## 466270 Application of Model-Predictive Control to a Feeding Blending Unit

Monday, November 14, 2016
Grand Ballroom B (Hilton San Francisco Union Square)
Jakob Rehrl1,2, Julia Kruisz2, Stephan Sacher2, Martin Horn1, Johannes G. Khinast2,3 and Isabella Aigner2, (1)Institute of Automation and Control, Graz University of Technology, Graz, Austria, (2)Research Center Pharmaceutical Engineering GmbH, Graz, Austria, (3)Institute of Process and Particle Engineering, Graz University of Technology, Graz, Austria

Continuous pharmaceutical manufacturing is pursued by the FDA [1][2]. By continuously monitoring the process and by actively taking actions in case of deviating process parameters, the product quality will be improved while the production costs decrease. The proper operation of the continuous production line requires the development and implementation of sophisticated control strategies. On the one hand, the control strategy has to ensure that intermediates of bad quality are discharged. On the other hand, this missing material has to be compensated by means of adjusting the production speed of the upstream and/or downstream unit operation. This operation mode poses the requirement of adjusting the production speed of individual unit operations.

The paper at hand focuses on the design of a model predictive control (MPC) strategy [3][4] for a feeding blending unit (FBU), see Figure 1. The considered setup utilizes two feeders and a blender in order to create a blend of a given composition. The controller aims at tracking a prescribed blender outlet mass flow. Simultaneously, the mass hold-up in the blender, as well as the blender speed should remain within given bounds. A mathematical model of the plant, which is based on physical relations, is given. Model parameters have to be identified from measurement data. Based on the presented plant model, a model predictive control strategy is designed.

Figure 1: Considered setup

The presented plant model is based upon the idea of dividing the blender into several compartments. For each compartment, the mass balance equation is solved. The inlet mass flow of the compartments is known: for the first compartment, it is equal to the sum of the feeder mass flows, and for the following compartments, the inlet mass flow is equal to the outlet mass flow of the preceding one. The outlet mass flow of one compartment is computed from the hold-up in the compartment and the mean residence time of the material in this compartment. The mean residence time depends on the hold-up and on the blender speed. A simple relation, which relates the mean residence time to the blender speed is fitted from measured data. By considering the mass hold-ups of the individual compartments as state variables, a state space realization [5] of the plant is derived. The number of state variables equals the number of compartments.

Furthermore, the input/output behavior of the blender regarding concentration is modeled by means of a third order low pass filter with additional dead-time. The time constants and the dead time have to be identified from measurements.

A model predictive control strategy, which is based on the presented plant model, is proposed. The formulation of the objective function is discussed. Constraints on the state and the actuating signal are taken into account by the suggested control strategy. Several test scenarios, which investigate the fluctuations of feeder mass flows on the outlet of the blender are set up.

Simulation studies demonstrate the advantages of the proposed concept. The tuning of the controller is very intuitive, multi-input multi-output systems with constraints can be handled naturally.

[1] U.S. Department of Health and Human Services, “Guidance for Industry - PAT - A Framework for Innovative Pharmaceutical Development, Manufacturing, and Quality Assurance,” 2004.

[2] S. Chatterjee, “FDA Perspective on Continuous Manufacturing,” IFPAC Annual Meeting, 2012. [Online]. Available: http://www.fda.gov/downloads/AboutFDA/CentersOffices/OfficeofMedicalProductsandTobacco/CDER/UCM341197.pdf. [Accessed: 21-Mar-2016].

[3] J. B. Rawlings and D. Q. Mayne, Model Predictive Control: Theory and Design. Nob Hill Publishing, 2015.

[4] J. Maciejowski, Predictive Control with Constraints. Prentice Hall, 2001.

[5] B. Friedland, Control System Design: An Introduction to State-Space Methods. New York: McGraw-Hill, 1986.