Normling for the ‘Merck Research Challenge’

Reducing machine downtime, price pressure and an ever-increasing degree of customisation are just some of the challenges facing the industry in times of artificial intelligence, digitalisation and globalisation. Optimising your own production processes is therefore a goal that every plant operator knows all too well.

At adesso, we understand these challenges in your industry and help you to master them - for example with our virtual standard plant ‘Normling’, which demonstrates the advantages of AI, simulation and complex algorithms in the production context.

The adesso-Normling is not just a mental construct: it is a virtual standard plant that complies with DIN SPEC 77221 and makes it possible to compare plants and machines with each other in terms of the processes used, production and maintenance. The 3D model serves as a neutral comparison plant for a wide variety of production facilities and enables a direct comparison without having to take local conditions and special features into account. Together with our customers, we can move through this virtual plant and run through individual production processes and identify optimisation potential, model access to the plant or develop benchmarks for predictive maintenance.

As part of the Merck Research Challenge, we developed a concept for the operation of industrial plants in process and procedural chemistry for Merck KGaA, the Darmstadt-based science and technology company in the health care sector, with the following objectives in mind:

• Significant increase in production efficiency
• Optimisation of production processes
• Simplification of data management
• Automation of workflows and process characteristics

By using our concept, it would be possible for Merck to automatically determine specific mixing ratios of individual active ingredients during production, as well as the operating modes of the systems. To do this, many internal and external influences are analysed and harmonised to create an optimum solution. The standardised model serves as a virtual image of the plants. The data obtained in the respective systems can be compared with the established benchmarks of the standardised system and filtered. This filtered data then forms the basis for efficient optimisation of the production processes.

As a further level, we offer the possibility of horizontally networking the numerous applications currently in use and thus creating a comprehensive solution. In other words, we are not creating another solution that runs parallel to all existing programmes, but a solution that brings the existing programmes together.