An Introduction to Hybrid Modelling:
Concept and Workflow
How combining data-driven, kinetic, and CFD modelling creates the foundation for digital twins in bioprocess development.
Bioprocesses are inherently complex — governed simultaneously by biological kinetics, fluid dynamics, mass transfer, and data-rich process signals. No single modelling paradigm captures this complexity alone. Hybrid modelling integrates three complementary model types into a unified, predictive framework that is greater than the sum of its parts — and forms the backbone of modern bioprocess digital twins.
What Is Hybrid Modelling?
Hybrid modelling is the strategic integration of mechanistic (first-principles) knowledge with data-driven (empirical) learning. In bioprocessing, this means combining three distinct modelling disciplines — each contributing what the others cannot — to build models that are both physically interpretable and data-adaptive.
- Learns patterns from experimental and process data
- Enhances model accuracy and real-time predictions
- Enables adaptive control and optimization
- Handles complex, non-linear data relationships
- Understands core reaction and biochemical mechanisms
- Predicts conversion rates, yields, and rate-limiting steps
- Supports rational process design
- Provides mechanistic interpretability
- Captures fluid flow, mixing, and mass/heat transfer
- Resolves spatial gradients and reactor dynamics
- Aids in scale-up and equipment design
- Models impeller dynamics and gas dispersion
Hybrid vs Pure Mechanistic vs Pure Data-Driven
Understanding where hybrid modelling excels requires comparing it to the two pure-approach alternatives. Each has distinct strengths and weaknesses in a bioprocess context.
Built entirely from physical and biochemical laws. Highly interpretable and generalisable but requires complete mechanistic knowledge.
Learns directly from experimental data. Powerful pattern recognition but acts as a "black box" — poor extrapolation beyond training data.
Combines mechanistic structure with data-driven flexibility. Physically interpretable, generalisable beyond training data, and adaptive.
Hybrid Modelling Process Flow
The hybrid model operates as an integrated pipeline. Input process variables enter a data-driven model layer, which computes specific rates that feed into the kinetic model. CFD modelling runs in parallel, informing spatial and hydrodynamic boundary conditions. Together, they produce the output process variable.
Variable
Model
Modelling
Modelling
Variable
Where Hybrid Modelling Is Applied in Bioprocessing
CFD resolves mixing and mass transfer while kinetic/data-driven models predict cell behaviour across scales.
Data-driven layers process online sensor data (pH, DO) to update model states and enable predictive control.
The computational engine that runs in parallel with the real process, predicting outcomes in silico.
Identifies rate-limiting steps and quantifies factor impacts on CQAs for systematic optimisation.
Hybrid Modelling as the Foundation for Digital Twins
A digital twin is a living, continuously updated virtual replica of a physical bioprocess. The hybrid modelling framework — data-driven + kinetic + CFD — provides the computational engine that makes this possible. As real-time data flows in, the model adapts, self-corrects, and predicts process trajectories without touching the physical system.
Driving Innovation in Bioprocessing
Graphtal brings deep expertise in hybrid modelling and advanced bioprocess analytics — from first-principles mechanistic models through to AI-aided data-driven solutions and CFD-based bioreactor simulation.
Computational fluid dynamics integrated with kinetic models to resolve mixing, mass transfer, and spatial gradients for scale-up and equipment design.
First-principles models of cell growth, substrate consumption, and product formation — providing interpretable, generalisable process understanding.
Machine learning and AI models trained on process historian data to enhance prediction accuracy, enable soft sensing, and support adaptive control strategies.
Integrated data-driven + kinetic + CFD frameworks forming the core engine for digital twin development and in silico process optimisation.
Design of Experiment planning, execution support, and response surface model building for systematic process characterisation and design space definition.
Applied ML for anomaly detection, predictive maintenance, soft sensor development, and classification of batch outcomes from multivariate process data.
Multivariate data analysis for annual product quality review, batch trending, and process capability monitoring across commercial manufacturing campaigns.
New technology evaluation, software assessment, and tailored training workshops to upskill your bioprocess team in advanced modelling and analytics methods.
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