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How Digitalization Helps the Specialty Chemicals Industry to Accelerate Their R&D Efforts

Dr. Alexander Madl – Oct 11, 2019

TAGS:  Science-based Formulation   

Digitalization in Specialty Chemicals Industry: Accelerate Your R&D Efforts TodayChallenging times are ahead for the Specialty Chemicals Industry. The only constant is change. The rising political uncertainty and potential conflicts impact the raw material supply and make the prices volatile. All over the world, environmental concerns are rising and the chemical markets are exposed to stricter regulations. At the same time, we are on the brink of the next industrial revolution.

Increasing computing power, the creation of hyper-connectivity and therefore the digitalization of our interactions have created new industries and changed the incumbent, from retail to media. Likewise, the Chemical Industry will not remain untouched. Every company, big or small, needs to find its own digital strategy.

Digitalization works on three layers and makes three promises to the industry that it must hold true. The fundamentals are enabling innovations such as:

  • The exponential increase in computing power (Moore’s law) over the past half a century
  • Progress in miniaturization, and
  • New materials or the development of effective net-work protocols.

Based on these fundamentals, the increasing interconnection of systems and objects, de-centralized data storage (cloud) and automatization of even complex tasks are at the core of the digital transformation. Digital applications, such as artificial intelligence or distributed ledgers (Blockchain) are built on these fundamentals. (See figure below)

Layers of Digital Transformation
The Layers of Digital Transformation

The promises of the digital transition are:

  1. Trusted and secure automatic transactions without involving a human agent, leading to the release of humans from getting involved into routine tasks.
  2. High information transparency and accessibility, across borders and in real-time, allowing for more informed decision making.
  3. An increased capability to identify and to visualize patterns from diverse data pools allowing a new level of understanding of complex, and often non-linear systems.
On the perception level, our interactions will be more intuitive, fluid & mobile.
The machines (like desktop computers, laptops & keyboards) will disappear, in advance of mobile devices and voice control.

Digitalization will catalyze many transformations — in the business models, in supply chain, in production as well as in our work organization and therefore in HR. But how does the digital revolution impact the way the Specialty Chemical Industry does innovation?

The laboratory, be it a synthesis lab in the raw material industry or an application lab for adhesives or paint formulations, is the core of chemistry-based innovation. But in the lab itself, the procedures and work processes, haven’t changed much from the past. There is still a high amount of qualified manual labor.

The information sources or lab journals moved from paper files to a computer drive, but are often not easy to access than before. We are still miles away from having all data in one place and evaluation remains a largely manual task. But even more, there are many distractions that keep the chemists away from creative work.

There is an increasing demand put on administration and documentation, for safety and compliance reasons. And with all (digital) information in hand, it is not easy to navigate through the information overload to find what one needs in the moment.

So, let’s take the promises of the digital transformation & see how they hold true for special chemistry and formulation R&D.

Assistance, Transparency, and Accessibility

Any assistance that helps R&D staff to focus on the creative part of their work, that eliminates distractions and non-value contributing tasks should automatically enable more innovation. Information that took time and effort to gather are now available on finger tips.

Sigma-Aldrich (now part of Merck) transferred its whole paper-based chemicals catalogue into a web-shop. They are now one of the biggest on-line market places in the world, amongst giants like AMAZON or ALIBABA.

But this was not the real transition. By making the data digital, they became searchable.

Additional-useful-information can be fed in, such as the link to most recent papers of scientist who used the same chemical in their synthesis. Algorithms can find additional offers or bundles.

Digital Transformation in ChemicalsPlatforms to enrich listed chemicals with useful data and provide this as service to the customer now also appear for formulators in the Chemical Industry. SpecialChem is one of the leading providers here1.

Imagine, that this information is available anytime, when you need it. Imagine, the search is not controlled by a keyboard entry but voice control. Chances are high, that you use that technology at home namely SIRI or CORTANA.

Just imagine you can just tell your digital assistant to order a chemical in the moment you use it up, without stopping your process, getting out of your gloves and making a note at a Post-it, that then might get forgotten anyway.

Merging safety googles with “Google-Glasses” technology, your assistant can see, what you see.

  • It can note the numbers from the scale or instrument, and put the data into the lab notebook, just operated by your voice.
  • Enriched by Augmented Reality (AR), important data or information can be projected into your “field of vision”: safety information or procedures, for instance.

These systems are already tested in paint manufacturing and in other fields of chemical operations2. Start-ups developing this technology further for applications in the chemistry labs3. Also, big Specialty Chemicals producers, like Evonik, already providing solutions for lab assistance for complex formulations and applications4.

Lab Automation

Lab work doesn’t have to be human-focused in a digitalizing world anymore. Be it a synthesis or an application lab, too many of the operations are still based on the manual work and the skills of trained lab technicians. But many of these tasks can be automatized.

High-throughput screening has long been standard in drug development5 and catalyst research6, but is still in its infancy in materials science. But now also more complex operations can be done by robots. Robots can cook7, so robots can formulate and tests paints, adhesives and other formulations8!

Isn’t it true, that due to budget and time constraints, often only a limited number of factors can be tested and optimized? Imagine that an automated, high throughput lab can provide a full picture faster and more cost-effective than possible before.

Data quality and reliability rises, when a robot that does not get tired or distracted takes on the work, not a human being.

Data Centered Development and Modeling

To build strong scientific models of the complex chemical interactions was always a goal and a driver for progress in chemistry innovation. Many years ago, many approaches were introduced such as:

  • Molecular modeling
  • Computational fluid dynamics (CFD), or
  • The Monte Carlo method for statistical simulation of processes and scenarios

But outside of academia research they didn’t find practical application in large scale. The problems in chemical industry were too complex and too multi-variant for the computing power available at time. But this is changing now9.

The vast increase in low-cost computing power and advances in software development enable modeling to contribute to value creation through the shortening of development and scale-up cycles and more targeted application development at minimal cost.

BASF invested recently in a super-computer to provide the best infrastructure for their innovators to build better models. The new computer already found applications in e.g. modeling the complex interactions of detergents in the washing process10. This is very relevant for chemistry applicators downstream the value chain.

The task is not to understand quantum mechanics and breaking / rebuilding chemical bounds. The task is to understand the complex interactions of diverse ingredients in a formulation and predict the performance in the desired application. 

Large data pools are being created that are too complex, too dynamic, too variant in their formats or source and too poorly structured to be evaluated by conventional methods. These applications call for the use of new types of data storage and analysis systems, such as those provided in the cloud.

Software, algorithms and processing methods are also continuously evolving to meet the new requirements. The visualization of information out of large data pools is one important feature.

In the past, the full information provided by the analytical methods could not be used. The full spectrum, containing 2D information had to be collated to one or two single numbers to get processed (e.g. Mw and Mn from a full GPC graph). But often, changes in the performance properties in the final application are impacted by an incremental change in one of these properties, such as for example a higher fraction of lower or higher molecular weight polymers. Now tools are available, that extract this type of detailed information and make it available to the researchers11.

Furthermore, Artificial Intelligence (AI) will help us in the future to navigate through the vast amount of data and information to find the right clue to process it. Artificial intelligence will help:

  • Identify patterns in our data
  • Support us in extracting information from literature or
  • Suggest starting formulations based on the application problem to be solved.
Elements of Data-Driven Development
Elements of a Data-Driven Development

The Vision: Data-Driven Formulation Development

As shown in the figure above, the vision for the Specialty Chemistry, and the downstream formulators & applicators is a data-driven development of new:

  • Chemicals
  • Materials
  • Formulations, and
  • Applications

This requires a new technical approach and a different mindset. The industry needs to aim for much higher automation and consequent digital data retrieval. The IT landscape, today often very fragmented needs to get updated to cloud storage and data pooling.

Raw material data, data from experiments, analytical data, as well as performance test data needs to get accessed and analyzed with digital tools to get a bigger picture. The benefits are better models and therefore faster, more targeted developments according to the changing customer needs.

We, the innovators, the human beings in the labs, the research chemists, application engineers or lab technicians don’t need to be afraid of the new technologies. We get assistance. We can focus more on what we can do best: creation. But we need to learn to embrace the new technologies.

Most of us are not “digital natives”. We grew up with pen and paper, not with smart phones. But we are innovators, at the end. We can adapt. Our aim must be to become “digital migrants”. There is a lot of information out there in the net12. Learning a new skill has never been as easy as it is now.

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  1. Another provider is for instance: https://www.allchemist.net
  2. https://www.bergolin.de/en/news/?tx_kmnews_pi1[id]=73
  3. https://web.ars.de/cleverlab/
  4. https://www.european-coatings.com/Raw-materials-technologies/Technologies/The-Alexa-for-coatings-labs
  5. https://www.git-labor.de/forschung/pharma-drug-discovery/hochdurchsatz-screening
  6. Modern Applications of High Throughput R&D in Heterogeneous Catalysis, Hersg. A. Hagemeyer, A. F. Volpe, Bentham Science Publishers, 2014
  7. https://www.moley.com
  8. https://www.chemspeed.com
  9. https://www.chemanager-online.com/themen/anlagenbau-komponenten/trendbericht-modellierung-und-simulation-theorie-statt-praxis
  10. https://www.basf.com/global/en/who-we-are/innovation/our-way-to-innovations/supercomputer.html
  11. https://sciencedesk.net/
  12. https://www.datacamp.com/

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