A data-driven approach to chemical process alarm threshold optimization

LIU Heng; LIU Zhenjuan; LI Hongguang

doi:10.3969/j.issn.0438-1157.2012.09.010

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A data-driven approach to chemical process alarm threshold optimization

更新时间：2025-12-10

- A data-driven approach to chemical process alarm threshold optimization
- CIESC Journal Issue 9, Pages: 2733-2738(2012)
- 作者机构：
  
  北京化工大学信息科学与技术学院,北京,100029
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0438-1157.2012.09.010
  CLC： P277
- Online First：05 September 2012，
  
  Published：05 September 2012
- 稿件说明：
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LIU Heng, LIU Zhenjuan, LI Hongguang. A data-driven approach to chemical process alarm threshold optimization[J]. CIESC Journal, 2012, (9): 2733-2738.
DOI：

LIU Heng, LIU Zhenjuan, LI Hongguang. A data-driven approach to chemical process alarm threshold optimization[J]. CIESC Journal, 2012, (9): 2733-2738. DOI： 10.3969/j.issn.0438-1157.2012.09.010.

摘要

为了提高化工过程报警系统的性能

需要对过程参数的报警阈值进行优化设置。针对传统阈值方法存在的问题

采用核密度估计方法、基于历史数据对过程报警状态进行估计

从最小化误报警和漏报警概率的角度建立了优化过程报警阈值的目标函数

并采用数值优化的方法进行求解。应用于TE过程的仿真结果表明

此方法能够有效地减少过程误报警的次数

并且对漏报警的次数进行抑制。

Abstract

In order to improve the performance of chemical process alarm systems

it is imperative to optimize assignments of process alarm thresholds.In response to limitations of traditional threshold assignment methods

based on historical data

this paper firstly invokes kernel density estimation methods to identify process alarm states before an objective associated with alarm threshold optimization in terms of minimizing the probabilities of false and missed alarms is established along with enabling numerical solvers.Simulation results on TE process demonstrate that the proposed approaches can effectively reduce the number of false alarms as well as limit that of missed alarms.

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