Bending Force of Hot Rolled Strip Based on Improved Whale Optimization Algorithm and Twinning Support Vector Machine

Abstract

Bending control is one of the main methods of shape control for the hot rolled plate. However, the existing bending force setting models based on traditional mathematical methods are complex and have low control accuracy, which leads to poor strip exit shapes. Aiming at the problem of complex bending force setting of the traditional algorithm, an improved whale swarm optimization algorithm and twin support vector machine-based bending force model for hot rolled strip steel (LWOA-TSVR) is proposed. Based on the hot rolling field production data of a steel plant, the research group established the bending force prediction model by using the nonlinear approximation ability of the twin support vector machine. The introduction of the Levy flight improvement algorithm improves the generalization ability, prediction accuracy, and convergence speed of the whale swarm optimization algorithm with the help of the convergence of coefficient vectors, solves the problem of a random selection of the parameters of the traditional whale swarm optimization algorithm and optimizes the ability of the whale swarm algorithm to jump out of the local optimum. Based on the actual rolling database, the hit rate of the proposed method reaches 91% (from −5 to 5 KN), which fully meets the requirements of the detection accuracy on the actual production line. The model is not only able to overcome the local search to obtain the global optimal solution, but also has the advantages of fast convergence and higher prediction accuracy. A comparison of the model with twin support vector machines and traditional whale swarm algorithms shows that the prediction accuracy is higher. The experimental results also show that this model has advantages over existing bending force prediction models in terms of improving the accuracy of the strip shape control and providing theoretical guidance for practical bending force settings.

Improved Whale Optimization Algorithm with Deep Learning-Driven Retinal Fundus Image Grading and Retrieval

“…The use of IWOA optimally chooses the parameters related to the LS-SVM model. It has a certain capability to escape from the local optima, a faster operational speed, and a simple adjustment parameter [20]. However, the algorithm exploits an arbitrary method for exploration, and overreliance on the random limit search speed of the model, convergence accuracy, and the speed of WOA increased.…”

Prediction model of BOF end-point phosphorus content and sulfur content based on LWOA-TSVR

“…Li et al [11] used the Levenberg-Marquardt (LM) algorithm based on BP neural network to establish a prediction model of end-point phosphorus content in BOF steelmaking process. Shi et al [12] proposed a PCA-GA-BP (backpropagation) multiple optimised end-point phosphorus content and oxygen content prediction model, using (PCA) to reduce the dimension of influencing factors, and using genetic algorithm (GA) to optimise the model. To some extent, the prediction model based on intelligent algorithms can predict the end-point phosphorus content and sulfur content, but the situation of converter steelmaking is complicated, and the indicators affecting end-point phosphorus content and sulfur content affect each other, which increases the difficulty of model training.…”