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论文类型：期刊论文
发表时间：2017-11-05
发表刊物：APPLIED THERMAL ENGINEERING
收录刊物：EI、SCIE
卷号：126
页面范围：566-582
ISSN号：1359-4311
关键字：Multi-stage condensation Rankine cycle; Liquefied natural gas; Power generation; Multi-population genetic algorithm
摘要：Due to the large temperature change of LNG regasification process (from about -160 degrees C to ambient temperature), the irreversible loss of the LNG regasification prnress is large, which affects the efficiency of power generation systems for LNG cold energy recovery. Our previous study proposed a two-stage condensation Rankine cycle, which can effectively improve the utilization efficiency of LNG. On this basis, this paper continues to discuss the effect of stage number of condensing process on the performance and economy of the power generation systems by LNG cold energy. Seven systems are considered in this paper, including three traditional power generation systems (direct expansion, organic rankine cycle and combined cycle) and four proposed systems (the two-stage and three-stage condensation Rankine cycles, as well as the two-stage and three-stage combined cycles). These seven systems are simulated by Aspen Hysys software, and with net power output, electricity production cost (EPC) and annual net income as the objective functions respectively, the key system parameters (the condensation temperature and the inlet pressure of LNG turbine, etc.) are optimized under the three different objective functions. Results showed that when the net power output is the objective function, the net power output of the three stage combined cycle is the largest. If EPC is chosen as the objective function, the cost of the combined cycle is always lower than that of the Rankine cycle system with the same condensation stage number, but net power output of the Rankine cycle is higher than that of the combined cycle. When annual net income is the objective function, the three-stage combined cycle has the largest annual net income. But if considering the difficult degree of control system, the two-stage combined cycle is recommended. (C) 2017 Elsevier Ltd. All rights reserved.