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.
|Journal||E3S Web of Conferences|
|Publication status||Published - Mar 23 2020|
|Event||7th International Conference on Environment Pollution and Prevention, ICEPP 2019 - Melbourne, Australia|
Duration: Dec 18 2019 → Dec 20 2019
ASJC Scopus subject areas
- Environmental Science(all)
- Earth and Planetary Sciences(all)