Sensor Selection and State Estimation of Continuous mAb Production Processes

Sandra A. Obiri, Bernard T. Agyeman, Sarupa Debnath, Siyu Liu and Jinfeng Liu ()
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Sandra A. Obiri: Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada
Bernard T. Agyeman: Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada
Sarupa Debnath: Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada
Siyu Liu: Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada
Jinfeng Liu: Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada

Mathematics, 2023, vol. 11, issue 18, 1-20

Abstract: The production of monoclonal antibodies ( mAbs ) plays a pivotal role in therapeutic treatments, and optimizing their production is crucial for minimizing costs and improving their accessibility to patients. One way of improving the production process is to improve model accuracy through the correct estimation of its states and parameters. The contributions of this paper lie in the provision of guidelines for sensor selection in the upstream production process of mAbs to enhance the accuracy of state estimation. Furthermore, this paper applies an effective variable selection technique for simultaneous state and parameter estimations for enhanced estimation results in the biomanufacturing processes of mAbs . An estimation framework of MHE is designed for three different case studies to demonstrate the efficiency of the proposed approach. The estimation performance is compared and assessed using the Root Mean Squared Error ( RMSE ) as an evaluation criterion.

Keywords: monoclonal antibodies; moving horizon estimation; state estimation; parameter estimation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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