Project summary

Mapping industrial capabilities to develop sustainable production systems and eco-innovations is crucial to support management of complex carbon neutral value chains, identification of latent innovation opportunities as well as development of multi-stakeholder system layouts design and re-design (e.g. new industrial symbiosis projects based on complementary capabilities and resources).

However, current approaches rely mostly on ad-hoc and expensive data-collection methods, are not very scalable or reusable, fail to integrate relevant but fragmented data-sources, use mostly proprietary data, and their results are hard to trace and validate empirically. These limitations translate into climate-related projects having either a very limited overview of technological capabilities available to address their technological needs or into high costs for ad-hoc studies that are expensive to update and hard to reuse.

In this pathfinder project – AMICa – we developed a proof-of-concept for an advanced, data-driven, system-oriented, and reusable industrial capability mapping platform. To scope this project, we explored the target innovation opportunity using as a driving case requirements set by organisations based in the Nordic countries and that perform research and development activities in the area of sustainable biofuels and other sustainable bioenergy-related applications. Activities performed during the development of the project included: requirement elicitation, conceptual design, iteration and validation of the platform, a technology development plan, and activities towards consolidation and expansion of the consortium.

With the results and learnings obtained through the proof-of-concept implemented during this pathfinder project, AMICa can now further develop the necessary technology blocks required to fill the innovation gap that exists between the current demand for advanced capability mapping and the limitations of the methods currently available to serve this demand. Our long-term objective is to be the most effective and efficient platform to map worldwide industrial capabilities able to support the development of new technologies, products and services with positive climate change impact.

AMICa’s project partners included key cleantech ecosystem stakeholders with complementary expertise and roles. DTU, as project leader, led and executed the project leveraging its expertise in mapping complex engineering systems; Chalmers University of Technology provided their experience in the aerospace sector, knowledge-based models and concept selection methods for early system design; The Nordic Initiative for Sustainable Aviation (NISA) provided their industry knowledge as an active industrial organisation working to promote and develop a more sustainable aviation industry with members that include Nordic airports, airlines, aviation authorities and the support of Airbus and Boeing; Novozymes contributed with insights into the challenges of open innovation and R&D orchestration as a large corporate company and world leading developer of biological and bioenergy solutions for sustainable industrial manufacturing processes; and MASH Biotech contributed with experience and ideas based on their work as an innovative SME developing affordable next-generation biofuels. As a result, the project incorporated viewpoints representing the needs and knowledge of SMEs, large private companies, industry organisations, and academia.

AMICa’s foundations

AMICa developed a data-driven approach to map sustainable production capabilities in industrial ecosystems. The approach consists of a conceptual framework that allows to model the problem using already available digital records and an analytical method that leverages network analysis and open data sources to provide an enhanced system overview of technological capabilities available for sustainable production.

AMICa’s objective is to provide improved data-driven decision-support for designing, developing and implementing more sustainable and cleaner production systems using pre-existent technological capabilities. Target users include technology developers, system designers, industry organisations, and policymakers.

Given a specific technological challenge within sustainable production, the data-driven mapping of technological capabilities aims to contribute to answering questions such as:

– What technological capabilities are sufficiently complementary, available, but have not been combined yet to fill an existent technical gap?

– Where in the world do we have hotspots of unexploited but highly complementary capabilities that can serve as starting point for new regional industrial symbiosis projects?

– Which organisations have uniquely positioned capabilities that can serve to bridge sectoral boundaries or technology niches required to develop new system level-solutions?

The theoretical lens applied in this study integrates two complementary perspectives. First, an information-centred view on industrial ecosystems focused on technological capabilities instead of flows of material and energy. Second, a networked view of technological capabilities at industry level, informed by previous studies of complex systems and by a contextually enriched input-process-output (IPO) model.

 Platform requirement elicitation

In this section, we focus on the results and findings connected to our work with partners and other stakeholders. The aim was to gather user needs and ideas for AMICa’s current and future users.

Results of our one-to-one meetings with project partners

Throughout the whole project duration, we conducted one-to-one meetings with NISA, MASH Biotech and Novozymes. These meetings followed a meeting guide, which was developed based on the agreements and discussions during the project kick-off meeting.

Consistent with the original project plans, we focused on the development of technologies that enable the production of sustainable biofuels. During the meetings, we:

• Elicited technological and organisational challenges faced by the project partners.
• Investigated current practices.
• Examined potential problems faced when applying current practices.
• Seek to determine the current baseline when it comes to mapping technological capabilities.
• Explored the potential effects and relevance of regulations, standards and certifications that set constraints to potential solution spaces.
• Asked about other people and organisations we should contact, as well as information sources we should examine.

As a result of these meetings, together with the project partners we identified and conceptualised two complementary challenges that require improved support and that fall within the scope of the AMICa project:

a) Difficulties to map the current and potential network of relations between feedstocks, processing technologies and outputs.

b) Need for a biofuels capability graph that allows mapping relevant organisations based on their capabilities, relations and geographies.

Conceptual framing for our guiding challenges and solutions

In what follows, this document develops a brief conceptual framework for our two guiding challenges.

a) Mapping the current and potential network of relations between feedstocks, processing technologies and outputs.

A key finding of our meetings and literature review is that exploring all relevant combinatorial possibilities between potential feedstocks (e.g. microalgae), processing technologies (e.g. microwave-assisted transesterification) and outputs (e.g. biodiesel), is a difficult but crucial task in the development of new sustainable biofuels.

Mapping the current and potential relations between those feedstocks, processing technologies and outputs is important due to the wide range of possible combinations that can lead to innovations in biofuels and the many more dead-ends that can be found in this process.

Furthermore, there are significant geographical differences in feedstock availability, local path dependencies and geographical specialisation in the development of different processing technologies, as well as different types of local output needs and uses for sub-products. This complexity makes it hard for both practitioners and researchers to have an appropriate overview of relevant sets of “feedstock-technologies-outputs” in a way that shows already tried combinations (successful and not) as well as combinations that have not been tried, and that therefore could lead to new discoveries.

For example, given a feedstock such as heterotrophic microalgae biomass:

• Which sets of processing technologies/methods have been tested? (e.g. different types of thermal treatment)
• Which technologies have been explored the most?
• Which processing technologies have not been tested but are technologically related?
• Which types of oils and sub-products have been generated by each of these processing technologies?
• What about the geography and organisations involved in this biomass and processing technology?

Currently, this mapping task is performed primarily in an ad-hoc and top-down fashion, e.g. by industry experts, specialised agencies, or researchers. Results are presented in the form of written reports, presentations and publications. Although this can lead to valuable insights, this approach has some of the following drawbacks:

Results are often presented in a static and “flat” format. Therefore, it is not possible to explore different levels of analysis or segmentation for the data (e.g. different types of aggregation or filtering).

The raw underlying data is not made available and the presented data is usually a mix between quantifiable explicit information and tacit expert knowledge. This creates barriers for data reuse, makes drawing alternative interpretations difficult, creates analytical “black-boxes”, and as a result, hinders potential replicability and incremental updates by third parties.
Over time, ad-hoc and top-down methods that rely on expert knowledge are more expensive and less scalable. This limits the access that SMEs and other small organisations have to up-to-date and customised technology insights.

The approach selected by AMICa to support organisations dealing with this challenge is to develop an alternative method that derives the relations between feedstocks, processing technologies, and outputs in a bottom-up and data-driven fashion from open data sources. More specifically, we derive a connection of the type feedstock-technology, technology-output, feedstock-feedstock, etc., if we find in our data sources co-occurrences of the keyword(s) associated with a given feedstock, technology or output within the same digital trace (e.g. patent, EU project, biofuel production facility, scientific publication).

As an example, if a body of scientific publications and patents simultaneously mention “white sweet clover” and “anaerobic fermentation”, then there are good reasons to believe that this specific biomass is connected to that specific processing technology. The power of this approach is that it can map in relative terms and over time a vast number of direct and indirect connections, making explicit those technology pathways that have been already taken and those connections that hold potential but remain unexplored. This approach also enables forecasting which pathways might strengthen over time based on historical trends.
Figure 1 illustrates fictional data examples of within and between connections for feedstocks, processing technologies, and outputs.

Figure 1: Illustration for the web of connections between feedstocks, processing technologies and outputs (all connections are fictional and for illustrative purposes only)

b) Towards a biofuels capability graph that allows mapping relevant organisations based on their capabilities, relations and geographies

The second challenge identified refers to the development of new multi-stakeholder projects where is hard to have a good visibility of all relevant actors, their capabilities, and their connections (e.g. previous projects, collaborations or shared third parties).

Two cases that were used to exemplify this challenge during our meetings are the innovation tender process for “Trash to Cash” (e.g. Ladhe, Magnusson, and Nilsson 2014) initiatives and the development of one or more “bioports” (e.g. Mackinnon Lawrence 2013) in Denmark.

The case of innovation tenders for Trash to Cash projects illustrates how the collaborative emphasis of new tender processes show the inefficiencies of current instruments and approaches to map organisational and technological capabilities. In this type of innovation tenders two important challenges are a) to make sure that all those that are relevant get to know and participate in the tendering process, and b) to facilitate the assembly of the best possible teams between those participating in the tendering process, since the complexity of the technical requirements requires combining the capabilities of a number of organisations.

The case of the development of potential aviation “bioports” in Denmark is illustrative of the challenges faced by large capital-intensive biofuel projects. The idea behind the development of aviation bioports is to create hubs, usually around airports, that can supply sustainable biofuels for aviation, combining locally available feedstocks with regional expertise and technology at a competitive price. So far, with different degrees of ambition, bioports projects have been created around the Schiphol airport in the Netherlands, the Oslo airport in Norway, and around the Stockholm and Gothenburg airports in Sweden. However, these projects have only managed to supply a relatively minor amount of biofuel at a high price. What is more, in many cases a significant proportion of the biofuel that these bioports provides is imported from the US instead of locally produced. The idea is that future bioports can better leverage local feedstock and technological expertise from nearby organisations, increasing the amount of sustainable biofuel locally produced and decreasing its price.

The approach selected by AMICa to support organisations dealing with this challenge is to provide access to an up-to-date and data-driven interactive map of relevant organisations in a given geographical and technological area. Such map should integrate explicit capabilities of the relevant organisations as well as their direct and indirect relations within the capability graph.

In this challenge, we reuse the same data sources gathered to answer the first challenge, namely patents, publications, EU projects, list of biofuel facilities and other industry databases. However, instead of focusing on capability, here we focus first on organisations and potential consortia, emphasising the element of collaborative sustainable production and sustainable production systems.

 

Technology

Figure 2 presents an overview of AMICa´s technology infrastructure plan that provides a summary of the results linked with the activities described above. The design of this infrastructure pursues the following principles, which were considered important element within our consortium:

• Scalability: it should be possible to significantly increase the number of digital records hosted and analysed.

• Use of open source technologies in combination with stable services for integration and distribution: open source technologies allow more easily for the integration and modification of features based on the natural learning and changes that occur during the process.

• Analytic transparency that enables replicability, re-use and documentation: In order to create the maximum impact and allow others to build on our results our technological infrastructure has to be understandable and easy to recreate by third parties.

• Flexible infrastructure that allows to consolidate data workflows and analyses for multiple challenges: We strive for a modular architecture that can reduce the cost of future modifications and allow us to try new features incrementally.

Figure 2: Overview of the technology infrastructure behind AMICa

Data architecture

Figure 3 shows an overview of data sources that have been examined and analysed to-date.

Figure 4 shows the current data model highlighting the connections at the meta-level between the available data fields and data sources.

Figure 3: Overview of data sources

Figure 4: Data model draft based on the key data sources currently in use and their metadata

Descriptive statistics of curated data sources

Until now, from a data acquisition and analysis perspective, AMICa has a data coverage larger than any other single public biofuel repository. This is possible thanks to the integration of several data sources. In total, the current dataset contains more than 14.000 unique records, each of them representing a digital trace, including biofuel-related projects, patents, publications, organisations and industrial facilities in more than 140 countries.

The capability mapping platform

For more information about the results of this project please visit:

Our data exploration dashboards

Our Sankey diagrams that allow us to visualise interconnected industrial capabilities

References

Alaswad, A., M. Dassisti, T. Prescott, and A. G. Olabi. 2015. “Technologies and Developments of Third Generation Biofuel Production.” Renewable and Sustainable Energy Reviews 51. Elsevier: 1446–1460. doi:10.1016/j.rser.2015.07.058.

Faaij, André. 2013. “Biomass Resources, Worldwide.” In Renewable Energy Systems, 567–619. New York, NY: Springer New York. doi:10.1007/978-1-4614-5820-3_259.

Faaij, André. 2014. Biomass and Bioenergy. Edited by Khalid Rehman Hakeem, Mohammad Jawaid, and Umer Rashid. Renewable Energy Systems. Vol. 44. Cham: Springer International Publishing. doi:10.1007/978-3-319-07578-5.

Ladhe, Tobias, Johan Magnusson, and Andreas Nilsson. 2014. “From Trash to Cash: A Case of Waste Management Business Model Formation.” In , 275:323–335. doi:10.1007/978-3-319-05951-8_31.

Mackinnon Lawrence. 2013. “Next Step For Green Aviation: ‘Bioports.’” https://www.forbes.com/sites/pikeresearch/2013/12/12/next-step-for-green-aviation-bioports/#6f612cddd91c.

Nordic Council of Ministers. 2016. Sustainable Jet Fuel for Aviation – Nordic Perpectives on the Use of Advanced Sustainable Jet Fuel for Aviation. Copenhagen.

OECD. 2015. System Innovation: Synthesis Report.

OECD / International Energy Agency. 2010. Sustainable Production of Second-Generation Biofuels: Potential and Perspectives in Major Economies and Developing Countries. doi:9789461739698.

Ridley, Caroline E., Christopher M. Clark, Stephen D. LeDuc, Britta G. Bierwagen, Brenda B. Lin, Adrea Mehl, and David A. Tobias. 2012. “Biofuels: Network Analysis of the Literature Reveals Key Environmental and Economic Unknowns.” Environmental Science & Technology 46 (3): 1309–1315. doi:10.1021/es2023253.

Sarasini, Steven. 2015. “(Failing to) Create Eco-Innovation Networks: The Nordic Climate Cluster.” Technology Analysis & Strategic Management 27 (3): 283–299. doi:10.1080/09537325.2014.983894.

Tuominen, Anu, Nina Wessberg, and Anna Leinonen. 2015. “Participatory and Prospective Value Network Analysis: Supporting Transition towards Biofuels in Finnish Road Transport.” European Journal of Futures Research 3 (1): 6. doi:10.1007/s40309-015-0064-y.

Uhlbach, Wolf-hendrik, Pierre-alexandre Balland, and Thomas Scherngell. 2017. R&D Policy and Technological Trajectories of Regions: Evidence from the EU Framework Programmes.

van der Valk, Tessa, and Govert Gijsbers. 2010. “The Use of Social Network Analysis in Innovation Studies: Mapping Actors and Technologies.” Innovation: Management, Policy & Practice 12 (1): 5–17.

 

Data visualisations examples