Your process data offers more savings potential than you might think

Based on your own data, we’ll show you where measurable improvements can be made in your plant and validate potential adjustments during ongoing operations. Untapped potential often remains hidden, even when processes have already been thoroughly analyzed, simulated, and optimized. This is because plants often behave in ways that are too complex for traditional models to fully capture during ongoing operations.

This is exactly where the Data Science —comes into play with We use real operational data and AI to identify additional opportunities for optimization and to validate these optimizations in a controlled manner during operation.

Want to see a successful real-world example?

In a pilot project with Syngenta, a dewatering column was analyzed and optimized using data-driven methods. The plant was already well understood and operating stably. Nevertheless, the actual operating data revealed additional opportunities for optimization.

Over the course of approximately 2.5 months, process and quality data were consolidated, relevant influencing factors were identified, and optimized parameter sets were derived. These were reviewed with process experts and gradually validated during ongoing operations while maintaining consistent product quality.

Result: Energy consumption was significantly reduced, and economically significant annual savings were demonstrated.

Learn more about the project

The Data Science Framework (DSF) helps identify potential cost savings!

When is the DSF a good option for you?

DSF is a useful tool if you work in the process industry and want to base your optimization efforts on actual operating data rather than assumptions.

's production facility is energy-intensive. We identify areas where savings can be made without compromising quality or operations.

Process data is stored in various systems
We consolidate relevant data, organize it, and make it ready for analysis.

Models and simulations are availableat
. We supplement this view with data on the actual plant behavior during operation.

Typical applications: Physical processes such as distillation, drying, or similar processes in the chemical, pharmaceutical, biochemical, and food industries.

Lassen Sie Ihr Potenzial direkt prüfen!

Achieve better results while the system is running!

In many plants, the relevant factors are generally known. However, it often remains unclear how they actually interact during operation.

Which recipe parameters actually influence the process?
Where is there untapped potential for optimization?
What is possible without compromising quality or operations?

There is untapped potential between the model and reality

Simulation models and traditional calculations show ideal conditions. However, actual operating conditions often differ from these. It is precisely this discrepancy that reveals measurable opportunities for optimization:

real-world interactions between parameters
previously unrecognized connections within the process
Deviations from the "optimal" model state

Without access to actual system performance data, these opportunities remain untapped. Or they are not implemented due to risk considerations.

Using the Data Science Framework, we leverage real-time operational data to optimize recipe parameters. This approach poses no risk to your ongoing operations. Recommendations are reviewed with your process experts and validated in a controlled operational setting.

The Data Science Framework does not follow a rigid analytical process; rather, it is consistently geared toward operational implementation. The goal is to derive concrete, verifiable optimizations from existing data—and to validate them under real-world conditions.

Sascha Zeller, Head of Data Management, ControlTech Engineering AG

You can benefit from this right away!

Transparency Instead of Model Assumptions
You see how your investment actually performs—not how it is supposed to perform in the model.

Practical parameters instead of theoretical optimization
You’ll receive concrete, tested sets of parameters that work in real-world applications.

Identify savings potential instead of increasing resource costs
You’ll benefit from greater control, increased efficiency, and lower operating costs.

Leverage potential systematically instead of starting from scratch over and over again
The iterative DSF approach with machine learning lays the foundation for scaling.

How the Data Science Framework Works

3 components of the DSF process (target definition, data screening, assessment) presented in a cycle. — The Data Science Framework is a standardized process for data science projects with three steps, which are repeated iteratively (round after round).

Step 1: Identify relevant data and make it usable

Not all available data is critical for optimization. We identify which process data actually influences your specific issue, assess its quality, and prepare it so that it can be used directly for analysis. Typically, this involves focusing on several relevant process parameters.

Result: A robust database instead of an unstructured collection of data

Step 2: Identify connections and identify opportunities

Based on real-world operational data, models are developed that replicate the actual behavior of the system. This process also reveals interactions between parameters that often remain hidden in traditional approaches. This leads to specific recommendations for optimized parameter sets.

Result: Clear correlations and concrete optimization strategies

Step 3: Test in operation and refine as needed

The derived parameter sets are not evaluated theoretically, nor are they implemented without verification. They are proposed based on data, reviewed using your process expertise, and validated through ongoing operational monitoring. New insights are fed directly back into the models, improving the proposals iteratively—in alignment with your operational requirements.

Result: Validated optimizations that work in real-world operations

Ready for your existing system environment

We are system-neutral and implement your requirements
regardless of the technology used.

Picture of Sascha Zeller, Team Leader OT Solutions at ControlTech Engineering AG.

Let us analyze your initial situation together.

Together, we’ll review what data is already available, what realistic opportunities for improvement exist, and what an initial DSF cycle might look like in your organization.

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