Timothy M. Young

Extraction of information from laser-induced breakdown spectroscopy spectral data by multivariate analysis

Laser-induced breakdown spectroscopy (LIBS) is being proposed more and more as a high-throughput technology to assess the elemental composition of materials. When a specific element is of interest, semiquantification is possible by building a calibration model using the emission line intensity of this element for known samples. However, a unique element has usually more than one emission line, and there are many examples where several emission lines used in combination give dramatically better results than any of the individual variables used alone. With a multivariate approach, models can be constructed that take into account all the emission lines related to a specific element; therefore more robust models can be developed. In this work, chemometric methods such as principal component analysis and partial least squares are proposed to resolve and extract useful information from the LIBS spectral data collected on biological materials.

Prediction of internal bond strength in a medium density fiberboard process using multivariate statistical methods and variable selection

This paper presents new data mining-based multivariate calibration models for predicting internal bond strength from medium density fiberboard (MDF) process variables. It utilizes genetic algorithms (GA) based variable selection combined with several calibration methods. By adopting a proper variable selection scheme, the prediction performance can be improved because of the exclusion of non-informative variable(s). A case study using real plant data showed that the calibration models based on the process variables selected by GA produced better performance than those without variable selection, with the exception of the radial basis function (RBF) neural networks model. In particular, the calibration model based on supervised probabilistic principal component analysis (SPPCA) yielded better performance (only when using GA) than partial least squares (PLS), orthogonal-PLS (O-PLS), and radial basis function neural networks models. The SPPCA model benefits most from the use of GA-based variable selection in this case study.

Integrating Separation and Conversion—Conversion of Biorefinery Process Streams to Biobased Chemicals and Fuels

The concept of the integrated biorefinery is critical to developing a robust biorefining industry in the USA. Within this model, the biorefinery will produce fuel as a high-volume output addressing domestic energy needs and biobased chemical products (high-value organics) as an output providing necessary economic support for fuel production. This paper will overview recent developments within two aspects of the integrated biorefinery—the fractionation of biomass into individual process streams and the subsequent conversion of lignin into chemical products. Solvent-based separation of switchgrass, poplar, and mixed feedstocks is being developed as a biorefinery “front end” and will be described as a function of fractionation conditions. Control over the properties and structure of the individual biomass components (carbohydrates and lignin) can be observed by adjusting the fractionation process. Subsequent conversion of the lignin isolated from this fractionation leads to low molecular weight aromatics from selective chemical oxidation. Together, processes such as these provide examples of foundational technology that will contribute to a robust domestic biorefining industry.

Ecosystem Services of Woody Crop Production Systems

Short-rotation woody crops are an integral component of regional and national energy portfolios, as well as providing essential ecosystem services such as biomass supplies, carbon sinks, clean water, and healthy soils. We review recent USDA Forest Service Research and Development efforts from the USDA Biomass Research Centers on the provisioning of these ecosystem services from woody crop production systems. For biomass, we highlight productivity and yield potential, pest susceptibility, and bioenergy siting applications. We describe carbon storage in aboveground woody biomass and studies assessing the provision of clean and plentiful water. Soil protection and wildlife habitat are also mentioned, in the context of converting lands from traditional row-crop agriculture to woody production systems.

On-Line Monitoring of the Buffer Capacity of Particleboard Furnish by Near-Infrared Spectroscopy

This paper reports on a study of on-line monitoring of the buffer capacity of particleboard furnish using near-infrared (NIR) spectroscopy and multivariate analysis models (chemometrics). The buffer capacity of wood furnish is known to affect the quality of polymerization and the curing rates of urea-formaldehyde (UF) resins, which may affect the mechanical properties of manufactured panel. The first phase of the study consisted of building multivariate calibration and validation models from NIR spectroscopy data to predict the buffer capacity of particleboard furnish in a laboratory environment. During this phase, a spectrometer (Ocean Optics USB2000) operating in the 550–1100 nm spectral range was evaluated. The second phase of the study took place at a North American particleboard plant over several weeks. Several multivariate calibration models were constructed and tested on-line during a four-day test period. The on-line root mean square error of prediction (RMSEP) and the coefficient of variation (CV) for buffer capacity predictions ranged from 3.45 to 0.92 and 22.4% to 5.8%, respectively.

The economic availability of woody biomass for the southeastern United States.

Abstract A deterministic model was developed to estimate the average total cost of producing whole-tree chips from woody biomass for energy production. The model, IFCHIPSS (Industrial Fuel Chip Supply Simulator), estimated harvest, transportation and stumpage costs. Average total cost estimates were made for 62 potential plant locations in the southeastern United States. The model employed a spatial analytical component and used a geographic information system to locate potential plant sites. The model also measured the impact of market and non-market conditions on the economic availability of woody biomass. Northeast Florida, Southern Georgia, Southern Alabama and the Coastal Plain of South Carolina were considered low cost regions for any production level of woody biomass. Costs in these regions ranged from

Understanding the Characteristics of Non-industrial Private Forest Landowners Who Harvest Trees

8.89 t−1 (1987 US dollars per green metric ton) to

Improved estimation of the lower percentiles of material properties

11.58 t−1 up to 90 700 t (green metric ton) of annual production. A green metric ton was considered to be pre-dried forest wood fiber. Higher cost regions were the South Delta of Louisiana, Kentucky, West Virginia and the mountains of Tennessee and Virginia. Costs in these regions ranged from

Kernel Ridge Regression with Lagged-Dependent Variable: Applications to Prediction of Internal Bond Strength in a Medium Density Fiberboard Process

16.46 t−1 to