铜闪速熔炼过程参数预测模型

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铜闪速熔炼多相流模型化研究进展

铜闪速熔炼多相流模型化研究进展
姜保成;郭学益;王亲猛;王松松;陈建儒
【期刊名称】《中国有色金属学报》
【年(卷),期】2024(34)1
【摘要】铜闪速熔炼具有高处理量、高富氧浓度、高冶炼效率和低环境污染的优势,已在世界范围内广泛推广应用,深入研究闪速熔炼过程中的物理化学现象和原理具有重要意义。

然而,闪速炉内多相流间相互作用及熔炼动力学行为受到多方面因素的共同影响,仅依靠生产经验、试验观测和理论分析难以全面解析炉内多相流传质传热过程、获取流体流动特性、揭示气固多相流相互作用规律,阻碍了闪速炉冶炼效能的进一步提升。

当下计算流体力学数值模拟发展迅速,正为预测和分析闪速炉内多相流问题提供了高效、准确、直观的帮助。

本文在介绍铜闪速炉结构、冶炼原理、理论模型的基础上,阐述了近年来铜闪速熔炼动力学反应机理、炉内流动特性方面模型化的研究成果及发展趋势,全面综述了铜闪速炉在喷嘴、沉淀池、上升烟道、锅炉优化方面的研究成果,为闪速炉生产优化及设计改进提供指导。

【总页数】22页(P207-228)
【作者】姜保成;郭学益;王亲猛;王松松;陈建儒
【作者单位】中南大学冶金与环境学院
【正文语种】中文
【中图分类】TF811
【相关文献】
1.基于Rand-MQC耦合算法的高强度铜闪速熔炼多相平衡热力学分析
2.铜闪速熔炼过程操作参数预测模型及应用
3.铜闪速熔炼多相平衡数模的建立与应用
4.铜闪速熔炼过程能效优化模型研究
5.铅闪速熔炼过程的多相平衡模型
因版权原因，仅展示原文概要，查看原文内容请购买。

高投料量下炼铜闪速炉内熔炼过程的数值模拟

高投料量下炼铜闪速炉内熔炼过程的数值模拟陈卓;王云霄;宋修明;赵荣升;殷术贵【摘要】A numerical model was developed and the simulation was carried out with FLUENT 6.3 to investigate the distributions of multi-fields in the reaction shaft of a flash furnace at high loading rate. The results show that the process air expands quickly after it is fed into the furnace and forms a large downward gas column in the centre of the reaction shaft. Great gradients are found in the gaseous temperature and concentration destinations across the main gas column. Moreover, a low temperature region is found under the concentrate burner while a steady high temperature region appears in the lower part of the reaction shaft. The computation also reveals that the poor mixing between the gas and the concentrate particles is the main reason accounting for the decreased reaction efficiency of the smelting process in the reaction shaft of high productivity.%以FLUENT 6.3为计算平台,建立了铜闪速炉熔炼过程数值模型,并针对高投料量下反应塔气粒两相变物理场信息分布变化特点展开仿真研究.结果表明:工艺风入炉后迅速膨胀,并在反应塔中心形成轮廓明显的主体气流柱；主体气流柱内外的温度和氧浓分布梯度变化较大；局部低温出现在精矿喷嘴下方,而高温反应核心区域则下移至反应塔中下部.综合多场耦合仿真结果可知:高投料量条件下精矿粒子与反应配风之间混合力度欠佳是造成高投料量反应塔内熔炼过程反应效率降低的主要原因.【期刊名称】《中国有色金属学报》【年(卷),期】2011(021)011【总页数】6页(P2916-2921)【关键词】铜闪速炉;反应塔;熔炼反应;数值模拟【作者】陈卓;王云霄;宋修明;赵荣升;殷术贵【作者单位】中南大学能源科学与工程学院,长沙410083;中南大学能源科学与工程学院,长沙410083;金隆铜业有限公司,铜陵244000;金隆铜业有限公司,铜陵244000;中南大学能源科学与工程学院,长沙410083【正文语种】中文【中图分类】TF811金隆铜业有限公司闪速炉是我国自行设计和建造的第一座炼铜闪速炉，其最初设计生产能力为年产阴极铜10万t；在历经多次技术升级改造后，2009年该闪速炉精矿喷嘴处理能力已提高至170 t/h，闪速炉生产能力也由此达到年产矿铜35万t，阴极铜40万t[1−5]。

铜闪速吹炼过程多相平衡热力学分析

铜闪速吹炼过程多相平衡热力学分析
铜是一种普遍存在于日常生活和工业生产中的金属物质，具有良好的电热绝缘性、良好的抗腐蚀能力、优良的延展性、高弹性模量、质轻等特点。

传统的铜吹炼过程由于材料等大容量间接温度测量及控制困难，很难实现多相平衡，而闪速吹炼过程则可以实现多相平衡，有利于铜冶炼过程的优化和可持续发展。

闪速吹炼过程是一种基于热力学分析的多相平衡技术，能够实现铜的多相平衡，可以提高铜的比表面积、抗腐蚀性、强度和耐热性，以及形成更丰富的多孔形结构，增加铜材料的力学特性。

通过建立对吹炼过程的多相力学模型，对“温度-压力-流量”三者之间的相互影响关系进行量化，从而实现多相平衡铜吹炼工艺的全自动控制。

热力学分析是一种获取吹炼过程动态变化的重要方法，它能有效地探究铜的多
相平衡过程的变化规律，为吹炼过程的实验研究提供基础。

根据内部物理特性，确定多相平衡模型，进行多相态分析，分析和模拟各相的质量流换热，并建立全相定势及动态状态，从而获取过程中变化的定势计算与参数优化。

通过热力学分析，可以获得铜吹炼过程中多相热力学性质的准确信息，从而更
好地控制工艺，而且可以就温度、压力和流量等参数之间的直接相互影响进行精确计算，以达到多相平衡效果。

因此，热力学分析对于优化铜吹炼过程，获得高质量铜产品具有重要作用，是了解和分析铜吹炼过程的重要手段之一。

Inco公司闪速熔炼

第六章Inc0公司闪速熔炼Inco公司闪速熔炼把工业氧气、干燥的Cu—Fe—s精矿、SiO2造渣剂和返回料从水平方向喷吹入高温(1250℃)炉中。

一旦进入炉中氧气就和精矿按反应式(1.1)和式(1.2)发生反应。

生成：①熔锍，55％～60％Cu②熔渣，1％～2％Cu③烟气，60％～75(体积)％SO2冰铜出到钢包中被送去吹炼，见图1．6所示。

渣出到钢包中被送到储料堆，进行渣中铜的回收，也可不回收，见第11章。

烟气经过水冷、除尘后送到硫酸厂。

Inco公司闪速炉也可用于从转炉返回的熔渣中回收铜。

渣从一个倾斜的钢槽从水冷门倒入炉中，见图6．1(a)所示。

2002年初，已有五座Inco公司闪速炉投人生产，乌兹别克斯坦的Almalyk、亚利桑那州的Hayden、新墨西哥的Hurley以及加拿大安大略省的Sudbury。

A1malyk、Hayden和Hurley的闪速炉熔炼Cu—Fe—S精矿，Sudbury的闪速炉熔炼Ni—Cu—Co—Fe—S精矿，生产约45~Ni—Cu—Co冰铜和1％Ni—Cu—Co的渣。

6．1闪速炉参数Inco公司的闪速炉由高质量MgO和MgO—CrzOa砖砌成，见图6．1(a)。

主要包括：①闪速炉的两端各有一个精矿燃烧室；②端部和侧墙装有铜质水冷套；③一个中央烟气上升烟道；④侧壁上有出铜口；⑤末端墙上有一个出渣口；⑥端墙上有一个加入转炉炉渣的溜槽。

6．1．1精矿燃烧室Inco公司精矿燃烧室是一个直径0．25m、长lm、壁厚1cm、带有陶瓷内衬的水冷不锈钢管。

干精矿从上部一个倾斜的料管加入炉中，工业氧气从炉子的水平方向吹人，见图6．1(b)。

此炉燃烧室的直径应保证氧气和炉料能以40m／s的速度喷人炉中。

这个速度产生的精矿／氧气火焰，能够到达中央上升烟道，该炉大约向下倾斜7。

左右，使火焰在渣的表面上燃烧，而不是在顶部和墙h。

6．1．2水冷Inco公司炉子的侧面墙和端墙装有水冷冷凝铜管、冷却板和隔板，以保证炉子结构的完整性，对于奥托昆普闪速炉，水冷会使富含磁铁矿的炉渣在炉壁上沉积，这样可以保护炉体砖衬和水冷铜管，延长炉子的寿命。

铜闪速熔炼过程操作模式的多类分类策略研究

铜闪速熔炼过程操作模式的多类分类策略研究针对铜闪速熔炼操作模式易获取而标记困难的特点，文章利用支持向量机在解决小样本、非线性及高维模式识别问题中特有的优势，构造了一种基于边缘交叉的支持向量机决策树模型，能有效的减小传统决策树方法出现的误差积累现象，提高铜闪速熔炼过程操作模式分类的准确度。

标签：操作模式；支持向量机；多类分类引言铜是重要的有色金属之一，在能源、航空、冶金、机械、石油、化工、电器、医疗卫生等工业部门中有着重要的应用。

熔炼是提取铜、铅、锌、镍等有色金属的主要工艺方法，世界上85%的铜是通过熔炼工艺生产的，但我国铜熔炼工艺能耗比发达国家高出21.2%，有价金属随炉渣损失大。

因此，研究研究铜闪速熔炼过程的操作参数优化，对于实现铜闪速熔炼过程的节能降耗、提高资源利用率以及充分发挥生产潜力、提高生产过程的技术经济指标，实现企业的可持续发展，都具有重大意义[1]。

铜闪速熔炼过程是一个复杂的物理化学变化过程，具有非线性、时变性、强耦合、大滞后等特点。

Goto和Maruyama等[2-5]开发了符合热力学反应条件和物料平衡、热量平衡的操作参数优化模型。

然而，由于数学模型是通过大量简化得到的，很难应用数学模型来实现操作参数的优化。

无法完全依靠传统方法建立精确的物理模型进行管理监控。

但在长期的运行过程中产生了大量反映其运行机理和运行状态的数据。

由于实际需求和成本优化等因素考虑，如何利用这些海量数据来优化系统操作参数，提高产量已成为亟待解决的问题。

文献[6]针对铜闪速熔炼过程的特点，充分利用在生产过程中长期积累的工业数据，提出了基于数据驱动的操作模式优化方法。

文章在此基础上，针对铜闪速熔炼过程生产过程的特点，进行了铜闪速熔炼过程操作模式的分类策略研究，提出一种改进的多类支持向量机分类方法，并将其应用到铜闪速熔炼过程的操作模式分类。

1 铜闪速熔炼过程控制机理铜闪速熔炼工艺机理为：将深度脱水的精矿粉末，在闪速炉喷嘴处与空气或氧气混合，然后从反应塔顶部喷入反应塔内并发生反应，形成熔融硫化物和氧化物的混合熔体，并下降到反应塔底部，在沉淀池中汇集并沉淀分离，最终形成冰铜与炉渣。

铜闪速炉悬浮熔炼反应过程机理的研究(Ⅱ)——粒子脉动碰撞模型

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文章编号－０９— ８２２０）４—０１０１０３４（０７００９— ８
铜闪速炉悬浮熔炼反应过程机理的研究（ＩＩ）
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粒子脉动碰撞模型
锴梅，炽任鸿九梅显，。
冷取样，并进行了外观形态检验、矿相显微镜分析、扫描电镜分析、化学物相分析和ｘ荧光分析，得出如下主要结论：（）１反应塔中颗粒分布极不均匀，在反应塔中央区域较集中；
（）２反应塔中粒子氧化程度极不均匀，的粒有子反应速度极快，氧化程度高，产生了大量的磁性氧
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地稳定了此时颗粒的原始状态，但是在实际的闪速炉反应塔内反应并没有到此结束。所以粒子分裂模型只是对精矿粒子进入反应塔发生一系列初期反应的分析和描述。１２两粒子碰撞模型溪３５２；３４４
２中南大学能源科学与工程学院，．湖南长沙４０８）１０３
摘要：总结前人阗速熔炼反应模型——粒子分裂模型和两粒子碰撞模型的基础上，在根据对金隆阗速炉反
应塔工艺物料颗粒高温急冷试样的分析结果，通过从微粒运动学、两相流体力学以及统计分析和归纳等方面的论
是：ａ铜精矿粒子的燃烧速度非常快；ｂ各种精（）（）矿粒子在反应塔内的氧化程度不一致。基于上述试验结果和分析研究，出了关于反应塔内反应机理提
的两粒子碰撞模型，图２所示。如

铜的直接闪速熔炼

第12章铜的直接闪速熔炼前面几章已阐明从硫化物精矿中提取铜有两个主要的步骤：熔炼和吹炼。

同时也表明熔炼和吹炼具有同样的化学工艺，例如从Cu—Fe—S相中氧化Fe和S。

很久以来，冶金和化学工程师的目标就是想把这两个步骤结合起来，变成一个连续的直接炼铜熔炼工艺。

这个结合最主要的优点在于：①将排出的SO2气体隔离，形成一个单独的连续气流；②减少能量消耗；③减少投资和成本。

本章主要介绍：①2002年铜的直接熔炼情况；②对这种工艺潜在优点的认识程度。

该工艺最主要的问题在于：①进入铜的直接熔炼炉中的大约25％的Cu最终熔解在渣中；②回收这些渣的成本将可能限制未来铜的直接熔炼向处理含Fe量低的铜精矿发展[如辉铜矿(Cu2S)和斑铜矿(Cu5FeS4) ]．而是向处理含Fe量高的黄铜精矿发展。

12．1直接炼铜的理想工艺图12·1是一个直接炼铜的理想工艺示意图，该工艺主要的加入料为精矿、氧气、空气、造渣剂和返回料。

主要产物为：铜水、低含铜量的渣、高SO2含量的烟气。

该工艺是自热式的，随着高富氧鼓风，有充足的反应热去熔化所有熔炼炉和邻近精炼厂提供的含铜返回料，包括碎电极。

该工艺也是连续的。

本章其余的部分讲述如何更快地实现这个理想。

理想的情况是：铜中杂质含量低；渣直接丢弃，不做铜的回收处理；烟气中有足量的SO2用于制硫酸。

12．2直接炼铜的工业单炉2002年，只有一个工艺——奥托昆普闪速熔炼，实现了单炉直接炼铜，如图1．4所示。

采用这个工艺的厂家有两个：波兰的Glogow和澳大利亚的奥林匹亚大坝。

这两座炉子都是处理辉铜矿和斑铜矿的。

前些年，诺兰达浸入式风口工艺(见图1．5)也能直接炼铜。

现在用于生产含铜72%～75％的高品位冰铜。

这个改变提高了熔炼速率，同时改善了杂质的脱除。

铜的直接闪速熔炼的产品(见表12．1)是：铜：99％Cu，0．44％～0.9％S，0．01％Fe，0．4％O，1280℃：渣：14％～24％Cu，约1300℃；烟气：15％～20％SO2，1350℃。

基于DLSSVM的铜闪速熔炼过程工艺参数预测

对信号力进行小波变换，可将数据信号划到不同的频带上，从而在各频带上设置阈值去除测量信号的噪
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摘要：针对铜闪速熔炼工业数据中含有噪声和随机干扰，难以精确地建立工艺参数预测模型的缺陷，提出小波去噪最小二乘支持向量机（ＬｓＤＳⅥ 的工艺参数预测方法。首先采用小波变换对数据进行去噪处理，然后利用最小二乘支持向量机建立熔后样本数据进行模型参数辨识，实现铜闪速
过程影响因素与工艺指标之间的非线性关系模型，实
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若ｆｔ）处连续，则）在可重构为
ｆ击『（））ｄ。（（西ｆ６ｔＲ）Ｊ２４ ¨ ）
Ａｂｔａｔｓｒｃ：Ｃｏｓｄｒｎｈｄｓｒａａａｏｏｐｒｆｓｍｅｔｇｉｃｕｉｇｎｉｅａｄｒｎｏｄｓｕｂｃ，ｔｉｄｆｉｕｔｏｎｉｅｉｇｔｅｉｕｌｉｄｔｆｃｐｅａｈｓｌｎｌｄｎｏｓｎｄｍｉｔｒａｅｉｓｉｃｌｔｎｌｌｉｎａｎａｒｔｌｕｌｈｒｄｃｉｎｍｏｅｏｃａｔａａｔｒ．ｒｄｃｉｎｍｅｏｆｒｆｐｒｍｅｅｓｂｅｎｗａｅｅｄｎｉｉｇｒａｅｙｂｉ￣ｕｄｔｅｐｅｉｔｏｄｌｆｒｆｐｒｍｅｅｓＡｐｅｉｔｏｈｔｄｏｃａａａｔｒａｄｏｔｓｖｌｔｅｏｓｎ

铜闪速熔炼多相平衡数模的建立与应用

铜闪速熔炼是一个高温、多相反应过程，操作变量多、变量间交互耦合效应复杂。因此，研究建立一套能较为准确的反映铜闪速熔炼过程的数学模型。对于指导实际生产实践，优化工艺参数有着十分重要的意义。本文基于最小自由能原理 … Ｒｎ与ａｄ算法［立２１建了铜闪速熔炼数学模型，并利用建立的数学模型进行仿真模拟，研究各种工艺参数与铜锍品位渣含铜等目标参数的关系，为优化工艺条件提供了理论依据和指导
ａｅ１１，００ｎ．ｒｓｅｔｅｙａｄｔｅｒｌｔｅｅｒｒｆｔｅａｅ３１ｒ．９．６ａｄ０５ｅｐｃｉｌｎｈｅａｉｒｏｓｏｍｒ．％。０．％ａｄ１．５ｒｓｅｔｅｙｖｖｈ２ｎ４４％ｅｐｃｉｌ．Ａｔｔｅｓｍｅｔ，ｔｅｖｈａｉｍｅｈ
ＡｂｔａｔｓｒｃＭｕｔｈｓｑｉｂｉｍｔｅｔａｄｌｏｈｏｐｒｆｓｍｅｔｇｐｏｅｓｉｅｔｂｉｅａｅｎｔｅＧｉｂｌｐａｅｅｕｌｒｌｉｕｍａｈｍａｉｌｍｏｅｒｔｅｃｐｅａｈｓｌｎｒｃｓｓｓａｌｈｄｂｓｄｏｈｂｓｃｆｌｉｓ
维普资讯
第２７卷６期
有色冶金设计与研究
２０正０６１月２
铜闪速熔炼多相平衡数模的建立与应用
童长仁，吴卫国，周小雪
（江西理工大学材料与化学工程学院，江西赣州３１０）４００
（要］摘基于吉布斯最小自由能原理，建立了铜闪速熔炼过程的多相平衡数学模型。实例验证表明，该数模能较好的反映铜闪速熔炼过程，与生产实际数据相比，中Ｃ、ｅＳ铜锍ｕＦ、含量（绝对误差分别为１９１２％）．、．、４５Ｏ７，对误差为２６９７３３炉渣中Ｆ、ｉ、．１相．％、．％、．％；ｅＳＯ２Ｃｕ含量（绝对误差分别为１９００、．，对误差为％）．、．６０５相１

闪速熔炼渣含铜的数值模拟

闪速熔炼渣含铜的数值模拟
陈红荣;梅炽;谢锴;任鸿九;王晓华;张源;刘安明
【期刊名称】《有色金属工程》
【年(卷),期】2008(060)002
【摘要】通过数值仿真的方法,分析闪速熔炼反应塔内Fe3O4含量的分布与变化规律,提出降低渣含铜的途径和措施.计算表明,精矿喷嘴操作参数的优化匹配,可以减少反应塔底部Fe3O4的生成量.氧浓或氧量分布为调控的主要参数;工艺风氧浓高,工艺风速低,有利于减少Fe3O4生成量;增加SiO2台量,颗粒中的Fe3O4含量明显降低,但需综合考虑此因素对渣含铜的影响.调整反应塔精矿喷嘴的工艺风氧量和氧浓,使反应塔内的氧势呈合理的梯度分布,将有助于渣含铜的降低.
【总页数】4页(P71-74)
【作者】陈红荣;梅炽;谢锴;任鸿九;王晓华;张源;刘安明
【作者单位】中南大学,热工设备仿真与优化研究所,长沙,410083;中南大学,热工设备仿真与优化研究所,长沙,410083;中南大学,热工设备仿真与优化研究所,长
沙,410083;中南大学,热工设备仿真与优化研究所,长沙,410083;金隆铜业公司,安徽铜陵,244000;金隆铜业公司,安徽铜陵,244000;金隆铜业公司,安徽铜陵,244000【正文语种】中文
【中图分类】TF811;TF803.11
【相关文献】
1.降低闪速熔炼渣含铜实践 [J], 昂正同
2.铜闪速熔炼贫化电炉渣含铜的线性回归分析 [J], 周俊
3.铜闪速熔炼过程中渣含铜的研究——计算机模拟 [J], 谭鹏夫;张传福
4.降低转炉高品位冰铜吹炼渣含铜生产实践 [J], 张定乾
5.转炉渣含铜影响因素分析及生产措施 [J], 余彬;张鑫;赵立恒;张建波;李江平;王恩志;任军祥
因版权原因，仅展示原文概要，查看原文内容请购买。

基于自适应模糊神经网络的铜闪速熔炼冰铜温度模型研究

０１，ｔｅｓｍｕａｉｎｒｓｌｓｓｏｔａｈｖｒｇｂｏｕｅｅｒｒｉ６７Ｃ，ａｄｔｅｒｌｔｖｒｏｅ — ．４ｈｉｌｔｅｕｔｈｗｈｔｔｅａｅａｅａｓｌｔｒｏｓ．＂ｏｎｈｅａｉｅｅｒｒｐｒ
摘要：于自适应模糊神经网络系统（ＦＳ及利用某闪速炼铜厂生产实践的稳定数据，立了网络结基ＡＮＩ）建构为３入、输出、属度函数个数为（，，７的闪速炼铜过程的冰铜温度模型。结果显示其训练数输单隶７５）据平均绝对误差为５０Ｃ，对误差为０４；真检验数据平均绝对误差为６７，对误差为．＂相．１仿．＂相Ｃ０５，明所建立的模型预测值与生产操作数据基本吻合，模型对铜熔炼过程的最优化具有参考价．５表该值，以代替现有的静态配料模型用于工业在线计算机控制。可关键词：；速熔炼；糊控制；铜闪模神经网络；真仿
中图分类号：８１０４．ＴＦ１；２２１文献标识码：Ａ文章编号：０７５５２０）２０２３１０ —７４（０７０ —００ —０
பைடு நூலகம்
ＲｅｅｒｈｏｈｅＭａｔｍｐｅａｕｅＭｏｌｏｐｒｓａｃｎｔｔｅＴｅｒｔｒｄｅｆＣｏｐｅ

铜精矿的闪速熔炼

表5-1 闪速炉各产物成分
12
二、闪速熔炼的基本原理
1、反应塔内的传输现象
闪速炉的主要熔炼过程发生在反应塔内。气流中的精矿颗粒在离开反应塔底部进入沉淀池之前完成氧化和熔化等过程。
发生在反应塔内的是一个由热量传递、质量传递、流体流动和多相多组分间的化学反应综合而成的复杂过程。
研究反应塔内的传输现象，对获得高的生产率与金属回收率、长的炉寿命和低的能源消耗的具有理论指导意义，也为喷嘴和炉型设计的改进提供基础。
体中。因此，颗粒的速度等于气流速度加上颗粒的下落速度。
在实际条件下,混合流中的颗粒分散度是很大的，相邻两颗
粒间的平均距离大约等于20个颗粒的直径，甚至更多。
颗粒的终点速度就可以用斯托克斯公式来描述:
up=gc(ρp-ρg)d2p/18η
(5-2)
式中, up为颗粒的终点速度（m/s）；gc为重力加速度(m/s2)；
根据图5.6，可以确定出沉淀池终渣中Fe3O4的含量(%)与锍品位的关系。
28
条件： PSO2=10kPa; Fe3O4% 含量除1270℃时，渣含SiO2为26% 外，其余均为渣饱和SiO2
图5.6 锍-渣-炉气体系中锍品位与炉渣中的Fe3O4%关系
29
控制Fe3O4的一般途径有:
1. 提高反应塔温度 2. 增加沉淀池燃油量，降低锍品位 3. 降低Fe/SiO2，加入煤，以及优化喷嘴结构与
15
等温气体喷射时的速度衰减由下式表达:
Ux=12.4U0r0/x
(5-1)
式中，Ux为从入口点开始的x距离上的中心喷射速度 (m/s)；U0为入口初始速度(m/s)；r0为入口喷嘴半径(m) 。式(5-1)说明，气流的终点速度乃由入口初始速度决定，

基于智能融合策略的冰铜品位预测模型

中图分类号：Ｐ７文献标识码：文章编号：１０－９２２０）４０２－４Ｔ２３Ａ００３３（０７０－０６０１引言
体下降到反应塔底部，在沉淀池中汇集并沉淀分离，
最终形成冰铜与炉渣。当铜闪速炉处理物料量不变时，闪速炉产出冰铜的品位是铜闪速熔炼过程的综
化控制问题。目前，用于冶炼过程质量预测的方法主要还是机理建模和神经网络， ’ 机理模型包含大
过程的建模问题，考虑到该反应过程中物质存在固、液、气三相，影响因素多且相互之间耦合严重，以难实现对重要参数冰铜品位的在线预测，在分析铜冶炼机理的基础上建立了铜闪速熔炼多相多组分数学模型，Ｊ将生产数据中的精矿量、渣精矿量、酸矿硅量、不定物料量、富氧空气量及组成它们的各元素的百分含量，以及将计算所得的冰铜相中ｃ、ｅＳ０ｕＦ、、、Ａ和ｚｓｎ这６种主要元素摩尔量，分别代人多相多组分数学模型，以得到冰铜品位的机理模型：可
Ｃ％：ｍｕｃｕｃｕＸ
ｍ
量假设与统计信息，精度不高，但在一定程度上可以
反映生产状况。神经网络虽然可任意逼近非线性系统以及自学习、自适应等特点，建模依据测量数但据，缺乏物理基础。针对铜闪速熔炼工业过程，文本以预测铜闪速熔炼过程中的冰铜品位为目标，采用
并联融合形式，立冰铜品位机理模型与基于规则建的模糊神经网络模型的智能融合模型，并运用智能协调器确定融合模型的输出；出一种模糊神经网提络参数更新学习的受约束梯度下降算法，高了参提数的学习效率。工业数据的仿真结果验证了所提出的方法能有效抑制工况波动，高冰铜品位的预测提

铜闪速炉内颗粒受热过程的数学建模与数值优化

Trans.Nonferrous Met.Soc.China31(2021)1506−1517Mathematical modelling and numerical optimization ofparticle heating process in copper flash furnaceDong-bo GAO1,Xiao-qi PENG1,2,Yan-po SONG1,Zhen-yu ZHU1,Yang DAI11.School of Energy Science and Engineering,Central South University,Changsha410083,China;2.Hunan First Normal University,Changsha410205,ChinaReceived16October2020;accepted10March2021Abstract:A mathematical model of the particle heating process in the reaction shaft of flash smelting furnace was established and the calculation was performed.The results indicate that radiation plays a significant role in the heat transfer process within the first0.6m in the upper part of the reaction shaft,whilst the convection is dominant in the area below0.6m for the particle heating.In order to accelerate the particle ignition,it is necessary to enhance the convection,thus to speed up the particle heating.A high-speed preheated oxygen jet technology was then suggested to replace the nature gas combustion in the flash furnace,aiming to create a lateral disturbance in the gaseous phase around the particles,so as to achieve a slip velocity between the two phases and a high convective heat transfer coefficient.Numerical simulation was carried out for the cases with the high-speed oxygen jet and the normal nature gas burners.The results show that with the high-speed jet technology,particles are heated up more rapidly and ignited much earlier,especially within the area of the radial range of R=0.3−0.6m.As a result,a more efficient smelting process can be achieved under the same operational condition.Key words:flash smelting process;particle heating;mathematical model;high-speed jet;numerical simulation1IntroductionThe flash smelting has been recognized as a highly efficient and environmentally benign process for extracting Cu or Ni metals from their sulfides. In the past decades,the flash furnaces have been widely used,producing more than50%of copper and70%of nickel in China.High productivity is one of the outstanding advantages of the flash smelting technology.However,when great efforts are committed to continuously increase the capacity of the flash furnace,similar technical problems are encountered in different smelters,such as the overheating in the lower part of the reaction shaft, the accelerated corrosion to the furnace walls,and high dust generation ratio of the process[1,2].A number of studies including experiments[3−5],neural network[6],and numerical simulation[7−9] were conducted to investigate the root causes. Besides,thermodynamic simulation[10,11]was also applied to obtaining the phase equilibria in the flash smelting process[12].THEMELIS et al[13]originally applied numerical simulation in the study of the flash smelting process.In1995,JOKILAAKSO et al[14] used the CFD software Phoenics to study the fluid flow around the concentrate burner of a copper flash smelting ter in1998,they simulated the temperature distributions in the reaction shaft with Phoenics again,by simplifying the3D cylindrical structure into a2D axial computational domain,but taking some basic chemical reactions into consideration[15].After-wards,the numerical method was adopted for the study of the flash smelting process of nickel[16],Corresponding author:Yan-po SONG,E-mail:*****************.cnDOI:10.1016/S1003-6326(21)65594-21003-6326/©2021The Nonferrous Metals Society of China.Published by Elsevier Ltd&Science PressDong-bo GAO,et al/Trans.Nonferrous Met.Soc.China31(2021)1506−15171507zinc[17,18]and lead[19].And simulation was also carried out for purposes of optimizing the furnace structures and operational parameters.In2004, CHEN et al[20]performed numerical simulation of the flash smelting process with CFX,and the optimization of the concentrate burner operation was advanced.They also investigated the factors affecting the Fe3O4formation inside the reaction shaft by changing the oxygen-enrichment and the flow rate of the process air in the numerical studies. As a result,the solution was concluded to reduce the copper loss in the slag[21].Moreover,an in-depth analysis of the flow characteristics in the settler[22]was also reported by the same research group recently based on numerical simulations.The concentrate combustion is an important topic in the numerical study of the flash smelting process,since it directly affects the completeness of reactions and the composition of final products of the process.It was believed that the concentrate particles completely reacted soon after they are fed into the reaction shaft.And this was proven in the simulation results of XIE[23](Fig.1(a))and LI et al[24],which showed that most particles at the height(the vertical distance from the roof of the reaction shaft to the position where the sampling point locates,also referred to as the shaft height)of 2m in the reaction shaft were fully reacted. However,for the same flash furnace,as its concentrate feed rate was doubled,a significant delay was found in the particle ignition[25],as shown in Fig.1(b).In order to speed up the particle ignitions in the reaction shaft,many studies have then been carried out.CHEN et al[26]suggested to insert a nature gas lance through the center of the concentrate burner to supply heat to the ignition of particles.Instead of making improvement of the furnace structure,ZHOU et al[27]developed an indicator named the momentum ratio of the distribution air over the process air,and found that it has dramatic influence on the particle dispersion in the furnace.The indicator then has been widely accepted and used to optimize the operational parameters of the concentrate burner.Nevertheless, few theoretical analyses have been reported for the particle heating process in the reaction shaft.To further improve the efficiency and the capacity of the flash smelting process,it is crucial to investigate the causes of the ignition delay of particles.Therefore,a mathematical model was developed to analyze the heat transfer in the particle heating process inside the reaction shaft,to clarify the key factor that is helpful to accelerate the heating process and the ignition of particles.Numerical simulation was also carried out,to assess the effectiveness of the technical measure proposed in the study.Fig.1Temperature increasing along reaction shaft: (a)Result of XIE[23];(b)Result of CHEN et al[25]2Theoretical modelling of heating process of concentrate particlesSince the concentrate particles are fed into the flash furnace at a temperature much lower than their ignition point,the particles must undergo a process of heating and temperature increasing before they reach their ignition point.This process is generally considered to be very fast,because the smelting reactions are completed in only a second or so in the furnace,but few detailed analyses have been reported how the particles are heated up inside the furnace.Hence,a mathematical model wasDong-bo GAO,et al/Trans.Nonferrous Met.Soc.China31(2021)1506−1517 1508established to investigate the heat transfer and the temperature change of the concentrate particles as they fall through the reaction shaft.2.1Mathematical model for analysis of particleheating processThe“concentrate cone”formed by the dispersed particles inside the furnace is approximated as a truncated cone in the model, extending from the exit of the process air to the bottom of the reaction shaft.The top diameter of the truncated cone takes the exit diameter of the pipe for the process air,and the bottom diameter is equal to the actual dispersion diameter of the concentrate cone measured in the industrial test[28].An element of infinitesimal heightΔh(as shown in Fig.2)was taken for consideration.In order to simplify the analysis of heat transfer between the element and its surroundings,a few assumptions were made as follows.(1)The element is approximated to be a cylinder because of its infinitesimal height,and the particles in the element are considered to be of the same size.(2)By considering the element as a particle group,the heat received by the element is to heat up the particles inside.(3)The heat transfer between the element and its surroundings includes the convection and radiation.For the radiation,only the radiative heat transfer Q r between the element and the shaft wall is considered,and the element is regarded as a gray body.Besides,the convective heat Q c is supposed to be received by the element only through the circumferential surface of the cyliner.Fig.2Schematic diagram of element for particle heating process analysisHence,an equation of energy balance can be used to describe how the temperature of the element changes with the heat received:pc rd+dpTc M Q Qt=(1) where c p is the specific heat capacity of concentrate particles,J/(kg·K).M is the total mass of particles in the element,kg.T p is the particle temperature of the element,K.t is the residence time of particles through the element,s.Q c and Q r are the heat fluxes that the element receives by convection and radiation,respectively,W.By replacing the variable Q c in Eq.(1)with Newton equation and Q r with Stephan−Boltzmann law,a differential equation of particle temperature changing with time can be obtained:()() p44g p0w pd+dpTc M D h T T D h T Ttαεσ=π∆-π∆-(2) where D denotes the diameter of the cylinder element,m.αis the convective heat transfer coefficient,W/(m2·K).T g is the temperature of the hot gas around the element,K.εdenotes the emissivity of the element surface.σ0is the Stephan−Boltzmann constant,andσ0=5.67×10−8 W/(m2·K4).T w is the wall temperature of reaction shaft,K.Then,the formula to calculate the temperature of the particle group can be obtained by integrating Eq.(2),that is()()110g pd+tpc M T T D h T T tα-=π∆-⎰()1440w pdt D h T T tεσπ∆-⎰(3) where t1is the time duration of the heating process, s.And T0and T1are the temperatures of the particle group at the beginning and the end of the time duration,K.With Eq.(1),it can be found that,when the concentrate feed rate increases,more thermal energy is required to heat up the particles.In other words,if the heat flux remains the same,it will take longer,either in time or distance for the particle group to reach the same temperature.This then explains why the particles seem more difficult to be ignited in the reaction shaft at a higher concentrate feed rate.Also,if we wish to heat up the particles at the same rate as before,a higher heat flux must be provided,that is,the conditions of heat transfer must be improved so that the particles can receive much more heat in the same time duration.Dong-bo GAO,et al/Trans.Nonferrous Met.Soc.China31(2021)1506−15171509 The energy for the temperature rising of theparticles is the sum of the convective heat from thehot gas and the radiative heat from the wall of thereaction shaft.In order to distinguish the role ofradiation and convection in the heating process ofparticles,a program was developed to calculate thetemperature of the particle group as it moves alongthe reaction shaft,to positions of different height,and the proportions of the convection and radiationin the total heat received by the element aresummarized.2.2Effects of convection and radiation inparticle heating processA calculation was carried out for a case at aconcentrate feed rate of200t/h.The proportions ofconvection and radiation in the total heat thatparticles receive within3m of the shaft height arelisted in Table1.Table1Proportions of convection and radiation in totalheat received by particle group at different heights inreaction shaftHeight of reaction shaft/mProportion in total heatreceived by particles/% Convection Radiation0.534.365.70.650.050.01.065.634.41.580.719.32.084.715.32.584.915.13.085.914.1It was suggested that,in0.5m where the particles move along the reaction shaft,the radiation plays a major role in the heating process, providing2/3heat for the increasing of the particle temperature.However,by moving down just0.1m (to the height of0.6m),the effect of the convection increases significantly and its proportion becomes equivalent to that of the radiative heat.When the particles move down further,to the height of1m, the convective heat is dominant in the total heat. The proportion then keeps increasing,until exceeding80%at the height of1.5m.Afterwards, the convective heat is changed slightly,increasing by5%in the following1.5m.As the results indicate,the radiation plays a greater role in the early stage of the particle heating process,while the convection is more importantin the area below the height of0.6m in the reaction shaft.For the radiation,since the wall temperature is actually the temperature of the molten slag sticking to the shaft refractory linings,it is unlikely to change with the concentrate feed rate.Hence,the radiative heat flux can be hardly improved when the wall conditions are the paratively, the convection can be normally enhanced by increasing the convective heat transfer coefficient, and thus the focus of study is put on the convection heat transfer.2.3Suggestion for convection strengtheningThe convection is a heat transfer process associated with fluid flow.The Dittus−Boelter equation gives the correlation of the Nusselt number with the Reynolds number(Re)and the Prandtl number(Pr)under the forced convection heat transfer[29]:0.50.33=2+0.6Nu Re Pr(4) The convective heat transfer coefficient can be calculated bypg=dNuαλ(5)By substituting the definition of Re and Pr into Eq.(5),the convection coefficient can be related to the velocities of the gaseous and particle phases as0.50.33p p gg g g gp g g2+0.6d u u cdλυραυλ⎡⎤⎛⎫-⎛⎫⎢⎥⎪= ⎪⎪⎢⎥⎪⎝⎭⎝⎭⎢⎥⎣⎦(6)where d p is the diameter of particles,and it usually takes the average of particle diameters in the group,m.u p and u g denote the velocities of the particle and the gas,respectively,m/s.υg is the gaseous viscosity,m2/s.ρg is the gaseous density, kg/m3.c g is the specific heat capacity of the gas, J/(kg·K).λg denotes the gaseous thermal conductivity,W/(m·K).The convective heat transfer coefficient between the particles and the gas is proportional to the square root of the velocity difference(i.e.slip velocity)of the two phases.Since it is unrealistic to change physical properties of either the gas or the particles,increasing the slip velocity is more practical to improve the convection inside the furnace.Based on above considerations,the high-speed preheated oxygen jet technology wasDong-bo GAO,et al/Trans.Nonferrous Met.Soc.China 31(2021)1506−15171510recommended,aiming to enhance the convection heat transfer and thus to accelerate the particle heating process.Numerical simulation was then carried out to analyze the effectiveness of adopting this technical measure in the flash smelting process.3Numerical analysis of high-speed jet measureThe main air streams in the flash furnace include the process air,the distribution air and the central oxygen,which all come into the reaction shaft through the concentrate jet distributor (CJD)burner that locates in the center of the shaft roof.Besides,there are three combustion flows from the natural gas burner around the CJD burner.All these gaseous streams move axially along the reaction shaft,except that the distribution air exiting from the side of the distribution cone blows in the radial direction.The schematic diagrams of the flash furnace and the air streams through the CJD burner are shown in Figs.3and 4,respectively.Fig.3Schematic diagram of flash furnaceFig.4Schematic diagram of CJD burner and airflow through itDispersed through the curved surface of the distribution cone,the concentrate particles at 100μm are susceptible to the gaseous flow,makingtheir speed soon approach that of the gas.So,the slip velocity between the gaseous phase and the particles normally reduces quickly within a short distance below the CJD burner.Then,a more effective way to increase the slip velocity between the two phases is to create a lateral disturbance perpendicular to the vertical movement of the particles.As a result,the high-speed preheating oxygen jet is suggested to replace the natural gas combustion in the shaft roof.Specifically,the oxygen is injected at a high speed from three burners in the shaft roof at an angle of 15°to the vertical direction.Meanwhile,to compensate the heat loss by cancellation of the natural gas combustion,the oxygen is preheated to 1500K as the supplement heat into the furnace.3.1Mathematical model for numerical simulationNumerical simulation was carried out for the flash smelting process respectively with the standard natural gas burners and the high-speed preheated oxygen jet burners.The flash furnace is 7m in diameter and 8m in height.The gaseous passage of the settler is also included,which is 18.65m long,1.6m high and 9.2m wide inside (as shown in Fig.3).A hybrid grid scheme was used for the discretization of the computation domain,of which the structured grids were adopted mainly in the gaseous passage of the settler,and the unstructured grids were applied to the domain of the reaction shaft.Meanwhile,the grids in the areas near the burners and in the center of the reaction shaft were locally refined.The total number of the grids is about 700000.The flash smelting process involves complex interactions of the gaseous phase and the particles.The transfer processes of the gaseous phase are described and solved based on the Eulerian method,and those of the particle phase are based on the Lagrangian method.The governing equations to describe the transfer processes between the gaseous and the particle phases include the momentum equation,the energy equation and the species equation for each phase.All these equations can be referred to in literatures [27,28,30,31].The chemical reactions involved in the smelting process and the natural gas combustion are given in Table 2.Table 2Chemical reactions included in numerical simulationProcessNo.ReactionsDong-bo GAO,et al/Trans.Nonferrous Met.Soc.China31(2021)1506−15171511Decompositionof concentrate 12CuFeS2+O2→Cu2S+2FeS+SO2 2FeS2+O2→FeS+SO232Cu5FeS4+O2→5Cu2S+2FeS+SO2Oxidization and reduction 43FeS+5O2→Fe3O4+3SO2 52Cu2S+3O2→2Cu2O+2SO2 6FeS+3Fe3O4→10FeO+SO2 73Cu2O+FeS→6Cu+FeO+SO2Slag reaction82FeO+SiO2→Fe2SiO4 Natural gascombustion9CH4+2O2→CO2+2H2ONumerical computation was performed for cases at a concentrate feed rate of200t/h,of which one is with the normal natural gas burner(named 200t-S)and the other with the high-speed preheated oxygen jet burner(named200t-J).An industrial test was also carried out independently at the same concentrate feed rate(named200t-T),to verify the accuracy of the numerical model.Operational parameters for all three cases are listed in Table3.Table3Production parameters of simulation cases Production parameter200t-T200t-J200t-S Load of concentrates/(t·h−1)200200200 Load of ash/(t·h−1)151818Air volume flow ofprocess air/(m3·h−1)102611220012200Oxygen volume flow ofprocess air/(m3·h−1)238722532025320 Velocity of process air/(m·s−1)909090 Volume flow of distributionair/(m3·h−1)315031503150 Volume flow of centraloxygen/(m3·h−1)200022002200 Volume flow of centralCH4/(m3·h−1)200150150 Volume flow of CH4of ordinaryburner/(m3·h−1)245−150 Volume flow of air of ordinaryburner/(m3·h−1)2450−1500 Gaseous velocity of high-speedburner/(m·s−1)−400−Gaseous temperature ofhigh-speed burner/K−1500−The gaseous temperature at three points of the flash furnace was measured by inserting the thermocouple into the furnace through the sampling holes.The positions of the sampling holes in the flash furnace are illustrated in Fig.5.Fig.5Positions of sampling holes used for measurement of gaseous temperatureThe temperatures measured in the industrial test were compared with the results of the numerical simulation(Case:200t-T),as given in Table4.The relative errors between the test data and the simulation results are all less than5%. Therefore,the numerical models of the copper flash smelting process are considered to be accurate and reliable for following performance analysis of the high-speed preheated oxygen jet.Table4Comparison of gaseous temperatures of industrial test and numerical results(200t-T)PositionDepth ofthermocoupleinserted intofurnace/mmTemperaturemeasured inindustrialtest/°CTemperatureofsimulationresult/°CRelativeerror/% R130013581382 1.8 R23001414−142214230.4S7001330−13401303−2.43.2Numerical results and discussionThe performance analysis of the high-speed preheated oxygen jet was made based on the comparison of simulation results in the cases of 200t-S and200t-J,including the velocity field,the temperature field and the concentration field of the gaseous phase.3.2.1Comparison of velocity distribution in twocasesThe velocity distribution in the central section along the X-axis(i.e.the direction along the furnace length)in the cases of200t-J and200t-S is shown in Fig.6.From Fig.6(a),it is suggested that after being injected into the furnace at a high speed,the oxygen stream is guided into the main stream and merges inDong-bo GAO,et al/Trans.Nonferrous Met.Soc.China31(2021)1506−1517 1512it at the height of about2m.Influenced by thehigh-speed jet at the top,the expansion of thecentral gas column to the right is slightlyconstrained,resulting in the whole column in thereaction shaft deflecting a bit to the left.In the caseof200t-J,the diameters of the gas column are alittle larger than those in the normal condition(listed in Table5),which is likely due to the hotoxygen injecting into the gas column.Fig.6Velocity distribution in high-speed jet condition:(a)200t-J;(b)200t-STable5Comparison of gas column diameters in cases of200t-J and200t-S(m)Case H=1m H=2mH=3mH=4mH=5mH=6mH=7mH=8m200t-J 1.68 2.74 3.42 3.84 4.17 4.14 3.97 4.44 200t-S 1.60 2.73 3.12 3.70 3.97 3.86 3.65 4.13 H−Height of reaction shaft3.2.2Comparison of temperature distribution in twocasesFigure7shows the temperature contour of the high-speed jet case and the normal natural gas combustion case.The adoption of the high-speed jet seems not to affect the temperature distribution significantly in the reaction shaft.However,by replacing the natural gas burner with the preheated high-speed oxygen jet,the local high temperature areas at the exit of the natural gas burner and around the central gas column disappear.Instead, the range of the central low temperature area reduces,and the temperature distribution inside the furnace tends to be more uniform.Fig.7Temperature distribution in different conditions: (a)200t-J;(b)200t-SFigure8shows the temperature changes along the height of the reaction shaft at different radial positions in the central section(the schematic diagram of the position is illustrated in Fig.8(f))in the cases of200t-J and200t-S.By preheating the oxygen to1500K,it is to compensate the heat loss caused by the cancellation of the natural gas combustion.However,since the enthalpy of the high-temperature oxygen is only half of the heat released by the natural gas combustion,and the high-speed jet can hardly penetrate the gas column to inject the heat into the center of the column,the temperature distribution in the case of200t-J is not much different in the center of the reaction shaft from that in the case of200t-S,as shown in Fig.8(a).But at the position of R=0.3m and R=0.6m,the gaseous temperature increases much faster along the shaft height,which indicates a faster heating process for the particles.By taking the melting point of the Cu−Fe−S−O solution as the ignition point of the particle group[32],the particles are found to be ignited earlier in the case of200t-J,at the radial positions of R=0.3m and R=0.6m.Furthermore, the point of the highest gaseous temperature usuallyDong-bo GAO,et al/Trans.Nonferrous Met.Soc.China31(2021)1506−15171513 Fig.8Comparison of temperature change in two cases of200t-S and200t-J:(a)R=0m;(b)R=0.3m;(c)R=0.6m;(d)R=0.9m;(e)R=1.2m;(f)Schematic diagram of radial positions for plottingcorresponds to the position where most particles are reacted.And in the case of200t-J,these points are at the positions of R=0.3m and R=0.6m,indicating a faster smelting reaction.When extended to the area of R>1.2m,the adoption of the high-speed jet seems to have no obvious impact on the temperature distribution,except that a local high temperature area can be found in the case of200t-S due to the combustion of the natural gas.3.2.3Comparison of SO2concentration distributionin two casesSO2is an important product of the smelting process,so in the numerical studies,the distribution of the SO2concentration field is often used to analyze the efficiency of the smelting process in the reaction shaft.The SO2concentration changing with the shaft height in two cases is summarized in Fig.9.Dong-bo GAO,et al/Trans.Nonferrous Met.Soc.China31(2021)1506−15171514Fig.9Comparison of SO2concentration in two cases of200t-S and200t-J:(a)R=0m;(b)R=0.3m;(c)R=0.6m;(d)R=0.9m;(e)R=1.2m;(f)Schematic diagram of radial positions for plottingSimilar to the temperature distribution,the adoption of the high-speed oxygen jet has limited impact on the central area of the furnace.But at the bottom of the reaction shaft,as indicated by the larger gaseous column diameter in the case of 200t-J,the particles are more dispersed,so the concentration of SO2is lower than that in the case of200t-S,due to a less dense particle distribution per unit area.While at the positions of R=0.3m and R=0.6m,the SO2concentration increases more quickly and reaches the highest point much earlier in the case adopting the high-speed oxygen jet.In addition,the SO2concentration is also higher at other radial positions in the case of200t-J, indicating a more efficient smelting process in the case with the high-speed jet technology.3.3Performance analysis of high-speed jettechnologyBy introducing the high-speed preheatedDong-bo GAO,et al/Trans.Nonferrous Met.Soc.China31(2021)1506−15171515oxygen jet into the flash smelting process,a lateral disturbance is created in the gaseous flow,thus to increase the slip velocity between the gaseous phase and the particles in the upper part of the reaction pared to the numerical results of the case with normal natural gas combustion(200t-S)，the key findings in the smelting process with the high-speed preheated oxygen jet are summarized as follows.(1)By preheating the oxygen to1500K,the heat loss caused by cancellation of the natural gas combustion is partly compensated.The local high temperature areas caused by natural gas combustion near the shaft roof disappear.In addition,the low temperature area below the CJD burner is smaller, and the temperature inside the shaft is more uniform,with no great temperature gradient forming around the central gas column,which will be beneficial to the particle heating process as well as the furnace maintenance.(2)After adopting the high-speed preheated oxygen jet,the gaseous temperature in the center and the areas with the radius R>1.2m changes slightly.However,in the area of the radius R=0.3−0.6m,the particle heating is found to be significantly accelerated,and thus the particles are ignited earlier.Specifically,for the particles at the radial position of R=0.3m and R=0.6m,their ignition height rises0.2and1.1m,respectively.(3)Similar characteristics can also be found in the gaseous SO2concentration fields.But more importantly,the SO2concentration in the gas column in the case of200t-J is generally higher than that in the case of200t-S,indicating that more sulfur is combusted in the reaction shaft,and thus a more efficient smelting reaction can be achieved.4Conclusions(1)The radiation and convection are key heat transfer modes for particle heating in the reaction shaft.However,the radiation plays a greater role in the upper part of the furnace,specifically,within the shaft height of0.6m.Thereafter,the convection is dominant in the heating process of particles.(2)The radiation heat flux inside the reaction shaft can be hardly improved due to the constant wall paratively,the convection can be enhanced,by increasing the slip velocity between the gaseous phase and the particle phase so as to achieve a higher convective heat transfer coefficient.(3)The movement of the concentrate particles is easily impacted by the gaseous flow,with their velocity approaching to that of the gas. Thus,introducing a lateral gaseous disturbance perpendicular to the vertical movement of the particles will be effective to create a slip velocity between the particles and their surrounding gaseous phase.(4)The high-speed preheated oxygen jet is suggested to replace the normal natural gas combustion in the flash smelting process.With pointing15 to the vertical direction,the oxygen stream is injected into the main gas column,causing a lateral disturbance around the surface of the falling particles.(5)The use of the high-speed preheated oxygen jet has no significant influence on the gaseous temperature in the center of the reaction shaft.However,within the area of R=0.3−0.6m, particles are heated up more quickly and ignited earlier.(6)The distribution of the SO2concentration field also shows that the particles in the furnace are ignited and react faster when the high-speed oxygen jet is adopted.Moreover,the SO2concentration in the gas column is generally higher,indicating a higher desulfurization rate,and thus a higher smelting efficiency can be achieved. AcknowledgmentsThe project was funded by Jinguan Copper of Tongling Non-ferrous Metals Group Co.,Ltd.Deep appreciations are given to persons providing technical supports in the research and the industrial tests.Special thanks are given to Prof.Jun ZHOU for his valuable suggestion to this research. References[1]AGRAWAL A,SAHU K K.Problems,prospects and currenttrends of copper recycling in India:An overview[J].Resources,Conservation and Recycling,2010,54(7): 401−416.[2]KOJO I V,STORCH H.Copper production with Outokumpuflash smelting:An update[J].Advanced Processing of Metals and Materials,2006(8):226−238.[3]JORGENSEN F R A,KOH P T bustion in flashsmelting furnaces[J].Journal of The Minerals,Metals& Materials Society,2001,53:16−20.[4]HAPP J V,JORGENSEN F R A.Experimental study of flash。

铜闪速熔炼过程冰铜品位预测模型的研究与应用

铜闪速熔炼过程冰铜品位预测模型的研究与应用
阳春华;谢明;桂卫华;彭晓波
【期刊名称】《信息与控制》
【年(卷),期】2008(37)1
【摘要】针对铜闪速熔炼过程中的冰铜品位在线检测难题,在组元分析的基础上,研究了独立化学反应以及组分间的摩尔数关系,并建立了数学模型;但由于反应机理的复杂性与建模时的简化,冰铜品位预测精度难以满足实际应用的要求.同时基于工业数据,建立了神经网络冰铜品位预测模型,它能很好地描述训练样本数据之间的关系,但泛化能力不强.为克服单一模型的局限性,引入了包含自适应调整隶属度函数的模糊协调器;将数学模型和神经网络模型有机结合,提出了一种冰铜品位智能集成预测模型.工业数据验证了模型的有效性.
【总页数】6页(P28-33)
【关键词】铜闪速熔炼过程;冰铜品位;数学模型;神经网络;智能集成模型
【作者】阳春华;谢明;桂卫华;彭晓波
【作者单位】中南大学信息科学与工程学院
【正文语种】中文
【中图分类】TP273
【相关文献】
1.铜闪速冶炼冰铜品位在线控制数学模型应用 [J], 鲍镇;赵辉;孙凤来
2.铜闪速熔炼过程操作参数预测模型及应用 [J], 谢锴;米沙;严兵;李启
3.铜闪速熔炼过程参数预测模型 [J], 顾毅;颜青君
4.基于自适应模糊神经网络的铜闪速熔炼冰铜温度模型研究 [J], 曾青云;周立;汪金良;刘桦
5.生产高品位冰铜新闪速熔炼技术的开发 [J], 柯英
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１引言生产实践表明，在铜闪速熔炼过程中，当闪速炉处理料量不变时，闪速炉产出的冰铜温度、冰铜品位及渣中铁硅比是闪速熔炼过程的综合判断指标，也是对闪速炉的操作参数（即热风、氧气量）进行调控的重要依据。

目前，由于对这三大参数的检测只能放在出冰铜时进行人工测量，而冰铜每隔一段时间才从冰铜口放出，这样测得的数据将滞后熔炼过程１ｈ以上，再加上人为因素的影响，使得测量得到的三大参数难以及时起到修正操作参数的作用。

此外，由于对冰铜温度的检测是使用消耗式热电偶在炉前冰铜口处测得［１］，这种一次性热电偶测温存在不可重复性，测量成本较高。

因此，研究开发闪速熔炼过程模型，用三大参数的预测值代替其实测值来指导闪速炉的反馈控制，将极大提高对熔炼过程操作参数调控的实时性，从而可以优化操作参数，进而提高生产过程的稳定性。

目前，闪速炉计算机在线控制多采用基于物料平衡和热平衡的机理模型来模拟熔炼过程［２］。

与其它方法建立的模型相比，机理模型的可解释性强、外推性能好。

但是机理模型的建立通常是基于一定假设条件的，而这些假设条件与实际情况存在一定差距，难以保证机理模型的精确性。

而对于机理模型不清楚的对象，可以采用基于数据驱动的建模方法建立过程模型。

其中，模糊神经网络（ＦＮＮ）由于具有很强的容错能力，在处理和解决问题时不需要对象的精确数学模型，ＦＮＮ通过其结构的可变性，逐步适应外部环境的各种因素的作用，因此在解决具有高度非线性和严重不确定性的复杂系统控制方面具有巨大潜力。

收稿日期：２００７－０９－２８※本项目获２００４年国家发改委高技术产业化专项资助，资助文号：发改高技［２００４］２０８０号。

作者简介：顾毅（１９６２—），男，广西人，高级工程师，主要从事工业自动化设计与研究工作。

铜闪速熔炼过程参数预测模型顾毅１，颜青君２（１．南昌有色冶金设计研究院，江西南昌３３０００２；２．中南大学信息科学与工程学院，湖南长沙４１００８３）〔摘要〕针对铜闪速炉的冰铜温度、冰铜品位与渣中铁硅比的预测问题，提出了一个基于模糊神经网络的预测模型。

仿真结果表明，该模型的预测精度较高，可以较准确地反映冰铜温度、冰铜品位与渣中铁硅比的变化趋势，为生产操作提供有益的指导。

〔关键词〕闪速炉；预测模型；模糊神经网络中图分类号：ＴＦ８０１．３，ＴＰ１５文献标识码：Ｂ文章编号：１００４－４３４５（２００７）０６－００１３－０３ＦｏｒｅｃａｓｔｉｎｇＭｏｄｅｌｏｆｔｈｅＰａｒａｍｅｔｅｒｓｉｎＣｏｐｐｅｒＦｌａｓｈＳｍｅｌｔｉｎｇＰｒｏｃｅｓｓＧＵＹｉ１，ＹＡＮＱｉｎｇ－ｊｕｎ２（１．ＮａｎｃｈａｎｇＥｎｇｉｎｅｅｒｉｎｇ＆ＲｅｓｅａｒｃｈＩｎｓｔｉｔｕｔｅｏｆＮｏｎｆｅｒｒｏｕｓＭｅｔａｌｓ，Ｎａｎｃｈａｎｇ，Ｊｉａｎｇｘｉ３３０００２，Ｃｈｉｎａ；２．ＳｃｈｏｏｌｏｆＩｎｆｏｒｍａｔｉｏｎＳｃｉｅｎｃｅ＆Ｅｎｇｉｎｅｅｒｉｎｇ，ＣｅｎｔｒａｌＳｏｕｔｈＵｎｉｖｅｒｓｉｔｙ，ＣｈａｎｇｓｈａＨｕｎａｎ４１００８３，Ｃｈｉｎａ）ＡｂｓｔｒａｃｔＩｎｏｒｄｅｒｔｏｆｏｒｅｃａｓｔｔｈｅｍａｔｔｅｔｅｍｐｅｒａｔｕｒｅ，ｍａｔｔｅｇｒａｄｅ，ａｎｄｒａｔｉｏｏｆＦｅｔｏＳｉＯ２ｉｎｓｌａｇｆｒｏｍｃｏｐｐｅｒｆｌａｓｈｓｍｅｌｔｅｒ，ａｎｆｏｒｅｃａｓｔｉｎｇｍｏｄｅｌｂａｓｅｄｏｎｆｕｚｚｙｎｅｕｒａｌｎｅｔｗｏｒｋｓｗａｓｐｕｔｆｏｒｗａｒｄ．Ｔｈｅｒｅｓｕｌｔｓｏｆｓｉｍｕｌａｔｉｏｎｉｎｄｉｃａｔｅｄｔｈａｔ，ｔｈｅｐｒｅｃｉｓｉｏｎｏｆｔｈｅｆｏｒｅｃａｓｔｉｎｇｍｏｄｅｌｉｓｓａｔｉｓｆｙｉｎｇ．Ｓｏｔｈｉｓｍｏｄｅｌｃｏｕｌｄｅｘａｃｔｌｙｒｅｆｌｅｃｔｔｈｅｃｈａｎｇｅｔｒｅｎｄｓｏｆｔｈｅｍａｔｔｅｔｅｍｐｅｒａｔｕｒｅ，ｍａｔｔｅｇｒａｄｅ，ａｎｄｒａｔｉｏｏｆＦｅｔｏＳｉＯ２ｉｎｓｌａｇ，ａｎｄｃｏｕｌｄｂｅｕｓｅｄａｓａｇｕｉｄｅｉｎｐｒａｃｔｉｃａｌｏｐｅｒａｔｉｏｎ．ＫｅｙｗｏｒｄｓＣｏｐｐｅｒｆｌａｓｈｓｍｅｌｔｅｒ；ｆｏｒｅｃａｓｔｉｎｇｍｏｄｅｌ；ｆｕｚｚｙｎｅｕｒａｌｎｅｔｗｏｒｋｓ有色冶金设计与研究第２８卷２００７年第６期１２月２闪速熔炼工艺简介闪速熔炼是现代火法炼铜的主要方法之一。

它克服了传统方法未能充分利用粉状精矿的巨大表面积、将焙烧和熔炼分阶段进行的缺点，从而，大大减少了能源消耗，提高了硫利用，改善了环境。

奥托昆普闪速炉的结构见图１。

闪速熔炼是将经过深度脱水（含水＜０．３％）的粉状精矿，在喷嘴中与空气或氧气混合后，以高速度（６０￣７０ｍ／ｓ）从反应塔顶部喷入高温（１４５０℃￣１５５０℃）的反应塔内。

此时，精矿颗粒被气体包围，处于悬浮状态，在２～３ｓ内基本上完成了硫化物的分解、氧化和熔化等过程。

熔融硫化物和氧化物的混合熔体落下到反应塔底部的沉淀池中汇集起来继续完成锍与炉渣最终形成过程，并进行澄清分离。

炉渣在单独贫化炉或闪速炉内贫化区处理后再弃去。

３模糊神经网络的结构设计模糊神经网络有多种，人们采用较多的是简化的Ｔ－Ｓ模糊神经网络［３］，其特点是：在系统的模糊规则中，“Ｉｆ”部分是模糊的，而后件“Ｔｈｅｎ”部分是确定的，为各输入变量的线性组合或常量。

这里每条模糊规则的结论都是采用常值形式，其简化的一种形式为Ｒｊ：Ｉｆｘ１ｉｓＡｊ１ａｎｄｘ２ｉｓＡｊ２ａｎｄ…ｘｍｉｓＡｊｍ，Ｔｈｅｎｙ１＝ｗｊ１ａｎｄｙ２＝ｗｊ２ａｎｄ…ｙｎ＝ｗｊｎ式中：ｘ和ｙ是输入、输出变量；ｗｊｋ是常值，表示系统第ｊ条规则的ｋ个输出；Ａｊｉ也为论域Ｕｉ上的模糊集合。

模糊神经网络的结构示意图如图２所示。

第１层为输入层，该层有ｍ个神经元，输入向量Ｘ＝［ｘ１，ｘ２，…，ｘｍ］Ｔ传送到下一层。

第２层为模糊化层，设每个输入变量均有５个模糊集合，分别为ＰＢ、ＰＳ、ＺＥ、ＮＳ、ＮＢ，则该层共有ｍ×５个神经元，隶属函数选择为高斯函数，输出为μｉｋ＝ｅｘｐ－（ｘｉ－ｍｉｋ）２σ２ｉｋ!"（１）式中：ｉ＝１，２，…，ｍ；ｋ＝１，２，…，５，分别表示输入量的维数和模糊集合数。

ｍｉｋ和σｉｋ分别表示高斯隶属函数的中心和宽度。

第３层为推理层，该层的神经元个数为ｎ，每个神经元代表１条模糊规则，采用Ｓｕｍ－Ｐｒｏｄｕｃｔ模糊推理规则，即πｊ＝ｍｉ＝１#μｉｌ（２）式中：ｊ＝１，２，…，ｎ表示规则数；ｌ＝１，２，…，５。

第４层为解模糊层，该层的作用是实现归一化计算，避免在学习过程中由于各修正量过大而产生振荡。

该层的输出可表示为πｊ＝πｊ／ｎｊ＝１$πｊ（３）第５层为输出层，只有一个神经元。

该层采用加权线性求和法，求出清晰的输出值，即ｙ＝ｎｊ＝１$ωｊπｊ（４）式中：ωｊ表示解模糊层与输出层之间的连接权值。

由以上５层神经元组成的模糊神经网络具有很强的学习能力。

在模糊神经网络中，学习的方法有很多种，因为ＢＰ算法是一种运算简单、计算效率高的方法，因此采用ＢＰ算法，按照反方向误差修正ωｊ，σｉｊ，ｍｉｊ，从而完成隶属函数的和规则的自我学习和完善。

图２模糊神经网络模型图１奥托昆普闪速炉结构简图有色冶金设计与研究第２８卷１４・・４三大参数模糊神经网络模型及其仿真研究根据机理分析，确定三大参数神经网络模型的输入变量为：反应塔热风量ｘ１、反应塔富氧浓度ｘ２、装入干矿总量ｘ３、装入物含Ｃｕ率ｘ４、装入物含Ｆｅ率ｘ５、装入物含Ｓ率ｘ６、装入物含ＳｉＯ２率ｘ７、空气水分率ｘ８，分别建立三大参数的模糊神经网络模型。

冰铜温度模糊神经网络模型：Ｔｍ＂＝ｆ１（ｘ１，ｘ２，ｘ３，ｘ４，ｘ５，ｘ６，ｘ７，ｘ８）（５）冰铜品位模糊神经网络模型：Ｐｍ＂＝ｆ２（ｘ１，ｘ２，ｘ３，ｘ４，ｘ５，ｘ６，ｘ７，ｘ８）（６）渣中铁硅比模糊神经网络模型：Ｃ铁硅＂＝ｆ３（ｘ１，ｘ２，ｘ３，ｘ４，ｘ５，ｘ６，ｘ７，ｘ８）（７）其中，Ｔｍ＂、Ｐｍ＂、Ｃ铁硅＂分别为冰铜温度、冰铜品位、渣中铁硅比的预测结果。

选取３００组工业数据分别对这三个模糊神经网络进行训练，并选取５０组数据分别对这三个模型进行验证，三个模型的预测结果见图３、图４、图５。

冰铜温度的最大相对误差为８．８５％，平均相对误差为２．０３％；冰铜品位的最大相对误差为４．４８％，平均相对误差为１．５３％；渣中铁硅比的最大相对误差为１１．７４％，平均相对误差为４．３０％。

由图可以看出，当工况较为稳定，三大参数比较接近目标值时，模糊神经网络的预测精度较高；但当工况不稳定时，模糊神经网络的预测精度不太理想。

这是因为工况不稳定时，样本的质量和数量都达不到训练的要求，所以才导致模糊神经网络不够精确。

５小结铜闪速熔炼过程是一个典型的复杂工业过程，由于闪速熔炼过程中存在大量的不确定性因素，因此给过程模型化带来了巨大的困难。

本文采用基于实际工业运行数据的模糊神经网络模型建立了铜闪速熔炼过程预测模型，对三大参数进行预测。

从工业数据的验证结果可以看出，该模型取得了较好的效果，三大参数的预测精度较高，对工业操作具有指导作用。

〔参考文献〕［１］张卫华．冰铜温度测量方法研究［Ｊ］．江西铜业工程，１９９９，（４）：１￣５．［２］朱祖泽，贺家齐．现代铜冶金学［Ｍ］．北京：科学出版社，２００３．［３］刘凤霞，刘前进．基于模糊神经网络的故障测距［Ｊ］．电力自动化设备，２００６，２６（５）：３２￣３４．图３冰铜温度预测模型仿真图图４冰铜品位预测模型仿真图图５渣中铁硅比预测模型仿真图铜闪速熔炼过程参数预测模型第６期１５・・。