Applications of Polynomial Chaos Expansions in optimization and control of bioreactors based on dynamic metabolic flux balance models. (10th August 2017)

• Record Type: Journal Article
• Authors: Kumar, Divya; Budman, Hector
• Is Part Of: Chemical engineering science. Volume 167(2017)
• Page Start: 18
• Page End: 28
• Publication Date: 2017-08-10
• Subjects: Dynamic Flux Balance Model -- Fed-batch bioreactor -- Robust control -- Robust optimization -- Model predictive control -- Polynomial Chaos Expansions; Chemical engineering -- Periodicals; Génie chimique -- Périodiques; Chemical engineering; Periodicals; Electronic journals; 660
• DOI: 10.1016/j.ces.2017.03.035
• Languages: English
• ISSNs: 0009-2509

Abstract

Highlights: A new model-based online robust control methodology maximizing the end point property of a fed-batch bioreactor is proposed. A new model-based offline robust optimization methodology for fed-batch bioreactor is proposed. Both the algorithms use Polynomials Chaos Expansions for uncertainty propagation and show superior performance than nominal counterparts. Abstract: This work proposes model based approaches for on-line or off-line economic optimization of batch reactors in the presence of model error (uncertainty). Polynomial Chaos Expansions are used as an effective and computationally efficient tool to propagate the error in model parameters into the optimizations' cost functions. The computational efficiency of the proposed uncertainty propagation approach is essential for the implementation of the on-line approach that takes into account feedback corrections. The role of feedback, as applied in the on-line formulation, is proved to be instrumental for reducing conservatism of the optimization results. The proposed approaches can serve to design recipes for maximizing productivity in batch, fed-batch or perfusion operation of bioreactors.