CFD Predictive Models for Biopharma Mixing Protocol Development
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional methods for developing mixing protocols in biopharmaceutical production are time and labor intensive, often resulting in inferior protocols and failing to address factors like air-liquid interfacial stress, air entrainment, and the risk of visible or sub-visible particle formation, leading to excessive shear stress and costly full-scale investigations.
Innovation Solution
The development of predictive models using computational fluid dynamics (CFD) simulations to evaluate mixing protocols, identifying key parameters such as impeller speed, batch size, solution viscosity, and vessel geometry, and ranking candidate models based on R2 values and complexity to optimize mixing conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional methods are used to develop mixing protocols, then the protocols can be developed through traditional experimentation, but the process is time and labor intensive and results in inferior protocols
Solution Approach 1:
The patent performs preliminary CFD simulations to evaluate multiple mixing protocol candidates before full-scale implementation. By conducting virtual assessments of flow patterns, shear stress, and mixing efficiency for various impeller speeds and configurations, the system identifies optimal protocols in advance, avoiding time-consuming trial-and-error experimentation while ensuring high-quality results
Solution Approach 2:
The patent creates virtual copies of the mixing system through CFD computational models that replicate physical mixing vessel geometry, fluid properties, and operating conditions. These digital twins allow comprehensive evaluation of mixing protocols without requiring physical prototypes or extensive laboratory testing, dramatically reducing development time while maintaining protocol quality
2Object-affected harmful factors
If conventional methods are used to develop mixing protocols, then traditional experimentation can be performed, but air-liquid interfacial stress, air entrainment, and particle formation risks are not adequately addressed
Solution Approach 1:
The patent segments the evaluation of harmful factors into distinct CFD analysis components: air-liquid interfacial stress calculation, air entrainment detection, and particle formation risk assessment. By dividing the complex evaluation into separate computational modules, each harmful factor can be independently quantified and optimized, enhancing evaluation capability without overwhelming system complexity
Solution Approach 2:
The patent introduces CFD simulation as an intermediary tool between protocol design and physical implementation. This computational mediator calculates and visualizes harmful factors like air-liquid interfacial stress and air entrainment that are difficult to measure directly in physical experiments, enabling comprehensive assessment of previously undetectable parameters
3Object-generated harmful factors
If conventional methods are used to develop mixing protocols, then traditional testing can be performed, but excessive shear stress occurs and costly full-scale investigations are required
Solution Approach 1:
The patent performs preliminary CFD-based shear stress analysis to identify and eliminate excessive shear conditions before physical manufacturing or full-scale testing. By calculating shear stress distributions across different mixing protocols in advance, the system optimizes impeller speed and configuration to minimize harmful shear effects, avoiding costly failures in later stages
Solution Approach 2:
The patent uses CFD computational models as virtual copies to simulate and evaluate shear stress effects without requiring expensive physical testing. These digital models replicate fluid dynamics and shear stress conditions, enabling comprehensive assessment of multiple protocols at low cost before committing to expensive full-scale investigations
4Productivity
If CFD simulations are used to evaluate mixing protocols, then high-throughput evaluation can be performed, but computational resources and model complexity increase
Solution Approach 1:
The patent segments the CFD evaluation process into standardized modular steps: geometry setup, boundary conditions, solver configuration, and result analysis. By creating reusable templates for common mixing vessel geometries and operating conditions, the system maintains high throughput while managing model complexity through systematic organization
Solution Approach 2:
The patent systematically varies key parameters like impeller speed, batch size, and solution viscosity across multiple CFD simulations to evaluate their impact on mixing performance. By identifying and focusing on the most influential parameters, the system achieves high-throughput evaluation of protocol variations without requiring equally complex models for every possible parameter combination
Data Source
AI summary
A method of developing a predictive model may include identifying mixing protocol parameters for the predictive model, identifying an evaluation criterion for the predictive model, selecting test values for the mixing protocol parameters, identifying a computational fluid dynamics (CFD) simulation required to be performed in order to generate the evaluation criteria, conducting the CFD simulation for each combination of test values, thereby generating evaluation criteria corresponding to each combination of test values, generating a domain of potential predictive models relating the mixing protocol parameters to the evaluation criterion, identifying a pool of candidate predictive models from the domain of potential predictive models, and ranking the pool of candidate predictive models.