An integrated approach of artificial neural networks and polynomial chaos expansion for prediction and analysis of yield and environmental impact of oil shale retorting process under uncertainty

Shale oil reserves exploration is getting huge investments due to the depletion of conventional reserves. Computational methods have been used to realize optimum design and operation of shale oil and gas reserves exploration and processing. The uncertainty associated with the composition of shale reserves and operating conditions during processing put a challenge to the realization of high yield and mitigation of environmental impact. In the current work, machine learning (ML) based models are proposed for the estimation of the yield and environmental impact of the oil shale retorting process under uncertainty. An artificial uncertainty of 1% was inserted in feed composition and process conditions of an Aspen model of the process to generate data for the development of the ML models. Artificial Neural Network (ANN), Least Square Boosting (LSB), and Bagging techniques were compared to find the best ML model. ANN models, with the highest correlation coefficient of 0.995 and 0.999 for the oil yield and CO2 content respectively, are used as surrogates in a Polynomial Chaos Expansion (PCE) framework for the uncertainty analysis of the process. For 1% uncertainty in feed composition and process conditions, a mean absolute deviation of 0.319 and 0.580 was obtained for the oil yield and carbon dioxide emissions respectively. To find the hierarchy in the process inputs in terms of their effect on the oil yield and carbon dioxide emissions, the ANN model is used as a surrogate in sensitivity analysis through Sobol and Fourier Amplitude Sensitivity Test (FAST) indices. The most sensitive input variables were feed temperature and air molar flow rate. The proposed modeling framework will provide a base for future real-time monitoring and analysis of the oil shale retorting processes