Modelling and optimization of a multistage flash desalination process

Abstract

The multistage flash (MSF) desalination process is a widespread and vitally important process for satisfying the needs of citizens of arid land such as in the Middle East Countries. MSF processes are large and complex plants, and a number of simplifying assumptions must be used in order to provide first principle models for simulating and predicting their operation. This article describes the development and application of artificial neural networks (ANNs) as a modelling technique for simulating, analyzing, and optimizing MSF processes. Real operational data is obtained from an existing MSF plant during two modes of operation: a summer mode and a winter mode. ANNs based on a feed-forward architecture and trained by the backpropagation algorithm with momentum and a variable learning rate are developed. The networks can predict different plant performance outputs including the distilled water produced and top brine temperature. The inputs to the ANNs are based on engineering know-how of the operation of the plant. The predictions of the prepared networks were compared to actual measurements. Good agreements were obtained. In addition to their use as a training tool for new operators and for decision-making, the prepared networks were used to optimize the performance of the plant. A composite objective function that consists of the different plant performance measures was used in conjunction with the prepared ANNs within an optimization model. The ANN model serves as an accurate and more convenient replacement of first principle models or plant data. The decision variables over which optimization was carried out are subjected to constraints to ensure that maximum and minimum bounds are adhered to as well as safety considerations.