Abstract Mathematical models can be a powerful tool in the operation of reverse osmosis (RO) facilities which is often challenged by a varying feed water quality. Most models, however, do not consider both full-scale and good modelling practice, which makes them less suited in practice. In this paper, a generic steady state model for RO was set-up and applied to a unique three-year data set from a full-scale RO process according to state-of-the-art good modelling practice. It was found that the model outputs are most sensitive towards the water and the solute permeability, and the feed spacer channel height, and therefore, only these parameters were calibrated. Furthermore, manufacturer's tests do not always reflect the full-scale situation, which highlights the importance of calibration. The model was validated with online conductivity data as input taking into account the uncertainty originating from online sensors, and compared to the commercial software Winflows. Despite the lack of long-term predictive power since fouling was not included, the model with online conductivity data as input showed satisfactory results, i.e. an average deviation from the data of 2.7%, 12.7%, 34.1% and 18.7% for respectively the recovery, the concentrate pressure, the permeate and concentrate solute concentration.

中文翻译：

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用于全面运行的通用反渗透模型

摘要 数学模型可以成为反渗透 (RO) 设施运行的有力工具，该设施经常受到给水水质变化的挑战。然而，大多数模型并没有考虑全面和良好的建模实践，这使得它们不太适合实践。在本文中，根据最先进的良好建模实践，建立了 RO 的通用稳态模型，并将其应用于来自全面 RO 过程的独特的三年数据集。发现模型输出对水和溶质渗透率以及进料间隔通道高度最敏感，因此，仅校准这些参数。此外，制造商的测试并不总是反映满量程的情况，这凸显了校准的重要性。该模型使用在线电导率数据作为输入进行了验证，同时考虑了来自在线传感器的不确定性，并与商业软件 Winflows 进行了比较。尽管由于不包括结垢而缺乏长期预测能力，但以在线电导率数据为输入的模型显示出令人满意的结果，即回收率与数据的平均偏差分别为 2.7%、12.7%、34.1% 和 18.7% ，浓缩压力，渗透和浓缩溶质浓度。