Multi-Criteria Decision Making Approach to the Formulation and Selection of Anti-icing Liquids

摘要

To effectively fight snow storms in the challenging funding environment, many maintenance agencies in North America have started to produce their own anti-icing liquids, instead of procuring commercial anti-icers. This work demonstrates a systematic approach to data-driven, multi-criteria decision-making, by conducting a set of laboratory tests to assess twenty blended chloride-based anti-icing formulations. The laboratory data were then used to establish predictive models correlating the multiple design parameters with the anti-icer performance and impacts or with an anti-icer composite index. We used artificial neural networks for modeling and examined anti-icer performance (characteristic temperature and ice-melting capacity at 30DGF and 15DGF respectively) and impacts (splitting tensile strength of concrete after ten freeze-thaw cycles and corrosivity to mild steel) as a function of the formulation design. The anti-icer composite index was calculated for four different user priority scenarios (cost-first, performance-first, impacts-first, or a balanced approach), each of which placed a different set of decision weights on various target attributes. Three-dimensional response surfaces were then constructed to illustrate such predicted correlations and to guide the direction for formulation improvements.