Why digital twins mean process engineers don’t need to become data scientists
Proven processes and software technologies make analytics do-able for every industrial organisation, says GE Digital’s Cobus van Heerden
Today, staying competitive means progressing on a digital transformation journey, including machine learning and analytics.
Not only can industrial organisations capitalise on the IoT opportunity, optimise operations and generate greater profitability, but engaging in the latest technologies also helps to attract and retain the best talent.
Fortunately, the journey to success with machine learning and analytics doesn’t mean that process engineers need to be data scientists. Proven processes and software technologies make analytics do-able for every industrial organisation.
Create a Process Twin
Process engineers have exceptional domain expertise to put together process models — or Process Twins — and be able to interpret the models. This is the foundation for improving competitive advantage and success with analytics.
To drive analytics and improve processes, process engineers can align domain expertise to five capabilities:
Analysis — automatic root cause identification accelerates continuous improvement Monitoring — early warnings reduce downtime and waste Prediction — proactive actions improve quality, stability, and reliability Simulation — what-if simulations accelerate accurate decisions at a lower cost Optimisation — optimal process setpoints improve throughput at acceptable quality by up to 10%
Advanced analytics techniques are available to industrial process engineers to fulfill these capabilities. To support the journey to machine learning and analytics, GE Digital provides analytics technology training in the form of a self-serve product university, detailed demo videos, and application advice.
Additionally, while today’s software features enhanced ease of use and no-code implementation extensible with Python, process engineers can still lean on product experts in combination with their own domain expertise to mine data and leverage analytics to improve operations.
Success with analytics
As an example, a leading food manufacturer was able to drive down customer complaints by more than 33% through analytics. The manufacturer had struggled with weight control on a cube-shaped product. Make the cubes too heavy, and the manufacturer was giving away product or producing watery product if the excess weight was due to too much water. When the cubes were too light, the company was in regulatory jeopardy as well as having trouble compacting the product into a stable cube shape.
The team used software to get a complete, correlated-by-lot and period picture of: ingredient specs, process variables as run, and lab data — using it to look for controllable factors that correlated to excess giveaway and then comparing periods with better weight control to the factors that were true then.
Now, when the team sees how a raw material variance was successfully corrected for or a process disturbance was overcome, that understanding is embedded into a new material spec, recipe or SOP. This smart analysis yielded other benefits as well.
Another example involves applying a smart predict project at a pulp and paper manufacturer to predict Critical to Quality (CTQ) KPIs to improve productivity and eliminate wastewater regulatory issues. As a final example, a partner in mining delivered an Advanced Process Control solution that increases throughput by 10% using smart optimisation technology.
From small projects to multi-plant optimisation
All process engineers can and need to develop capabilities in analytics and machine learning to remain competitive — both at an individual professional level as well as to help their industrial organisation — in our world of digital transformation.
Over time, engineers can go from small projects to pilots to multi-plant optimisation with deep application of analytics. Engineers’ deep domain expertise provides a foundation for modelling processes and developing the analytics that are game changers in very specific applications. The combination of applied analytics technology with those Process Twin models uncovers hidden opportunities for improvement over and over again.