The development of complex engineering models using artificial neural network-based proxy models for life cycle assessments of energy systems

Abstract

The energy industry has been using life cycle assessment (LCA) to determine the environmental impact of projects. Obtaining accurate data of certain industrial activities requires complex engineering models that have long computing times, are difficult for non-experts to use, and may contain confidential data. This work examines using proxy models based on quadratic and artificial neural network (ANN) regression to create an accurate, easy-to-use, black-box model that can be easily shared. Generating target values from the engineering software needed for training can be time-consuming, hence, adaptive sampling methods are examined (random, spread, high error, and the combo method [50/50 random/high error]). Two case studies were examined: a transportation fuel LCA of Maya, Bow River, and mined bitumen crude oils; and an LCA of a natural gas transmission pipeline (NGTL). This work found that ANN proxy models are more accurate than quadratic regression, and the high error sampling method reduced the maximum error but increased the average error. The combo and high error methods using 3000 to 4000 samples achieved similar maximum errors to the random method using 10,000 samples. Because of uncertainty in LCA input values, reducing average error is less valuable than reducing extreme errors. For the NGTL case study, the ANN model was able to reduce the average and max error by 67% and 68%, respectively, while using 35% fewer coefficients; ANN models are more appropriate for complex nonlinear models.