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The aim of the study was to investigate the changes in ultimate, proximate analysis, and calorific properties of Miscanthus x giganteus with three types of planting materials (two rhizomes - R1 and R2 - and one seedling - S) and three ash fertiliser treatments (P0, P2, and P5) were included in the study. The research further examined their effects on crop yield, stem height and various chemical properties. The results showed that the maximum yield was obtained with the R1 x P2 plant type, while the minimum yield was recorded with the R2 x P2 plant type. In addition, the greatest average stem height (3.34 m) was recorded for the R2 x P5 plant type. Significant differences were also found in the chemical components between the plant types and treatments. For example, the highest ash content of 2.25% was found in plant type 'S' x P5, while the highest coke content of 14.48 % was found in plant type R1 x P5. The statistical analysis confirmed that planting material and ash fertilisation had significant influence on the physicochemical properties of Miscanthus x giganteus. This consequently affects the calorific value, with the average higher and lower heating value being 18.32 and 17.04 MJ/kg, respectively. The neural regression network models showed robust predictive performance for the higher heating value and lower heating value, with low chi-square values, X², and high coefficients of determination, R². [ABSTRACT FROM AUTHOR]

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]

Miscanthus and Virginia Mallow are energy crops characterized by high yields, perenniality, and low agrotechnical requirements and have great potential for solid and liquid biofuel production. Later harvest dates result in lower yields but better-quality mass for combustion, while on the other hand, when biomass is used for biogas production, harvesting in the autumn gives better results due to lower lignin content and higher moisture content. The aim of this work was to determine not only the influence of the harvest date on the energetic properties but also how accurately artificial neural networks can predict the given parameters. The yield of dry matter in the first year of experimentation for this research was on average twice as high in spring compared to autumn for Miscanthus (40 t/ha to 20 t/ha) and for Virginia Mallow (11 t/ha to 8 t/ha). Miscanthus contained 52.62% carbon in the spring, which is also the highest percentage determined in this study, while Virginia Mallow contained 51.51% carbon. For both crops studied, delaying the harvest date had a positive effect on ash content, such that the ash content of Miscanthus in the spring was about 1.5%, while in the autumn it was 2.2%. Harvest date had a significant effect on the increase of lignin in both plants, while Miscanthus also showed an increase in cellulose from 47.42% in autumn to 53.5% in spring. Artificial neural networks used to predict higher and lower heating values showed good results with lower errors when values obtained from biomass elemental composition were used as input parameters than those obtained from proximity analysis. [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]

In this study, an evaluation of food waste generation was conducted, using images taken before and after the daily meals of people aged between 20 and 30 years in Serbia, for the period between 1 January and 31 April in 2022. A convolutional neural network (CNN) was employed for the tasks of recognizing food images before the meal and estimating the percentage of food waste according to the photographs taken. Keeping in mind the vast variates and types of food available, the image recognition and validation of food items present a generally very challenging task. Nevertheless, deep learning has recently been shown to be a very potent image recognition procedure, while CNN presents a state-of-the-art method of deep learning. The CNN technique was implemented to the food detection and food waste estimation tasks throughout the parameter optimization procedure. The images of the most frequently encountered food items were collected from the internet to create an image dataset, covering 157 food categories, which was used to evaluate recognition performance. Each category included between 50 and 200 images, while the total number of images in the database reached 23,552. The CNN model presented good prediction capabilities, showing an accuracy of 0.988 and a loss of 0.102, after the network training cycle. The average food waste per meal, in the frame of the analysis in Serbia, was 21.3%, according to the images collected for food waste evaluation. [ABSTRACT FROM AUTHOR]

The world today faces several pressing challenges: energy from non-renewable sources is becoming increasingly expensive, while at the same time the use of agricultural land for food production is decreasing at the expense of biofuel production. Energy crops offer a potential solution to maximizing the use of land. In order to provide new value to the by-product, it is necessary to investigate its possible nutritional and functional potential. Therefore, the main objective of this study was to determine the energetic, nutritional, and functional potential of the species Sida hermaphrodita L. and Silphium perfoliatum L. in different phenophases. The analyzed energy potential of the mentioned species is not negligible due to the high determined calorific value (17.36 MJ/kg for Virginia mallow and 15.46 MJ/kg for the cup plant), high coke content (15.49% for the cup plant and 10.45% for Virginia mallow), and desirably high carbon content, almost 45%, in both species. The phenophase of the plant had a significant influence on the content of the analyzed specialized metabolites (SM) in the leaves, with a high content of ascorbic acid at the full-flowering stage in Virginia mallow (229.79 mg/100 g fw) and in cup plants at the end of flowering (122.57 mg/100 g fw). In addition, both species have high content of polyphenols: as much as 1079.59 mg GAE/100 g were determined in the leaves of Virginia mallow at the pre-flowering stage and 1115.21 mg GAE/100 g fw in the cup plants at the full-flowering stage. An HPLC analysis showed high levels of ellagic acid and naringin in both species. In addition, both species have high total chlorophyll and carotenoid concentrations. Due to their high content of SM, both species are characterized by a high antioxidant capacity. It can be concluded that, in addition to their energetic importance, these two plants are also an important source of bioactive compounds; thus, their nutritional and functional potential for further use as value-added by-products should not be neglected. [ABSTRACT FROM AUTHOR]

Miscanthus is a perennial energy crop that produces high yields and has the potential to be converted into energy. The ultimate analysis determines the composition of the biomass and the energy value in terms of the higher heating value (HHV), which is the most important parameter in determining the quality of the fuel. In this study, an artificial neural network (ANN) model based on the principle of supervised learning was developed to predict the HHV of miscanthus biomass. The developed ANN model was compared with the models of predictive regression models (suggested from the literature) and the accuracy of the developed model was determined by the coefficient of determination. The paper presents data from 192 miscanthus biomass samples based on ultimate analysis and HHV. The developed model showed good properties and the possibility of prediction with high accuracy (R2 = 0.77). The paper proves the possibility of using ANN models in practical application in determining fuel properties of biomass energy crops and greater accuracy in predicting HHV than the regression models offered in the literature. [ABSTRACT FROM AUTHOR]

Wastewater treatment plants are facilities where wastewater is treated by technological processes. A byproduct of a wastewater treatment plant is sewage sludge, which can be both a good soil conditioner and a source of nutrients for the crops to which it is applied. Energy crops are non-food plants that can cleanse the soil of heavy metals through their ability to phytoremediate. The purpose of this study is to determine the effects of different amounts of sewage sludge on soil and plants. In the experiment Virginia mallow (Sida hermaphrodita L.) was used and the influence of stabilized sewage sludge in the amounts of 1.66, 3.32 and 6.64 t/ha dry matter on the energy composition and biomass yield was observed.The obtained results showed a yield of 8.85 t/ha at the maximum amount of sewage sludge used. Hemicellulose content was 20.20% in the application of 6.64 t/ha of sewage sludge and 19.70% in the control, while lignin content was 17.97% in the control and 16.77% in the maximum amount of sewage sludge. The heavy metals molybdenum and nickel did not differ significantly under the influence of larger amounts of sewage sludge, while manganese increased from 23.66 to 35.82 mg/kg. [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]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]