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The higher heating value (HHV) is an important parameter to indicate the energetic potential of biomass from different sources. Bomb calorimeter is the standard to obtain this data; however, in the absence of this equipment, estimative through proximate analysis aided with multiple regression formulas is an acceptable alternative. Still, this is a time-consuming technique and highly dependable of operator skills. In this context, thermogravimetric analysis has been used to obtain proximate analysis results with higher precision and demanding a small amount of samples. Here, a simple and effective method to estimate HHV from data obtained by thermogravimetric analysis using different sources of biomass is presented. The proposed method showed to be more accurate to estimate HHV than proximate analysis laboratory procedure. Greater correlation was observed between the HHV values obtained from calorimeter–thermogravimetry than the ones obtained from calorimeter–proximate analysis. Multiple regression analysis indicated that fixed carbon and ashes have an inverse influence over HHV, while the former is responsible for high calorific value and the latter affects energy release. Finally, the proposed method showed to be fast and accurate to estimate HHV of biochar samples and should be easily adaptable to other studies that require fast assessment of calorific value from biochar.

To understand the fuel characteristics of a waste shiitake mushroom bed derived from hardwood chips, the moisture content at the time of disposal and after 1 month, as well as its calorific value, ash content, and elemental composition, were investigated. The moisture content on a wet basis (MC w ) was 78% at the time of disposal and was as high as 63% even 1 month after disposal. It is considered that the slow drying process is caused by the low moisture permeability of the skin of mushroom bed, and therefore, it is preferable to crush the waste mushroom bed before drying. Comparing the gross calorific value on a dry basis of the waste mushroom bed with that of the cultivation bed wood chips, the value inside of the waste mushroom bed was similar, while that of its skin was significantly lower (by 11%). The reason for this lies in the significantly higher ash content and nitrogen content compared to those of wood. When analyzed from the combustion heat of the contained elements, it was found that both the cultivation bed wood chips and the waste mushroom bed had almost no hydrogen contributing to combustion due to their high oxygen content, and they were dependent on the heat generation of carbon. As a result of finding the relationship between the net calorific value that can be used as a boiler fuel and MC w , for example, the value at an MC w of 50% was calculated to be 7.6 MJ/kg, which was almost the same as that of sugi ( Cryptomeria Japonica ) sapwood and bark. The ash content of the waste mushroom bed was about 7%, which is close to that of bark and about ten times that of the wood used for the cultivation bed. When the waste mushroom bed is used as boiler fuel, appropriate ash treatment is required as in the case of using bark.

Accurately determining the calorific value of aviation kerosene is crucial for optimizing aircraft engine efficiency and design testing. However, current measurement methods exhibit significant uncertainties, necessitating an assessment of the uncertainty associated with measuring the calorific value of aviation kerosene to establish precise results. The present paper introduces the oxygen bomb calorific value measurement method and the probability box model, proposing an improved aviation kerosene calorific value uncertainty evaluation method by combining it with the MCM method. An uncertainty evaluation model based on GUM method, MCM method, and improved MCM method is established in conjunction with experimental data. The analysis of different models demonstrates that the improved MCM method effectively considers the uncertainty of corresponding distribution parameters of variables based on their own uncertainties, providing a reliable approach for calculating the heating value uncertainty of aviation kerosene.

The gross calorific value (GCV) of coal is pivotal in shaping policies across various sectors of the Indian economy. It plays a crucial role in classification and valuation of coal and is a major factor in determining electricity tariffs charged by thermal power plants. With coal production escalating year-on-year to meet India's increasing electricity demand, there is significant rise in coal testing activities along the pit-to-power supply chain at multiple points and by multiple testing agencies often driven by sector-specific policy requirements. While laboratory testing accurately determines GCV, it is costly and time-consuming due to the reliance on expensive equipment and skilled personnel. Global researchers have previously devised a plethora of empirical formulae predicting GCV based on its correlations with easy-to-measure properties like moisture and ash content. However, the applicability and utility of these formulae to the prevalent policy matrix of coal and power sector remain to be explored. The introduction of independent third-party assessment of coal quality by Coal India Limited in 2016 has generated a vast dataset of coal sample-test results, offering an opportunity to reassess existing empirical formulae, test their alignment with existing policies, and explore possibility of a unified, region-neutral formula for rapid GCV prediction with a special focus on alleviating the current overload in coal testing.

In this study, the gross calorific value (GCV) of coal was accurately and rapidly determined using eight artificial intelligence models based on big data of 2583 observations of coal samples in the Mong Duong underground coal mine (Vietnam). Accordingly, the volatile matter, moisture, and ash were considered as the key variables (inputs) for determining GCV. Seven artificial neural network (ANN) models were developed to estimate GCV as the first stage. Subsequently, the best ANN model (with the highest performance) was selected as the initial ANN model for the optimization process, i.e., ANN 3-12-9-1 model. The particle swarm optimization (PSO) algorithm was applied to perform a global search for the optimal weights/biases of the selected ANN model. This novel procedure is denoted as PSO-ANN. A variety of performance metrics was used to assess the quality of the training process, as well as the models’ performance in the testing dataset. The results revealed that the models developed in this study could determine GCV rapidly and accurately. Of those, the PSO-ANN model provided the highest accuracy in estimating GCV of coal with a root-mean-squared error of 182.476, the correlation coefficient of 0.964, the variance accounted for of 96.411, and mean absolute percentage error of 0.016. Besides, the analyzed and compared results also indicated that the PSO algorithm played a significant role in improving the accuracy of the ANN model. It was introduced as an alternative solution to determine the GCV of coal in practical engineering rapidly.

This study is aimed at utilizing three waste materials, i.e., solid refuse fuel (SRF), tire derived fuel (TDF), and sludge derived fuel (SDF), as eco-friendly alternatives to coal-only combustion in co-firing power plants. The contribution of waste materials is limited to ≤5% in the composition of the mixed fuel (coal + waste materials). Statistical experimental design and response surface methodology are employed to investigate the effect of mixed fuel composition (SRF, TDF, and SDF) on gross calorific value (GCV) and ash fusion temperature (AFT). A quadratic model is developed and statistically verified to apprehend mixed fuel constituents’ individual and combined effects on GCV and AFT. Constrained optimization of fuel blend, i.e., GCV >1,250 kcal/kg and AFT >1,200 °C, using the polynomial models projected the fuel-blend containing 95% coal with 3.84% SRF, 0.35% TDF, and 0.81% SDF. The observed GCV of 5,307 kcal/kg and AFT of 1225 °C for the optimized blend were within 1% of the model predicted values, thereby establishing the robustness of the models. The findings from this study can foster sustainable economic development and zero CO 2 emission objectives by optimizing the utilization of waste materials without compromising the GCV and AFT of the mixed fuels in coal-fired power plants.

Calorific value is an important index for evaluating coal quality, and it is important to achieve the rapid detection of calorific value to improve production efficiency. In this paper, a calorific value detection method based on NIRS-XRF fusion spectroscopy is proposed, which utilizes NIRS to detect organic functional groups and XRF to detect inorganic ash-forming elements in coal. NIRS, XRF and NIRS-XRF fusion spectrum were separately used to establish partial least squares (PLS) regression models for coal calorific value, and better prediction performance was obtained by using fusion spectrum (the determination coefficient of calibration set (R2) was 0.98, the root mean square error of prediction set (RMSEP) was 0.19 MJ/kg, the average relative deviation for prediction (MARDP) was 0.95%). The variable selection is very important for model performance. The effective variables were extracted using Pearson correlation coefficients to further optimize the prediction model, and the evaluation indexes of the optimized model are R2 = 0.99, RMSEP = 0.16 MJ/kg and MARDP = 0.70%. In addition, the repeatability of the proposed method was briefly evaluated. The results show that the proposed method is an effective analysis method to detect the calorific value of coal, which provides a new idea and technique for coal quality detection.

From this study, the quantity, composition, and energy content of waste material in Perungudi dump yard of Chennai, Tamilnadu, India were examined. Based upon the past waste generation data from the documentary evidence and field data was used to predict the quantity of waste generated in future. This study reviews the potential uses of solid wastes generated at the perungudi dump yard as a sustainable energy source. Physical properties of waste sample like specific gravity (Sg), moisture content (MC), dry density, particle size distribution and unit weight of MSW are analyzed using ASTM guidelines. Proximate analysis (For Physical Characteristics) and Ultimate analysis (For elemental Analysis) were analysed in its ash content, volatile matter (Vm) and fixed carbon (Fc). The net calorific energy stored in solid waste was determined using empirical analysis. The element content like carbon (C), hydrogen (H), oxygen (O), nitrogen (N) and sulphur (S) value in the solid wastes are derived from standard value of material and this values was substituted into the “Modified Dulong’s” equation to determine the energy content (GCV) of solid waste. Final estimation of energy output was arrived using Gross Calorific Value (GCV) and Net Calorific Value (NCV).

The calorific value of coal is a key parameter for pricing, trade, and combustion management. Conventional bomb calorimetry provides accurate results but is time-consuming, labor-intensive, and destructive. Near-infrared (NIR) spectroscopy offers a rapid and non-destructive alternative, yet its application is limited by strong band correlations, nonlinear spectral responses, and the lack of interpretability in many predictive models. In this study, the Kolmogorov–Arnold Network (KAN) is applied to the prediction of coal calorific value, demonstrating its capability to describe nonlinear spectral relationships within an interpretable mathematical structure. Based on this framework, a Radial Basis Function KAN (RBF-KAN) is further developed by replacing the B-spline bases in the KAN with radial basis functions, allowing improved representation of localized and irregular spectral variations while maintaining model transparency. Using 671 coal-powder samples measured by a portable MicroNIR spectrometer, the RBF-KAN achieved an RMSE of 1.35 MJ/kg and an MAE of 0.92 MJ/kg under five-fold cross-validation, outperforming conventional regression models, deep neural networks, and other KAN variants. Analysis of RBF activations and spectral attribution maps indicates that the model consistently responds to characteristic O-H and C-H overtone regions, which correspond to known absorption features in coal. These results suggest that the RBF-KAN provides a practical and interpretable framework for on-site estimation of coal calorific value, complementing traditional calorimetric analysis.

Research scientists worldwide are continuously driving innovations toward achieving a safe and healthy environment across the entire ecosystem. An integral component of this pursuit, as captured in SDG-7, is ensuring access to affordable, reliable, sustainable, and modern energy for all. The discovery of the vastness of bioresources embedded in agricultural and forestry residues mirrors hope and presents an array of challenges. Over the decades, biomass densification has been implemented to upgrade and consolidate the energy value of loose biomass for industrial and domestic applications. This is projected to mitigate the overreliance on fossil fuels as energy sources. However, the combustion and energy performance of biomass have not sufficiently met the energy mix requirements for extensive renewable energy use. The performance of the compacted material is dependent on the type of binder used in the manufacturing process, among other factors. This study explored the details of the available binders and biomass compositions investigated in previous studies. The authors also reported their performance, primarily regarding energy value and combustible behavior. Limitations such as low yield and low energy content, among other performance-related issues in biomass briquettes, can be highly enhanced with the appropriate selection of biomass and compatible binders. Hence, various research attempts, approaches, and methodologies have been conducted to develop solid fuel, and the binder’s influence on the energy content, density, combustion behavior, and other physical attributes of fuel briquettes has been reported.