Membrane plants for drinking water treatment should not only deliver a good water quality but should also operate cost effective. Therefore a two stage procedure was applied for optimization of a ceramic membrane microfiltration processes with coagulation pretreatment. First neural networks were applied for prediction of the course of transmembrane pressure (TMP) over several cycles with high precision. With a sensitivity analysis relationships between influencing parameters could be shown. In a second step these models were applied for operational costs optimization by genetic algorithms. Based on the idea of Darwin’s survival of the fittest, settings for filtration time, flux and aluminum dosage were optimized leading to minimized operational costs with a costs reduction of about 30 %. The selected study proved the effectiveness of genetic algorithms and the applicability for online optimization being planned for further studies.

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