Influencing Factor

However, if the current charge is not controlled, then a high current density can lead to a thicker and more porous anodic film. It has also been found that (Chai et al, 2008) the corrosion resistance of an anodic film can be related to the applied cnrrent density. An anodized coating formed at a higher cnrrent density should be thicker and have higher corrosion resistance. 

8 Cross-sectional microstructures of (a) Anomag and (b) Tagnite coatings. 

Furthermore, anodizing of magnesium is even possible in organic electrolytes 

The solution temperature has a negative effect on the anti-corrosion property of an anodized film This is probably because the large quantity of heat caused by sparking could not be released effectively (Chai et ah, 2008) when anodized at a higher temperature. 

9 Anodizing voltage curves for different Mg alloys (Shi et al., 2006c). 

Martin (119) showed that under the conditions of his experiment rats 

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This change is influenced by metaboHc hormone action rarely do any of the hormones or other influencing factors act independent of each other to regulate nutrient partitioning. Complex interactions aHow for integration of influences to accommodate a coordinated chronic regulation of nutrient use for maintenance or growth so that an animal may adapt to its environment (see Feeds AND FEED ADDITIVES). 

At the next organizational level are factors directly causing error 1) job characteristics such o Complexity, time stress, noise, lighting, environment, or mental requirements, and 2) individual factors such as personality, and team performance. These, collectively, are called performance-influencing factors, or PIFs. 

Chapter 3, Factors Affecting Human Performance in the Chemical Industry, describes how a knowledge of "performance-influencing factors" (PIFs), can be used to identify and then eliminate error-causing conditions at the plant. 

Use the flow charts as a basis for asking questions relating to each stage of the sequential causal block diagram. Work backward from the observable error to the initiating event. A careful analysis of the performance-influencing factors (Chapter 3) will form part of this analysis. 

Internal error mechanisms can be regarded as intrinsic human error tendencies. The particular error mechanisms that will be triggered depend on the performance-influencing factors (PIFs) in the situation (see Chapter 3). However, use of Figure 2.16 allows certain preliminary conclusions to be drawn. 

Performance-influencing factors are general conditions which increase or decrease the likelihood of specific forms of error. They can be broadly grouped into the following categories ... 

In the previous chapter, a comprehensive description was provided, from four complementary perspectives, of the process of how human errors arise during the tasks typically carried out in the chemical process industry (CPI). In other words, the primary concern was with the process of error causation. In this chapter the emphasis will be on the why of error causation. In terms of the system-induced error model presented in Chapter 1, errors can be seen as arising from the conjunction of an error inducing environment, the intrinsic error tendencies of the human and some initiating event which triggers the error sequence from this imstable situation (see Figure 1.5, Chapter 1). This error sequence may then go on to lead to an accident if no barrier or recovery process intervenes. Chapter 2 describes in detail the characteristics of the basic human error tendencies. Chapter 3 describes factors which combine with these tendencies to create the error-likely situation. These factors are called performance-influencing factors or PIFs. 

Performance-influencing factors analysis is an important part of the human reliability aspects of risk assessment. It can be applied in two areas. The first of these is the qualitative prediction of possible errors that could have a major impact on plant or personnel safety. The second is the evaluation of the operational conditions under which tasks are performed. These conditions will have a major impact in determining the probability that a particular error will be committed, and hence need to be systematically assessed as part of the quantification process. This application of PIFs will be described in Chapters 4 and 5. 

TABLE 3.2 A Classification Structure of Performance Influencing Factors ... 

The analyst also records opportunities to recover errors, and various performance shaping factors (called performance-influencing factors in this book) which will subsequently be needed as part of the quantification process. 

PREDICTED ERROR DESCRIPTION PERFORMANCE-INFLUENCING FACTORS... 

During the PHEA stage, the analyst has to identify likely human errors and possible ways of error detection and recovery. The PHEA prompts the analyst to examine the main performance-influencing factors (PIFs) (see Chapter 3) which can contribute to critical errors. All the task steps at the bottom level of the HTA are analyzed in turn to identify likely error modes, their potential for recovery, their safety or quality consequences, and the main performance-influencing factors (PIFs) which can give rise to these errors. In this case study, credible errors were found for the majority of the task steps and each error had multiple causes. An analysis of two operations from the HTA is presented to illustrate the outputs of the PHEA. Figure 7.12 shows a PHEA of the two following tasks Receive instructions to pump and Reset system. 

All critical errors and recovery points for task steps are recorded in accordance with the conventions of the PHEA and provide a valuable input to the specification of warnings and cautions for the design of procedures. In addition, various performance-influencing factors which contribute to the occurrence of critical errors can be identified which can provide input to the development of other error reduction measures such as training, and control panel design. 

The areas that may be considered as part of such an evaluation are the performance-influencing factors (PIFs) described in Chapter 3. The human factors assessment methodology (HFAM) method described in Chapter 2, provides a systematic framework for the evaluation process. 

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