(448b) Model-Based Optimization of An Algal Bioreactor Coupled to An Anaerobic Digester

Since the early 1980s, several studies have shown that certain strains of microalgae can build up high concentrations of fatty acids (in the range 30-70%), from which oil can be extracted, processed and refined into biofuel [1]. Sialve et al. [2] argue that one of the key issues that must be tackled on the path to making algal-derived biodiesel feasible is the reuse of waste biomass and the recycling of nutrients, both to reduce feed costs and to abate nitrogen and phosphorus derived pollution. The current consensus on the best way to achieve this is to couple algal bioreactors and anaerobic digesters so that effluent organic nutrients are broken down to their inorganic forms and recycled, while organic carbon is converted to methane and collected; see figure below. In addition to treating CO₂-rich flue gas, this process configuration also gives the ability to treat wastewater feeds, thereby increasing its value.

The general objective of this work is to study such integrated systems. The idea is to use a model-based optimization methodology in order to gain knowledge leading to promising designs, operational strategies, and hopefully successful deployment.

To conduct the optimization, great care must be taken that the mathematical models provide an accurate description of the biological and physico-chemical processes in the system, especially regarding the anaerobic digestion of algal biomass. A half-year study of the anaerobic digestion of Chlorella vulgaris was performed at LBE-INRA, Narbonne (France), including daily readings of total and solution COD, VFA and ammonia concentrations along with pH and methane outflow rate. Using PCA-based analysis [3], it is found that the 4 reactions are required to describe >95% of the variance described by the experimental data. A four-part model containing the hydrolysis of insoluble matter, acidogenesis of carbohydrates and LCFAs, acidogenesis of proteins, and methanogenesis of VFAs is therefore considered, and its (yield and kinetic) parameters are identified using the available measurements [4].

In coupling an algal bioreactor to an anaerobic digester, the two units can no longer be considered to operate independently. Therefore, operational decisions, such as the feed rate of CO₂ gas and the recycling rate of nutrients to the algal bioreactor, must be taken at the same time as design decisions, such as the volumes of the algal and anaerobic digestion compartments. Both sets of variables are therefore decision variables in the optimization problem formulations. Several objective functions are investigated, namely (i) maximization of the algal biomass (as an indicator of fatty acid content), (ii) maximization of the production of methane in the anaerobic digester, and (iii) maximization of CO₂ capture. The resulting optimization problems can be solved efficiently and reliably using state-of-the-art numerical methods. However, because the mathematical models carry a great deal of uncertainty, post-optimal sensitivity analysis is systematically applied to determine the level of confidence that can be placed in the calculated optimal design and operation strategies. Such sensitivity analysis is also useful to figure out which part of the model should be refined in priority.

References

[1] Y. Chisti, "Biodiesel from microalgae." Biotechnology Advances, 25:294-306, 2007.

[2] B. Sialve, N. Bernet, and O. Bernard. "Anaerobic digestion of microalgae as a necessary step to make microalgal biodiesel sustainable." Biotechnology Advances, 27:409-416, 2009.

[3] O. Bernard, B. Chachuat, A. Helias, and J. Rodriguez "Can we assess the model complexity for a bioprocess: theory and example of the anaerobic digestion process." Water Science & Technology, 53(1):85-92, 2006.

[4] B. Chachuat, O. Bernard, J.-Ph. Steyer, "A two-step procedure for estimating the parameters in mass-balance-based bioprocess models." 6^th International Symposium on Systems Analysis and Integrated Assessment in Water Management (Watermatex'04), Beijing, China.