**4. Scenario modeling: a case study on a petrochemical plant**

The model was applied in a real case study concerning the management of an emergency in a petrochemical company (see **Figure 6**). The plant consists of

**7**

*The Analytic Functional Resonance Analysis to Improve Safety Management*

process and service plants. Plant processes include: Predistillation unit; Propane unit; Distillation unit; Catalytic hydrogenation unit and Diesel oil purification. While service facilities include: Diathermic oil system; Steam and hot water production unit; Refinery torch; Hydrogen production unit; Cooling water system and Refinery storage area. The plant preserves extremely dangerous substances in quantities equal to or greater than the limits. Thus, it is a plant with a high risk activity, where it is necessary to analyze all the deviations from the operating standards (emergency conditions) such as: gas leakage, hydrocarbon release, fire, earthquake,

**STEP#1 "Identification of the Essential Functions".** The case study analyzes the emergency generated by the *loss of propane* gas during the transfer from tanker to tank. The **goal** of the model was to evaluate the variability of performance between upstream activities and downstream activities. An **expert team** was formed. The expert team consisted of 1 safety manager, 1 AHP expert, 1 chemical engineer, 1 mechanical engineering and 1 risk manager. The expert team analyzed the scenario and summarized the main activities are carried out during emptying the propane from the vehicle and placing it in the treatment plant. In fact, propane is a very dangerous hydrocarbon as the compound appears as a colorless and odorless gas, which can however be easily liquefied by compression and therefore highly flammable. **Table 2** describes the activities carried out during the emergency and

**Figure 7** shows the FRAM of the emergency management activity. FRAM Model Visualiser (FMV) was used to create a graphical representation of a FRAM model

**STEP#2 "Identification of variability".** In the second step the variability of the functions was characterized and highlighted in red in **Figure 7**. In the scenario analysis, the *human functions* revealed more criticality, which could present different variability. In particular, the analysis focused on two main activities and related

((https://functionalresonance.com/FMV/index.html).

• **Scenario A** "activate ESD control";

• **Scenario B** "activate hydro-foam cannon".

*DOI: http://dx.doi.org/10.5772/intechopen.93998*

flood, sabotage, pollution, etc.

**Figure 6.** *Petrochemical plant.*

the responsibilities.

emergency scenarios:

#### **Figure 5.**

*Summary of the main steps and phases of the study.*

*The Analytic Functional Resonance Analysis to Improve Safety Management DOI: http://dx.doi.org/10.5772/intechopen.93998*

**Figure 6.** *Petrochemical plant.*

*Operations Management - Emerging Trend in the Digital Era*

main phases as described below.

over the component j. [35].

main steps and phases of the study.

*Summary of the main steps and phases of the study.*

based on matrix algebra to "measure" decisions [32]. AHP is characterized by three

**Phase #1 "Define hierarchy".** The aim of the first step is to define the goal and the hierarchy of the decision problem. The decision maker or the experts team identifies a set of criteria for evaluating the *n* decision alternatives and assigns a percentage weight to each criterion; then assigns a score that is the impact of the criterion on the decision. The score of each decision alternative is the weighted average of the scores of each criterion on the decision by the weight assigned to each criterion. The top of hierarchy represents the goal of the decision problem. Lower levels represent criteria and sub-criteria in which the decision-making model is broken down. The bottom

**Phase #2 "Perform pairwise comparison and relative weight estimation".** After defining the hierarchy, the criteria are compared in pairs, the sub criteria and alternatives are compared in pairs by assigning a score of relative importance to the other. The sum of the weights must be 100%. Saaty suggested an increasing scale of values form 1 (*equal importance*) to 9 (*extreme importance*) when comparing two components [34]. The result of the comparison is the so-called *dominance coefficient* aij that represents the relative importance of the component on row (i) over the component on column (j), i.e., *a*ij *= w*i*/w*j*.* The pairwise comparisons can be represented in the form of a square matrix (n x n), symmetric and diagonal. The number of pairwise comparisons grows quadratically with the number of criteria and alternatives. The score of 1 represents equal importance of two components and 9 represents extreme importance of the component i

**Phase#3 "Perform consistency index".** Saaty (1990) proposed utilizing consistency index (CI) to verify the consistency of the comparison matrix [36]. The CI could then be calculated by: CI = (λmax − n)/n − 1. In general, if CI is less than 0.10, satisfaction of judgments may be derived. **Figure 5** shows a summary of the

The model was applied in a real case study concerning the management of an emergency in a petrochemical company (see **Figure 6**). The plant consists of

**4. Scenario modeling: a case study on a petrochemical plant**

level represents all alternatives to evaluate in terms of the criteria [33].

**6**

**Figure 5.**

process and service plants. Plant processes include: Predistillation unit; Propane unit; Distillation unit; Catalytic hydrogenation unit and Diesel oil purification. While service facilities include: Diathermic oil system; Steam and hot water production unit; Refinery torch; Hydrogen production unit; Cooling water system and Refinery storage area. The plant preserves extremely dangerous substances in quantities equal to or greater than the limits. Thus, it is a plant with a high risk activity, where it is necessary to analyze all the deviations from the operating standards (emergency conditions) such as: gas leakage, hydrocarbon release, fire, earthquake, flood, sabotage, pollution, etc.

**STEP#1 "Identification of the Essential Functions".** The case study analyzes the emergency generated by the *loss of propane* gas during the transfer from tanker to tank. The **goal** of the model was to evaluate the variability of performance between upstream activities and downstream activities. An **expert team** was formed. The expert team consisted of 1 safety manager, 1 AHP expert, 1 chemical engineer, 1 mechanical engineering and 1 risk manager. The expert team analyzed the scenario and summarized the main activities are carried out during emptying the propane from the vehicle and placing it in the treatment plant. In fact, propane is a very dangerous hydrocarbon as the compound appears as a colorless and odorless gas, which can however be easily liquefied by compression and therefore highly flammable. **Table 2** describes the activities carried out during the emergency and the responsibilities.

**Figure 7** shows the FRAM of the emergency management activity. FRAM Model Visualiser (FMV) was used to create a graphical representation of a FRAM model ((https://functionalresonance.com/FMV/index.html).

**STEP#2 "Identification of variability".** In the second step the variability of the functions was characterized and highlighted in red in **Figure 7**. In the scenario analysis, the *human functions* revealed more criticality, which could present different variability. In particular, the analysis focused on two main activities and related emergency scenarios:



#### **Table 2.**

*Functions of the system.*

**Figure 7.** *FRAM representation of the system.*

According to the analysis, the *expert team* characterized the environmental conditions in which the operators work. Historically, human performance is investigated through specific *performance shaping factors* (PSFs), as described below:


**9**

**Figure 8.** *AHP model.*

*The Analytic Functional Resonance Analysis to Improve Safety Management*

difficult, very difficult, difficult beyond standards).

• PSF#3 Ergonomics and Human Machine Interaction. It refers to the adequacy

• PSF#5 Complexity. It refers to the difficult of the task to perform (simple, easy,

**STEP#3 "Aggregation of variability and definition of functional resonance".** 

**Table 3** summarized the weights of variability in which operators are involved

Furthermore, the AHP model allows to define the probability of occurrence of the most critical scenario or, as the results show in **Figure 10**, the most critical

• PSF#6 Workload, Stress and Stressors. It refers to mental stress or excessive

• PSF#7 Work Processes. It refers to the adequacy or inadequacy of safety

The AHP hierarchical structure created for characterizing the variability and define functional resonance is shown in **Figure 8**. AHP model was created using Super Decision Software (http://www.superdecisions.com/). When two items of the "Performance Shape Factor" level are compared with respect to the main goal, the expert answers the question "*Which PSF is more important*?". The AHP helps to

according to PSFs. The weights of the factors are defined through AHP. More specifically, it emerges that PSF#1, PSF#4 and PSF#6 present a higher probability

• PSF#4 Time Available. It refers to the adequacy or inadequacy of the time

*DOI: http://dx.doi.org/10.5772/intechopen.93998*

available to complete a task.

workload.

of occurrence.

or inadequacy of machine (i.e. computer).

culture, management policies/support, etc.

assess the subjective probability of an event or trigger cause.

A graphic representation of **Table 3** is shown in **Figure 9**.

scenario is scenario B: Scenario A (47%%) and Scenario B (53%).

*The Analytic Functional Resonance Analysis to Improve Safety Management DOI: http://dx.doi.org/10.5772/intechopen.93998*

*Operations Management - Emerging Trend in the Digital Era*

the transfer and close the cut-off valves

and the gas is depressurized

**# Operations Responsible**

2 Activate the shower cooling system on the truck through the 10HC1V system Desk operator 3 If possible, intercept the ATB side valve Truck driver 4 Disconnect the unloading arms Internal

5 Turn away truck Internal

7 Activate hydro-foam cannon Internal

8 Turn off furnace and cool surrounding equipment Internal

Desk operator

operator

operator

Internal operator

operator

operator

1 Activate Electrostatic discharge (ESD) control from the control room to stop

6 Alternatively, continue the unloading operations until the tanker is emptied,

**8**

**Figure 7.**

**Table 2.**

*Functions of the system.*

*FRAM representation of the system.*

operator(s) involved in the task.

use of formal operating procedures.

According to the analysis, the *expert team* characterized the environmental conditions in which the operators work. Historically, human performance is investigated through specific *performance shaping factors* (PSFs), as described below:

• PSF#1 Training and Experience. It refers to the experience and training of the

• PSF#2 Procedures and Administrative Controls. It refers to the existence and


**STEP#3 "Aggregation of variability and definition of functional resonance".**  The AHP hierarchical structure created for characterizing the variability and define functional resonance is shown in **Figure 8**. AHP model was created using Super Decision Software (http://www.superdecisions.com/). When two items of the "Performance Shape Factor" level are compared with respect to the main goal, the expert answers the question "*Which PSF is more important*?". The AHP helps to assess the subjective probability of an event or trigger cause.

**Table 3** summarized the weights of variability in which operators are involved according to PSFs. The weights of the factors are defined through AHP. More specifically, it emerges that PSF#1, PSF#4 and PSF#6 present a higher probability of occurrence.

A graphic representation of **Table 3** is shown in **Figure 9**.

Furthermore, the AHP model allows to define the probability of occurrence of the most critical scenario or, as the results show in **Figure 10**, the most critical scenario is scenario B: Scenario A (47%%) and Scenario B (53%).

#### *Operations Management - Emerging Trend in the Digital Era*


#### **Table 3.** *Weighting of output variability.*

**Figure 9.** *Probability of occurrence of the most critical PSFs.*

#### **Figure 10.**

*Probability of occurrence of the most critical scenario.*

**STEP#4 "Monitor and manage the variability".** From the numerical analysis FRAM emerges a critical value (considering the values shown in table n) for the "activate hydro-foam cannon" function which must be analyzed to limit its variability, which affects the downstream variable. While the "Activate ESD control" function presents a lower variability.

Considering the most critical PSF or PSF#1 a sensitivity analysis was performed to evaluate the variability of this factor and the robustness of the model. As shown in **Figure 11** it emerges that if the vertical line is at 0.5 shows the scenario A is more likely. For any PSFs greater than that, the Scenario B is the more likely.

The general result or PSF#1 highlights that *Training and Experience* is a critical point. It is an unsurprising result. In fact, it is clear that training is essential to taught

**11**

**5. Conclusions**

**Figure 11.** *Sensitivity analysis.*

tions or industrial settings.

The authors declare no conflict of interest.

**Conflict of interest**

*The Analytic Functional Resonance Analysis to Improve Safety Management*

workers to manage complex scenarios at achieving those skills that allow them to work both by reducing risks and protecting personal and community safety. Safety training

is the only "measure" that can be validly opposed to situations of residual risk.

This is a pilot study that is based on the awareness that the increasingly complexity of industrial plants and the need to analyze safety systems lead researchers to develop new methodological approach. In the present research the main gap of the qualitative approach of FRAM method was overcome with the integration of a multi-criteria decision-making method. The research proposes the integration of the traditional FRAM method with AHP. The integration of AHP with FRAM allows to investigate a new perspective in the field of risk management. The model was applied in a real case study to evaluate the performance of emergency operations in a petrochemical company. Considering the variability of each system function, the research numerically shows the level of variability generated by an upstream function on a downstream function. The results obtained are aimed at identifying function couplings that could generate high variability. Future development of research is the integration technological and organizational aspects, beyond human ones. Moreover, the model can be applied in different socio-technical systems where a high level of complexity requires the use of innovative tools. Thus, the proposed model will be tested in other situa-

*DOI: http://dx.doi.org/10.5772/intechopen.93998*

*The Analytic Functional Resonance Analysis to Improve Safety Management DOI: http://dx.doi.org/10.5772/intechopen.93998*

*Operations Management - Emerging Trend in the Digital Era*

**PSFs Weighting of variability** PSF#1 0,29,448 PSF#2 0,14,892 PSF#3 0,04571 PSF#4 0,15,991 PSF#5 0,10,006 PSF#6 0,18,167 PSF#7 0,06925

**10**

**Figure 9.**

**Figure 10.**

**Table 3.**

*Weighting of output variability.*

*Probability of occurrence of the most critical PSFs.*

function presents a lower variability.

*Probability of occurrence of the most critical scenario.*

**STEP#4 "Monitor and manage the variability".** From the numerical analysis FRAM emerges a critical value (considering the values shown in table n) for the "activate hydro-foam cannon" function which must be analyzed to limit its variability, which affects the downstream variable. While the "Activate ESD control"

Considering the most critical PSF or PSF#1 a sensitivity analysis was performed to evaluate the variability of this factor and the robustness of the model. As shown in **Figure 11** it emerges that if the vertical line is at 0.5 shows the scenario A is more

The general result or PSF#1 highlights that *Training and Experience* is a critical point. It is an unsurprising result. In fact, it is clear that training is essential to taught

likely. For any PSFs greater than that, the Scenario B is the more likely.

workers to manage complex scenarios at achieving those skills that allow them to work both by reducing risks and protecting personal and community safety. Safety training is the only "measure" that can be validly opposed to situations of residual risk.
