Projections on the profitability of the pharmaceutical industry predict a large amount of growth in the coming years. Stagnation over the last 20 years in product development has led to the search for new processing methods to improve profitability by reducing operating costs or improving process productivity. This work proposes a novel multifeed bioreactor system composed of independently controlled feeds for substrate(s) and media used that allows for the free manipulation of the bioreactor supply rate and substrate concentrations to maximize bioreactor productivity and substrate utilization while reducing operating costs. The optimal operation of the multiple feeds is determined a priori as the solution of a dynamic optimization problem using the kinetic models describing the time-variant bioreactor concentrations as constraints. This new bioreactor paradigm is exemplified through the intracellular production of beta-carotene using a three feed bioreactor consisting of separate glucose, ethanol and media feeds. The performance of a traditional bioreator with a single substrate feed is compared to that of a bioreactor with multiple feeds using glucose and/or ethanol as substrate options. Results show up to a 30% reduction in the productivity with the addition of multiple feeds, though all three systems show an improvement in productivity when compared to batch production. Additionally, the breakeven selling price of beta-carotene is shown to decrease by at least 30% for the multifeed bioreactor when compared to the single feed counterpart, demonstrating the ability of the multifeed reactor to reduce operating costs in bioreactor systems. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 33:902-912, 2017.
The measurement or estimation of bioprocess states is critical for process control and optimization applications. However, certain disturbances or unknown inputs can generate significant model−plant mismatch if not considered by the process models. To better estimate the process states in the presence of these disturbances or unknown inputs, a nonlinear unknown input observer is applied. Experimental studies of batch and fed-batch operations of a bioreactor were performed using a recombinant Saccharomyces cerevisiae to produce β-carotene. Previously developed kinetic models produce model−plant mismatch with changes to the initial conditions or operating mode of the bioreactor. The observer is applied to the bioreactor system to estimate the batch and fed-batch state variables. State estimates from the designed observer are compared to model predictions and experimental measurements. Results show improved state estimation over first-principles model predictions when applying the unknown input observer to the nonlinear dynamic process with unknown disturbances.
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