摘要
对羧基苯甲醛(4-CBA)作为精对苯二甲酸(PTA)装置氧化反应过程中产生的一种有害杂质,是影响PTA产品质量指标的关键因素,4-CBA杂质一直采用人工取样测试、化验室离线分析,由于样品分析周期长,测试结果滞后,难以提供实时信息以指导生产操作。现用装置DCS采样及相关的过程工艺参数,基于人工神经网络建立的数学模型用DCS的CL语言编程进行4-CBA的软测量,实现了在线连续测量,提高了响应速度,优化了装置的操作,提高了产品质量的稳定性。
As a harmful impurity generated during oxidation reaction process from PTA device, 4-CBA （carboxy benzaldehyde） is the key factor to PTA quality. It is difficult to provide real- time information to guide the production since 4- CBA has being sampled manually, off-line analysised with long analysis time and lagged test results. Adopting DCS to take sample and related process parameters, artificial neural network based mathematical model with CL language programmed DCS for 4- CBA soft measurement, on-line continuous measurement is realized with improved response speed, optimized device operation and improved product quality stability.
出处
《石油化工自动化》
CAS
2012年第5期40-43,共4页
Automation in Petro-chemical Industry
