Adjoint optimization for the general rate model of liquid chromatography

Osterroth, Sebastian; Menstell, Peter; Schwämmle, Achim; Ohser, Joachim; Steiner, Klaus

doi:10.1016/j.compchemeng.2019.106657

Cited by 6 publications

(1 citation statement)

References 45 publications

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“…Optimization problems can be solved in many ways (e.g., genetic algorithm) 4,32,33 . However, they can be classified into two main categories, derivative‐free and derivative‐based optimization 34,35 . The solver choice is based on the structure and complexity of the problem, such as linearity or non‐linearity of the cost function and constraints.…”

Section: Model Predictive Controlmentioning

confidence: 99%

Control strategy for biopharmaceutical production by model predictive control

Eslami,

Jungbauer

2024

Biotechnology Progress

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The biopharmaceutical industry is rapidly advancing, driven by the need for cutting‐edge technologies to meet the growing demand for life‐saving treatments. In this context, Model Predictive Control (MPC) has emerged as a promising solution to address the complexity of modern biopharmaceutical production processes. Its ability to optimize operations and ensure consistent product yields has made it an attractive option for manufacturers in this sector. Furthermore, MPC's alignment with the Process Analytical Technology (PAT) initiative provides an additional layer of assurance, facilitating real‐time monitoring and enabling swift adjustments to maintain process integrity. This comprehensive review delves into the various applications of MPC, ranging from robust control to stochastic model predictive control, thereby equipping biotechnologists and process engineers with a powerful toolset. By harnessing the capabilities of MPC, as elucidated in this review, manufacturers can confidently navigate the intricate bioprocessing landscape and unlock this approach's full potential in their production processes.

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Section: Model Predictive Controlmentioning

confidence: 99%