Use of Artificial Neural Networks to Model Biomass Properties of Miscanthus (Miscanthus × giganteus) and Virginia Mallow (Sida hermaphrodita L.) in View of Harvest Season

Abstract

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 Quality of Virginia Fanpetals Biomass as an Energy Source, Depending on the Type of Propagating Material and Plantation Age

“…In the present study, the carbon content of Sida biomass was bound by a weak, but significant, correlation with the HHV. Carbon concentration was similar to that noted by Stolarski et al [42] and Šurić et al [38], and it was higher than that reported by Šiaudinis et al [43]. In the work of Bilandžija et al [37], carbon concentration was much lower when Sida biomass was harvested in January (32.22% DM) than in November and March (46.79% and 50.08% DM, respectively).…”

“…In other studies, the HHV and LHV of Virginia fanpetals biomass varied across years, and minor differences resulting from the harvest date (fall, winter, early spring) were noted [9,34,35,37,43,44]. The HHV and LHV of the biomass were generally higher when Virginia fanpetals was harvested in the spring rather than in the fall [38], but these trends varied [34,37]. Szyszlak et al [45] observed that the HHV was affected by the thickness of Virginia fanpetals shoots and suggested that the value of this parameter could be influenced by plant density.…”

“…The moisture content of Virginia fanpetals biomass is lower than that of lignified biomass [34]. Numerous studies have shown that delayed harvest decreases the moisture content of Virginia fanpetals shoots from more than 40% in the fall to around 20-25% in the winter [35][36][37][38]. According to Jablonowski et al [9], the moisture content of Sida biomass decreased to around 10% when dry shoots were left in the field until March (data not published).…”

Biomass Higher Heating Value Estimation: A Comparative Analysis of Machine Learning Models

“…The application of the ML model in the estimation of HHV biomass shows clear advantages in terms of accuracy and reliability of predictions. Models such as ANNs often achieve high R 2 values (>0.90) and lower RMSE and MAE values, demonstrating their ability to estimate by managing the data [46,47]. On the other hand, it should be noted that although high accuracy and lower modeling errors are achieved, longer runtimes are often required due to higher learning complexity [48], which can be a limiting factor when computational resources are limited.…”

Enhancing Cookie Formulations with Combined Dehydrated Peach: A Machine Learning Approach for Technological Quality Assessment and Optimization

“…The output value, denoted as Y , is determined by transfer functions, with f 1 representing the transfer function for the hidden layer and f 2 for the output layer. The input layer matrix is denoted by X [ 32 , 33 ]: …”