Dynamic Modeling of Catalyst Feeding in Multiphase Reactors

Catalyst Feeder Unit (Source)

Dynamic Modeling of Catalyst Feeding in Multiphase Reactors

You may be forgiven for not having heard of multiphase reactors, but these are the unsung heroes that power a range of industries from petrochemicals to pharmaceuticals. These interesting systems can deal with multi-phase reactions; gas, liquid and solid phases are essential for synthesising most fuels, pharmaceuticals and more. But there’s a crucial component at the heart of these reactors: this factor is the catalyst. And it’s not just about having the catalyst sometimes, it is about how much and how often you feed it into a reactor. Well here’s where dynamic modelling comes in.

In this blog, we will look at why the ability to model the feeding of catalysts to multiphase reactors dynamically is a big deal, how it is done, and what this means for the future.

Process intensification in multiphase reactors (image)

Why Multiphase Reactors and Catalyst Feeding Matter

What can be compared to cooks in a kitchen preparing a fascinating menu for several gourmets? Cooking is a process that involves blending different types of ingredients and balancing such ingredients in a dish. Some ingredients must be put at the correct time otherwise the whole meal could be spoiled with the inclusion of that certain ingredient. Multiphase reactors work similarly. These are built to hold together elements in different phases of existence, say a gaseous state interacting with a liquid one and using a catalyst to increase the rate of the process.

They may be in a different phase from the reactant, which raises the level of how they are introduced into the reactor and how they will interact with the other substances present in the reactor. Mixing is a delicate process; care must be taken to achieve the best reaction efficiency, reproducibility of the product, and minimisation of side products.

Consider this: currently the turbulent growth of the market indicates that the global catalyst market is expected to touch USD 35. 5 billion by 2025 with a CAGR of 4% and the auto finance market which amounts to $940 billion is expected to expand to $1.6% to a global market size of USD 63.50 billion from 2020 to 2025 (Market Research Future). That’s a huge market informing the demand for efficient methods of operation and application of catalysts in the industry.

Dynamic Modeling: The Key to Better Catalyst Feeding

How do you decide the mode of charging the catalyst into the reactor? To achieve this, dynamic modelling is needed. It’s a bit like having a video link with a psychic medium that enables you to see through the veil of the web. Dynamic modelling entails the use of computed methods to assess a system’s behaviour over time. As applied to multiphase reactors, it allows an understanding of the movement, interaction, and impact of the catalyst on the process in real-time.

Why is this important?

Optimised Catalyst Usage: You don’t want to feed the catalyst at a time when it is not going to be optimally utilised, so you apply it precisely where and when it can be of the most use. Studies have found that catalyst-feeding optimisation in conventional multiphase reactors could lead to a reduction in the use of catalysts by as much as 15%, which is highly cost-effective. ScienceDirect.
Improved Product Quality: Steady catalyst feeding leads to more uniform products, whether you’re making chemicals, fuels, or pharmaceuticals. Research shows that precise feeding can improve product consistency by 20-30% in pharmaceutical applications. American Chemical Society.
Reduced Downtime: It indicates that with improved control it will take a shorter time to undertake the activities such as maintenance or replacement of catalysts. Chemical Engineering Journal report shows that if catalyst feeding is optimised in the reactor, annual downtime can be reduced by 10%, increasing the efficiency of operations.
Cost Efficiency: Keto, firms can reduce operational costs by carefully fine-tuning, the amount of catalyst they need and how it is best to use it. Some industries have revealed that they cut down millions of dollars yearly by implementing dynamic modelling techniques. McKinsey & Company.

To get dynamic modelling right, there are several factors to consider:

The size, shape and distribution density of the catalyst particles may affect the behaviour of the particles in the reactor. If the particles for instance are small they will react faster but they will introduce things such as clogging or high pressure.
How the catalyst and other phases composed of gas, liquid or solid influence one another can be dramatic. Therefore, the interactions underlying this reaction must be well understood to effectively fine-tune the process.
The specific design and configuration of the reactor and the presence of such appendages as stirrers or baffles, for example, can have a monumental influence on the way that the catalysts are dispersed and blended.
The speed, direction, and turbulence of the flow inside the reactor will determine how well the catalyst does its job.

DEM-CFD modelling and simulations of hydrodynamic characteristics and flow resistance coefficient in fixed-bed reactors (source)

Methods for Dynamic Modeling of Catalyst Feeding

Computational Fluid Dynamics (CFD): Consider CFD as the methodology that allows for the visual observation of how fluids (and catalysts) act within the given reactor. This is very similar to watching the currents of a river, only this time what can be observed is how catalysts propagate and interact.
For instance, ANSYS Fluent software is one of the most valuable tools companies use to simulate these complicated flows in multiphase reactors. Previous research indicated that the application of CFD can cut down the duration while designing the reactor by 25% to 40%. (Source: Journal of Computational Physics.)
Population Balance Models (PBM): These models allow you to estimate the behaviour of catalyst particle sizes in the reactor in the future this data is important when using a catalyst. For instance, knowing the changes to particles of a catalytic converter, where the particles may break or clump together, is a virtue of PBM. A particular case analysis revealed that, with 95% accuracy, PBM could estimate catalyst life, thus decreasing the number of catalyst replacements in Chemical Engineering Science.
Kinetic Models: As stated previously and clearly, kinetic modelling is mainly about the actual chemical reactions. It is similar to having a formulation that indicates the speed at which each reaction will occur about conditions necessary to determine the speed with which a catalyst influences given reaction speeds.
Machine Learning Algorithms: Given the ever-increasing popularity of artificial intelligence, machine learning is proving to be an essential method of applying the results of these reactors to the wealth of data produced by them. These algorithms can predict how the catalyst would act with varying conditions and in turn, the process can be optimised.
For instance, DataRobot has an AI Cloud that provides machine learning capabilities for the analysis of reactor data and enhancing the catalyst feeding methods. There is evidence that the use of machine learning solutions can increase the precision of catalyst feeding by as much as 20%, Journal of Process Control.

Challenges in Dynamic Modeling

Computational Complexity: Multiphase reactors are incredibly complex, with many factors to consider at once. Modelling all these factors accurately requires serious computing power. One way to tackle this complexity more efficiently is to combine different methods, like CFD with machine learning.
Data Accuracy: The model’s accuracy depends on the quality of your input data. If the data is off, the predictions will be, too, which could lead to costly mistakes.
Scalability: What works in a small lab might work better when scaled up to a full-sized industrial reactor. Ensuring that your models can scale effectively is crucial.

Looking to the Future

Integration with Real-Time Monitoring: Imagine adjusting the catalyst feeding in real-time, based on what’s happening in the reactor. Advanced sensors and data analytics make this possible. The OSIsoft PI System enables real-time monitoring of industrial processes, which can be integrated with dynamic modelling for better control.
Hybrid Modeling Approaches: By combining different modelling techniques, like CFD with machine learning, you can get more accurate predictions and better results.
Use of Digital Twins: Digital twins are virtual replicas of physical reactors. By creating a digital twin, you can continuously simulate and optimise the process in real-time, leading to better performance and less downtime. Digital twins can improve operational efficiency by up to 30% by allowing real-time adjustments and predictive maintenance Deloitte Insights.
Green Chemistry and Sustainable Practices: As industries move towards more sustainable practices, future models will focus on minimising the environmental impact by optimising catalyst usage, reducing waste, and enhancing energy efficiency.

Wrapping Up

Dynamic modeling of catalyst feeding in multiphase reactors is transforming industries by optimizing catalyst use, improving product quality, and reducing costs. Techniques like CFD, PBM, and machine learning enhance efficiency and sustainability in reactor operations. These advancements promise better reactor performance, aligning with global sustainability goals.