Human Factors Analysis and Safety Management Systems:a Case Study from a Refinery
The study of human factors is a scientific discipline involving the systematic application of information regarding human characteristics and behavior to enhance the performance of man-machine systems. In this work a new procedure based on SLIM (Success Likelihood Index Methodology) method is developed in order to integrate human factor in a refinery risk management system. The drivers for developing a new Human Factor Risk Management (HFRM) model are follows. (1) Integrate of human factor risk management into the organization as a part of achieving their overall goal of a managed corporate culture. (2) Increase the human factor contribution to company functions and activities. (3) Meet requirements for managing human factors. (4) Reduce costs arising from human performance limitations and add value through improved human performance. (5) Meet demand of business owners and high level managers. The success likelihood index methodology (SLIM) was developed under the sponsorship of Brookhaven National Laboratory and the U.S. Nuclear Regulatory Commission to quantify operator actions in the plant response model of a probabilistic risk assessment. It is based on the assumption that the human error rate in a particular situation depends on the combined effects of a relatively small set of performance-shaping factors (PSFs) that influence the operators ability to perform the action successfully. PSFs account for both the plant conditions, or scenarios, under which the action must be performed and the psychological and cognitive state of the individuals performing the action. In the case study the quantitative evaluation of the human error rate for the action is accomplished by judges who were assumed to be able to rank the PSFs in two ways. (1) A numerical rating, n, of the degree to which the PSF helps or hinders the performance of the action. (2) A ranking of the relative importance, or weight, w,, of each PSF for influencing the reliability of the action. The method proposed in this work can be divided into six steps. (1) Data collection regarding risk events. (2) Constitution of a panel of experts and first approach to the case study. (3) Definition and selection of the Performance shaping factors for the case study. (4) Assignment of weighting factors for each PSF. (5) Scoring of each PSF. (6) Calculation of the success likelihood index (SLI). It is then possible, through the use of this index, to assign a priority to each possible corrective action by considering. (1) Risk index (real or potential according to which one is the highest) referred to the non conformance, identified through the use of the risk matrix reported in the accident reporting system modules of the refinery. (2) SLI index, the lowest the value is, the more attention is required: the non conformance analysis finds out some critical pint and corrective actions are needed. (3) In relation to each event among all the possible corrective actions is better to assign resources first to the one presenting the highest w, and lowest r i and following these criteria the resources are assign step by step to the others as well.
human factor refinery success likelihood Index methodology performance shaping factors
BEVILACQUA M CIARAPICA FE GIACCHETTA G
Energy Department, University Politecnica delle Marche, via Brecce Blanche, Ancona, Italy
国际会议
The 2010 International Symposium on Safety Science and Technology(2010 安全科学与技术国际会议)
杭州
英文
2280-2286
2010-10-26（万方平台首次上网日期，不代表论文的发表时间）