Hydraulic Modelling and Scheduling Scheme of Blast Furnace Gas Pipeline Network

doi:10.12068/j.issn.1005-3026.2023.01.010

Abstract

Abstract: To develop a set of accurate scheduling scheme of blast furnace gas (BFG) for the whole plant， the structure and operation characteristics of the BFG pipeline network of an integrated steelworks are analyzed. Considering the correlation between the pressure and flow， a hydraulic model of BFG pipeline network is established to make scheduling strategy of BFG pipeline network. The results of case study show that the pressure of the pipeline network increases with the decrease of the flow rate in the 1700 hot rolling mill. When the pressure rises to the upper limit of 20 kPa， it is necessary to open the 1# and 2# flare towers and adjust the volume of discarding BFG. The pipeline network pressure can be maintained within the allowable range if the discarding reaches 84400m³/h. When 4# and 6# blast furnaces take blowing-down， the pipeline network pressure drops sharply. Therefore， it is necessary to shut down the auxiliary boilers and 2# 80 MW gas generator unit to restore the pipeline network pressure. The pipeline network pressure is stable at 13 kPa by setting a gasholder. When the pressure relative error is less than 0.01% and the flow relative error is less than 0.8%， the calculation time of the model for each working condition is shorter， ranging from 3.59 to 4.46 s.

Key words: blast furnace gas pipeline network; hydraulic modelling; flow rate; pressure; scheduling scheme

CLC Number:

TK01

FANG Xiao-qing， LIU Shu-han， SUN Wen-qiang. Hydraulic Modelling and Scheduling Scheme of Blast Furnace Gas Pipeline Network[J]. Journal of Northeastern University(Natural Science), 2023, 44(1): 69-75.

References

[1]Sun W，Wang Q，Zhou Y，et al.Material and energy flows of the iron and steel industry: status quo，challenges and perspectives［J］.Applied Energy，2020，286: 114946.
[2]Tore B N，Henrik L，Poul A ，et al.Perspectives on energy efficiency and smart energy systems from the 5th SESAAU2019 conference［J］.Energy，2021，216: 119260.
[3]Huang Y，Lyu Z，Liu Y，et al.A fuzzy modeling method based on Dirichlet process mixture model for blast furnace gas system［J］.IFAC-Papers OnLine，2018，51(21):301-306.
[4]刘书含，孙文强，石晓星，等.基于BP神经网络的热风炉群煤气消耗预测［J］.中国冶金，2022，32(2):77-83.(Liu Shu-han，Sun Wen-qiang，Shi Xiao-xing，et al.Prediction of gas consumption of a hot blast stove group based on BP neutral network［J］.China Metallurgy，2022，32(2):77-83.)
[5]Cui G，Jia Q S，Guan X，et al.Data-driven computation of natural gas pipeline network hydraulics［J］.Results in Control and Optimization，2020，1: 100004.
[6]张琦，刘帅，徐化岩，等.钢铁企业智慧能源管控系统开发与实践［J］.钢铁，2019，54(10):125-133.(Zhang Qi，Liu Shuai，Xu Hua-yan，et al.Development and practice of smart energy management and control system in iron and steel works［J］.Iron & Steel，2019，54(10):125-133.)
[7]滕卯寅，周伟国，王海，等.高炉煤气管网事故工况实验系统的设计［J］.油气储运，2014，33(1):15-19.(Teng Mao-yin，Zhou Wei-guo，Wang Hai，et al.Design of experimental system of blast furnace gas pipeline networks under accident condition［J］.Oil & Gas Storage and Transportation，2014，33(1):15-19.)
[8]宋军，蔡九菊，张琦.钢铁联合企业煤气系统动态仿真［J］.东北大学学报(自然科学版)，2010，31(5):685-688.(Song Jun，Cai Jiu-ju，Zhang Qi.Dynamic simulation of gas system in iron and steel complex［J］.Journal of Northeastern University (Natural Science)，2010，31(5):685-688.)
[9]Kim J H，Yi H S，Han C.A novel MILP model for plantwide multiperiod optimization of byproduct gas supply system in the iron-and steel-making process［J］.Chemical Engineering Research and Design，2003，81(8):1015-1025.
[10]Yang J，Cai J，Sun W，et al.Optimization and scheduling of byproduct gas system in steel plant［J］.Journal of Iron and Steel Research International，2015，22(5):408-413.
[11]Leonid D，Michael R，Leonid K.A method of analysis of complex hydraulic networks［J］.E3S Web of Conferences，2020，219: 1002-1006.
[12]Su L，Zhao J，Wang W.Hybrid physical and data driven transient modeling for natural gas networks［J］.Journal of Natural Gas Science and Engineering，2021，95: 104146.
[13]刘书含，孙文强，范天骄，等.事件和数据融合的加热炉煤气消耗量预测方法［J］.材料与冶金学报，2021，20(4):304-309.(Liu Shu-han，Sun Wen-qiang，Fan Tian-jiao，et al.A hybrid event-driven and data-driven method for predicting the gas consumption of reheating furnaces［J］.Journal of Materials and Metallurgy，2021，20(4):304-309.)
[14]Zhou J，Zhou X，Liang G，et al.An MINLP model for network layout of underground natural gas storage［J］.Journal of Intelligent & Fuzzy Systems，2020，38(4):4618-4642.
[15]Kou J，Chen Y，Zhou X，et al.Optimal structure of tree-like branching networks for fluid flow［J］.Physica A: Statistical Mechanics and Its Applications，2014，393: 527-543.
[16]Fan Y，Chai Y，Wu J，et al.Behavior of coke in the blast furnace for smelting Bayan Obo Mine［J］.Fuel，2022，309: 122147.
[17]Yang C，Kong X，Zhang L，et al.Analysis of ‘5.29’ blast furnace burst accident of Fangda special steel［J］.IOP Conference Series: Earth and Environmental Science，2020，608: 012007.(上接第68页)
[2]王伟臣，魏志军，张峤，等.后燃对火箭发动机羽流红外特性的影响［J］.航空动力学报，2010，25(11):2612-2618.(Wang Wei-chen，Wei Zhi-jun，Zhang Qiao，et al.Influence of afterburning on infrared signature of rocket motor exhaust plume［J］.Journal of Aerospace Power，2010，25(11):2612-2618.)
[3]韩炜，赵跃进，胡新奇，等.超高声速飞行器光学窗口气动光学效应分析［J］.光学技术，2010，36(4):622-626.(Han Wei，Zhao Yue-jin，Hu Xin-qi，et al.Study on aerooptical effects of hypersonic vehicles optical window［J］.Optical Technique，2010，36(4):622-626.)
[4]Farooq M，Green A A，Hutchins M G.High performance sputtered Ni:SiO₂ composite solar absorber surfaces［J］.Solar Energy Materials and Solar Cells，1998，54: 67-73.
[5]Dominguez J B，Berube-Lauziere Y.Diffuse optical tomographic imaging of biological media by time-dependent parabolic SPN equations: a two-dimensional study［J］.Journal of Biomedical Optics，2012，17(8):86012-1-86012-14.
[6]Das R，Mishra S C，Ajith M，et al.An inverse analysis of a transient 2-D conduction-radiation problem using the lattice Boltzmann method and the finite volume method coupled with the genetic algorithm［J］.Journal of Quantitative Spectroscopy and Radiative Transfer，2008，109(11):2060-2077.
[7]Chopade R P，Mohan V，Mayank R，et al.Simultaneous retrieval of parameters in a transient conduction-radiation problem using a differential evolution algorithm［J］.Numerical Heat Transfer，Part A，2013，63: 373-395.
[8]Qi H，Ruan L M，Zhang H C，et al.Inverse radiation analysis of a one-dimensional participating slab by stochastic particle swarm optimizer algorithm［J］.International Journal of Thermal Sciences，2007，46(7):649-661.
[9]Qi H，Niu C Y，Gong S，et al.Application of the hybrid particle swarm optimization algorithms for simultaneous estimation of multi-parameters in a transient conduction-radiation problem［J］.International Journal of Heat and Mass Transfer，2015，83(7):428-440
[10]Zhang B，Qi H，Ren Y T，et al.Application of homogenous continuous ant colony optimization algorithm to inverse problem of one-dimensional coupled radiation and conduction heat transfer［J］.International Journal of Heat and Mass Transfer，2013，66(3):507-516.
[11]Ren Y T，Qi H，Zhao F Z，et al.Simultaneous retrieval of temperature-dependent absorption coefficient and conductivity of participating media［J］.Scientific Reports，2016，6: 21998.
[12]Liu D，Yan J H，Wang F，et al.Inverse radiation analysis of simultaneous estimation of temperature field and radiative properties in a two-dimensional participating medium［J］.International Journal of Heat and Mass Transfer，2010，53(21):4474-4481.
[13]Tan H P，Ruan L P，Tong T W.Temperature response in absorbing，isotropic scattering medium caused by laser pulse［J］.International Journal of Heat and Mass Transfer，2000，43(2):311-320.
[14]Sun J，Feng B，Xu W B.Particle swarm optimization with particles having quantum behavior［C］//Congress on Evolutionary Computation.Portland，OR，USA:IEEE，2004:325-331.