Practical modeling for assessing the efficiency of a full-scale reverse osmosis (RO) system may be a challenging task. This is because the operating conditions of RO systems can change significantly in actual practice owing to high seasonal variations and different progress of membrane fouling during long-term filtration. Accordingly, it is difficult to reliably model the RO performance if such conditions are excluded. In this study, we model a full-scale installation of a RO membrane system, considering actual operations of the industrial water treatment plant. A numerical model is built to describe spatiotemporal behavior of (water, salt, and foulant) mass transport inside a full-dimension pressure vessel. By performing a global sensitivity analysis, we evaluate the relative importance of key influential factors on model accuracy and specific energy consumption (SEC). The model and its parameters are optimized based on the sensitivity result and validated using best-fitted time-series measurement data of 3875 h. The results demonstrate the practical behaviors of fouling development and separation performance of the primary RO process. A regression tree analysis of SEC for 27 different operational scenarios in simulations may benefit decision making for energy efficient RO. Results reveal the high dependence of SEC on cleaning frequency in the feed temperature range.

用于评估全尺寸反渗透 (RO) 系统效率的实用建模可能是一项具有挑战性的任务。这是因为在长期过滤过程中，由于季节性变化大和膜污染的不同进展，反渗透系统的运行条件在实际操作中会发生显着变化。因此，如果排除这些条件，则很难对 RO 性能进行可靠建模。在本研究中，我们考虑工业水处理厂的实际操作，对 RO 膜系统的全尺寸安装进行建模。建立了一个数值模型来描述全尺寸压力容器内（水、盐和污染物）质量传输的时空行为。通过执行全局敏感性分析，我们评估了关键影响因素对模型精度和特定能耗 (SEC) 的相对重要性。该模型及其参数根据灵敏度结果进行优化，并使用 3875 h 的最佳拟合时间序列测量数据进行验证。结果证明了初级反渗透工艺的污垢发展和分离性能的实际行为。模拟中 27 种不同操作场景的 SEC 回归树分析可能有利于节能 RO 的决策制定。结果揭示了 SEC 在进料温度范围内对清洗频率的高度依赖性。结果证明了初级反渗透工艺的污垢发展和分离性能的实际行为。模拟中 27 种不同操作场景的 SEC 回归树分析可能有利于节能 RO 的决策制定。结果揭示了 SEC 在进料温度范围内对清洗频率的高度依赖性。结果证明了初级反渗透工艺的污垢发展和分离性能的实际行为。模拟中 27 种不同操作场景的 SEC 回归树分析可能有利于节能 RO 的决策制定。结果揭示了 SEC 在进料温度范围内对清洗频率的高度依赖性。