Polyharmonic Systems Involving Critical Nonlinearities with Sign-Changing Weight Functions

Abstract

The aim of the present study is to develop a neural network model for the prediction of slurry erosion (SE) of heavy-duty pump impeller steels and casing material. The heavy-duty pump impeller has a wide range of applications like slurry transportation system and coal conveying system of thermal power stations, and various other industries like mining, chemical and marine industry. In the present study, the slurry erosion performance of three different pump impeller steels namely 18Cr-8Ni, 16Cr-10Ni-2Mo and super duplex 24Cr-6Ni-3Mo-N, and one pump casing material namely Grey cast iron was tested. The SE experiments were carried out on a laboratory scale pot tester using the sand as an erodent. A data set was produced to develop a prediction-based neural network (NN) model and 70% of this data set was used as input to NN model. The learning of NN model was based on an artificial neural network (ANN) and used to build a prediction model that can predict the results while the input data was supplied to it. Firstly, the data were divided into training, validation, and testing. The NN model was trained on 70% of the original dataset and validated by another 15% data. A total of 30 training epochs were performed for training, testing, and validation of the model. The validation of the model indicates that the build NN model was the best fit and comprises no over-fitting and under-fitting issues. In the end, the prepared NN model was given the remaining 15% data for testing. It outputs the corresponding values for each input. These predicted values are then compared with the actual ground truth to check the robustness of the designed model. The various measures used for evaluating the model were R2 (coefficient of determination), Root mean square error, etc. Results show that 18Cr-8Ni steel exhibits the best SE performance followed by 16Cr-10Ni-2Mo, 24Cr-6Ni-3Mo-N, and Grey cast iron. © 2021 Elsevier B.V.