Total Coal Consumption Research Articles

Simulation of total coal consumption control under air quality constraints based on machine vision

Simulation of total coal consumption control under air quality constraints based on machine vision

Coal Is Dirty, but Where It Is Burned Especially Matters.

Coal abatement actions for pollution reduction often target total coal consumption. The health impacts of coal uses, however, vary extensively among sectors. Here, we modeled the sectorial contributions of coal uses to emissions, outdoor and indoor PM2.5 (particulate matter with an aerodynamic diameter of less than 2.5 mm) concentrations, exposures, and health outcomes in China from 1970 to 2014. We show that in 2014, residential coal accounted for 2.9% of total energy use but 34% of premature deaths associated with PM2.5 exposure, showing that effects were magnified substantially along the causal path. The number of premature deaths attributed to unit coal consumption in the residential sector was 40 times higher than that in the power and industrial sectors. Emissions of primary PM2.5 were more important than secondary aerosol precursors in terms of health consequences, and indoor exposure accounted for 97% and 91% of total premature deaths attributable to PM2.5 from coal combustion in 1974 and 2014, respectively. Our assessment raises a critical challenge in the switching of residential coal uses to effectively mitigate PM2.5 exposure in the Chinese population.

Research on the Improved Neural Network of Coal Price Forecast Based on Co-integration Theory

China is a big coal consumer, and changes in coal prices will have a huge impact on the entire market economy system. In this paper, aiming at the prediction of coal price, the grey correlation analysis method is firstly used to describe the correlation degree between the main factors and the coal price, and the main influencing factors are sorted according to the correlation coefficient. Finally, it is determined that a coal price prediction system needs to be established on such indicators as coal reserves, raw coal production, total coal consumption, the proportion of coal in the energy mix, coal road traffic volume, coal imports, etc. Next, the quantitative indicators are combined with the economic cycle and cointegration theory to study, with the help of cointegration theory to improve the traditional neural network, so that it is more suitable for the prediction of coal prices. Finally, this paper obtains an effective coal price prediction model, which can better predict and analyze the future coal price and can play an important reference role in the formulation of government policies.

Measurement and influential factors of the efficiency of coal resources of China’s provinces: Based on Bootstrap-DEA and Tobit

A more efficient use of China’s coal resources is key to rapidly promoting the growth of the country’s industrializing economy. As such, it is essential that an effective approach for measuring and evaluating the current efficiency of coal resources is developed. This paper measures the efficiency of coal resources in 30 provinces in China from 2000 to 2015 using an improved DEA model of Bootstrap, and it uses Tobit regression to analyze the influencing factors on the efficiency of coal resources. The results illustrate that: 1) The proposed approach could significantly improve upon the accuracy of the measurement of the traditional DEA model. 2) The efficiency of coal resources shows an uptrend of fluctuation for the research period. With respect to the influential factors, total coal consumption has the least influence on the efficiency of coal resources. In contrast, local financial science and technology expenditures have greatest influence on the efficiency of coal resources. Among all the influential factors, local financial science and technology expenditures have the greatest negative impact on energy efficiency of coal resources and it is very critical that the government increases investment in scientific and technological investment in the mining industry.

Examining the Impact and Influencing Channels of Emission Trading Pilot Markets in China

The global warming has become a serious issue in the world since the 1980s. The targets for the first commitment period of the Kyoto Protocol cover emissions of the six main greenhouse gasses (GHGs). China is the world's largest CO2 emitter and coal consumer and was responsible for 27.3 percent of the global total CO2 emission and 50.6 percent of the global total coal consumption in 2016 (BP, 2017). As China plays an important role in the global climate change, China has set goals to improve its environmental efficiency and performance. In 2011, the Chinese government for the first time announced an intent to establish carbon emission trading market in China. Eight regional emission trading schemes have been operating since 2013 (seven pilot markets during the 12th Five Year Plan period and one pilot market during the 13th Five Year Plan period) including provinces of Guangdong, Hubei, and Fujian, and cities of Beijing, Tianjin, Shanghai, Shenzhen, and Chongqing. The goal of these regional emission trading pilot markets is to help the government establish an efficient carbon emission trading scheme at national level. Some researchers have been focused on examining the impact of emission trading schemes in China using CGE model by constructing different scenarios and ex-ante analysis using data prior to emission trading pilot markets implementation. While this paper tries to conduct an ex-post analysis with data of 2005-2017 to evaluate the impact of emission trading pilot markets in China at provincial level using difference-in-difference (DID) model. By including both CO2 and SO2 as undesirable outputs to calculate Malmquist-Luenberger (ML) Index to measure green total factor productivity, this paper plans to evaluate the impact of carbon emission trading pilot markets in China via emission reduction, regional green development, synergy effect and influencing channels. This paper tries to answer the following research questions: (1) Do emission trading pilot markets reduce CO2 emission and increase regional green total factor productivity? (2) Is there any synergy effect from emission trading pilot markets? (3) What are the influencing channels of emission trading pilot markets? Keywords: Emission trading, CO2 emissions, Different-in-difference

The integration of hybrid hydrogen networks for refinery and synthetic plant of chemicals

Hydrogen is the core source to both refinery and synthetic plant of chemicals. Refinery consumes high purity hydrogen while synthetic plant of chemicals needs syngas consists of hydrogen and carbon oxides. As main hydrogen production technologies, industrial coal gasification and steam methane reforming based pathways generate H2, CO and CO2, which is actually the mixture of hydrogen and carbon oxides. Hence, the gases demand of refinery and synthetic plant of chemicals and their supply from hydrogen production can form hybrid hydrogen networks. On the basis of complementary reuse, this paper firstly proposes integration of hybrid hydrogen network for refinery and synthetic plant of chemicals. Superstructures of individual and hybrid hydrogen networks are employed as problem illustration and corresponding linear programming (LP) mathematical models are formulated. Practical refinery and synthetic plant of chemicals cases are employed to demonstrate its application. Compared with individual networks, the natural gas conservation case can recover 8660.4 Nm3·h-1 hydrogen in purge gas, reduce 1386.6 Nm3·h-1 CO2 emission, equaling to reduction of 278.11 kmol·h-1 natural gas feedstock and 14.8% of total gas production load; the coal conservation case can even waive the total coal consumption and extra 104.1 kmol·h-1 natural gas, recover 8660.4 Nm3·h-1 hydrogen in purge gas, reduce 5255.8 Nm3·h-1 of CO2 emission and decrease 21.2% of the total gas production load. Furthermore, economic evaluation is also placed to account for the economic advantage of hybrid network.

Research on influencing factors of coal price based on improved principal component analysis and grey correlation method

As an important basic energy in China, the price fluctuation of coal is related to the operation of China’s coal market and the whole energy market and coal price analysis has always been the focus of the industry. By establishing an improved principal component-grey correlation analysis (PCA-GRA) model, the influencing factors of coal price were analyzed and tested one by one, and the influence degree of the influencing factors such as total coal production, total coal consumption, coal import and export volume, and world coal price on China’s coal price was studied. The results show that coal price fluctuation is greatly influenced by coal energy storage level and market environment level. In order to promote the development of coal industry, some suggestions are put forward.

Operation scheduling of a coal-fired CHP station integrated with power-to-heat devices with detail CHP unit models by particle swarm optimization algorithm

The accommodation of high-penetration renewable power poses a considerable challenge to power grids. Coal-fired combined heat and power (CHP) stations are forced to enhance their operational flexibility by applying heat-power decoupling technologies. Power-to-heat devices, including electric boilers and heat pumps, are capable to enhance the operational flexibility of coal-fired CHP stations. The problem regarding the operation scheduling of a CHP station with multiple CHP units and power-to-heat devices is addressed in this study. Operation optimization models integrated with detail CHP unit models are developed, and the particle swarm optimization algorithm is utilized as the optimization algorithm. Then, a case study are carried out. Results show that the unequal distribution of heating and power loads among coal-fired CHP units can decrease the total irreversibility caused by heating steam pressure regulation. The operation scheduling method provided in this study can decrease the total coal consumption by 14.14 and 14.70 t/day for the CHP station integrated with an electric boiler and a heat pump, respectively. As a result, 1204.7 and 1252.44 ton CO2, and an additional ∼182 and ∼190 kUSD/year can be saved for the reference CHP station integrated with an electric boiler and a heat pump, respectively.

Hierarchical dispatch method for integrated heat and power systems considering the heat transfer process

The coordinated operation of integrated heat and power systems (IHPSs) is a promising solution for accommodating renewable energy and improving energy efficiency. A practical, effective dispatch method that coordinates the electric power system (EPS) and district heating system (DHS) is of great concern. This paper proposes a practical, non-iterative hierarchical dispatch method that introduces the concepts of the feasible region of boundary variables (FRBV) and optimal function of boundary variables (OFBV). In the proposed method, the private information of the DHS is protected in both the FRBV and OFBV. Moreover, they are transmitted between the EPS and the DHS operators only once to reduce communication overhead. This paper further proposes a method to conservatively estimate the FRBV and OFBV of the DHSs when the heat transfer process is considered in the model. Case studies are presented for an actual IHPS located in a northern city in China, and the results show that: 1) The heat generation capacity is over-estimated when the heat transfer process is not considered. 2) The hierarchical dispatch method obtains a larger regulating range of the combined heat and power units compared to those of the traditional “heat-led” operation mode and reduces the total coal consumption and the curtailed wind power by 4.46% and 38.50%, respectively. 3) The approximation-based hierarchical dispatch method obtains a solution that is less optimal than the exact method, but this difference in optimal value is less than 1%.

Environmental Kuznets Curve: Empirical Relationship between Energy Consumption and Economic Growth in Upper-Middle-Income Regions of China.

This paper examines the environmental Kuznets curve (EKC) relationship between energy consumption and economic growth in upper-middle-income regions of China with the panel data of 21 provinces from 2000 to 2017. The influence mechanism of socio-economic factors on the EKC of these regions is also detected. The results show that the energy consumption EKC fitting map in these regions conforms to the classical environmental Kuznets curve, which is an inverted “N” type, and the inflection point is ahead and more accurate after adding spatial effects. The direct effect of energy consumption has delayed the inflection point, indicating that the level of industrialization, urbanization, and population density have a significant impact on EKC. At the same time, it is found that the level of industrialization and population density have a positive relationship with energy consumption, while the level of urbanization has a negative correlation with energy consumption. The spatial spillover effect of the indirect effects of total energy consumption, coal consumption, and crude oil consumption shows that the level of industrialization has a significant and negative link with EKC. The increase in the level of industrialization will affect the total energy consumption of neighboring areas and the consumption of coal and crude oil.

Improved estimation of rural residential coal emissions considering coal-stove combinations and combustion modes.

Rural residential coal combustion (RRCC) has detrimental effects on air quality, climate, and human health. There are large uncertainties regarding emissions from RRCC owing to the lack of consideration of several key factors (e.g. combination modes of coal and stoves, combustion modes, and high temporal resolution). In this study, we provided a new estimation framework for RRCC emissions through a case study in the Beijing-Tianjin-Hebei (BTH) region, China. The emission estimations were improved according to four aspects, namely (1) coal-specific and stove-specific coal consumption was calculated based on face-to-face field interviews of 6700 valid volunteers/households covering 288 villages in 50 counties; (2) the influences of combustion modes (flaming and smoldering modes) on emissions were considered; (3) emissions of different fuel-stove combinations were estimated based on coal, stove, and combustion mode-specific RRCC consumption and localised emission factors; and (4) a method for emission estimation with high temporal resolution (1h) was developed. The results indicated that RRCC emitted 413.6kt SO2, 55.7kt NOx, 5717.3kt CO, 149.4kt VOCs, 167.1kt PM2.5, 18.2ktEC, 32.5kt OC, and 8.2kt NH3 in 2016. The combination of bituminous coal and an advanced coal stove was the most significant contributor (20.7-71.8%) to various pollutant emissions. Coal combusted under the flaming mode contributed to most (81.9%) of the total coal consumption, and thus emitted the majority (50.8-99.8%) of pollutants, except for VOCs. Meanwhile, that under the smoldering mode only accounted for 18.1% of the total consumption, but contributed 49.2% and 74.7% of the CO and VOCs emissions, respectively. Two clear emission peaks occurred at approximately 7:00-9:00 and 18:00-20:00. The detailed coal consumption and emissions with high temporal and spatial resolution can provide sound data for further research on rural environmental issues and scientific support to pollution control strategies.

A novel cascade heating system for waste heat recovery in the combined heat and power plant integrating with the steam jet pump

Waste heat utilization is an essential approach for improving the energy utilization efficiency of the combined heat and power (CHP) plant and a significant low-carbon way to achieve clean urban heating. To recover the excess exhaust steam heat of the CHP plant with the high back-pressure turbine (CHP-HBP), this paper proposed a novel heating system integrated with the steam jet pump (SJP). The EBSILON software was applied for modeling the proposed thermal system and analyzing the thermal performance of the CHP-HBP heating system under different operating conditions. On this basis, the coupled component-system design solution was proposed by combining the 1-D mathematical design model of the SJP with the heating system performance. Compared with the conventional system, under the design condition, the exhaust steam recovery rate and the heating capacity of the novel system had a significant increment of 8.66% and 31.8 MW with the same power output. Meanwhile, the total exergy loss and standard coal consumption rate for electricity generation of the novel system reduced by 5.74 MW and 6.77 g/kWh, respectively, with about 2.32% improvement in electricity generation efficiency. The critical parametric influence analysis on the overall performance showed that the novel system has better adaptability with some fluctuations of turbine back-pressure, supply/return water temperatures, and heating load. Under off-designed conditions, the recovery rate of the exhaust steam of the novel system was 5–20% higher than that of the conventional system, and the coal consumption rate and electricity generation efficiency both performed better. In all, the proposed system provided a promising method for the effective utilization of waste heat in the field of clean heating and the energy system optimization and integration for the coal-fired CHP plants.

Evaluating the coal rebound effect in energy intensive industries of China

Abstract The energy intensive industries of China consumes the highest share of coal that accounts for 93.69% of China’s total coal consumption. Hence, these industries are important to achieve the goals to reduce carbon emission and energy conservation. The LMDI decomposition analysis is used to investigate the factors affecting coal consumption in these industries. The empirical findings indices that first, gross industrial output and structural changes had accelerated coal consumption, whereas energy intensity de-accelerated coal consumption. Second, technological improvements boosted industrial growth rate. Finally, results also reveal that there exists a coal rebound effect in energy intensive industries with the maximum at 35.07% in 2016, with an increasing trend during 1990–2016. The present research can help the policymakers to design a pinpointed policy framework to reduce carbon emission and coal conservation.

Coordination of Electricity and Heat Dispatch Strategy Combining Carbon Trading and Peak Shaving Compensation Mechanism

Integrated power generation group’s participation in the carbon trading market and peaking auxiliary service market is one of the effective ways to promote low carbon power development and wind power consumption. Aiming at the serious problem of wind abandonment during the low temperature period of winter heating period in Northeast China, a multi-market mechanism is proposed. The carbon trading is analysed. Firstly, and the regional peak demand optimization model based on multi-scenario stochastic programming and the maximum wind power acceptance power calculation model of regional power grid is constructed to calculate the peak shaving demand and existing peak shaving in each period of the regional power grid. Maximum wind power acceptance under resources; Based on the coordination of carbon quotas among power generation groups and the subsection compensation mechanism of compensated peak shaving costs, and aiming at the minimum sum of total coal consumption costs, wind abandonment penalties and compensation costs, integrated dispatching model considering carbon trading and peak shaving compensation is established. Finally, an example is given to verify the rationality and effectiveness of the proposed strategy.

Scheduling of electric-thermal integrated energy system considering environmental cost

Coordinated scheduling of electric heating comprehensive energy system is an effective measure to promote wind power consumption and solve environmental problems fundamentally. In the existing coordinated scheduling of electric heating, the optimization goal usually only considers the minimum of abandoned wind or the minimum of overall coal consumption of the system, and does not directly take pollutant emission as the optimization index. In the actual operation, the target of minimum total coal consumption or light rejection rate of abandoned air is likely to be inconsistent with the target of minimum pollutant emission. Therefore, this paper proposes a coordinated scheduling model of electric heating comprehensive energy system considering environmental cost, selects the appropriate expression form of environmental cost, unifies the economic cost and environmental cost, and takes its weighted sum value as the optimization goal, so as to achieve the lowest comprehensive economic cost and environmental cost on the basis of maximizing the consumption of wind power. Finally, the comparison between the traditional optimization model and the model proposed in this paper is carried out by the way of example simulation. The results show that the new optimization model is helpful to reduce the environmental expenditure, and the cost of considering the environmental cost is 5.2% lower than not considering the environmental cost.

Public health effect and its economics loss of PM2.5 pollution from coal consumption in China

China's energy structure is based on coal resource and it accounts for main proportion in the primary energy consumption. Coal consumption produces PM2.5 pollution, which seriously affects public health. Considering that there are few studies on the effect PM2.5 pollution produced by coal consumption, this paper uses the Poisson Regression model to estimate the impacts on public health and the economic loss of PM2.5 pollution produced by coal consumption using the data in 2015. Based on these results, the paper also predicts the impacts on public health effect and its economic loss caused by PM2.5 pollution from coal consumption under the baseline scenario and total coal consumption control scenario in 2020 and 2030. Finally, based on the research conclusions, suggestions are proposed to reduce the public health economic loss from PM2.5 pollution caused by coal consumption.

Design and Research of a Three-Phase AC Magnetic Separator for Coal Desulfurization and Ash Reduction

China’s total coal consumption accounts for 50% of total energy consumption. However, every ton of coal in the process of production and use will bring huge losses to the environment. Desulfurization and ash removal of coal have been a continuous focus of researchers in various countries. The three-phase alternating current (AC) magnetic separator is a device for desulfurization and ash reduction of coal based on the principle of generating an alternating magnetic field generated by a three-phase flat linear motor. It is optimized by finite element analysis and its electromagnetic thrust is improved by 114% after optimization. Factors such as current size, magnetic particle size, and installation angle of the device are also analyzed. The simulation results show that the structure design of the three-phase AC magnetic separator is reasonable.

Operation optimization on the large-scale CHP station composed of multiple CHP units and a thermocline heat storage tank

Combined heat and power (CHP) is used around the world to enhance energy efficiency. The power load and heating loads are coupled in CHP units. Therefore, heat-power decoupling is essential for CHP units because it serves as the balancing power units to accommodate intermittent renewable power. Heat storage is an effective way to enhance CHP station flexibility. Considering the detail energy consumption characteristics of CHP units, this study investigated how to operate the CHP station, which is composed of multiple parallel CHP units and a thermocline heat storage tank, under constrained heating and power loads. Operation optimization models were developed, and particle swarm optimization was applied. Then, a case study was carried out. The minimum adjustable power load of the CHP unit is decreased by 12% through integrating heat storage when the heating load is 300 MW. The operation of the thermocline storage tank and CHP units could be scheduled globally with the developed optimization models. The total coal consumption could be decreased by 10.27 tons for the analyzed typical day, and the average standard coal consumption rate for power generation reduces by 1.16 g·kW−1h−1. Additional ~133 kUSD per heating period can be saved.

Trade-off between environmental benefits and time costs for public bicycles: An empirical analysis using streaming data in China.

In recent years, bicycle sharing has become more popular in cities and towns around the world. Public bicycles are one of the important forms of bicycle sharing. The use of public bicycles instead of motor vehicles has brought about changes in environmental benefits for the society, and at the same time, it has also changed the travel time of residents. Because these changes are potential and cannot be measured directly, we put forward the calculation methods of environmental benefits and time costs of public bicycles according to the concept of opportunity cost. Taking Hohhot, the capital city of Inner Mongolia, as an example, according to the streaming data of 9.76×106 valid orders of public bicycle trips in 2016, we found that the use of public bicycles could reduce consumption of standard coal by as much as 5,796.11 tons (accounting 0.16‱ of the total coal consumption in Inner Mongolia) and carbon emissions were reduced by 4,381.28 tons (accounting 0.30‱ of the total carbon emissions in Inner Mongolia). However, the promotion of public bicycles is not without disadvantages. The use of public bicycles has increased the average travel time of residents by 0.06 hours, which is neglected in previous studies. The contribution of public bicycles to saving 1kg standard coal corresponds to 0.10 hours of travel time waste. Thus, reducing 1kg of carbon emissions is corresponding to a waste of 0.14 hours of travel time. The use of public bicycles varies among different income groups, which is caused by the value of travel time. Low-income groups use more frequently than high-income groups. Generally speaking, low-income groups bring more environmental benefits to society through the use of public bicycles, but bear more time costs. Therefore, we propose a carbon tax on high-carbon transportation modes to encourage green travel and balance the environmental benefits and time costs of public bicycles.

A Scheduling Model for Wind Power Consumption Considering Source-Charge Coordination of Combined Heat and Power System in Low-Carbon Environment

Increasing the penetration and consumption rate of new energy sources will help reduce or control carbon dioxide emissions and better promote green and low-carbon development. Based on the establishment of deep peak-load regulation model of pure condensing thermal power units, the model for the expansion of combined heat and power plant adjustment range, interruptible electro-thermal flexible load model and combined heat and power units carbon emission model, this paper introduces a quota-based carbon emission trading mechanism and constructs a source-load coordination scheduling model of combined heat and power system that considers the characteristics of heating network and aims at minimizing the total cost of units operation and carbon emission trading. The total coal consumption and wind curtailment absorption effect in three cases are compared by cases study. The outcomes display that the model in this paper can coordinate the scheduling resources on both sides of source and load, further diminish the total coal consumption and improve wind power consumption capacity based on improving the flexibility of source and load side, and achieve wind power consumption in low-carbon environment.