锻压技术

面向加热炉利用率的锻坯装炉节能调度

英文标题：Forging billet charging energy-conservation scheduling for heating furnaces efficiency

作者：江明明何非李东波童一飞

单位：南京理工大学

分类号：TF089

出版年,卷(期):页码：2016,41(8):115-121

摘要：

为了实现锻造行业的节能目标，首先分析了锻坯的特征，然后根据锻坯的加热规范，考虑锻坯形状和尺寸对加热时间的影响，建立了以锻坯装炉容量差率为目标函数的锻造装炉节能调度模型。基于遗传算法，采用二进制编码及精英保留策略，构造出面向目标函数的求解算法。同时，通过相关算例，验证了锻坯装炉节能调度模型及其求解方法的有效性及可行性。最后，通过应用实例分析，得到每批加热炉工作时间减少量分别为11，14，18和15 min，平均每批锻坯的生产能耗将减少1174.5 GJ的能量。该节能调度模型和求解方法能够满足现场装炉需求，在一定生产工艺条件下，能有效提高加热炉利用率，减少加热炉工作时间，实现了节能降耗的目标，有助于锻造企业生产过程中的节能。

In order to achieve energy efficiency goals for forging industry, firstly, the characteristics of forging billet were analyzed, then according to specifications of forging billet heating, a forging billet charging scheduling model with an objective function, which was capacity residuals of charging, was established considering influences of the shape and size of the forging billet on heating time. Then, based on genetic algorithm, an algorithm for objective function was constructed by genetic algorithm binary coding and elite reserved strategy. At the same time, the forging billet charging energy-saving scheduling model and its effectiveness and feasibility were verified by the relevant examples. Finally, by analyzing application examples, the amount of work time of each furnace is reduced 11 min,14 min,18 min and 15 min respectively, so the power of 1174.5 GJ is reduced for each batch of forging billet. Thus, it is proved that the energy-efficient scheduling model and solving methods meet the needs of field charging. Under the certain production conditions, the furnace utilization is effectively improved, furnace working period is shortened, and the goal of energy saving is achieved. At last, it helps to save energy in the forging process.

基金项目：

国家自然科学基金资助项目（51575280）

江明明（1989-），男，硕士研究生何非（1982-），男，博士，讲师

参考文献：

［1］Zhao Y, Liu X, Xia L, et al. Theoretical studies on the water-colded black mark of steel slab in a walking beam type reheating furnace[J]. Industrial Heating, 2005, 34(4):43-46.
［2］Si M, Thompson S, Calder K. Energy efficiency assessment by process heating assessment and survey tool (PHAST) and feasibility analysis of waste heat recovery in the reheat furnace at a steel company[J]. Renewable & Sustainable Energy Reviews, 2011, 15(6):2904-2908.
［3］Tian W, Chiu W K S. Calculation of direct exchange areas for non-uniform zones using a reduced integration scheme [J]. Journal of Heat Transfer, 2003, 125(5):839-844.
［4］Mui C, Osinski E, Meech J A, et al. A SCADA-based expert system to provide delay strategies for a steel billet reheat furnace [A]. Proceedings of the Second International Conference on IEEE [C]. Paris，1999.
［5］杨业建, 姜泽毅, 张欣欣. 钢坯热轧加热炉区生产调度模型与算法[J]. 北京科技大学学报, 2012, 34(7):841-846.Yang Y J, Jiang Z Y, Zhang X X. Model and algorithm of furnace area production scheduling in slab hot rolling [J]. Journal of University of Science and Technology Beijing, 2012, 34(7):841-846.
［6］Parali J，Malindzak D，Csonto J.Scheduling of slabs into push furnaces-two different approaches [A]. Proceedings of the 12th International Conference on Process Control and Simulation[C]. Kosice，1996.
［7］Ning S, Wang W, Liu Q. An optimal scheduling algorithm for reheating furnace in steel production [J]. Control & Decision, 2006, 21(10):1138-1142.
［8］王志刚, 刘全利, 王伟. 改进的装炉组合问题建模与优化算法[J]. 控制工程, 2011, 17(2):197-201.Wang Z G, Liu Q L, Wang W. Improved modelling and optimal algorithm for combination stacking[J]. Control Engineering of China, 2011, 17(2):197-201.
［9］朱柏青, 卢海星, 夏勇,等. 基于离散混合蛙跳算法的锻件装炉组合优化模型研究[J]. 中国农机化学报, 2013, 34(6):197-201.Zhu B Q, Lu H X, Xia Y, et al. Design and test of tractor's hydraulic steering by-wire system [J]. Journal of China Agricultural Mechanization, 2013, 34(6):197-201.
［10］孙文安, 刘发久, 孙克斌,等. 基于线性规划的炼钢装炉最小成本控制[J]. 数学的实践与认识, 2006, 36(9):158-162.Sun W A, Liu F J, Sun K B, et al. Minimum cost control of steel-making-furnacing based on linear programming[J]. Mathematics in Practice and Theory, 2006, 36(9):158-162.
［11］李颢, 任德祥. 基于遗传算法的均热炉群装炉出炉调度[J]. 控制与决策, 1999, 2(2):135-139.Li H, Ren D X. Baked charging scheduling based on genetic algorithm soaking pits [J]. Control and Decision, 1999, 2(2):135-139.
［12］史海波, 马玉林, 彭威. 冷轧企业热处理工序的优化配料装炉问题研究[J]. 计算机集成制造系统, 2004, 10(1):116-120.Shi H B, Ma Y L, Peng W. Load optimization of furnace problem for heat treatment operation of cold-rolled sheets plant [J]. Computer Integrated Manufacturing Systems, 2004, 10(1):116-120.
［13］ Knoop P, Van Nerom L. Scheduling requirements for hot charge optimization in an integrated steel plant [A]. 38th IAS Annual Meeting. Conference Record of the. IEEE [C]. Utah, 2003.
［14］ Baker J E.Adaptive selection methods for genetic algorithms[A].Proceedings of the 1st International Conference on Genetic Algorithms[C].Hillsdale，1987.

服务与反馈：

【文章下载】【加入收藏】