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How one company created a virtual lab that uses MVDA to screen potential new plasticizer molecules

January 25, 2018

In chemical manufacturing, the process involved in creating a breakthrough new solvent or substance often takes years, or even decades, with ongoing tests that may be based on trial and error as much as specifically applied knowledge. One area of development that is particularly important in the chemical industry is creating new substances called plasticizers that can help resins or polymers retain a more supple or flexible nature.



Plasticizers are used in a variety of products ranging from electrical cords to shoe soles to toothbrushes.

What is a plasticizer?

A plasticizer is a substance (typically a solvent) that can be added to a synthetic resin to produce or promote plasticity and flexibility, and to reduce brittleness.

Plasticizers act as a kind of lubricant between segments of polymer chains. Without the plasticizer, chains of molecules sit rigidly on top of one another, like uncooked noodles in a box. But when the right plasticizer is added, the molecules can move freely, just like noodles softened in water can.

Plasticizers are an essential component in a variety of materials ranging from vinyl to rubber to PVC. You’ll find plasticizers in products such as car tires, seat cushions, shoe soles, glasses, electrical cords, plastic forks, dishes and much more.

Identifying the right plasticizer for a specific molecule requires testing, data and analytics. Problems with a plasticizer can seriously interfere with a product’s function and quality, particularly in regard to flexibility or performance. So confidence in the suitability of a plasticizer for a particular molecule or polymer is essential for manufacturing companies.

Streamlining the molecule screening process

Developing a streamlined process to screen potential new plasticizer molecules was the goal behind the creation of a new virtual lab by an international specialty chemical company. Chemometrics specialists used SIMCA software as the heart of a new multivariate data analytics process that serves as a customized virtual lab. The virtual lab is able to screen molecules using data-driven methods rather than relying on trial and error and specific domain knowledge.

The multivariate data analytics approach using SIMCA structured and visualized the computed properties of each molecule in a way that allowed project teams to compare performance with respect to the specifications for the plasticizers. By testing ideas in a virtual lab, the team of specialists was able to challenge the results before the product was synthesized. The visualization of the data opened up discussions that made the process more productive and efficient.

As a result, the company was able to streamline the process for identification of new plasticizer molecules, saving time and resources.

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Topics: Multivariate Data Analysis, Manufacturing Processes

Erik Johansson

Written by Erik Johansson

Lead Data Scientist

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