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The application of municipal sewage sludge as fertilizer in the production of non-food energy crops is an environmentally and economically sustainable approach to sewage sludge management. In addition, the application of municipal sewage sludge to energy crops such as Miscanthus x giganteus is an alternative form of recycling nutrients and organic material from waste. Municipal sewage sludge is a potential source of heavy metals in the soil, some of which can be removed by growing energy crops that are also remediation agents. Therefore, the objective of the research was to investigate the effect of municipal sewage sludge applied at three different rates of 1.66, 3.22 and 6.44 t/ha on the production of Miscanthus. Based on the analyses conducted on the biomass of Miscanthus fertilized with sludge from the wastewater treatment plant in three fertilization treatments, it can be concluded that the biomass of Miscanthus is a good feedstock for the process of direct combustion. Moreover, the application of the largest amount of municipal sewage sludge during cultivation had no negative effect on the properties of Miscanthus biomass. Moreover, the cellulose and hemicellulose content of Miscanthus is ideal for the production of second-generation liquid biofuels. Fertilizer treatments had no effect on the content of cellulose and lignin, while a significant statistical difference was found for hemicellulose.

Invasive plant species (IAS), with their numerous negative ecological, health, and economic impacts, represent one of the greatest conservation challenges in the world. Reducing the negative impacts and potentially exploiting the biomass of these plant species can significantly contribute to sustainable management, protect biodiversity, and create a healthy environment. Therefore, the main objective of this study was to evaluate the nutritional potential, phytochemical status, and antioxidant capacity of nine alien invasive plant species: Abutilon theophrasti , Amaranthus retroflexus , Ambrosia artemisiifolia , Datura stramonium , Erigeron annuus , Galinsoga ciliata , Reynoutria japonica , Solidago gigantea , and Sorghum halepense . Multivariate statistical methods such as cluster and PCA were performed to determine possible connections and correlations among selected IAS depending on the phytochemical content. According to the obtained results, R. japonica was notable with the highest content of vitamin C (38.46 mg/100 g FW); while E. annuus (1365.92 mg GAE/100 g FW) showed the highest values of total polyphenolic compounds. A. retroflexus was characterized by the highest content of total chlorophylls (0.26 mg/g) and antioxidant capacity (2221.97 µmol TE/kg). Therefore, it can be concluded that the selected IAS represent nutrient-rich plant material with significant potential for the recovering of bioactive compounds.

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]

The main objective of this research is to evaluate how fertilisation with different amounts of municipal sewage sludge, a by-product of wastewater treatment, affects the yield and energetic properties of Miscanthus biomass. The influence of municipal sewage sludge on the energy properties of Miscanthus was analysed in four dosages, namely: 0 t/ha DM, 1.66 t/ha DM, 3.32 t/ha DM and 6.64 t/ha DM. The study showed that the highest amount of municipal sewage sludge did not negatively affect the analysed energy properties of Miscanthus in any way. The yields were almost the same at all dosage of municipal sewage sludge application at around 32 t/ha, the higher calorific value was 18.90 MJ/kg at all dosage of municipal sewage sludge application, coke and carbon, as one of the more important parameters for direct combustion, also remained statistically unchanged. The composition of lignocellulose also showed no significant differences in relation to fertilization. [ABSTRACT FROM AUTHOR]

The aim of this study was to investigate the potential of using structural analysis parameters for estimating the higher heating value (HHV) of biomass by obtaining information on the composition of cellulose, lignin, and hemicellulose. To achieve this goal, several nonlinear mathematical models were developed, including polynomials, support vector machines (SVMs), random forest regression (RFR) and artificial neural networks (ANN) for predicting HHV. The performed statistical analysis "goodness of fit" showed that the ANN model has the best performance in terms of coefficient of determination (R2 = 0.90) and the lowest level of model error for the parameters X2 (0.25), RMSE (0.50), and MPE (2.22). Thus, the ANN model was identified as the most appropriate model for determining the HHV of different biomasses based on the specified input parameters. In conclusion, the results of this study demonstrate the potential of using structural analysis parameters as input for HHV modeling, which is a promising approach for the field of biomass energy production. The development of the model ANN and the comparative analysis of the different models provide important insights for future research in this field. [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]