The most important design parameter of “minimum fluidization velocity” in tapered fluidized beds is studied by robust smart models focusing on the particle size distribution. The smart models are developed based on the multilayer perceptron (MLP), Gaussian process regression (GPR), and genetic programming (GP) techniques. For this purpose, 675 data samples are measured for different geometrical dimensions and material properties. Basically, it is shown that accounting for the Gaussian distribution width of the particles in the models improves their outcomes considerably. The MLP and GPR based-models show excellent results with average absolute relative error (AARE) values of 0.36 and 0.80%, respectively, for 125 data points used for the testing process. The data samples are also used for studying the accuracy of available empirical models, which shows that the recent model proposed by Rasteh et al. obtains the best outputs among the others. The developed models also present favorable trends under different conditions. Finally, in addition to accomplishing the sensitivity analysis, an explicit and integrated form of empirical correlation is also proposed by GP with an AARE value of 6.87% for total data points.
