Model Learning and Smart Data

In practice, model-based optimization can lead to substantial improvements in processes – whether planning and implementing supply chains, scheduling the tasks in a production chain, or for the layout and operation of a production plant. Reliable models that lead to meaningful, practice-relevant statements are an essential requirement for success in these challenges.

ITWM has many years of experience in adapting models to the current problem, in addition to training of the models by means of data obtained from the process. Modeling, simulation, and optimization (MSO) methods are employed in such a way that the result is the continuous improvement of the model parameters through continuous comparison of the model predictions with actual practice. This also enables data quality to be evaluated with increasing reliability, for example, for the early identification of outliers or system error.

In this way, data and models are given equal consideration – models learn from data (model learning), data can be compared and assessed with models (Smart Data). This approach has been implemented in a series of projects.

Clusters of Projects

Our projects can be pooled in the following clusters:

 

Databased Planning of Production Processes

The new design and improvement of processes in chemical engineering nowadays is based most often on process simulations. Nearly always objectives for quality and costs have to be considered.

Finding the best compromises between these competing objectives is difficult, but essential for the decision process.

 

Systemic Therapies

Especially the planning process of heavily affecting therapies like tumor diseases or long-term-therapies of chronic diseases like diabetes demands decision support tools. The projects in the cluster »Systemical Therapies« help physicians with the search for an adequate therapy.