The relationship between oxygen consumption rate and temperature during coal spontaneous combustion

Zhu, Jinying; He, Ning; Li, Dengji

doi:10.1016/j.ssci.2011.08.023

Cited by 59 publications

(19 citation statements)

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“…The configuration of a CNF CC power plant can utilize the pure oxygen produced by the electrolysis, the anodic product of the carbon dioxide splitting, to improve the efficiency of the combustion include happen for a variety of reasons, ranging from a decrease in partial combustion as more oxidant is present, the reduction of NO x present as less nitrogen is available, and the higher temperatures reached which allow for improved thermodynamic efficiency [35][36][37][38][39][40]. This transition to an oxy-fuel combustion process reduces the impurities in the exhaust gas, which will prevent side reaction from occurring in the electrolysis chamber.…”

Section: Carbon Nanofiber Combined Cycle Power Plantmentioning

confidence: 99%

Thermodynamic assessment of CO2 to carbon nanofiber transformation for carbon sequestration in a combined cycle gas or a coal power plant

Lau

Dey

Licht

2016

Energy Conversion and Management

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a b s t r a c tMolten carbonate electrolyzers offer a pathway to capture emitted CO 2 from the flue gas of the power plants and transform this greenhouse gas emission at low energy and high yield instead into a specific, value added, hollow carbon nanofiber product, carbon nanotubes. The present day value of the carbon nanotubes product is $10,000 that of proposed, or in place, current carbon tax costs of $30 per ton, strongly incentivizing carbon dioxide removal. The recent progress in high-temperature molten carbonate electrolysis systems for carbon dioxide utilization and the impact these advances have on developing a CO 2 -free fossil fuel power plant for electricity generation is presented. A thermodynamic model analysis is presented for a molten Li 2 CO 3 electrolysis system incorporated within a combined cycle (CC) natural gas power plant to produce carbon nanofibers (CNF) and oxygen. Such a CC CNF plant system is shown to require 219 kJ to convert one mole of CO 2 to carbon, and generates electricity at higher efficiency due to pure oxygen looped back to the gas turbine input from the CO 2 splitting, with the added advantages that (i) the CC CNF plant emits no CO 2 and (ii) all CO 2 is converted to value added carbon nanotubes useful for strong, lightweight construction, batteries and nanoelectronics. Converting to power and ton units, per metric ton of methane fuel consumed the CC CNF plant is thermodynamically assessed to produce 8350 kW h of electricity and 0.75 ton of CNT and emits no CO 2 , while the CC plant produces 9090 kW h of electricity and emits 2.74 ton CO 2 . The required energy balance for a carbon nanotube production from an analogous coal power plant consumes a larger fraction of the coal energy, and encourages co-generation with renewable electric energy.

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Section: Carbon Nanofiber Combined Cycle Power Plantmentioning

confidence: 99%

Thermodynamic assessment of CO2 to carbon nanofiber transformation for carbon sequestration in a combined cycle gas or a coal power plant

Lau

Dey

Licht

2016

Energy Conversion and Management

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show abstract

“…1 and Fig. 2 shows the fitting relationship of the temperature about the gas concentration of CO and CH 4 , if given the gas concentration, multiple fitting relationship about temperature is derived. Temperature fitting value about the gas concentration is the weighted average, and then gets mean value.…”

Section: A Data Level Fusion Recognitionmentioning

confidence: 99%

“…O 2 , N 2 , CO, CO 2 , C 2 H 4 , C 2 H 2 , CH 4 and C 2 H 6 gas concentration was used as input information of feature level.…”

Section: B Feature Level Fusion Recognitionmentioning

confidence: 99%

“…These problems have brought a lot of confusion for prevention and control work of goaf coal spontaneous combustion. For a long time, many scholars at home and abroad has carried on the basic theory research and scientific experiments to conquer these difficulties [4]- [7]. Due to the complexity of the problem, many influence factors, the limitation of the detection means, although some progress has been made, but has not been satisfactory solution.…”

Section: Introductionmentioning

confidence: 99%

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Study on the Prediction Method of Fusion Recognition for Characteristic Information of Coal Spontaneous Combustion

Wang¹,

Deng²,

Hou³

et al. 2017

IJFCC

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Abstract-To solve the technical problems of identification for the dangerous degree of coal spontaneous combustion, the coal spontaneous combustion process is divided into three stages: slow oxidation, accelerating oxidation and intense oxidation in the paper. The prediction method of fusion identification for characteristic information of coal spontaneous combustion is proposed and the sensitivity index of coal spontaneous combustion degree is determined. The quantitative relationship of the characteristic temperature of coal spontaneous combustion and the gas concentration of each index is determined by polynomial least squares fitting method according to the sample test data. The different feature states are classified by SVM and PSO-SVM algorithm. The criterion of the diagnosis and early warning of coal spontaneous combustion is given according to the results of the data level and feature level. The proposed method can effectively solve the problem of low recognition rate. The experiment shows that the prediction classification accuracy of SVM is 80%, the prediction classification accuracy of PSO -SVM is approximately 100%. The PSO-SVM algorithm can significantly improve the prediction accuracy compared with the traditional method, which provides criterion for the diagnosis and early warning of coal spontaneous combustion. The classification identification of the dangerous degree of coal spontaneous combustion is implemented. It is of great significance and practical application value for improving the level of prevention and control technology of coal spontaneous combustion early hazards.

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“…Although these indexes have been playing positive role in combustion prediction, it could be easily affected by gases such as CO and CO 2 emanating from adjacent gobs or air entering fire zones. Another compound index, representing the ratio between carbon and effective hydrogen of coal oxidation products, called C/H was proposed by Ghosh and Banerjee in India (Wang et al, 2003;Zhu et al, 2012). Owing to the disturbance of methane released from coal, this index is only effectively applied in low gaseous mines (Singh et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Coal spontaneous combustion prediction in gob using chaos analysis on gas indicators from upper tunnel

Yang

Zhou

et al. 2015

Journal of Natural Gas Science and Engineering

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The relationship between oxygen consumption rate and temperature during coal spontaneous combustion

Cited by 59 publications

References 0 publications

Thermodynamic assessment of CO2 to carbon nanofiber transformation for carbon sequestration in a combined cycle gas or a coal power plant

Thermodynamic assessment of CO2 to carbon nanofiber transformation for carbon sequestration in a combined cycle gas or a coal power plant

Study on the Prediction Method of Fusion Recognition for Characteristic Information of Coal Spontaneous Combustion

Coal spontaneous combustion prediction in gob using chaos analysis on gas indicators from upper tunnel

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