Due to significant lipid and carbohydrate production as well as other useful properties such as high production of useful biomolecular substrates (e.g., lipids) and the ability to grow using non-potable water sources, algae are being explored as a potential high-yield feedstock for biofuels production. In both natural and engineered systems, algae can be exposed to a variety of environmental conditions that affect growth rate and cellular composition. With respect to the latter, the amount of carbon fixed in lipids and carbohydrates (e.g., starch) is highly influenced by environmental factors and nutrient availability. Understanding synergistic interactions between multiple environmental variables and nutritional factors is required to develop sustainable high productivity bioalgae systems, which are essential for commercial biofuel production. This article reviews the effects of environmental factors (i.e., temperature, light and pH) and nutrient availability (e.g., carbon, nitrogen, phosphorus, potassium, and trace metals) as well as cross-interactions on the biochemical composition of algae with a special focus on carbon fixation and partitioning of carbon from a biofuels perspective.

While advantages of biofuel have been widely reported, studies also highlight the challenges in large scale production of biofuel. Cost of ethanol and process energy use in cellulosic ethanol plants are dependent on technologies used for conversion of feedstock. Process modeling can aid in identifying techno-economic bottlenecks in a production process. A comprehensive techno-economic analysis was performed for conversion of cellulosic feedstock to ethanol using some of the common pretreatment technologies: dilute acid, dilute alkali, hot water and steam explosion. Detailed process models incorporating feedstock handling, pretreatment, simultaneous saccharification and co-fermentation, ethanol recovery and downstream processing were developed using SuperPro Designer. Tall Fescue (Festuca arundinacea Schreb) was used as a model feedstock.

During cellulosic ethanol production, cellulose hydrolysis is achieved by synergistic action of cellulase enzyme complex consisting of multiple enzymes with different mode of actions. Enzymatic hydrolysis of cellulose is one of the bottlenecks in the commercialization of the process due to low hydrolysis rates and high cost of enzymes. A robust hydrolysis model that can predict hydrolysis profile under various scenarios can act as an important forecasting tool to improve the hydrolysis process. However, multiple factors affecting hydrolysis: cellulose structure and complex enzyme-substrate interactions during hydrolysis make it diffucult to develop mathematical kinetic models that can simulate hydrolysis in presence of multiple enzymes with high fidelity. In this study, a comprehensive hydrolysis model based on stochastic molecular modeling approch in which each hydrolysis event is translated into a discrete event is presented. The model captures the structural features of cellulose, enzyme properties (mode of actions, synergism, inhibition), and most importantly dynamic morphological changes in the substrate that directly affect the enzyme-substrate interactions during hydrolysis.