Modeling the Production of Microalgal Biodiesel

Biodiesel produced from microalgal lipids is being extensively researched as an alternative to petroleum-derived diesel. Literature reports of prior modeling to estimate the likely energy and carbon footprints and manufacturing cost of microalgal biodiesel are inconclusive, with wide ranges for performance measures such as Net Energy Return and manufacturing cost. The goals of this research are to develop an integrated techno-economic life cycle inventory model of microalgal biodiesel production, create a base case that simulates proven manufacturing processes, identify potential barriers to technical, financial, and/or environmental viability, perform sensitivity analyses that identify the input parameters and modeling assumptions that have significant influence on key biodiesel performance indicators (KPI's), and perform case studies involving alternative microalgal properties, manufacturing processes and operating conditions, modeling time-scales, and geographic locations.;The model created to meet these objectives, called "TELCIM", is the first publicly available, integrated techno-economic life cycle inventory model of microalgal biodiesel manufacture. TELCIM was initially populated with data representing conventional microalgae cultivation and harvesting and vegetable oil extraction and conversion technologies deployed in a southern California location. The Net Energy Return for this case is below 1.0, the minimum threshold for long-term sustainability; the carbon intensity is similar to that of petrodiesel; and the manufacturing cost is uncompetitive with current transportation fuels. Detailed breakdowns show the contributions of each major process step and use category to these performance metrics, allowing identification of the major barriers to viability. Among the biggest obstacles is the large amount of energy used to dry the biomass to the 10% residual moisture content required by the conventional oilseed extraction process.;Two types of single-parameter sensitivity analyses are used to identify input parameters that have significant influence on the KPI's. Tornado plots reveal that biological properties including lipid fraction, intracellular water content, and growth rate are among the most influential with respect to one or more of the KPI's. Trend analyses of several of the anaerobic digester operating parameters show that factors affecting biogas production have much stronger influence on the KPI's than factors relating to nutrient recovery. Several variants to the base case were performed, including cases in which the microalga's growth rate and lipid content conform to R&D targets established by the National Alliance for Advanced Biofuels and Bioproducts; a hypothetical wet extraction process allows the drying step to be bypassed; and there is no anaerobic digestion step. The results from these cases suggest that one or more breakthroughs in cell biology and/or process engineering are necessary to make microalgal biodiesel a sustainable large-scale alternative to petroleum diesel. They also show that including anaerobic digestion in the manufacturing scheme improves the KPI's of greatest interest, and delivers an acceptable financial return on incremental investment.;TELCIM is a steady-state model, and the climatological data used in the base case represents annual average conditions. The effects of monthly variations in sunlight intensity are simulated under several facility design bases and operating strategies; it appears that designing for maximum sunlight intensity is more cost and energy effective, but necessitates underutilization of available carbon dioxide and reduces the biodiesel production rate. Enhancing the correlation between sunlight intensity and microalgal growth rate to account for the effect of light saturation on photosynthetic efficiency significantly dampens seasonal variations in biomass productivity. Analysis of hourly average versus daily average sunlight intensity indicates that daily average data is sufficiently precise if the long-term average biomass productivity is known; otherwise substantial error can be introduced if biomass productivity is estimated directly from sunlight intensity data, and daily average data is used instead of hourly average data. Five alternative manufacturing locations along the southern rim of the continental United States are simulated using local climatological inputs, including sunlight intensity. The only performance measure that differs significantly among these sites is water intensity, which is predicted to be much lower east of the Rockies due to higher precipitation rates. (Abstract shortened by UMI.).