Modelling case study for holistic production control

The paper describes the part of research based on the idea of a holistic production control and optimisation concept which would be based on a simple model of only a few principal production Performance Indicators (pPI) (such as production rate, quality of products and associated costs). The model would be extracted from historical plant data using various statistical, identification and data mining techniques. Such a concept would enable simple upgrading of existing MES and similar tools which enable collection of a vast amount of data, but are relatively weak in offering support for decisions and control. The paper presents the modelling part of the procedure for the design of the holistic control and optimisation of the production, i.e. the derivation of a simple pPI model using neural networks. The modelling is demonstrated on the well known Tennessee Eastman benchmark process, which represents a plant-wide industrial production of chemical components.