An Ontological Approach to Chemical Engineering Curriculum Development

Continuous reflection and evolution of curricula in chemical engineering is beneficial for adaptation to evolving industries and technologies and for improving student experience. To this end it was necessary to develop a method to enable a holistic reflection on the curriculum and to examine potential areas of improvement and change. The curriculum was modelled using knowledge modelling through the development of an ontology, Chemical Engineering Education Ontology (ChEEdO) in the Protégé 3.5 environment. ChEEdO models topics, taught modules and the learning outcomes of the modules within the domain of chemical engineering. The learning outcomes were related to the topics using verb properties from Bloom’s taxonomy and the context of each learning outcome. The functionality of semantic reasoning via the ontology was demonstrated with a case study. The modelling results showed that the ontology could be successfully utilised for curriculum development, horizontal and vertical integration and to identify appropriate pre-requisite learning

Towards a Re-Usable Ontology for Waste Processing

The potential of ontologies and knowledge modeling in process systems engineering has been realised and researched, efforts were directed to create semantic models representing the process industry domain. In this paper we present a re-usable ontology that consists of two main classification modules: i) Waste and ii) Processing Technology. The ontology has been developed, validated and used for processing of waste within the framework of Industrial Symbiosis. It supports a web platform that enables Industrial Symbiosis practice. The ontology is used for collecting information, user registration and semantic input output matching. © 2014 Elsevier B.V

A semantic framework for enabling model integration for biorefining

This paper introduces a new paradigm for establishing a framework that enables interoperability between process models and datasets using ontology engineering. Semantics are used to model the knowledge in the domain of biorefining including both tacit and explicit knowledge, which supports registration and instantiation of the models and datasets. Semantic algorithms allow the formation of model integration through input/output matching based on semantic relevance between the models and datasets. In addition, partial matching is employed to facilitate flexibility to broaden the horizon to find opportunities in identifying an appropriate model and/or dataset. The proposed algorithm is implemented as a web service and demonstrated using a case study.The authors wish to acknowledge the financial support by the Marie Curie Initial Training Networks Program, under the RENESENG project (FP7-607415)

An ontological approach to chemical engineering curriculum development

Continuous reflection and evolution of curricula in chemical engineering is beneficial for adaptation to evolving industries and technologies and for improving student experience. To this end it was necessary to develop a method to enable a holistic reflection on the curriculum and to examine potential areas of improvement and change. The curriculum was modelled using knowledge modelling through the development of an ontology, Chemical Engineering Education Ontology (ChEEdO) in the Protégé 3.5 environment. ChEEdO models topics, taught modules and the learning outcomes of the modules within the domain of chemical engineering. The learning outcomes were related to the topics using verb properties from Bloom’s taxonomy and the context of each learning outcome. The functionality of semantic reasoning via the ontology was demonstrated with a case study. The modelling results showed that the ontology could be successfully utilised for curriculum development, horizontal and vertical integration and to identify appropriate pre-requisite learning

Industrial symbiosis implementation by leveraging on process efficiency methodologies

Resource efficiency is a crucial step for manufacturing companies to improve their operations performance and to reduce waste generation. However, there is no guarantee of a zero waste scenario and companies need to look for new strategies to complement their resource efficiency vision. Therefore, it is important to enroll in an industrial symbiosis strategy as a means to maximize industrial value capturing through the exchange of resources (waste, energy, water and by-products) between different processes and companies. Within this, it is crucial to quantify and characterize the waste, e.g. to have clear understanding of the potential industrial symbiosis hot spots among the processes. For such characterization, it is proposed to use an innovative process efficiency assessment approach. This empowers a clear understanding and quantification of efficiency that identifies industrial symbiosis hot spots (donors) in low efficiency process steps, and enables a plausible definition of potential cold spots (receivers), in order to promote the symbiotic exchanges

Next generation supply chains: making the right decisions about digitalisation

This white paper is based on research carried out by the IfM’s Centre for International Manufacturing and insights emerging from our work with industrial partners. In it we share our latest findings to help global companies consider their digital supply chain strategies

Model Integration Using Ontology Input-Output Matching

This paper introduces ontology controlled model integration framework using inputoutput matching in the domain of biorefining. The framework builds upon the existing framework and replaces the Common Object Request Broker Architecture (CORBA) object bus with more flexible semantic repository. Semantic Web Services Description Ontologies (OWL-S) are used to describe model inputs, outputs, preconditions, operating environment and its functionality. The OWL-S enables the automation of model integration through (i) discovery, (ii) selection, (iii) composition, and (iv) execution stages. This concept has been verified with a small scale model integration to demonstrate the flexibility of model integration through all four stages of the process