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This research aims to use artificial neural networks (ANNs) to estimate the yield and energy characteristics of Miscanthus x giganteus (MxG), considering factors such as year of cultivation, location, and harvest time. In the study, which was conducted over three years in two different geographical areas, ANN regression models were used to estimate the lower heating value (LHV) and yield of MxG. The models showed high predictive accuracy, achieving R2 values of 0.85 for LHV and 0.95 for yield, with corresponding RMSEs of 0.13 and 2.22. A significant correlation affecting yield was found between plant height and number of shoots. In addition, a sensitivity analysis of the ANN models showed the influence of both categorical and continuous input variables on the predictions. These results highlight the role of MxG as a sustainable biomass energy source and provide insights for optimizing biomass production, influencing energy policy, and contributing to advances in renewable energy and global energy sustainability efforts.

Abstract In response to the EU's REPowerEU initiative (COM (2022) 108) which encourages an increase in biogas production by 20% in member states by 2030 to boost energy independence, it has become essential to identify sustainable alternatives to traditional feedstocks for biogas production, especially in the EU Member states where there is still high dependence on corn silage as the main raw material in biogas plants. While corn silage, predominantly used in the European biogas plants today, serves primarily for the livestock sector, alternative sources need to be explored. Therefore, this study aimed to evaluate the potential of Arundo donax, a perennial energy crop, as an alternative feedstock in a continuous anaerobic process. The biogas yield and its quality, characterized by CH4, CO2, H2S and O2 content, were determined during a continuous process with A. donax, compared with two mixed feedstocks of A. donax and corn silage over a 5‐month period in a continuous anaerobic digestion process. The results revealed that A. donax exhibits a biogas yield and methane content comparable to corn silage, indicating its potential as a viable and sustainable alternative feedstock for biogas production.

Considerable efforts have been made to replace formaldehyde-containing adhesives in the manufacturing of wood products, particularly particleboard, with natural alternatives. One promising solution is the liquefaction of lignocellulosic materials such as wood using glycerol (C3H8O3) under sulfuric acid catalysis (H2SO4). The aim of this study was to investigate the chemical composition and properties of spruce and oak biomass after liquefaction and to evaluate its potential as a formaldehyde-free adhesive substitute. All samples were liquefied at 150 °C for 120 min in five different wood–glycerol ratios (1:1 to 1:5). The liquefaction percentage, the insoluble residue, the dry matter and the hydroxyl (OH) number were determined as characteristic values for the polymer properties of the liquefied samples. The results showed the liquefaction percentage was up to 90% for spruce and oak. The insoluble residue ranged from 10 to 29% for spruce and from 10 to 22% for oak, the dry matter ranged from 54 to 70% for spruce and from 51 to 62% for oak, while the highest xydroxyl number was 570 mg KOH/g for spruce and 839 mg KOH/g for oak. Based on these results, liquefied wood was shown to be an effective natural alternative to synthetic resins in particleboard adhesives and a way to reduce formaldehyde emissions. This research not only supports environmentally sustainable practices but also paves the way for various bioproducts derived from liquefied biomass and points to future avenues for innovation and development in this area.

The objective of this study was to investigate the changes in the nutrient and fatty acid profiles of hazelnuts (Corylus avellana) and walnuts (Juglans regia) subjected to continuous drying. Samples from two consecutive years (2020 and 2021) were analyzed for nutritional value both before and after conduction drying. Thermal conduction drying was performed at 60 and 80°C at intervals of 15, 30, 45, and 60 min. The results showed that hazelnuts had increased ash, protein (from 16.4 to 18.7%), carbohydrate and starch content, while walnuts had a higher pH and fat content (from 60.97 to 71.02%). After drying, increasing temperatures resulted in significant changes in nutrient concentrations for both nuts, including changes in ash, protein, fat (hazelnuts from 58.69 to 71.48% at 60°C for 60 min), carbohydrate and starch content, and pH. Monounsaturated and polyunsaturated fatty acid content varied by sample and year, with notable trends such as the increase in oleic acid in walnuts from 82.26 to 83.67%. Longer drying times and higher temperatures correlated with an increase in monounsaturated fatty acids and a decrease in polyunsaturated fatty acids in both nut types. In conclusion, conduction drying, especially at higher temperatures and longer durations, significantly affects the nutrient and fatty acid profiles of hazelnuts and walnuts. The study provides new insights into the effects of drying conditions on the nutrient composition and fatty acid profiles of hazelnuts and walnuts and reveals significant changes that warrant further investigation. It sets the stage for future research to extend these findings to other nut species and alternative drying processes and highlights the importance of optimizing processing parameters for improved health benefits and sustainability.

The research conducted focused on the capabilities of various non-linear and machine learning (ML) models in estimating the higher heating value (HHV) of biomass using proximate analysis data as inputs. The research was carried out to identify the most appropriate model for the estimation of HHV, which was determined by a statistical analysis of the modeling error. In this sense, artificial neural networks (ANNs), support vector machine (SVM), random forest regression (RFR), and higher-degree polynomial models were compared. After statistical analysis of the modeling error, the ANN model was found to be the most suitable for estimating the HHV biomass and showed the highest specific regression coefficient, with an R2 of 0.92. SVM (R2 = 0.81), RFR, and polynomial models (R2 = 0.84), on the other hand, also exhibit a high degree of estimation, albeit with somewhat larger modelling errors. The study conducted suggests that ANN models are best suited for the non-linear modeling of HHV of biomass, as they can generalize and search for links between input and output data that are more robust but also more complex in structure.

Energy crops are all crops that are grown with the ultimate goal of energy production, and are characterized by high yield per unit area, low agro-technical investments and the possibility of conversion into various forms of biofuels. In order to reduce the consumption of primary energy, and increase the efficiency of production and collection of biomass of energy crops, in recent times, special emphasis has been placed on the development of modern mechanization. Producers offer various technical and technological solutions, and the aim of this paper is to present the possibility of development and using field mechanization in the harvest of energy crops in the process of biomass production.

Energetske kulture su sve kulture koje se uzgajaju s krajnjim ciljem proizvodnje energije, a karakterizira ih visok prinos po jedinici površine, niska agrotehnička ulaganja te mogućnost konverzije u različite oblike biogoriva. Kako bi se smanjila potrošnja primarne energije, a povećala efikasnost proizvodnje i prikupljana biomase energetskih kultura, u novije vrijeme je poseban naglasak stavljen na razvoj suvremene mehanizacije. Proizvođači nude različita tehnička i tehnološka rješenja, a cilj ovoga rada je predstaviti značajnije trendove razvoja poljoprivredne mehanizacije za žetvu i prikupljanje energetskih kultura.

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.

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.

Biomass as a renewable energy source includes energy crops that are not used for food but solely for biomass production with the goal of conversion to various forms of biofuel. Switchgrass, a perennial grass native to North America, has been explored as an energy crop for many years. It is suitable because it does not require much agrotechnical input, is highly resistant to pest infestation and disease development, and can provide very high biomass yields. The aim of this work was to determine the biomass quality of the mentioned plant in relation to the autumn and spring harvest, considering its use in direct combustion processes. Significant differences were found in the percentages of ash, carbon, nitrogen, sulfur, oxygen, and water, as well as in the percentages of micro and macro elements, in the harvest dates studied. Compared to the autumn, the moisture content decreased from 33.88% to 10.95% and ash content from 4.59% to 3.1% in the spring harvest, while the carbon content increased from 47.02% to 47.49%, dry matter from 38.91% to 89.22%, and heating value from 18.60 MJ kg−1 to 18.73 MJ kg−1. Shifting the harvest date from autumn to spring resulted in the production of higher quality biomass for use in combustion processes.

In this study, the physical properties of two hazelnut species were investigated before and after drying at different temperatures and durations. The results showed that the physical properties of the hazelnut samples, including size, volume, density, weight, kernel mass, and shell mass, were significantly affected by temperature, duration, and their interactions. In addition, the moisture content of the samples decreased with increasing temperature and drying duration. The lowest value for the Istarski duguljasti variety was 5.36% (160 °C and 45 min), while the lowest value for Rimski okrugli was measured at 160 °C and 60 min (5.02%). Ash content was affected by both temperature and time, with the Istarski duguljasti variety having a minimum value of 0.84% at 120 °C and 60 min and Rimski okrugli a maximum value of 1.24% at 100 °C and 30 min. The variables of the ultimate analysis, such as nitrogen, carbon, sulfur, and hydrogen, increased with increasing temperature and time. The oxygen content and the higher heating value decreased with increasing temperature. Energy optimization in the drying process is crucial to reduce costs and save time. Effective energy optimization measures can lead to significant cost savings and improved operational efficiency in the drying process.

One of the essential factors for the selection of the drying process is energy consumption. This study intended to optimize the drying treatment of capers using convection (CD), refractive window (RWD), and vacuum drying (VD) combined with ultrasonic pretreatment by a comparative approach among artificial neural networks (ANN) and response surface methodology (RSM) focusing on the specific energy consumption (SEC). For this purpose, the effects of drying temperature (50, 60, 70 °C), ultrasonication time (0, 20, 40 min), and drying method (RWD, CD, VD) on the SEC value (MJ/g) were tested using a face-centered central composite design (FCCD). RSM (R2: 0.938) determined the optimum drying-temperature–ultrasonication-time values that minimize SEC as; 50 °C-35.5 min, 70 °C-40 min and 70 °C-24 min for RWD, CD and VD, respectively. The conduct of the ANN model is evidenced by the correlation coefficient for training (0.976), testing (0.971) and validation (0.972), which shows the high suitability of the model for optimising specific energy consumption (SEC).

Hazelnut fruit samples were collected over 2 years (2020 and 2021) and subjected to four different drying temperatures (100, 120, 140, and 160 °C) and four different drying times of 15, 30, 45, and 60 min using conduction drying. The analyses performed showed that conduction drying at different temperatures and different drying times had a significant effect on the change in the composition of the hazelnut shell fuel and its mass properties. Comparing the untreated samples over two years and the samples after drying, it can be seen that in 2020, the drying treatment causes a decrease in the percentage of C and H, while in 2021, drying at 160 °C and 45 min causes an increase in C and H values. After treatment, the S content decreased on average, while the value of O increased or remained the same. The greatest increase in heating values (HHV and LHV) was observed at temperatures of 140 °C and 120 °C and the duration of 45 min. When drying was applied, a significant difference in mass change was observed at 120 °C, 100 °C, and 140 °C and 30 and 60 min process durations. The change in heating value is significantly affected by the parameters year of sampling, temperature, and time, while the change in mass of the hazelnut shell is most affected by drying time.

Agricultural biomass is one of the most important renewable energy sources. As a byproduct of corn, soybean and sunflower production, large amounts of biomass are produced that can be used as an energy source through conversion. In order to assess the quality and the possibility of the use of biomass, its composition and calorific value must be determined. The use of nonlinear models allows for an easier estimation of the energy properties of biomass concerning certain input and output parameters. In this paper, RFR (Random Forest Regression) and SVM (Support Vector Machine) models were developed to determine their capabilities in estimating the HHV (higher heating value) of biomass based on input parameters of ultimate analysis. The developed models showed good performance in terms of HHV estimation, confirmed by the coefficient of determination for the RFR (R2 = 0.79) and SVM (R2 = 0.93) models. The developed models have shown promising results in accurately predicting the HHV of biomass from various sources. The use of these algorithms for biomass energy prediction has the potential for further development.

A number of measures to diversify its energy supply sources and reduce its dependence on imported energy sources has been taken by the EU. These include pursuing new energy sources, such as renewable energy and liquefied natural gas; increasing the storage capacities; and investing in interconnectors and other infrastructure. However, these actions require long-term adjustment, while there is a need to find an option to meet the energy needs at a moment. One possible option is to utilize seed production wastes for energy production. This research paper aims to investigate the potential of utilizing seed production wastes (SPWs) for energy production in continental Croatia, and assess its feasibility. Eight different SPWs were used in this research, where their energy characteristics were determined and the theoretical thermal potential was calculated if they are used as raw material in the production of thermal energy through biomass and cogeneration power plants, or in biogas power plants. By using the available feedstock, it is theoretically possible to produce a total of 38,051.10 GJ of thermal energy by direct combustion of SPWs and 34,727.91 GJ by combustion of the produced biomethane. The SPWs of oilseed rape and beans contain the highest specific heat potential per hectare.

The increasing amount of residual waste presents several opportunities to use biomass as a renewable energy source. Agricultural biomass is a raw material with a high ash content, which can be a problem in any form of energy conversion. To obtain better quality biofuel, excess mineral matter must be removed. Demineralization is a simple form of mixing and washing biomass with various liquids to reduce ash content. Water, acetic acid, hydrochloric acid and nitric acid are common solvents used for this purpose. Ash is composed of different micro (Zn, Cu, Fe) and macro elements (Mg, Ca, K), which can have different consequences for the use of biomass for thermal energy. Different solvents have different effects on the individual elements, with inorganic acids having the greatest effect in demineralization processes, with a reduction in ash content of up to 80% for corn and about 99% for soybeans.

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.

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.

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.