E3S Web Conf.
Volume 158, 20202019 7th International Conference on Environment Pollution and Prevention (ICEPP 2019)
|Number of page(s)||5|
|Section||Environmental Economy and Management|
|Published online||23 March 2020|
Prediction of capital cost of ro based desalination plants using machine learning approach
1 PhD Candidate, United Arab Emirates University, Al Ain, UAE
2 Professor, United Arab Emirates University, Al Ain, UAE
3 Assistant Professor, United Arab Emirates University, Al Ain, UAE
∗ Corresponding author: firstname.lastname@example.org
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.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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