Efficiency prediction of control room operators based on human reliability analysis and dynamic decision‐making style in the process industry

Process industries have a potential for the occurrence of major accidents. These accidents can have severe adverse effects on human health and the environment and they can cause extensive damage to equipment and buildings. During major process upsets, central control rooms are among the most stressful workplaces in the world. Therefore, Human Reliability Analysis and Dynamic Decision‐Making Styles (DDMSs) play an important role in safety management in these industries. This study employs the intelligent Adaptive Neuro Fuzzy Inference System model associated with two questionnaires along with Cognitive Reliability Error Analysis Method to analyze the Human Reliability Influencing Factors (HRIFs) and DDMSs of the control room operators and to determine the efficiency of operators as well as their dominant and efficient decision‐making styles. Nine influencing factors on human reliability and five DDMSs are evaluated and the correlation between the HRIFs is investigated. Efficiency of the operators, according to the HRIFs, is determined and they are ranked. Next, the most dominant and efficient of the DDMSs among the operators was identified. Finally, an intelligent algorithm for determining the efficiency of a control room's operators is developed. © 2015 American Institute of Chemical Engineers Process Saf Prog 35: 192–199, 2016