Smart Data Visualization for High-Throughput Data

Whether you are operating high-throughput equipment or you only work with the results, visualizing the data efficiently is key for sending a quick and clear message. At Avantium, we operate units that test up to 64 reactors in parallel. This means that the amount of data generated quickly builds up and conventional visualization plots provide more confusion than clarity. This makes it necessary to use smart visualization figures allowing operators and scientist to quickly decide on unit performance or interpret data in a fast and efficient way.

How to visualize Trending Data?

Assume that the reaction pressure is a critical process parameter, which you want to monitor closely. A common approach would be to create 64 scatter plots (one per reactor) showing the trends of reaction pressure versus time (see Figure 1). Reviewing these 64 individual figures would be a lengthy process and it could even become confusing with so many plots. In a different approach, you could make a scatter plot that includes the data of the 64 reactors and choose different colors (or markers) to differentiate them. In this case, the density of the data is so high that one could barely distinguish data from one reactor to the other, and there will be little understanding of what is happening with this critical process parameter, as shown in the picture.

Figure 1 : Reaction Pressure in dependence of time 

A smarter way to visualize trending data

The use of a smart figure would allow both operators and scientists to quickly identify if a process variable is within specifications or if actions are required, e.g. a pressure drift could indicate the plugging or bypassing of the catalytic bed. The smart plot we have chosen for this case is called a Cell plot. At Avantium we automatically generate these type of figures using JMP and using the trending data from our Flowrence software. A typical cell plot representing the reaction pressure as a function of time, for 64 reactors working in parallel, is shown in Figure 2.

In this case, the color represents a continuous scale, showing red and blue for the upper and lower specification limits, respectively. When you look at the graph, you can now easily see that the pressure in all 64 reactors is within the limits of the specifications. In addition, as the color per reactor varies slightly one could interpret that the pressure per reactor is stable. Besides, one could quickly detect that R21-R23 have a higher-pressure trend compared to the other reactors. This fast analysis could be followed by a more detailed review of process conditions of reactors showing irregular behavior, for example by use of Shewhart control charts, but instead of drawing 64 plots, we would need to focus only in 3 of them, corresponding to reactors R21 to R23.

Figure 2 : Cell Plot of Reaction Pressure in dependence of time 

Visualize Catalyst Performance Data

In high-throughput catalyst testing programs, the target is usually the evaluation of different catalyst formulations to identify the most promising materials considering key performance indicators, catalyst synthesis price and/or catalyst synthesis recipes. Within Avantium, we have specialized catalyst-testing services to evaluate a broad range of catalysts in terms of conversion and yield to the desired product while keeping an eye on the final price. In these large screening campaigns, the priority is to quickly identify the catalysts with the highest yield. However, we observe that there is an increasing desire to cluster the data based on the composition, which allows the customer to understand better the results.

Figure 3 : Catalyst Yield vs. Catalyst Price

In Figure 3, one could identify the circled catalyst as the most promising candidates, considering that the price is relatively low and the yield is relatively high. However, immediately questions arise: “How many of the ZSM-5 containing catalysts where tested?” or “Are these promising catalysts outliers? “

Figure 3 : Catalyst Yield vs. Catalyst Price

In Figure 3, one could identify the circled catalyst as the most promising candidates, considering that the price is relatively low and the yield is relatively high. However, immediately questions arise: “How many of the ZSM-5 containing catalysts where tested?” or “Are these promising catalysts outliers? “
One way to answer these questions is to transform the data and use a bar chart, grouped by active component as shown in Figure 4. Now, one can easily identify which catalysts perform better, based on the Yield/Price ratio, and the color map provides a direct link to rank material based on their cost.

A disadvantage is that the x-axis is saturated because of the number of catalysts screened in this test, and therefore, its analysis becomes cumbersome. This is especially ineffective for presenting data or results at management level meetings. The use of a smart figure would allow clear and fast interpretation of the data.

Ideally, everyone should be able to look at the plot and in an instant be able to judge which catalysts are performing better, and answer questions like “Did I select a reasonable amount of materials from each class?” and “What cost level is the catalyst?”. For this case, we have selected a Treemap as a smart plot visualization.

Figure 4 : Bar chart of Catalyst Yield vs. Catalyst Price

Figure 4 : Treemap plot of Catalyst Yield vs. Catalyst Price

The figure shows two axis, Yield/Cost versus Catalyst and the color as the total cost. However, the technique is now to segregate catalysts in different areas of the plot to indicate the main zeolite component, and to use the size of the individual boxes to provide information on the Yield/Cost ratio. Please note that the area of each box is proportional to the normalized Yield/Cost ratio, which allows comparison across catalysts with different zeolites.

It can easily be observed that, within these tests, a similar number of total catalysts containing USY compared to the sum of SAPO-34 and BETA were tested, and approximately a third of the samples tested contained ZSM-5 as active zeolite material. Within each of these areas, one can identify the best performing catalyst on the upper left corner and the color indicates the normalized cost.

The results shown in this Treemap indicate that the best catalyst is Cat 11 (among the tested materials, of course); the number of catalysts screened with this active component is significant (box size of ZSM-5 is a 1/3 of the total amount screened); and lastly, that a proposal for follow-up test could consider more SAPO-34 and BETA material to show that an even selection solids has been done. In our busy day-to-day activities such smart figures simplify the interpretation of results by conveying a clear message in a single picture.

General guidelines for a clear visualization

No matter which smart visualization you choose, the message should be conveyed in a simple and concise form to facilitate the communication process. Always keep in mind, advanced statistical and modeling tools are needed to make quantitative conclusions and identify optimal values for key parameters. In this newsletter, we emphasize those that must be considered sine qua non:

1. Keep it simple

Whenever possible, make simple figures and avoid saturation with unnecessary information.

2. Know your audience and define the key message

Determine your audience to tailor the graphics based on your key message. Consider three main types of audiences: managerial, technical or academics and non-technical. They all have different needs.

3. Use colors effectively

Minimize the use of color whenever possible. If you have troubles selecting colors, remember the color wheel.

4. Use the correct tool

Plots need to be informative and engaging. The correct tool, e.g. Matplotlib, R or JMP, can facilitate the preparation of your story and the visual context around it.

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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