Higher Heating Value Determination Research Articles

Synthesis of Hydrochars via Hydrothermal Carbonization of Zinc Chloride Activated Cotton Textile Waste

Anthropogenic pollution from fossil fuel combustion due to increasing global populations and emerging industries poses a serious threat to the environment. Therefore, it is vital to explore alternative green energy sources. Hence, this study synthesized renewable solid fuel hydrochars by utilizing biomass of cotton textile waste (CTW) as feedstock. ZnCl2 was selected as a catalyst due to its Lewis acid properties promoting hydrolysis and dehydration of raw materials towards hydrochar formation. The ZnCl2-activated cotton textile waste (ZnCl2-CTW) was prepared via the wet impregnation method at different ZnCl2 loading. The hydrochars were synthesized via hydrothermal carbonization of ZnCl2-CTW in a Parr batch reactor at the reaction temperature of 200 °C for 3 h. The hydrochars and CTW were characterized using a thermogravimetric analyser (TGA) to evaluate the proximate analysis, whereas an elemental analysis and determination of higher heating value (HHV) were conducted employing a CHNS analyser. The proximate analysis indicates the hydrochars volatile matter increases at higher catalyst loading, possibly due to the high content of bio-oil adsorbed on the hydrochars surface. The elemental analysis shows higher carbon and lower oxygen content for hydrochars compared to CTW, mainly due to the dehydration, decarboxylation, and aromatization reactions during HTC. Meanwhile, the HHV of hydrochars increases as catalyst loading increases to 1.5 g, which obtained the highest value of 18.41 MJ/kg, indicating good quality of solid fuel. This demonstrated that the ZnCl2 catalyst was useful in improving the energy density of hydrochars. Overall, ZnCl2 catalyst loading of 1.5 g has exhibited good performance in producing hydrochars via HTC of CTW. Thus, ZnCl2-CTW has the potential to produce hydrochar suitable as solid fuel, contributing to the conversion of waste into energy and the preservation of the environmental.

Prediction of higher heating value of coal based on gradient boosting regression tree model

Higher heating value, also known as the coal calorific value, is an important indicator of coal quality. Nevertheless, traditional experimental determination of higher heating value is expensive, tedious, and time-consuming. Some regression models have been commonly used to solve this problem. However, the prediction accuracy still needs to be improved. Accordingly, based on the correlation between proximate analysis of coal and higher heating value, a gradient boosting regression tree model was established in this study to predict the higher heating value as a means to a less intensive procedure to estimate coal heating values. This approach used data from the U.S. Geological Survey Coal Quality database and Kentucky Coal Database to evaluate coal quality. The hyperparameters of the regression tree model were tuned by cross-validation. A comparative study was conducted between the model established in this paper and the existing methods. Experimental results indicate that the coefficient of determination of the gradient boosting regression tree model of two databases achieves 0.9862 and 0.9541, respectively. Thus, the prediction capability of the gradient boosting regression tree model outperforms the existing methods.

Physicochemical Characterization and SEM-EDX Analysis of Brewer’s Spent Grain from the Craft Brewery Industry

The brewing industry generates, as the primary coproduct, brewers’ spent grain (BSG). In Mexicali, Baja California, Mexico, there are 17 companies that generated 282 tons of BSG by 2016. Cattle feeding is the most common type of disposal for this waste. However, it can be valorized for the production of bioenergy or as a source of added-value products. Therefore, the objective of the present work was to assess the physicochemical properties of the brewers’ spent grain from a local craft brewery, to choose the most appropriate exploitation route. Chemical and morphological analyses were carried out by energy dispersive X-ray fluorescence spectroscopy (EDX), scanning electron microscopy, and the higher heating value determination. The results of the proximate analyses were 72.32% moisture, 78.47% volatile matter, 17.48% fixed carbon, and 4.05% ash. The results of the chemical analysis for extractables were 5.23% using organic solvent and 50.25% using hot water. The content determination were 17.13% lignin, 26.80% cellulose, and 37.17% hemicellulose. The results of the ultimate analysis were 43.59% C, 6.18% H, 3.46% N, and 37.22% O. The higher heating value experimentally obtained was 18.70 MJ/kg. Moreover, in the EDX analysis, Ca, P, K, and S were mainly found. It is recommendable to valorize the BSG through the xylitol, bioethanol or biogas production, because of its high moisture, hemicellulose and cellulose content.

Valorization of Eucalyptus Leaves by Essential Oil Extraction as an Added Value Product in Mexico

The eucalyptus leaves generated by the forestry plantations in Mexico represent a neglected biomass residual resource that has a potential for the extraction of essential oil and would allow achieving economic incomes, as well as employment in the forestry sector. Its revalorization as raw material to extract essential oil constitutes a sustainable method that adds it value. Likewise, it contributes to the reduction of national essential oil imports, which in 2003–2014 were in average 349 tons per year and had a price of 19.60 dollars/kg in 2014. In this study, the results of an essential oil extractor operation by applying the water steam distillation technique are presented with the goal of determining in which conditions is obtained the higher yield. Therefore, an experiments factorial design was developed. The influence of the next factors on the yield of the essential oil extracted were studied: steam pressure, operation time and particle size. The higher yield was 0.52 % m/m, an operation time of 80 min, saturated steam at 30 psi and a whole eucalyptus leaf. The eucalyptus essential oil was analyzed by FT-IR spectroscopy, where the 1,8 cineol was identified as the compound with the highest proportion. Besides, to characterize the eucalyptus leaves and the essential oil, it was made an analysis of X-Ray Fluorescence, refractometry, Higher Heating Value determination, Scanning Electron Microscopy, Energy-Dispersive X-Ray Spectroscopy and Thermogravimetric Analysis. According to the results, the processing of 20 % of eucalyptus leaves in Mexico satisfies to 1.45 times the eucalyptus essential oil internal demand.

Determination of higher heating value of petro-diesels using mid-infrared spectroscopy and chemometry

Higher heating value (HHV) is probably the most important property of the fuels. Bomb calorimeter and derived empirical formulae are often used for accurate determination of HHV of fuels. A useful empirical equation was derived to estimate HHV of petro-diesels from their C and H contents: HHV (in MJ/kg) = 0.3482(C) + 1.1887(H), r 2 = 0.9956. The derived correlation was validated against the most common formulae in the literature, Boie and Channiwala–Parikh correlations. Accordingly, accurate determination of C and H contents is essential for estimation of HHV and avoids using a bomb calorimeter. However, accurate estimation of C and H contents requires using expensive and laborious gas chromatographic techniques. In this work, chemometry offered a simple method for HHV determination of petro-diesels without using bomb calorimeter or even gas chromatography. PLS-1 calibration was used instead of gas chromatography to find C and H contents from the non-selective mid-infrared (MIR) spectra of petro-diesels, HHV was then estimated from the earlier empirical equation. The proposed method predicts HHV of petro-diesels with high accuracy and precision, with modest analysis costs. The present method may be extended to other fuels.

Development of a new proximate analysis based correlation to predict calorific value of coal

The experimental determination of higher heating value (HHV) of solid fuels is a cost intensive process, as it requires special instrumentation and highly trained analyst to operate it, where as proximate analysis data can be obtained relatively easily using an ordinary muffle furnace. Therefore, to simplify the task and to reduce the cost of analysis many correlations were developed for determining HHV from proximate analysis of solid fuels. An attempt has been made in this paper to evaluate the applicability of these correlations with a special focus on Indian coals. It has been observed that the developed correlations are either complex in nature or by-pass the effect of important variables like moisture and ash contents of coals. An effort has, therefore, been made to develop a simple correlation based on proximate analysis data for predicting HHV of coal (as-received basis). The model presented here is developed using analyses of 250 coal samples and its significance lies in involvement of all the major variables affecting the HHV. The developed model appears to be better than the existing models and has the following form: HHV = - 0.03 ( A ) - 0.11 ( M ) + 0.33 ( V M ) + 0.35 ( F C ) .