Evolutionary design of a generalized polynomial neural network for modelling sediment transport in clean pipes. Issue 10 (2nd October 2016) - Ebtehaj, Isa
Abstract : To determine the minimum velocity required to prevent sedimentation, six different models were proposed to estimate the densimetric Froude number (Fr). The dimensionless parameters of the models were applied along with a combination of the group method of data handling (GMDH) and the multi-target genetic algorithm. Therefore, an evolutionary design of the generalized GMDH was developed using a genetic algorithm with a specific coding scheme so as not to restrict connectivity configurations to abutting layers only. In addition, a new preserving mechanism by the multi-target genetic algorithm was utilized for the Pareto optimization of GMDH. The results indicated that the most accurate model was the one that used the volumetric concentration of sediment ( CV ), relative hydraulic radius ( d / R ), dimensionless particle number ( D gr ) and overall sediment friction factor ( λs ) in estimating Fr. Furthermore, the comparison between the proposed method and traditional equations indicated that GMDH is more accurate than existing equations.