Case study: Industrial Symbioses in EPOS

In 2015, a consortium of five process industries, five SMEs, and two academic institutes proposed the EPOS project. The industrial interest in the project lay in the development of a methodology and software for supporting energy and resource efficiency, along with the opportunity to explore mutually beneficial activities between the selected sites of the industrial partners. The academic interest lay in the testing and improvement of the tools developed by the two institutes; first, an engineering tool for the design and analysis of integrated energy systems, second, a methodology set in the objective to measure and monitor the effect of cluster management on the concerted actions of the companies (Van Eetvelde, 2017). The latter is built around the premise that non-technological aspects of industrial symbiosis are as – if not more – important when assessing the feasibility and impact of symbiotic activities. These non-technological aspects are grouped under five domains; Legal, Economic, Spatial, Technical, and Social (Van Eetvelde, Delange, et al., 2005) and the method is termed LESTS.

The boundary of the case study is limited to the agglomerates of industries in four coun-tries and five locations, as shown in Figure 1 1 (on the next page). For parsimony of words, the term cluster is used to identify each of the locations in case study I. The widely ac-cepted use of the word refers to similar companies and related institutions without a defined geographic boundary. Here a cluster refers to industrial sites belonging to differ-ent sectors, all partners of the EPOS project who volunteered to be assessed for their potential to engage in industrial symbiosis. The benefit is two-fold, first; it helped avoid sharing sensitive information between competing industries, second; similar sectors pro-duce similar wastes and require similar inputs, but dissimilar sectors can have higher chances of finding matches between wastes and energy streams. This is called cross-sec-torial industrial symbiosis.

These industrial clusters are located in Poland, France, and UK, while two district heating networks are located in France and Switzerland, each. The industrial sectors or districts include the following combination of partners in each geographical location.

1. The Rudniki cluster in Poland consists of the cement (Rudniki) and minerals (Jasice and Romanowo) and steel (Krakow) plants.

2. The Lavéra cluster (France) is comprised of chemicals (Lavéra) and steel (Fos) plants.

3. In Humber cluster (UK) is made up of minerals (Melton) and cement (South Ferriby) and chemicals (Hull) plants.

4. Steel (Dunkirk) represents industry in Dunkirk cluster (France). The steel plant engages with the city of Dunkirk in a district heating network (DHN).

5. City of Visp (Switzerland) has a district heating and cooling network fed by a biotechnology company.

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Figure 1-1: Map of the European industrial symbiosis hot spots and the locations of industrial symbioses in the case study (adapted from (Strane Innovation SAS, 2016))

Each of the industrial clusters was assessed for their existing and potential industrial sym-bioses. The method for the content analysis was divided into five aspects according to LESTS and conclusive remarks about the dynamics of symbiosis were drawn on the basis of the work of Boons et al. (2017), supplemented by the three-stage development process of industrial symbiosis by Chertow and Ehrenfield (2011). The methodology is defined in detail in Chapter 2. Furthermore, the potential industrial symbioses were proposed to the industries complemented with a numeration of their Strengths, Weaknesses, Opportuni-ties, and Threats (SWOTs). All the symbiotic opportunities that are proposed under this study focus bring two or more process industries together. Where no potential symbiosis could be found, the waste streams were valorised to replace primary raw materials or energy sources so that the industries could look for potential partners outside of the afore-mentioned sectors.

The case study is presented in two consecutive chapters in the thesis (Chapter 3 and Chap-ter 4).

TRANSITION TO RENEWABLE ENERGY

Globally we are transitioning to an energy system that is dependent on the renewable energy. In 2017, with 17% contribution of renewables in the total energy needs, EU was well on the way to achieve the 2020 target of 20% renewables (European Commission, 2017). As the EU Renewable Energy Directive aims to increase this number to 32% in 2030 (European Commission, 2018), considerable investments and infrastructural changes are needed in the European member states to transition to renewables energy systems.

As introduced above, transitions involve a range of possible development paths, whose direction, scale and speed government policy can influence, but never entirely control (Rotmans et al., 2001, p. 16). The German government has shown the most ambitious of these efforts, in the form of Energiewende, a set of plans to achieve to a low carbon, envi-ronmentally sound, reliable, and affordable energy supply (Tews, 2013) as referenced by (Frondel et al., 2015). This transition does not come without uncertainty and its own set of challenges. The German Energiewende has been questioned for its toll on the poor households as it increases the prices of electricity (Frondel et al., 2015) and for its fairness of cost and benefit distribution among different actors in the power system (Cludius et al., 2014). The inherent uncertainty and variability of the wind and solar power technolo-gies results in a high friction – technically, financially, and operationally – when integrating them in the existing energy system.

Rotmans et al. (2001) have narrated the evolution of energy system in the Netherlands from coal based to natural gas. The authors emphasised how a government can exert guidance to changing a regime by subsidising specific technologies, supporting research and development of the same technology, and supporting public awareness campaigns (Rotmans et al., 2001). After decades of supporting natural gas, the Dutch government is now committed to transitioning the energy system to a renewable based one (van Leeu-wen et al., 2017), mainly because of the adverse climatic effects of the fossil fuels and partly because of the geologic impact of prolonged gas mining in the Northern parts of the country (Osborne, 2019).

It is also mentioned in literature regarding system transition and the MLP that different regimes (science regime, economic regime, technology regime, etc.) interact in complex relationships. Hence, only incentivising technologies based on renewable energy through subsidies is not enough to bring about a transition; consumer behaviour, social norms, and markets also play a crucial role in defining the success of renewable energy technol-ogies. Authors of (van Leeuwen et al., 2017) suggested six areas for energy transition.

1. transition of energy source – move away from fossil fuels towards renewable sources

2. transition of energy consumption – other technology which use other forms of energy, i.e. electrification of heating demand

3. social transition – increased citizen awareness and involvement, e.g. develop-ment of local energy service companies

4. agricultural transition – balancing land use for food and biomass production, 5. tax transition – shifting energy taxes in favour of renewable energy

consump-tion and investments

6. macro-economic trade transition – changing dependence of industrial activi-ties and jobs from fossil fuel trade towards renewable energy trade

To the above six points, I would add the market support mechanisms for the renewable technologies. However, to realise this transition, the technological aspects of the existing electricity network or the grid need to be briefly defined first. The term “grid” is com-monly used to describe an electricity system supporting four operations; electricity generation, electricity transmission, electricity distribution, and electricity control (Stri-elkowski, 2017). With the inclusion of renewable energy technologies in the grid, the traditional grids are evolving to “smart grids”. The different aspects of the old grids and the changes that are occurring in these aspects are discussed in the section below.

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