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In this study machine learning (ML) models have been employed to predict the higher heating value (HHV) of biomass by utilizing input variables derived from ultimate, proximate, and structural analyses. In total, 180 models were developed, with 124 utilizing ultimate analysis data, 28 based on proximate analysis, and 28 relying on structural analysis. Various ML techniques, including polynomial models (SOP), support vector machines (SVM), random forest regression (RFR), and artificial neural networks (ANN), were employed for analysis. The study found that ANN models, when "fed" with FC and VM data, provided considerable accuracy in prediction results, with the best results obtained with 2-12-1 architecture (R2=0.96). In addition, a separate model configuration that processed inputs on biomass constituents such as cellulose, lignin, and hemicellulose showed remarkable agreement with empirical data. Additional findings revealed that the models created using SOP (R2=0.95), SVM (R2=0.95), and RFR (R2=0.90) demonstrated minimal discrepancies when predicting HHV. This study provides significant insights into the investigation of biomass analysis techniques employing ML tools, paving the way for future research aimed at constructing a robust tool for HHV prediction. Subsequent models may explore integrating inputs from diverse analysis methods and leveraging advanced machine learning techniques to enhance accuracy further [ABSTRACT FROM AUTHOR]

Arar (Juniperus phoenicea L.) is a small monoecious or dioecious evergreen species presenting as a shrub or tree around the Mediterranean. This widespread plant is also causing problems in Croatia, along its Adriatic coastal area and in particular on the island of Pag. It affects the establishment and growth of other species that share its habitat and has also reduced the grazing areas of local sheep breeding and beekeeping communities. Arar is also a frequent cause of wildfires in the region. Its spread is indeed far-reaching, from its impact on plants and livestock to its adverse effects on the local population and its tourism, which is one of the main components of the island's economy. This research aimed to evaluate biomass and biochar samples of arar, using standard methods to verify their potential energy value. Results of the study showed a favorable content of coke (16.28%) and volatiles (77.26%) in the samples. The C, H, S, N, and O ratios of the samples were 50.14%, 6.57%, 0.31%, 0.84% and 42.13%, respectively. The higher calorific value was 20.45 MJ·kg−1 for biomass and 29.01 MJ·kg−1 for biochar. Accordingly, this species can be used as a solid biofuel for direct combustion or similar processes and for other value-added applications. [ABSTRACT FROM AUTHOR]