Abstract Artificial neural networks (ANN) are black box models that are becoming more popular than transport-based models due to their high accuracy and less computational time in predictions. The literature shows a lack of ANN models to evaluate the forward osmosis (FO) process performance. Therefore, in this study, a multi-layered neural network model is developed to predict the permeate flux in forward osmosis. The developed model is tested for its generalization capability by including lab-scale experimental data from several published studies. Nine input variables are considered including membrane type, the orientation of membrane, molarity of feed solution and draw solution, type of feed solution and draw solution, crossflow velocity of the feed solution, and the draw solution and temperature of the feed solution and the draw solution. The development of optimum network architecture is supported by studying the impact of the number of neurons and hidden layers on the neural network performance. The optimum trained network shows a high R2 value of 97.3% that is the efficiency of the model to predict the targeted output. Furthermore, the validation and generalized prediction capability of the model is tested against untrained published data. The performance of the ANN model is compared with a transport-based model in the literature. A simple machine learning technique such as a multiple linear regression (MLR) model is also applied in a similar manner to be compared with the ANN model. ANN demonstrates its ability to form a complex relationship between inputs and output better than MLR.

中文翻译：

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使用人工神经网络 (ANN) 对正向渗透过程建模以预测渗透通量

摘要 人工神经网络 (ANN) 是一种黑盒模型，由于其高精度和预测中的计算时间更少，因此比基于传输的模型更受欢迎。文献显示缺乏 ANN 模型来评估正向渗透 (FO) 过程性能。因此，在本研究中，开发了一个多层神经网络模型来预测正向渗透中的渗透通量。通过包含来自几项已发表研究的实验室规模实验数据，对开发的模型的泛化能力进行了测试。考虑了九个输入变量，包括膜类型、膜的取向、进料溶液和汲取溶液的摩尔浓度、进料溶液和汲取溶液的类型、进料溶液的错流速度以及汲取溶液和进料溶液和汲取的温度解决方案。通过研究神经元和隐藏层的数量对神经网络性能的影响来支持最佳网络架构的开发。最佳训练网络显示 97.3% 的高 R2 值，这是模型预测目标输出的效率。此外，模型的验证和广义预测能力针对未经训练的已发布数据进行了测试。ANN 模型的性能与文献中基于传输的模型进行了比较。一种简单的机器学习技术，例如多元线性回归 (MLR) 模型，也以类似的方式应用以与 ANN 模型进行比较。ANN 展示了它比 MLR 更好地在输入和输出之间形成复杂关系的能力。