关键词: 大数据, 电脱盐, 运行优化, 智能监测, 影响因素, big data, electric desalination, operation optimization, intelligent monitoring, influence factors

Abstract: Based on 400 days-period of operating data of the electrical desalination system, the big data analysis method was used to establish a mapping relationship between the operating parameters and the after-desalination salt content. The model was then applied in intelligent prediction of the after-desalination salt content for a total of 1052 operating samples acquired form Apr. 2019 to Sept. 2020. An average error of 0.42mg/L was found by comparing the prediction salt content with actual value. In order to analysis the influence of operating parameters on salt removing efficiency, a model-based optimization method was proposed. The method was used to study several key factors that affect the salt removing efficiency for electric desalination process. Based on the screening results, several key operating parameters that role the salt removing efficiency for electric desalination system were analyzed and corresponding optimal operation suggestions provided. The after-desalination salt content can be significantly reduced by following suggestions provided with the optimization method.