(440f) Optimal Design and Synthesis of Integrated Algal Biorefinery for Biofuel and Chemical Production Under Economic and Environmental Criteria
Recent progress in developing sustainable products is largely spurred by the growing concerns over global climate change and dependence on fossil-based products [1, 2]. Domestic biomass resources can substitute a significant portion of petroleum utilized to produce fuels and chemicals, thus creating jobs across the country in various economy sectors . Microalgae is a prospective biomass feedstock in terms of high area productivity, minimized competition with conventional agriculture, capability to grow in various water conditions, close to zero net carbon dioxide emissions and providing valuable co-products . However, an algal biorefinery based on current processing technologies cannot be commercialized unless the energy efficiency of harvesting and dewatering equipment is significantly improved [5-9]. Glycerol is a byproduct when the algal biomass is converted to biodiesel via transesterification reactions and the utilization of glycerol for chemical production has a positive economic impact on biodiesel production . Therefore, our goal is to develop the optimal process design of an integrated algal biorefinery to simultaneously produce biodiesel and glycerol-derived chemicals under both economic and environmental criteria.
In this work, a superstructure of an algal biorefinery with a number of technology alternatives is developed to simultaneously produce biodiesel and several glycerol-derived chemicals. The superstructure begins with an open-pond cultivation system, and then the mature algae product from the open pond is harvested by auto-flocculation and dissolved air flotation sequentially. Later, the algal biomass is concentrated by either pressure filtration or centrifugation. We employ a cell disruption technology from bead beating, microwaving, high pressure homogenization, and sonication, before lipid materials are separated by the solvent extraction system, where the extractant is selected from hexane, isopropanol/hexane, butanol, and supercritical carbon dioxide. Lipid materials are converted to biodiesel through four transesterification technologies, while the remnant is decomposed by anaerobic digestion and the resulting biogas is utilized to generate electricity or synthesize methanol consumed on site. Glycerol obtained from transesterification can be converted to hydrogen, propylene glycol, 1,3-dihydroxyacetone, butyl glycerol either, or poly-3-hydroxybutyrate. Hydrogen production from glycerol includes three alternatives: steam reforming, autothermal reforming, and aqueous phase reforming.
In order to optimize the superstructure and establish an optimal process, we propose a multi-objective mixed-integer nonlinear programming (MINLP) model including four types of constraints. Mass balance constraints describe the mass flow rates of each unit process. Integer variables are chosen to account for the selection of one technology among the alternative options . Energy balance constraints enforce the energy conservation of all equipment by calculating the enthalpy differences between inlet and outlet. The economic and environmental performance is evaluated following the life cycle optimization frame work that integrates the multi-objective superstructure optimization scheme with life cycle assessment (LCA) and techno-economic analysis of the algal biorefinery [5, 11-16]. The life cycle stages of this cradle-to-gate LCA include raw material acquisition, transportation, and production. The life cycle inventories extracted from the process concern both the direct and indirect emissions, which could be aggregated into global warming potential (GWP) to quantify the environment impact to the atmosphere. The model includes two objective functions, which are minimizing the unit biodiesel production cost and the unit GWP. Both objective functions are associated with one gallon gasoline equivalent of the biodiesel produced. The multi-objective MINLP problem is solved using a global optimization framework that integrates the parametric algorithm and branch-and-refine algorithm. The optimal results are plotted in a Pareto-optimal curve which reveals the tradeoffs between the economic and environmental objectives.
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