Machine Learning and Hybrid Models

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. An essential prerequisite for the realization of such hybrid methods in process engineering are reliable models that lead to practically relevant statements.

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). In a number of projects, hybrid methods of process engineering based on our models have already been realized.