Practical Teaching of Modeling Tools for Ion-Exchange Chromatography: A Case Study

Abstract

The utilization of modeling tools has gained significant attention recently. These models typically involve a series of partial differential equations, which can be challenging for novice modelers. This paper presented an approach in practical teaching of modeling tools for ion-exchange chromatography with three parts: model introduction, model calibration through group learning using different calibration strategies, and model applications. This approach was integrated into the traditional bioseparation engineering curriculum as an activity, using the separation of monomer–dimer mixtures of monoclonal antibodies as an example. Results of competitive group learning and the Wilcoxon test revealed that the parameter-by-parameter method was more user-friendly than the Yamamoto method for novice modelers to obtain reasonable model parameters quickly. Then, students used the well-fitted model for process optimization and explored the effects of process parameters and material input variation on the chromatography process, which helped students appreciate the critical role of time and material savings achieved through modeling tools. Finally, the student questionnaire results revealed that over two-thirds of the students gave positive feedback on the activity. Through this practical teaching, students became familiar with chromatography modeling tools, moving away from the tedious formulaic descriptions found in traditional modeling courses. This well-designed activity can be expanded from academia to industry, transforming the novice modelers into experienced modelers who can meet the high demands of the modern biopharmaceutical industry.

Standardized approach for accurate and reliable model development of ion‐exchange chromatography based on parameter‐by‐parameter method and consideration of extra‐column effects

“…Evidently, this divergence aroused because the SLA method, which has an in-depth physical understanding of the SMA model, was employed to calculate σ. Conversely, the employment of a non-physically meaningful approach like the IM might not reveal this phenomenon. [35] Thus, the accuracy and interpretability of the SLA method for estimating σ were superior to those of the IM. Nevertheless, it remained uncertain whether the same molecular weight or equal contribution, is a better interpretation.…”

“…These calculations can be readily calculated by novice modelers and easily implemented by Excel spreadsheets. [35] The concept of PbP calibration has been extended from the SMA model in the IEC field to the field of hydrophobic interaction chromatography. [36] Nevertheless, the PbP method has more stringent requirements for experimental design than other methods (such as gradient lengths and loading of elution experiments), posing a challenge for its applications.…”

Exploration and Practice of Online–Offline Blended Teaching in Process Simulation Courses

“…Process simulation stands as one of the important cores of chemical engineering, offering users a virtual playground to explore and comprehend the complexities of real-world industrial processes. − In recent years, much attention has been paid to practical and hands-on learning experiences with these process simulation tools. − However, traditional chemical education has predominantly relied on theoretical instruction, often lacking a tangible connection to the practical applications of their studies. Process simulation can bridge this gap by immersing students in a virtual environment that mirrors the challenges and decision-making processes they will encounter in their professional careers. − This not only enhances their grasp of theoretical principles but also cultivates problem-solving skills essential for success.…”