Mass and energy allocation method analysis for an oil refinery characterization using multi-scale modeling

The goal of this study is to characterize a single oil refinery using a mass and energy multi-scale allocation method for 13 different fossil fuel-based products. This method structures a rigorous modeling and data collection approach to fill the gap that exists in traditional methods of life cycle assessment (LCA). The refinery system's information is used to subdivide a main process at different sub-process levels and obtain multi-scale information using calculated factors for the main process system's raw material inputs and outputs. The analysis was performed using derived sub-models from an overall technological model that individually simulate the production of each final refinery product. Based on the technological models of the individual product refineries, the same environmental indicators identified for mass and energy allocation were calculated. With this approach, it was possible to build a mass and energy LCA model of a fully parameterized refinery capable to accurately describe up to 98% the analyzed system. Incoming raw materials and effluents are weighted by specific factors calculated from the total of each principal process refining amount and the percentage outputs of the intermediate products. From a bottom-up approach, a primary data questionnaire was obtained with the total raw materials and waste quantities for each of the five key processes and their auxiliary processes. Information that previously appeared at the system (global refinery) and processes (main processes) level can now be accounted for in sub-process terms, thus allowing the monitoring information and environmental performance of the product and intermediate products to be found in a multiproduct system. The refining sub-process' environmental impact with regard to specific and intermediate products within the refinery processing steps was accurately estimated. This resulted not only in improvement of the property allocation but also in the identification of critical points in the processing steps, thus enabling optimization and innovation with regard to the processing lines and the refining process and producing a more efficient environmental impact analysis with great quality. The obtained formulations can be extended to more complex refining systems and other manufacturing processes.