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Valuation of Space Debris for a Circular Economy in Space

  • Participant: Atanu Chaudhuri, Lenos Trigeorgis, Zsofia Toth
  • Partners- Ralph “Dinz” Dinsley at 3S Northumbria Limited
  • There are more than 28,000 routinely tracked objects orbiting Earth larger than the average soda can. The vast majority (~75%) are space debris that no longer serve a purpose. These debris are dominated by fragments from 560 known breakups, explosions, and collisions of satellites or rocket bodies. These events have left behind an estimated 900,000 objects larger than 1 cm and a staggering estimated 130 million objects larger than 1 mm in commercially and scientifically valuable Earth orbits. Currently, there are almost 12,000 ‘payloads’ on orbit of which more than 8,000 are active – more than 5,000 of which are Starlink satellites. Current practice is that satellite operators deorbit from low Earth orbits or send the satellites to graveyard orbits (> 36,000 km out) at the end of their operational life. There are roughly 330 satellites in graveyard orbit. Little is known of the polluting effects of deorbiting large numbers of ‘dead’ satellites and what can be done to recover or reuse the materials from these satellites remaining on orbit. 
  • These objectives translate into the following research questions:
  • How can the optimal circular pathway for space debris and the decision to extract and use the materials from space debris now or in the future be determined?
  • What are appropriate service-led business models for commercialising services around the circular economy of space debris?

Community co-created distributed manufacturing platform

  • Participant: Atanu Chaudhuri (PI)
  • Partners- Loughborough University, University of Derby, Teesside University
  • Duration: October 2023- July 2024, funded by INTERACT, £112,250
  • The aim of this project is to develop a blueprint for a co-created, distributed, community-based manufacturing platform in the UK with a business model to support its financial viability and scalability. Our objectives and research questions in support of the aim seek to:
    • Understand the challenges which local communities face in getting objects repaired and delivered at reasonable cost, their perceptions about manufacturing as a career choice and in acquiring the necessary skills to gain employment in the manufacturing sector.
    • Understand the challenges faced by local manufacturers in upskilling their employees while embracing digital transformation and in attracting a future workforce to manufacturing.
    • Understand the challenges faced by local councils in creating meaningful learning and employment opportunities for the young population for the manufacturing sector and in supporting the elderly population in accessing manufactured goods and services, in reducing the digital divide and in improving youth engagement in manufacturing.
    • Assess the potential of a digital platform in changing the perception of the community towards manufacturing, in improving skills, in reducing the digital divide and in improving youth engagement in manufacturing.
    • Support sustainable and localized production: by promoting distributed manufacturing, the platform supports sustainable and localized production. It reduces the need for long-distance transportation and mass production, leading to a lower carbon footprint and decreased resource consumption. The platform will promote the use of recycled materials. This approach can contribute to a more environmentally friendly and resilient manufacturing system.
    • Create a business model for the long-term sustainability and scaling up of the above platform.

Critical Medical Resource Rationing in a Public Health Emergency: A Data-Driven Modelling Study

  • Participant: Li Ding (PI)
  • Partners: Loughborough University, NHS Bristol, Clinical Commissioning Group, City University of London, University of Bath 
  • Description: The Covid-19 Pandemic created “extraordinary and sustained” pressures on health systems and in some cases demands for rationing critical resource including intensive care (ICU) beds, medical equipment and health professionals. Advanced systems modelling and simulation approaches can help. To address the limitations of existing triage policies, The project aims to develop an empirically informed algorithmic model that assigns patient priority based on ICU operational data from one NHS trust. The performance of the prioritisation policies proposed is evaluated against existing triage benchmarks in a comprehensive computer simulation study.
  • Acknowledgement: This research was funded by UKRI/EPSRC Covid-19 Rapid Response Grant (Grant Ref: EP/V050761/1).

Project Kenten funded by British Council as part of Innovation for African Universities Programme

  • Participant: Atanu Chaudhuri (PI)
  • Partners: Regional Maritime University, Ghana, Graydon Lloyd, Spencer Bird, Wazuri Enterprises
  • Brief description: Project Kenten focussed on post-harvest losses (PHL) in agricultural supply chains in Ghana specifically for three crops- tomatoes, onions and okra. Field studies were conducted to analyse the extent and the causes of  PHL and specific interventions to address those were developed and implemented. Series of webinars on agricultural supply chains and business model development were conducted for students at Regional Maritime University, Ghana and Durham University, UK. A student competition was organised at  Regional Maritime University, Ghana to encourage students to come up with innovative solutions to address PHL in Ghana.  The project also led to developing linkages with other universities in Ghana such as University of Cape Coast and agricultural technology service providers     

Responsible Space Exploitation funded by Institute of Advanced Studies, Durham University

  • Participant: Atanu Chaudhuri (PI)
  • Partners: Department of Physics and Department of Education, Durham University  
  • Brief description: The project sought to seek answers related to defining responsible space exploitation and the role of different stakeholders through a series of workshops and interviews with academics and industry practitioners.  Two workshops were organised. The first in November 2021, was dedicated to colleagues of all faculties across Durham University, to explore the interests within Durham. The second in March 2022, was a two day workshop and attracted space industry leaders and UK Space Agency. The project gave us an opportunity to identify all Durham University colleagues, interested in “Responsible Space Exploitation” and paved the way for further development of multi-disciplinary Space Related Research capabilities within Durham Universities involving Physics, Computer Science, Business School, Law and School of Government and International Affairs

Synchronized deliveries with a bike and a self-driving robot

  • Participant: Yanlu Zhao (PI)
  • Partners: University of Padova, Univ. Lille, CNRS, Centrale Lille, Inria, Department of Intelligent Supply Chain Y,
  • Brief description: Online e-commerce giants are continuously investigating innovative ways to improve their practices in last-mile deliveries. Inspired by the current practices at (the largest online retailer by revenue in China), we investigate a delivery problem that we call Traveling Salesman Problem with Bike-and-Robot (TSPBR) where a cargo bike is aided by a self-driving robot to deliver parcels to customers in urban areas. We present two mixed-integer linear programming models and describe a set of valid inequalities to strengthen their linear relaxation. We show that these models can yield optimal solutions of TSPBR instances with up to 60 nodes. To efficiently find heuristic solutions, we also present a genetic algorithm based on a dynamic programming recursion that efficiently explores large neighborhoods. We computationally assess this genetic algorithm on instances provided by and show that high-quality solutions can be found in a few minutes of computing time. Finally, we provide some managerial insights to assess the impact of deploying the bike-and-robot tandem to deliver parcels in the TSPBR setting.