(383b) Enabling Discovery and Integration of Process Models and Data Using Ontology in the Domain of Biorefining | AIChE

(383b) Enabling Discovery and Integration of Process Models and Data Using Ontology in the Domain of Biorefining


Koo, L. - Presenter, University of Surrey
Kalemi, E., University of Surrey
Cecelja, F., University of Surrey
Process System Engineering (PSE) has been recognised as a method to design and operate chemical processes [1]. PSE has played an essential role to significantly improve our understanding of the processes and increasing efficiency of those processes by enabling the use of modelling, simulation, optimisation, planning and control. Due to the challenges in developing efficient and sustainable biorefining processes, the PSE tools have provided the vital links with notions of process systems engineering and biorenewables to address a variety of products and services [2].

With increasing number of specialising tools and methods [3], the ability to search different type of models and data involved in biorefining processes and concomitant integration of these models and data remains a challenge. The best way to retain these valuable models and data that often resides only within individuals, is to store in such a way to make their future retrieval and reuse as easy as possible. The InterCAPEmodel ontology represents knowledge of biorefining models and data in a systematic approach, where identifying, capturing, retrieving, sharing and effectively reusing these models and data are possible [4]. The ontological approach ensures the full utilisation of modelling knowledge that can be coupled with the potential of individual expertise to identify the best suited model for the integration of CAPE (computer aided process engineering) models and data in the domain of biorefining.

In this paper, an ontological approach towards the discovery of biorefining models and data for the purpose of integration is proposed. The InterCAPEmodel ontology is introduced as a foundation to support the discovery of the process models and data as well as their integration. The ontology provides a common vocabulary to eliminate any heterogeneity for effective communication among users. The modelling knowledge, both implicit knowledge that reflects modeller’s intuition and their experience in modelling, as well as explicit knowledge that identifies type, characteristics, and associated properties of the models and data are captured in the form of taxonomy and relations that clarify the semantic stronger, more coherent, and more broadly applied. In addition, the ontology supports the registration process where common reference is established for integration of the models and data in the domain of biorefining. The ontology guides user to navigate through the taxonomy and assists in registering a model or data (i.e. process, energy, economic, environmental models or data) as an instance, which can be searched, retrieved and reused by the community. The “input-output matching” [5] is employed as a method of model integration, which is a process of instance matching. The properties that have the most meaningful association in integration are established as a common reference characterising inputs and outputs of the model and/or data. This method of integration permits partial matching, which is more flexible in discovery of models and data, which further increases the reusability of the models and data. To highlight the main functionality of the ontology approach, the design of InterCAPEmodel ontology, as well as the semantic methods used to measure relevance are validated by a number of case studies using models and data related to biorefining process.

[1] M. Alvarado-Morales, N. Al-Haque, K. Gernaey, J. Woodley, R. Gani, 2008, CAPE methods and tools for systematic analysis of new chemical product design and development, Computer Aided Chemical Engineering, on CD. vol. 25, pp. 997-1002.

[2] R. Gani, I. E. Grossman, 2007, Process Systems Engineering and CAPE – What Next?, 17th ESCAPE Proceeding Book, Bucharest, Romania, vol. 24, pp. 1-5.

[3] M. Matzopoulos, 2011. Dynamic Process Modeling: Combining Models and Experimental Data to Solve Industrial Problems, Process Systems Engineering: Dynamic Process Modeling.

[4] L. Koo, N. Trokana, F. Cecelja, 2017. A Semantic Framework for Enabling Model Integration for Biorefining. Computers & Chemical Engineering, 38, pp.733-738.

[5] N. Trokanas, F. Cecelja, T. Raafat, 2014. Semantic input/output matching for waste processing in industrial symbiosis. Computers & Chemical Engineering, 66, pp.259–268.