Abstract
Bioprocesses are increasingly used for the production of high added value products. Microorganisms are used in bioprocesses to mediate or catalyze the necessary reactions. This makes bioprocesses highly nonlinear and the governing mechanisms are complex. These complex governing mechanisms can be modeled by a metabolic network that comprises all interactions within the cells of the microbial population present in the bioprocess. The current state of the art in bioprocess control is model predictive control based on the use of macroscopic models, solely accounting for substrate, biomass, and product mass balances. These macroscopic models do not account for the underlying mechanisms governing the observed process behavior. Consequently, opportunities are missed to fully exploit the available process knowledge to operate the process in a more sustainable manner. In this article, a procedure is presented for metabolic network-based model predictive control. This procedure uses a combined moving horizon-model predictive control strategy to monitor the flux state and optimize the bioprocess under study. A CSTR bioreactor model has been combined with a small-scale metabolic network to illustrate the performance of the presented procedure.
Highlights
Biochemical processes have gained attention in the past decades in producing high added value products
Microorganisms play an important role in biochemical processes as these mediate or catalyze the reactions that are required to produce the products of interest
The proposed combined moving horizon estimation (MHE)-model predictive control (MPC) strategy for metabolic network model-based predictive bioprocess control has been implemented on a continuous stirred tank (bio)reactor (CSTR) bioreactor using a lowcomplexity metabolic network
Summary
Biochemical processes have gained attention in the past decades in producing high added value products. Metabolic networks comprise all interactions within the cell and between cell and environment by modeling the metabolites (i.e., the chemical compounds produced, consumed, and interacted with by the microorganisms) as the nodes and the reactions or fluxes between these different chemical compounds as the edges of a network. This network can be summarized in a stoichiometric matrix S in which an element Sij corresponds with the stoichiometric coefficient of the i-th metabolite in the j-th reaction or flux [1,2]. The result of such a study is a highly complex metabolic network with often a myriad of reactions and metabolites
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