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Five Use Cases for Data-Driven Process Optimisation Industry

Today, production facilities generate more data than ever before. Sensors, process control systems, and historian databases collect information around the clock on plant conditions, energy consumption, production volumes, and product quality. Nevertheless, much of the potential for optimization remains untapped.

The challenge is rarely about collecting data. The real task lies in deriving concrete insights and measurable improvements from existing data. This is exactly where CTE’s Data Science Framework (DSF) comes in. It helps companies systematically analyze production data, reveal correlations, and pinpoint opportunities for optimization.

How do we proceed?

At CTE, you—the customer—are our top priority. Only with your process expertise can we optimize your production. We focus on the interplay between collecting, structuring, and validating data.

Go directly to the solution

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:

  1. Distillation processes

  2. Drying systems

  3. Chemical production processes

  4. 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:

  1. How to reduce travel times

  2. Why quality deviations occur

  3. 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:

  1. Faster root cause analysis

  2. Fewer unplanned downtimes

  3. Greater process stability

  4. 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:

  1. Faster decisions

  2. Less waste

  3. Higher product quality

  4. Shorter response times

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:

  1. Virtual process images

  2. Model-Predictive Control

  3. Digital Twins

  4. 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.
Sascha Zeller, Business Unit Manager, Data Management
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