Predicting the Chemical Composition of Aqueous Phase from

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Predicting the chemical composition of aqueous phase from hydrothermal liquefaction of model compounds and biomasses Rene Bjerregaard Madsen, Patrick Biller, Mads Mørk Jensen, Jacob Becker, Bo B. Iversen, and Marianne Glasius Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.6b02007 • Publication Date (Web): 27 Oct 2016 Downloaded from http://pubs.acs.org on October 30, 2016

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Energy & Fuels

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Predicting the chemical composition of aqueous phase from hydrothermal

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liquefaction of model compounds and biomasses

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René B. Madsen, Patrick Biller, Mads M. Jensen, Jacob Becker, Bo B. Iversen, Marianne Glasius*

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Department of Chemistry and iNANO, Aarhus University, Langelandsgade 140, 8000 Aarhus C,

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Denmark

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* Corresponding author: [email protected]

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Keywords

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Hydrothermal liquefaction

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Aqueous phase

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Quantitative analysis

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Principal component analysis

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Partial least squares regression

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Highlights

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Quantitative analysis of 67 compounds in the aqueous phase from HTL of model compounds

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Identification of reaction pathways forming pyrazines

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Prediction of compound concentrations, total organic carbon, total nitrogen, and pH in aqueous phase

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from DDGS, M. x giganteus, C. vulgaris

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Abstract

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Hydrothermal liquefaction (HTL) is a promising technique for conversion of wet biomasses containing

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varying amounts of carbohydrate, protein, lipid and lignin. In this work, mixtures of these model

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compounds were subjected to HTL at 335 ⁰C. As many as 67 compounds were quantitated in the aqueous

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phase, including small organic acids, cyclic oxygenates, fatty acids, nitrogenates, and oxygenated aromatics.

1 ACS Paragon Plus Environment

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The concentrations correlated with the ratio of the model compounds. Principal component analysis

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separated samples based on their quantitative results which could be linked to their biochemical

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composition. Concentrations of the analytes were modeled with partial least squares regression, and high

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quality predictions were made from quality control samples and to varying degrees from Dried Distillers

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Grains with Solubles (DDGS), Miscanthus x giganteus, and Chlorella. vulgaris which are useful in further

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processing methods. Values for total organic carbon (TOC), total nitrogen, and pH were also predicted from

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quality control samples, DDGS, Miscanthus x giganteus, and Chlorella. vulgaris. Carbohydrate and lipid

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contents mainly influenced TOC values and could be used for minimizing loss of organics, techno economic

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analysis, and for assessing potential for anaerobic digestion and thermal gasification. Pyrazines were

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modeled using linear, exponential and 2nd order polynomial fits depending on whether carbohydrate or

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protein was the limiting biochemical component which could be a way of controlling nitrogen and carbon

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displacement to the aqueous phase. This work shows that TOC, total nitrogen, pH, and concentrations of

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single compounds in the aqueous phase from HTL can, in many cases, be predicted from HTL of mixtures of

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biomass constituents.

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1. Introduction

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The apparent production cap and diminishing fossil oil reserves have led to rising and fluctuating oil prices

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affecting the global economy 1 which along with increasing public environmental concern is encouraging

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further research in conversion of biomass into second and third generation biofuels. Biofuels are usually

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classified as first, second, or third generation, where first generation biofuels produced from food and oil

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crops using existing technology, while second generation biofuels are based on biological and

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thermochemical processing of non-food feedstock such as lignocellulosics or waste products. Third

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generations biofuels are made from aquatic cultivated feedstocks 2. Hydrothermal liquefaction (HTL) has

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developed as one of the most promising technologies for sustainable production of a substitute for crude

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oil using wet biomass. The process leads to four product phases: 1) a desired bio-crude, 2) an aqueous


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phase rich in water-soluble organics and nutrients, 3) a gas phase consisting mainly of carbon dioxide, and

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4) a solid residue.

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The aqueous phase has been identified as a key fraction for making the process commercially feasible 3, 4.

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Despite this status, only few studies have identified the chemical composition and even fewer have

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quantified the aqueous phase 5-7. Substantial amounts of organic carbon are lost to the aqueous phase and

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should be recovered to improve the efficiency of the overall process. The concentration of total organic

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carbon (TOC) in the aqueous phase is highly dependent on type of biomass, solid loading of the reactor,

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reaction conditions, and whether the aqueous phase is recirculated. Typical aqueous phase TOC values for

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experiments with 10% dry biomass, without recirculation of aqueous phase, are 10 to 14 g L-1 8. Substantial

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amounts of total nitrogen (TN) are found in the aqueous phase, mainly in the form of ammonium 8,

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however, numerous nitrogenated organic compounds have been identified, many of which are toxic to the

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environment and even cytotoxic (to mammalian Chinese hamster ovary cells) 9. The large values for TOC

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and TN means that waste management would be required for the aqueous product. The large amount of

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TN means that the aqueous phase can be used as a nutrient for growing microalgae 10, however, high

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concentrations of phenolics, fatty acids and small organic acids still require considerable dilution 11, 12.

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There are several other possibilities for use of the aqueous phase from HTL. Recirculation of the aqueous

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phase has received much attention recently with several studies reporting higher oil yield and decreasing

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loss of organics to the aqueous phase with each recirculation 13, 14. However, one of these studies also

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suggested lower quality oils due to increasing contents of water and oxygenated compounds 14. Anaerobic

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digestion has been proposed for producing methane gas, however, cyclic oxygenates and phenol present in

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the aqueous phase have been proposed to lead to longer lag phases of the methane production 15.

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Condensed phase ketonization of organic acids of the aqueous phase has been proposed with further

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processing into olefins for valorization 16. Finally, complete thermal gasification has been proposed for

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producing hydrogen for subsequent upgrading of bio-crude to a petroleum-like bio-fuel 17.


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Extensive quantitative analysis is important for understanding potential effects and possibilities for use of

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the aqueous phase. Predictive models for the composition and properties would thus be of substantial

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benefit and provide means for adjusting the aqueous phase for optimal use. It was recently proposed that

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bio-crude and gas products are secondary reactions of water soluble compounds 18. Hence, quantitative

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results can provide much needed information on the building blocks of the desired bio-crude.

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Some previous studies have performed HTL on model compounds such as cellulose, hemicellulose, protein,

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lipid, and lignin 19-21. In all cases they addressed the potential yields of the product fractions and in most

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cases the composition of the oil phase. Other studies have reported fitted models based on either model

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compounds or biomass for prediction of yields from biochemical composition based on carbohydrate,

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protein, and lipids 22-24. Pedersen and Rosendahl 25 predicted formation of cyclic oxygenates and

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oxygenated aromatics based on carbohydrate and lignin composition. Another study reported varying

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success with the use of a design of experiments (DoE) in predicting product yields from the conversion of

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carbohydrate, protein, and lipid 26.

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The current work presents results from quantitative analysis of aqueous phase from HTL of mixtures

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resembling lignocellulosic biomass. Mixtures were prepared based on a DoE along with quality control

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samples (QC) used for validation of the models. A total of 67 compounds, including small organic acids,

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cyclic oxygenates, oxygenated aromatics, nitrogenates, and fatty acids, were quantitated. Results were

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subjected to exploratory principal component analysis (PCA), and partial least squares regression (PLS-R)

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was used to predict the concentration of selected compounds and to predict the TOC, TN, and pH values of

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aqueous phase from QC samples, DDGS, M. x giganteus, and C. vulgaris.

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To our knowledge this is the first study to perform extensive positive characterization and the most varied

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quantification of the aqueous phase from HTL of model compounds. Furthermore, it is the first work to

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effectively predict TOC, TN, pH, and the concentration of single compounds based on the biochemical

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composition of the feedstock. The results show the potential for optimizing the composition of the aqueous

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phase of HTL through the biochemical composition of the feedstock which could enhance its further use,


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tackle waste treatment, and potentially minimize loss of organics, thereby improving the carbon recovery

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of the process.

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2. Materials and methods

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2.1 Chemicals and reagents

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Methyl chloroformate (MCF), methanol and pyridine were obtained in analytical grade from Sigma-Aldrich.

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All standards were obtained from either Sigma-Aldrich, Fluka or Acros Chemicals. 4-bromotoluene was used

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as internal standard and was obtained from Sigma-Aldrich. Cellulose was obtained from Sigma-Aldrich,

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hemicellulose was from Merck chemicals, soy protein, unrefined rape seed oil, and C. vulgaris were from

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commercial sources, the Protobind 1000 lignin was from GreenValue SA, DDGS was from Lantmännen

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Agroetanol AB, Norrköping, Sweden, and M. x giganteus was from Danish Institute of Agricultural Sciences,

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Research Centre Foulum (Denmark). Potassium carbonate was from Sigma-Aldrich. Helium carrier gas for

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gas chromatography was of 99.999% purity.

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2.2 Experimental design

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In an attempt to mimic the highly varied composition of biomass, a mixed model design was employed. The

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maximum content of lignin in the biomass was constrained to 30 w/w% as only few biomasses contain

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more. Protein was constrained to 95 w/w% which was limited by the purity. Carbohydrates were composed

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of a 3:1 mixture of cellulose and hemicellulose. A total of 15 calibration experiments and seven quality

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control (QC) experiments were conducted with biochemical composition as listed in Table 1.

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2.3 Hydrothermal liquefaction

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Reactions were carried out in 20 mL Swagelok batch reactors conditioned with demineralized water. The

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reactors were loaded with 10 mL biomass slurry containing 10 w/w% biomass, 2 w/w% potassium

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carbonate, and 88 w/w% demineralized water. Potassium carbonate has been shown to reduce coke

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formation of especially lignocellulosics, and it was added to all experiments in order to maintain constant

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conditions. Unfortunately it leads to saponification for high lipid containing feedstocks. Reactions were

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carried out at 340 ⁰C for 20 min (including heating and cooling time) in a fluidized sand bath. Rapid cooling


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in a water bath quenched the reaction. Reactors were subsequently vented, and the aqueous phase was

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decanted into a centrifuge tube. The tube was centrifuged at 6500 rpm for 5 min, and the aqueous phase

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was decanted into a preparative glass. The samples were stored at 5 ⁰C until further analysis. Each

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experiment was performed in duplicate.

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Sample 7A was not available for analysis since it was lost during product separation.

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2.4 Analysis

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The TOC and TN were determined by a Hach Lange DR2800 spectrophotometer with TOC-kit LCK387 and

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TN-kit LCK338, respectively.

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Samples were analyzed by gas chromatography coupled to mass spectrometry (GC-MS) with prior

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derivatization with MCF. Aqueous phase (300 µl) was mixed with 10 w/w% sodium hydroxide solution (10

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µl), methanol (200 µl), and pyridine (40 µl). MCF (2 x 25 µl) was added, and the vial was vortexed for 30 s

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with each addition. Chloroform (400 µl) containing internal standard (4-bromotoluene) was added,

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followed by 10 s vortexing. Sodium bicarbonate 50 mmol L-1 (400 µl) was added and the vial was again

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vortexed for 10 s. The aqueous layer was removed, and the organic layer was transferred to a vial with

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insert 27. Multicomponent standard solutions were prepared, and they were derivatized and analyzed in

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duplicates. Analyte responses were normalized with the response of the internal standard before

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construction of calibration curves. Replicate batch experiments showed only minor differences and the

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average concentration determined for each experiment is reported and was used for chemometric analysis.

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Analytes for which a standard was not available were quantitated with the calibration curve of an analyte

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with a similar mass spectrum. Calibration curves could not be obtained for pyrocatechol instead normalized

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peak areas have been included in the results since it is of high interest.

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The biochemical composition of DDGS, C. vulgaris, and M. x giganteus was determined according to the

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following methods. Total carbohydrate content was determined from reaction of phenol and sulfuric acid

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with carbohydrate followed by colorimetric determination at 420 nm 28. Total protein content was

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determined according to the Lowry protein assay with reduction of folin reagent and subsequent


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colorimetric determination at 720 nm 29. Total lipid contents were determined gravimetrically after

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extraction in 2:1 chloroform and methanol aided by sonication. The total lignin content was determined

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with the acetyl bromide method upon extensive washing to remove proteins 30. The compositional analysis

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of feedstocks, on dry and ash free basis is presented in Table 2. The methods for determination of

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carbohydrate, protein, and lipid content were adapted from analysis of microalgae and some degree of

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matrix effects is expected, especially for carbohydrate content of lignocellulosics.

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Moisture content was determined as loss on drying by evaporation at 105 °C until constant weight. Ash

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content was determined by incineration at 575 °C for 5 h. The CHNS content was measured using a CHNS

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Elementar Vario Macro cube analyzer by Elementar Analysensysteme GmbH, Hanau, Germany. The

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samples were analyzed for CHNS content without prior drying, and the oxygen content was determined by

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difference.

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2.5 Principal component analysis

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Principal component analysis (PCA) is a useful tool for elucidating the covariance of multiple variables of

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importance in order to provide further insight into the complex mechanism. Furthermore, time-consuming

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and tedious univariate data exploration is likely to leave latent variables undetected. Simplifying the visual

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interpretation of multivariable data is efficiently carried out with PCA. Furthermore, adding quality control

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samples provides a simple way to validate and assess the performance of the PCA model. The model

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provides a linear combination of the raw data, from maximizing covariance, which are displayed in scores,

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loadings and residuals. Thus, the raw data is projected onto a set of orthogonal principal components (PC)

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representing the largest variation. The scores present the projection of the raw data onto the PC’s and are

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used to estimate the similarities and differences between samples while the loadings show the weight of

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each variable on the given PC 31. The raw data can be subjected to a number of preprocessing techniques of

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which auto-scaling is commonly used and was applied in this work. Auto-scaling typically involves mean

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centering, where the mean value of each variable is subtracted from the corresponding variable, and


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scaling is performed by dividing each variable with the corresponding standard deviation. This

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preprocessing is applied to allow each variable to contribute to the model.

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Preprocessed data was treated to PCA performed in PLS toolbox.

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2.6 Partial least squares regression

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Partial least squares regression (PLS-R) is a supervised method with the ultimate goal of predicting a set of

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dependent variables (Y) from a set of independent variables (X). In PLS-R, X (biochemical composition) and

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Y (concentrations of analytes, TOC, TN, and pH) are decomposed to scores (T and U) and loadings (P’ and

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U’) along with residuals (E and F). A set of latent variables (LV) are found which maximizes the co-variance

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of the scores. Thus, the scores are related through regression coefficients (B) which show the effect of each

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independent variable.

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ܺ = ܶܲᇱ + ‫ܧ‬ ܻ = ܷܳ ᇱ + ‫ܨ‬ ܻ = ‫ܺܤ‬

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The regression coefficients can then be used to predict Y of unknown samples (QC samples, DDGS, M. x

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giganteus, and C. vulgaris) from the independent variable. It is important to note that the X and Y values of

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the calibration set were subjected to data preprocessing in the form of auto-scaling by subtracting the

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mean value and dividing by the standard deviation. This means that when predicting Y values of an

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unknown sample, the X values have to be auto-scaled with the values of the calibration set (provided in

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supplementary information) prior to obtaining the scalar product (Y) of BX. The predicted Y value will again

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be a value subjected to the preprocessing performed on the calibration set. Hence, the predicted Y value

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has to be multiplied by the standard deviation of Y from the calibration set and the mean value of Y from

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the calibration set has to be added (provided in supplementary information).

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To assess the quality of the models a number of parameters are evaluated for cross validation (cv) and

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predictions (pred). Cross validation was carried out with contiguous block splitting, removing a set of

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replicate samples at a time. Predictions were evaluated on QC samples. The performance of each model


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was evaluated based on R2, Root Mean Square Error (RMSE), and bias for cross validated and predictive

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models. Finally, predictions were performed on aqueous phase from DDGS, M. x giganteus, and C. vulgaris.

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3. Results and discussion

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3.1 Quantitative analysis of aqueous phase

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Calibration curves were constructed using a multi component standard solution (supplementary section A).

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Sixteen compounds were pseudo-quantified using the calibration curve of a standard with a similar mass

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spectrum. In total 67 compounds were quantified (Table 3). These included 23 carboxylic acids, nine fatty

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acids, eight dicarboxylic acids, one tricarboxylic acid, nine oxygenated aromatics, 10 cyclic oxygenates, and

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seven nitrogenated compounds (apart from hydroxypyridines).

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Fatty acids (≥ C14) constitute the most abundant analytes due to saponification when employing alkali

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catalyst, while cyclic oxygenates are almost solely formed from carbohydrates (Fig. 1). Small organic acids

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(≤ C12) are equally formed from carbohydrates and proteins, while oxygenated aromatics are mainly formed

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from lignin with considerable contribution from protein. As expected, mixtures containing lignin had the

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highest concentration of phenol, m-cresol, and pyrocatechol from partial degradation of lignin.

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Pyrocatechol was found in high abundance for lignin containing samples with values estimated up to 1200

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mg l-1. Interestingly, concentrations of dicarboxylic acids and nitrogenates are higher for 1:1 ratios of

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protein:lipid and carbohydrate:protein, respectively, than the pure biochemical components, which is

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further described in section 3.4.

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Numerous organic acids (isovaleric acid, 3-methylpentanoic acid, 4-methylpentanoic acid, 3-methylpent-3-

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enoic acid, and hydrocinnamic acid) are solely formed from degradation of protein through deamination.

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Interestingly, protein formed the highest concentration of acetic acid, which is likely due to deamination of

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glycine. Previous studies on HTL of microalgae have shown large amounts of acetamide in the aqueous

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phase originating from acetic acid and ammonia, thus, even higher amounts of acetic acid are expected to

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have been formed in our samples 7. Some organic acids were mainly formed from carbohydrates including

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methacrylic acid, butyric acid, pent-4-enoic acid, valeric acid, tiglic acid, 4-oxohexanoic acid, 4-methylpent-


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3-enoic acid, and pent-3-enoic acid. Cyclic oxygenates were almost exclusively formed from carbohydrates

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with only minor contributions from protein and lipid.

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As expected, pyrazines are mainly formed in the presence of protein. Deamination of amino acids releases

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nitrogen as ammonium into the aqueous phase with secondary reactions leading to pyrazine formation.

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However, up to a 20 fold increase in pyrazine formation is observed when mixing with carbohydrates

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(sample 6). Addition of small amount of lignin or lipid greatly reduces the amount of pyrazines which is

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likely due to formation of amides with organic acids. Several studies have reported large amounts of

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hexadecanamide in bio-crude, while acetamide and propanamide have been reported in aqueous phase [6,

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29]. Smaller symbiotic effects (two fold increase) were observed for several dicarboxylic acids (succinic acid,

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glutaric acid, 2-oxoglutaric acid, methylsuccinic acid, and ethylsuccinic acid) in the presence of varying

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biochemical compositions. While numerous cyclic oxygenates were detected, protein formed only δ-

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valerolactone in high amounts (233 mg L-1). Many phenolic compounds were formed from protein (phenol,

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p-cresol, and 3-hydroxypyridine).

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Fatty acids were almost exclusively formed from rapeseed oil, as expected from degradation of

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triglycerides. Oleic acid was especially abundant with concentrations approaching 10 g L-1 for the aqueous

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phase from HTL of pure rape seed oil.

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Glycerol is expected to be an abundant compound in the aqueous phase of high lipid containing samples.

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However, glycerol is not detected with the present analytical method used since it is not derivatized.

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It is interesting to note that dicarboxylic acids (succinic acid, glutaric acid, methylsuccinic acid, and

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ethylsuccinic acid) are found in higher concentrations in mixtures with protein constituting more than 45%

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of the biomass than in mixtures with less protein or in samples made from pure protein (see Table 3).

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Samples of pure protein would contain high amounts of ammonia which would react to form succinimides

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and glutarinimide 7.

24 25


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3.2 PCA

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The quantitative results of the aqueous phase analysis were subjected to PCA (Fig. 2). Based on Scree plots,

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the eigenvalue rule, and visual interpretation, three PC’s were chosen which accounted for 82.5 % of the

4

total variance (PC1 43.9%, PC2 27.9%, PC3 10.6%).

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Generally, replicate samples showed only minor variation, indicating good repeatability of the HTL process,

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product separation and sample preparation. Samples from rapeseed oil or carbohydrate were fully

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separated by PC1, while sample mixtures of rapeseed oil and carbohydrate were more similar to samples of

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high carbohydrate (i.e. sample 4 and 13). The higher similarity for samples of carbohydrates is mainly a

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consequence of the larger number of analytes originating from carbohydrates when applying the analytical

10

method of this work. The highest positive loadings were for cyclopentenones and different four and five

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carbon small organic acids, while highest negative loadings were observed for different fatty acids. Protein

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samples (sample 2 and 10) were fully separated from rapeseed oil (sample 3 and 11) and carbohydrate

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samples (sample 1 and 9) by PC2. The highest positive loadings were for different valeric acids and succinic

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acids. High positive loadings were also obtained for propane-1,2,3-tricarboxylic acid, glutaric acid, levulinic

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acid, acetic acid, and δ-valerolactone.

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Samples of mixtures of protein with rapeseed oil and with carbohydrate are in both cases more similar to

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protein samples, indicating interaction effects. This interaction is even more distinguished with PC3 which

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completely separates mixtures (sample 6 and 12) from pure protein and carbohydrate samples. The highest

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loadings were obtained for pyrazines and 3-hydroxypyridines. However, fatty acids also presented a high

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positive loading. The highest negative loadings were obtained for δ-valerolactone and hex-5-enoic acid,

21

while also several aromatic acids (benzoic acid, hydrocinnamic acid, and phenyl acetic acid), different

22

valeric acids, and small dicarboxylic acids (oxalic acid, malonic acid, and fumaric acid) showed negative

23

loadings. The effect of adding lignin to the feedstock is observed with PC4. Increasing amounts of lignin

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lead to separation from oil, protein, and carbohydrate samples. However, carbohydrate samples were also

25

separated from protein and oil samples, showing that carbohydrates produce more of the compounds


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made from lignin degradation. The highest positive loadings were observed for phenolic compounds

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(phenol, p-cresol, m-cresol, and pyrocatechol) from lignin, while several dicarboxylic acids (fumaric acid,

3

succinic acids, and glutaric acid) and fatty acids (caprylic acid, lauric acid, and oleic acid) showed smaller

4

positive loading. The highest negative loadings were obtained from protein-related compounds.

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Validation of the model with QC samples was best displayed from PC3 and PC4 (Fig 2C). The QC samples

6

were samples with biochemical composition between the extreme points and center point of the

7

experimental design. Therefore, QC4 (high lignin) and QC6 (1:7 carbohydrate:protein) are modeled close to

8

what would be expected while the remaining QC samples are clustered together. It also displays that the

9

interaction between protein and carbohydrate is especially strong when approaching a 1:1 ratio as none of

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the QC samples contained this ratio.

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3.3 PLS-R of compounds

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The complexity of the aqueous phase from HTL is well recognized and several compound classes are, as

13

previously described, expected to influence its further processing. Therefore, predicting the concentration

14

of single compounds provides possibilities for adjusting the composition.

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A total of 16 small organic acids, fatty acids, cyclic oxygenates, and phenolic analytes were selected for PLS

16

analysis based on their high concentrations and their previously reported presence 5, 6, 27. In the case of

17

fatty acids, samples without any rapeseed oil were removed, since they were not relevant for the models

18

and also introduced increased uncertainty. The sample of pure protein was considered an outlier when

19

modelling dicarboxylic acids (succinic acid and glutaric acid). This is likely due to the high concentration of

20

ammonia which will react with especially succinic acid to form succinimide. However, this sample also had

21

the highest TOC value and previous results have shown that the derivatization efficiency for dicarboxylic

22

acids is highly sensitive to the excess of MCF 27. Similarly, the sample of pure carbohydrate was considered

23

an outlier when modelling phenolics (phenol and p-cresol) as the concentrations were much lower than the

24

expected value. The reason for the low value is unknown and requires further knowledge of the reaction

25

pathways that are not yet fully understood.


Page 13 of 39

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Energy & Fuels

1

A model for each analyte is presented in Table 4. Calibration curves and predicted values for each analyte

2

are included in supplementary information. The models were evaluated based on R2, RMSE, and bias

3

values. The displayed evaluation criteria presented in Table 4 were generally found to be similar when

4

other analytes within the compound classes were modelled. Models for small organic acids and cyclic

5

oxygenates performed very well with R2pred > 0.943 and RMSEP and bias values appropriate for the given

6

concentrations detected. The performance of dicarboxylic acids and phenolics were dependent on the

7

specific analytes. Thus, succinic acid (R2pred = 0.921, RMSEP = 238, bias = 140) and phenol (R2pred = 0.882,

8

RMSEP = 42.5, bias = -11.6) were modelled well. The poor modeling of p-cresol (R2pred < 0.585, RMSEP =

9

10.5, bias = -3.1) could indicate inconsistent derivatization. While the method used in this work has been

10

validated for phenol in a single biomass, it has not been validated over a range of different biomass

11

compositions. Fatty acids were poorly predicted (R2pred > 0.497) which is mainly reflected in the low number

12

of samples for calibration. Thus, increasing the number of sample mixtures with rapeseed oil would most

13

likely improve the model significantly. Due to the DoE, the samples spanned from neat model compound to

14

mixtures of equal percentage of each model compound while the QC samples were within this range. The

15

fact that RMSEP values were lower than RMSECV indicates that the models could be improved with the

16

addition of more data points which is expected considering the small size of the calibration set.

17

Acetic acid is one of the most abundant analytes of the aqueous phase and could affect further utilization

18

of the aqueous phase. Thus, predicting the concentrations of acetic acid would be highly valuable. The

19

results for predicting acetic acid in QC samples are presented in Figure 3 (the plots of remaining 12 analytes

20

have been included in the electronic supplement). In most cases, it is observed that the concentration of

21

acetic acid can be predicted with less than 10% error which is slightly higher than the estimated

22

repeatability error determined for acetic acid 27.

23

Overall the results show that PLS-R is able to predict concentrations of small organic acids and cyclic

24

oxygenates close to or within the experimental error of the method. Further data points are likely to show

25

similar results for lipids. Oxygenated aromatics can in certain cases, such as phenol, be predicted well.


Energy & Fuels

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 14 of 39

1

3.4 Predicting nitrogenated aromatics in the aqueous phase

2

Pyrazines have been extensively studied in food science since they are an important class of aroma

3

compounds adding to the taste sensation 32, 33. They are formed by two consecutive Maillard-like reactions

4

of precursors from degradation of protein and carbohydrates. Formation of pyrazines from model systems

5

of saccharides and amino acids has previously been investigated 34, as well as the effect of pH 35. The

6

conditions used in this work are not expected to result in pH effects, however, these effects should be

7

considered in case acidic conditions are employed. While the toxicity of pyrazines is generally low, they

8

display low odor thresholds and potentially make nitrogen inaccessible to algae or plants if the aqueous

9

phase is to be recycled as nutrient or used as plant fertilizer. Recently, a significant increase in pyrazines

10

formation in HTL bio-crude was reported for a combined glucose and protein feedstock, however, the

11

composition of the aqueous phase was not reported 36.

12

Pyrazines presented a special case regarding modelling outcomes. The compound class could not be

13

modelled with PLS-R based on the DoE experiments which indicate that the regression is not linear and that

14

there is a change in reaction chemistry. Instead it was necessary to implement three fittings to fully model

15

the outcome. Each model was based on the limiting component: 1) linear fit when protein/carbohydrate

16

ratio was >1, 2) exponential fit when protein/carbohydrate ratio was equal to 1, 3) 2nd order polynomial fit

17

when protein/carbohydrate ratio was carbohydrate>lipids (Fig. 6A).

2

Several cyclic oxygenates are formed from dehydration of carbohydrates which means that fewer

3

heteroatoms are likely to remain in the organic compounds when compared to protein. The larger number

4

of heteroatoms will increase the polarity of the resulting compounds leading to a higher aqueous phase

5

TOC value. Furthermore, addition of lignin leads to lower TOC values which are explained by increased

6

losses during the product separation and by fewer heteroatoms in the biomass feedstock compared to

7

carbohydrates and protein. When lignin was added to samples, it was possible to extract additional dark-

8

colored bio-crude with acetone. This fraction was discarded in order to keep the sample work-up

9

consistent.

10

As expected, the total nitrogen concentration is highly dependent on the amount of protein used (R2pred =

11

0.986, RMSEP = 509, bias = 212), while addition of carbohydrates and lipids leads to less nitrogen in the

12

aqueous phase (Fig. 6B). This is likely due to formation of N-heterocyclic compounds and fatty acid amides

13

that are partially soluble in the bio-crude. The release of ammonia and formation of nitrogen-containing

14

compounds leads to an increase in pH, while carbohydrates result in lower pH values from the release of

15

small organic acids without the added release of ammonia (Fig. 6C). Samples of lipids did not affect the pH

16

value despite producing fatty acids. This is likely due to the saponification effect of potassium carbonate.

17

These samples have approximately the same pH values as samples containing varying amounts of

18

carbohydrate and protein. The produced N-heterocyclic compounds of pyridine and pyrazines create a

19

buffering effect which places the samples in three groups with similar pH values (< 8, 8-9, and > 9.5).

20

Models were found to perform very well regarding R2, RMSE, and bias values cross validation and

21

predictions for QC samples (Table 6). Predictive values for QC samples are found in supplementary

22

information.

23

3.6 Biomasses

24

The quality of the models and differences regarding model compound mixtures were further evaluated

25

from predictions of the aqueous phase composition from HTL of DDGS, M. x giganteus, and C. vulgaris


Page 17 of 39

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Energy & Fuels

1

(Table 7). For single compounds the predictions were, in most cases, highly dependent on the origin of the

2

biochemical components of the biomasses. DDGS is a processed biomass, subjected to fermentation and

3

subsequent work-up, which means that the acetic acid in the aqueous phase is likely to form mainly from

4

polymer degradation instead of deacetylation leading to very precise predictions. Similarly the model

5

compounds have been purified leading to acetic acid concentrations of DDGS aqueous phase similar to

6

model compound mixtures. In contrast M. x giganteus and C. vulgaris are likely to contain large amounts of

7

naturally occurring acetyl groups which are hydrolyzed during the HTL process yielding much higher

8

concentrations than in model compound mixtures.

9

Simple degradation products, such as levulinic acid, phenol, cyclopentanone, and 2,3-dimethylcyclopent-2-

10

enone were generally well predicted for all biomasses. Organic acids such as isovaleric acid, 4-

11

methylpentanoic acid, glutaric acid, and hydrocinnamic acid formed from deamination of amino acids. They

12

were well predicted for DDGS and C. vulgaris indicating that the content of isoleucine, glutaminic acid, and

13

phenylalanine are similar to those of soy protein, while the aspartic acid content is much different as

14

succinic acid was poorly predicted. In contrast both succinic acid and glutaric acid were well predicted in

15

aqueous phase of M. x giganteus while isovaleric acid and 4-methylpentanoic acid were poorly predicted

16

indicating a similar content of aspartic acid and glutaminic acid in soy protein. Cyclic oxygenates

17

(cyclopentanone, butyrolactone, and 2,3-dimethylcyclopent-2-enone) were generally well predicted, except

18

for butyrolactone in aqueous phase from M. x giganteus. The regression coefficients of these compounds

19

were highly similar with large positive values for carbohydrates and lower negative values for protein and

20

lipids. These compounds are mainly formed from ring closure and dehydration of saccharides and the

21

monomer composition of polysaccharides from biomass is much less varied than that of proteins leading to

22

a more uniform formation of cyclic oxygenates. Phenol and p-cresol were very well predicted for all

23

biomasses apart from phenol in aqueous phase of M. x giganteus. The poor prediction from M. x giganteus

24

is due to the high carbohydrate content which is beyond the range of the calibration set because the

25

sample of pure carbohydrate had to be removed for phenol. Phenol had a negative regression coefficient


Energy & Fuels

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 18 of 39

1

for carbohydrate and the likely overestimation of carbohydrate content in M. x giganteus leads to a low

2

prediction. Ketoacids (levulinic acid and 4-oxohexanoic acid) were very well predicted for all biomasses.

3

These compounds were formed in high concentrations in pure samples of carbohydrate and protein.

4

The linear dependency of methylpyrazine formation on the limiting component of carbohydrate was found

5

in aqueous phase of DDGS and C. vulgaris which were predicted with relative differences of 23.9% and

6

43.9%, respectively. Furthermore, M. x giganteus had a 2nd order polynomial dependency on the protein

7

content. This indicates that the intermediate products from degradation of carbohydrates and protein

8

reacting further to produce pyrazines are similar across biomasses and model compound mixtures. Further

9

work could potentially help to explore these pathways to partly control the displacement of nitrogen to the

10

aqueous phase. Similarly, TN was well predicted for DDGS and C. vulgaris while excellent predictions were

11

obtained for TOC values of all three biomasses. Hence, the values of TOC and TN are highly similar to those

12

obtained for mixtures of model compounds indicating that their values are mainly dependent on the

13

amounts of carbohydrate, protein, lipid, and lignin rather than the monomer ratios of the specific

14

biochemical components. Especially the prediction of TOC values is of importance as it provides valuable

15

information for TEA and further processing such as anaerobic digestion and thermal gasification.

16

The pH values of biomasses were somewhat different from those of model compound mixtures. Especially

17

the pH value of M. x giganteus was lower than predicted which is attributed to the high concentrations of

18

acetic acid. Concentrations of fatty acids could not be predicted with acceptable relative differences due to

19

the poor models obtained from the small calibration set. The regression coefficients of fatty acids show

20

that they are mainly produced from lipids; however, it also shows that the concentration is highly

21

dependent on the amount of carbohydrates present. The negative correlation of carbohydrates may be

22

caused by pH differences since less saponification was observed with higher carbohydrate contents.

23

4. Conclusion

24

Model compound mixtures were prepared according to a mixed model design and subjected to HTL. A total

25

of 67 analytes were quantitated in the aqueous phase using GC-MS. The variations of the analytes were


Page 19 of 39

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Energy & Fuels

1

explored using PCA and concentrations of selected analytes were predicted in QC samples, DDGS, M. x

2

giganteus, and C. vulgaris using PLS-R. Carbohydrate samples showed particularly high concentrations of 2-

3

oxo-glutaric acid, 4-oxohexanoic acid, succinic acid, 3-methylcyclopent-2-enone, and butyrolactone. Protein

4

samples had high concentrations of acetic acid, 4-methylpentanoic acid, and δ-valerolactone. Samples of

5

lipids were particularly high in palmitic acid, linoleic acid, and oleic acid. Finally, samples containing lignin

6

mainly showed higher phenol, m-cresol, and pyrocatechol concentrations. High quality predictions of

7

especially carboxylic acids from model compounds and Dried Distillers Grains with Solubles (DDGS) were

8

obtained which could be useful for further processing methods such as ketonization and recycling of

9

aqueous phase.

10

Concentrations of pyrazines were highly dependent on the model compound mixture. Three different fits

11

were necessary depending on whether carbohydrate or protein was the limiting component or if they were

12

present in equal amounts. Formation of pyrazines was well predicted in model compounds, M. x giganteus,

13

and C. vulgaris. These findings were explained based on different reaction pathways and competing

14

reactions of amide formation. Controlling these pathways could be a way of changing the displacement of

15

nitrogen and carbon to the aqueous phase.

16

PLS-R successfully modeled the TOC, TN, and pH values and predicted the values for quality control

17

samples. Values for TOC were found to follow the order of protein>carbohydrate>lipid. High quality

18

predictions of TOC values were obtained for both model compounds and biomasses which could be

19

valuable for TEA, potentially minimizing loss of organics, and assessment of further processing such as

20

anaerobic digestion and thermal gasification. Values for TN were mainly dependent on the protein content

21

while the presence of lipid further decreased the value. Values for pH were grouped into three sets (pH < 8,

22

8-9, or > 9.5) depending on which model compound was present at the highest concentration.

23

The results provide valuable information for tuning the aqueous phase composition for further utilization,

24

however, it should be recognized that these predictions were based on a given set of conditions. Especially

25

the addition of alkali catalyst is likely to effect the concentration of fatty acids in the aqueous phase.


Energy & Fuels

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

1

Acknowledgements

2

We thank the Innovation Fund Denmark (Grant No. 1305-00030B), the Danish National Research

3

Foundation (DNRF93), and the Danish Centre for Food and Agriculture for funding this work.

4

References

5

(1) Murray, J., King, D., Nature 2012, 481, 433-435.

6

(2) Saladini, F.; Patrizi, N.; Pulselli, F. M.; Marchettini, N.; Bastianoni, S., Renewable and Sustainable

7

Energy Reviews 2016, 66, 221-227.

8

(3) Connely, E. B.; Colosi, L. M.; Clarens, A. F.; Lambert, J. H., Energy & Fuels 2015, 29, 1653-1661.

9

(4) Marcilla, A.; Catalá, L.; Garcia-Quesada, J. C.; Valdés, F. J.; Hernández, M. R., Renewable and

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Sustainable Energy Reviews 2013, 27, 11-19.

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(5) Panisko, E.; Wietsma, T.; Lemmon, T.; Albrecht, K.; Howe, D., Biomass and Bioenergy 2015, 74, 162-

12

171.

13

(6) Villadsen, S. R.; Dithmer, L.; Forsberg, R.; Becker, J.; Rudolf, A.; Iversen, S. B.; Iversen, B. B.; Glasius,

14

M., Energy & Fuels 2012, 26, 6988-6998.

15

(7) Maddi, B.; Panisko, E.; Albrecht, K.; Howe, D., J. Vis. Exp. 2016, 109, 1-11.

16

(8) Barreiro, D. L.; Riede, S.; Hornung, U.; Kruse, A.; Prins, W., Algal Research 2015, 12, 206-212.

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(9) Pham, M.; Schideman, L.; Scott, J.; Rajagopalan, N.; Plewa, M. J., Environmental Science and

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Technology 2013, 47, 2131-2138.

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(10) Hognon, C.; Delrue, F.; Texier, J.; Grateau, M.; Thiery, S.; Miller, H.; Roubaud, A., Biomass and

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Bioenergy 2015, 73, 23-31.

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(11) Biller, P.; Ross, A. B.; Skill, S. C.; Lea-Langton, A.; Balasundaram, B.; Hall, C.; Riley, R.; Llewellyn, C.

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A., Algae Research 2012, 1, 70-76.

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(12) Bagnoud-Velasquez, M.; Schmid-Staiger, U.; Peng, G.; Vogel, F.; Ludwig, C., Algae Research 2015, 8,

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76-82.

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(13) Ramos-Tercero, E. A.; Bertucco, A.; Brilman, D. W. F., Energy & Fuels 2015, 29, 2422-2430.


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(14) Biller, P.; Madsen, R. B.; Klemmer, M.; Becker, J.; Iversen, B. B.; Glasius, M., Bioresour. Technol.

2

2016, 220, 190-199.

3

(15) Tommaso, G.; Chen, W.; Li, P.; Schideman, L.; Zhang, Y., Bioresour. Technol. 2014, 178, 139-146.

4

(16) Albrecht, K.; Cooper, A. R.; Frye, J. G.; Dagle, R. A.; Dagle, V. In Condensed Aqueous Phase

5

Ketonization of Organic Acids Produced By the Hydrothermal Liquefaction of Lignocellulosic Biomass,

6

Catalysis at the Confluence of Science and Technology, Pittsburgh, PA, 17th June, 2015.

7

(17) Cherad, R.; Onwudili, J. A.; Biller, P.; Williams, P. T.; Ross, A. B., Fuel 2016, 166, 24-28.

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(18) Luo, L.; Sheehan, J. D.; Dai, L.; Savage, P. E., Sustainable Chem. Eng. 2016, 4, 2725-2733.

9

(19) Kruse, A.; Krupka, A.; Schwarzkopf, V.; Gamard, C.; Henningsen, T., Ind. Eng. Chem. Res. 2005, 44,

10

3013-3020.

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(20) Kruse, A., Maniam, P., Spieler, F. , Ind. Eng. Chem. Res. 2007, 46, 87-96.

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(21) Gao, Y.; Chen, H.; Wang, J.; Shi, T.; Yang, H.; Wang, X., J. Fuel Chem. Technol. 2011, 39, 893-900.

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(22) Biller, P.; Ross, A. B., Bioresour. Technol. 2011, 102, 215-225.

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(23) Leow, S.; Witter, J. R.; Vardon, D. R.; Sharma, B. K.; Guest, J. S.; Strathmann, T. J., Green Chemistry

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2015, 17, 3584-3599.

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(24) Valdez, P. J.; Tocco, V. J.; Savage, P. E., Bioresour. Technol. 2014, 163, 123-127.

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(25) Pedersen, T. H.; Rosendahl, L., Biomass and Bioenergy 2016, 83, 206-215.

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(26) Teri, G.; Luo, L.; Savage, P. E., Energy & Fuels 2014, 28, 7501-7509.

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(27) Madsen, R. B.; Jensen, M. M.; Mørup, A. J.; Houlberg, K.; Christensen, P. R.; Klemmer, M.; Becker, J.;

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Iversen, B. B.; Glasius, M., Anal. Bioanal. Chem. 2016, 408, 2171-2183.

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(28) Gerchakov, S. M.; Hatcher, P. G., Limnol. Oceanogr. 1972, 17, 938-943.

22

(29) Waterborge, J., The Lowry method for protein quantification. Human Press: New Jersey., 2002.

23

(30) Moreira-Vilar, F. C.; Siqueira-Soares, R. C.; Finger-Teixeira, A.; Oliveira, D. M.; Ferro, A. P.; Rocha, G.

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J.; Ferrarese, M. L. L.; Santos, W. D.; Ferrarese-Filho, O., PLOS ONE 2014, 9.

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(31) Bro, R.; Smilde, A. K., Anal. Methods 2014, 6, 2812-2831.

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(32) van Praag, M.; Stein, H. S.; Tibbetts, M. S., J. Agric. Food. Chem. 1968, 16, 1005-1008.


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(33) Mûller, R.; Rappert, S., Appl Microbiol Biotechnol 2010, 85, 1315-1320.

2

(34) Hwang, H.; Hartman, T. G.; Ho, C., J. Agric. Food. Chem. 1995, 43, 179-184.

3

(35) Meynier, A.; Mottram, D. S., Food Chem. 1995, 52, 361-366.

4

(36) Zhang, C.; Tang, X.; Sheng, L.; Yang, X., Green Chemistry 2016, 18, 2542-2553.

5

(37) Yaylayan, V. A., Food Sci. Technol. Res. 2003, 9, 1-6.

6

(38) Chiaberge, S.; Leonardis, I.; Fiorani, T.; Bianchi, G.; Cesti, P.; Bosetti, A.; Crucianelli, M.; Reale, S.; De

7

Angelis, F., Energy & Fuels 2013, 27, 5287-5297.

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(39) Zhu, Y.; Biddy, M. J.; Jones, S. B.; Elliott, D. C.; Schmidt, A. J., Applied Energy 2014, 129, 384-394.

9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25


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1

Energy & Fuels

Table 1 Overview of the theoretical design of experiments. Cellulose/hemicellulose

Protein (soy)

Lipid (unrefined rapeseed oil)

Lignin (wheat straw soda extracted)

3:1 1 – apex

1.000

0

0

0

2 – apex

0

1.000

0

0

3 – apex

0

0

1.000

0

4 – edge

0.500

0

0.500

0

5 – edge

0

0.500

0.500

0

6 – edge

0.500

0.500

0

0

7 – face

0.330

0.330

0.330

0

8 – apex

0.233

0.233

0.233

0.300

9 – edge

0.850

0

0

0.150

10 – edge

0

0.850

0

0.150

11 – edge

0

0

0.850

0.150

12 – face

0.450

0.450

0

0.100

13 – face

0.450

0

0.450

0.100

14 – face

0

0.450

0.450

0.100

15 – center

0.308

0.308

0.308

0.075

16 – QC1

0.667

0.167

0.167

0

17 – QC2

0.167

0.667

0.167

0

18 – QC3

0.167

0.167

0.667

0

19 – QC4

0.258

0.258

0.258

0.225

20 – QC5

0.750

0.113

0.113

0.025

21 – QC6

0.113

0.750

0.113

0.025

22 – QC7

0.113

0.113

0.75

0.025

2 3 4 5


Energy & Fuels

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

1

Page 24 of 39

Table 2 Biochemical composition, moisture and ash content of DDGS, C. vulgaris, and M. x giganteus. Composition

DDGS

C. vulgaris

M. x giganteus

Carbohydrate

35.0 ± 1.3

26.5 ± 1.1

88.0 ± 5.0

Protein

42.2 ± 2.2

48.5 ± 0.3

4.2 ± 0.4

Lipid

22.4 ± 0.3

16.3 ± 1.3

5.4 ± 6.1

Lignin

2.8 ± 0.1

3.9 ± 0.3

17.8 ± 1.8

Moisture

6.7 ± 0.5

2.1 ± 0.9

4.3 ± 0.7

Ash

5.4 ± 0.0

11.4 ± 0.2

2.8 ± 0.0

Carbon

44.97 ± 0.04

47.86 ± 0.07

46.68 ± 0.01

Hydrogen

6.92 ± 0.06

6.94 ± 0.00

6.24 ± 0.02

Nitrogen

5.22 ± 0.03

7.85 ± 0.01

0.48 ± 0.03

Sulfur

0.85 ± 0.05

0.51 ± 0.01

0.09 ± 0.02

42.04

36.84

46.51

Proximate

Elemental

Oxygen


Page 25 of 39

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Energy & Fuels

1

Table 3 Quantitative results of model compound mixtures. Analytes marked by an asterix were quantitated using calibration curves of an analyte with

2

similar mass spectra. Car

Pro

Lip

Car/Lip

Pro/Lip

Car/Pro

Car/pro/Lip

Car/Pro

Car/Lig

Pro/Lig

Lip/Lig

Car/Pro/Lig

Car/Lip/Lig

Pro/Lip/Lig

Car/Pro/

100

100

100

50/50

50/50

50/50

33/33/33

/Lip/Lig

85/15

85/15

85/15

45/45/10

45/45/10

45/45/10

Lip/Lig

mg L-1

mg L-1

mg L-1

mg L-1

mg L-1

mg L-1

mg L-1

23/23/23/30

mg L-1

mg L-1

mg L-1

mg L-1

mg L-1

mg L-1

31/31/31/7

-1

-1

mg L

mg L

Carboxylic acid Acetic acid 1712 ± 28

2600 ±

69.6 ±

145

4.1

31.4 ±

Isobutyric acid

2112 ±

98

127

17.6 ±

20.3 ±

34.8 ±

0.9

1.0

2.1

1782 ± 10

1405 ± 80

25.2 ± 0.1

21.3 ± 1.0

1541 ±

2348 ±

51

64

36.1 ±

39.2 ±

2.8

1.6

80.8 ±

20.5 ±

4.0

363 ± 33

2101 ± 74

912 ± 2

2034 ± 45

1559 ± 48

1.8 ± 0.2

36.6 ± 0.7

16.8 ± 0.6

25.0 ± 1.4

23.7 ± 1.4

0.7

5.0 ± 0.0

50.4 ± 0.1

78.8 ± 4.0

14.4 ± 0.4

56.8 ± 2.9

42.9 ± 3.5

0.7

82.6 ±

18.6 ±

85.3 ±

13.0 ±

52.5 ±

11.6

0.0

7.7 ± 2.1

3.8

1.5

0.3

54.7 ± 1.3

44.5 ± 1.4

15.4 ± 0.3

7.6 ± 0.2

0.3 ± 0.1

8.3 ± 0.4

4.3 ± 0.2

9.5 ± 0.6

7.5 ± 0.1

7.0 ± 0.3

0.3

7.8 ± 0.3

1.4 ± 0.1

10.5 ± 0.1

7.7 ± 0.5

4.7 ± 0.3

7.2 ± 0.2

Crotonic acid

5.0 ± 0.5

5.8 ± 0.2

ND

9.9 ± 1.4

5.1 ± 0.3

7.5 ± 0.0

8.6 ± 0.9

6.0 ± 0.1

5.6 ± 0.2

6.9 ± 0.2

ND

7.1 ± 0.8

9.6 ± 0.4

6.5 ± 0.3

8.5 ± 0.2

Isovaleric acid

0.8 ± 0.0

182 ± 3

ND

ND

107 ± 5

104 ± 9

75.4 ± 2.0

55.8 ± 3.2

1.6 ± 0.2

183 ± 9

1.2 ± 0.2

99.6 ± 0.5

0.1 ± 0.1

105 ± 5

66.5 ± 3.4

10.7 ± 1.3

3.6 ± 0.1

0.3 ± 0.1

8.6 ± 0.3

2.1 ± 0.2

7.4 ± 0.1

6.5 ± 0.2

8.9 ± 0.4

4.9 ± 0.0

1.1 ± 0.0

8.2 ± 0.0

7.2 ± 0.5

3.0 ± 0.2

6.6 ± 0.1

12.2 ± 0.6

3.6 ± 0.2

0.3 ± 0.0

2.1 ± 0.2

7.4 ± 0.1

6.7 ± 0.1

9.0 ± 0.4

4.8 ± 0.1

1.1 ± 0.1

8.2 ± 0.1

8.6 ± 0.1

3.1 ± 0.2

6.7 ± 0.0

17.3 ± 1.7

3.3 ± 0.0

2.1 ± 0.2

2.2 ± 0.1

9.7 ± 0.0

8.4 ± 0.4

10.0 ± 0.1

4.4 ± 0.1

6.6 ± 0.1

11.6 ± 0.2

10.7 ± 1.1

3.5 ± 0.2

8.8 ± 0.4

Methacrylic acid

ND

2202 ± 962 ± 7

15.0 ±

Butyric acid

10.5 ±

Pent-4-enoic acid

10.4 ±

Pent-3-enoic acid*

0.2

12.7 ±

12.4 ±

Valeric acid

2.6

0.2

0.3 19.0 ± 0.7

25

ACS Paragon Plus Environment

Energy & Fuels

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

25.1 ±

Tigllic acid*

22.4 ±

Page 26 of 39

21.5 ±

21.5 ± 4.2

4.2 ± 0.1

1.6 ± 0.1

3.7

3.7 ± 0.5

0.5

21.9 ± 0.1

15.6 ± 0.3

1.7

7.4 ± 0.0

2.4 ± 0.3

15.9 ± 1.0

21.8 ± 2.4

4.5 ± 0.3

21.8 ± 1.2

2.3 ± 0.2

1.7 ± 0.0

0.1 ± 0.0

2.2 ± 0.3

0.9 ± 0.1

2.3 ± 0.1

1.9 ± 0.1

1.5 ± 0.1

2.5 ± 0.2

1.9 ± 0.4

0.3 ± 0.0

1.8 ± 0.0

1.9 ± 0.5

1.2 ± 0.1

1.9 ± 0.1

72.7 ±

65.2 ±

ND

123 ± 2

ND

ND

4.5

6.0

49.9 ± 1.2

33.0 ± 2.0

ND

114 ± 5

ND

60.6 ± 0.1

ND

67.3 ± 3.1

43.5 ± 1.9

ND

249 ± 5

ND

ND

148 ± 7

133 ± 13

102 ± 2

63.3 ± 3.5

0.3 ± 0.1

225 ± 11

ND

122 ± 0

ND

136 ± 5

86.5 ± 3.1

4.1 ± 0.4

3.4 ± 0.1

1.1 ± 0.1

3.7 ± 0.7

1.6 ± 0.1

2.2 ± 0.2

2.6 ± 0.0

2.1 ± 0.0

3.9 ± 0.0

3.0 ± 0.1

2.4 ± 0.2

2.3 ± 0.1

3.7 ± 0.2

1.9 ± 0.2

2.5 ± 0.0

6.4 ± 1.1

5.8 ± 0.2

ND

5.9 ± 1.4

3.8 ± 0.6

9.1 ± 0.7

6.7 ± 1.8

5.5 ± 0.2

6.6 ± 0.8

7.7 ± 0.6

ND

7.8 ± 0.0

5.1 ± 0.3

3.1 ± 0.7

7.0 ± 1.0

3-methylbut-2-enoic acid* 3-methyl-pentanoic acid 4-methyl-pentanoic acid 5-hexenoic acid 3-methylpent-2-enoic acid* 3-methylpent-3-enoic acid*

16.3 ±

13.6 ±

1.2 ± 0.1

28.8 ± 1.4

ND

0.7 ± 0.1

1.2

0.1

9.3 ± 0.5

5.8 ± 0.2

1.3 ± 0.0

23.1 ± 0.6

0.0 ± 0.0

11.1 ± 0.5

0.6 ± 0.0

12.4 ± 0.8

7.1 ± 1.1

6.7 ± 1.6

2.7 ± 0.1

ND

5.6 ± 1.0

2.2 ± 0.5

8.2 ± 0.3

7.4 ± 0.1

5.1 ± 0.1

6.9 ± 0.3

5.0 ± 0.2

0.3 ± 0.4

5.9 ± 0.3

5.3 ± 1.0

2.4 ± 0.1

6.8 ± 0.7

197 ± 5

247 ± 10

7.6 ± 0.1

121 ± 2

159 ± 6

303 ± 21

243 ± 5

161 ± 5

177 ± 3

228 ± 6

285 ± 0

117 ± 0

149 ± 5

210 ± 9

1114 ± 35

594 ± 27

2.1

446 ± 32

351 ± 22

880 ± 51

681 ± 13

474 ± 15

830 ± 34

581 ± 19

5.4

832 ± 11

411 ± 16

357 ± 10

581 ± 23

5.1 ± 1.4

0.3 ± 0.0

0.5 ± 0.1

6.5 ± 1.3

0.4 ± 0.0

1.6 ± 0.3

2.1 ± 0.0

2.2 ± 0.3

5.3 ± 0.8

0.4 ± 0.0

0.3 ± 0.0

1.8 ± 0.1

6.2 ± 0.7

0.5 ± 0.2

2.2 ± 0.3

21.3 ± 0.5

6.7 ± 0.4

2.1 ± 0.1

4.4 ± 0.1

19.6 ± 0.3

12.5 ± 0.2

6.0 ± 0.2

14.5 ± 0.7

1.6 ± 0.1

4.9 ± 0.1

4.8 ± 0.1

8.5 ± 0.3

16.0 ± 0.9

11.4 ± 0.4

2-methylpent-2-enoic acid

40.7 ±

Levulinic acid

13.6 ±

4-oxohexanoic acid

0.6 54.1 ±

4-methylpent-3-enoic acid

11.6 ±

5-oxohexanoic acid

18.2 ±

21.2 ±

0.2

4.7 ± 0.2

1.3

15.0 ± 0.0

14.3 ± 0.4

1.4

9.2 ± 0.0

8.4 ± 0.1

9.8 ± 0.4

4.6 ± 0.1

9.8 ± 0.9

13.9 ± 0.4

2.2 ± 0.0

7.3 ± 0.1

19.2 ±

Octanoic acid *

1.0

22.4 ± 0.2

26


Page 27 of 39

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

Energy & Fuels

11.4 ±

Dodecanoic acid *

1.2

10.4 ±

3.0 ± 0.3

1.2 ± 0.2

2.8 ± 0.4

6.6 ± 0.0

0.8 ± 0.1

0.9 ± 0.0

1.7

0.9 ± 0.1

ND

2.1 ± 1.0

172 ± 6

ND

24.2 ±

2445 ±

5.2

91

ND

22

10.3 ±

3636 ±

28.6 ±

1262 ±

1.0

152

26.1

247

1.0 ± 0.0

4.9 ± 0.6

6.1 ± 0.7

3.8 ± 0.2

1.3 ± 0.1

0.2

1.1 ± 0.0

2.8 ± 0.4

6.7 ± 0.8

5.0 ± 0.7

0.7 ± 0.0

15.6 ± 1.9

13.3 ± 2.1

1.0 ± 0.0

0.8 ± 0.1

1.6

0.7 ± 0.0

1.0 ± 0.2

21.4 ± 2.2

13.1 ± 0.1

4.2 ± 3.7

44.8 ± 6.1

24.4 ± 5.0

1.3 ± 1.9

1.6 ± 1.8

110 ± 4

1.5 ± 2.1

ND

55.3 ± 8.6

32.0 ± 1.1

ND

953 ± 116

721 ± 135

ND

7.1 ± 0.3

1802 ± 38

ND

ND 1272 ± 92

796 ± 11

24.6 ±

2422 ±

8.6 ± 0.8

729 ± 156

309 ± 106

8.7 ± 0.2

0.9

177

Fatty acids 58.3 ±

Myristic acid *

23.5 ±

Palmitic acid ND Linoleic acid 8.9 ± 0.1

1417 ±

9.4 ± 9.0

11.5 ± 2.6

758 ± 153

548 ± 26

18.6 ±

9435 ±

55.9 ±

6200 ±

32.6 ±

14.5 ±

7585 ±

3.2

483

73.8

182

7.3 ± 0.2

4539 ± 543

4072 ± 658

5.7

3.5

137

11.1 ± 1.3

25.6 ± 26.7

6195 ± 278

4142 ± 83

0.9 ± 0.3

6.8 ± 0.9

30

3.4 ± 2.0

499 ± 11

0.9 ± 0.2

343 ± 46

262 ±.61

0.8 ± 0.1

3.9 ± 0.2

713 ± 38

0.8 ± 0.0

1.9 ± 1.5

479 ± 69

267 ± 4

6.3 ± 0.6

6.7 ± 2.0

407 ± 9

5.0 ± 0.5

154 ± 3

5.0 ± 0.4

90.7 ± 13.3

67.4 ± 17.2

5.8 ± 0.1

7.0 ± 2.4

262 ± 14

5.7 ± 0.9

6.1 ± 0.6

140 ± 17

74.2 ± 0.9

ND

ND

ND

97.8 ±

ND

ND

ND

ND

ND

59.0 ± 9.5

43.2 ± 10.4

87.8 ± 9.0

47.0 ± 0.1

23.2 ± 3.5

19.1 ± 4.7

35.7 ± 4.0

18.7 ± 0.1

1045 ±

Stearic acid

255 ± 3 Lignoceric acid

0.6

6.7 ± 1.8

Oleic acid

Behenic acid

38.0 ±

78.1 ±

Palmitoleic acid

Eicosanoic acid

0.2

ND

ND

3.0 ND

100 ± 1

39.9 ±

ND

1.8

166 ± 8 ND

ND

66.1 ±

ND

ND

3.3

Dicarboxylic acids 16.6 ±

Oxalic acid

Malonic acid

19.1 ±

13.8 ±

26.4 ± 1.5

7.1 ± 1.0

ND

1.7

4.9 ± 2.3

5.5 ± 3.2

11.4 ± 2.1

2.5 ± 1.9

6.1

2.0 ± 1.1

8.2

4.0 ± 0.1

18.1 ± 0.3

6.2 ± 2.0

8.8 ± 0.4

0.8 ± 0.1

0.9 ± 0.0

0.5 ± 0.0

1.1 ± 0.0

0.5 ± 0.0

0.9 ± 0.0

0.6 ± 0.0

2.0 ± 0.1

1.1 ± 0.0

1.7 ± 0.0

3.3 ± 0.7

0.8 ± 0.0

0.9 ± 0.1

2.3 ± 0.5

1.0 ± 0.2

27


Energy & Fuels

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Page 28 of 39

1383 ±

Succinic acid 402 ± 76

712 ± 23

7.7 ± 0.5

176 ± 37

709 ± 34

48

939 ± 68

726 ± 17

343 ± 65

1671 ±

91.9 ±

71

0.5

185 ± 36

1047 ± 40

925 ± 95

421 ± 63

164 ± 28

644 ± 9

447 ± 35

0.2

241 ± 41

80.4 ± 17.6

306 ± 4

268 ± 22

67.2 ±

Methylsuccinic acid * 336 ± 68

317 ± 18

5.6 ± 0.4

577 ± 48

606 ± 30

494 ± 1

416 ± 8

294 ± 62

539 ± 34

184 ± 42

160 ± 6

4.5 ± 0.5

8.1 ± 2.0

6.2 ± 0.0

4.6 ± 0.0

161 ± 36

16

260 ± 5

343 ± 18

284 ± 10

237 ± 7

162 ± 31

283 ± 6

0.9 ± 0.0

4.5 ± 0.8

8.6 ± 0.8

0.6

12.8 ± 0.2

13.0 ± 0.5

7.8 ± 1.2

0.6

2.3 ± 0.0

13.7 ± 1.9

4.8 ± 0.8

12.4 ± 0.5

12.7 ± 1.0

2.7 ± 0.2

1.5 ± 0.0

3.3 ± 0.1

2.5 ± 0.5

4.4 ± 0.3

3.6 ± 0.8

3.4 ± 0.3

4.7 ± 0.2

2.9 ± 0.2

1.6 ± 0.2

5.4 ± 0.5

2.9 ± 0.5

2.0 ± 0.2

4.0 ± 0.2

77.5 ±

Glutaric acid

2.1 21.0 ±

19.0 ±

Ethylsuccinic acid *

Adipic acid

956 ± 185

15.0 ±

57.6 ±

12.7 ±

460 ± 80

2.3

2.3

272 ± 60

169 ± 24

169

533 ± 60

350 ± 28

410 ± 83

220 ± 26

7.7

701 ± 3

267 ± 5

336 ± 39

336 ± 53

1.6 ± 0.1

2.3 ± 0.1

1.2 ± 0.0

1.4 ± 0.1

2.2 ± 0.1

4.0 ± 0.1

2.8 ± 0.0

2.7 ± 0.0

1.5 ± 0.1

4.5 ± 0.1

1.8 ± 0.0

2.9 ± 0.2

1.5 ± 0.0

2.4 ± 0.1

2.8 ± 0.2

6.5 ± 1.1

4.1 ± 0.1

ND

3.2 ± 0.0

2.6 ± 0.2

5.4 ± 0.1

4.0 ± 0.4

6.7 ± 0.5

7.3 ± 0.1

9.0 ± 1.0

2.3 ± 0.2

5.5 ± 0.1

4.3 ± 0.3

5.1 ± 0.3

5.0 ± 0.0

21.3 ±

11.9 ±

13.5 ±

14.7 ±

62.0 ±

14.8 ±

19.9 ±

12.2 ±

0.6

0.0

1.0

0.2

0.7

37.4 ± 1.6

21.5 ± 0.5

0.5

0.6

0.0

53.9 ± 1.1

13.7 ± 0.0

15.3 ± 0.6

31.6 ± 1.5

139 ±

ND 8.1 ± 0.2

143 ± 2

2.4

89.7 ± 5.0

493 ± 51

124 ± 11

288 ± 6

264 ± 12

146 ± 1

106 ± 13

324 ± 14

169 ± 5

0.3 ± 0.0

0.5 ± 0.0

4.5 ± 0.1

3.9 ± 0.3

2.2 ± 0.1

2.0 ± 0.1

0.6 ± 0.0

5.1 ± 0.3

0.6 ± 0.0

2.8 ± 0.0

0.5 ± 0.0

3.7 ± 0.1

1.9 ± 0.1

5.7 ± 0.1

339 ± 11

9.6 ± 0.8

40.3 ± 1.5

187 ± 10

5.1 ± 0.1

6.1 ± 0.9

14.8 ± 1.0

11.0 ± 0.5

2-oxoglutaric acid

598 ±

72.6 ±

Propanetricarboxylic acid Oxygenated aromatics Benzoic acid 3-hydroxypyridine, monoacetate

15.4 ± 1.9

Phenol

Phenyl acetic acid

19.9 ± 2.3

2.6

0.6 ± 0.0

4.9 ± 0.5

59.9 ±

12.7 ±

90.1 ±

11.8 ± 0.3

92.7 ± 1.7

3.5 ± 0.0

9.2 ± 0.3

0.9

436 ± 23

236 ± 15

89.8 ± 4.8

1.5

0.9

7.1 ± 1.0

2.1 ± 0.2

ND

3.8 ± 0.3

4.4 ± 0.3

2.9 ± 0.0

8.8 ± 0.4

21.3 ± 3.4

9.4 ± 0.8

6.6 ± 0.3

3-hydroxypyridine

31.8 ±

11.6 ±

m-cresol*

1.3

28


Page 29 of 39

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Energy & Fuels

28.2 ±

p-cresol 4.5 ± 0.5

0.0

0.1 ± 0.0

2.3 ± 0.0

Hydrocinnamic acid 1.0 ± 0.0

161 ± 4

0.8 ± 0.1

0.9 ± 0.1

6.6 ± 0.6

ND

ND

3.5 ± 0.3

307 ± 7

2.6 ± 0.4

1.8 ± 0.2

148 ± 12

0.5

3.2 ± 0.2

188 ± 14

Pyrocatechol

46.7 ±

11.6 ±

1.6

0.2

10.9 ±

39.5 ±

24.5 ±

33.2 ± 2.8

51.5 ± 8.0

0.6

1.1

1.6

17.5 ± 0.1

7.7 ± 0.9

67.8 ± 4.3

34.4 ± 0.6

94.9 ±

77.2 ±

4.5

7.2

61.3 ± 1.0

37.8 ± 0.9

1.3 ± 0.1

145 ± 6

1.4 ± 0.0

70.8 ± 1.2

1.2 ± 0.0

86.5 ± 1.5

52.0 ± 1.5

ND

ND 0.9 ± 0.3

41.3 ± 2.9

1.1

6.5 ± 0.5

16.2 ± 0.8

6.6 ± 0.3

10.0 ± 0.3

115 ± 13

81.9 ± 6.8

87.1 ± 4.3

268 ± 1

114 ± 5

147 ± 1

4.8 ± 0.3

88.1 ± 2.9

132 ± 6

133 ± 1

88.9 ± 8.0

264 ± 3

123 ± 3

164 ± 5

21.5 ± 0.2

120 ± 4

113 ± 2

88.9 ± 2.0

78.4 ± 0.6

427 ± 3

108 ± 2

255 ± 19

36.2 ± 1.4

75.7 ± 3.6

342 ± 0

404 ± 32

979 ± 4

433 ± 21

560 ± 74

83.3 ± 7.9

365 ± 14

124 ± 3

21.9 ±

32.2 ± 2.2 ± 0.3

4.0

11.1 ±

10.1 ±

0.3

0.1

18.9 ±

10.5 ±

0.2

0.1

39.1 ±

17.3 ±

0.3

0.4

60.8 ±

72.8 ±

0.8

3.7

26.3 ±

17.3 ±

0.3

1.0

90.8 ± 0.5

70.0 ± 2.9

24.3 ± 0.6

76.4 ± 2.6

229 ± 1

5.6 ± 0.0

97.4 ± 5.8

46.2 ± 2.4

94.1 ± 1.6

64.0 ± 3.4

3.0 ± 0.1

2.6 ± 0.0

9.7 ± 0.2

11.1 ± 0.1

3.5 ± 0.1

11.6 ± 0.3

2.2 ± 0.0

2.1 ± 0.1

6.0 ± 0.1

9.1 ± 0.6

2.4 ± 0.0

6.7 ± 0.3

8.8 ± 0.1

3.1 ± 0.2

38.0 ± 1.1

34.4 ± 0.2

9.6 ± 0.0

39.8 ± 0.3

Cyclic oxygenates Cyclopentanone

2-methylcyclopent-2enone

13.3 ± 330 ± 6

enone*

23.0 ±

5.4

1.6

309 ± 11

1235 ±

36.4 ±

148

0.7

9.9 ± 0.1

507 ± 4

30.8 ±

2.4

385 ± 19

74.5 ±

13.9 ±

90.1 ±

2.0

0.2

0.3

84.3 ± 0.8

70.5 ± 7.8

52.1 ±

87.7 ±

4.3

0.0

113 ± 9

74.6 ± 0.0

55.8 ± 0.0

2.2 34.1 ±

19.0 ± 171 ± 7

0.6

2.9 ± 0.3

101 ± 2

233 ± 5

6.6 ± 0.0

21.9 ± 0.5

3.0 ± 0.0

1.8 ± 0.1

15.1 ± 1.0

2.0 ± 0.1

1.5 ± 0.0

78.8 ±2.5

6.9 ± 0.4

1.6 ± 0.0

δ-valerolactone

3,4-dimethylcyclopent2-enone*

0.2

40.6 ±

2,3-dimethylcyclopent2-enone

14.4 ±

524 ± 7

Butyrolactone

3-methylcyclopent-2-

2.2 ± 0.6

13.5 ± 1.1

81.4 ±

10.5 ±

2.1 17.5 ±

3.1 ± 0.1

0.4

13.9 ± 0.4

8.4 ± 1.0

2.0 ± 0.0

6.1 ± 0.3

7.3 ± 0.2

5.4 ± 0.4

47.0 ± 1.8

25.7 ± 3.7

0.7

(?,?)dimethylcyclopent-2enone*

11.0 ±

4,4-dimecyclopent-2enone*

1.0

12.0 ±

38.3 ± 2.4

46.5 ± 6.6 ± 0.0

1.0

0.2 57.1 ± 0.2

29


Energy & Fuels

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3-ethylcyclopent-2enone*

11.1 ± 28.7 ± 1.1

2.6 ± 0.1

11.5 ±

Page 30 of 39

20.9 ±

1.6 ± 0.0

1.1

2.3 ± 0.1

0.5

11.0 ± 0.3

11.1 ± 1.3

0.8

4.2 ± 0.1

4.8 ± 0.6

8.8 ± 1.9

194 ± 18

69.1 ± 0.5

27.9 ± 0.1

4.6 ± 0.2

13.3 ±

1134 ±

3.4 ± 0.1

3.4 ± 0.0

0.1

96

344 ± 43

79.5 ± 3.6

4.1 ± 0.1

ND

ND

28.2 ± 2.9

4.7 ± 0.0

13.7 ± 1.6

4.0 ± 0.0

2.3 ± 0.0

11.7 ± 0.9

12.8 ± 1.2

4.4 ± 0.1

11.8 ± 0.2

4.7 ± 0.4

148 ± 22

4.6 ± 0.5

8.1 ± 0.1

59.9 ± 0.0

0.6

3.9 ± 0.5

800 ± 98

4.9 ± 0.5

14.1 ± 0.3

247 ± 10

ND

7.6 ± 0.0

ND

61.9 ± 6.5

ND

0.6 ± 0.0

18.7 ± 1.3

3.3 ± 0.3

0.3 ± 0.0

2.6 ± 0.0

0.3 ± 0.0

34.0 ± 4.3

0.4 ± 0.0

0.8 ± 0.1

10.2 ± 0.5

9.7 ± 1.0

10.0 ± 2.1

0.3 ± 0.0

1.6 ± 0.0

ND

16.8 ± 1.7

0.2 ± 0.0

1.6 ± 0.3

7.3 ± 0.2

0.2 ± 0.1

1.3 ± 0.1

0.2 ± 0.0

17.0 ± 1.0

0.2 ± 0.0

0.2 ± 0.1

7.0 ± 0.5

494 ± 10

20.6 ± 0.0

320 ± 14

324 ± 21

Nitrogenates 15.0 ±

Pyrazine

Methylpyrazine

4.6 ± 1.1

0.2

ND

71.5 ± 3.6

2,5-dimethylpyrazine

ND

14.2 ±

9.3 ± 0.4

62.9 ±

96.5 ± 1.0 ± 0.2

6.6 51.5 ±

Ethylpyrazine 0.3 ± 0.0

3.2 ± 0.0

0.3 ± 0.0

0.3 ± 0.0

0.8 ± 0.0

ND

1.6 ± 0.2

ND

ND

0.6 ± 0.1

3.6

0.4 ± 0.1

1.9 ± 0.0

0.2 ± 0.0

0.3 ± 0.1

0.4 ± 0.2

0.2

11.6 ± 0.7

1.4 ± 0.1

21.8 ±

27.8 ±

0.7

9.9

341 ± 4

539 ± 30

375 ± 20

234 ± 5

24.2 ±


1.2 26.8 ±

2,3,5-trimethylpyrazine

2-pyrrolidone

7.4

45.4 ± 34.7

809 ± 24

38.3 ± 23.8

20.2 ± 686 ± 14

0.1

1 2 3 4 5

30


Page 31 of 39

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Energy & Fuels

1

Table 4 Regression coefficients and validation parameters for PLS1 models of 13 selected analytes.

2

Abbreviations are explained in the text. Carbohydrate Regression coefficient

Protein Regression coefficient

Lipid Regression coefficient

Lignin Regression coefficient

R2cv

R2pred

RMSECV

RMSEP

Biascv

Biaspred

Acetic acid

0.0235

0.5555

-0.5286

-0.0827

0.821

0.943

293

175

21.3

105

Isovaleric acid

-0.3368

0.6772

-0.3193

-0.0451

0.983

0.997

8.3

4.7

0.4

2.8

4-methylpentanoic acid

-0.3406

0.6799

-0.3061

-0.0878

0.993

0.996

7.0

5.9

0.5

3.8

Levulinic acid

0.2050

0.4271

-0.5756

-0.0863

0.798

0.959

36.2

21.1

2.4

-13.9

4-oxohexanoic acid

0.5123

0.1304

-0.5845

-0.0888

0.923

0.978

83.1

38.0

-2.5

-12.9

Succinic acid

-0.1692

0.6904

-0.4009

-0.0135

0.883

0.921

168

238

1.9

140

Glutaric acid

0.0160

0.5603

-0.4665

0.0032

0.655

0.384

63.0

74.6

1.1

14.4

Phenol

-0.3786

0.1716

-0.1022

0.8788

0.837

0.882

52.3

42.5

1.6

-11.6

p-cresol

-0.4494

0.3471

-0.0705

0.4255

0.284

0.585

17.1

10.5

1.2

-3.1

Hydrocinnamic acid

-0.3506

0.6784

-0.2966

-0.0886

0.989

0.997

5.4

4.60

0.0

3.9

Cyclopentanone

0.6794

-0.3656

-0.2919

-0.0068

0.976

0.996

14.3

7.4

-1.1

0.8

Butyrolactone

0.6396

-0.3651

-0.2415

-0.0638

0.784

0.951

72.2

54.7

-1.2

35.5

3-methylcyclopent-2-

0.6664

-0.3599

-0.2943

0.0246

0.923

0.970

96.9

57.0

-6.7

-13.3

Palmitic acid

-0.6411

0.1278

0.4127

-0.0497

0.721

0.607

400

562

-38.3

-180

Linoleic acid

-0.4992

-0.1131

0.5155

-0.1040

0.730

0.866

599

720

-62.7

-581

Oleic acid

-0.6750

0.2116

0.3455

0.0185

0.691

0.497

1663

2576

-134

-454

enone

3 4 5 6 7 8 9


Energy & Fuels

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1

Page 32 of 39

Table 5 Model parameters for linear, exponential and polynomial fits of pyrazines. linear

exponential

2nd degree polynomial

Pyrazine

434.3 x A -1.777

5.482 x exp(8.088 x A)

840.3 x A2 + 60.13 x A + 3.131

Methylpyrazine

2593 x A – 21.19

12.97 x exp(10.13 x A)

5070 x A + 333.6 x A – 4.613


214.1 x A + 2.698

0.758 x exp(10.94 x A)

478.1 x A2 + 7.513 x A -0.279

Ethylpyrazine

118.2 x A – 0.943

0.437 x exp(10.77 x A)

234.8 x A2 + 12.54 x A + 0.003


55.11 x A -0.092

1.119 x exp(6.891 x A)

95.82 x A + 12.79 x A – 0.195

trimethylpyrazine

61.20 x A + 0.275

0.459 x exp(9.142 x A)

109.6 x A2 + 11.95 x A + 0.046

2

2

2 3

Table 6 Validation parameters for linear, exponential and polynomial fits of pyrazines. R2 linear

RMSE linear

R2 exp

RMSE exp

R2 poly

RMSE poly

Pyrazine

0.974

12.0

0.989

6.3

0.994

5.6

Methylpyrazine

0.993

36.9

0.989

36.2

0.995

30.1


0.987

4.2

0.988

3.2

0.997

2.1

Ethylpyrazine

0.993

1.7

0.992

1.4

0.996

1.2


0.985

1.1

0.927

2.0

0.998

0.4

trimethylpyrazine

0.976

1.6

0.966

1.5

0.996

0.6

4 5

Table 7 Regression coefficients and validation parameters for PLS1 models of TOC, TN, and pH Protein Regression coefficient 0.5916

Lipid Regression coefficient -0.4356

Lignin Regression coefficient -0.1404

R2cv

R2pred

RMSECV

RMSEP

Biascv

Biaspred

TOC

Carbohydrate Regression coefficient -0.1197

0.782

0.851

2683

1819

-110

419

TN

-0.3585

0.6628

-0.2964

-0.0825

0.981

0.986

732

508

-48.31

212

pH

-0.5508

0.5642

0.0024

-0.1002

0.901

0.903

0.29

0.23

0.01

-0.06

6 7 8 9 10


Page 33 of 39

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

1

Energy & Fuels

Table 8 Measured and predicted values in aqueous phase from HTL of DDGS DDGSMeas

DDGSPred

M. x giganteusMeas

M. x giganteusPred

C. vulgarisMeas

C. vulgarisPred

mg L-1

mg L-1

mg L-1

mg L-1

mg L-1

mg L-1

Acetic acid

1885

1775

4323

1549

3310

1837

Isovaleric acid

55.2

79.6

5.7

24.6

120

88.8

4-methylpentanoic acid

54.0

105

4.0

27.9

147

117

Levulinic acid

225

198

188

199

279

201

4-oxohexanoic acid

684

610

855

787

764

600

Succinic acid

1423

849

599

505

1591

912

Glutaric acid

262

228

151

194

174

238

Phenol

69.5

94.4

383

139

76.1

94.4

p-cresol

13.4

21.7

24.2

13.7

18.9

24.9

Hydrocinnamic acid

26.0

65.6

2.8

16.3

81.4

73.2

Cyclopentanone

105

93.5

188

205

57.1

81.5

Butyrolactone

105

150

233

313

38.8

130

2,3-dimethylcyclopent-2-enone

95.7

65.8

86.5

116

62.1

60.9

Methylpyrazine

670

870

12.0

35.6

963

688

TOC

23510

23855

20740

20323

24150

24456

TN

4520

5918

238

1463

7400

6606

pH

8.14

8.68

6.80

7.57

9.10

8.81

2


Energy & Fuels

18000 16000 Concentration - mg L-1

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 34 of 39

14000 12000 Nitrogenates

10000

Cyclig oxygenates

8000

Oxygenated aromatics

6000

Fatty acids

4000

Dicarboxylic acids Small organic acids

2000 0

1 2

Figure 1 Summed concentrations of analyte classes from HTL of pure compounds and mixtures of

3

carbohydrates (Car), protein (Pro), lipid (Lip), and lignin (Lig).

4


Page 35 of 39

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Energy & Fuels

A

B

C

1


Energy & Fuels

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

1

Figure 2 A: Scores plot of PC1 and PC2 for model compound mixtures. B: Scores plot of PC3 and PC4 for

2

model compounds mixtures. C: Scores plot of PC3 and PC4 for QC samples.

Page 36 of 39

3

4 5

Figure 3 Plot of measured and predicted concentrations of acetic acid in QC samples. The green line shows

6

calibration values. The red line shows cross validated calibration values.

7


Page 37 of 39

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Energy & Fuels

1 2

Figure 4 Plots of linear, exponential and polynomial fits for pyrazines. The abscissa presents the ratio of the

3

limiting model compound to the total biomass loaded. Linear fit – red squares and line, exponential fit –

4

blue crosses and line, 2nd degree polynomial fit – orange triangles and line.

5 6


Energy & Fuels

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 38 of 39

1 2

Figure 5 Reaction pathways for producing pyrazines from carbohydrates and protein degradation.

3


Page 39 of 39

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Energy & Fuels

A

B

C

1 2

Figure 6 Plot of predicted and measured values of TOC (A), TN (B), and pH (C). The green line shows

3

calibration values. The red line shows cross validated calibration values.


Predicting the Chemical Composition of Aqueous Phase from

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