This work investigates the capability of multiple linear regression (MLR) and artificial neural networks (ANN) to model permeate flux in a thermodynamically complex forward osmosis (FO) process. Whey-permeate was concentrated to a dry matter content of more than 55 %, creating a highly supersaturated metastable solution and exceeding the established boundaries of conventional membrane technology. Different ANN architectures were trained and tested with a varying number of hidden layers and neurons to find an accurate structure.

Furthermore, the evaluated significance of the input parameters was used to reduce the model's complexity. This work shows that both approaches (MLR: R²_test = 0.9718, ANN: R²_test = 0.9849) were able to model the FO's permeate flux accurately, even with a reduced number of inputs. Finally, due to its slightly better performance, the ANN was used to outline the influence of FS inlet flow and process temperature.

这项工作研究了多元线性回归 (MLR) 和人工神经网络 (ANN) 在热力学复杂的正向渗透 (FO) 过程中模拟渗透通量的能力。乳清渗透液浓缩至干物质含量超过 55%，形成高度过饱和的亚稳态溶液，并超越了传统膜技术的既定界限。使用不同数量的隐藏层和神经元对不同的 ANN 架构进行了训练和测试，以找到准确的结构。

此外，输入参数的评估显着性被用来降低模型的复杂性。这项工作表明 ，即使输入数量减少，两种方法（MLR：R²_测试 = 0.9718，ANN：R²_{测试= 0.9849）都能够准确地模拟 FO 的渗透通量。}最后，由于其性能稍好，ANN 被用于概述 FS 入口流量和过程温度的影响。