Multi‐Objective Optimization of Slab Heating Process in Walking Beam Reheating Furnace Based on Particle Swarm Optimization Algorithm,Steel Research International

当前位置： X-MOL 学术 › Steel Res. Int. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

Multi‐Objective Optimization of Slab Heating Process in Walking Beam Reheating Furnace Based on Particle Swarm Optimization Algorithm
Steel Research International ( IF 1.9 ) Pub Date : 2020-10-19 , DOI: 10.1002/srin.202000382
Jing-Guo Ding ₁ , Ling-Pu Kong ₁ , Jin-Hua Guo ₁ , Meng-Xue Song ₁ , Zhi-Jie Jiao ₁

Affiliation

The unreasonably high heating process temperature of slab in furnace leads to many heating defects. To avoid these, a multi‐objective optimization method is proposed for furnace temperature setting based on particle swarm optimization (PSO) algorithm. A 2D model of the finite difference scheme, in which the thickness and width were unequally partitioned, is investigated. The distance between two neighbouring nodes increased with the thickness and width, and the effects of a more detailed grid on the surface or side are observed along with influence of a rough grid in the core. Then, a multi‐objective optimization function of the temperature setting, from which energy consumption and the oxidation and burning loss should be minimized, is established. The PSO algorithm is implemented to calculate the optimal value of the multi‐objective optimization function. The application results show that average temperature differences at the quarter and midpoint thicknesses between the predicted model and measured values decrease from 53.4 to 8.5 °C and 43.6 to 11.4 °C, respectively; furthermore, the average oxidation and burning loss rate decrease from 0.93% to 0.79%. Average energy consumption decreases from 1.57 to 1.33 GJ t⁻¹, thereby considerably reducing the energy consumption of the reheating furnace and minimizing the production cost.

中文翻译：

基于粒子群算法的步进梁式加热炉板坯加热过程多目标优化

炉中板坯的加热过程温度过高导致许多加热缺陷。为避免这种情况，提出了一种基于粒子群算法的多目标优化炉温设定方法。研究了厚度和宽度不均等的有限差分方案的二维模型。两个相邻节点之间的距离随着厚度和宽度的增加而增加，并且随着核心中粗糙网格的影响，可以观察到更细的网格在表面或侧面上的影响。然后，建立了温度设置的多目标优化函数，从中可以最大程度地减少能耗以及氧化和燃烧损失。实施PSO算法以计算多目标优化函数的最优值。应用结果表明，预测模型和测量值之间的四分之一和中点厚度的平均温差分别从53.4降低至8.5°C和43.6降低至11.4°C。此外，平均氧化和燃烧损失率从0.93％降低到0.79％。平均能耗从1.57 GJ t降低到1.33 GJ t^-1，从而大大减少了再加热炉的能耗，并使生产成本最小化。

更新日期：2020-10-19

点击分享查看原文

点击收藏

阅读更多本刊最新论文本刊介绍/投稿指南11