An intelligent algorithm for performance evaluation of job stress and HSE factors in petrochemical plants with noise and uncertainty

This study presents an intelligent algorithm based on Adaptive Neuro-Fuzzy Inference System (ANFIS) and statistical methods for measuring job stress in noisy and complex petrochemical plants. Job stress is evaluated against health, safety, environment and ergonomics (HSEE) program in the integrated algorithm. The algorithm is composed of seventeen distinct steps. To achieve the objectives of this study, standard questionnaires with respect to HSEE are completed by operators. The average results for each category of HSEE are used as inputs and job stress is used as output for the algorithm. Moreover, operators' stress level with respect to HSEE is evaluated by the algorithm. Finally, operators with weak stress level are identified. The advantage and superiority of the intelligent algorithm are shown by error analysis in contrast with conventional regression approaches. This is the first study that introduces an integrated intelligent algorithm for assessment and improvement of job stress and HSEE in noisy, complex and uncertain environment.

► An intelligent algorithm is proposed to rank job stress with respect to HSEE.
► The proposed algorithm is ideal for complex and noisy petrochemical plants.
► HSEE and stress factors are considered as input and output variable, respectively.
► The algorithm is applied to control room operators.
► The algorithm is capable of handling complexity and uncertainty.