(441d) An Ontology for Information and Data Management for Synthesis and Design of Processing Networks

An ontology for information and data management for synthesis and design of processing networks

A. Quaglia, A. Anantpinijwatna, G. Sin,* R. Gani

¹CAPEC, Department of Chemical and Biochemical Engineering, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark; email: aq@kt.dtu.dk. *email:gsi@kt.dtu.dk.

The solution of complex decision-making problems related to the chemical industry (e.g. plant design, plant operation, planning, scheduling, etc), requires gathering and managing a huge amount of data and information.

Because of the increase in competition caused by globalization, the ability of quickly taking decisions to adapt to new trends and situation have emerged as one of the key aspects for maintaining profitability, urging the chemical sector (as other technology-oriented industries) to develop systems for efficient information management (Harris et al. 2008). The function of this information management systems is to provide a structure for the storage, maintenance and update of large amount of information, which can be quickly and easily accessible for future use.

While traditionally the most important information and knowledge constituting the intellectual capital of a company was owned by a group of experienced employees, playing the role of expertsin most of the decision-making activity (Jaaskelainen, 2011), the need for a more structured system for management of information emerged in recent years, due to an increase professional mobility and employment turnover, as well as because of the increased amount and complexity of information to be managed.

In this contribution, we focus on the problem of data management with respect to early stage design of chemical processes. The design of a chemical process (or of a network of processes), requires a large number of technical, economical and regulatory information, which are related to the different process technologies employed.

In order to provide a structure for storage and management of these information, we propose a multi-level ontology, based on the concept of process tasks (which represents a functional description of the transformation to which a process stream is subject, such as reaction or separation). Tasks are aggregated in different combinations to form process intervals, which constitute technological alternatives for the execution of a process step, which represent a step in the transformation of raw materials into products.

Based on the proposed ontology, the knowledge available with respect to process technologies is organized in libraries of process tasks, process intervals and process steps, where the most relevant information required for the formulation and the solution of the early stage design problem are stored.

The structure is designed in order to be compatible with our integrated business and engineering framework for synthesis and design of processing networks (Quaglia et al. 2012), allowing to efficiently use the information stored for the formulation of design problems. Furthermore, the generic and flexible nature of data structure allows for a diverse application range, spanning from form process synthesis and design of vegetable oil refining network to biorefinery and industrial  water/wastewater treatment networks.

In order to test and highlight the features of the proposed information management structure, a case study dealing with the design of edible oil wastewater treatment is formulated. Available knowledge with respect to wastewater treatment engineering is collected and systematized according to the above described information management system, resulting in the definition of a library of wastewater treatment technologies, organized according to the described ontology.

The problem of wastewater treatment design is then formulated (on the base of the information available in the library) and solved, leading to the identification the optimal treatment configuration, with respect to the minimization of treatment costs.

References

J.A. Harris, T.M. Tworoger, L.C. Tworoger, 2008, Long term survival and quality information systems: a longitudinal case study, Academy of information & Management Science Journal (11) 89

A. Jaaskelainen, 2011, How to measure and manage the risk of losing key employees? International Journal of Learning and Intellectual Capital  (8) 63-75

A.Quaglia, B.Sarup, G.Sin, R.Gani, 2012, Integrated business and engineering framework for synthesis and design of enterprise-wide processing networks, Computers & Chemical Engineering (38) 213-223.