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Open AccessFeature PaperArticle

Model-Based Process Optimization for the Production of Macrolactin D by Paenibacillus polymyxa

Technische Universität Berlin, Faculty III Process Science, Chair of Measurement and Control, D-10623 Berlin, Germany
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Processes 2020, 8(7), 752; https://doi.org/10.3390/pr8070752
Received: 4 June 2020 / Revised: 22 June 2020 / Accepted: 24 June 2020 / Published: 28 June 2020
(This article belongs to the Special Issue Fermentation Optimization and Modeling)
In this study, we show the successful application of different model-based approaches for the maximizing of macrolactin D production by Paenibacillus polymyxa. After four initial cultivations, a family of nonlinear dynamic biological models was determined automatically and ranked by their respective Akaike Information Criterion (AIC). The best models were then used in a multi-model setup for robust product maximization. The experimental validation shows the highest product yield attained compared with the identification runs so far. In subsequent fermentations, the online measurements of CO 2 concentration, base consumption, and near-infrared spectroscopy (NIR) were used for model improvement. After model extension using expert knowledge, a single superior model could be identified. Model-based state estimation with a sigma-point Kalman filter (SPKF) was based on online measurement data, and this improved model enabled nonlinear real-time product maximization. The optimization increased the macrolactin D production even further by 28% compared with the initial robust multi-model offline optimization.
Keywords: online optimization; fermentation; NIR spectroscopy; nonlinear state estimation; multi-model approach online optimization; fermentation; NIR spectroscopy; nonlinear state estimation; multi-model approach
MDPI and ACS Style

Krämer, D.; Wilms, T.; King, R. Model-Based Process Optimization for the Production of Macrolactin D by Paenibacillus polymyxa. Processes 2020, 8, 752.

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