To do this, Nimet Sterneberg, a chemical engineer who works in the data science division at Clariant, is helping his chemical engineering colleagues embrace a concept that is mostly new to them: Data science. Clariant decided to try a hybrid approach where data scientists work together with chemical engineers.
Traditionally, chemical engineers in the manufacturing industry who want to know more about their process behaviors turn to data scientists for answers. But this approach creates both a communications and knowledge gap between data scientists and engineers. Engineers can spend significant time explaining a process to a data scientist so he or she can crunch numbers.
So Sterneberg is now one of the many chemical process engineers who embraced the power of Industry 4.0 and self-service data analytics solutions to keep data scientists in the loop. Clariant has also implemented a TrendMiner feature that helps them apply machine learning (ML) models for an even deeper dive into process behavior.
Unlocking machine learning: Clariant’s model
Clariant’s digital maturity model is advanced. Working with TrendMiner, the company has evolved its plants into augmented factories by using more robust solutions to determine the root cause of process anomalies. It applies ML capabilities to its processes so that process engineers at Clariant can set up advanced monitoring and alert systems to tell them when known parameters deviate from learned behavior.
To do this, Sterneberg and his colleagues use a TrendMiner feature: The integration of Python notebooks. “What we do is get the data from the historian using TrendMiner,” said Sterneberg.
They then gather the time-series data out of TrendMiner and use their own algorithms and data science platform, where they then create analytics on top of it, discuss the results, and get opinions for the next steps. Closing the gap between engineers and data scientists brings them all into the loop so they can work with end users to do the analysis together.
“This is where we started to use the Python notebooks at Clariant.”
Unlocking machine learning: The power of analytics
Python is a computer language that has been around since the 1980s. It was invented by a Dutch programmer who wanted to create a language that was powerful but easy enough for anyone to learn and use.
Data scientists use Python because short scripts of its code efficiently and effectively sort large amounts of data, which allows them to gain more insights. But the language can be used for a variety of tasks, including establishing ML techniques.
TrendMiner included proprietary Python notebooks to improve data analysis and enable machine learning. At Clariant, the data science team took advantage of the integration by enhancing the company’s DashHub view. It created new visualizations using ML tags in TrendMiner with the Python notebooks integration.
Then the company began supercharging its digitalization program with machine learning capabilities that allow process experts to establish an even stronger golden fingerprint. The software records patterns of good behavior over time. When a process does not match the learned good behavior, process engineers can set up a monitoring and alert system to allow them to take quick action.
They will use this machine learning model (or machine learning tasks) that they built to create more monitoring and alarms.
Unlocking machine learning: Realizing greater efficiency
Sterneberg and his colleagues have increased their skill set with the addition of ML capabilities, and discovered they gained greater efficiency across Clariant’s operations. The team found TrendMiner’s machine-learning notebooks feature to be a very powerful and very useful tool.
“We were not chemical data scientists from the beginning. We evolved ourselves into data scientists. So, we are using advantages of classical engineering or chemical engineering and data-driven modeling. Each site has their own needs or pain points. In most of the cases, however, increasing throughput to improve production and make money is the goal,” Sterneberg concluded.
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