(483g) Optimal Design of Microalgal Biomass Processing Network

Microalgae has the potential to serve as a promising feedstock for the production of biofuels and value chemicals due to its high quantity of natural lipids, proteins and carbohydrates. It grows fast and is a non-food feedstock. Economical significant production of carbon neutral biofuels from microalgal biomass has been touted as an alternative to dwindling reserves of fossil fuels. There are many processing pathways in hand to produce a plethora of end products form microalgal biomass under the concept of microalgal biorefineries. A possible way to advance is through a systematic and optimal biorefinery design.

In this study, we propose a design methodology for the superstructure-based optimization of microalgal biorefinery network. The proposed methodology has four steps; (1) problem definition, (2) superstructure development, (3) formulation of optimization model, and (4) determination of the optimal biorefinery network. The superstructure is developed to represents all potential alternatives in the processing network.  The core part of the design methodology is the development of optimization formulation which includes the mass balance constraints (must be satisfied at each processing stage) and objective functions. The selection of alternatives is represented by introducing binary variables. These binary variables are the main decision variables of the ensuing optimization, which determines the optimal processing network. The optimization model results in the formulation of mixed integer nonlinear programming (MINLP) problem. The solution to the optimization problem provides the optimal route for the synthesis of biofuels and value chemicals from microalgal biomass.

A specific case study to find the optimal processing network for the production of biodiesel from microalgal biomass is solved in order to demonstrate the applicability of the proposed approach. The proposed superstructure includes a number of major processing steps for the production of biodiesel from microalgal biomass, such as harvesting of microalgal biomass, pretreatments including drying and cell disruption of harvested biomass, lipid extraction, transesterification, and post-transesterfication purification. Each processing stage has several options/technological alternatives to perform the respective task. The MINLP model is implemented and solved in GAMS using a database built in Excel. The objective function for the optimization is chosen as the maximization of biodiesel yield. It also includes other objectives such as the maximization of gross operating margin and/or the minimization of waste products. Depending upon the different objective functions that have been chosen, different optimal processing routes have been determined.

This method is a systematic way for an optimal processing pathway and has the capacity to screen through all potential processing alternatives to locate the optimal processing network. Based on the obtained information, detailed process flowsheet can be synthesized for a more detailed economic evaluation. As future work, the modelling framework needs to be extended to accommodate choices of multiple products/pathways and further processing of waste streams into useful products.