Abstract
This paper presents a neural network tool for predicting the capital cost of desalination plants based on reverse osmosis technology. A multi-layer feedforward neural network with back propagation learning method is used to model the investment cost of RO plants. The model is developed using the data sets of 1806 RO plants of capacity at least 1000 m3/day, which involved training, testing and validation. The model used six inputs that included both categorical and numerical data elements, namely: plant location, plant capacity, project award year, raw water salinity, plant types, and project financing type. The output is the capital cost of the RO plants planned. This prediction model can be used by governments, investors or other stakeholders in desalination industry to make a reasonable estimate of investment costs of upcoming RO plant projects.
Highlights
With the increase in water demands over years many of the water scarce countries are depending on desalination technologies for producing drinking water
A desalination plant is a large-scale project which is usually undertaken by governments; an estimation of the project cost is important for governments, investors and stakeholders
Cost data on reverse osmosis (RO) plants was collected from the database available with Global Water Intelligence (GWI) [17], who keeps the inventory of all desalination plants
Summary
With the increase in water demands over years many of the water scarce countries are depending on desalination technologies for producing drinking water. RO is one of the newer desalination technologies that’s on the rise all over the world. A desalination plant is a large-scale project which is usually undertaken by governments; an estimation of the project cost is important for governments, investors and stakeholders. When new RO plants are proposed, an estimation of the expected project cost is key in deciding the budget allocation. This can help identify whether the project is cost effective or an alternative is to be looked for
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