Engineering topics

Wolfrum, Edward

Publications and source records attributed to Wolfrum, Edward.

3 records

DOE OSTI2024

Rapid measurement of soluble xylo-oligomers using near-infrared spectroscopy (NIRS) and multivariate statistics: calibration model development and practical approaches to model optimization

Rapid monitoring of biomass conversion processes using techniques such as near-infrared (NIR) spectroscopy can be substantially quicker and less labor-, resource-, and energy-intensive than conventional measurement techniques such as gas or liquid chromatography (GC or LC) due to the lack of solvents and preparation methods, as well as removing the need to transfer samples to an external lab for analytical evaluation. The purpose of this study was to determine the feasibility of rapid monitoring of a biomass conversion process using NIR spectroscopy combined with multivariate statistical modeling, and to examine the impact of (1) subsetting the samples in the original dataset by process location and (2) reducing the spectral range used in the calibration model on model performance. We develop multivariate calibration models for the concentrations of soluble xylo-oligosaccharides (XOS), monomeric xylose, and total solids at multiple points in a biomass conversion process which produces and then purifies XOS compounds from sugar cane bagasse. A single model using samples from multiple locations in the process stream showed acceptable performance as measured by standard statistical measures. However, compared to the single model, we show that separate models built by segregating the calibration samples according to process location show improved performance. We also show that combining an understanding of the sample spectra with simple multivariate analysis tools can result in a calibration model with a substantially smaller spectral range that provides essentially equal performance to the full-range model. We demonstrate that real-time monitoring of soluble xylo-oligosaccharides (XOS), monomeric xylose, and total solids concentration at multiple points in a process stream using NIR spectroscopy coupled with multivariate statistics is feasible. Segregation of sample populations by process location improves model performance. Models using a reduced spectral range containing the most relevant spectral signatures show very similar performance to the full-range model, reinforcing the importance of performing robust exploratory data analysis before beginning multivariate modeling.

09 BIOMASS FUELS↗

DOE OSTI2022

NIR Dataset and Models (Near-Infrared Spectroscopy Calibration Dataset for Butanediol Fermentation and Multivariate Calibration Models) [SWR-22-60]

2,3-butanediol (2,3-BDO) is an economically important platform chemical that can be used in a variety of chemical feedstocks, liquid fuels, and biosynthetic building blocks. While 2,3-BDO can be efficiently produced by fermentation, the fermentation requires continuous monitoring and control to maximize 2,3-BDO yields and minimize inhibitory coproducts. Because of the time required for sampling and at-line measurement of fermentation samples with high pressure liquid chromatography (HPLC), the ability for operators to perform real-time modification to fermentation conditions is limited. To overcome this challenge, researchers from the National Renewable Energy Laboratory (NREL) have developed a calibration model which can predict the concentration of several analytes in real-time using near-infrared (NIR) spectra of the filtered fermentation broth. While significantly reducing the need for off-line sampling, NREL expects this technology to play a critical role in maximizing 2,3-BDO production. 2,3-butanediol (2,3-BDO) is a useful chemical platform that can be used to create a variety of products. For instance, 2,3-BDO can be (1) dehydrated and converted into methyl ethyl ketone, a liquid fuel additive or (2) deoxydehydrated into 1,3-butadiene for synthetic rubber, which can also be oligomerized in high yields to gasoline, diesel, and jet fuel. In order to maximize 2,3-BDO production, frequent measurement of fermentation samples is needed, as small changes in oxygen concentration can drive the fermentation to undesired products. For example, oxygen-deficient conditions result in glycerol production, while excess oxygen concentrations result in acetoin production. This results in the need for measuring dissolved oxygen, glucose, and xylose concentrations in order to optimize the aeration rate of the fermentation. Traditional monitoring methods occurs off-line and can take up to 30 minutes per sample. With multiple fermenters and high-pressure liquid chromatography (HPLC) injectors, resulting in the need for multiple samples, the sampling process can take hours to complete. NREL’s calibration model can predict the glucose, xylose, 2,3-BDO, acetoin, and glycerol concentrations from NIR spectra of filtered fermentation liquor samples. Using a partial least-squares (PLS) calibration model, NREL’s model can monitor the concentration of these analytes during subsequent fermentations at bench- and pilot-scale, demonstrating the utility of NIR spectroscopy combined with chemometrics for real-time, at-line monitoring of 2,3-BDO fermentations.

Wolfrum, Edward↗

DOE OSTI2021

Reliability metrics and their management implications for open pond algae cultivation

The prevalence of contaminating organisms in outdoor algae cultivation, with the often-associated dramatic crop failures, necessitates the need for metrics describing production system reliability. Standard metrics for algae cultivation reliability are critically needed to be able to map improvements in operational parameters, but do not currently exist. In this work, we present a set of standard metrics including mean time to failure (MTTF) and mean time between failures (MTBF) as the basis for the calculation of pond failure rate (FR) and reliability coefficient (RC). Metrics associated with the numbers of contaminating organisms such as abundance ratio (AR), prevalence of infection (PI), and mean intensity of infection (MII) are also relevant. These metrics, based on measured experimental values of outdoor pond performance during the Algae Testbed Public-Private Partnership (ATP3) Unified Field Studies (UFS), provide a basis for quantifying pond failure and provide insight into potential pond management and contaminant mitigation strategies. From these reliability metrics applied to this dataset, we are able to link operational parameters, such as harvest frequency and inoculum source, to pond reliability for different algae strains and seasons, and from an assessment of AR, provide contamination thresholds beyond which a culture may be unrecoverable. Ultimately, the implementation of widely-practiced and simple-to-calculate algae pond reliability metrics calculated from rapid and easy to collect data or observations will reduce risk and uncertainty in large-scale algae deployment and aid in the development of integrated pest management (IPM) strategies.

09 BIOMASS FUELS↗