Predictive packaging
We partnered with a chemical engineering company to address challenges in predicting the diffusion characteristics of materials used in their packaging solutions. The company needed to accurately forecast how various materials would behave when exposed to specific chemical environments. This was crucial for optimizing material selection, reducing waste, and ensuring long-term packaging integrity.
The Challenge
Our client struggled to predict how materials used in their packaging would degrade or corrode under different chemical conditions. This affected their ability to ensure product safety and longevity, as the diffusion of chemicals could compromise packaging quality over time. Accurate prediction was essential to select the right materials and minimize costly testing.
The solution
We implemented a detailed solution combining in-depth material analysis, advanced feature engineering, and machine learning models. By analysing the physical and thermodynamic properties of packaging materials and understanding their interactions with different chemicals, to understand potential degradation and corrosion. Using feature engineering, we identified key factors like molecular size, porosity, and temperature to ensure precise predictions. We then employed cutting-edge machine learning models to analyse data to accurately predict chemical diffusion rates, offering actionable insights into the selection of optimal, long-lasting packaging materials.
The features
Pinpointed the necessary physical and thermodynamic properties that influence chemical diffusion through domain knowledge expertise.
Application of Principal Component Analysis, Genetic Algorithms and SHAP values to identify the key features affecting diffusion, leveraging both scientific knowledge and literature.
Utilising a range of machine learning techniques, including neural networks, Xgboost, and gaussian process regression to enhance the accuracy of diffusion predictions, helping to identify the optimal packaging solutions.
The result
Our solution improved the accuracy of chemical diffusion predictions, helping the company optimize material selection, reduce testing costs, and enhance packaging durability. As a result, the company confidently designed safer, longer-lasting packaging solutions, extending the product lifecycle and improving overall efficiency.
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