Machine learning and AI to optimise the cost of ownership for small-scale reverse osmosis processes
The demand for agricultural, industrial, and potable water for domestic use has increased continuously over the last thirty years, reportedly increasing by 1% year on year since the 1980s (UN Water report, 2019). By 2050 consumption is expected to exceed current usage by 20 to 30%, leaving many countries experiencing severe water stress. It is evident that effective and efficient management of this vital resource is critical. Desalination technologies are becoming increasingly necessary to meet water demand, with reverse osmosis being the most prevalent technology, accounting for greater than 60% of installed global capacity (Desal data, 2016). Reverse osmosis, in conjunction with its necessary pre-treatment processes, is resource intensive, particularly in terms of energy, chemicals, and membranes. Economies of scale mitigate operating costs somewhat for large seawater desalination plants. However, smaller-scale systems are becoming more common to treat low volume saline water for industrial and agro-industrial applications, and these smaller systems pose specific challenges in terms of process and operational cost optimisation. ML techniques such as support vector machines and artificial neural networks have been applied to model various desalination processes that pose multivariate and time series challenges. However, it is unclear whether these approaches are optimal for smaller-scale industrial seawater treatment. The aim of this project is to develop models using AI and ML techniques to optimise the cost of ownership in small-scale desalination and water treatment processes. An instrumented and automated reverse osmosis rig will be used to collect data under different operating conditions. Using a combination of existing reverse osmosis operational data and results from experimental work, AI/ML techniques will be applied based on current methodologies and engineering techniques to establish the benefits and limitations of computational intelligence and propose methods for optimisation of small-scale desalination processes.