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Numerical Modeling and Optimization of Wastewater Treatment Using Porous Polymeric Membranes

机译：多孔聚合物膜处理废水的数值模拟与优化

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A new modeling approach was developed for prediction of ammonia removal from water by means of porous membranes. The model was based on adaptive neuro-fuzzy interface system (ANFIS) to simulate ammonia stripping from water by means of hollow-fiber membrane contactors. The predictions aimed to obtain optimum conditions for ammonia stripping using the Taguchi method. The initial concentrations of ammonia, pH of the ammonia solution, velocity of the feed, and the presence of excess ions in the ammonia feed solution were considered as the input properties. On the other hand, mass transfer coefficient was considered as output. The prediction results revealed that the pH of the ammonia feed has a significant effect on the separation of ammonia from water. The results also showed that the prediction of ANFIS model and experimental data match well and that the model can be used for prediction of porous membranes. Furthermore, simulated annealing was also used to determine controllable conditions to find the highest mass transfer coefficient.
机译：开发了一种新的建模方法，用于预测通过多孔膜从水中去除氨的能力。该模型基于自适应神经模糊接口系统（ANFIS），以模拟通过中空纤维膜接触器从水中剥离氨气的过程。这些预测旨在获得使用田口法进行氨气汽提的最佳条件。氨的初始浓度，氨溶液的pH，进料速度和氨进料溶液中过量离子的存在被视为输入特性。另一方面，传质系数被认为是输出。预测结果表明，氨进料的pH值对氨从水中的分离有重大影响。结果还表明，ANFIS模型的预测结果与实验数据吻合良好，可用于多孔膜的预测。此外，模拟退火还用于确定可控制的条件，以找到最高的传质系数。

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