Below, we highlight five typical industrial use cases for our data science framework.
1. Continuous Process Optimisation
Many energy-intensive production processes run for years using the same parameters. Small inefficiencies often go undetected. Data-driven analyses can reveal correlations between process parameters, energy consumption, and production output. This makes it possible to identify operating conditions that enable higher efficiency or better product quality.
Typical applications:
Distillation processes
Drying systems
Chemical production processes
Thermal processes
2. Gaining a Better Understanding of Dynamic Processes
Processes that are constantly changing are particularly challenging. These include startup procedures, batch processes, and transition phases between different operating states. While traditional analyses often focus on steady-state conditions, data science enables the investigation of complex temporal relationships.
This allows companies to recognize:
How to reduce travel times
Why quality deviations occur
Which process steps offer potential for optimization
3. Identify deviations early on
Production facilities generate thousands of measurement values every day. The challenge lies in filtering out the relevant signals from this volume of data. Data-driven process monitoring identifies interactions between various process variables and detects anomalies at an early stage. The benefits:
Faster root cause analysis
Fewer unplanned downtimes
Greater process stability
Improved transparency
4. Predicting quality rather than measuring it
Many quality characteristics can only be determined through time-consuming laboratory analyses.
Data-driven models often make it possible to estimate these quality metrics even during production. Process data serves as the basis for what are known as soft sensors.
As a result, companies benefit from:
Faster decisions
Less waste
Higher product quality
Shorter response times
This approach is often referred to as predictive quality.
5. Digital Models for Intelligent Control Systems
Traditional control approaches often reach their limits when applied to complex production processes. Data-driven models provide a deeper understanding of process behavior and form the basis for modern optimization and control strategies.
Possible applications include:
Virtual process images
Model-Predictive Control
Digital Twins
Smart Controllers
This allows processes to be operated more stably, efficiently, and reliably.
The key factor: the right data foundation
Not every use case immediately requires the use of artificial intelligence or complex machine learning models. The greatest impact often comes from systematically analyzing existing data, identifying relevant factors, and deriving concrete optimization steps from them.
This is exactly where CTE’s Data Science Framework comes in: It helps companies identify suitable use cases, make use of existing data, and validate data-driven improvements in a controlled manner during ongoing operations.
After all, successful Process Optimisation doesn't Process Optimisation with algorithms—it starts with understanding your own data.
