Department of Water Engineering and Hydraulic Structure, Faculty of Civil Engineering, Semnan University, Semnan, Iran.
Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam.
Environ Sci Pollut Res Int. 2020 May;27(13):15278-15291. doi: 10.1007/s11356-020-08023-9. Epub 2020 Feb 19.
The scarcity of freshwater causes the necessity for water delineation of brackish water. Reverse osmosis (RO) is one of the popular strategies characterized with lower cost and simple processing procedure compared to the other desalination techniques. The current research is conducted to investigate the efficiency the RO process based on one-week advance prediction of total dissolved solids (TDS) and permeate flow rate for Sistan and Bluchistan provinces located in Iran region. The water parameters including pH, feed pressure temperature, and conductivity are used to construct the prediction matrix. A newly hybrid data-intelligence (DI) model called multilayer perceptron hybridized with particle swarm optimization (MLP-PSO) is developed for the investigation. The potential of the proposed MLP-PSO model is validated against two predominate DI models including support vector machine (SVM) and M5Tree (M5T) models. The results evidenced the potential of the proposed MLP-PSO model over the SVM and M5T models in predicting the TDS and permeate flow rate. In addition, the proposed model attained lower uncertainty for the simulated data. Overall, the feasibility of the hybridized MLP-PSO achieved remarkable predictability for the RO process.
淡水资源短缺促使人们需要对咸水进行水划分。与其他海水淡化技术相比，反渗透（RO）是一种具有成本低、处理工艺简单等特点的流行策略。本研究旨在基于对伊朗锡斯坦和俾路支省总溶解固体（TDS）和渗透流量的一周提前预测，调查 RO 工艺的效率。研究中使用了包括 pH 值、进料压力、温度和电导率在内的水参数来构建预测矩阵。开发了一种名为多层感知器与粒子群优化（MLP-PSO）混合的新型数据智能（DI）模型，用于调查。将提出的 MLP-PSO 模型的性能与两种主要的 DI 模型（支持向量机（SVM）和 M5Tree（M5T）模型）进行了验证。结果表明，与 SVM 和 M5T 模型相比，提出的 MLP-PSO 模型在预测 TDS 和渗透流量方面具有更大的潜力。此外，该模型对模拟数据的不确定性较低。总的来说，混合 MLP-PSO 模型实现了 RO 过程的显著可预测性。
