Data-driven models of steady state and transient operations of spiral-wound RO plant

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• 作者：Xavier Pascual^a ; Han Gu^b ; Alex R. Bartman^b ; Aihua Zhu^b ; Anditya Rahardianto^b ; Jaume Giralt^a ; Robert Rallo^c ; Panagiotis D. Christofides^b ; Yoram Cohen^b ; ^{yoram@ucla.edu}
• 关键词：Desalination ; Data-driven models ; Support vector regression ; Spiral-wound RO plant ; Process models
• 刊名：Desalination
• 出版年：2013
• 出版时间：1 May, 2013
• 年：2013
• 卷：316
• 期：Complete
• 页码：154-161
• 全文大小：734 K

文摘

The development of data-driven RO plant performance models was demonstrated using the support vector regression model building approach. Models of both steady state and unsteady state plant operation were developed based on a wide range of operational data obtained from a fully automated small spiral-wound RO pilot. Single output variable steady state plant models for flow rates and conductivities of the permeate and retentate streams were of high accuracy, with average absolute relative errors (AARE) of 0.70 % -2.46 % . Performance of a composite support vector regression (SVR) based model (for both streams) for flow rates and conductivities was of comparable accuracy to the single output variable models (AARE of 0.71 % -2.54 % ). The temporal change in conductivity, as a result of transient system operation (induced by perturbation of either system pressure or flow rate), was described by SVR model, which utilizes a time forecasting approach, with performance level of less than 1 % AARE for forecasting periods of 2 s to 3.5 min. The high level of performance obtained with the present modeling approach suggests that short-term performance forecasting models that are based on plant data, could be useful for advanced RO plant control algorithms, fault tolerant control and process optimization.