Chemical Engineering Journal, Vol.165, No.2, 545-553, 2010
Simulation and optimization of MSF desalination process for fixed freshwater demand: Impact of brine heater fouling
The most costly design and operation problems in seawater desalination are due to scale formation and corrosion of plant equipment. Fouling factor (a measure of scale formation) is one of the many important parameters that affect the operation of MSF processes. In this work, a steady state model of MSF is developed based on the basic laws of mass balance, energy balance, and heat transfer equations with supporting correlations for physical properties calculations. The model includes parameters such as the brine flow rate, freshwater flow rate, the temperature profiles for all stages, top brine temperature and steam flow rate. gPROMS model builder 2.3.4 software is used for model development, simulation and optimization. The model is validated against the simulation results reported in the literature. The model is then used to study the role of a changing brine heater fouling factor with varying seawater temperatures and its effect on the plant performance for fixed water demand, for a given steam and top brine temperature. For fixed water demand, this paper also studies the effect of brine heater fouling factor with seasonal variation of seawater temperatures on the operating cost. Based on actual plant data, a simple linear dynamic fouling factor profile is developed which allows calculation of fouling factor at different time (season of the year). January is considered to be the starting time (when the fouling factor is minimum) of the process after yearly overhauling. The total monthly operation cost of the MSF process is selected to minimize, while optimizing the operating parameters such as make up, brine recycle flow rate and steam temperature. This leads to a seasonal optimal operation policy for the whole year. (C) 2010 Elsevier B.V. All rights reserved.
Keywords:MSF desalination process;Brine heater fouling;Fixed water demand;Annual operating cost;Simulation;Optimization