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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.

Sve veće količine generiranog otpada od hrane odbačene na odlagališta velika su prijetnja u postizanju održivog razvoja, stabilnosti tržišta hrane, rastu ljudske populacije i dobrobiti ljudi. Odbačena hrana ima dalekosežne posljedice, kako na nacionalnoj tako i na globalnoj, ali i okolišnoj razini. Sve je manje obradivih poljoprivrednih površina, smanjuju se zalihe fosilnih goriva i sve je teže proizvesti hranu. Poljoprivredna proizvodnja se okreće obnovljivim izvorima energije i održivoj proizvodnji uz maksimalno iskorištavanje dostupnih resursa. Uzimajući u obzir sve to potpuno je neprihvatljivo proizvoditi velike količine otpada od hrane, dok se istovremeno bilježi stalan porast gladi. S obzirom da su potrošači odgovorni za veliki stupanj odbačene hrane, institucije uz pomoć znanosti orijentiraju se na istraživanja koja mogu ponuditi moguća rješenja u preventivnom djelovanju ili barem djelomičnom zaustavljanju negativnog trenda u bacanju hrane.

Porastom stanovništva dolazi do stvaranja sve većih količina otpada od hrane. Za planiranje smanjenja i stvaranja mehanizama rješavanja otpada od hrane potrebno je različitim modelima predvidjeti količinu otpada od hrane. Na stvaranje otpada od hrane utječu brojni socioekonomski i društveni čimbenici. Kao alat za nelinearno modeliranje i predviđanje nude se modeli u obliku umjetnih neuronskih mreža. U ovom istraživanju kreiran je model umjetnih neuronskih mreža za predviđanje ukupne količine otpada od hrane na području Europske unije. Provedenom analizom pogodnosti (R2=0, 99), utvrđena je mogućnost razvijenog za predviđanje ukupne količine otpada od hrane.

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.

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.

The use of Posidonia oceanica (PO) waste biomass for energy production is under consideration in several countries of the Mediterranean region. The aim of this paper is an evaluation of using PO waste as a biomass source for direct combustion in territorial contexts where the presence of large quantities of beached seagrass is a problem for the tourism-orientated economy such as Istrian peninsula in Croatia. The presence of large PO leaves deposits on the Istrian beaches is responsible for some negative inconveniences, such as the odor released during the natural degradation processes. Other problems are connected with the visible appearance of the beach and negative connotation of PO with pollution of the touristic area. This condition reduces the possibility of touristic competitiveness of the hotels, apartments, bars, and restaurants that are located along the seaside promenade or beaches covered with PO biomass. This paper shows data of PO waste properties as a resource for energy generation which can help to decrease costs of their collection and could improve the quality of life of residents and tourists in the nearby areas. From energy properties, the heating values of fresh and dried onshore biomass samples of PO biomass were within the range of main agricultural and forest biomass and it was around 9–11 MJ/kg ; ash content is very high (16–36%) compared with other types of biomass. Due to the abovementioned data, PO waste carries some inherent limitations to its use as biomass in direct combustion and energy generation, primarily due to high ash content. Furthermore, PO waste can be recognized as a potential fuel for combustion or co- combustion due to its low sulphur (1.15–2.36%) and nitrogen (1.47–2.39%) content but the low carbon content (25–32%) is not a favorable characteristic. It can be concluded that PO waste is a potentially interesting source for energy production.

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.

Two artificial neural network (ANN) models were developed to predict the main quality parameters of the corn used for ethanol production. Five Croatian corn hybrids were evaluated in this study (introducing the hybrid type as the first input categorical variable for ANN modelling), grown during three vegetation periods (the second categorical variable), under two levels of agrotechnology (the third categorical variable), dried at four temperatures (the fourth input variable), using two different heating and pressure pre-treatments of corn kernels (the fifth variable for ANN calculation) in order to improve the properties of corn for ethanol production. The first model (ANN1) was used to predict the hectolitre weight, 1000-kernels weight, the gelatinisation rate, and the contents of: glucose, reducing sugars and ethanol during the drying process, according to the type of the corn hybrid and the drying temperature. The ANN2 model was developed to predict the corn weight and moisture during the process, based on the input parameters. The artificial neural network models gave a good fit to experimental data and were able to predict the output variables successfully, showing a reasonably good predictive capability (overall r2 for the corn kernel weight and moisture was 0.989, while r2 for other outputs was 0.856). On the basis of a developed ANN models, multi-objective optimization was performed showing the possible practical use in the corn kernel drying process.