Operation Optimization of Slab Reheating Process Based on Differential Evolution

Abstract

In the slab reheating process, the temperature distribution of walking beam furnace is critical for the temperature of slab, as well as the subsequent hot rolling. Besides, this process is dynamic, nonlinear, and time varying due to coupling with complex physical and chemical reactions. In this paper, we focus on an operation optimization problem of tuning furnace temperature to optimize the slab reheating process, which considers the reheating quality and production cost. For this purpose, we develop a novel operation optimization method. Specifically, first, from the view of mechanism, a heat transmit model based on the heat-exchange of slab and furnace, an energy consumption function, and especially, an oxidation loss function are established. Therefore, incorporating the mechanism models, we build an optimization model to minimize temperature deviation of slab, energy consumption, and oxidation loss to describe our problem. Then, based on the features of the problem, we propose a modified differential evolution algorithm, which includes a space contraction scheme, a new self-adaptive parameter strategy, and a new mutation operator. Finally, to evaluate the performance of our method, extensive numerical experiments are implemented by comparing it with other well-known evolutionary algorithms based on practical data. The experimental results demonstrate the effectiveness of proposed operation optimization method on solving our problem.

A Data-Driven Multiobjective Dynamic Robust Modeling and Operation Optimization for Continuous Annealing Production Process

“…In recent years, there have been many researches focused on the operation optimization problems in process industries such as the iron and steel industry and the chemical industry. [3][4][5][6][7][8][9][10][11] For the gas stirred ladle systems in the steelmaking process, Mazumdar et al 3) formulated a multiobjective optimization model with constraints to investigate inert gas injection in steelmaking ladles. In view of the hot stamping process, Mu et al 4) established the response surface model to optimize the heating parameters of hot stamping.…”

“…Chen et al 9) established the operation optimization model of hot rolling process with the more practical constraints and presented a hybrid self-adaptive genetic algorithm to solve this model. Xia et al 10,11) developed a novel optimization method to solve the operation optimization problem of furnace temperature in the slab reheating process and proposed a mechanism and data analytics based hybrid model to study the furnace operation optimization problem. However, these research usually did not consider the dynamic disturbances, which meant that the production process was static.…”

“…In recent years, many kinds of multiobjective evolutionary algorithms (MOEAs) have been proposed in the literature. [19][20][21][22][23][24][25][26][27][28][29] Among these MOEAs, NSGA-II is one of the most widely adopted algorithms for practical optimization problems for process operation, 3,4,6,[8][9][10][11][27][28][29][30] due to its characteristics of fast computation speed and good convergence. Therefore, in this paper we prefer to adopt the NSGA-II as the solving method.…”

Predictive Modeling of Strip Temperature in Continuous Annealing Furnace: An Improved Optimization Algorithm

“…Its main functions include but not limited to recrystallization and annealing after rolling, improving the mechanical properties, yield strength, tensile strength, and surface quality of the strip to provide thin steel strips with excellent performance and high surface quality for manufacturing enterprises. 2,3) As an important piece of equipment in the continuous annealing production line, the CAF consists of the preheating section (PHS), heating section (HS), soaking section, cooling section, over-aging section, final cooling section and water cooling section. The vertical CAF is heated by the heat radiation of the radiant tube (RT) with a better surface quality, which is widely used in practical production.…”

Sustainable Scheduling of the Production in the Aluminum Furnace Hot Rolling Section with Uncertain Demand

“…The results come from the following aspects. Firstly, to tackle the scheduling problem in the furnace hot rolling section, most research concentrates on the iron and steel industry [10,[15][16][17][18][19][20][21][22][23][24][25][26][27], and the aluminum industry is hardly considered in the literature [28]. Even if the aluminum and steel industries have similar production processes, which also have different characteristics, the two types of production cannot be treated as the same.…”