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Operational Optimization of Shale Gas Processing under Feedstock Uncertainties with Updated Back-off Strategy

Uncertainties have a great influence on the optimal performance of chemical processes. In the industrial operation, the ignorance of uncertainty may result in substandard products. With the prosperous development of shale gas, the processing process of shale gas faces large uncertainties in the feedstock, which will influence industrial operations. Aiming at the maximization of the profit of shale gas processing, this study optimizes the corresponding chance constrained optimization problem. The updated back-off strategy is used to reformulate the stochastic problem into the deterministic problem. To reduce the computational burden, meta-models based on Polynomial Chaos Expansion are developed instead of the computationally expensive rigorous process model. With efficient computation, the optimization result of shale gas processing implements the trade-off between profit and product quality.