Smart testing strategies for catalysts

Using statistical design and mathematical models to maximize chance of success

Designing experiments is something every researcher does on a daily basis, by carefully considering not only objectives but also experiments carried out previously. Applying statistical and mathematical models to interpret the results is also common practice. But, designing the experiments themselves with statistical models is a rarity.

The benefit of statistical design-of-experiments (DoE) is the acceleration of discoveries by selecting the most appropriate tests, in many cases reducing the number of tests required, and zooming in on the best multi-dimensional space of variables much quicker. In some cases, it can make pursuing a line of research feasible when traditional methods would have entailed a prohibitively large program.

DoE in the research cycle

The picture illustrates how statistical design-of-experimentation contributes to the cycle of research. Clearly, it’s application will impact the way of working. Rather than planning the next experiment on the basis of the results of the previous one, experiments are designed with available knowledge of possible outcomes. By asking questions about the relationship between parameters and performance indicators, and by considering the “feasible” parameter space upfront, key information for process design is incorporated.

Why DoE is not used and Why it still matters so much

A key factor why statistical design of experiments is being used less than one would expect is a lack of knowledge of key statistical and mathematical concepts needed by the people directly involved in developing a new catalyst or new catalytic process. And yet, catalysis is necessarily a multi-parameter challenge – one which every researcher will recognize – making the potential benefits of a systematic DoE approach very tangible.

From our own experience, we have seen many examples where statistical methods were applied successfully. Even more relevant – we have seen examples in which the problem at hand would probably not have been solved without such a strategy. 

A customer example

Consider the direct oxidation of propane to acrylic acid using a four-component mixed metal oxide catalyst as an example in Fig 1.

This project was brought to us by a client that had already spent several months, unsuccessfully, trying to reproduce the state-of-the-art performance. In this study, we started by attempting to synthesize a number of catalysts based on information in a number of patents. Not surprisingly, performance was underwhelming (the two areas highlighted in orange in Fig.1).

Figure 2: Fractional conversion of propane, and fractional selectivity towards the desired product acrylic acid. Although obvious to the expert – the impact of pretreatment parameters is large, difficult to capture in a single parameter and needs to be dealt with systematically.
It was only after digging deeper in available literature that we identified a large number of parameters in catalyst pretreatment which had a larger than usual impact. A two-tier strategy was applied, in which first all possible parameters associated with pretreatment were tested for significance.

In a second stage, the parameters found significant (in this case, mostly parameters related to drying/calcination rates and temperatures), were optimized further. The result obtained was equivalent to the reference performance (the area highlighted in green in Fig.1).

The right DoE saved over 50% of time and effort

This finding had a huge impact on the design used for the subsequent screening for novel catalyst materials. Rather than focusing “just” on catalyst composition, a lot of attention was spent on catalyst pretreatment during the entire program. The very large set of potentially important parameters was reduced to a top 5 of most significant parameters. These most significant parameters were optimized in a subsequent experimental campaign.

The overall process took less than Save & Exit a month, which is a significant gain compared to multiple months of trial-and-error experimentation in a more traditional method. Statistics and mathematics were key enablers for this accelerated approach.

Scheme 2 shows how this approach significantly reduced the number of experiments to be executed, and saved over 50% of time and effort.

Conclusions

When considering these strategies, it is important to realize each project is unique – the specific research objectives and the complexity of the underlying chemistry determine what is the best strategy. In short, there are no magic bullets: discussion of objectives and results is key to design the right program, and to adjust this design as the amount of data available increases. However, the methodology as such is generally applicable, and can be adapted to fit the needs of your project.

Single-Pellet-String-Reactors (SPSR)

No dead-zones, no bed packing & distribution effects. The catalyst packing is straightforward and does not require special procedures. A single string of catalyst particles is loaded in the reactors with an internal diameter (ID) that closely matches the particle average diameter. This applies to single catalyst systems, as well as stacked-bed systems. The use of a narrow reactor avoids any maldistribution of gas and liquid over the catalyst bed, thereby eliminating catalyst-bed channeling and incomplete wetting of the catalyst.

The most accurate and stable pressure regulator for 16-parallel reactors

The most accurate and stable pressure regulator for a multi-parallel reactors with just ±0.1bar RSD at reference conditions. The Reactor Pressure Controller (RPC) uses microfluidics technology to individually regulate the back-pressure of each reactor. By measuring the inlet pressure of each reactor, the RPC maintains a constant inlet pressure by regulating the backpressure. As a result, the distribution of the inlet flows over the 16 reactors is unaffected and a low reactor-to-reactor flow variability is achieved.

Reactor pressure control is not only important to ensure accurate pressure control, but also to help maintaining equal distribution of the inlet flow over the 16 reactors.

Automated liquid sampling system

Programmable, fully automated liquid product sampling robot for 24/7 hands-off operation. Robot equipped with a compact manifold aiming at depressurizing the effluent immediately after each reactor to atmospheric pressure. Reactor effluent is depressurized by a miniaturized (low volume) parallel dome regulator, allowing a stable control of gas or gas/liquid product streams. This eliminates the use of valves at high pressure (such as multi-position valves), which are prone to leakage.

Gas liquid separation is sone directly by collecting the liquid products in sample vials and directing the gas products to the online gas analyzer. This approach minimizes required flushing times in the downstream section of the reactor eliminating the need for high pressure gas-liquid separators, level sensors, and drain valves.

EasyLoad® reactor closing system

Unique reactor closing system, no connections required. With a rapid reactor replacement minimizing delays, improving uptime and reliability. Sealing of up to 16 reactors by simply closing the ‘top-box’ in a single action. No leak testing required!

Stable evaporation by liquid injection into reactor

The direct injection of liquid into the top of the reactor and the consecutive conditioning zone allows feeding of broad range of liquids and concentrations. Various types of liquids, both aqueous and oil phase are successfully evaporated and fed to the reactors.

Tube-in-tube reactor technology with effluent dilution

This unique tube-in-tube feature allows an easy and rapid exchange of the reactor tubes (within minutes!) with a single o-ring at the top of the reactor without the need for any connections. The use of an inert diluent gas (outside of reactor) to maintain the pressure stops undesirable reactions immediately after the catalyst bed while serving as a carrier gas to the GC, facilitating the analysis of high boiling point components, preventing dead volumes and back flow, and reducing the time required to transfer gas and liquid effluent products to the analytical instruments.

The tube-in-tube design enables the use of quartz reactors at high pressure applications.

Compact TinyPressure module glass-chip holder with integrated pressure measurement

Holds the microfluidic glass-chips for gas distribution and measures inlet (and outlet) pressure of the 16 parallel reactors at ambient temperature, allowing online measurement of catalyst bed pressure drop.

No high-temperature pressure sensors required. Pressure range of 10 – 200 bar (high pressure) or 0.5 – 10 bar (low pressure).

The modular design enables easy calibration and quick exchange of the microfluidic glass-chip, without the need for time-consuming leak testing.

Microfluidics modular gas distribution

Unrivalled accuracy in gas distribution with patented glass-chips for 4 and 16 reactors, tested with a guaranteed flow distribution of 0.5% RSD channel-to-channel variability. Quick exchange for different operating conditions, offering the unique flexibility to cover a wide range of applications using the same reactor system.

Auto-calibrating liquid feed distribution, measurement, and control

The most accurate liquid distribution for high throughput systems with real-time liquid flow measurement and control for 16-parallel reactors. Auto-calibrating function enabled by a single flow sensor guarantees that all 16 reactors are continuously operated at the desired LHSV, all the time. Innovative design based on our microfluidic glass-chips with integrated temperature-control. The system continuously regulates the liquid distribution to all 16 reactors, and together with our Reactor Pressure Control technology, eliminates the impacts of pressure variations in the flow distribution.

Proven technology with difficult feedstocks with high viscosity, such as VGO, HVGO and DAO: no blockage and or breakage observed. Different glass-chips available for different viscosities.

Liquid distribution errors below 0.2% RSD, making it the most accurate parallel liquid flow distribution device on the market.

Option to selectively isolate the liquid flow to any of the 16-parallel reactors.

 

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