(54d) Performance of Fed-Batch Relative to Batch Processes in Unsteady State Fermentation and in situ Gas Stripping Simulations Under Different Conditions

The production of biofuels and chemicals from lignocellulosic biomass on the fermentation platform is considered a sustainable energy alternative to fossil fuels mainly because of the mitigation of environmental impacts and energy independence. Butanol and ethanol have properties that make them excellent liquid transportation fuel alternatives. Fermentation is frequently operated as a batch or semi-batch process. The batch fermentation using microorganisms is characterized by low reactor productivities, yield and total solvent produced because of product toxicity and substrate inhibition to the microorganisms. The integrated fed-batch fermentation and in situ gas stripping circumvents substrate inhibition by using a substrate feeding strategy that utilizes a lower initial substrate concentration at the beginning of fermentation. As fermentation proceeds and the substrate is used up, fresh substrate is added at concentrations that sustains microbial growth and product formation, and below substrate concentrations inhibitory to the microorganisms. Furthermore, product inhibition is alleviated by removing solvents as they are produced to prevent the build-up of solvents to inhibitory concentrations.

In this study, the batch reactor, RBatch block, in Aspen Plus is linked to a Fortran user kinetics subroutine to simulate an unsteady state fed-batch fermentation process. A fed-batch and in situ gas stripping process will be simulated by continuously feeding nitrogen gas to an RBatch block. With this simulation approach, the advantages of the integrated fed-batch and in situ gas stripping to improve the productivity, yield and total solvent production by reducing substrate and product inhibitions relative to a batch fermentation will be highlighted under different conditions. An important feature of the procedure developed in this study will be to show that the trends predicted from the model are consistent with general trends and microbial growth kinetics observed in fermentation systems that use microorganisms in the face of high substrate and product inhibitions. This study provides a platform that can be used to as guide to the fermentation experimentalist and assess the accuracy of literature data. Additionally, a universally accepted process simulator, Aspen Plus, can be used in the simulation of fermentation processes linked with separation unit operations to provide strategies for the design and optimization of bioprocesses.