Identifying and addressing critical improvements in biomass, bioproduct and biofuel productivity is a priority for the nascent algae-based bioeconomy. Economic and sustainability principles should guide these developing improvements and help to unravel the contentious water–food–energy–environment nexus that algae inhabit. Understanding the biochemistry of the storage carbon metabolism of algae to produce biofuels and bioproducts can bring to light the key barriers that currently limit the overall carbon efficiency and the photosynthetic efficiency, and ultimately guide productivity and commercial viability in the context of limiting resources. In the analysis reported here, we present different potential pathways for a conceptual algae biorefinery framework, with each pathway addressing one of the main identified barriers to future deployment. We highlight the molecular identification, in the form of an extensive literature review, of potential bioproducts that may be derived directly from both biomass and fractions produced through a conversion pathway, for three important commercially-relevant genera of algae, Scenedesmus, Chlorella and Nannochloropsis. We establish a relationship between each of the potential bioproducts, describe relevant conversion and extraction processes, and discuss market opportunities with values and sizes as they relate to commercial development of the products.

Algal biomass compositional analysis data form the basis of a large number of techno-economic process analysis models that are used to investigate and compare different processes in algal biofuels production. However, the analytical methods used to generate these data are far from standardized. This work investigated the applicability of common methods for rapid chemical analysis of biomass samples with respect to accuracy and precision. This study measured lipids, protein, carbohydrates, ash, and moisture of a single algal biomass sample at 3 institutions by 8 independent researchers over 12 separate workdays. Results show statistically significant differences in the results from a given analytical method among laboratories but not between analysts at individual laboratories, suggesting consistent training is a critical issue for empirical analytical methods. Significantly different results from multiple lipid and protein measurements were found to be due to different measurement chemistries. We identified a set of compositional analysis procedures that are in best agreement with data obtained by more advanced analytical procedures. The methods described here and used for the round robin experiment do not require specialized instrumentation, and with detailed analytical documentation, the differences between laboratories can be markedly reduced.

One of the major challenges associated with algal biofuels production in a biorefinery-type setting is improving biomass utilization in its entirety, increasing the process energetic yields and providing economically viable and scalable co-product concepts. We demonstrate the effectiveness of a novel, integrated technology based on moderate temperatures and low pH to convert the carbohydrates in wet algal biomass to soluble sugars for fermentation, while making lipids more accessible for downstream extraction and leaving a protein-enriched fraction behind. We studied the effect of harvest timing on the conversion yields, using two algal strains; Chlorella and Scenedesmus, generating biomass with distinctive compositional ratios of protein, carbohydrate, and lipids. We found that the late harvest Scenedesmus biomass had the maximum theoretical biofuel potential at 143 gasoline gallon equivalent (GGE) combined fuel yield per dry ton biomass, followed by late harvest Chlorella at 128 GGE per ton. Our experimental data show a clear difference between the two strains, as Scenedesmus was more successfully converted in this process with a demonstrated 97 GGE per ton. Our measurements indicated a release of >90% of the available glucose in the hydrolysate liquors and an extraction and recovery of up to 97% of the fatty acids from wet biomass. Techno-economic analysis for the combined product yields indicates that this process exhibits the potential to improve per-gallon fuel costs by up to 33% compared to a lipids-only process for one strain, Scenedesmus, grown to the mid-point harvest condition.

In the context of algal biofuels, lipids, or better aliphatic chains of the fatty acids, are perhaps the most important constituents of algal biomass. Accurate quantification of lipids and their respective fuel yield is crucial for comparison of algal strains and growth conditions and for process monitoring. As an alternative to traditional solvent-based lipid extraction procedures, we have developed a robust whole-biomass in situ transesterification procedure for quantification of algal lipids (as fatty acid methyl esters, FAMEs) that (a) can be carried out on a small scale (using 4–7 mg of biomass), (b) is applicable to a range of different species, (c) consists of a single-step reaction, (d) is robust over a range of different temperature and time combinations, and (e) tolerant to at least 50% water in the biomass. Unlike gravimetric lipid quantification, which can over- or underestimate the lipid content, whole biomass transesterification reflects the true potential fuel yield of algal biomass. We report here on the comparison of the yield of FAMEs by using different catalysts and catalyst combinations, with the acid catalyst HCl providing a consistently high level of conversion of fatty acids with a precision of 1.9% relative standard deviation. We investigate the influence of reaction time, temperature, and biomass water content on the measured FAME content and profile for 4 different samples of algae (replete and deplete Chlorella vulgaris, replete Phaeodactylum tricornutum, and replete Nannochloropsis sp.). We conclude by demonstrating a full mass balance closure of all fatty acids around a traditional lipid extraction process. Accurate quantification of different types of lipids in algal biomass using a comprehensive study of reaction parameters.

Methane-utilizing bacteria (methanotrophs) are capable of growth on methane and are attractive systems for bio-catalysis. However, the application of natural methanotrophic strains to large-scale production of value-added chemicals/biofuels requires a number of physiological and genetic alterations. An accurate metabolic model coupled with flux balance analysis can provide a solid interpretative framework for experimental data analyses and integration. A stoichiometric flux balance model of Methylomicrobium buryatense strain 5G(B1) was constructed and used for evaluating metabolic engineering strategies for biofuels and chemical production with a methanotrophic bacterium as the catalytic platform. The initial metabolic reconstruction was based on whole-genome predictions. Each metabolic step was manually verified, gapfilled, and modified in accordance with genome-wide expression data. The final model incorporates a total of 841 reactions (in 167 metabolic pathways). Of these, up to 400 reactions were recruited to produce 118 intracellular metabolites. The flux balance simulations suggest that only the transfer of electrons from methanol oxidation to methane oxidation steps can support measured growth and methane/oxygen consumption parameters, while the scenario employing NADH as a possible source of electrons for particulate methane monooxygenase cannot. Direct coupling between methane oxidation and methanol oxidation accounts for most of the membrane-associated methane monooxygenase activity. However the best fit to experimental results is achieved only after assuming that the efficiency of direct coupling depends on growth conditions and additional NADH input (about 0.1–0.2 mol of incremental NADH per one mol of methane oxidized). The additional input is proposed to cover loss of electrons through inefficiency and to sustain methane oxidation at perturbations or support uphill electron transfer. Finally, the model was used for testing the carbon conversion efficiency of different pathways for C1-utilization, including different variants of the ribulose monophosphate pathway and the serine cycle. We demonstrate that the metabolic model can provide an effective tool for predicting metabolic parameters for different nutrients and genetic perturbations, and as such, should be valuable for metabolic engineering of the central metabolism of M. buryatense strains.