Optimizing a Cogeneration sCO 2 CSP–MED Plant Using Neural Networks
Optimizing a Cogeneration sCO 2 CSP–MED Plant Using Neural Networks
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使用神经网络优化热电联产 sCO 2 CSP™MED 工厂
DOI：
10.1021/acsestengg.0c00132
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发表时间：
2021
影响因子：
7.1
通讯作者：
Hatzell, Marta C.
中科院分区：
文献类型：
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作者：
Caceres Gonzalez, Rodrigo A.;Zheng, Yanjie;Hatzell, Kelsey B.;Hatzell, Marta C.
定义最佳热电联产系统设计需要使用复杂的分析，能够捕获多个子系统和单个设备并行的动态过程。这是由于一个众所周知的事实，即最佳热电联产系统的性能并不总是与单个子系统的最佳性能相关。此外，子系统和单个设备通常表现出内在的设计权衡，这在子系统级模型中不容易捕获。在这里，我们对聚光太阳能（CSP）热电联产系统进行了稳态热力学和经济分析，该系统通过超临界二氧化碳（sCO2）布雷顿循环发电，并通过多效蒸馏（MED）装置供水。使用三个人工神经网络可以预测经济性能（水和电的平均成本）和系统性能（热效率、第二定律效率、性能比和太阳能性能比）。热电联产系统的平均电力成本（LCOE）高于最先进的CSP sco2电厂。然而，这种功率性能的降低使得水的平准化成本为1.1美元/立方米，与传统的膜基工艺（1.25美元/立方米）相当，明显低于其他太阳能热脱盐系统（1.8美元/立方米）。sCO2Brayton循环和MED装置之间的非寄生集成还允许在不改变热电联产系统第二定律效率的情况下实现产水量最大化，在分析过程中，热电联产系统的第二定律效率保持在11%。
Defining optimal cogeneration system design requires the use of complex analyses capable of capturing dynamic processes within multiple subsystems and individual devices in parallel. This is due to the well-known fact that optimal cogeneration system performance does not always correlate with the optimal performance of a single subsystem. Furthermore, subsystems and single devices often present inherent design trade-offs which are not easily captured in subsystem level models. Here, we perform a steady state thermodynamic and economic analysis for a concentrated solar power (CSP) cogeneration system producing power through a supercritical carbon dioxide (sCO2) Brayton cycle and water through a multieffect distillation (MED) plant. The use of three artificial neural networks allows for the prediction of economic performance (levelized cost of water and electricity) and system performance (thermal efficiency, second law efficiency, performance ratio, and solar performance ratio). The cogeneration system results in a higher levelized cost of electricity (LCOE) than state-of-the-art CSP sCO2plants. However, this reduction in power performance allows for a levelized cost of water of 1.1 $/m3, which is comparable to conventional membrane-based processes (1.25 $/m3) and significantly less than other solar thermal (1.8 $/m3) desalination systems. The nonparasitic integration between the sCO2Brayton cycle and MED plant also allows for maximization of water production without altering the second law efficiency of the cogeneration system, which remains at 11% during the analysis.