Life Cycle Assessment Based Greenhouse Gas Emission Reductions, Cross Country Analysis and Algorithm Aided Prediction for Lightweighted Composite Auto Parts

Algorithms made our life simpler and enables Machine to learn and predict and seems like a promising revolutionary science of the future. Even though this has been a hotspot in many aspects of science; there are some areas still behind. One of these areas is the Environmental and lifecycle assessments (LCA). The main problem with these areas is that the collected data are not comparable as every research has its own unique details even though LCA has a standard set of guidelines to perform. The second main problem is that we have very limited data avail-able for machines to be able to use in these areas. Here in this thesis, several normalization methods and a coefficient were used to make these data comparable. Also, because we had lim-ited data available, we performed several LCA studies to generate more data to be able to use it in Machine learning algorithms for the purpose of testing, validation and training. One of the advantages of having limited data here was that it enabled us to implement several machine learning algorithms and methods to compare their performances. The results of this study created a powerful tool that can help researchers, original equipment manufacturers, policymakers and automotive companies to make better environmental decisions prior to any design stages just by knowing the percentage of lightweighted automotive parts. This tool is specifically created to work with lightweighting that is resulted from replacing a glass fibre automotive part with natural fibre-reinforced composites. Also, with the developed coefficient of countries, one can transfer the LCA data from one country to another by using a simple equation. These coefficients were developed by using the countries posted total primary energy supply and electricity grid mix. By using these tools, we will have a better understanding of our emissions prior to any design with relatively high accuracy.Ph.D