Abstract The main target of this study is to apply thermal integration on a five-steps ZnSI thermochemical hydrogen production cycle in order to improving the process efficiency. In this process water enters the Bunsen system at a 298 K, 1 bar with flow rate of 0.5 mol/s, and CO2 is the input stream into the zinc system at 298 K and 1 bar. Oxygen and CO are output streams of Bunsen and zinc systems, respectively. Hydrogen is output of the HI system, which is obtained as a final product at 313 K, 1 bar with the flow rate of 0.5 mol/s. A solar thermal system which uses solar dish collector is applied in order to provide a portion of the required thermal load in the process. Modeling of the solar system is done through Matlab software. By using ZnSI instead of SI process, temperature of reaction decreases. Also hydrogen and carbon monoxide are produced. The results demonstrate that by this modification number of units of the Zn system reduces and the chemical process is simplified. By performing thermal integration, heat duty of the system decreases and accordingly thermal efficiency increase by 61.06%. As well as, the minimum required hot and cold utilities decrease by 51.94% and 65.52%, respectively. Solar fraction and collector thermal efficiency were obtained 42.57% and the 82% respectively. Finnaly a parametric assessment is done on the key parameters which influence performance of the process.

中文翻译：

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利用太阳能的热集成五步ZnSI热化学循环制氢工艺

摘要 本研究的主要目标是将热集成应用于五步 ZnSI 热化学制氢循环，以提高工艺效率。在此工艺中，水以 298 K、1 bar 的压力以 0.5 mol/s 的流速进入本生系统，CO2 是在 298 K 和 1 bar 下进入锌系统的输入流。氧气和 CO 分别是本生灯和锌系统的输出流。氢气是 HI 系统的输出，在 313 K、1 bar 和 0.5 mol/s 的流速下作为最终产品获得。应用使用太阳能盘式集热器的太阳能热系统，以便在过程中提供一部分所需的热负荷。太阳系的建模是通过 Matlab 软件完成的。通过使用 ZnSI 代替 SI 工艺，反应温度降低。还会产生氢气和一氧化碳。结果表明，通过这种修改，Zn 系统的单元数量减少，化学过程得到简化。通过进行热集成，系统的热负荷降低，热效率提高61.06%。此外，所需的最低冷热公用设施分别减少了 51.94% 和 65.52%。太阳能分数和集热器热效率分别为 42.57% 和 82%。最后，对影响过程性能的关键参数进行参数评估。06%。此外，所需的最低冷热公用设施分别减少了 51.94% 和 65.52%。太阳能分数和集热器热效率分别为 42.57% 和 82%。最后，对影响过程性能的关键参数进行参数评估。06%。此外，所需的最低冷热公用设施分别减少了 51.94% 和 65.52%。太阳能分数和集热器热效率分别为 42.57% 和 82%。最后，对影响过程性能的关键参数进行参数评估。