Abstract
The present work pertains to modelling and identification of seawater desalination system using reverse osmosis. Initially the manipulated variable (feed pressure and recycle ratio) and the measured variables (flowrate, concentration and pH of permeate) are identified from reverse osmosis desalination system. The model of reverse osmosis was developed from the first principle approach using the mass balance equation (taking into consideration effect of concentration polarisation) from which the transfer function model was developed. The parameters of multi-input multi-output model are identified using the autoregressive exogenous linear identification technique. The states of the process model were also estimated using Kalman filter and parameters are identified by nonlinear least square (NNLS) algorithm. The plant’s data of spiral wound model are given as input to all the identification methods. The results obtained from the predicted and the linear models are in good agreement with these obtained for the same plant data.
Highlights
Water has become scarce as consumption has increased due to the increase in population and higher standards of living. 97.5% of the world’s water resources are exited salt water in the ocean and seas in addition to brackish water
Desalination is a process to convert high total dissolved solid (TDS) sea water into low TDS portable water so that water can be used for the public
A number of large desalination plants were operated by semi permeable membrane techniques
Summary
Water has become scarce as consumption has increased due to the increase in population and higher standards of living. 97.5% of the world’s water resources are exited salt water in the ocean and seas in addition to brackish water. Zilouchion (2001) identified multivariable RO desalination photovoltaic process in the form of a transfer function matrix as well as state space representation by the recursive least square techniques Assef.et al (1995) presented the model by step response data and fitting, where input and output were defined in a different way. Fkirin .et al (1997) presented an optimal identification which was applied for the time varying dynamic process based on linear combination of the recursive least square method This scheme was applied to identify the parameters and to predict the ARMAX model of online desalination process. Feed water is transformed into portable water using the high pressure developed by HP pump to overcome the osmotic pressure, when the feed water travelling in the RO bed In this process the measured variables are the flow rate, concentration of the dissolved solids in the product water and the pH of the portable water. In order to estimate the parameters from input/output data, a model structure representing the RO process is necessary
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