(184o) Risk-Based Quality Control for the Safe and Efficient Biotechnological Batch Production of Lactic Acid

Process control plays a critical role in pharmaceutical fed-batch and batch bioreactors to ensure safe and high-quality products [1-2]. More than 90% of the bioprocessing industry still relies on conventional error-reactive single-input single-output (SISO) control techniques, which may identify sub-optimal control actions given the complex system dynamics under potential disturbances in pH, temperature, nutrient intake, etc. [3-4] Advanced model-based control is thus essential to enhance the operations in these bioprocesses, such as model predictive control (MPC) which leverages repetitive online dynamic optimization to determine the optimal control actions [5-6]. However, key research questions remain on: (i) How to simultaneously account for process control and batch-end quality control? (ii) How to ensure the resulting optimal process operating pathway is safe? and (iii) How to address online computational efficiency?

In this work, we present a systematic approach to integrate risk-based control and end-point quality control for pharmaceutical processes using the fermentation production of lactic acid as a case study. We first discuss the data-driven surrogate modeling for the control of this process based on a verified high-fidelity model [7]. An explicit MPC problem is then formulated using the surrogate model to maintain the process risk within an acceptable level [8]. Herein, risk is defined based on the historical operating data of the process. For example, the temperatures or pH levels at which the substrate may begin to degrade, or the high pressures at which the reactor vessel may be damaged. These safety-critical parameters are holistically evaluated in real time quantified via a dynamic risk index [9]. A multi-level optimization problem is then formulated to incorporate both closed-loop risk control and end-point quality control which has been demonstrated on a chemical batch production process [10]. This optimization problem is recast as a multi-parametric problem and solved offline to obtain explicit solutions to the quality control problem. These solutions are tabulated as functions of measurable process parameters so that the optimal bioreactor control input can be quickly evaluated in real-time to ensure batch quality is met in a safe fashion. Moreover, online optimization can be replaced with function evaluation to significantly reduce computational load during actual operations. This approach is showcased to identify the safely optimal operating trajectory of the lactic acid production process while satisfying the end-batch quality target.

References

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