Biomass compositional analysis for energy applications.

In its broadest definition, biomass can be described as all material that was or is a part of a living organism. For renewable energy applications, however, the definition of biomass is usually limited to include only materials that are plant-derived such as agricultural residues (e.g., wheat straw, corn stover) by-products of industrial processes (e.g., sawdust, sugar cane bagasse, pulp residues, distillers grains), or dedicated energy crops (e.g., switchgrass, sorghum, Miscanthus, short-rotation woody crops). This chapter describes analytical methods developed to measure plant components with an emphasis on the measurement of components that are important for biomass conversion. The methods described here can be viewed as a portfolio of analytical methods, with consistent assumptions and compatible sample preparation steps, selected for simplicity, robust application, and the ability to obtain a summative mass closure on most samples that accurately identifies greater than 95% of the mass of a plant biomass sample. The portfolio of methods has been successfully applied to a wide variety of biomass feedstock as well as liquid and solid fractions of both thermochemical pretreatment and enzymatic saccharification (1).

Rapid biomass analysis: new tools for compositional analysis of corn stover feedstocks and process intermediates from ethanol production.

New, rapid, and inexpensive methods that monitor the chemical composition of corn stover and corn stover-derived samples are a key element to enabling the commercialization of processes that convert stover to fuels and chemicals. These new techniques combine near infrared (NIR) spectroscopy and projection to latent structures (PLS) multivariate analysis to allow the compositional analysis of hundreds of samples in 1 d at a cost of about $10 each. The new NIR/PLS rapid analysis methods can also be used to support a variety of research projects that would have been too costly to pursue by traditional methods.

Chemical analysis of wood chips in motion using thermal-emission mid-infrared spectroscopy with projection to latent structures regression.

The opacity and random surface orientation of process streams of solids complicates the application of spectroscopy to on-line analysis of solid streams. We have used a specialized form of thermal-emission mid-infrared spectroscopy, called transient infrared spectroscopy, combined with PLS regression to analyze a moving stream of wood chips for lignin, hemicellulose, glucan, and extractives content. Sixty-eight softwood samples from 6 species were analyzed using one regression, and 28 hardwood samples from 14 species were analyzed with another. Root-mean-square errors of prediction for the properties varied from 0.8 to 1.9 wt %, depending on property and wood type, with lignin and hemicellulose being the most accurately determined and glucan being the least.