Franjo Cecelja

An ontological approach towards enabling processing technologies participation in industrial symbiosis

Abstract A new ontological framework which supports processing technologies participation in Industrial Symbiosis (IS) is proposed. The framework uses semantic web service formalism to describe technology based on tacit knowledge embedded in the domain ontology and explicit knowledge acquired from the users. To enhance technology discovery, IS relevant processing technology classification and characterization is proposed. Technology participation in IS is addressed. Partial semantic input–output matching is used to propose complex and innovative networks assessed and ranked by their technological, economic and environmental benefits. The proposed framework is implemented as a web service and operation demonstrated using a case study.

Semantic input/output matching for waste processing in industrial symbiosis

Industrial symbiosis (IS) is a subdiscipline of Industrial Ecology that aims to bring together companies from different sectors to share resources, namely energy, materials, and water. The main goal of IS is to improve resource (materials, waste, energy) efficiency and lead to mutual environmental, financial and social benefits to participants.In this paper we present a semantic approach for IS input/output matching. This approach is based on knowledge modelling and ontologies.Ontologies are used to model all resources - waste, water, energy - along with details about their composition, characteristics (chemical and physical) and tacit knowledge about their flow.The input/output matching algorithm presented enables the valorisation of resources through industrial symbiosis networks.

Semantic Formalism for Waste and Processing Technology Classifications Using Ontology Models

Abstract Ontologies and semantics are widely used in many areas in an effort to enable the sharing, reusability and interoperability of knowledge. Industrial Symbiosis (IS) is an area which can be highly benefited by such technologies in order to eliminate the jargon barriers that exist in the resource/waste and processing technologies domains. In this paper, we present the use of these technologies in IS practice by demonstrating the development of different knowledge models that exploit the potential of semantics in order to automate the IS practice and make it more effective.

Integration of superstructure-based optimization and semantic models for the synthesis of reactor networks

Abstract The paper presents a novel framework for the optimisation and synthesis of complex reactor networks that combines superstructure-based optimisation, semantic models and analytical tools. The work addresses the representation and extraction of process synthesis knowledge during the optimisation process with the purpose to simplify and interpret design results. The simplification is achieved with a gradual evolution of the superstructure and corresponding adjustments of the optimisation search. The interpretation is accomplished with the use of analytical tools to translate data into descriptive terms understood by users. Means of analysis include dynamic ontologies populated by computer experiments and continuously upgraded in the course of optimisation.

Semantic algorithm for Industrial Symbiosis network synthesis

Abstract The paper introduces a semantic algorithm for building Industrial Symbiosis networks. Built around ontology modelling of knowledge in the domain of Industrial Symbiosis, the algorithm enables the acquisition of the explicit knowledge from the user through ontology instantiation and input/output matching based on semantic relevance between the participants. Formation of innovative Industrial Symbiosis networks is enabled by decomposition of properties characterising respective resources and solutions, the process optimised for set environmental criteria. The proposed algorithm is implemented as a web service. The potential of the algorithm is demonstrated by several case studies using real-life data.

Ontology evaluation for reuse in the domain of Process Systems Engineering

Ontologies are a useful tool for knowledge representation, sharing and reuse. Although the number of available ontologies is increasing, the concomitant reuse activities are not following respectively. This is particularly true in the domain of Process Systems Engineering where the ontology development has been proven to be a challenging task and respective reusability is at its infancy. This paper presents a framework for evaluation of ontology for reuse. The proposed framework benefits from information about ontologies, such as terminology and ontology structure, to calculate a compatibility metric of ontology suitability for reuse and hence integration. The framework was demonstrated using a Chemical and Process Engineering case.

OFIS – Ontological Framework for Industrial Symbiosis

This paper proposes an ontological framework to support Industrial Symbiosis (IS) operation. The framework exploits semantic knowledge modeling and enables structural data transformation for identification of potential synergies between various industries and hence formation of one to one and complex symbiotic networks.

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.

A spreadsheet calculator for estimating biogas production and economic measures for UK-based farm-fed anaerobic digesters

This paper presents a spreadsheet calculator to estimate biogas production and the operational revenue and costs for UK-based farm-fed anaerobic digesters. There exist sophisticated biogas production models in published literature, but the application of these in farm-fed anaerobic digesters is often impractical. This is due to the limited measuring devices, financial constraints, and the operators being non-experts in anaerobic digestion. The proposed biogas production model is designed to use the measured process variables typically available at farm-fed digesters, accounting for the effects of retention time, temperature and imperfect mixing. The estimation of the operational revenue and costs allow the owners to assess the most profitable approach to run the process. This would support the sustained use of the technology. The calculator is first compared with literature reported data, and then applied to the digester unit on a UK Farm to demonstrate its use in a practical setting.

Optical sensors for the validation of electromagnetic field distributions in biological phantoms

We present optical sensors for the measurement of electric and magnetic fields in an anatomically and dielectrically correct biological phantom of the human head generated by a personal communication device. The sensor is based on all-dielectric optical probes, coupled by fiber optics to the remote light sources and signal processing electronics. The electric field probe utilizes Pockels effect in cadmium telluride, while the magnetic field probe is based on Faraday effect in cadmium manganese telluride. The anticipated measurement sensitivity is 2 V/m (E field) and 0.25 (mu) T (H field), with spatial resolution of 1 cm. Frequency range is 1.8 GHz, and dynamic range is > 60 dB. Both sensors utilize polarimetric optical scheme.