Modeling and Nonlinear State Estimation for Advanced Process Control of the Enzymatic Conversion of Lactose into Value-Added Products
- Type: Conference Presentation
- Conference Type: AIChE Annual Meeting
- Presentation Date: November 19, 2020
- Duration: 15 minutes
- Skill Level: Intermediate
- PDHs: 0.30
A recent advance in enzymatic modeling corresponds to a new kinetic model to convert lactose waste into value-added galactooligosaccharide (GOS) products using Î²-galactosidase . This specific reaction has been studied by several researchers in the past, but each proposed model presented challenges such as mass balance issues and overfitting due to the excessive number of parameters . The proposed kinetic model contains no mass balance errors, while minimizing the number of parameters to accurately depict the process characteristics. Furthermore, this model uniquely distinguishes between purely galactose derived disaccharides, trisaccharides, and tetrasaccharides and those containing a glucose saccharide group. Due to the similarity of glucose and galactose, there are no online measuring techniques that can discern between the specific di, tri, or tetrasaccharides stereoisomers. A High Performance Liquid Chromatography (HPLC) method coupled with an ion-exchange column and refractive index detector can distinguish between glucose and galactose, but has no resolution for other isomers such as disaccharide, trisaccharide, and tetrasaccharide stereoisomers. Without the ability to measure specific component concentrations, state estimation must be used to predict the individual states using the online measurements and the proposed kinetic model.
This work addresses the state estimation and advanced control employing the developed enzymatic model for the first time. Unique estimation challenges are posed and addressed, with the specific novelty of using nonlinear state estimation to find stereoisomer concentrations from available online measurements and the nonlinear kinetic model. Both an Extended Kalman Filter (EKF) and a Moving Horizon Estimator (MHE) are analyzed for process implementation. The EKF is an unconstrained estimator that allows for rapid estimation but does not guarantee feasible and optimal results. The MHE is a constrained estimator that guarantees feasibility at the cost of increased computational time  . Using these estimation techniques, a Biologically-Inspired Optimal Control Strategy (BIO-CS) algorithm is applied to optimize the feeding of lactose and enzyme into a semi-batch reactor . The proposed estimation and advanced control framework allows the feasibility of this process to be examined and provides a guide for other bioprocesses to follow.
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|AIChE Member Credits||0.5|
|AIChE Graduate Student Members||Free|
|AIChE Undergraduate Student Members||Free|