A method for differentiating between exogenous and naturally embedded ash in bio-based feedstock by combining ED-XRF and NIR spectroscopy. (March 2019)

• Record Type: Journal Article
• Authors: Thyrel, Mikael; Aulin, Robert; Lestander, Torbjörn A.
• Is Part Of: Biomass and bioenergy. Volume 122(2019)
• Page Start: 84
• Page End: 89
• Publication Date: 2019-03
• Subjects: Spectroscopy -- X-ray fluorescence -- XRF -- Near-infrared -- NIR -- Ash mass fraction -- Exogenous ash; Biomass energy -- Periodicals; Biomass -- Periodicals; Energy-Generating Resources -- Periodicals; Bioénergie -- Périodiques; 333.9539
• DOI: 10.1016/j.biombioe.2018.12.018
• Languages: English
• ISSNs: 0961-9534

Abstract

Abstract: Characterization of ash-generating elements is of great importance in bio-based processes using lignocellulosic biomass as feedstock. Spectral data using energy dispersive X-ray fluorescence (ED-XRF) spectroscopy and near-infrared (NIR) spectroscopy were recorded from 119 lignocellulosic samples collected at bio-based combined heat and power plants. These spectra were used in regression modeling by using orthogonal projections to lateral structures (OPLS) to predict ash mass fraction varying between 0.2 and 5.7% in the dry biomass. The ED-XRF models produced more robust calibrations with lower prediction errors than corresponding NIR models that underestimated ash mass fractions >2%, especially when extra samples contaminated with 0.2–4.3% exogenous ash to reach 5% ash mass fraction were validated using the constructed OPLS models. Thus, by combining these spectral techniques, it has been shown for the first time that it is possible to distinguish between naturally embedded bioash and ash originating from contamination in biomass samples. This opens up new routes and instrumentation development to monitor and control varying ash mass fractions better in bio-based feedstocks entering combustion processes or biorefinery processes. Graphical abstract: Image 1 Highlights: XRF shows excellent predictions of ash in biomass - reflects all ash-forming elements. Excellent predictions of total ash by XRF also for exogenous ash. NIR underestimates ash contents if contaminants of inorganic elements are present. NIR-predictions are good at low ash contents - reflects non-contaminated bioash. XRF and NIR in combination can differentiate between bioash and inorganic impurities in biomass.