Predictive Analysis of Cognitive Errors of Control Room Operators: a Case Study in a Petrochemical Industry

The aim of this study was to identify and assess human errors in a petrochemical plant using the Technique for the Retrospective and Predictive Analysis of Cognitive Errors (TRACEr).

The sample size was all the eight operators of control room working in four shifts. In the first step, all tasks were analyzed using the hierarchical task analysis in order to identify sub-tasks. Then, for all the subtasks, different error modes (external and internal), Psychological Error Mechanism (PEM) and Performance Shaping Factors (PSFs) were identified and recorded in TRACEr sheet.

The analysis of TRACEr sheets indicated that of a total number of 1171 detected errors, the internal and external errors were 50.67% (n=593) and 49.33% (n=578), respectively. In this line, ̔timing/sequence̕ errors with 35.36% and chr(chr('39')39chr('39'))quality/selectionchr(chr('39')39chr('39')) errors with 30.03% were identified as the highest and lowest external error modes, respectively. In classifying the internal error modes, action errors with 31.87% and decision making with 10.73% were identified as the highest and lowest external error modes, respectively. Within PEMs, ̔distraction/preoccupation̓ (23.61%) was identified as the main causes of perception errors. The analysis of the PSFs shows that ‘Organization’ with 27.95% and ‘task complexity’ with 8.74% were two main factors affecting the task errors. 

The current study could identify many of the errors and conditions that affect the performance of operators. Therefore, this study can be introduced as a basis for managers and stockholders of chemical industries with complexity and high risk in order to prioritize human error prevention programs.