The creation of a novel predictive model for pPVC using spectroscopic techniques

Irvin, Kate Sarah (2024) The creation of a novel predictive model for pPVC using spectroscopic techniques. Doctoral thesis (PhD), Manchester Metropolitan University.

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Abstract

POLEMERS (Property and fOrmuLation predictivE Model for the polymEr industRy using Spectroscopy), a novel model to predict the bill of materials (BOM) and/or the properties of general-purpose plasticised PVC compounds was created for the first time. The polymer industry, as many others, are change adverse, in lieu of the fact that current standard procedures work. However, with an increasingly competitive market, improvements in industrial processes are vital for sustainability. The current process to either develop a new PVC compound fit for purpose or test a competitor’s product is extremely time consuming and labour intensive. Many of the tests to be performed are based on technical expertise and experience, which both create potential bias as well as limit the human resources that can be used for it. To that end, this study investigated and created a fast, simple, inexpensive, and fit for purpose method by which BOMs and material properties can be predicted and achieved. The research conducted herein entails producing a large set of samples of known composition, which were extensively characterised by various chemical measurements as well as physical/mechanical property tests to produce a comprehensive dataset that could be employed for the development of the proposed model. This approach enables the model to make predictions with a high degree of accuracy while minimising the need for extensive and resource-intensive experimental testing. Key findings indicate that POLEMERS successfully predicts multiple raw material levels and material properties of pPVC for general-purpose applications with industrially acceptable accuracy. FT-IR spectroscopy was found to outperform Raman spectroscopy in predicting raw material concentrations, offering a faster, more accurate, and cost-effective technique that requires less skilled labour. The extensive databank created serves not only as a foundation for POLEMERS but also as a valuable resource for the industry, offering deeper insights into material properties and guiding future formulation strategies. POLEMERS has the potential to reduce laboratory staffing costs by nearly 50%, freeing significant time for technical staff to focus on more complex projects. However, the model's effectiveness is currently more pronounced in midrange raw material concentrations, requiring further refinement for higher and lower concentrations and specialised raw materials. Despite these limitations, the initial results are promising, and ongoing refinement and validation are expected to enhance its robustness and applicability. By introducing this novel predictive model, the industry can benefit from streamlined processes that enhance efficiency and productivity. The ability to swiftly determine the BOMs and material properties of PVC compounds has the potential to revolutionise product development and quality control. Manufacturers can now save valuable time and resources by utilising this cost-effective solution, leading to improved competitiveness and a more sustainable future. This proposed model, while being a first attempt, can clearly be further optimised in future, with a more diverse dataset of compositions and properties.