Temporal Decomposition Scheme for Designing Large-Scale CO2 Supply Chains Using a Neural Network-Based Model for Forecasting CO2 Emissions

The battle against climate change and the search for innovative solutions to mitigate its effects have become the focus of the researchers� attention. One potential approach to reduce the impacts of the global warming could be the design of a Carbon Capture and Storage Supply Chain (CCS SC). However, the high complexity of the model requires exploring alternative ways to optimise it. In this work, a CCS multi-period supply chain for Europe is designed. Data on CO2 emissions have been sourced from the EDGAR database, which includes information spanning the last 50 years. Since this problem involves optimising the cost and operation decisions over a 10-year time horizon, it would be advisable to forecast carbon dioxide emissions to enhance the reliability of the data used. For this purpose, a neural network-based model is implemented for forecasting N-Beats. Furthermore, a temporal decomposition scheme is used to address the intractability issues of the model. The selected method is Lagrangean decomposition, which has been employed in other high complexity works, demonstrating strong performance and significant computational savings.