Cost Optimization of Nanofiltration with Fouling by Natural Organic Matter
Publication: Journal of Environmental Engineering
Volume 128, Issue 10
Abstract
An artificial neural network and genetic algorithm routine has been developed for predicting and optimizing membrane system performance. The model predicted system behavior in response to operating conditions of applied pressure and crossflow velocity. Artificial neural networks accurately modeled mechanisms involved in fouling of membranes by natural organic matter. The model correctly predicted the effects of calcium within the solution in exacerbating fouling, binding of the divalent calcium ions to the natural organic matter macromolecules, and the formation of complexes. The model also correctly predicted the role of increased pressure in inducing fouling and the reverse scenario of mitigating fouling with increased crossflow velocity. The model was applied to membrane plant design for determining cost-effective operations. The genetic algorithm routine searched the predictions of the system model to determine the optimal operating conditions. Fouling conditions induced by the presence of calcium resulted in escalating costs with increases in calcium concentration. Membrane-related cost components were shown to be a significant cost factor that is sensitive to operating conditions and represents a prime target for optimization.
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Published In
Journal of Environmental Engineering
Volume 128 • Issue 10 • October 2002
Pages: 967 - 973
Copyright
Copyright © 2002 American Society of Civil Engineers.
History
Received: Jul 10, 2001
Accepted: Feb 28, 2002
Published online: Sep 13, 2002
Published in print: Oct 2002
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