(120c) Developing Innovation Roadmaps for Plastics Value-Chain Using Sustainable Circular Economy Framework

The plastics value-chain is leading to large amounts of nutrient pile up in the environment through oceanic gyres, over-filled landfills, and micro-plastics. Grocery carrier bags, films and beverage bottles form a major chunk of this pollution, an estimated 45%. Since this plastics problem is very pressing, it has inspired researchers from multiple disciplines to propose innovations in various parts of the value-chain. It is crucial to re-evaluate our current value-chain operation for minimizing this pile-up while also keeping a check on the life-cycle impact of circularizing the value-chain. Researchers at The Global Kaiteki Center have systematically built a list of innovations in the plastics value-chain and categorized them based on the sectors which they are likely to displace [1]. This includes information ranging from novel AI based separation techniques and chemical recycling to household collection schemes and incentives. This list of innovations is an exhaustive set of emerging technologies, novel policy action and behavioral strategies. In collaboration, we at The Ohio State University have developed a general Sustainable Circular Economy framework which can design cradle-to-cradle life-cycle value chains for multiple stakeholders using multi-objective optimization [2]. This work aims to systematically rank the list of innovations and construct a roadmap towards a Sustainable Circular Economy (SCE), using the developed optimization framework in conjunction with hotspot and sensitivity analyses.

In our previous work [2,3], we have found optimal value-chains within the design space of conventional alternatives (called ‘superstructure’ networks) for the grocery sacks value-chain. This included decision variables ranging from type of bags, collection methods, primary and secondary waste treatment methods, etc. We also found optimal value-chain designs for the three objectives: circularity, global warming potential and life-cycle cost. Further, we constructed a pareto surface containing trade-off or compromise solutions. As mentioned earlier, the focus of this work is to study each innovation tabulated in the developed list based on enhancement of Sustainable Circular Economy. We also broaden the scope of our previous work [2] to also include beverage bottles, since a major chunk of emerging technologies are aimed at dealing with PET bottle waste.

We use life-cycle allocation and displacement methods to find hotspots of emissions, loss of circularity and cost in the PET beverage bottles value-chain. This yields information on sectors of the value-chain which have the highest scope of improvement. Additionally, we have categorized the list of innovations based on stage of adoption, namely: Innovation gap, Theoretical, Experimentation, Commercialization and Diffusion. Information from both hotspot and adoption stage are used to select which technologies to introduce in the value-chain network of alternatives, or ‘superstructure’. This is followed by data collection and generation for introducing life-cycle of the chosen ‘emerging technology’ within the design space. This requires generation of life-cycle inventory for novel technologies using prospective LCA [4,5]. In order to fill several data voids and gaps, simulation of chemical plants and physical processes are performed to find parameters of the life-cycle model. For example, within the PET bottles case study we explore novel technologies to make bio-based compostable polyhydroxy-alkanoate and bio-PET bottles; and chemical recycling using glycolysis. Following this, the SCE framework is implemented using multi-objective optimization to construct a new pareto surface. Ultimately, the presence of the innovation within the pareto optimal set is investigated to determine if the innovation is worthy of being included within the roadmap. This process, starting from hotspot analysis is repeated, until every sector of the value-chain is covered, thereby developing a roadmap for innovation. As a result, the generated innovation roadmap contains novel technologies in each sector ranked according to the potential of enhancing the SCE of the system. In conclusion, this talk will contain a systematic method to convert a list of innovations into a Roadmap of most promising innovations using systemic multi-objective optimization approaches on cradle-to-cradle life-cycle superstructures. The method will be demonstrated for products such as grocery sacks and beverage bottle value-chains, thereby aiding corporate strategy and policy action towards most effective SCE innovations.

References

1. Hafsa, F., Dooley, K., Basile, G., and Buch, R. (2020), “Innovations for more circular plastic packaging”, under review at Journal of Cleaner Production.
2. Thakker, Vyom, and Bhavik R. Bakshi. "Toward sustainable circular economies: A computational framework for assessment and design." Journal of Cleaner Production295 (2021): 126353.
3. Thakker, Vyom, and Bhavik R. Bakshi. “Towards Sustainable Circular Economy: Design Framework and Application to Grocery Sacks”. 2020 AIChE Annual Meeting. Video available at: https://youtu.be/dWj6R0UXMjM
4. Yao, Yuan, and Eric Masanet. "Life-cycle modeling framework for generating energy and greenhouse gas emissions inventory of emerging technologies in the chemical industry." Journal of Cleaner Production 172 (2018): 768-777.
5. Thonemann, Nils, Anna Schulte, and Daniel Maga. "How to conduct prospective life cycle assessment for emerging technologies? A systematic review and methodological guidance." Sustainability3 (2020): 1192.