Biomass pellets are required as a source of energy because of their abundant and high energy. The rapid measurement of pellets is used to control the biomass quality during the production process. The objective of this work was to use near infrared (NIR) hyperspectral images for predicting the properties, i.e., fuel ratio (FR), volatile matter (VM), fixed carbon (FC), and ash content (A), of commercial biomass pellets. Models were developed using either full spectra or different spatial wavelengths, i.e., interval successive projections algorithm (iSPA) and interval genetic algorithm (iGA), wavelengths and different spectral preprocessing techniques. Their performances were then compared. The optimal model for predicting FR could be created with second derivative (D2) spectra with iSPA-100 wavelengths, while VM, FC, and A could be predicted using standard normal variate (SNV) spectra with iSPA-100 wavelengths. The models for predicting FR, VM, FC, and A provided R² values of 0.75, 0.81, 0.82, and 0.87, respectively. Finally, the prediction of the biomass pellets’ properties under color distribution mapping was able to track pellet quality to control and monitor quality during the operation of the thermal conversion process and can be intuitively used for applications with screening.

The main objective of this study was to investigate the impact of grinding (pretreatment) with a pin mill on the crude extract yields of Dipterocarpus alatus (Yang-Na) leaves. A factorial design in a completely randomized design was conducted to study the combinational effects of sieve sizes (1.0, 1.5, and 3.0mm) and feed rates (1.0, 1.5, and 3.0kg min⁻¹), examining the interaction of parameters for grinding oven-dried Yang-Na leaves. Ethanol extraction initially evaluated the influence of Yang-Na leaf powder with diverse particle sizes. When sorting particle size, the crude extract yield increased as the particle size decreased, with 0.0380.150mm particles yielding the highest extraction, although yields decline when the particle size is lower than 0.038mm. The average particle sizes, production capacity, and fineness modulus all exhibited a significant decrease as the sieve size and feeding rate were reduced, while the specific energy consumption showed an inversely proportional relationship with these parameters. Intriguingly, the crude extract yield remained independent of the average particle size. Notably, the highest yield (14.79g kg⁻¹) was derived from a 0.31mm average particle size, ground with a 1.5mm sieve and a 3kg min⁻¹ feeding rate. This suggests that the pretreatment, involving both grinding conditions and sorting size, has an impact on the performance of the extraction process. However, this study offers an energy-efficient alternative, advocating for using average particle sizes without prior sorting, streamlining the extraction process while maintaining substantial yields. These insights underline the crucial influence of particle size and grinding techniques, advancing our understanding of efficient herbal extraction techniques for industrial applications.

Effective air circulation is crucial for plant growth, requiring adequate airflow and environmental stability. This study utilized Computational Fluid Dynamics (CFD) to analyze airflow patterns in a controlled testing chamber, focusing on how miniature fan placement affects airflow direction and temperature distribution. Ten case studies were conducted, with the CFD model validated against experimental data collected from six monitoring locations on the plant growth table. Model validation was performed using statistical analyses including coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE). The validation results showed strong agreement between simulated and experimental data, with R² values of 0.92 for temperature and 0.89 for airflow velocity. Statistical analysis showed significant differences in both airflow and temperature models at the 0.05 level, with the CFD model validation yielding an RMSE of 2.02 and an average absolute error of 1.17. Among the tested configurations, case M1 achieved the highest air velocity (0.317 m/s) and lowest temperature (27.03 °C), compared to M2 (0.255 m/s, 27.17 °C) and M3 (0.164 m/s, 27.18 °C). The temperature variations between cases significantly impacted cold storage efficiency, with case M1’s superior airflow distribution providing more uniform cooling. These findings offer practical guidelines for optimizing ventilation system design in medicinal plant cultivation facilities, particularly in maintaining ideal storage conditions through strategic fan placement and airflow management.

As global energy demand has increased, bioenergy has emerged as a viable option for reducing greenhouse gas emissions. This study focuses on using waste materials from the sugar industry, such as sugarcane straw, bagasse, and filter cake, to compress into pellets to investigate pelletizing behavior and energy usage. Raw material preparation was a critical phase influencing pelletizing efficiency. Biomass pellet quality depended on a uniform particle size distribution and adequate moisture content. A moisture content of 20% (wb) was found to be suitable for biomass pelletization from the sugar sector. Specific energy in the pelletizing process ranged from 144.28 to 197.85 Wh/kg. The suggested mixing ingredients (sugarcane leaves: bagasse: filter cake) of 0% sugarcane leaves, 90% bagasse, 10% filter cake, and 5% sugarcane leaves, 93.5% bagasse, and 1.5% filter cake resulted in pellets with a bulk density of over 600 kg/m³ and a durability of at least 97.5%. All aspects were assessed according to standardized criteria for developing biomass pellet processing technology from sugar industry byproducts. This method could improve efficiency, boost production volume, lower production costs, and promote the efficient and cost-effective use of renewable energy.