In chemical engineering, the primary purpose of process simulation is to give engineers the capability to deeply understand the interactions between materials in a given medium. Before committing to expensive, full-scale production runs, process engineers must be able to predict how materials will behave. Whether the objective is to perform complex crystallizations, catalyze chemical reactions, or achieve stable emulsification, predicting these behaviors accurately is the cornerstone of modern manufacturing.
To achieve this predictive power, the industry generally relies on two distinct approaches to understand material interactions: pure mathematical models based on fundamental physical theories, and empirical trials based on physical experiments. Both methods are valid, and both play an important role in the unit operations of chemical engineering. However, when we need to compare mathematical models and empirical trials, the critical question becomes: which of them provides a better, faster, and more accurate representation of what will actually happen in the real world?
The Limits of Pure Mathematical Modelling
On one side of the spectrum, we have pure mathematical modeling. These models are theoretical, built from the ground up using first principles and complex differential equations. While theoretically sound, pure mathematical models often lack a direct connection to the chaotic, realistic situations found on the factory floor.
The reality of industrial manufacturing is that the interactions between materials—especially in complex, multiphase systems involving solid-liquid mixing or highly viscous fluids—are often so incredibly complicated that it is practically impossible to run calculations based solely on pure theory. Generalized theoretical models often fail to catch the specific localized physical phenomena that occur inside a large industrial tank, such as dead zones, unexpected shear variations, or uneven heat distribution. Relying entirely on theoretical equations can create a false sense of security that shatters once the process is moved into physical equipment.
The Burden of Pure Empirical Trials
Because pure theory cannot always solve the complexities of dynamic chemical processes, engineers naturally turn to the laboratory. They run extensive physical experiments to generate empirical data based on previous knowledge and historical observations.
While empirical data provides an undeniable ground truth, relying solely on physical trials creates a massive bottleneck for process development. Running pilot tests is incredibly expensive, requiring significant labor, dedicated equipment, and vast amounts of costly raw materials. Furthermore, empirical data is notoriously difficult to scale. A successful trial in a 1-liter benchtop flask does not linearly translate to a 10,000-liter production vessel. The surface-area-to-volume ratio changes, heat transfer dynamics shift, and mixing times alter dramatically. Relying strictly on iterative, trial-and-error testing to bridge this gap can delay a product's time-to-market by months or even years.
The Scale-Up Dilemma
The ultimate goal for any process engineer is the seamless scale-up of chemical processes. When choosing a pathway, the tension between these two methods becomes obvious. Lean too heavily on theoretical math, and you risk missing critical physical anomalies. Lean too heavily on empirical trials, and your R&D budget and project timeline will rapidly spiral out of control.
This bottleneck is exactly why engineers are constantly seeking a better, more unified way to perform accurate chemical process calculations without sacrificing either speed or real-world accuracy.
The Sweet Spot: Semi-Empirical Models
Both theoretical models and empirical trials are fundamentally important, but if we are able to combine them, we achieve the absolute best of both worlds. This powerful combination is known as semi-empirical modeling.
By running a targeted, highly specific number of experimental tests, engineers can accurately characterize specific physical phenomena within a certain geometry. From these experiments, empirical coefficients are extracted. These real-world data points are then plugged into a robust mathematical model based on the physical description of the fluid dynamics.
This is where specialized industrial process simulation software truly shines. A semi-empirical model represents the perfect compromise between the theoretical and empirical fields. It uses real-world data to anchor the math, ensuring the equations reflect reality, while using the math to extrapolate the data, eliminating the need for endless physical trials.
By utilizing semi-empirical models, chemical engineers can explain the complex interactions between materials far better than either individual method could alone. The generated results are highly representative of actual production conditions, providing process engineers with the confidence they need to reduce pilot testing, optimize operations, and secure a much faster, safer path to industrial scale-up.
The Influence of Mixing in the Process
New methodology using VisiMix software for the purpose of checking the influence of mixing in the processes.
VisiMix Demo Operation
Learning how to input data into the VisiMix software and get results the will help us understand the influence of mixing in our processes.
Lab Experiments
Learning how to set up the relevant experiments at the lab scale, to develop the processes from an engineering point of view.
VisiMix
User friendly, universal and reliable Computational Process Engineering software for analysis of processes facilitated by mixing.
