X. Philip Ye

A Comparative Review of Petroleum‐Based and Bio‐Based Acrolein Production

Acrolein is an important chemical intermediate for many common industrial chemicals, leading to an array of useful end products. This paper reviews all the synthetic methods, including the former (aldol condensation) and contemporary (partial oxidation of propylene) manufacturing methods, the partial oxidation of propane, and most importantly, the bio-based glycerol-dehydration route. Emphasis is placed on the petroleum-based route from propylene and the bio-based route from glycerol, an abundantly available and relatively inexpensive raw material available from biodiesel production. This review provides technical details and incentives for industrial proyduction that justify a transition toward bio-based acrolein production.

Influence of pyrolysis condition on switchgrass bio-oil yield and physicochemical properties.

The poor and inconsistent physicochemical properties of bio-oil are inhibiting its industrialized production. We investigated the variability in properties of switchgrass bio-oil produced at three pyrolysis temperatures (T=450, 500, and 550 degrees C) and three feedstock moisture contents (MC=5%, 10%, and 15%) in a 3x3 factorial experiment in order to exploit opportunities to improve bio-oil properties through optimization of pyrolysis parameters. Results showed that even with the single type of feedstock and pyrolysis system, the two main factors and their interaction caused large variations in bio-oil yield and most of the measured physicochemical properties. Following improvements of bio-oil properties could be individually achieved by selecting an optimal pyrolysis condition (shown in parenthesis) comparing with the worst case: increase of bio-oil yield by more than twofold (MC=10%, T=450 degrees C), increase of pH by 20.4% from 2.74 to 3.3 (MC=10%, T=550 degrees C), increase of higher heating value by 18.1% from 16.6 to 19.6 MJ/kg (MC=10%, T=450 degrees C), decrease of density by 5.9% from 1.18 to 1.11 g/cm(3) (MC=5%, T=550 degrees C), decrease of water content by 36% from 31.4 to 20.1 wt.% (MC=5%, T=450 degrees C), decrease of viscosity by 40% from 28.2 to 17 centistokes (MC=5%, T=550 degrees C), decrease of solid content by 57% from 2.86 to 1.23 wt.% (MC=15%, T=550 degrees C), and decrease of ash content by 41.9% from 0.62 to 0.36 wt.% (MC=15%, T=550 degrees C). There is no single, clear-cut optimal condition that can satisfy the criteria for a bio-oil product with all the desired properties. Trade-offs should be balanced according to the usage of the end-products.

Fast classification and compositional analysis of cornstover fractions using Fourier transform near-infrared techniques.

The objectives of this research were to determine the variation of chemical composition across botanical fractions of cornstover, and to probe the potential of Fourier transform near-infrared (FT-NIR) techniques in qualitatively classifying separated cornstover fractions and in quantitatively analyzing chemical compositions of cornstover by developing calibration models to predict chemical compositions of cornstover based on FT-NIR spectra. Large variations of cornstover chemical composition for wide calibration ranges, which is required by a reliable calibration model, were achieved by manually separating the cornstover samples into six botanical fractions, and their chemical compositions were determined by conventional wet chemical analyses, which proved that chemical composition varies significantly among different botanical fractions of cornstover. Different botanic fractions, having total saccharide content in descending order, are husk, sheath, pith, rind, leaf, and node. Based on FT-NIR spectra acquired on the biomass, classification by Soft Independent Modeling of Class Analogy (SIMCA) was employed to conduct qualitative classification of cornstover fractions, and partial least square (PLS) regression was used for quantitative chemical composition analysis. SIMCA was successfully demonstrated in classifying botanical fractions of cornstover. The developed PLS model yielded root mean square error of prediction (RMSEP %w/w) of 0.92, 1.03, 0.17, 0.27, 0.21, 1.12, and 0.57 for glucan, xylan, galactan, arabinan, mannan, lignin, and ash, respectively. The results showed the potential of FT-NIR techniques in combination with multivariate analysis to be utilized by biomass feedstock suppliers, bioethanol manufacturers, and bio-power producers in order to better manage bioenergy feedstocks and enhance bioconversion.

Recent progress in converting biomass to biofuels and renewable chemicals in sub- or supercritical water

This review covers recent research and development undertaken in converting biomass (lignocelluloses, carbohydrates, waste vegetable oil and algae) to biofuels and renewable chemicals using sub- or supercritical water (SCW) as thermochemical reaction media. Applications of SCW technology in pretreating biomass for bioethanol production by fermentation and in hydrogen/methane production by gasification are not covered in this review. The focus is on research progress in understanding the conversion characteristics of model biomass compounds, such as hemicellulose, cellulose, triglycerides and, to a lesser extent, real heterogeneous biomass in SCW. Specific attention is given to promising reaction pathways and novel process development in SCW conversion of biomass and its model compounds. There are good opportunities for the use of biomass feedstocks and SCW technology in the production of liquid fuels for the transportation sector, as well as renewable chemicals for the chemical industry. However, a broad range of fundamental and exploratory research is still needed to advance understanding of the complex conversion chemistry of real biomass in SCW.

Improved prediction of biomass composition for switchgrass using reproducing kernel methods with wavelet compressed FT-NIR spectra

Fourier transform near-infrared (FT-NIR) technique is an effective approach to predict chemical properties and can be applied to online monitoring in bio-energy industry. High dimensionality and collinearity of the FT-NIR spectral data makes it difficult in some applications to construct the reliable prediction model. In this study, two nonlinear kernel methods with wavelet-compressed data, Kernel Partial Least Squares (KPLS) regression and Kernel Ridge Regression (KRR), are presented to resolve those data into a few predictors and then, more sophisticated models are created to capture the nonlinear relationships between the spectral data and concentrations determined by wet chemistry. A wavelet transform is adopted as a preprocessing procedure to reduce the data size for supporting real-time implementation of assessing biomass properties with FT-NIR spectroscopy. A real-life data of switchgrass is presented to illustrate the performance of the developed models and the results advocated that the use of nonlinear kernel procedure with wavelet compression improved the prediction performance of the model.

Neutron Scattering for Moisture Detection in Foamed Asphalt

AbstractFoamed warm-mix asphalt (WMA) has been widely accepted and used in the United States and many other countries around the world. However, several key concerns about WMA technology still need to be answered, including the major issue of moisture-induced damage. Because of the reduced production temperatures and the foaming process with water, moisture may be entrapped in pavements after compaction. The trapped moisture decreases the adhesion between asphalt binder and aggregates and the cohesion among asphalt binder, resulting in stripping and other forms of pavement distress. The neutron scattering technique provides a unique tool for the determination of the microscopic structure of asphalt and for the detection of the presence of moisture and its spatial distributions in asphalt. In particular, small-angle neutron scattering (SANS) in the wave vector transfer range from 0.003−0.5  A−1 is suitable to probe the spatial density fluctuations in the real space from 200−1  nm, which has a resolution se...

Pretreatment of near Infrared Spectral Data in Fast Biomass Analysis

The ability to rapidly evaluate the chemical composition of biomass feedstock for purposes of process monitoring and optimisation is useful for gauging the potential applications and value of such feedstocks. Near infrared (NIR) spectroscopy, coupled with multivariate analysis and data pretreatment, was evaluated to remove interference from physical heterogeneity that could mask chemical property responses. Pretreatment methods included standard normal variate (SNV), multiplicative scattering correction (MSC), 1st derivative with the Savitzky-Golay algorithm (1st derivative), 2nd derivative with the Savitzky-Golay algorithm (2nd derivative), extended multiplicative signal correction (EMSC) and combinations of 1st derivative/2nd derivative with SNV. Results indicated that, of these methods, EMSC was most effective for diffuse reflectance NIR analysis of lignocellulosic biomass. The EMSC-pretreated data not only best accessed the chemical similarity of the probed feedstocks in our hierarchical cluster analysis but also consistently led to the overall best prediction of the chemical composition of the biomass.

Glycerol Dehydration to Acrolein Catalyzed by ZSM‐5 Zeolite in Supercritical Carbon Dioxide Medium

Abstract Supercritical carbon dioxide (SC‐CO2) has been used for the first time as a reaction medium for the dehydration of glycerol to acrolein catalyzed by a solid acid. Unprecedented catalyst stability over 528 hours of time‐on‐stream was achieved and the rate of coke deposition on the zeolite catalyst was the lowest among extensive previous studies, showing potential for industrial application. Coking pathways in SC‐CO2 were also elucidated for future development. The results have potential implications for other dehydration reactions catalyzed by solid acids.

Tandem catalytic conversion of glycerol using solid catalysts followed by transesterification to produce alkyl lactate

This study investigated alkyl lactate production from glycerol by tandem processes which included glycerol conversion to calcium lactate using solid catalysts and subsequent transesterification of calcium lactate to alkyl lactate using methanol/ethanol and carbon dioxide. The effect of reaction conditions on alkyl lactate production was systematically investigated. A central composite design (CCD) and response surface methodology were used to design the experiments and evaluate the optimum process conditions. At the optimum conditions, the methyl lactate yield reached 57 mol% with a glycerol conversion of 94 mol% using refined glycerol. A yield of approximately 42 mol% for ethyl lactate was obtained using refined glycerol at the optimum conditions. Similar glycerol conversion and alkyl lactate yields were obtained using crude glycerol, indicating that the impurities in the crude glycerol had no significant effects on alkyl lactate production. The examination of regenerated catalysts that had been reused twice showed no negative effects on glycerol conversion and methyl lactate production. Compared to the traditional lactic acid and alkyl lactate production, this two-step process for alkyl lactate production from glycerol is both economical and environmentally benign since no gypsum was produced and the solid catalysts can be regenerated and reused.

A Quasi-chemical Model for Bacterial Spore Germination Kinetics by High Pressure

High pressure processing (HPP) is an emerging non-thermal technology that is growing exponentially in use worldwide for the pasteurization of commercial foodstuffs. At combinations of elevated pressures and temperatures, HPP inactivates bacterial spores, but HPP has not yet been implemented commercially for food sterilization. Studies of the mechanisms of bacterial spore inactivation by HPP using primarily spores of Bacillus species have shown that spore germination precedes inactivation, with the release of dipicolinic acid from the spore core as the rate-determining step. Investigations probing spore resistance to and germination by HPP using Bacillus subtilis, a number of selected B. subtilis mutants, Bacillus amyloliquefaciens, and Clostridium difficile spores have compiled a wealth of detailed mechanistic information, while also accumulating abundant germination kinetics data that has not previously been analyzed by predictive models. Presently, we devise a “quasi-chemical” model for bacterial spore germination dynamics by HPP. This quasi-chemical germination model (QCGM) hypothesizes a three-step mechanism and derives a set of ordinary differential equations to model the observed germination dynamics. The results with this model are viewed in the context of historical studies of spore activation, germination, and inactivation, with an eye toward potentially integrating differential equation models for germination and inactivation into a single, comprehensive model for spore dynamics by HPP. With the increasing use of high hydrostatic pressure to investigate mechanisms of bacterial spore resistance and physiology, the QCGM results help promote the efficient control of bacterial spores, whether for the inactivation of Clostridium botulinum spores in low-acid foods or aerosolized Bacillus anthracis spores on textiles used in protective clothing, tents, or shelters.