Abstract
A successful industrial application of a novel type biomass estimator based on Genetic Programming (GP) is described in the paper. The biomass is inferred from other available measurements via an ensemble of nonlinear functions, generated by GP. The models are selected on the Pareto front of performance-complexity plane. The advantages of the proposed inferential sensor are: direct implementation into almost any process control system, rudimentary self-assessment capabilities, better robustness toward batch variations, and more effective maintenance. The biomass inferential sensor has been applied in high cell density microbial fermentations at The Dow Chemical Company.
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Kordon, A. et al. (2004). Biomass Inferential Sensor Based on Ensemble of Models Generated by Genetic Programming. In: Deb, K. (eds) Genetic and Evolutionary Computation – GECCO 2004. GECCO 2004. Lecture Notes in Computer Science, vol 3103. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24855-2_118
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DOI: https://doi.org/10.1007/978-3-540-24855-2_118
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