A novel process to recovery natural gas liquids from oil field associated gas with liquefied natural gas (LNG)cryogenic energy utilization is proposed.Compared to the current electric refrigeration process,the proposed process uses the cryogenic energy of LNG and saves 62.6%of electricity.The proposed process recovers ethane, liquid petroleum gas(propane and butane)and heavier hydrocarbons,with total recovery rate of natural gas liquids up to 96.8%.In this paper,exergy analysis and the energy utilization diagram method(EUD)are used to assess the new process and identify the key operation units with large exergy loss.The results show that exergy efficiency of the new process is 44.3%.Compared to the electric refrigeration process,exergy efficiency of the new process is improved by 16%.The proposed process has been applied and implemented in a conceptual design scheme of the cryogenic energy utilization for a 300 million tons/yr LNG receiving terminal in a northern Chinese harbor.
Today’s changeable market and resultant disturbance of crude oil supply require agile and flexible scheduling of crude oil operation. The objective of flexible scheduling is to keep stable crude oil op-eration and satisfy production demands under the circumstances of supply disturbance. In this paper, a new mixed integer non-linear programming (MINLP) formulation is set up for crude oil scheduling firstly, and then some heuristic rules worked out by some experts are proposed to linearize bilinear terms and prefix some binary variables in the MINLP model. These rules not only reduce the complexity of the MINLP model, but also can be used to solve the scheduling problems in various conditions. In case study, the new model with heuristic rules and the best models reported in the literature are com-pared and evaluated in three benchmark examples in the normal situation, and then three abnormal situations of supply delay are considered based on the new approach. The results of case study show that the new flexible approach can handle crude oil scheduling problems efficiently in both normal and abnormal conditions.
