Estimation of higher heating value of biomass from proximate analysis: A new approach

Nhuchhen, Daya Ram; Salam, P. Abdul

doi:10.1016/j.fuel.2012.04.015

Cited by 255 publications

(79 citation statements)

References 14 publications

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“…In this study, R 2 values are calculated along with the derivation of regression models while the AAE and ABE of regression models are calculated separately with Microsoft Excel. The developed model is considered to be the best fit if the estimation errors, AAE and ABE, tended to be zero and the R 2 value was close to 1 [6,8,28]. The accuracy of the new regression models is tested by comparing the experimental results with predicted results from the new regression models.…”

Section: Evalation and Validation Of New Regression Modelsmentioning

confidence: 99%

“…This indicates that a proximate-based regression model in HHV predictions of PW samples should consider FC, VM and ash content. Parikh et al [20] and Nhuchhen [8] used a similar approach and concluded that developed models with all three components of proximate analysis are required to lower estimation errors. AAE in N1 to N8 is also observed to be lower than AAE in N9 and N10 (about 3%).…”

Section: Derivation Of the New Regression Modelsmentioning

confidence: 99%

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Regression Model to Predict the Higher Heating Value of Poultry Waste from Proximate Analysis

et al. 2018

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Improper land application of excess poultry waste (PW) causes environmental issues and other problems. Meanwhile there is an increasing trend of using PW as an alternative energy resource. The Higher Heating Value (HHV) is critical for designing and analyzing the PW conversion process. Several proximate-based mathematical models have been proposed to estimate the HHV of biomass, coal, and other solid fuels. Nevertheless, only a small number of studies have focused on a subclass of fuels, especially for PW. The aim of this study is to develop proximate-based regression models for an HHV prediction of PW. Sample data of PW were collected from open literature to develop regression models. The resulting models were then validated by additional PW samples and other published models. Results indicate that the most accurate model contains linear (all proximate components), polynomial terms (quadratic and cubic of volatile matter), and interaction effect (fixed carbon and ash). Moreover, results show that best-fit regression model has a higher R 2 (91.62%) and lower estimation errors than the existing proximate-based models. Therefore, this new regression model can be an excellent tool for predicting the HHV of PW and does not require any expensive equipment that measures HHV or elemental compositions.

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Section: Evalation and Validation Of New Regression Modelsmentioning

confidence: 99%

Section: Derivation Of the New Regression Modelsmentioning

confidence: 99%

Regression Model to Predict the Higher Heating Value of Poultry Waste from Proximate Analysis

et al. 2018

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“…The comparison is shown in Figure 5, and the models are presented in Table 6. In accordance with (Nhuchhen & Salam, 2012), the proximate analysis provides empirical information on the composition of the biomass, so some of the correlations based on proximate analysis may be low. Additionally, the evidence offered by the literature indicates that few researchers have centered their studies on particular types of biomass, preferring to focus on models of more general use.…”

Section: Discussionmentioning

confidence: 99%

“…Other studies and experimental correlations have been used to determine the HHV of biomass by proximate analysis, but the value can differ significantly among analyses (Erol et al, 2010;Chun-Yang, 2011;Vargas-Moreno et al, 2012). In some cases, a representative approximate value of the fluctuations in multiple biomasses is not available because of the varying characteristics of raw materials and even greater differences among biomasses (Erol et al, 2010;Nhuchhen & Salam, 2012). The variables evaluated in the proximate analyses have been used in multiple models to estimate the HHV of biomass and lignocellulosic materials, as reported by different authors (Chun-Yang, 2011;Vargas-Moreno et al, 2012).…”

Section: Introduction 1 2 Introductionmentioning

confidence: 99%

“…The variables evaluated in the proximate analyses have been used in multiple models to estimate the HHV of biomass and lignocellulosic materials, as reported by different authors (Chun-Yang, 2011;Vargas-Moreno et al, 2012). However, in some cases, the models do not properly fit the measured value for the HHV (Nhuchhen & Salam, 2012). For this reason, we propose a quick solution as a simple tool for estimating the Higher Heating Value (HHV, MJ/kg) from the experimental analysis of EFB.…”

Section: Introduction 1 2 Introductionmentioning

confidence: 99%

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Quick-scan estimating model of Higher Heating Value of oil palm empty fruit bunches based on ash from proximate analysis data

Villegas¹,

Ávila²

2014

ing.inv.

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Modelo de exploración para estimación rápida del Poder Calorífico Superior de racimos vacíos del fruto de palma de aceite basado en el contenido de Cenizas obtenido a partir de datos de análisis próximo J. M. Villegas 1 and H. Avila 2 ABSTRACTA correlation model developed for the quick-scan estimation of the Higher Heating Value (HHV) based on the ash content from a proximate analysis of Oil Palm Empty Fruit Bunches (EFB) is presented in this paper. The correlation was developed using a best subsets regression method with data of biomass samples. EFB were taken directly from the end of the processing line (the process exit after the fruit removal section) in agro-industrial palm oil extraction facilities that are located on the Colombian coast. The correlation is also compared with other published correlations of lignocellulosic biomass. After conducting a statistical analysis from proximate analysis variables expressed in the dry basis for Fixed Carbon (FC), Ash content (Ash), and Volatile Matter (VM), colinearity was identified between Ash -VM -FC, VM -FC, in developed models that show unsatisfactory behavior when these variables are included, indicating that these models are inadequate. Finally, the correlation model for a quick-scan estimation on the dry basis was obtained based on the Ash content from a proximate analysis of EFB (HHV= 0.827Ash + C, with C between 9.97 and 12.4), with a mean absolute error (MEA) lower than 3% and a marginal mean bias error (MBE) of 0.19%, and R 2 = 0.8, indicating that the model has an HHV with single input variable predictive capability. This model can be used as a support tool for quick-scan estimation when evaluating the available bioenergy in the processing of EFB using economical and efficient energetic indicators. RESUMENUn modelo de correlación desarrollado para la estimación rápida del Poder Calorífico Superior (HHV) de racimos vacíos del fruto de palma de aceite (EFB) basado en el contenido de cenizas obtenido a partir de datos de análisis próximo es presentado en este artículo. La correlación se desarrolló a través del método de regresión de mejores subconjuntos mediante el uso de datos de muestras obtenidas directamente de la línea de procesamiento (la salida del proceso después de la sección de desfrutado) en instalaciones de empresas agro-industriales extractoras de aceite de palma ubicadas en la costa colombiana. La correlación obtenida se comparó con otros modelos publicados para estimar HHV para biomasa lignocelulósica. Luego de un análisis estadístico a las variables de análisis próximo expresadas en base seca Carbono Fijo (FC), Contenido de cenizas (Ash), Material Volátil (MV), se evidenció colinearidad en los modelos desarrollados entre Ash -VM -FC, VM -FC, mostrando comportamientos insatisfactorios cuando se incluyen estas variables indicando modelos inadecuados. Finalmente el modelo de correlación para estimación rápida en base seca se obtuvo a partir del contenido de cenizas del EFB de datos de análisis próximo (HHV= 0.827Ash + C, con C entre 9.97 y 12...

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Torrefaction and Pelletization of Lignocellulosic Biomass for Energy Intensified Fuel Substitute

Venkatachalam¹,

Sengottian²,

Ravichandran³

2022

Production Technologies for Gaseous and Solid Biofuels

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Estimation of higher heating value of biomass from proximate analysis: A new approach

Cited by 255 publications

References 14 publications

Regression Model to Predict the Higher Heating Value of Poultry Waste from Proximate Analysis

Regression Model to Predict the Higher Heating Value of Poultry Waste from Proximate Analysis

Quick-scan estimating model of Higher Heating Value of oil palm empty fruit bunches based on ash from proximate analysis data

Torrefaction and Pelletization of Lignocellulosic Biomass for Energy Intensified Fuel Substitute

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