A Hybrid Modeling Method for Aluminum Smelting Process Based on a Hybrid Strategy-Based Sparrow Search Algorithm

Abstract

In the production of aluminum, the regenerative aluminum smelting process is an important part for energy efficiency and product quality. Aluminum liquid temperature is a significant variable in the aluminum smelting process, and it is costly to measure timely because it requires protective temperature sensor. To handle this problem, a kind of modeling framework which combine a mechanism model with multi-scale kernel technique is proposed. First, the mechanism model is built for the aluminum liquid temperature by the energy conservation law and heat transfer mechanism. Since the mechanism model is based on some assumptions, it often results in unknown variables. Thus, the multi-scale kernel technique is used to obtain the unknown variables. Finally, a hybrid temperature prediction model is built by combining the multi-scale kernel and the mechanism model. The parameter identification of the hybrid model is described as an optimization problem, and a hybrid strategy-based sparrow search algorithm (HSSA) is proposed to solve this problem. The experiment results show that HSSA has higher convergence accuracy and stronger global search ability than the original sparrow search algorithm (SSA), and the proposed hybrid model can correctly estimate the aluminum liquid temperature.

A Multi-Strategy Sparrow Search Algorithm with Selective Ensemble

“…Therefore, many researchers have begun to conduct considerable studies on the improvement of SSA. Liu et al [21] proposed a hybrid strategy improved sparrow search algorithm (HSISSA). They constructed a hybrid circle piecewise mapping method to initialize the population and combined the spiral search method with the Lévy flight formula to update the locations of the discoverers and the alerters, expand the search range of the population, and enhance the search ability.…”

“…SSA has the advantages of simple structure, strong flexibility, and few parameters, and significant advantages in convergence speed, search accuracy, and stability. It has been applied to solve various practical application problems, such as intrusion detection [21], energy consumption [22], wireless sensor networks [23], feature selection [24], network planning [25], data compression [26], engineering problems [27][28][29][30][31][32][33], etc. However, the second challenge is that SSA is highly susceptible to interference from local solutions, resulting in reduced search capability and search accuracy.…”