摘要
热定型过程位于印染工业流程中的后处理工段,主要完成织物的拉幅定型处理。该工段能耗巨大,约占印染企业总能耗的二分之一。定型过程能耗主要源于为导热油加热的燃料煤耗以及导热风机消耗的电能。首先基于定型机的物理结构及工艺原理,依据牛顿热交换公式和换热平衡方程,推导出煤耗与定型机各级烘箱温度、布匹速度与导热风机风速之间的能耗关联模型;然后,依据风机风量与功率关系推导出导热风机的电耗关联模型。最后将总能耗模型(煤耗+电耗)转化为能耗最小的优化问题,运用粒子群优化(particle swarm optimization,PSO)算法对其进行优化求解。求解结果可解释为定型过程能耗最小时关键参数对应的最优工作点;该优化求解结果经实践验证后可进一步作为定型过程节能降耗的有效操作指导。
As the post-processing section in dyeing and priming industry,heat-setting process mainly completes fabric stentering and setting operations,its energy consumption almost accounts for about 1/2 of total energy consumption in dyeing and printing enterprises. The energy consumption of heat-setting process mainly consists of the fuel coal consumption used for heating the heat conducting oil and the electricity consumption caused by thermal fans. First, based on the physical structure and processing principle ,the energy relation models between coal consumption and each oven temperature ,as well as the fabric running speed and thermal fan speed are derived according to Newton＇s heat exchange formula and heat balance equation. Then,the thermal fan electricity consumption relation model is deduced according to the relation between the fan air volume and its consumed power. Finally, the total energy consumption model （coal consumption ＋ electricity consumption） is transformed into the minimum energy consumption optimization problem,which is then solved with particle swarm optimization algorithm. The optimization results can be explained as the key parameter optimal operating points of heat-setting process under minimum energy consumption condition. After practical test verification,the proposed optimization results can be used as the effective operation guidance for process energy-saving and consumption reduction.
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2013年第3期582-587,共6页
Chinese Journal of Scientific Instrument
基金
浙江省自然科学基金项目(LQ12F03013)
国家自然科学基金项目(61203177
51005213)
浙江大学工业控制技术国家重点实验室开放课题(ICT1230)资助
关键词
能耗模型
粒子群优化
热定型机
energy consumption model
particle swarm optimization （PSO）
heat-setting machine
作者简介
任佳，1999年于华北电力大学获得学士学位，2002年于沈阳工业大学获得硕士学位，2006年于浙江大学获得博士学位，现为浙江理工大学副教授，主要研究方向为数据挖掘，智能优化算法及其应用。E—mail：jren@zstu．edu．cn苏宏业，1990年于南京工业大学获得学士学位，1993年于浙江大学获得硕士学位，1995年于浙江大学获得博士学位，现为浙江大学教授，主要研究方向为．非线性系统控制理论与应用，鲁棒控制理论与应用，复杂工业过程先进控制和优化技术、软件开发与应用。E—mail：hysu@iipc．zju．edu．cn
