Multivariate NIR spectroscopy models for moisture, ash and calorific content in biofuels using bi-orthogonal partial least squares regression

Abstract

The multitude of biofuels in use and their widely different characteristics stress the need for improved characterisation of their chemical and physical properties. Industrial use of biofuels further demands rapid characterisation methods suitable for on-line measurements. The single most important property in biofuels is the calorific value. This is influenced by moisture and ash content as well as the chemical composition of the dry biomass. Near infrared (NIR) spectroscopy and bi-orthogonal partial least squares (BPLS) regression were used to model moisture and ash content as well as gross calorific value in ground samples of stem and branches wood. Samples from 16 individual trees of Norway spruce were artificially moistened into five classes (10, 20, 30, 40 and 50%). Three different models for decomposition of the spectral variation into structure and noise were applied. In total 16 BPLS models were used, all of which showed high accuracy in prediction for a test set and they explained 95.4-99.8% of the reference variable variation. The models for moisture content were spanned by the O-H and C-H overtones, i.e. between water and organic matter. The models for ash content appeared to be based on interactions in carbon chains. For calorific value the models was spanned by C-H stretching, by O-H stretching and bending and by combinations of O-H and C-O stretching. Also -C=C- bonds contributed in the prediction of calorific value. This study illustrates the possibility of using the NIR technique in combination with multivariate calibration to predict economically important properties of biofuels and to interpret models. This concept may also be applied for on-line prediction in processes to standardize biofuels or in biofuelled plants for process monitoring.

Qualitative and quantitative analysis of lignocellulosic biomass using infrared techniques: A mini-review

“…Other physical properties, such as mechanical properties, also could be investigated via NIRS, and a reduced spectral range (650-1150 nm) was used successfully for prediction [75]. The NIRS method is also a powerful tool for predicting other properties, such as moisture, ash, and char content [76,77], which is helpful in evaluating biomass processing. Real-time monitoring of biomass composition is important for industrial applications, because the composition of biomass may vary according to location and variety.…”

Ash Content vs. the Economics of Using Wood Chips for Energy: Model Based on Data from Central Europe

“…Wood chips are a product of chipping wood with a size up to 120 mm (STN 48 0057 2004;STN 48 0058 2004) with a varying calorific value, moisture content, bark content, and impurities content. The moisture content affects the lower calorific value of the wood chips (Lestander and Rhen 2005) and other properties of the wood chips, which then affects their storage management and handling properties (Mattsson 1990;Evald and Jacobsen 1993;Jensen et al 2004Jensen et al , 2006Jirjis 2005). The content of the impurities in the wood chips (such as dirt, mineral matter, or foreign material) is difficult to determine before combustion, but relatively easy to determine afterwards, as they are mostly non-combustible and increase the amount of ash after combustion.…”

High Throughput Screening of Elite Loblolly Pine Families for Chemical and Bioenergy Traits with Near Infrared Spectroscopy

“…Contrasting results have been reported in the literature about the capability of infrared spectroscopy to model the ash content of lignocellulosic biomass. For instance, whereas some models performed well in predicting the ash content of Norway spruce wood [30], others had varying degrees of prediction success based on the wavenumber range and spectral pretreatment technique used [14]. Among the four properties modeled, the prediction errors (i.e., SEP) for volatile matter was the highest-more than twice the SECV.…”