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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.

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.

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).

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.

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.