A reference data model to support biomass supply chain modelling and optimisation
Introduction
The “Climate Action and Renewable Energy Package” of the European Union encourages the EU member states to improve their energy efficiency and to promote alternative and renewable energy sources in order to reduce greenhouse gas emissions and fossil fuel dependency (European Commission, 2010a). Studies forecasting the use of alternative and renewable energy sources in 2050 indicate that biomass will occupy a significant share (40–50%) in the production of electricity, heat and transport fuels (Dufresne et al., 2009, IPCC, 2011, Singer et al., 2011) because biomass is abundantly present and it can be stored to generate energy on demand (Mafakheri and Nasiri, 2014, Rentizelas et al., 2009b). In addition, biomass can also be a sustainable alternative for the currently fossil based production of chemicals and medicines such as cosmetics and food additives and materials such as bioplastics and paper. Unfortunately, uncertainties related to weather variability, policy conditions and market fluctuations (Shabani et al., 1998) as well as the barriers induced by high costs for transport and handling of biomass feedstock (Gold and Seuring, 2011, Shabani et al., 1998) have prevented the bio-based sector from making a greater contribution to the present market.
To address these prohibiting factors for the take-off of bio-based systems, recent research efforts combine supply chain management and operations research (De Meyer et al., 2014, Mafakheri and Nasiri, 2014, Shabani et al., 1998, Sharma et al., 2013). Most of the developed optimisation models address a specific (part of the) supply chain for a specified biomass type (De Meyer et al., 2014). Since most barriers for the development of a sustainable bio-based sector relate to the characteristics of the biomass products, most optimisation models and simulation models address the upstream part of the supply chain (De Meyer et al., 2014, Shabani et al., 1998, Sharma et al., 2013). This implies that the operations are considered from biomass production up to the delivery to the conversion facility. Each of these models comes with its own database or its own way to structure the input data needed in the optimisation model (Ayoub et al., 2007, De Mol et al., 1997, Freppaz et al., 2004, Frombo et al., 2009). De Mol et al. (1997) store all data concerning possible network structures in their database, such as costs, capacities, storage losses and seasonality in supply and demand. These data are transferred to the optimisation model by writing them to ASCII files (De Mol et al., 1997). The database developed by Ayoub et al. (2007) covers the data of biomass resources for a pre-defined country or region. Data mining techniques are used to determine the optimal locations of facilities, available biomass quantities and the allocation of biomass feedstock based on spatial distribution (Ayoub et al., 2007). Freppaz et al. (2004) and Frombo et al. (2009) both come with a relational database connected to the optimisation model by means of an open database connectivity (ODBC) interface. Both databases cover the input data of the optimisation model required to characterise the problem (Freppaz et al., 2004, Frombo et al., 2009). None of the publications describes the data model in sufficient detail to allow re-use.
These different approaches and the lack of transparency on the data structure hamper the application of the simulation and optimisation models to other types of supply chains than the one for which they have been developed and constraints the exchange of the models among users. A ‘reference’ data model can purvey genericity and model interoperability by enabling the representation of typical biomass supply chains capturing different sorts of products, operations and attributes. This would result in a holistic platform that is capable of addressing different types of problems for different types of biomass-based supply chains and different optimisation or simulation models. Such unifying frameworks support analysis of alternatives with stakeholders and assessing and communicating their results in a transparent way (Kelly et al., 2013). In addition, a generic reference data model is a precondition to achieve a standard exchange format. Data (in case studies) can then be made public in a usable (XML) format, so that different tools can be benchmarked in the same case studies. The need for standardisation in modelling and data transfer has also been recognised in other fields such as ecosystem modelling (Mooij et al., 2014), hydrogeology (Wojda and Brouyère, 2013), soil survey (de la Rosa et al., 2002), space physics (Todd et al., 2010), public transport (Comité Européen de Normalisation, 2001), medical care (Beeler, 1998, Canfield et al., 1994) and financial support (Ecofin Data Model AG, 2006). Furthermore, the data model should be spatially enabled for visualisation of the problem and/or computation of spatial parameters (absolute and relative locations) involved in the problem (Ayoub et al., 2007, de la Rosa et al., 2002, Frombo et al., 2009, Perpina et al., 2009).
This paper ambitions to present a generic and flexible reference data model, based on knowledge-based engineering (Kelly et al., 2013), meant as the blueprint of the database component of information and decision support systems related to typical biomass-based supply chains (e.g., agricultural and silvicultural crops and residues, sewage sludge, municipal solid waste, industrial residues and animal residues for production of bioenergy and biomaterials). Such knowledge based models are often used in Expert Systems in which the quality of the model is decisive for the succes of the system (Kelly et al., 2013). Section 2 describes the procedure used to design and validate the reference data model, while Section 3 presents the resulting conceptual data model, its physical implementation and an exercise for validation of its genericity and applicability. This results in the evaluation of the reference data model and the definition of opportunities for its further elaboration (Section 4).
Section snippets
Design of the reference data model
In general, decision support systems (DSS) described in the field of biomass supply combine three modules: (1) a database module, (2) a query module and (3) a decision module (Fig. 1) (Freppaz et al., 2004, Frombo et al., 2009, Lin et al., 2015, Zambelli et al., 2012). The database module stores the input data of the optimisation or simulation model to characterise the problem and the supply chain to be analysed. These input data encompass non-georeferenced data such as the characteristics of
Generic high level process model
To ensure a comprehensive modelling of the biomass supply chain, a first step consists of defining a high level process model based on a generic cradle-to-gate life cycle inventory (LCI) of biomass-based supply chains. The goal of this cradle-to-gate analysis is to identify and group the possible product and operation types and to define their relationships and possible combination sequences. In general, three major chain segments can be distinguished in bioenergy supply chains (Fig. 2) (An
Discussion and conclusion
The data model elaborated in this paper is meant to serve as a template of the database component of information and decision support systems related to the upstream segment of supply chains covering different types of biomass feedstock, different types of destinations (biomaterials and bioenergy) and, hence, different typesof handling techniques. This generic reference data model enables the exchange of optimisation and simulation models among users and is a precondition to achieve a standard
Acknowledgments
This research is funded by a Ph.D. grant of the Agency for Innovation by Science and Technology (IWT) in Flanders, Belgium (Grant number: 101203).
References (75)
- et al.
Economic optimization of a lignocellulosic biomass-to-ethanol supply chain
Chem. Eng. Sci.
(2012) - et al.
Spatial optimization of Jatropha based electricity supply chains including the effect of emissions from land use change
Biomass Bioenergy
(2016) - et al.
Biofuel and petroleum-based fuel supply chain research: a literature review
Biomass Bioenergy
(2011) - et al.
Two levels decision system for efficient planning and implementation of bioenergy production
Energy Convers. Manag.
(2007) HL7 Version 3—an object-oriented methodology for collaborative standards development
Int. J. Med. Inf.
(1998)- et al.
Bioethanol supply chain system planning under supply and demand uncertainties
Transp. Res. Part E Logist. Transp. Rev.
(2012) - et al.
The biological sustainability of biomass harvesting
Agric. Ecosyst. Environ.
(1998) An entity-relationship approach to decision support and expert systems
Decis. Support Syst.
(1988)- et al.
A multilingual soil profile database (SDBm Plus) as an essential part of land resources information systems
Environ. Model. Softw.
(2002) - et al.
Optimisation methods for the design and management of biomass-for-bioenergy supply chains: a review
Renew. Sustain. Energy Rev.
(2014)
Considering biomass growth and regeneration in the optimisation of biomass supply chains
Renew. Energy – Spec. issue Optim. Methods Renew. Energy Syst. Des.
A generic mathematical model to optimise strategic and tactical decisions in biomass-based supply chains (OPTIMASS)
Eur. J. Oper. Res.
Analyzing the design and management of biomass-to-biorefinery supply chain
Comput. Ind. Eng.
Biomass energy transport. Analysis of bioenergy transport chains using life cycle inventory method
Biomass Bioenergy
Optimizing forest biomass exploitation for energy supply at a regional level
Biomass Bioenergy
Planning woody biomass logistics for energy production: a strategic decision model
Biomass Bioenergy
Supply chain and logistics issues of bio-energy production
J. Clean. Prod.
Waste biomass-to-energy supply chain management: a critical synthesis
Waste Manag.
Optimal design and global sensitivity analysis of biomass supply chain networks for biofuels under uncertainty
Comput. Chem. Eng.
CyberGIS-enabled decision support platform for biomass supply chain optimization
Environ. Model. Softw.
Modelling of biomass-to-energy supply chain operations: applications, challenges and research directions
Energy Policy
Serving many at once: how a database approach can create unity in dynamical ecosystem modelling
Environ. Model. Softw.
Methodology based on Geographic Information Systems for biomass logistics and transport optimisation
Renew. Energy
An optimization model for multi-biomass tri-generation energy supply
Biomass Bioenergy
Logistics issues of biomass: the storage problem and the multi-biomass supply chain
Renew. Sustain. Energy Rev.
Biomass supply chain design and analysis: basis, overview, modelling, challenges, and future
Renew. Sustain. Energy Rev.
Development and implementation of integrated biomass supply analysis and logistics model (IBSAL)
Biomass Bioenergy
Relational database design based on the entity-relationship model
Data Knowl. Eng.
The bioenergy potential of conservation areas and roadsides for biogas in an urbanized region
Appl. Energy
Design of regional production networks for second generation synthetic bio-fuel – a case study in Northern Germany
Eur. J. Oper. Res.
An object-oriented hydrogeological data model for groundwater projects
Environ. Model. Softw.
A GIS decision support system for regional forest management to assess biomass availability for renewable energy production
Environ. Model. Softw.
Challenges and models in supporting logistics system design for dedicated-biomass-based bioenergy industry
Bioresour. Technol.
Optimization-based approaches for bioethanol supply chains
Ind. Eng. Chem. Res.
Nieuwe perspectieven voor beheerresten
The standard data model approach to patient record transfer
Landschapsstroom – Energetische Benutting Van Biomassa Uit Natuurterreinen
Cited by (12)
An integrated approach to prioritise parameters for multi-objective optimisation: A case study of biomass network
2020, Journal of Cleaner ProductionCitation Excerpt :On the other hand, Lim et al. (2019) optimised a biomass supply network model to achieve circular utilisation via element targeting approach, which the work was extended to consider the multi-period problem of resources and demand fluctuations (Lim et al., 2019b). In the macroscopic view, De Meyer et al. (2016) developed a reference data model which serves as a template to support biomass supply network modelling and optimisation that allowed the benchmarking of different tools of modelling. Mathematical model gives an optimal solution for single objective problem and a range of feasible solutions for multi-objective problem.
Optimisation tool for logistics operations in silage production
2019, Biosystems EngineeringCitation Excerpt :The majority of the barriers for the development of efficient biomass supply chains are related to the characteristics of the biomass products (De Meyer, Snoeck, Cattrysse, & Van Orshoven, 2016), and thus, to the processes performed at the first links of the chain.
Techno-economic evaluation of biomass-to-end-use chains based on densified bioenergy carriers (dBECs)
2019, Applied EnergyCitation Excerpt :The supply chain literature relevant to our research can be found, to a certain extent, in [12] and can be categorised into four types. Thorough analysis was performed to optimise feedstock allocation, storage, pre-treatment and end-use locations in specific regions and countries [13–17], or to related topics and also based on Geographic Information System (GIS) methodologies on deriving biomass allocation potentials in specific regions and countries [18–20]. Without specific network structures and significant predetermined temporal- and/or spatial granularity, relevant publications discuss different densification technologies with the aim to optimise a specific end-use application [21–25].
Simulation and evaluation of a biomass gasification-based combined cooling, heating, and power system integrated with an organic Rankine cycle
2018, EnergyCitation Excerpt :However, due to the issues of energy crisis and environment pollution, an even higher advantage can be obtained by feeding the system with renewable energy. Biomass is an attractive alternative to fossil fuels of the CCHP system owing to its abundance [2], wide distribution [3], and CO2 neutrality [4]. Among the energy conversion technologies of biomass, gasification is a self-sufficient thermo‒chemical process that can be used to produce clean gaseous fuel [5].
Electricity generation prospects from clustered smallholder and irrigated rice farms in Ghana
2017, EnergyCitation Excerpt :Among the important drawbacks of modern bioenergy is the complexity of the supply chain (from biomass sourcing to energy consumption) and the economic costs associated with the conversion of the resource. For this reason, the integration of biomass in the energy planning of a community/country requires the development of advanced planning and economic tools that allow for assessing and optimizing costs in order to identify the optimal location for biomass investments [9,14]. Indeed, if bioenergy is to have a long-term future, it must be able to provide affordable, clean and efficient energy forms.
Integrated optimization of sustainable supply chains and transportation networks for multi technology bio-based production: A decision support system based on fuzzy ε-constraint method
2016, Journal of Cleaner ProductionCitation Excerpt :A modelling approach that can accommodate this diversification will be more resilient and may support longer term supply, and reduce the effects of seasonal fluctuations and price instabilities as well as technological uncertainties on the supply chain performance. In addition, the representation of typical biomass supply chains capturing different sorts of products, operations and attributes would result in a holistic approach that is capable of addressing different types of problems for different types of biomass-based supply chains and different optimization models (De Meyer et al., 2016). To address these gaps in the literature, this paper proposes a new mathematical programming based optimization approach to design sustainable supply chains along with logistics networks.