Interest in biomass has increased due to current environmental issues, and biomass analysis is usually performed using element and proximate analyses to ascertain its fuel characteristics. Mainly, element component prediction models have been developed based on proximate analysis, yet few studies have predicted proximate components based on element analysis. Hence, this study developed a proximate component prediction model following the calorific value calculation. Analysis of Pearson’s correlation coefficient showed that volatile matter (VM) and fixed carbon (FC) were positively correlated with hydrogen and oxygen, and with carbon, respectively. Thus, the model correlation was developed using a combination of the “stepwise” and “enter” methods along with linear or nonlinear regressions. The optimal models were developed for VM and ash content (Ash). The VM optimal model values were: R² = 0.9402, root-mean-square error (RMSE) = 7.0063, average absolute error (AAE) = 14.8170%, and average bias error (ABE) = −11.7862%. For Ash, the values were: R² = 0.9249, RMSE = 2.9614, AAE = 168.9028%, and ABE = 167.2849%, and for FC, the values were: R² = 9505, RMSE = 6.3214, AAE = 18.3199%, and ABE = 15.0094%. This study provides a model to predict the proximate component by element analysis. Contrary to existing method, proximate analysis can be predicted based on elemental analysis, and shows that consume samples can be performed at once.

Emerging global environmental pollution issues have caused a reduction in coal utilization, leading to an increased research focus on biomass use as an alternative. However, due to the low heat values of biomass, studies in this field are still in progress. Biomass primarily comprises cellulose, hemicellulose, and lignin. To determine the composition of these three components, the measurement methods recommended by TAPPI (Technical Association of the Pulp and Paper Industry) and NREL (National Renewable Energy Laboratory) are typically employed involving equipment such as HPLC. However, these methods are time consuming. In this study, we proposed a model for predicting cellulose, hemicellulose, and lignin contents based on elemental and industrial analyses. A dataset comprising 174 samples was used to develop this model. This was validated using 25 additional samples. The R²_P values for cellulose, hemicellulose, and lignin were 0.6104–0.6362, 0.4803–0.5112, and 0.7247–0.7914, respectively; however, the R²_CV values obtained from the validation results were 0.7387–0.7837, 0.3280–0.4004, and 0.7427–0.7757, respectively. The optimal models selected for cellulose, lignin, and hemicellulose were C1, L2, and 100-(C1-L2) or H2, respectively. Our predictions for woody and herbaceous biomass, including torrefied samples, should be applied with caution to other biomass types due to the potential accuracy limitations. To enhance the prediction accuracy, future research should broaden the range of biomass types considered and gather more data specifically related to woody and herbaceous biomass.

Wood biomass is an alternative to fossil fuels. However, biomass use has several limitations. Torrefaction, in which reduction conditions prevail to overcome these limitations, has been suggested. Here, torrefaction using different wood chips (Liriodendron tulipifera, Populus canadensis, Pinus rigida, and Pinus koraiensis) was conducted under oxygen-rich and oxygen-lean conditions to determine the effects of oxygen. Torrefaction was conducted at 230–310 °C for 1 h. A mass yield difference of 3.53–20.02% p (percentage point) was observed between oxygen-lean and oxygen-rich conditions. The calorific value increased by a maximum of 50.95% and 48.48% under oxygen-rich and oxygen-lean conditions, respectively. Decarbonization (DC), dehydrogenation (DH), and deoxygenation (DO) occurred in the following order because of dehydration and devolatilization during biomass torrefaction: DO > DH > DC. The calorific value of the torrefied biomass increased linearly with the extent of all three processes. The combustibility index and volatile ignitability were calculated based on proximate composition to suggest the optimal conditions for replacing anthracite and bituminous coal. This study provides suggestions for stable operation in a standard boiler design.

We developed an internal environment prediction model for smart greenhouses using machine learning models. Machine learning models were developed by finding certain rules based on the data obtained from the target system and have the advantage of learning various characteristics that are difficult to define theoretically. However, the model accuracy and precision can change according to the model structure (hyperparameters, algorithms, epoch) and data characteristics. In this study, the analysis was performed according to the collected weather data characteristics. The model performance was low when the amount of training data was obtained over less than three days (4320 ea). The model performance improved with an increase in the amount of training data. Model performance stabilized when the training data volume exceeded seven days (10,080 ea). The optimal amount of data was determined to be between three and seven days, with an average model r² of 0.8811 and an RMSE of 2.056 for the gated recurrent unit algorithm. This study verified the possibility of developing a predictive model for the internal environment of a greenhouse based on weather data from outside. This study is limited to a specific target greenhouse, and further analysis of data from various greenhouses and climates is necessary to achieve global optimization.

This study aims to identify an equation for predicting the calorific value for heat-treated biomass using structural analysis. Different models were constructed using 129 samples of cellulose, hemicellulose, and lignin, and calorific values obtained from previous studies. These models were validated using 41 additional datasets, and an optimal model was identified using its results and following performance metrics: the coefficient of determination (R²), mean absolute error (MAE), root-mean-squared error (RMSE), average absolute error (AAE), and average bias error (ABE). Finally, the model was verified using 25 additional data points. For the overall dataset, R² was ~0.52, and the RMSE range was 1.46–1.77. For woody biomass, the R² range was 0.78–0.83, and the RMSE range was 0.9626–1.2810. For herbaceous biomass, the R² range was 0.5251–0.6001, and the RMSE range was 1.1822–1.3957. The validation results showed similar or slightly poorer performances. The optimal model was then tested using the test data. For overall biomass and woody biomass, the performance metrics of the obtained model were superior to those in previous studies, whereas for herbaceous biomass, lower performance metrics were observed. The identified model demonstrated equal or superior performance compared to linear models. Further improvements are required based on a wider range of structural biomass data.