Oxygen consumption as the definitive factor in predicting heat of combustion

Merckel, Ryan David; Labuschagne, F.J.W.J.; Heydenrych, Mike D.

doi:10.1016/j.apenergy.2018.10.111

Cited by 12 publications

(3 citation statements)

References 24 publications

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“…Table 1 summarizes the existing HHV models based on the composition of the three major components, C, H, and O from an ultimate analysis of biomass fuels. In the previous studies, HHV models were developed for wood [23], biomass [21,[23][24][25][26], sewage sludge [27], municipal solid waste [28,29], poultry waste [15], and any fuel [30,31]. Unfortunately, some published HHV models did not indicate the biomass fuel conditions of the samples used in their studies (e.g., dry-basis, dry ash free, or as received basis) or failed to indicate the range of biomass types to which their models can be employed.…”

Section: Introductionmentioning

confidence: 99%

Thermal Properties and Combustion-Related Problems Prediction of Agricultural Crop Residues

et al. 2021

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The prediction and pre-evaluation of the thermal properties and combustion-related problems (e.g., emissions and ash-related problems) are critical to reducing emissions and improving combustion efficiency during the agricultural crop residues combustion process. This study integrated the higher heating value (HHV) model, specific heat model, and fuel indices as a new systematic approach to characterize the agricultural crop residues. Sixteen linear and non-linear regression models were developed from three main compositions of the ultimate analysis (e.g., C, H, and O) to predict the HHV of the agricultural crop residues. Newly developed HHV models have been validated with lower estimation errors and a higher degree of accuracy than the existing models. The specific heat of flue gas during the combustion process was estimated from the concentrations of C, H, O, S, and ash content under various excess air (EA) ratios and flue gas temperatures. The specific heat of agricultural crop residues was between 1.033 to 1.327 kJ/kg·K, while it was increased by decreasing the EA ratios and elevating the temperature of the flue gas. Combustion-related problems, namely corrosions, PM1.0 emissions, SOx, HCl, and ash-related problems were predicted using the fuel indices along with S and Cl concentrations, and ash compositions. Results showed that agricultural crop residues pose a severe corrosion risk and lower ash sintering temperature. This integrated approach can be applied to a wide range of biomass before the actual combustion process which may predict thermal-chemical properties and reduce the potential combustion-related emissions.

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Section: Introductionmentioning

confidence: 99%

Thermal Properties and Combustion-Related Problems Prediction of Agricultural Crop Residues

et al. 2021

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“…He shows that this difference of −306 kJ/mol accounts for the major part of ∆H° c , making it the driving force for all combustion reactions involving O 2 (the majority) as an oxidant ( Figure 4 ). In other words, the double bond in O = O is weaker than the double bonds in other molecules; the difference makes the combustion reaction highly exothermic [ 24 , 25 , 26 ]. Accordingly, O 2 provides about 75% of the energy of combustion, while the organic part contributes only 25% [ 24 ].…”

Section: Redox Potential and The Chemical Basis For The Role Of O ...mentioning

confidence: 99%

Reduced Nucleotides, Thiols and O2 in Cellular Redox Balance: A Biochemist’s View

Bettendorff

2022

Antioxidants

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In the present review, which is aimed at researchers, teachers and students in life sciences, we try to show how the physicochemical properties of the elements and molecules define the concept of redox balance. Living organism are open systems traversed by fluxes of energy and matter. During catabolic oxidative metabolism, matter—mostly hydrogenated organic molecules—is oxidized and ultimately released as CO2. Electrons are passed over to coupling molecules, such as NAD+ and FAD, whose reduced forms serve as electrons donors in anabolic reactions. Early photosynthetic activity led to the accumulation of O2 and the transformation of the reduction to an oxidizing atmosphere, favoring the development of oxidative metabolism in living organisms. We focus on the specific properties of O2 that provide the chemical energy for the combustion reactions occurring in living cells. We explain the concepts of redox potential and redox balance in complex systems such as living cells, we present the main redox couples involved in cellular redox balance and we discuss the chemical properties underlying their cellular roles and, in particular, their antioxidant properties in the defense against reactive oxygen species (ROS). Finally, we try to provide an integrative view emphasizing the interplay between metabolism, oxidative stress and metabolic compartmentation in mammalian cells.

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“…The models were explained using two groups (molecular and functional), with different numerical algorithms producing nonlinear and multilinear predictive models. In determining the effects of fuel compounds on predicting the heat of combustion, Merckel et al [19] observed that the heat of combustion is more sensitive to variations in the percentage of oxygen in the fuel than to similar changes in the content of carbon and hydrogen.…”

Section: Introductionmentioning

confidence: 99%

Comparison of an Artificial Neural Network and a Multiple Linear Regression in Predicting the Heat of Combustion of Diesel Fuel Based on Hydrocarbon Groups

Younis¹,

Abbas²,

Najim³

et al. 2020

Rev. Chim.

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A comparison between artificial neural network (ANN) and multiple linear regression (MLR) models was employed to predict the heat of combustion, and the gross and net heat values, of a diesel fuel engine, based on the chemical composition of the diesel fuel. One hundred and fifty samples of Iraqi diesel provided data from chromatographic analysis. Eight parameters were applied as inputs in order to predict the gross and net heat combustion of the diesel fuel. A trial-and-error method was used to determine the shape of the individual ANN. The results showed that the prediction accuracy of the ANN model was greater than that of the MLR model in predicting the gross heat value. The best neural network for predicting the gross heating value was a back-propagation network (8-8-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.98502 for the test data. In the same way, the best neural network for predicting the net heating value was a back-propagation network (8-5-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.95112 for the test data.

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Oxygen consumption as the definitive factor in predicting heat of combustion

Cited by 12 publications

References 24 publications

Thermal Properties and Combustion-Related Problems Prediction of Agricultural Crop Residues

Thermal Properties and Combustion-Related Problems Prediction of Agricultural Crop Residues

Reduced Nucleotides, Thiols and O2 in Cellular Redox Balance: A Biochemist’s View

Comparison of an Artificial Neural Network and a Multiple Linear Regression in Predicting the Heat of Combustion of Diesel Fuel Based on Hydrocarbon Groups

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