Energy aware knowledge extraction from Petri nets supporting decision-making in service-oriented automation

This paper introduces an approach to decision support systems in service-oriented automation control systems, which considers the knowledge extracted from the Petri nets models used to describe and execute the process behavior. Such solution optimizes the decision-making taking into account multi-criteria, namely productive parameters and also energy parameters. In fact, being manufacturing processes typically energy-intensive, this allows contributing for a clean and saving environment (i.e. a better and efficient use of energy). The preliminary experimental results, using a real laboratorial case study, demonstrate the applicability of the knowledge extracted from the Petri nets models to support real-time decision-making systems in service-oriented automation systems, considering some energy efficiency criteria.