Data Science and Artificial Intelligence for smart sustainable plastic

High-density polyethylene (HDPE) is a semi-crystalline polymer, it is this combination of crystalline and amorphous regions that provide the polymer with its attractive properties that result in the polymer's widespread use in day-to-day life. The crystals within the polymer contribute towards the stiffness, barrier properties and toughness while the amorphous regions provide flexibility, ductility and contribute towards toughness. HDPE can be collected and mechanically recycled to produce a post-consumer resin (PCR), promoting a circular economy with a plastic that has a lower environmental footprint compared to new, virgin plastic. The sorted packaging is mechanically recycled through grinding, washing and then extruding to produce PCR pellets.

One of the major issues with using PCR to replace virgin plastic in packaging is that it is a variable material. It may contain different grades of plastic differing in molecular weight or flexural strength, it can be contaminated with other materials and the recycling process itself can lead to degradation of the plastic. Many of these changes influence the crystallinity in the polymer. In order to be able to more effectively reuse PCR HDPE we need to understand how crystal size, degree of crystallinity, tie molecule concentration control the performance of the polymer.

One application area where HDPE is widely used is packaging, particularly for containing home/personal care products and food, with approximately 270,000 tonnes used annually. HDPE is an excellent packaging material due to its low density, chemical inertness, and toughness. It is a thermoplastic that can be heated and then processed into bottles by extrusion blow-moulding.
A practical outcome of the project is to understand how the crystallinity changes in recycled HDPE and how this subsequently impacts the in-user properties of the polymer. This understanding could be used in inform the selection of PCR for use in packaging, ultimately increasing the sustainability of plastics in packaging.

This PhD studentship will run alongside our project funded by the UK Industrial Strategy Challenge Fund in Smart Sustainable Plastic Packaging, and is co-funded by Unilever. We will use existing literature data on virgin plastics, combined with new experimental data from various in-house chemical techniques (FTIR/Raman/DSC/XRD) performed on recycled plastics, to try and predictively link the chemical structure of the plastic to its properties. These properties ultimately determine whether a sample of recycled plastic will be desirable for use in manufacturing. Therefore, if an accurate predictive model could be established, we would be able to save time and money and not have to put a recycled PCR through the manufacturing process before we could determine its properties.

We will do this using cutting edge data science techniques in the space of Topological Data Analysis. This should allow us to understand relationships between certain chemical techniques and certain chemical properties, and since peaks in the spectra correspond to certain physical phenomena, we may be able to establish new structure-property relationships. We also aim to understand how contamination with other plastics, physical weathering and oxidation in the extrusion process impact the physical properties of the plastic. This will help us to understand the viability of the current plastic recycling stream if challenges to shift to 100% recycled plastic in manufacturing are to be met.

This CDT's focus on using "Future Computing Systems" to move "Towards a Data-driven Future" resonates strongly with two themes of non-academic organisation. In both themes, albeit for slightly different reasons, commodity data science is insufficient and there is a hunger both for the future leaders that this CDT will produce and the high-performance solutions that the students will develop.

The first theme is associated with defence and security. In this context, operational performance is of paramount importance. Government organisations (e.g., Dstl, GCHQ and the NCA) will benefit from our graduates' ability to configure many-core hardware to maximise the ability to extract value from the available data. The CDT's projects and graduates will achieve societal impact by enabling these government organisations to better protect the world's population from threats posed by, for example, international terrorism and organised crime.

There is then a supply chain of industrial organisations that deliver to government organisations (both in the UK and overseas). These industrial organisations (e.g., Cubica, Denbridge Marine, FeatureSpace, Leonardo, MBDA, Ordnance Survey, QinetiQ, RiskAware, Sintela, THALES (Aveillant) and Vision4ce) operate in a globally competitive marketplace where operational performance is a key driver. The skilled graduates that this CDT will provide (and the projects that will comprise the students' PhDs) are critical to these organisations' ability to develop and deliver high-performance products and services. We therefore anticipate economic impact to result from this CDT.

The second theme is associated with high-value and high-volume manufacturing. In these contexts, profit margins are very sensitive to operational costs. For example, a change to the configuration of a production line for an aerosol manufactured by Unilever might "only" cut costs by 1p for each aerosol, but when multiplied by half a billion aerosols each year, the impact on profit can be significant. In this context, industry (e.g., Renishaw, Rolls Royce, Schlumberger, ShopDirect and Unilever) is therefore motivated to optimise operational costs by learning from historic data. This CDT's graduates (and their projects) will help these organisations to perform such data-driven optimisation and thereby enable the CDT to achieve further economic impact.

Other organisations (e.g., IBM) provide hardware, software and advice to those operating in these themes. The CDT's graduates will ensure these organisations can be globally competitive.

The specific organisations mentioned above are the CDT's current partners. These organisations have all agreed to co-fund studentships. That commitment indicates that, in the short term, they are likely to be the focus for the CDT's impact. However, other organisations are likely to benefit in the future. While two (Lockheed Martin and Arup) have articulated their support in letters that are attached to this proposal, we anticipate impact via a larger portfolio of organisations (e.g., via studentships but also via those organisations recruiting the CDT's graduates either immediately after the CDT or later in the students' careers). Those organisations are likely to include those inhabiting the two themes described above, but also others. For example, an entrepreneurial CDT student might identify a niche in another market sector where Distributed Algorithms can deliver substantial commercial or societal gains. Predicting where such niches might be is challenging, though it seems likely that sectors that are yet to fully embrace Data Science while also involving significant turn-over are those that will have the most to gain: we hypothesise that niches might be identified in health and actuarial science, for example.

As well as training the CDT students to be the leaders of tomorrow in Distributed Algorithms, we will also achieve impact by training the CDT's industrial supervisors.