Improvement of proximate data and calorific value assessment of bamboo through near infrared wood chips acquisition

Abstract

In a previous study, a near infrared (NIR) spectroscopy model was developed using a spectra of ground bamboo samples. Although the previous report on ground bamboo described a good performance, operation in the power plant was found to be inconvenient due to preparation costs and labour required for the necessary preparation of ground samples. Thus, this study presents the comparison of the performance of an NIR model that was developed by direct scanning of bamboo chips to the previously developed model for ground samples. Special emphasis is put on the comparison of the spectral reproducibility. Bamboo chip models were developed based on PLS regression with variable selection methods in order to achieve the optimal model. The moisture content (MC) and ash content (A) of the developed bamboo chip models could be applied toward quality assurance. The volatile matter (VM) and fixed carbon (FC) models could be used for approximating predictions. The gross and net calorific value (GCV and NCV) models could be used for most applications. The root mean square (RMS) value of pre-treated spectra of different particle size had no statistically significant differences. The study’s findings indicate that the model developed using NIR spectroscopy protocol with wood chips spectra can be used as a classification tool and is an effective method for estimating bamboo chip energy quality. The big particle size of wood chips affect negatively the prediction model, however, it could be solved through spectral pre-processing technique, thus eliminating the need for grinding feedstock samples.