Estimation of coal gross calorific value based on various analyses by random forest method

Matin, S.S.; Chelgani, S. Chehreh

doi:10.1016/j.fuel.2016.03.031

Cited by 128 publications

(43 citation statements)

References 30 publications

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“…Thus, it can be reported that FW releases more energy upon complete combustion than cow dung, lawn grass and pig waste. As per the investigation conducted by Matin and Chelgani [19], it can be concluded that FW, grass and cow dung possess a highly energy potential than pig waste.…”

Section: B Calorific Value Of Substratesmentioning

confidence: 72%

Section: B Calorific Value Of Substratesmentioning

confidence: 99%

“…CV is defined as the potential amount of energy per unit mass obtained after complete combustion [19]. It can also be seen as an indicator of the chemical energy stored in substrate, and a factor of good quality of components [19]. To determine and predict the performance of each component during the AD, the amount of energy per unit mass of cow dung, grass clippings, FW, and pig waste were compared in table II.…”

Section: B Calorific Value Of Substratesmentioning

confidence: 99%

See 2 more Smart Citations

Impact of Substrate Composition in Biomethane Production under Thermophilic Conditions

Bambokela¹,

Matheri²,

Belaid³

et al. 2016

International Conference on Advances in Science, Engineering, Technology and Natural Resources (ICASETNR-16) Nov. 24-25, 2016 P

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Abstract-In this investigation, the anaerobic digestion (AD) results from different substrates were evaluated. By means of two co-digestion processes with cow dung, a good performance was observed in the first process and a poor production trend was seen in the second one. NaOH was used as chemical absorbent in two different bottles in the CO2 fixing unit to compare the volume of biomethane produced after purification. A batch anaerobic co-digestion was carried out in 500 mL digesters placed into a water bath at 45°C and maintained at the initial pH of 7. Important AD factors such as pH, Carbon and Nitrogen ratio (C/N), concentration, substrate composition were considered. Through an ultimate analysis, amount of Carbon (C), Hydrogen (H), Nitrogen (N), and Sulphur (S) were also discussed. The toxicity of other elements found in substrates appeared higher in the second process than in the first. It was concluded that the inhibitory elements identification and control before the biogas production process were primordial.

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Section: B Calorific Value Of Substratesmentioning

confidence: 72%

Section: B Calorific Value Of Substratesmentioning

confidence: 99%

Section: B Calorific Value Of Substratesmentioning

confidence: 99%

See 1 more Smart Citation

Impact of Substrate Composition in Biomethane Production under Thermophilic Conditions

Bambokela¹,

Matheri²,

Belaid³

et al. 2016

International Conference on Advances in Science, Engineering, Technology and Natural Resources (ICASETNR-16) Nov. 24-25, 2016 P

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“…As far as proximate analysis based correlations are concerned Cordero et al [13] used it for HHV prediction of lignocellulosic and carbonaceous materials, whereas Özyuğuran and Yaman [3] applied it for different biomass sub-classes. Hybrid analysis based models were used mainly for coal HHV estimation [14]. Higher heating value was estimated also applying artificial neural network [2].…”

Section: *Corresponding Author: Krzysztof_gornicki@sggwplmentioning

confidence: 99%

Estimating the higher heating value of forest and agricultural biomass

2020

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The objectives of study were to investigate the ash content, carbon content and the higher heating value and modelling of HHV of forest and agricultural biomass. Five types of biomass were used for the experiments: sunflower husk pellets, wood pellets, straw and hay briquettes, and forest chips. The investigated biomass properties (their average values) change: for ash content between 1.3% (woods pellets) and 7.3% (hay briquettes), for carbon content between 37.4% (forest chips) and 52.0% (wood pellets), for HHV between 14.8 kJ/kg (forest chips) and 20.1 kJ/kg (sunflower husk pellets). Two mathematical models from literature and model proposed by the authors were used to the HHV calculation. The model proposed by the authors gave the best results in determination of sunflower husk pellets HHV.

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“…Palmer, et al, predicted aqueous solubility using random forest methods [30]. An estimation of coal gross calorific values has been analyzed based on various RF methods [31]. Furthermore, Random Forest, have been applied as a Classification and Regression Tool for Compound Classification and QSAR Modeling tool for compound classification with QSAR modeling [32].…”

Section: Introductionmentioning

confidence: 99%

Novel Approaches to Modelling Flammability Characteristics of Polymethyl Methacrylate (PMMA) via Multivariate Adaptive Regression Splines and Random Forest Methods

Alhassan

Jiang

Mensah

et al. 2020

AJRCoS

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Soft-computing techniques for fire safety parameter predictions in flammability studies are essential for describing a material fire behaviour. This study proposed, two novel Artificial Intelligence developed models, Multivariate Adaptive Regression Splines (MARS) and Random Forest (RF) methods, to model and predict peak heat release rate (pHRR) of Polymethyl methacrylate (PMMA) from Microscale Combustion Calorimetry (MCC) experiment. From the statistical analysis, MARS presented the highest coefficient of determination (R 2 ) values of (0.9998) and (0.9996) for training and testing respectively, with low MAD, MAPE and RMSE values. Comparatively, MARS Alhassan et al.; AJRCOS, 4(4): 1-14, 2019; Article no.AJRCOS.53584 2 outperformed RF in the predictions of pHRR, through its model algorithms that generated optimized equations for pHRR predictions, covering all non-linearity points of the experimental data. Amongst the input variables (sample mass, THR, HRC, pTemp and pTime), heating rate (β), highly influenced pHRR outcome predictions from MARS and RF models. However, to validate the performance and applicability of the proposed models. Results of MARS and RF were benchmarked with that from Artificial Neural Network (ANN) methods. The MARS and RF models observed the least error deviation when compared with pHRR results for PMMA from the ANN models. This study therefore, recommends the adoption of MARS and RF in the predictions of flammability characteristics of polymeric materials. Original Research Article

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Estimation of coal gross calorific value based on various analyses by random forest method

Cited by 128 publications

References 30 publications

Impact of Substrate Composition in Biomethane Production under Thermophilic Conditions

Impact of Substrate Composition in Biomethane Production under Thermophilic Conditions

Estimating the higher heating value of forest and agricultural biomass

Novel Approaches to Modelling Flammability Characteristics of Polymethyl Methacrylate (PMMA) via Multivariate Adaptive Regression Splines and Random Forest Methods

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