Clean Coal Technologies in China: Current Status and Future Perspectives

Chang, Shiyan; Jiang, Zhuo; Meng, Shuo; Qin, Shiyue; Yao, Qiang

doi:10.1016/j.eng.2016.04.015

Cited by 175 publications

(69 citation statements)

References 30 publications

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“…According to the Beijing Statistical Year Books (2012-2017), coal consumption in Beijing declined remarkably by 56 % in 6 years as shown in Figure 6 (Karplus et al, 2018;BMBS, 2013BMBS, -2017. The slightly faster decrease in SO2 concentrations relative to coal consumption ( Figure S8) was likely due to the adoption of clean coal technologies that were enforced by the "Action Plan for Transformation and Upgrading of Coal Energy Conservation and Emission Reduction (2014-2020)" (Karplus et al, 2018;Chang et al, 2016). In summary, energy re-structure, e.g., replacement of coal with natural gas (Figure 6; Section S2), is the most effective measure in reducing ambient SO2 pollution in Beijing.…”

Section: Evaluating the Effectiveness Of The Mitigations Measures In mentioning

confidence: 99%

Assessing the impact of Clean Air Action Plan on Air Quality Trends in Beijing Megacity using a machine learning technique

Shi

Cheng

et al. 2019

Preprint

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Abstract. A five-year Clean Air Action Plan was implemented in 2013 to reduce air pollutant emissions and improve ambient air quality in Beijing. Assessments of this Action Plan is an essential part of the decision-making process to review the efficacy of the Plan and to develop new policies. Both statistical and chemical transport modelling were applied to assess the efficacy of this Action Plan. However, inherent uncertainties in these methods mean that new and independent methods are required to support the assessment process. Here, we improved a novel machine learning-based random forest technique to quantify the effectiveness of Beijing's Acton Plan by decoupling the impact of meteorology on ambient air quality. Our results demonstrate that meteorological conditions have an important impact on the year to year variations in ambient air quality. Further analysis show that the favorable meteorological conditions in winter 2017 contributed to a lower PM2.5 mass concentration (58&#8201;&#956;g&#8201;m&#8722;3) than predicted from the random forest model (61&#8201;&#956;g&#8201;m&#8722;3), which is higher than the target of the Plan (2017 annual PM2.5&#8201;<&#8201;60&#8201;&#956;g&#8201;m&#8722;3). However, over the whole period (2013 to 2017), impact of meteorological conditions on the trend of ambient air quality are small. It is the primary emission control, because of the Action Plan, that has led to the significant reduction in PM2.5, PM10, NO2, SO2 and CO from 2013 to 2017, which are approximately 34&#8201;%, 24&#8201;%, 17&#8201;%, 68&#8201;%, and 33&#8201;% after meteorological correction. The marked decrease in PM2.5 and SO2 is largely attributable to a reduction in coal combustion. Our results indicate that the Action Plan is highly effective in reducing the primary pollution emissions and improving air quality in Beijing. The Action Plan offers a successful example for developing air quality policies in other regions of China and other developing countries.

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Section: Evaluating the Effectiveness Of The Mitigations Measures In mentioning

confidence: 99%

Assessing the impact of Clean Air Action Plan on Air Quality Trends in Beijing Megacity using a machine learning technique

Shi

Cheng

et al. 2019

Preprint

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“…As an important fossil energy source, coal has always played a dominant role in China's primary energy consumption structure [1,2]. Although the safety situation of coal mines has been improved in recent years, mine water has always been a substantial threat to mining safety [3].…”

Section: Introductionmentioning

confidence: 99%

Predicting the Water-Conducting Fracture Zone (WCFZ) Height Using an MPGA-SVR Approach

et al. 2020

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Mine water that inrushes from coal-roof strata has always posed a substantial threat to mining activities every year. Therefore, an accurate prediction of the water-conducting fracture zone (WCFZ) height in the mining overburden strata is of great significance for the prevention and control of mine water accidents. The support vector regression (SVR) is proposed to predict the height of the WCFZ based on the mining depth, hard rock proportional coefficient, mining thickness and length of the working face. Simultaneously, the multi-population genetic algorithm (MPGA) is employed to search for the optimal SVR parameters. The MPGA-SVR model is trained and tested with a total of 69 collected data samples, and it is also applied to a field test. The accuracy and stability of the model were measured by the mean squared error and correlation coefficients. The obtained results show that the MPGA-SVR model achieves a higher accuracy and stability than the traditional empirical formula and genetic algorithm (GA)-SVR model. In terms of the process for optimizing the SVR parameters, the MPGA can find the optimal parameters more quickly and accurately, and it can effectively overcome the problem of premature and slow convergence of the genetic algorithm (GA). The proposed model improves the prediction accuracy and stability, which will help to avoid accidents caused by the inrush of water inrush in mining overburden strata and protect the ecological environment of the mining area.

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“…It is believed that a combination with supercritical steam cycle to increase the efficiency of energy conversion is one of the futures of CFB combustion technology [4]. The first 600 MW supercritical CFB boiler demonstration project was put into commercial operation in 2013 [5]. By the end of 2017, more than eighty-two supercritical CFB boilers were in operation or under construction in China [6].…”

Section: Introductionmentioning

confidence: 99%

On the Flexible Operation of Supercritical Circulating Fluidized Bed: Burning Carbon Based Decentralized Active Disturbance Rejection Control

Zhang

Xue

et al. 2019

Energies

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Supercritical circulating fluidized bed (CFB) is one of the prominent clean coal technologies owing to the advantages of high efficiency, fuel flexibility, and low cost of emission control. The fast and flexible load-tracking performance of the supercritical CFB boiler-turbine unit presents a promising prospect in facilitating the sustainability of the power systems. However, features such as large inertia, strong nonlinearity, and multivariable coupling make it a challenging task to harmonize the boiler’s slow dynamics with the turbine’s fast dynamics. To improve the operational flexibility of the supercritical CFB unit, a burning carbon based decentralized active disturbance rejection control is proposed. Since burning carbon in the furnace responds faster than throttle steam pressure when the fuel flow rate changes, it is utilized to compensate the dynamics of the corresponding loop. The parameters of the controllers are tuned by optimizing the weighted integrated absolute error index of each loop via genetic algorithm. Simulations of the proposed method on a 600 MW supercritical CFB unit verify the merits of load following and disturbance rejection in terms of less settling time and overshoot.

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Clean Coal Technologies in China: Current Status and Future Perspectives

Cited by 175 publications

References 30 publications

Assessing the impact of Clean Air Action Plan on Air Quality Trends in Beijing Megacity using a machine learning technique

Assessing the impact of Clean Air Action Plan on Air Quality Trends in Beijing Megacity using a machine learning technique

Predicting the Water-Conducting Fracture Zone (WCFZ) Height Using an MPGA-SVR Approach

On the Flexible Operation of Supercritical Circulating Fluidized Bed: Burning Carbon Based Decentralized Active Disturbance Rejection Control

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