Machine Learning and Analytics: Process Engineers Don’t Have to Be a Data Scientist

Today, staying competitive means progressing on a digital transformation journey, including machine learning and analytics. With ML and analytics, industrial organizations can capitalize on the IoT opportunity, optimize operations, and generate greater profitability. Additionally, engaging in the latest analytics 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 suddenly need to become data scientists. Proven processes and software technologies make analytics achievable for every industrial organization.

Process engineers have exceptional domain expertise to put together process models – or Process Digital 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:

1. Analysis - automatic root cause identification accelerates continuous improvement
2. Monitoring – early warnings reduce downtime and waste
3. Prediction – proactive actions improve quality, stability, and reliability
4. Simulation – what-if simulations accelerate accurate decisions at a lower cost
5. Optimization – optimal process setpoints improve throughput at acceptable quality by up to 10%

Advanced analytics techniques are available to industrial process engineers to fulfill on 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.

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 Proficy CSense to get a complete, correlated-by-lot and period picture of: ingredient specs, process variables as run, and lab data – using the software 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. The smart analysis with Proficy CSense 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 optimization technology.

 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 organization – in our world of digital transformation.

Over time, engineers can go from small projects to pilots to multi-plant optimization 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 Digital Twin models uncovers hidden opportunities for improvement over and over again.

If you’re ready to optimize with analytics, GE Digital’s Proficy CSense turns raw data into real-time value with a Process Twin. The software uses AI and machine learning to enable process engineers to combine data across industrial data sources and rapidly identify problems, discover root causes, and automate actions to continuously improve quality, utilization, productivity, and delivery of production operations.