The determination of calorific value (in terms of higher heating value or HHV) is of much significance for the assessment of the energy recovery potential of biomass. The literature reported that HHV of the biomass can be computed using the proximate composition of the biomass. The prediction of HHV from the proximate composition of biomass is of great interest as it can save expensive and time-consuming laboratory trials. The present work evaluates the available prediction models for the estimation of HHV of the biomass from the literature. A thorough review showed that most literature reported models are limited in terms of applicability across different biomass categories. The present study proposed a non-linear prediction model that can be universally used to predict calorific value across biomass categories. Accordingly, the proposed model was validated using the proximate composition of various biomass specimens extracted from the literature to confirm the universal applicability of the model. The accuracy and precision of model prediction were assessed in terms of statistical indicators. The values of the statistical indicators showed that the proposed model had better prediction efficiency (in terms of accuracy and precision) and less bias compared to the other literature reported models for all biomass categories. The model predicted maximum energy recovery potential for biomass having high fixed carbon, high volatile matter, and low ash. The maximum energy recovery of 19.148 MJ/kg predicted by the model on validation was found to be associated with less than 2% (1.19%) percent error.

热值的确定（就较高的热值或HHV而言）对于评估生物质的能量回收潜力具有重要意义。文献报道生物质的HHV可以使用生物质的接近组成来计算。从生物质的最接近组成来预测HHV备受关注，因为它可以节省昂贵且耗时的实验室试验。本工作评估了从文献中估算生物质HHV的可用预测模型。全面的审查表明，大多数文献报道的模型在不同生物量类别的适用性方面都受到限制。本研究提出了一种非线性预测模型，该模型可以普遍用于预测跨生物质类别的热值。因此，利用从文献中提取的各种生物量标本的近邻组成对所提出的模型进行了验证，以确认该模型的通用性。根据统计指标评估模型预测的准确性和准确性。统计指标的值表明，与针对所有生物质类别的其他文献报道的模型相比，所提出的模型具有更好的预测效率（在准确性和精确度方面）且偏差较小。该模型预测了具有高固定碳，高挥发性物质和低灰分的生物质的最大能量回收潜力。该模型在验证时预测的最大能量回收为19.148 MJ / kg，发现误差小于2％（1.19％）。