The plant polymer lignin is the most abundant renewable source of aromatics on the planet and conversion of it to valuable fuels and chemicals is critical to the economic viability of a lignocellulosic biofuels industry and to meeting the DOE’s 2022 goal of $\$2.50$/gallon mean biofuel selling price. Presently, there is no efficient way of converting lignin into valuable commodities. Current biological approaches require mixtures of expensive ligninolytic enzymes and engineered microbes. This project was aimed at circumventing these problems by discovering commensal relationships among fungi and bacteria involved in biological lignin utilization and using this knowledge to engineer microbial communities capable of converting lignin into renewable fuels and chemicals. Essentially, we aimed to learn from, mimic and improve on nature. We discovered fungi that synergistically work together to degrade lignin, engineered fungal systems to increase expression of the required enzymes and engineered organisms to produce products such as biodegradable plastics precursors.
Fusel alcohol mixtures containing ethanol, isobutanol, isopentanol, and 2-phenylethanol have been shown to be a promising means to maximize renewable fuel yield from various biomass feedstocks and waste streams. We hypothesized that use of these fusel alcohol mixtures as a blending agent with gasoline can significantly lower the greenhouse gas emissions from the light-duty fleet. Since the composition of fusel alcohol mixtures derived from fermentation is dependent on a variety of factors such as biocatalyst selection and feedstock composition, multi-objective optimization was performed to identify optimal fusel alcohol blends in gasoline that simultaneously maximize thermodynamic efficiency gain and energy density. Pareto front analysis combined with fuel property predictions and a Merit Score-based metric led to prediction of optimal fusel alcohol-gasoline blends over a range of blending volumes. The optimal fusel blends were analyzed based on a Net Fuel Economy Improvement Potential metric for volumetric blending in a gasoline base fuel. The results demonstrate that various fusel alcohol blends provide the ability to maximize efficiency improvement while minimizing increases to blending vapor pressure and decreases to energy density compared to an ethanol-only bioblendstock. Fusel blends exhibit predicted Net Fuel Economy Improvement Potential comparable to neat ethanol when blended with gasoline in all scenarios, with increased improvement over ethanol at moderate to high bio-blendstock blending levels. The optimal fusel blend that was identified was a mixture of 90% v/v isobutanol and 10% v/v 2-phenylethanol, blended at 45% v/v with gasoline, yielding a predicted 4.67% increase in Net Fuel Economy Improvement Potential. These findings suggest that incorporation of fusel alcohols as a gasoline bioblendstock can improve both fuel performance and the net fuel yield of the bioethanol industry.
Corynebacterium glutamicum has been successfully employed for the industrial production of amino acids and other bioproducts, partially due to its native ability to utilize a wide range of carbon substrates. We demonstrated C. glutamicum as an efficient microbial host for utilizing diverse carbon substrates present in biomass hydrolysates, such as glucose, arabinose, and xylose, in addition to its natural ability to assimilate lignin-derived aromatics. As a case study to demonstrate its bioproduction capabilities, L-lactate was chosen as the primary fermentation end product along with acetate and succinate. C. glutamicum was found to grow well in different aromatics (benzoic acid, cinnamic acid, vanillic acid, and p-coumaric acid) up to a concentration of 40 mM. Besides, 13 C-fingerprinting confirmed that carbon from aromatics enter the primary metabolism via TCA cycle confirming the presence of β-ketoadipate pathway in C. glutamicum . 13 C-fingerprinting in the presence of both glucose and aromatics also revealed coumarate to be the most preferred aromatic by C. glutamicum contributing 74 and 59% of its carbon for the synthesis of glutamate and aspartate respectively. 13 C-fingerprinting also confirmed the activity of ortho-cleavage pathway, anaplerotic pathway, and cataplerotic pathways. Finally, the engineered C. glutamicum strain grew well in biomass hydrolysate containing pentose and hexose sugars and produced L-lactate at a concentration of 47.9 g/L and a yield of 0.639 g/g from sugars with simultaneous utilization of aromatics. Succinate and acetate co-products were produced at concentrations of 8.9 g/L and 3.2 g/L, respectively. Our findings open the door to valorize all the major carbon components of biomass hydrolysate by using C. glutamicum as a microbial host for biomanufacturing.
In 2018 13.7 EJ of fuel were consumed by the global commercial aviation industry. Worldwide, demand will increase into the foreseeable future. Developing Sustainable Aviation Fuels (SAFs), with decreased CO2 and soot emissions, will be pivotal to the on-going mitigation efforts against global warming. Minimizing aromatics in aviation fuel is desirable because of the high propensity of aromatics to produce soot during combustion. Because aromatics cause o-rings to swell, they are important for maintaining engine seals, and must be present in at least 8 vol% under ASTM-D7566. Recently, cycloalkanes have been shown to exhibit some o-ring swelling behavior, possibly making them an attractive substitute to decrease the aromatic content of aviation fuel. Cycloalkanes must meet specifications for a number of other physical properties to be compatible with jet fuel, and these properties can vary greatly with the cycloalkane chemical structure, making their selection difficult. Building a database of structure-property relationships (SPR) for cycloalkanes greatly facilitates their furthered inclusion into aviation fuels. The work presented in this paper develops SPRs by building a data set that includes physical properties important to the aviation industry. The physical properties considered are energy density, specific energy, melting point, density, flashpoint, the Hansen solubility parameter, and the yield sooting index (YSI). Further, our data set includes cycloalkanes drawn from the following structural groups: fused cycloalkanes, n-alkylcycloalkanes, branched cycloalkanes, multiple substituted cycloalkanes, and cycloalkanes with different ring sizes. In addition, a select number of cycloalkanes are blended into Jet-A fuel (POSF-10325) at 10 and 30 wt%. Comparison of neat and blended physical properties are presented. One major finding is that ring expanded systems, those with more than six carbons, have excellent potential for inclusion in SAFs. Our data also indicate that polysubstituted cycloalkanes have higher YSI values.
In-silico screening of novel biofuel molecules based on chemical and fuel properties is a critical first step in the biofuel evaluation process due to the significant volumes of samples required for experimental testing, the destructive nature of engine tests, and the costs associated with bench-scale synthesis of novel fuels. Predictive models are limited by training sets of few existing measurements, often containing similar classes of molecules that represent just a subset of the potential molecular fuel space. Software tools can be used to generate every possible molecular descriptor for use as input features, but most of these features are largely irrelevant and training models on datasets with higher dimensionality than size tends to yield poor predictive performance. Feature selection has been shown to improve machine learning models, but correlation-based feature selection fails to provide scientific insight into the underlying mechanisms that determine structure–property relationships. The implementation of causal discovery in feature selection could potentially inform the biofuel design process while also improving model prediction accuracy and robustness to new data. In this study, we investigate the benefits causal-based feature selection might have on both model performance and identification of key molecular substructures. We found that causal-based feature selection performed on par with alternative filtration methods, and that a structural causal model provides valuable scientific insights into the relationships between molecular substructures and fuel properties.
In the last 20 years, biodiesel consumption in the United States has rapidly increased to ∼2 billion gallons per year as a renewable supplement to fossil fuel. However, further expansion of biodiesel use is currently limited in part by poor cold weather performance, which prevents year-round blending and necessitates blend walls ≤5% v/v. In order to provide a diesel fuel blendstock with improved cold weather performance (cloud point, pour point, and cold filter plug point), while at the same time maintaining other required fuel performance specifications, several biodiesel redox analogues were synthesized and tested. The best performing candidate fuels from this class showed improvement in the derived cetane number (29.3% shorter ignition delay), lower heating value (+4.7 MJ/kg), relative sooting tendency (-7.4 YSI/MJ), and cloud point (15 °C lower) when compared to a B100 biodiesel composed of an identical fatty acid profile. It was observed as a general trend that the reduced form of biodiesel, fatty alkyl ethers (FAEs), shows performance improvements in all fuel property metrics. The suite of improved properties provided by FAEs gives biodiesel producers the opportunity to diversify their portfolio of products derived from lipid and alcohol feedstocks to include long-chain alkyl ethers, a biodiesel alternative with particular applicability for winter weather conditions across the US.
We demonstrated production of a superior performance biodiesel referred to here as fatty acid fusel alcohol esters (FAFE) – by reacting fusel alcohols (isobutanol, 3-methyl-1-butanol, and (S)-(-)-2-methyl-1-butanol) with oil (glyceryl trioleate) using lipase from Aspergillus oryzae. Reaction conditions corresponding to a molar ratio of 5:1 (fusel alcohols to oil), enzyme loading of 2% w/w, reaction temperature of 35 °C, shaking speed of 250 rpm, and reaction time of 24 h achieved >97% conversion to FAFE. Further, FAFE obtained from reacting a fusel alcohol mixture with corn oil were evaluated for use as a fuel for diesel engines. FAFE mixtures showed superior combustion and cold-flow properties, with the derived cetane numbers up to 4.8 points higher, cloud points up to -6 °C lower, and the heat of combustion up to 2.1% higher than the corresponding FAME samples, depending on the fusel mixture used. This represents a significant improvement for all three metrics, which are typically anti-correlated. Finally, FAFE provides a new opportunity for expanded usage of biodiesel by addressing feedstock limitations, fuel performance, and low temperature tolerance.
Background: The efficient biological production of industrially and economically important compounds is a challenging problem. Brute-force determination of the optimal pathways to efficient production of a target chemical in a chassis organism is computationally intractable. Many current methods provide a single solution to this problem, but fail to provide all optimal pathways, optional sub-optimal solutions or hybrid biological/non-biological solutions. Results: Here we present RetSynth, software with a novel algorithm for determining all optimal biological pathways given a starting biological chassis and target chemical. By dynamically selecting constraints, the number of potential pathways scales by the number of fully independent pathways and not by the number of overall reactions or size of the metabolic network. This feature allows all optimal pathways to be determined for a large number of chemicals and for a large corpus of potential chassis organisms. Additionally, this software contains other features including the ability to collect data from metabolic repositories, perform flux balance analysis, and to view optimal pathways identified by our algorithm using a built-in visualization module. This software also identifies sub-optimal pathways and allows incorporation of non-biological chemical reactions, which may be performed after metabolic production of precursor molecules. Conclusions: The novel algorithm designed for RetSynth streamlines an arduous and complex process in metabolic engineering. Our stand-alone software allows the identification of candidate optimal and additional sub-optimal pathways, and provides the user with necessary ranking criteria such as target yield to decide which route to select for target production. Furthermore, the ability to incorporate non-biological reactions into the final steps allows determination of pathways to production for targets that cannot be solely produced biologically. With this comprehensive suite of features RetSynth exceeds any open-source software or webservice currently available for identifying optimal pathways for target production.