Leachability and adverse effects of coal fly ash: A review

Wang, Nannan; Sun, Xiaodan; Zhao, Qiang; Yang, Ying; Wang, Peng

doi:10.1016/j.jhazmat.2020.122725

Cited by 207 publications

(83 citation statements)

References 170 publications

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“…By modifying the weights of neurons in each layer, the error signal is transmitted to the input layer successively for calculation, and then goes through the forward propagation process again. After repeated application of these two processes, the learning process is end when the final output of the modified network is close to the expected value [47][48][49][50].…”

Section: Principle Of Bp Neural Networkmentioning

confidence: 99%

Safety Warning Model of Coal Face Based on FCM Fuzzy Clustering and GA-BP Neural Network

Meng

2021

Symmetry

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Risk and security are two symmetric descriptions of the uncertainty of the same system. If the risk early warning is carried out in time, the security capability of the system can be improved. A safety early warning model based on fuzzy c-means clustering (FCM) and back-propagation neural network was established, and a genetic algorithm was introduced to optimize the connection weight and other properties of the neural network, so as to construct the safety early warning system of coal mining face. The system was applied in a coal face in Shandong, China, with 46 groups of data as samples. Firstly, the original data were clustered by FCM, the input space was fuzzy divided, and the samples were clustered into three categories. Then, the clustered data was used as the input of the neural network for training and prediction. The back-propagation neural network and genetic algorithm optimization neural network were trained and verified many times. The results show that the early warning model can realize the prediction and early warning of the safety condition of the working face, and the performance of the neural network model optimized by genetic algorithm is better than the traditional back-propagation artificial neural network model, with higher prediction accuracy and convergence speed. The established early warning model and method can provide reference and basis for the prediction, early warning and risk management of coal mine production safety, so as to discover the hidden danger of working face accident as soon as possible, eliminate the hidden danger in time and reduce the accident probability to the maximum extent.

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Section: Principle Of Bp Neural Networkmentioning

confidence: 99%

Safety Warning Model of Coal Face Based on FCM Fuzzy Clustering and GA-BP Neural Network

Meng

2021

Symmetry

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“…During coal consumption, a large amount of particulate matter such as y ash (FA) and CO 2 can be emitted into the atmosphere. 1 For sustainable development, low CO 2 or particulate matter emission into the atmosphere is required. Unfortunately, to date, coal power plants are still a critical source for power generation, especially in developing countries, 2 e.g., about 750 Mt of ash per year is discharged from coal-burning power plants in the world.…”

Section: Introductionmentioning

confidence: 99%

“…Particularly, FA particles can be blown into the atmosphere by the wind, thereby cause air pollution and harm the human respiratory system, eyes, and skin. 1 Moreover, the unsanitary landll can change the soil salinity, contaminate groundwater, and release toxic compounds into the soil. 3,11,12 Researchers have been focusing on developing new techniques for recycling coal FA waste to use in production and life toward sustainable development goals.…”

Section: Introductionmentioning

confidence: 99%

Enhancing the removal efficiency of methylene blue in water by fly ash via a modified adsorbent with alkaline thermal hydrolysis treatment

Dinh¹,

Vo²,

Tran³

et al. 2021

RSC Adv.

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“…The total annual production of coal ash worldwide, mainly fly ash (FA), is about 700 to 800 million tons, of which less than one-third is recycled [1,2]. The accumulated FA has become a problem for the environment because of its heavy metal content, which can be leached out [3,4]. The mineral composition of FA is mainly represented by the glass phase and also by quartz, mullite, magnetite, and other minerals.…”

Section: Introductionmentioning

confidence: 99%

Geopolymers Based on Mechanically Activated Fly Ash Blended with Dolomite

et al. 2021

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This study reports the effect of natural dolomite addition to fly ash and the mechanical activation of this blend on the geopolymerization process. Dolomite was replaced with fly ash at 1, 3, 5, and 10 wt.%. Geopolymers were synthesized at ambient temperature using NaOH solution as an alkaline agent. The geopolymerization process, reactivity of the raw material, compressive strength, and microstructure were studied using X-ray diffraction, Fourier-transform infrared spectroscopy, thermogravimetry, and scanning electron microscopy. It was shown that blending fly ash with dolomite and mechanical activation improved the geopolymer strength, especially during the early age of curing. For geopolymers prepared using a 90% fly ash + 10% dolomite blend cured for 7 d, the strengths were 8.2-, 2.3-, and 1.4-fold higher than those for geopolymers prepared using 100% FA for 30 s, 180 s, and 400 s milling times, respectively. A simple method for evaluating the increments of mechanical activation, carbonate additives, and the synergistic effect in the increase in the compressive strength of the composite geopolymer is proposed.

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Leachability and adverse effects of coal fly ash: A review

Cited by 207 publications

References 170 publications

Safety Warning Model of Coal Face Based on FCM Fuzzy Clustering and GA-BP Neural Network

Safety Warning Model of Coal Face Based on FCM Fuzzy Clustering and GA-BP Neural Network

Enhancing the removal efficiency of methylene blue in water by fly ash via a modified adsorbent with alkaline thermal hydrolysis treatment

Geopolymers Based on Mechanically Activated Fly Ash Blended with Dolomite

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