Crecimiento de plántulas de tomate ( Lycopersicum sculentum Mill.)

To partially validate the developed approach, sensitivity analysis was carried out. To 20 25 30 35 40 45 50 55 60 H1 H3 H5 H7 H9 H11 H13 H15 H17 H19 H21 H23 H25 H27 H29 H31 H33 H35 H37 H39 H41 Utili ty V a lu e

the sensitivity of the model in terms of the changes that happened to the output of the risk evaluation when the prior probability changed. The model of MRHAA was examined as follows:

Figure 4.10: The analysis of MRHAA by HUGIN software given the evidence for node “𝑳𝒊𝒌𝒆𝒍𝒊𝒉𝒐𝒐𝒅𝑽𝒆𝒓𝒚 𝑯𝒊𝒈𝒉 = 𝟏𝟎𝟎%"

The prior probability value of the node "likelihood" was updated to 100% “Very High” (see Figures 4.10). As a result, the output value of the node MRHAA_{𝑉𝑒𝑟𝑦 𝐻𝑖𝑔ℎ} increased from 8.26% to 38.63%. The response indicates that a slight change in the prior probability value of each input node affects the value of the output node, causing a relative increase/decrease of the output value (axiom 1) (see Figures 4.8 and 4.10).

Figure 4.11: The analysis of MRHAA by HUGIN software given the evidence for node “𝑳𝒊𝒌𝒆𝒍𝒊𝒉𝒐𝒐𝒅_{𝑽𝒆𝒓𝒚 𝑯𝒊𝒈𝒉} = 𝟏𝟎𝟎%” and “𝑷𝒓𝒐𝒃𝒂𝒃𝒊𝒍𝒊𝒕𝒚_{𝑽𝒆𝒓𝒚 𝑯𝒊𝒈𝒉} = 𝟏𝟎𝟎%”

Changing the prior probability value of the node "Probability" and the node "Likelihood" to be set at 100% “Very High”, led to a change in the posterior probability value of the output "MRHAA_{𝑉𝑒𝑟𝑦 𝐻𝑖𝑔ℎ} " (see Figure 4.11). This change resulted in a further increase of the posterior probability value of the output "MRHAA_{𝑉𝑒𝑟𝑦 𝐻𝑖𝑔ℎ} " from 8.26 % to 70%, as shown in Figures 4.8 and 4.11. Therefore, the output value of "MRHAA_{𝑉𝑒𝑟𝑦 𝐻𝑖𝑔ℎ} " in Figure 4.11 is higher than the output value in both Figures 4.8 and 4.10. This result means that the total influence magnitude of the combination of the probability variation, namely the influence of both attributes at once on the values, is always greater than that of any single attribute (axiom 2).

By performing axiom 1 (i.e. increasing the belief degree of Likelihood𝑉𝑒𝑟𝑦 𝐻𝑖𝑔ℎ =

100%" ) and axiom 2 (i.e. increasing the belief degree for both “Likelihood_{𝑉𝑒𝑟𝑦 𝐻𝑖𝑔ℎ} and “Probability_{𝑉𝑒𝑟𝑦 𝐻𝑖𝑔ℎ} = 100%") for all the hazards, the results of these axioms showed the sensitivity of the models (see Figure 4.12). For example, the posterior probability value of the output H13 (i.e. Breakdown of

Communication (MRHCFA)) increased from 14.44% to 40.19% when the prior probability value of the node "Likelihood" was changed to be set at 100%, “Very High”. Moreover, by changing the prior probability value of the nodes "Likelihood" and "Probability" to be set at 100%, “Very High”, the output value of H13 increased from 14.44% to 70.19%, as shown in Figure 4.12.

Figure 4.12: The sensitivity analysis of the 42 hazards

4.6

Conclusion

This is one of the first studies that deals with data uncertainty problems in PTSs through developing a new integrated model. This new model integrates fuzzy rule-based (FRB) approach and Bayesian Network (BN) to analyse petroleum ports and transportation modes failures. The fuzzy rule-based Bayesian reasoning (FRBBR) method uses domain expert knowledge in the form of fuzzy IF-THEN rules. Output degree (i.e. belief degree) of the FMEA parameters was integrated by using a BN for risk ranking to provide a supporting system for decision-makers in analysing the failures. After

0 10 20 30 40 50 60 70 80 90 H1 H3 H5 H7 H9 H11H13H15H17H19H21H23H25H27H29H31H33H35H37H39H41 Th e Ve ry Hi gh Ou tp u t Val u e The Hazards

Sensitivity Analysis

utilising the belief degree of the hazards associated with PTSs (i.e. port and transportation modes hazards), Procedural Failure(MRHDA), Ship Collision due to Human Fatigue (SCHF) and Geological Hazards (PTGH), were the most important hazards in port, ship and pipeline transportation systems. Based on the ranked outputs of this method, the most significant hazard within the PTSs was recognised (i.e. Procedural Failure(54.44)). The output of this technique may be changed based on 1) experts’ backgrounds and 2) number of participant experts (i.e. more or less than nine), and different inputs can be included.

The proposed assessment methodology highlights the issues associated with PTSs. The proposed method shows more realistic and flexible results by describing the failure information based on real-life situations. Additionally, the proposed method provides a decision-support system for enhancing the safety practices of petroleum ports and other engineering and management systems through providing decision-makers with a reliable risk-ranking technique.

This chapter mainly focused on evaluating the local levels of PTSs. In addition, a brief discussion of Chapter 4 is presented in Chapter 7. However, controlling the operational risk at the local level may not ensure the safety of PTSs. In the following chapters, the global level of PTSs will be evaluated. While the FRBN technique was used to assess the local level of the PTSs, the Evidential Reasoning (ER) approach is going to be applied to accomplish the PTSs evaluation, due to the approach’s capability in synthesising the risk from the local level to the system level. Moreover, other techniques, such as Analytical Hierarchy Process (AHP) and network analysis technique, will be used as well.

5

Chapter 5: An Advanced Risk Assessment Framework

for PTNs using Evidential Reasoning Approach

Petroleum transportation systems (PTSs) play an important role in the flow of crude product from its place of production to the customer. In order to ensure an effective port-to-port transportation system, safety of the PTSs is a key element that must not be neglected for the successful petroleum transportation networks (PTNs). This chapter proposes a novel mathematical model that assesses PTSs locally and globally. An Evidential Reasoning (ER) approach is introduced due to the technique’s ability to combine the local/internal and global/external levels of PTSs. This chapter’s novel approach starts with the identification of the petroleum transportation hazards and finishes with the model validation process. Whilst the hybrid Fuzzy Rule-Based Bayesian Reasoning (FRBBR), Analytic Hierarchy Process (AHP), and Evidential Reasoning (ER) approaches are used at the internal level, the network analysis technique is used for the external level. The results gained from using these techniques can be used by decision makers to measure and improve PTSs safety.

5.1

Introduction

In the previous chapter, Fuzzy Rule-Based Bayesian Reasoning (FRBBR) was used to evaluate the identified hazards in order to enhance the safety practices of petroleum transportation systems (PTSs). However, the phrase ‘PTSs’ clarifies that there is more than just one system. PTSs contain two focal points that are linked to each other in order to complete the product transportation cycle. These focal points are ports and transportation modes.

PTSs play an important role in the flow of petroleum product from the production phase to the refinery or customer. If there is no preparation built into the system, or if the

system lacks facilities, it might be too late to take emergency actions if a failure strikes. A disruption in a PTS will affect the overall supply chain’s desired functions, which can result in various failures (Baublys, 2007). For example, factors such as planning and scheduling may influence the operation of the supply chain and may cause a breakdown in the fluency of the product’s movement (Ding and Tseng, 2013; Grossmann, 2005; Shah et al., 2010). Safety in design and operation is a prime concern for operating companies because the hazards could have a high economical and financial impact apart from causing other failures (Elsayed et al., 2009). With regard to the safety of petroleum transportation as a complete system, risk assessment is an important tool for maintaining the safety and reliability of the petroleum industry. Therefore, this chapter proposes a novel risk evaluation model in order to enhance the safety of the PTSs and applies that model in a real transportation system. To reach this goal, this chapter is organised as follows: the second sub-section provides a literature review for the approaches introduced in this chapter, Evidential Reasoning (ER) and Analytical Hierarchy Process, (AHP). The third sub-section is a step-by-step explanation of the techniques that have been used to evaluate PTSs. This section begins by identifying the petroleum transportation hazards and finishes with the validation process. A real-life case study is presented in the fourth sub-section to demonstrate the methodology proposed in this chapter. Finally, the conclusion is presented in the fifth sub-section.