摘要
制浆漂白过程中,漂白药品的加入量应随着运行工况的变化而不断调整,以保证产品漂白质量(白度)并节约药品用量。针对漂白过程常规DCS过程控制的不足,提出了氯漂过程的运行优化控制方法,对漂白化学药品加入量进行优化控制。通过对专家知识和生产数据的分析,集成案例推理、模糊推理、专家系统等智能技术和统计过程控制(SPC)技术,建立了氯漂前卡伯值的在线计算模型和漂后白度的预测模型,并实现了漂白化学药品加入量的优化。基于所提出方法,以WinCC为平台设计开发了氯漂过程运行优化控制系统。在某制浆厂氯漂过程工业试验表明,所提出方法和系统能够对氯气加入量进行及时有效地调整。对试验结果进行统计分析,得到优化后每分钟可以节约0.4 kg的氯气量,并且漂后白度和用药波动更小。
In the pulp bleaching process, the dosage of chemical agent usage must adjust to the variation of process conditions in order to maintain the quality（ as brightness）and save chemical agent. Because of the inefficiency of general DCS control in optimizing the dos- age of chemical agent usage, a operational optimization control method for chlorination stage （ C/D stage） is proposed. The method is combined intelligence technique as CBR, fuzzy reasoning, expert system etc, and statistical process control（SPC）. Based on the meth- od, a model can be used for calculating the brown stock kappa number and pulp brightness prediction model after C/D stage are estab- lished. The system is designed with WinCC and can be applied to optimize the bleaching chemical agent usage for chlorination stage. After applying in a pulp mill C,/D stage, the online industrial tests show that, the proposed method and system can have the dosage of chlorine usage tracked the variation of pulp properties and keep the brightness and agent usage fluctuation in limited range. Besides, it can save 0. 4 kg chlorine per minute.
出处
《控制工程》
CSCD
北大核心
2014年第2期303-308,共6页
Control Engineering of China
基金
国家自然科学基金资助项目(61104084)
广东省产学研基金项目(2010B090400410)
关键词
漂白过程
运行优化
白度
智能技术
统计过程控制
bleaching process
operation optimization
brightness
intelligence technique
SPC
