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This study focuses on the investigation and comparison of combustion characteristic parameters and combustion performance indices between fast-growing trees and agricultural residues as biomass sources. The investigation is conducted through direct combustion in an air environment using a thermogravimetric analyzer (TGA). Additionally, partial least squares regression (PLSR)-based models were developed to assess combustion performance indices via near-infrared spectroscopy (NIRS), serving as a non-destructive alternative method. The results obtained through the TGA reveal that, specifically, fast-growing trees display higher average ignition temperature (227 °C) and burnout temperature (521 °C) in comparison to agricultural residues, which exhibit the values of 218 °C and 515 °C, respectively. Therefore, fast-growing trees are comparatively difficult to ignite, but sustain combustion over extended periods, yielding higher temperatures. However, despite fast-growing trees having a high ignition index (Di) and burnout index (Df), the comprehensive combustion performance (Si) and flammability index (Ci) of agricultural residue are higher, indicating the latter possess enhanced thermal and combustion reactivity, coupled with improved combustion stability. Five distinct PLSR-based models were developed using 115 biomass samples for both chip and ground forms, spanning the wavenumber range of 3595–12,489 cm−1. The optimal model was selected by evaluating the coefficients of determination in the prediction set (R2P), root mean square error of prediction (RMSEP), and RPD values. The results suggest that the proposed model for Df, obtained through GA-PLSR using the first derivative (D1), and Si, achieved through full-PLSR with MSC, both in ground biomass, is usable for most applications, including research. The model yielded, respectively, an R2P, RMSEP, and RPD, which are 0.8426, 0.4968 wt.% min⁻4, and 2.5; and 0.8808, 0.1566 wt.%2 min⁻2 °C⁻3, and 3.1. The remaining models (Di in chip and ground, Df, and Si in chip, and Ci in chip and ground biomass) are primarily applicable only for rough screening purposes. However, including more representative samples and exploring a more suitable machine learning algorithm are essential for updating the model to achieve a better nondestructive assessment of biomass combustion behavior. [ABSTRACT FROM AUTHOR]

Accurate non-destructive assessment of biomass energy properties is essential for optimizing its use as an alternative fuel. In this study, 200 biomass samples were used to determine higher heating value (HHV) and 120 biomass samples for analyzing ultimate analysis parameters using near-infrared spectroscopy within the full wavenumber range of 12489.48 - 3594.87 cm-1. The samples were grounded, and five different types of partial least squares regression (PLSR) models were developed using traditional preprocessing, multi-preprocessing (MP) with 5 range, MP with 3 range, genetic algorithm, and successive projection algorithm. Limit of detection (LOD) and quantification (LOQ) were calculated using the best-performing model among five different PLSR models for HHV in kJ/kg, as well as the weight percentage (wt.%) of carbon (C), oxygen (O), hydrogen (H), and nitrogen (N). The LOD and LOQ for HHV were calculated as 622.42 kJ/kg and 1886.13 kJ/kg, respectively. Additionally, LOD and LOQ for ultimate analysis parameters, including C, O, H, and N were calculated as: 3.24 weight percentage (wt.%) and 9.81 wt.% for C, 2.04 wt.% and 6.18 wt.% for O, 0.35 wt.% and 1.05 wt.% for H, and 0.22 wt.% and 0.68 wt.% for N. The LOD and LOQ values for HHV, C, O, and H were lower than the minimum reference values used for model development, demonstrating the models' high sensitivity and potential to reliably detect and precisely quantify these parameters. However, the LOD and LOQ values exceeded the minimum reference value used during model development for the N, indicating that the selected models have certain limitations in assessing the N content in biomass. The sample range should be expanded for wt.% of N to enhance the model's performance, surpassing the LOD and LOQ values. This will improve the overall sensitivity of the model for reliable detection and quantification of N content in biomass samples. [ABSTRACT FROM AUTHOR]

This research proposes an artificial neural network (ANN)-based repair and maintenance (R&M) cost estimation model for agricultural machinery. The proposed ANN model can achieve high estimation accuracy with small data requirement. In the study, the proposed ANN model is implemented to estimate the R&M costs using a sample of locally-made rice combine harvesters. The model inputs are geographical regions, harvest area, and curve fitting coefficients related to historical cost data; and the ANN output is the estimated R&M cost. Multilayer feed-forward is adopted as the processing algorithm and LevenbergMarquardt backpropagation learning as the training algorithm. The R&M costs are estimated using the ANN-based model, and results are compared with those of conventional mathematical estimation model. The results reveal that the percentage error between the conventional and ANN-based estimation models is below 1%, indicating the proposed ANN model’s high predictive accuracy. The proposed ANN-based model is useful for setting the service rates of agricultural machinery, given the significance of R&M cost in profitability. The novelty of this research lies in the use of curve-fitting coefficients in the ANNbased estimation model to improve estimation accuracy. Besides, the proposed ANN model could be further developed into web-based applications using a programming language to enable ease of use and greater user accessibility. Moreover, with minor modifications, the ANN estimation model is also applicable to other geographical areas and tractors or combine harvesters of different countries of origin. [ABSTRACT FROM AUTHOR]

The higher heating value (HHV) plays a significant role in determining of the energy potential of biomass. The method to receive this value using a bomb calorimeter takes a long time approximately of 15 minutes. The implementation of near infrared spectroscopy (NIR) technique by generating a prediction model can be replaced the former method in order to reduce time into 2 minutes. Therefore, the precision of both techniques have to be concerned in order to get the reliable values. The repeatability and reproducibility processes were carried out to evaluate the precision of both methods. It was found that the percentage difference of standard deviation (SD) from the mean of repeatability for the NIR was less than 5 % and for reproducibility was slightly over 5 % (but not over than 10%). The SD of repeatability and reproducibility of the bomb calorimeter were 54.6 J/g (0.3 % difference of average HHV value) and 81.3 J/g (0.5 % difference of average HHV value) respectively. The average HHV from the bomb calorimeter was 18,166.77±236.29 J/g. Both techniques showed very low percentage difference of the SD from the mean that revealed very high reliability and precision of measurements.

The maize cob biomass is one of important biomass crops in Thailand. Nowadays, the use of the biomass as renewable resource is increasing, especially residue agriculture waste. As we know that the biomass properties impact combustion, in order to achieve boiler efficiency, its energy characteristics of biomass was required immediately before burning. This work uses the FT-near infrared spectroscopy to estimate gross calorific value (GCV) of maize cob as the rapid method. Each sample was scanned using diffuse reflectance mode at a wavenumber range between 12500-3600 cm-1. The scanning was done with a resolution of 8 cm-1 and completed 32 scans per sample, then averaged to be one spectrum. The results showed that this technique could decrease a processing time to 1-2 minutes per sample to determine GCV whereas alternatively the current method used a processing time of 25-30 minutes per sample. The capacity of the model gave root mean square error of cross validation (RMSECV) of 91.1 Jg-1, which was low. Hence, the model was acceptable and cloud be used for screening.

The aim of this study was to use the near infrared spectroscopy for predicting the gross calorific value (GCV) and ash content (AC) of recycled sawdust from mushroom cultivation. The wavenumber was in range of 12500-4000 cm-1 with the diffuse reflection mode was used. The NIR models was established using partial least square regression (PLSR) and was validated via using full cross validation. GCV model provided the coefficient of determination (R2), root mean square error of cross validation (RMSECV), ratio of prediction to deviation (RPD), and bias of 0.90, 445 J/g, 3.19 and 4 J/g, respectively. The AC model gave the R2, RMSECV, RPD and bias of 0.83, 1.7000 %wt, 2.44 and 0.0059 %wt, respectively. For prediction of unknow samples, GCV model provided the standard error of prediction (SEP) and bias of 670 J/g and -654 J/g, respectively. The AC model gave the SEP and bias of 1.84 %wt and 0.912 %wt, respectively. The result represented that the GCV and AC model probably used as the rapid method and non-destructive method.

The use of an unmanned aerial system (UAS) equipped with multispectral cameras is a potential approach to acquire canopy reflectance to make various correlations with desired crop parameters. However, the acquired reflectance data are mixed with unwanted data, such as reflectance from soil, which significantly affects some commonly used vegetation indices, such as the NDVI. This study compares the performance of three methods for detecting the canopy area of 3-month-old sugarcane crops. These methods extract the canopy areas using 5 NDVI thresholds (0.2, 0.3, 0.4, 0.5, and 0.6), a principal component analysis (PCA) threshold, and a digital surface model (DSM) threshold. The performance assessment will deliberately consider the quality percentage (QP) of each method to correctly detect the canopy area of short sugarcane crops in 10 selected images. The results show that filtration by the PCA threshold method provides the best result with a QP of 65.89-78.72%. The NDVI threshold method at levels of 0.3 and 0.4 follow with QPs of 58.42-68.81% and 40.80-70.81%, respectively, and the lowest accuracy is obtained by the DSM threshold method, which has QPs of 14.80-30.78%. [ABSTRACT FROM AUTHOR]