Multi-Objective Optimization Design of Ladle Refractory Lining Based on Genetic Algorithm

Abstract

Genetic algorithm is widely used in multi-objective mechanical structure optimization. In this paper, a genetic algorithm-based optimization method for ladle refractory lining structure is proposed. First, the parametric finite element model of the new ladle refractory lining is established by using ANSYS Workbench software. The refractory lining is mainly composed of insulating layer, permanent layer and working layer. Secondly, a mathematical model for multi-objective optimization is established to reveal the functional relationship between the maximum equivalent force on the ladle lining, the maximum temperature on the ladle shell, the total mass of the ladle and the structural parameters of the ladle refractory lining. Genetic algorithm translates the optimization process of ladle refractory lining into natural evolution and selection. The optimization results show that, compared with the unoptimized ladle refractory lining structure (insulation layer thickness of 0 mm, permanent layer thickness of 81 mm, and working layer thickness of 152 mm), the refractory lining with insulation layer thickness of 8.02 mm, permanent layer thickness of 76.20 mm, and working layer thickness of 148.61 mm has the best thermal insulation performance and longer service life within the variation of ladle refractory lining structure parameters. Finally, the results of the optimization are verified and analyzed in this paper. The study found that by optimizing the design of the ladle refractory lining, the maximum equivalent force on the ladle lining, the maximum temperature on the ladle shell and the ladle mass were reduced. The thermal insulation performance and the lightweight performance of the ladle are improved, which is very important for improving the service life of the ladle.

Substation instrumentation target detection based on multi‐scale feature fusion

“…Choice of downsampling method: downsampling operation refers to reducing the resolution of the image, and common downsampling methods are average pooling method and maximum pooling method 69–73 . Since the maximum pooling method can better preserve the image edge and texture information, this article selects the maximum pooling method to downsample the low‐level feature map, that is, conv3_3.…”

“…Choice of downsampling method: downsampling operation refers to reducing the resolution of the image, and common downsampling methods are average pooling method and maximum pooling method. [69][70][71][72][73] Since the maximum pooling method can better preserve the image edge and texture information, this article selects the maximum pooling method to downsample the low-level feature map, that is, conv3_3. The operation steps are: first of all, the conv3_3 need to be further feature extraction through the standard convolution of 3 × 3, and then the maximum pooling and resolution reduction are required, which will avoid the loss of detailed features in some positions.…”

Improved Multi-Stream Convolutional Block Attention Module for sEMG-Based Gesture Recognition

“…From machine learning to deep learning ( Lenz et al, 2015 ; Qi et al, 2020 ; Huang et al, 2021 ; Tao et al, 2022 ), the accuracy of the multi-classification of EMG signals has been improving. Deep learning performs better in the multi-classification problem of sEMG gestures because of its strong data fitting and feature extraction ability ( Li et al, 2019c ; Sun et al, 2022a ). In the method of gesture recognition using deep learning ( Han et al, 2018 ; Jiang et al, 2019b ; Liao et al, 2021 ), there are several major types of mainstream network algorithms: 1) convolutional neural networks ( Tao et al, 2022 ; Sun et al, 2022c ); 2) recurrent neural networks ( Liu et al, 2022b ); 3) network combining multi-class models ( Zhao et al, 2022 ); 4) some novel network ( Huang et al, 2019 ; Chen et al, 2021b ; Sun et al, 2021 ; Liu et al, 2022c ; Liu et al, 2022d ; Wu et al, 2022 ; Zhao et al, 2022 ).…”

Path Planning Optimization of Intelligent Vehicle Based on Improved Genetic and Ant Colony Hybrid Algorithm

“…The algorithm is simple. Relevant research mainly improves the heuristic function, but the efficiency is too low and the amount of calculation is large, and the searched path is not necessarily optimal (Sun et al, 2022b). Later, some scholars propose D* algorithm and its improvement (Zhang et al, 2022).…”