A methodology for screening of microalgae as a decision making tool for energy and green chemical process applications

The increasing interest for biotechnological use of microalgae demands a methodology for selection of species suitable to support the development of technologies based on the use of such non-conventional renewable raw material, i.e., green industrial applications. The vast and expanding collection of experimental data on both cell growth and biomass composition available in the literature can be used to reduce the cost of the experimental investigations required to support process engineering and optimization. Selecting the appropriate organism requires extracting useful information from such data, a cumbersome task since various multidisciplinary factors must be considered. This paper presents a computer-aided methodology for selecting appropriate algal species given an energy or green chemical process application employing microalgae as a renewable raw material. The approach is “system oriented”, based on biomass composition and chemical processing of the biomass downstream of the CO₂ biofixation and harvesting operations. Quantitative performance results are supported by professional process simulation. Besides comparison of a set of species performances, the proposed methodology also allows the discrimination among distinct algal compositions resulting from different growth conditions for a given species. Furthermore, three categories of screening metrics are proposed to be maximized by the decision making procedure in order to elicit the relevant information. To demonstrate the potential of the proposed methodology, a databank of both biochemical and elemental compositions of microalgal biomass was used in three green applications: Assessment of biomass heating value; production of syngas by gasification of the biomass; and production of Bio-H₂. Within the accuracy of the databank employed to illustrate the procedure, the methodology selected Botryococcus braunii and Isochrysis galbana as potential promising candidates, for the three examined applications.