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.
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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).
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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.
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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.
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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.
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.
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.
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.
