In this work, the design and operation of multistage flash (MSF) desalination processes are optimized and controlled in order to meet variable demands of freshwater with changing seawater temperature throughout the day and throughout the year. On the basis of actual data, the neural network (NN) technique has been used to develop a correlation which can be used for calculating dynamic freshwater demand/consumption profiles at different times of the day and season. A storage tank is linked to the freshwater line of the MSF process which helps avoiding dynamic changes in operating conditions of the process. A steady state process model for the MSF process coupled with a dynamic model for the storage tank is developed which is incorporated into the optimization framework within gPROMS modeling software. For a given design (process configuration), the operation parameters are optimized at discrete time intervals (based on the storage tank level which is monitored dynamically and maintained within a feasible limit) while the total daily cost is minimized.