectors will occur in tandem with additional climate change challenges. The industries will need to increase resilience to extreme events. Declining ore grades require greater energy input for extraction and processing so new methods to avoid associated emissions are required. While oil, gas and coal demand for energy consumption is projected to fall, non-fuel use of those resources will continue to have low-carbon alternative uses. Importantly, adaptation and mitigation opportunities have co-benefits. Profitable opportunities exist for the resource sectors to contribute to meeting the Paris Agreement and UN SDGs. A roadmap is presented that provides a synthesis of adaptation and mitigation strategies towards a climate-smart mining and recycling (CSM&R) strategy.

Analysis on the synergistic effect of sustainable development of coal industry under 1.5 °C scenario

Based on the coal demand under the 1.5 °C scenario, the amount of coal production in China was estimated in this study. According to the mutual relationship between the factors influencing coal production, an econometric model was constructed based on simultaneous equations, and the synergistic effect of each factor on the coal industry was estimated under the 1.5 °C scenario. Then, predictions were respectively made in the five aspects: coal production, coal science and technology progress, employment number, safe production level, and occupational health level, in different scenarios from 2016 to 2050. The results showed that before 2040, the comprehensive negative effect of the 1.5 °C situation is more than or equals to the positive effect. The 1.5 °C scenario has the biggest negative impact on employment, whose solution should be the focus. By 2050, the positive effects of the 1.5 °C scenario exceed the negative effect because of the enhancement in technical level and in the attention given to the whole production. Safety is improved, and health defects decline and the most obvious positive effect is on the ecological environment. The decrease of coal production will reduce the ecological environmental damage and significantly improve the ecological environment. In general, the prediction of 1.5 °C scenario promotes the increase of scientific production capacity and promotes the orderly development of coal. It has strengthened the safety and health protection degree, made the coal industry more efficient and competitive, and avoided or reduced the impact of coal development on the ecological environment and achieved environmental friendliness. However, the 1.5 °C situation also increases the employment pressure of the society, which affects the economic development of the major coal producing areas, but the situation can be overcome through the transformation and upgrading of the region. Finally, the impacts of various factors under the 1.5 °C scenario were evaluated through a unified comparison of the synergistic effect monetization using the cost and payment willingness methods. Based on the research results, suggestions on the regulation of coal production were proposed relating to resettlement of workers, protection of the ecological environment, and improvement of workers' health.