کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
625494 1455423 2011 4 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Prediction of the behaviour of the Silt Density Index (SDI) in the Caribbean Seawater and its impact on RO desalination plants
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی شیمی تصفیه و جداسازی
پیش نمایش صفحه اول مقاله
Prediction of the behaviour of the Silt Density Index (SDI) in the Caribbean Seawater and its impact on RO desalination plants
چکیده انگلیسی

The environmental characteristics of the seawater and its impacts on planning and operating RO desalination plants were always the concern of several scientists around the world. In this article, a study has been performed to investigate the seasonal characteristic of seawater in the Caribbean. The Radial Basis Function Networks (RBFN) was the methodology used in this paper where the redistribution of centres and the input training data possess significant effects in the model. The proposed method is based on clustering of input space vectors and computing weights of Euclidian distances and histogram equalization within each cluster for determining the centre and width of each receptive field. The model allows predicting the behaviour of the SDI using field data (turbidity and salinity) taking into account the tides (the Gulf of Paria) and seasonal changes (hurricane or dry seasons). The results indicated that during the hurricane season, the parameters can change up to 32% between seasons along year due to the change of direction of the inshore currents. Also values higher than 6 in the SDI indicated a very high potential for fouling. This article offers to the project engineers a new door in the application of precautionary principles in the design of desalination plants.

Research Highlights
► During the hurricane season, the fluctuations of the critical parameters are more frequent due to the change of direction of the inshore currents.
► The results indicated that the parameters can change up to 32% between seasons along year and the accuracy of the prediction obtained through RBFN can be around 98.7% per month.
► The method of prediction using RBFN is close enough to the real values thus the values of the SDI can be calculated with a good enough accuracy.
► Values higher than 6 in the SDI indicated a very high potential for fouling.
► This article offers to the project engineers a new door in the application of precautionary principles in the design of desalination plants.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Desalination - Volume 268, Issues 1–3, 1 March 2011, Pages 262–265
نویسندگان
, ,