Wastewater reclamation and reuse is being viewed increasingly as a sustainable approach to integrated water resources management in many countries including Australia. The technical feasibility of reclamation and reuse has been demonstrated by a number of successful projects. The current state-of-the art of reclamation technologies can produce water of any desired quality (including potable quality). However, the increasing number of efficient treatment processes has made the selection of an optimum treatment train a difficult task for planners and decision-makers. A decision support system (DSS) can be particularly useful in wastewater reclamation and reuse as it can provide assistance in the evaluation and selection of treatment alternatives for a given reuse application before exhaustive simulation or pilot studies are conducted. This paper highlights the ongoing research on the development of a computer based DSS named MOSTWATAR(©) (which stands for Model for Optimum Selection of Technologies for WAstewater Treatment And Reuse). MOSTWATAR(©) has a database of the performance characteristics and costs of commonly used reclamation technologies and an optimization module based on genetic algorithms to generate and optimize treatment trains. It also contains detailed reuse guidelines applicable in the various Australian States. This model is intended to assist planners and decision-makers in the techno-economic assessment of reclamation technologies and aid in the selection of the best 5 treatment trains for a given end use and location, wastewater characteristics, and flow rate. This paper describes salient features of the MOSTWATAR(©) package and demonstrates its application to a case study. The results from user-generated options are presented and it is shown that this model can be a very useful tool for selecting the best treatment trains for wastewater reclamation and reuse.
- Decision support system
- genetic algorithms
- treatment trains
- wastewater reclamation and reuse
- © IWA Publishing 2003