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 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.

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.

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).

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.

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.