(765e) Operational Level Integration System Based On An Ontological Framework by Means of Master/Control Recipe Semantic

Process industries are highly involved systems which entail complex decision-making. They require some advanced methods and/or tools in order to integrate and optimize information from different decision levels. In this sense, semantic based decision support systems seem to offer an efficient tool for facilitating interoperability across multiple, heterogeneous systems. In addition, they allow to share information and experience as well as to learn from collective knowledge.

Regarding the integration of different decision levels, an ontology (semantic structure) allows to coordinate the information exchange among the different modeling paradigms/conventions currently used for the different decisions to be taken. Its key role consists of capturing the relevant distinctions of the enterprise structure (specific domain) at the highest level of abstraction, embodying the results of academic research, and offers an operational method to put theory to practice [1].

This work focuses on the integration within the operational decision levels (control and scheduling). A description of the informatics system of batch process ontology[2] is provided. An additional level of detail regarding to master/control recipe element, is represented inside the ontology for mining quality data which will be translated into useful information by the decision maker. It is important to remark that the generality should be maintained since it is a basic principle in any ontological framework.

The recipes contain a variety of information about available raw materials, processing requirements, the manufacturing of a single batch of a specific product, etc., according to the World Batch Forum schemas [3] which are based on the ANSI/ISA-88 standards [4 - 8].

The presented framework is applied to a benchmark scheduling problem formulated by Westerberg-Kallrath [9].

[1]      Gruber, T., Springer-Verlag (ed.), Ontology, Encyclopedia of Database Systems, Ling Liu and M. Tamer özsu, 2008

[2]      Munoz, E., Espuna, A., Puigjaner, L. Towards an ontological infrastructure for chemical batch process management. "Computers & chemical engineering", 2010, vol. 34, núm. 5, p. 668-682.

[3]      Brandl, D., Emerson, D., September 2003. Batch markup language batchml.

[4]     International Society for Measurement and Control. (1995). Batch control. Part 1. Models and terminology. International Society for Measurement and Control.

[5]     International Society for Measurement and Control. (2001). Data structures and guidelines for languages. International Society for Measurement and Control.

[6]     International Society for Measurement and Control. (2003). Batch control. Part 3. General and site recipe models and representation. International Society for Measurement and Control.

[7]     International Society for Measurement and Control. (2006). batch Control part 4. Lot production registers. International Society for Measurement and Control.

[8]     International Society for Measurement and Control. (2007). Batch control part 5 automated equipment control models & terminology. International Society for Measurement and Control.

[9]     Kallrath, J. Planning and scheduling in the process industry.  ”OR Spectrum”, 2002, vol. 24,  219-250