The modelling of water treatment processes is challenging because of its complexity, nonlinearity, and numerous contributory\r\nvariables, but it is of particular importance since water of low quality causes health-related and economic problems which have a\r\nconsiderable impact on peopleââ?¬â?¢s daily lives. Linear and nonlinear modelling methods are used here to model residual aluminium\r\nand turbidity in treated water, using both laboratory and process data as input variables. The approach includes variable selection\r\nto find the most important factors affecting the quality parameters. Correlations of ~0.7ââ?¬â??0.9 between the modelled and real values\r\nfor the target parameters were ultimately achieved. This data analysis procedure seems to provide an efficient means of modelling\r\nthe water treatment process and defining its most essential variables.
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