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Abstract
A large number of new desalination plants are being contracted every year constituting huge strategic investments. Strategic decisions related to plant locations and capacity, the Selection of the desalination technology, and many other technical decisions related to the plant design and operation are very critical to maximize the economical and the social return on these investments. Viewing the desalination industry network as a supply chain provides a holistic view allowing decision makers to perform optimization of water desalination operations end to end. The proposed methodology provides a set of modular simulation components to allow the creation of complex models to optimize the entire water desalination supply chain quickly and easily. The optimization entails mathematical programming (MP) models that can be solved by external MP solvers. Saudi Arabia is the worldwide leader in the desalinated water capacity. A national water strategy is implemented by a number of governmental and privatized authorities engaged in major desalination and power generation projects aiming at covering 80% of municipal water demand by desalination by the end of 2014. We use the case of Saudi Arabia desalination supply chain to show the advantages of performing optimization using our proposed methodology. We solve a base case comprising the datasets collected from Saudi Arabia desalination authorities’ actual operational reports and the national development plans. We assume that the decision maker in this case wishes to find the optimal network flow given the specified demand. Then we show how the decision maker can develop additional alternatives to further investigate the base solution. The analysis shows that additional investments are essential since given the current infrastructure (i.e. desalination units, plants, distribution networks) only around 50% of the municipal water demand can be met. The analysis also provides the decision makers in Saudi Arabia with optimal operational values which maximize the flow while minimizing the total costs.
The authors would like to thank the King Fahd University of Petroleum and Minerals in Dhahran, Saudi Arabia, for funding the research reported in this paper through the Center for Clean Water and Clean Energy at MIT and KFUPM under the Ibn Khaldun Fellowship. The authors would like also to thank Effat University in Jeddah, Saudi Arabia, for the sabbatical assistance provided to the first author.