Zdravko Kravanja (Editor), Proceedings of the 26th European Symposium on Computer Aided Process Engineering – ESCAPE 26June 12th -15th, 2016, Portorož, Slovenia © 2016 Elsevier B.V. All rights reserved.

Utilising Semantics for Improved Decision Making in Bio-refinery Value Chains Nikolaos Trokanas, Madeleine Bussemaker, Franjo Cecelja

PRISE, FEPS, University of Surrey, Guildford, Surrey. GU2 7XH, UK

AbstractThis 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.

Keywords: ontology engineering, optimisation, biorefinery.

1. IntroductionThe need for sustainable and renewable energy resources has been identified. Biorefineries are considered a potential solution to the energy problem (King, Inderwildi, Williams, & Hagan, 2010). In addition to that, the complexity of the domain of biorefining has also been acknowledged in literature (Mansoornejad, Pistikopoulos, & Stuart, 2013). Complexity is present throughout the value chain of biorefineries. From biomass feedstocks which is highly variable to processing technologies that are characterised by a wide range of properties and pre-conditions.

Many tools and frameworks addressing the complexity of biorefineries have been developed. Most of them focus on computational techniques for the optimisation of biorefinery value chains (Akgul, Shah, & Papageorgiou, 2012; Čuček, Martín, Grossmann, & Kravanja, 2011) and modelling (Kokossis, 2014).

An effort to address the complexity of biomass and biorefineries has been presented in (Trokanas, Bussemaker, Velliou, Tokos, & Cecelja, 2015), where an ontology that describes the domain of biorefineries has been developed. Ontologies are a very useful tool for knowledge representation and they can play an important role in formalising the diverse and volatile information about biomass and biomass processing.

In this work, we present our efforts to use BiOnto to facilitate the optimisation of biorefinery value chains. For that, we have developed an optimisation platform (VCAP). The optimisation algorithm employed by the platform takes into account various steps of the value chain from the collection of biomass and its transportation to different biorefining technologies and end products and assesses the economic potential of different types of biomass and/or biorefinery technologies in various geo-economic environments.

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2. Using semantics for optimisationThe potential of semantics in optimisation and decision making has been identified and efforts have been made towards linking them. Ontologies are closely related to mathematical sets (classes) (Cecelja, Trokanas, Raafat, & Yu, 2015) and this aspect makes ontologies good candidates for improved decision making in stochastic search where the nature of ontologies can be used to classify and understand solutions (Labrador-Darder, Cecelja, Kokossis, & Linke, 2009). In addition to that, efforts have also focused on using ontologies to improve decision making in deterministic optimisation problems (Muñoz, Capón-García, Espuña, & Puigjaner, 2012).Deterministic optimisation represents the formal part of decision making while the ontology represents the informal part. In contrast to traditional deterministic approaches where the optimal solution is the objective, linking the two leads to a combination of the formal and informal part of optimisation allowing for the identification of the most acceptable solution. This solution is affected by the knowledge represented by the ontology taking into account, for example, different types of biomass and past experience.

3. Structure of domain ontologyThe BiOnto ontology (Figure 1) structured around two distinct streams/ontologies biorefining domain including biomass types and biorefining technologies. Both ontologies have been modelled in a number of ways forming separate sub-modules which in turn increase its spectrum of use and make the ontology application independent. Figure 1 presents an excerpt of the biorefining technologies module. The expanded sub-module (ByType) models processing technologies based on the type of the conversion. The other sub-modules are not presented for the sake of simplicity and presentation.

Figure 1 Excerpt of BiOnto Ontology Material Stream

4. Biomass characterisation and classificationThe detailed characterisation and the diverse classification of biomass and biorefining technologies helps towards improved decision making. The classification (Figure 1) allows users to explore information following different routes, i.e. WasteBiomass and FeedstockBiomass.Detailed characterisation (Figure 2) provides a source of information about different biomass types and technologies.

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Figure 2 Aspen Characterisation

Searching and retrieving information from the ontology is facilitated and enhanced taking advantage of the graph nature of ontologies and using the appropriate tools (Kapłanski & Weichbroth, 2015; Wróblewska, Kaplanski, Zarzycki, & Lugowska, 2013). For example, combining the detailed ontology modelling and characterisation with the reasoning capabilities of ontologies provides an intuitive search mechanism (Figure 3).

Figure 3 Reasoning with the ontology

The question presented in Figure 3 simply identifies the processes that have the potential to process a “thing” that is lignocellulosic biomass. Inference capabilities of the ontology reclassify any biomass that contains cellulose, hemicellulose and lignin as a lignocellulosic biomass. More complex questions can be formed to extract useful and “hidden” information from the ontology.

5. Optimisation problemThe developed platform (VCAP) facilitates the assessment of biorefinery value chains. It combines a user friendly interface and an optimisation algorithm. The optimisation algorithm employed by the platform takes into account various steps of the value chain from the collection of biomass and its transportation to different biorefining technologies and end products and assesses the economic potential of different types of biomass and/or biorefinery technologies in various geo-economic environments.

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Figure 4 VCAP Screenshot

The platform has been developed in Java and using GLPK solver via Java Optimisation Modeller (Mariño, 2011). Distance calculation is enabled by Graphhopper API (GraphHopper, 2015).

6. Linking knowledge modelling and optimisationThe optimisation problem is populated from the ontology. To achieve this the ontology has been expanded to represent the optimisation problem entities, namely collection points, sawmills, biorefineries etc.

Figure 5 VCAP Ontology

The concepts of VCAP ontology are linked to BiOnto via object properties. For example, CollectionPoint hasBiomassType Biomass and Biorefinery hasTechnology Process. This ontology is used to populate the optimisation problem.

The population of the optimisation problem starts by defining the country and biomass type that is available or demanded (Figure 6).

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Figure 6 Initiating Optimisation Problem

The ontology is then queried and the all stages of the optimisation problem are populated based on the user selection. As described in Figure 3, the reasoning capabilities of the ontology are used to define the processes that can process the selected biomass type that will be used to populate the Biorefinery stage. All other stages, such as storage and sawmills are also defined in a similar manner.

7. Case studyThe platform has been validated with a case study in Scotland. More specifically, three scenarios have been taken into account for the case study: i) Supply-driven, ii) Demand-driven and iii) Technology-driven. The biomass type considered in all three scenarios was softwood in the form of logs and chips (sawmill by-products). The moisture contents of the selected biomass types range between 30% - 60%.

Selecting Scotland as the location retrieves information about the types of biomass available in Scotland. After selecting the desired type of biomass, the technologies that can process this type of biomass are extracted and the problem is populated and solved (Figure 7).

Figure 7 Solving a problem

8. Conclusions and future workThe presented approach enables the use of knowledge modelling to enhance the process of optimisation and decision making for biorefinery value chains. It also demonstrated the use of ontologies for improved search and information retrieval using the reasoning capabilities of ontologies.Current implementation requires the user to define a country and biomass type in order to initiate and populate the optimisation problem. Future implementation will be

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expanded to allow a more flexible approach where the ontology will be used not only to identify appropriate technologies but also target specific products and specific technologies.

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