(494d) Keynote: Multi-Objective Optimization, State Estimation, and Advanced Control of a Semi-Batch Process for the Enzymatic Conversion of Lactose into Value-Added Products | AIChE

(494d) Keynote: Multi-Objective Optimization, State Estimation, and Advanced Control of a Semi-Batch Process for the Enzymatic Conversion of Lactose into Value-Added Products

Authors 

Schultz, G., UFSCar
Ribeiro, M. P. A., Federal University of São Carlos
Lima, F. V., West Virginia University
Dinh, S., West Virginia University
Some of the main challenges experienced when scaling a laboratory enzymatic process to commercial scale include achieving optimal operations, performing online monitoring, and ensuring process controllability. Often times, laboratory-scale enzymatic processes correspond to simply proof of concept experiments that are carried out to characterize the reaction phenomena and develop preliminary process models. For example, in laboratory settings, ideal process conditions, such as negligible spatial variation in concentration, temperature or pH are much more achievable than at industrial scales[1]. As a result, when applying these models to industrial scales, a higher level of uncertainty and error is expected when compared to laboratory scale. Additionally, when developing these models, highly specific offline analytical methods are employed and relied upon due to their increased precision and accuracy over traditional online monitoring methods. However, in many industrial processes, soft sensors or estimation methods must be used online to infer process states from measurements of variables such as the oxygen uptake rate or carbon dioxide production rate. When using these methods, an additional level of uncertainty is introduced due to reliance on calibration ranges and model accuracy[2]. Thus, considering all these factors, it is often challenging to predict if laboratory scale processes can be feasibility scaled up. In this work, a process optimization, state estimation, and advanced control framework is introduced to determine the feasible scaled-up operations of a laboratory enzymatic process, considering a new enzymatic model for the conversion of waste lactose into a series of value-added products known as galactooligosaccharides (GOS).

The enzymatic model used in this work utilizes β-galactosidase to convert lactose into GOS products while distinguishing purely galactose derived di-, tri-, and tetrasaccharides from those containing a glucose group[3]. Due to the structural similarities of these isomers, online monitoring techniques are unable to isolate and uniquely distinguish the specific isomer concentrations. Instead, such techniques are only able to measure the combined concentrations of the isomers and provide one total concentration, thus posing a more challenging monitoring problem. Additionally, this specific model was chosen as it is currently at laboratory scale and could potentially be scaled to a commercial level to address the lactose waste problem plaguing small-scale cheese manufacturers[4].

Due to the many process uncertainties, such as raw feed cost and enzyme cost in this preliminary stage, conducting a robust and accurate economic analysis is challenging and would likely exhibit a wide distribution of economics, ultimately providing unreliable results. Instead, to examine the feasibility of scaling up this process, a multi-objective optimization is carried out considering 2 main factors: maximizing lactose conversion and GOS production rate as these will have the greatest effect on the process economics. Using the results of this multi-objective optimization, a trajectory-based model predictive controller (MPC) is implemented to guarantee that the process adheres to the optimum profile and is able to respond to simulated disturbances that are typical of industrial processes. Finally, a Moving Horizon Estimator (MHE)[5] is implemented to cope with the noise from the online measuring techniques and to decouple the combined isomer concentrations, providing insight into the specific compound concentrations in the reactor for optimal operations and control.

References

[1] Villadsen, J., Nielsen, J., Lidén, G. (2011). Bioreaction Engineering Principles, 3. Springer, Boston, MA.

[2] Stanke, M., Hitzmann, B. (2013). Automatic Control of Bioprocesses. In: Measurement, Monitoring, Modelling and Control of Bioprocesses, Advances in Biochemical Engineering/Biotechnology, 1. Springer, Berlin, Heidelberg.

[3] Schultz, G., Alexander, R., Lima, F. V., Giordano, R. C., Ribeiro, M. P. A. (2021). Kinetic Modeling of the Enzymatic Synthesis of Galacto-Oligosaccharides: Describing Galactobiose Formation. Food and Bioproducts Processing, 127, 1-13.

[4] Illanes, A. (2011). Whey Upgrading by Enzyme Biocatalysis. Electronic Journal of Biotechnology. 14.

[5] Alexander, R., Campani, G., Dinh, S., Lima, F.V. (2020). Challenges and Opportunities on Nonlinear State Estimation of Chemical and Biochemical Processes. Processes, 8, 1462.