Software Sensors for Monitoring of Biopolymer Production

The topic of this paper is the design of software sensors for online monitoring of the biotechnological process of biopolymer production, in which biopolymers are accumulated in bacteria as an intracellular energy storage material. The proposed software sensors enabling on-line estimation of the biopolymer concentration represent in the case of these complicated bioprocesses an interesting alternative to the traditional off-line analytical techniques, which have only limited applicability for the real-time control of these bioprocesses. Due to the complexity of biochemical reactions, which make it difficult to create reasonably complex and therefore practically applicable first-principle mathematical models, a data-driven approach to the design of software sensors has been chosen in the presented study. Thus, regression methods were used in the design of the proposed software sensors, including multivariate statistical methods (PLS, PCR), which enabled the creation of software sensors using historical process data. The so obtained software sensors enable not only on-line estimation of the biopolymer concentration, but also the concentration of the cell biomass of the production bacterial culture. The model process for this study was fed-batch cultivation of the Pseudomonas putida KT2442 strain used for the production of medium-chain-length polyhydroxyalkanoates (mcl-PHAs).