Dr Franjo Cecelja

This book is a contribution towards better understanding of information technology and information systems and their application in manufacturing.

Previous research has shown that knowledge-based optimization models in process synthesis applications are more robust in both providing final outputs and improving computational performance. This expands this approach by implementing a general knowledge models which in turn enables interpretation of solutions so that non-experts understand detailed procedures of optimization. To this end, an automatic ontology based optimization system that links rule-based optimization model and ontology has been introduced for the purpose to both improve optimization performance and to present new extracted knowledge at optimization run-time. A benchmark reactor network design synthesis case is studied for comparison of performance.The concomitant results show that not only can ontology-based optimization system improve robustness of solutions and computational performance, but also it enables a more accurate understanding of the process synthesis procedures and presents extracted knowledge in a decent format.

Sugarcane bagasse (SCB) is an agro-industrial residue extracted during sugar processing. Sugar mills use basic process technologies for bagasse valorisation. Residues are often disposed improperly or burned inefficiently by thermal boilers causing environmental pollution. Biorefining bagasse as substrate for bio-based chemical production is superior to biomass disposal by incineration, burning and landfill. The conversion of bagasse for value-added applications may be economical with less environmental impact on humans and the ecosystem. Computer aided tools such as ontologies for knowledge modelling represent available information for bagasse feedstocks. This paper presents a reference model referred to as the SCB Ontology which is limited to a framework for the modelling of knowledge on the current utilisation of SCB feedstocks from principal sugarcane-cultivating countries. The SCB Ontology identifies opportunities for efficient bagasse valorisation by principal sugarcane producers. Potential application of the SCB Ontology for bagasse valorisation which is a circular bioeconomy initiative was also discussed.

This paper presents an expansion of an already developed ontology BiOnto (Trokanas, Bussemaker, Velliou, Tokos, & Cecelja, 2015) and processing technology eSymbiosis ontology (Raafat, Trokanas, Cecelja, & Bimi, 2013) towards valorisation of lignocellulosic biomass. The ontology provides a reference model interpretable by humans and computers by further classifying and characterizing lignocellulosic biomass (LCB) in several ways, such as: lignin, hemicellulose and cellulose content, C5 and C6 composition, elemental composition, and heat value. Similarly, LCB processing technologies are classified and characterised based on the input of LCB components, with related conversion rates of specific components. The combination of these classifications can elucidate additional information to assist in decision making for the ontology user. For example, the theoretical conversion rates of C5 and C6 polymeric sugars to ethanol are 0.5987 and 0.5679, then by employing the inference capabilities of the knowledge model, the user can gain insights into theoretical ethanol yields for various biomass types based on their C5 and C6 polymeric composition. This can also be applied to theoretical and actual yields of technologies modelled within the ontology, providing a useful reference tool for biorefinery development.

Renewable energy, especially biofuels, is seen as a viable solution to replace the depleting fossil fuels. Moreover, tighter environmental regulations across the world force many countries to develop strategic approaches to target and screen relevant replacement of resources and technologies. Because there are significant variations in types of biomass and conversion methods, determining the economical effectiveness of overall supply chain (SC) is a complex task. Thus, a holistic approach which accounts for economic, environmental and production aspects across the SC, starting from cultivation to the final products, and establishing appropriate links among them at each stage is imperative in analyzing overall chain efficiency.In this work, for analysis of SC, mathematical modelling is paired with linear programming (LP), which accounts for the variability of technologies that uses biomass as input. The analysis includes economic and environmental factors along the SC. Green House Gases (GHG) is analysed in terms of carbon-equivalent units and converted into monetary values to form financial objective. In addition, incentives for each party in the SC to have economical gain have also been considered.To validate the developed methodology, a case study from Northern Italy is investigated that takes corn stover as a feedstock and targeting production of ethanol with ash, carbon dioxide and DDGS being by-products. It should be highlighted that different types of ethanol conversion technologies are analyzed in terms of overall SC and compliance of GHG emission with existing regulation is also taken into account in the case study. © 2012 Elsevier B.V.

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. © 2013 Elsevier B.V.

We present novel electric and magnetic field measurement systems utilizing optical technologies, which have been developed, tested, and calibrated in the frequency region up to 2 GHz. They show an advantage over currently available measurement systems in that they are passive, all-dielectric, and EM immune. A detailed analysis of field perturbation by the measuring probes in the near-field region was performed using finite-difference time-domain (FDTD) algorithm for solving Maxwell's equations. Both probes were calibrated using a gigahertz transversal electric and magnetic cell, and the results show a linear response.

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.

Continuous reflection and evolution of curricula in chemical engineering is beneficial for adaptation to evolving industry requirements, novel technologies and enhances student experience by being up to date and inclusive of effective teaching strategies. To this end it was necessary to develop a method to enable a holistic reflection on the curriculum and to examine the effect and potential areas of improvement and change. The curriculum was modelled using semantic knowledge modelling through the development of an Ontology, ChEEdO in the Protégé 3.5 environment. ChEEdo models topics within the domain of chemical engineering (Topics), modules taught in chemical engineering courses (Modules) and the learning outcomes of these modules (LearningOutcomes). The learning outcomes were related to the topics using verb properties from Bloom’s taxonomy and using the context of each learning outcome. The functionality of semantic reasoning via the ontology was demonstrated with a case study based on curriculum development. The output of the modelling results demonstrated that the ontology could be successfully utilised for this purpose and this is discussed in relation to practicality and future direction.

This paper introduces a new framework to support synthesis of complex engineering problems using a paradigm that combines optimisation with ontological knowledge modelling. The framework registers and analyzes new solutions by introducing a mechanism of digital certificates to translate structural information and solution features through semantics of an ontology. The solutions are respectively clustered by design features. Tested against complex synthesis of reactor networks, the framework offers a potential to visualize optimization in the course of its development and demonstrates noticeable advantages over conventional methods of a similar basis in convergence and performance.

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.

Conversion of lignocellulose to value-added products is normally focussed on fuel production via ethanol or heat. In this work, a techno-economic assessment of a biorefinery with three product streams, cellulose, hemicellulose and lignin is presented. Moreover, the techno-economic assessment is evaluated in the context of the supply chain through optimisation. A mixed integer linear program was developed to allow for flexible scenarios in order to determine effects of technological and pre-processing variations on the supply chain. The techno-economic and optimisation model integration was demonstrated on a case study in Scotland using woody biomass, either as sawnlogs or sawmill chips. It was established that sawmill chips is the preferred option, however sawnlogs became competitive once passive drying to 30% moisture content (wet basis) was considered. The flexibility of the modelling approach allowed for consideration of technology savings in the context of the supply chain, which can impact development choices.

There is a movement towards implementation of 2nd generation biorefineries producing chemicals from renewable sources or residual feedstock such as woody biomass or woodchips. Value chain optimisation is used as a decision making tool to aide in the development and implementation of biorefining. To this end, an optimisation model for the value chain assessment of a lignocellulosic biorefinery was developed using mixed integer linear programming and verified with a softwood case study. The model allows for the comparison of different feedstock sources with different characteristics such as moisture content and size. Each source may be subjected to alternate pre-processing prior to biorefining to up to three product streams. Optimisation identified the most profitable locations for each process stage, including, collection points and intermediate storages, pre-processing locations, biorefining locations and customers for different production pathways according to the associated transport as well as capital and operational costs. The scene was set in Scotland, UK, with two source streams, logged softwood versus the chipped by-products from the sawmill industry. The biorefinery was based on a technology developed by Bio-Sep Ltd which converts the lignocellulosic feedstock into three product streams, cellulose, hemicellulose and lignin. The results demonstrate the significant effect of moisture content on drying and transportation costs. Overall, it was demonstrated that decision making tools for biomass processing must allow for the consideration of different pre-processing stages with respect to overall costs and/or profit

This paper presents a method for correcting dead reckoning parameters, which are heading and step size, for a pedestrian navigation system. In this method, the compass bias error and the step size error can be estimated during the period that the Global Positioning System (GPS) signal is available. The errors are used for correcting those parameters to improve the accuracy of position determination using only the dead reckoning system when the GPS signal is not available. The results show that the parameters can be estimated with reasonable accuracy. Moreover, the method also helps to increase the positioning accuracy when the GPS signal is available.

Renewable energy in general, and biofuels in particular, is seen as a viable solution for energy security and climate change problems. For this reason many countries, including Thailand, have set common objectives for utilisation of alternative resources. Thailand is an agricultural country and hence it has a great potential for generating renewable energy from a large amount of biomass resources. In consequence, a 15-year renewable energy development plan has been set by the Thai government, which targets an increase in electricity generation of 32%, from 2,800 MW in 2011 to 3,700 MW in 2022, and also an increase in consumption of ethanol by 200%, from 1,095 million litres in 2011 to 3,285 million litres in 2022 (Department of Alternative Energy Development and Efficiency of Thailand, 2008). Sugarcane and rice are the two main industrial crops in Thailand, with estimated production of 73.50 million tons of sugarcane per year (2009) and 31.50 million tons of rice per year (Sawangphol, 2011), and they are seen as a major source of biomass. This research focuses on the biomass from rice mill and sugar mill processes. In order to develop processing facilities that are capable of utilising available biomass and delivering the above set targets, a comprehensive and systematic methodology is required which will support the decision-making process by accounting for technological, economic and parameters. In this thesis, exhaustive simulation and optimisation are proposed as a tool. The first tool is the technology screening. The aim of the technology screening step is to show all profitability of technologies. This is done by considering various components of rice and sugar mills energy frameworks in Thailand: rice mill technology type, sugar mill technology type, ethanol technology type and biomass based power plant technology type. The modelling of processes for converting sugarcane and rice biomass into electrical energy and ethanol has been performed at the level of superstructure which has been chosen because the scope of the work is to screen available options and to compare them in different configurations in terms of economic aspects. The result of the simulation approach has shown the most profitable (shortest payback period) is the configuration that includes electrical rice mill, automated control sugar mill, gasification biomass based power plant and continuous ethanol plant. The sensitivity analysis has compared the cost of feedstock against profitability (payback period). The sensitivity analysis also compared the price of product against profitability (payback period). The result of the sensitivity analysis showed the change in the price of sugar product is the most sensitive for the rice and sugar mills energy framework. The second tool is the optimisation approach. The aim of the optimisation is to maximise the profit (NPV) impact. This is done by considering the various components of the biofuel supply chain in Thailand. All components were calculated based on candidate points including: the biofield(rice mill and sugar mill), biomass warehouse capacity and location, biofuel plant technology type, plant technology capacity, plant technology location, product warehouse capacity and location, transportation type is considered. There are four scenarios in the case study which were created to examine the proposed biomass optimisation model for Thailand to validate the mathematic formulation. The overall conclusion of the optimization approach is that the biomass power plant is profitable at the present time. The lignocellulosic plant will be the option when the process demand a lot of ethanol production. In summary, the proposed research fills the gap in the operational level and process level by multi-biomass from biofield to customer that includes warehouses and multi transportation modes towards the biofuel supply chain. From the business point of view, the research defines the data for the business investor and also analyses the risk of change in product price, feedstock cost and transportation cost.

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.

There is a push towards sourcing chemicals and materials from renewable feedstock such as lignocellulosic biomass. Value chain assessment can be used to evaluate the feasibility of the use of a certain technology and feedstock to produce various chemical sources in a given location. In this work an optimisation model for the value chain assessment of a lignocellulosic biorefinery was developed using mixed integer linear programing. The model allows for a comparison of two product sources which undergo mechanical and/or chemical pretreatment prior to processing by the biorefinery into three product streams, delivered to the customer. Optimisation chooses the source or sources of feedstock and the locations of intermediate storages, pretreatments, biorefinery(ies) and customers with respect to maximising profit. The model was verified based on a case study detailed in Scotland. The case study evaluates the use of felled softwood and/or to the use of sawmill by-products with the production of hemicellulose, lignin and cellulose. The results and implications of the optimisation of the scenario are discussed with respect to costs of transport, processing and product values.

The human food supply chain is placing great strain upon the environment. This is compounded by the creation of wastes at all points along the supply chain. Yet many of these “wastes” are instead surplus foodstuffs that may yet have the potential to be used. Recapturing the value in these surplus foodstuffs is essential in reducing environmental impact of the food supply chain. Insect bioconversion of such surplus foodstuffs back into animal feed is one promising way of doing this. In this study an optimization-based decision support tool is developed to inform bioconversion businesses what locations to source surplus foodstuffs from, where to locate processing facilities and what business model to pursue. A case study business is presented, which utilizes Hermetia illucens (black soldier fly larvae, BSFL) in small bioconversion units which have flexible location options, i.e. close to individual sources of surplus foodstuffs. Spent brewer's grains (SBG) are used as a case study surplus foodstuff. The quantities and locations of SBG are identified within the South East UK. Three business models are evaluated, one using the live BSFL to feed local poultry and two based upon dried BSFL-meal used in aquaculture feeds. The live BSFL business model is shown to be most viable at present with the best margins, and greatest resilience to model perturbations. The novelty of this study is the application of optimization understand the reality of how insect bioconversion may operate within current supply chains, as opposed to the technical or social aspects more usually studied.

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

Industrial Symbiosis (IS) is an innovative approach that aims in creating sustainable industrial networks set to process waste into materials, energy and water. Economic benefits are generated by normally low costs of waste or by-products, by using alternative energy sources and by environmental savings. Environmental benefits are inherent in IS and measured by landfill diversion but also by reduction in emissions and by water savings. Operating within confined geographic and administrative boundaries, IS also generates tangible social benefits to local communities, including job generation and retention, as well as new investments. The key to formation of IS networks is the mediation between participants, the process which requires expertise, hence knowledge in different areas, i.e. waste composition, capability of processing technologies and environmental effect, among the others. The whole process is currently managed by trained practitioners supported by proprietary databases. Limited to the level of expertise and intuition of practitioners and lacking readily available repository of tacit knowledge, the all operation is backward looking and focusing on past successful examples with innovative networks being incidental. This paper presents design and implementation of a semantic web platform which supports creation and operation of IS networks i) by screening the opportunities based on technological capability and resource availability of registered companies, and ii) by monitoring the IS operation and assessing sustainability using economic, environmental and social parameters. The platform employs ontologies to embed tacit knowledge in the domain of IS, knowledge gained from past experience but also from the latest research and otherwise advances in IS. More specifically, a set of integrated ontologies address off-spec nature of waste, i.e. variability in composition, dynamics in availability and pricing, as well as economic and environmental properties including hazardousness. Similarly, processing technologies are modeled in terms of processing capabilities, which include range of type of inputs, conversion rates, water and energy requirements, range of capacities, emissions as well as fixed and operational costs and environmental effects. Explicit knowledge is collected in the process of ontology instantiation with actual data collected from the IS participants during the registration. The ontologies are designed using ontology web language and hence prepared to grow and to share. In the current implementation more than 1500 different waste types and over 200 different technologies have been included. Purpose designed matchmaker identify synergies between participants on their semantic and explicit relevance, the process crucial to formation of IS networks. Semantic relevance defines suitability from the type of waste/by-product and range of technology inputs, including complex composites of waste, i.e. biodegradable waste, and it is calculated from distance between the two instances in the respective ontology. Semantic relevance also includes participant general suitability for particular type of IS. Explicit relevance is calculated using vector similarity algorithm for respective properties, such as quantity, availability, geographical location and hazardousness. More intuitive and complex IS networks are proposed by reclusively repeating matches between two participants which in turn gives an opportunity for even better economic and environmental savings and/or targeted production. Both semantic and explicit matching relevance are aggregated in into a numerical values use for match ranking. The eSymbiosis platform has been implemented as a web service with performance validated verified in the industrial region in Viotia, Greece and with several hundred participating company. The effort has been funded by the LIFE+ initiative (LIFE 09 ENV/GR/000300), which authors acknowledge.

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. © 2014 Elsevier Ltd.

The present study assesses the application of the forward osmosis process using a thermolytic draw solution for irrigation water supply. A novel forward osmosis desalination process with thermal depression regeneration for producing high quality irrigation water is also proposed aiming to provide a reliable and cost-effective method in terms of specific energy consumption. The modified forward osmosis process proposed in this chapter has the potential to provide low-energy production of fresh water from seawater and wastewater, therefore leading to a substantial cost reduction. The specific energy consumption was calculated at the optimum operating conditions of the forward osmosis membrane system producing 1 m3/h of fresh water from seawater at a recovery rate of 50%. The estimated specific energy consumption was 2.7 kWh/m3 and could be decreased when a heat recovery process was used. Furthermore, the combination of the proposed process with a salinity gradient solar pond was evaluated, which resulted in a major reduction of the specific energy consumption to 0.25 kWh/m3. This method can provide a financially viable and sustainable cycle for seawater desalination, particularly in areas of high solar irradiance.

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. © 2013 Elsevier Ltd. All rights reserved.

Organic waste and residue streams are ever increasingly seen as abundant and low-cost resources with great economic and environmental potential. Most of it comes from agriculture, forestry, biobased industry, and forestry. However, comparatively low quality, high cost of logistics and variability in availability and composition are still an obstacle to much greater utilization of these types of biomasses. This chapter is an approach to systemize knowledge about organic residue and waste streams to better promote them as feedstock.

There is a push towards sourcing chemicals and materials from renewable feedstock such as lignocellulosic biomass. Value chain assessment is used to evaluate the feasibility of the use of a certain technology and feedstock to produce various chemical sources in a given location. In this work an optimisation model for the value chain assessment of a lignocellulosic biorefinery was developed using mixed integer linear programing. The model allows for a comparison of two product sources which undergo mechanical and/or chemical pretreatment prior to processing by the biorefinery into three product streams, delivered to the customer. Optimisation identifies the optimal source or sources of feedstock and the locations of intermediate storages, pretreatments, biorefinery(ies) and customers with respect to maximising profit. The model was verified based on a case study detailed in Scotland. The case study evaluates the use of felled softwood and/or to the use of sawmill by-products with the production of hemicellulose, lignin and cellulose. The results and implications of the optimisation of the scenario are discussed with respect to costs of transport, processing and product values.

Remediation of per-and poly-fluoroalkyl substances (PFAS) is challenged with complexities of solutions, recalcitrance of end products and stringent, evolving regulations. Grouping, characterization and classification of PFAS compounds, environmental contaminations and treatment technologies through knowledge modelling has potential to overcome these challenges. Treatment technologies are often required to work in sequences, called treatment trains to achieve complete removal of PFAS from the environment i.e. a removal/separation stage followed by a degradation stage. Here, an ontology framework is presented to classify PFAS compounds and treatment technologies. Potential applications for the knowledge model to support decision making in environmental remediation and technology research and development is discussed.

This paper presents design and implementation of the BiOnto ontology in the domain of biorefining. The ontology models both biomass types and composition and biorefining processing technologies. The designed ontology is verified by a case study in the domain of Industrial Symbiosis.

Model reusability and integration with datasets are major contributors towards their interoperability, the concepts that follows process established by computer aided process engineering (CAPE) community (Belaud & Pons 2002). This paper proposes a semantic approach which enables model/data registration, their discovery and concomitantly model their integration. The functionality of the process is fully controlled by a biorefining domain ontology implemented using Ontology Web Language (OWL) and tested using biorefining related scenarios.

Industrial Symbiosis (IS) is an ecological approach aiming to promote waste valorization opportunities. To date, efforts related to IS process rely on data generated in the aftermath of IS network formation. We propose the integration of the process of screening of IS network options and optimisation of respective environmental performance with the use of semantics.

Continuous reflection and evolution of curricula in chemical engineering is beneficial for adaptation to evolving industry requirements, novel technologies and enhances student experience by being up to date and inclusive of effective teaching strategies. To this end it was necessary to develop a method to enable a holistic reflection on the curriculum and to examine the effect and potential areas of improvement and change. The curriculum was modelled using semantic knowledge modelling through the development of an Ontology, ChEEdO in the Protégé 3.5 environment. ChEEdo models topics within the domain of chemical engineering, modules taught in chemical engineering courses and the learning outcomes of these modules. The learning outcomes were related to the topics using verb properties from Bloom’s taxonomy and using the context of each learning outcome. The functionality of semantic reasoning via the ontology was demonstrated with a case study based on curriculum development. The output of the modelling results demonstrated that the ontology could be successfully utilised for curriculum development and this is discussed in relation to practicality and future direction.

Conversion of lignocellulose to value-added products is normally focussed on fuel production via ethanol or heat. In this work, a techno-economic assessment of a biorefinery with three product streams, cellulose, hemicellulose and lignin is presented. Moreover, the techno-economic assessment is evaluated in the context of the supply chain through optimisation. A mixed integer linear program was developed to allow for flexible scenarios in order to determine effects of technological and pre-processing variations on the supply chain. The techno-economic and optimisation model integration was demonstrated on a case study in Scotland using woody biomass, either as sawnlogs or sawmill chips. It was established that sawmill chips is the preferred option, however sawnlogs became competitive once passive drying to 30% moisture content (wet basis) was considered. The flexibility of the modelling approach allowed for consideration of technology savings in the context of the supply chain, which can impact development choices.

Industrial processes today are characterised by high dynamic and flexibility to adapt to new products and consequently by a short life time. Adding to it the unavoidable environmental component, Industrial Symbiosis (IS) is perhaps the most representative forms of integrated industrial processes. More precisely, IS is an innovative approach that aims in creating sustainable industrial networks set to process waste into materials, energy and water. Economic benefits are generated by normally low costs of waste or by-products, by using alternative energy sources and by environmental savings. Environmental benefits are inherent in IS and measured by landfill diversion but also by reduction in emissions and by water savings. Operating within confined geographic and administrative boundaries, IS also generates tangible social benefits to local communities, including job generation and retention, as well as new investments. The key to formation of IS networks is the mediation between participants, the process which requires expertise, hence knowledge in different areas, i.e. waste composition, capability of processing technologies and environmental effect, among the others. The whole process is currently managed by trained practitioners supported by proprietary databases. Limited to the level of expertise and intuition of practitioners and lacking readily available repository of tacit knowledge, the all operation is backward looking and focusing on past successful examples with innovative networks being incidental. This paper presents design and implementation of a semantic web platform which supports creation and operation of IS networks i) by screening the opportunities based on technological capability and resource availability of registered companies, and ii) by monitoring the IS operation and assessing sustainability using economic, environmental and social parameters. The platform employs ontologies to embed tacit knowledge in the domain of IS, knowledge gained from past experience but also from the latest research and otherwise advances in IS. More specifically, a set of integrated ontologies address off-spec nature of waste, i.e. variability in composition, dynamics in availability and pricing, as well as economic and environmental properties including hazardousness. Similarly, processing technologies are modeled in terms of processing capabilities, which include range of type of inputs, conversion rates, water and energy requirements, range of capacities, emissions as well as fixed and operational costs and environmental effects. Explicit knowledge is collected in the process of ontology instantiation with actual data collected from the IS participants during the registration. The ontologies are designed using ontology web language and hence prepared to grow and to share. In the current implementation more than 1500 different waste types and over 200 different technologies have been included. Purpose designed matchmaker identify synergies between participants on their semantic and explicit relevance, the process crucial to formation of IS networks. Semantic relevance defines suitability from the type of waste/by-product and range of technology inputs, including complex composites of waste, i.e. biodegradable waste, and it is calculated from distance between the two instances in the respective ontology. Semantic relevance also includes participant general suitability for particular type of IS. Explicit relevance is calculated using vector similarity algorithm for respective properties, such as quantity, availability, geographical location and hazardousness. More intuitive and complex IS networks are proposed by reclusively repeating matches between two participants which in turn gives an opportunity for even better economic and environmental savings and/or targeted production. Both semantic and explicit matching relevance are aggregated in into a numerical values use for match ranking. The platform has been implemented as a web service with performance validated verified in the industrial region in Viotia, Greece and with several hundred participating company. The effort has been funded by the LIFE+ initiative (LIFE 09 ENV/GR/000300), which authors acknowledge.

Mathematical modelling and optimisation at both household and energy supply network levels were developed to study the transformation of the natural gas-based domestic energy supply system with the introduction of biomethane generation, processing and utilisation based on a range of feedstock and conversion technologies. Biomethane processing includes, among other options considered, the conceptual development of a novel approach for upgrading biogas which utilises existing onshore natural gas processing capacity. Four different objective functions were considered for optimisation, representing different economic and environmental propositions, to identify the best path for introducing biomethane with multiple types of feedstock. Applying these objective functions to UK’s domestic energy supply, and assuming a range of subsidies available, it was established that a technically significant displacement of natural gas could be achieved, with displacement capabilities of 48%–72%, and greenhouse gas (GHG) reductions between 64% and 80%. Economically, these ranges of achievement would correspond to various levels of capital investment and economic viability, depending on the objective functions. Those cases leading to a positive net present value (NPV) appeared to heavily rely on subsidies and could run into a significant loss if subsidies were removed in the operational phase. In contrast, optimisation not assuming any subsidies in the first place could lead to a fundamentally economically viable system, but at the cost of a significantly lower level of biomethane penetration compared to the cases assuming subsidies. Overall, the results have indicated the importance of carefully selecting optimisation objectives, and revealed the potential consequences of adopting financial subsidies in developing the biomethane infrastructure.

The paper presents theoretical analysis and experimental verification of an electro-optic sensor for measurements of DC electric fields in space charge environment. Effects of both unipolar and bipolar charge were investigated. It was shown that in both cases the sensor output is linearly dependent on the intensity of measured electric field and independent of the space charge density. The achieved measurement resolution is 100 V/m with a dynamic range of 80 dB and a temperature stability of 0.1%/°C for temperatures near 20 °C. © 2006 Elsevier Ltd. All rights reserved.

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.

This paper presents a novel electric field measurement system utilizing optical technology, which has been developed, tested, and calibrated for the near-field measurement in the frequency range up to 1.8 GHz. The measuring probe is passive, all-dielectric, electromagnetic interference (EMI)-immune, and provides the information on the field strength, frequency, and phase. The achieved measurement resolution and minimum mode measurable fields were 10 V/m, with a spatial resolution of 10 mm. A finite-difference time-domain (FDTD) algorithm for solving Maxwell's equation was used to assess the field perturbation by the presence of the measuring probe and the suitability for the near-field measurement.

The number of ontologies available today, more than 10,000 as identified by dedicated search engines , is a good indicator of the progress made so far. Ontologies are developed for and used in several applications ranging from knowledge representation and semantic search up to data integration and web service discovery (Cheung, Cheung & Kwok 2012)(Trokanas, Cecelja & Raafat 2013, Raafat et al. 2013b)(Raafat et al. 2013a). Much of recent ontology development efforts have been reported in the domain of Process Systems Engineering (give references from review). It is the fact that ontologies are by definition conceptualisations that are supposed to be shared and reused (Gruber 1993, Gruber 1995). Ontology reuse is a practical and useful approach for knowledge engineers. It has the potential to reduce the cost of developing ontology from scratch (24), as well as promote interoperability among applications (6). In addition, many of the existing ontologies cover similar or overlapping domains (14). Ontology reuse is also part of many of existing frameworks and established methodologies (Noy, McGuinness 2001, López et al. 1999) for ontology development. However, by reviewing the literature (Bock et al. 2010, Brandt et al. 2008, Fernandes et al. 2011, Giménez et al. 2008), it became apparent that ontology reuse is a seldom occurring task. Researchers have identified a lack in robust (27) and pragmatic (24) methods for evaluating and identifying ontologies for reuse. This paper presents a metric for the evaluation of ontologies for reuse. The metric account for ontology metadata such as the datatypes, external resources, terminology and languages used for modelling. It also accounts for structural characteristics of ontologies such as size, breadth and width of the evaluated ontologies. All the available information is translated into vectors which are consequently compared. The resulting similarity scores for each aspect are aggregated and normalised into a single metric ranging between [0,1]. The metric has been tested and verified using experiments for the development of an ontology for the domain of Industrial Symbiosis. Bock, C., Zha, X., Suh, H. & Lee, J. 2010, "Ontological product modeling for collaborative design", Advanced Engineering Informatics, vol. 24, no. 4, pp. 510-524. Brandt, S.C., Morbach, J., Miatidis, M., Theißen, M., Jarke, M. & Marquardt, W. 2008, "An ontology-based approach to knowledge management in design processes", Computers & Chemical Engineering, vol. 32, no. 1, pp. 320-342. Cheung, C.F., Cheung, C. & Kwok, S. 2012, "A knowledge-based customization system for supply chain integration", Expert Systems with Applications, vol. 39, no. 4, pp. 3906-3924. Fernandes, R.P., Grosse, I.R., Krishnamurty, S., Witherell, P. & Wileden, J.C. 2011, "Semantic methods supporting engineering design innovation", Advanced Engineering Informatics, vol. 25, no. 2, pp. 185-192. Giménez, D.M., Vegetti, M., Leone, H.P. & Henning, G.P. 2008, "PRoduct ONTOlogy: Defining product-related concepts for logistics planning activities", Computers in Industry, vol. 59, no. 2, pp. 231-241. Gruber, T.R. 1995, "Toward principles for the design of ontologies used for knowledge sharing", International journal of human computer studies, vol. 43, no. 5, pp. 907-928. Gruber, T.R. 1993, "A translation approach to portable ontology specifications", Knowledge acquisition, vol. 5, no. 2, pp. 199-220. López, M.F., Gómez-Pérez, A., Sierra, J.P. & Sierra, A.P. 1999, "Building a chemical ontology using methontology and the ontology design environment", Intelligent Systems and their Applications, IEEE, vol. 14, no. 1, pp. 37-46. Noy, N.F. & McGuinness, D.L. 2001, "Ontology development 101: A guide to creating your first ontology", . Raafat, T., Trokanas, N., Cecelja, F. & Bimi, X. 2013a, "An Ontological Approach Towards Enabling Processing Technologies Participation in Industrial Symbiosis", Computers & Chemical Engineering, . Raafat, T., Trokanas, N., Cecelja, F. & Bimi, X. 2013b, "An ontological approach towards enabling processing technologies participation in industrial symbiosis", Computers & Chemical Engineering, vol. 59, no. 0, pp. 33-46. Trokanas, N., Cecelja, F. & Raafat, T. 2013, "Semantic approach for pre-assessment of environmental indicators in Industrial Symbiosis", Journal of Cleaner Production, .

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 paper introduces a semantic algorithm for building Industrial Symbiosis networks. Built around ontology modelling of knowledge in the domain of IS, 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 parameters 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.

design of InterCAPEmodel ontology, which contains a comprehensive description to represent the knowledge of models and data in the biorefining domain, is presented. Primarily, the InterCAPEmodel ontology aims at providing implicit knowledge that reflects process synthesis logic, and explicit knowledge including a complete set of input/output types and the parameters associated with each model and dataset to manage the repository. At present, the InterCAPEmodel ontology supports integration of model and/or data. To fully exploit the potential of providing the description of the model and data to sufficiently support semantic integration, the design of knowledge model is described and the use of ontology that demonstrates its functionality is presented using a case study of a lignocellulosic based biorefining models and data at supply chain level.

Agent-based Global Energy Management Systems for the Process Industry Y. Gao, Z. Shang, F. Cecelja, A. Yang, and AC Kokossis Abstract Energy utility systems ...

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.

Bioethanol is currently the most important biofuel for automotive transportation and the European Community has set common objectives about the utilization of biofuels for all member states. The current Italian production of bioethanol is not sufficient to achieve these goals. In this work the existing processes for ethanol production from corn and for energy generation from corn stover are analyzed with an exhaustive simulation approach. They are supplemented by local (internal) energy generation used to supply heat and/or electrical power to minimize the energy consumption from fossil sources. Different scenarios are analyzed to determine better, if not the best, way of production bioethanol from corn while minimizing the energy consumption from fossil sources. An economic and profitability analysis for every scenario is also provided.

The paper introduces a new paradigm for Industrial Symbiosis by pioneering the use of ontology engineering in the field. Semantics are used to model Industrial Symbiosis flows, to model enabling technologies and to systematise the development of a matching service. Combined with a systems engineering approach, semantics further combine tacit knowledge from Industrial Symbiosis experts with explicit knowledge from Industrial Symbiosis participants. The new approach promises systematic venues to discoveries, innovative solutions, and a holistic methodology in the development of Industrial Symbiosis networks. The paradigm has been implemented as a multilingual web service to support Industrial Symbiosis communities and to embrace small and medium enterprises that are currently side-lined from developments. The approach has been tested and validated using real-life data and its functions are demonstrated with illustrative examples.

Industrial Symbiosis (IS) is an ecological approach aiming to promote waste valorization opportunities. To date, efforts related to IS process rely on data generated in the aftermath of IS network formation. We propose the integration of the process of screening of IS network options and optimisation of respective environmental performance with the use of semantics.

This paper introduces an ontological approach to designing an integration platform for mobile telemedicine (MTM) services based on Service Oriented Architecture (SOA) and Semantic Web technologies. The meta-ontology is the core element of the platform, which coordinates the process of service registration and discovery between heterogeneous tele-medical applications, operating in a mobile environment while considering mobile, medical and environmental dynamic attributes.

Current optimisation methods, especially stochastic methods, are short of intermediate data analysis and control. Moreover the intermediate data has rarely been interpreted into the knowledge useful for either optimisation itself or industry productions. In this paper, we proposed a new optimisation method featuring on the capability of intermediate data analysis and control without setting any computational burden on optimisation. The method makes use of the long Markov process whose concept is borrowed from that in the simulated annealing but meanwhile bypasses its inherent sequential nature by using the proposed conceptual pools to populate the generated solutions. Four simple optimisation problems were selected to investigate the validity of the new algorithm. The algorithm was also studied on a complex engineering problem by comparing with the simulated annealing. Results showed that the new algorithm was as robust as the simulated annealing algorithm but can achieve quicker convergence.

This paper presents an effort to utilise semantics to improve the decision making process in biorefinery value chains. In more detail, an ontology describing biomass and biorefineries is used to facilitate the identification of the best options for the population of the optimisation problem. In addition to that, the reasoning capabilities of ontologies are used to enhance search of information. The approach has been verified with a case study for biomass available in Scotland.

This paper introduces a knowledge representation of process system models and data in the domain of biorefining. The semantic approach for model and data discovery is supported by the explicit description of the models and data representing biorefining technologies and their characterisation with matching properties. The domain ontology enables the process of registering models and data, instantiation of ontology through parsing, as well as facilitation of flexible model and data discovery. The proposed method of model and data discovery has been demonstrated using a process flowsheet related to biorefining process and the concomitant performance of the method has been verified.

Mathematical modelling and optimization of the natural gas based Distributed Energy Supply System (DESS), both at the building level and the overall energy supply network level was carried out for three types of micro-CHP - solid oxide fuel cells, Stirling engines, internal combustion engines - and for two operating strategies - cost-driven and primary energy-driven. The modelling framework has particularly allowed the quantification of the impact of micro-CHP on the total primary energy consumption at the whole network level. The result of a case study based on the UK reveals the range of the overall reduction in primary energy usage and central power plant capacity requirement and the range of the increase in natural gas supply to homes. The economic analysis shows that the coupling of different technologies, sizes of the CHP engine, and the operating strategies gives rise to a wide range of payback time. © 2013 Elsevier B.V.

Mathematical modelling and optimization of the Distributed Energy Supply System (DESS) using natural gas, both at the building level and the overall energy supply network level was carried out for various types of micro combined heat and power (micro-CHP) – solid oxide fuel cells, Stirling engines, internal combustion engines – and for two different operating strategies – cost-driven and primary energy-driven. The modelling framework captures the overall impact of the adoption of micro-CHP systems on the total primary energy usage in both generation and distribution. A detailed case study on the UK domestic energy supply was undertaken by applying both operating strategies to four different sizes of houses. The best technology selected in each case was evaluated in terms of the economics, total primary energy consumption, and reduction of central power generation requirement. It was shown that the primary energy consumption driven option selected technologies which could potentially achieve 6%-10% reduction of total primary energy use compared to the base case where micro-CHP was not adopted, which represents about 50% more reduction than the outcome of the cost-driven strategy.

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.

This paper proposes and integration platform for mobile services, based on Service Oriented Architecture (SOA)and Semantic Web technologies. Mobile technology has revolutionized many aspects of our lives. The wireless infrastructure is now capable of supporting applications and services in various domains such as transport, medicine and tourism, by providing increased coverage and quality of data transfer. With the rapid growth in number of mobile services there is a genuine need for a common platform to allow interoperability and reusability regardless of the underlying structure of each service. We have introduced an ontololgical approach to designing a platform which enables registration of mobile service providers and users as semantic web services. A meta-ontology has been designed as the core element of the platform to coordinate the overall process of registration and discovery and to enable interoperability between heterogeneous services. The meta-ontology supports the SOA concept and makes the process of service discovery robust by serving as a structuring basis and central reference point. The ultimate objective is to provide an integration platform to allow interoperability of heterogeneous applications operating in a mobile environment with a view to enhance service discovery considering, mobile, domain and environmental attributes of the services and users. As at present, parts of the meta ontology has been implemented and the process of registration verified, together with concomitant service discovery. Setting the scene for verifying interoperability of mobile services is taking place using "Mobile Telemedicine" as a use case.

Industrial Symbiosis (IS) is a growingly accepted paradigm for processing waste into material, energy and water with benefits to participants measured by economic, environmental and social gains. Despite of some attempts to quantify them no unified metrics or methods for calculating concomitant indicators have been proposed. This paper presents a systemisation of IS relevant environmental metrics and a semantic approach based on knowledge modelling using ontologies to facilitate “a priori” calculation of respective indicators. The approach and metrics are presented and verified with a case study.

Previous research has shown that knowledge-based optimization models in process synthesis applications are more robust in both providing final outputs and improving computational performance. This expands this approach by implementing a general knowledge models which in turn enables interpretation of solutions so that non-experts understand detailed procedures of optimization. To this end, an automatic ontology based optimization system that links rule-based optimization model and ontology has been introduced for the purpose to both improve optimization performance and to present new extracted knowledge at optimization run-time. A benchmark reactor network design synthesis case is studied for comparison of performance.The concomitant results show that not only can ontology-based optimization system improve robustness of solutions and computational performance, but also it enables a more accurate understanding of the process synthesis procedures and presents extracted knowledge in a decent format.