The Digital Pilot Plant: A Paradigm Shift in Scale-Up Engineering

Contents

1.  Rethinking process engineering: simulating, instead of testing

2. GIG Karasek IIoT platform: the central data hub of the digital pilot plant

3. The GIG Karasek pilot plant as a training ground for AI models

4. The digital pilot plant: the benefits of a self-learning platform 

5. Data aggregation as a competitive edge in the industrial sector  

6. Conclusion: the digital pilot plant – scale-up reconceived 

1. Rethinking process engineering: simulating, instead of testing

Traditional experimental trials at the GIG Karasek pilot plant typically require substantial volumes of test material to produce reliable results. But in real-world scenarios, especially within R&D, material availability is often limited. Additional challenges arise when working with expensive, hazardous, or slow-processing media. These constraints reduce the number of feasible tests and significantly limit the data pool.

That’s where the vision of the digital pilot plant comes into play: leveraging historical process and media data, advanced simulation tools, and supported with AI- and ML-powered predictive models, the plant enables highly accurate virtual prognoses and simulations. These offline simulations reduce the need for extensive physical testing.

The outcome? A radical transition from resource-intensive trials to data-driven digital methods to implement efficiency and flexibility within the process industries.

© GIG Karasek

“By combining AI and machine learning for intelligent analysis of existing data, we’re unlocking the potential to replace physical test series with ultra-precise simulations. This marks the beginning of a new era in process engineering, driven by the digital transformation of the pilot plant and enabling continuous optimization of material and process behavior without the need for physical trials.” 

Andreas Schnitzhofer, Managing Director, GIG Karasek

2. GIG Karasek IIoT platform: the central data hub of the digital pilot plant

At the core of GIG Karasek’s digital pilot plant lies its IIoT (Industrial Internet of Things) platform – fully integrated with the physical pilot facility. This platform acts as a centralized data hub, aggregating information from live pilot runs, historical datasets, and real-time feedback from operating production plants.

By leveraging these rich data resources, GIG Karasek develops intelligent, self-learning models powered by state-of-the-art AI and ML technologies. This technology enables test outcomes with high precision – without the need to run actual media through the equipment.

The result is a significant reduction in resource consumption. At the same time, adaptive algorithms create a continuously evolving system that gains and integrates knowledge from incoming data. This approach represents a true paradigm shift in scale-up engineering leading to the ability to precisely replicate plant operating conditions within the offline mode.

Further reading: GIG Karasek embraces digitalization with an innovative IIoT platform

3. The GIG Karasek pilot plant as a training ground for AI models

The GIG Karasek pilot plant provides an ideal environment for training algorithms. Operating under realistic conditions and tackling a wide range of process challenges, it generates a valuable, high-quality dataset that fuels continuous enhancement of algorithmic performance and growing reliability and accuracy of simulation results. 

Figure 1: The pilot plant offers an ideal combination of realistic data, a controlled environment and a variety of scenarios, which are prevalent for developing and enhancing ML algorithms. © GIG Karasek

Further reading: GIG Karasek Technical Center: how to test new concepts under real conditions

4. The digital pilot plant: the benefits of a self-learning platform

The digital pilot plant is a pioneering innovation hub that combines resource-efficient processing, intelligent automation, and AI-driven workflows to enable a smarter, more agile way of experimenting and capturing data and meanwhile creating a significant competitive edge for customers. 

• More efficient planning: AI-powered simulation models optimize process design from the earliest development stages, dramatically reducing design iterations and boosting planning efficiency.
• Faster development cycles: Digital models allow for rapid testing and adjustments, compressing experimental and pre-development phases and accelerating product readiness
• Reduced time to market: Optimized processes speed up market launch timelines, enabling companies to respond faster to customer needs and competitive pressures.
• Lower costs: The need for physical test materials and costly physical trials is greatly reduced.
• Proactive operational intelligence: AI-driven analytics detect anomalies and weak points early and allow rapid troubleshooting. This leads to reduced downtime, predictive maintenance, and leaner operations through real-time monitoring and accurate forecasting.
• Maximum security and data integrity: The platform connects to the customer’s process control system via a certified edge device. Secure certificate-based authentication, encryption, and data integrity checks ensure full compliance. Communication runs via MQTT and TLS 1.3 (the highest standard currently available). Customers retain full control over their data and access rights, while strict access policies safeguard against unauthorized use.

While digital technologies are becoming increasingly essential, human creativity remains irreplaceable. Visionary thinking, bold ideas, and innovative problem-solving continue to be the domain of human ingenuity. What’s new is how AI-powered simulations now make it possible to turn those ideas into reality more efficiently than ever before.

Example 1: Predictive design and automated proposal generation

The digital pilot plant offers a glimpse into a future where routine processes operate with a high degree of autonomy. One compelling example of the potential behind these technologies is AI-powered automated proposal generation:

• Request form: Customers enter physical data and target parameters.
• AI-driven simulations: Based on this input, simulation models generate precise pre-calculations and foundational data for further planning.

• Predictive design: The resulting design forms the basis for faster, more accurate proposals and initial project documentation. 

By leveraging digital simulation tools, companies can save valuable time and resources while significantly improving flexibility and process efficiency. For plant manufacturers like GIG Karasek, this means being able to respond to market demands more rapidly and to deliver projects both faster and with greater precision.

Example 2: “Operating assistant” for plant operators

Services such as predictive maintenance, condition monitoring, and optimization-as-a-service, once reliant on manual oversight and in-depth analysis, are now on the brink of a major transformation.

A key component is the integration of automated system routines that, much like driver-assistance systems in automotive engineering, enable seamless, real-time process monitoring in the background of operations.

By utilizing advanced machine learning techniques, the “operating assistant” will be capable of detecting anomalies at an early stage and providing targeted, actionable recommendations, including:

• Proactive fault management: AI models identify root causes and deviations early, enabling timely intervention.

• Targeted action guidance: Operators receive precise instructions and resolution strategies tailored to locate and solve the specific issue.

• Lifecycle-wide support: From daily plant operations to maintenance and ongoing process optimization, the operating assistant remains a continuous partner.

Figure 2: The operating assistant will excel through a combination of advanced technology, intuitive controls, and great extent of automation © AdobeStock

5. Data aggregation as a competitive edge in the industrial sector

The rapid evolution of artificial intelligence is reshaping the competitive landscape. Success will belong to those who can harness their data the fastest and most effectively.

For plant operators embracing digital transformation across their production infrastructure, this shift presents a unique opportunity to tap into a dynamically growing, intelligent data ecosystem and unlock significant strategic advantages:

• Anonymous data aggregation: By anonymously aggregating and analyzing operational data from all clients, GIG Karasek continuously expands and sharpens the scope and accuracy of its service offerings.

• Platform intelligence: With every new data point, the platform becomes more precise, more adaptive, and more powerful – an advantage passed directly on to customers.

• Cross-industry insights: By feeding data from a wide range of industries into its AI models, GIG Karasek delivers more robust, forward-thinking solutions that transcend the boundaries of any single sector.

The result: a dynamic ecosystem in which every customer benefits from collective intelligence – delivering faster, more accurate, and more efficient outcomes than ever before.

Conclusion: the digital pilot plant – scale-up reconceived

GIG Karasek’s digital pilot plant represents a paradigm shift, driven by the digital revolution sweeping through traditional industrial sectors. By integrating AI, machine learning, and the IIoT, it not only accelerates the entire value chain but also sets new benchmarks in efficiency, precision, and adaptability.

Through digital transformation, the pilot plant redefines the boundaries of scale-up engineering, leading to virtual simulations, rapid design generation, and pinpoint process optimization. This results in shorter development cycles, lower costs, greater flexibility, and vastly more efficient scaling of industrial systems and equipment.

The future of process technology will be shaped by advanced data-processing capabilities and GIG Karasek is proud to be a pioneer and enabler of this next generation of industrial innovation.

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