In order to make the risk assessment method of oil wharf handling more reasonable, basic data calibration method more accurate, and assessment findings more objective, the fuzzy weights of the relative probability of basic events are calibrated by ANP decisionmaking (Analytic Network Process). ANP decisionmaking is appropriate for reflecting the dependence between the basic events and the feedback relationship. The calibration value is used as the probability value of each basic event. Based on the fault tree model, the relationship between the accidents caused by the Bayesian network is constructed, and the important degree of the basic events is quantitatively evaluated. The case focuses on wharf handling gasoline fire and explosions, using ANP method to calibrate probability, and analyzing and sorting the structural importance, the probability importance, and critical degree of each basic event through forward and backward reasoning. The results showed that the evaluation model can better characterize the effect of the basic events on the top events, which can be targeted to identify security weaknesses in oil wharf handling process. It has some practical significance for finding security risks and improving working conditions and the overall system safety level.
The number of water transports of dangerous goods has increased by 20% annually over recent decades in China [
At present, research papers at home and abroad on risk assessment are easy to find and can be summed up into three categories.
(
(
(
The object system of study is complicated; thus, the above evaluation methods also encountered a number of problems while developing rapidly. For instance, the evaluation methods are unable to present the conditional probability relations among different levels and the interrelations of every node; short of database of all kinds of accidents; processing data by expert scoring methods that is not flexible enough and cannot well reflect the dependence and feedback relations among factors; lack of comprehensive studies across evaluation methods; and so on. Therefore, it is believed that establishing a fault tree for oil wharf handling, mapping it onto Bayesian Network, and, meantime, calibrating the probability of elementary event via Saaty’s ANP decisionmaking method will not only clarify the logic of tree causality clearly and express the conditional probability relations in different layers accurately, but also make the calibration method of probability value more flexible, reasonable, and reliable. Besides, it will be able to quantificationally calculate the structural importance, probable importance, and the critical importance of every elementary event, which can be targeted to identify security weaknesses in oil wharf handling process. It has practical significance for finding security risks, improving working conditions and the overall system safety level.
Oil wharf handling risk assessment needs an analysis of risk factors in handling process in the first place. Oil wharf handling mainly involves operations such as berthing, mooring, and (electrostatic) jumper connecting of the vessel, connecting pipelines (or marine loading arm), opening the valve, starting the pump, conveying supplies, turning off the pump, cleaning pipelines, disconnecting pipelines (or marine loading arm), closing the valve, disconnecting the jumper, unmooring, and departure. Each of the abovementioned operations may have certain risks. For example, when conveying supplies, physicochemical properties of different materials require different delivery pressure, temperature, flow rate, and mix heat. Once these conditions are out of control, a physical or chemical explosion or static electricity accumulation could occur. If cleaning pipelines does not follow operation rules, potential safety risks might therefore appear. In addition, pipes on the wharfs are in a wet and corrosive environment. As a result, if they are not comprehensively inspected or have inservice inspection in time, it might lead to leaking or even fire explosion.
Oil in this paper means petroleum products. Dangerous goods are divided into nine categories according to their dangerousness level or their major hazards [
In other articles Bayesian network and ANP calibration methods combined as a new assessment method have not been used. The following will focus on introducing the concept of Bayesian network and its building methods, ANP calibration methods of probability, and the methods of calculating the importance degree of basic events.
Bayesian network, also known as Belief Networks or Probability Networks, provides a natural method to reflect causality and describe the probability relations among variables. Bayesian network also works as a tool to analyze and reason uncertain events by utilizing the probability theory and graph theory. So far, it is one of the most effective theoretical models in the field of expressing and reasoning uncertain knowledge.
Suppose variable
A Bayesian network is composed of network structure
Suppose
Based on the connections between the node and its father node expressed in the above definitions, a function of joint probability distribution that contains all of the nodes can be deduced.
Figure
Simple model of Bayesian network.
The function of joint probability distribution shows the logical relationship among nodes and is the main basis of positive and negative reasoning and quantitative calculation.
A Bayesian network is composed of a DAG
Andgate structures.
Orgate structures.
Saaty came up with the core concept of analytic hierarchy process (AHP) as early as the beginning of 1980s. Related published literatures on AHP were very huge and exceeded the other decisionmaking methods [
ANP takes the mutual influence of factors or neighboring layers into consideration and makes a comprehensive analysis of the affected factors through hypermatrix to calculate its weight. ANP firstly divides system element into two parts, with the first part being named controlling factors layer. All decisionmaking guidelines, including the problem target, have been considered to be independent of each other and are subject only to the target element. There can be no decisionmaking guidelines within controlling factors, but there should be at least one target. The weight of each guideline in the controlling layer can be achieved through traditional AHP method. The second part is the network layer. It is composed of all the elements under control of the controlling layer, with mutually affected network structure inside. The calculation steps of ANP [
Then the weighted hypermatrix
Let
Top events are undesired events (accident or fault). Its occurrence probability is calculated by referring to the fault tree and the occurrence probability of basic events. In the Fault Tree Analysis, it first needs to solve all minimal cut sets (minimal path sets) before calculating the occurrence probability of top events and intermediate events, then using the inclusionexclusion theorem for precise calculations, or perform approximate calculation through exclusive approximate or independent approximate. In Bayesian network, there is no need to solve cut sets. Joint probability distribution can be used to directly calculate the occurrence probability of top events (
Quantitative assessment of oil wharf handling risks is based on analysis of the importance degree, which is to analyze the effect degree of every basic event to the occurrence probability of top events. It is an essential mean to provide information for us to modify the system. In Bayesian network, it is easy to calculate the importance degree of bottom events
(
Importance degree of structure is as follows:
(
Importance Degree of Probability is as follows:
(
Critical Importance Degree is as follows:
A binary simply connected Bayesian network is plotted through mapping fault tree. To calibrate the basic events by ANP decisionmaking method, it will be easy to calculate the importance degree of basic events and the probability of top events. The calculation rules are as follows.
If event is
If event is
Andgate:
Orgate:
Combined with the rules, steps for quantitative calculations are as follows.
Map the fault tree onto a Bayesian Network model and calibrating the occurrence probability of basic event
Let the occurrence probability of basic event
Taking the occurrence probability calibrated in Step
According to formulas (
A fire happens only when combustible, oxidant, and ignition sources are all available at the same time. Lack of any one of them will make a fire impossible; thus they make three necessary elements of fire. As for oil wharf handling, as long as there is ignition source, a fire or explosion might happen once oil leaks.
This paper chooses gasoline as the object of analysis as it is common goods in wharf handling. Gasoline is a flammable liquid with a low flash point in Section
This paper takes gasoline fire accident and explosion in oil wharf handling as the top event to analyze its risks by building a fault tree model. After a research in port of Dalian, Beibu Gulf, and some other ports in China, one top event, thirteen intermediate events, and thirtytwo basic events are chosen as trigger events and impact factors constitute the fault tree. Figure
Events list of gasoline fire accident and explosion in oil wharf handling.
Events type  Letter code  Events name 

Top event 

Fire accident and explosion in oil wharf handling 


Intermediate events 

Sources of ignition 

Leakage  

Open fire  

Electrostatic sparks  

Electrical sparks  

Friction, shock sparks  

Automobile exhaust sparks  

Tank leak  

Marine loading arm  

Pipe leak  

Electrostatic sparks on human body  

Electrostatic sparks of oil products  

Fracture on outside and inside of the arm  

Body static  

Static electricity accumulation of oil products  


Basic events 

Unlawful hot work 

Open fire on near vessels  

Fire brought by smoking or unlawful behavior  

Do not touch the electrostatic eliminating device before entering the field  

Touch conductors of different voltage  

Electrostatic sparks on chemical fiber clothes  

Flow too fast  

Poorly grounded  

Oil products are mixed with water and other impurities  

Design defects in process and devices  

Improper electric options (such as the selection of nonexplosionproof electrics)  

Electrical failure  

Operation against rules  

Short circuit or overload  

Insulating flange of marine loading arm malfunction (stray current sparks of vessel body)  

Personnel wear spikes  

Metal hosepipe, iron tools friction, or collision with ground  

The bearings of device has not been timely maintained  

Vehicles or equipment collide with each other  

Vehicles entering the field has not been equipped with backfire relief valve  

Backfire relief valve of vehicles malfunction  

Valve damaged  

Safety accessory abnormal  

Corrosion leak  

Fracture and leakage resulted from uneven settlement and adverse natural conditions  

Excessive tank filling  

The interface is not tight enough  

Poor natural conditions of typhoon and others  

Strike of mooring rope  

Pressure, temperature too high  

During loading and unloading operations, ship displacement over limit  

External shocks from vehicles, machines, and so forth 
Fault tree model of gasoline fire accident and explosion in oil wharf handling.
According to the mapping method described in Section
Bayesian network of gasoline fire and explosion in oil wharf handling.
In accordance with the logic relation shown in Figure
The essential data (basic calibration of absolute value [
The occurrence probability value of basic events.
Basic events  Calibration probability value 


0.00251 

0.0076 

0.0076 



0.0142 

0.02614 



0.04807 
Figure out the structural importance according to formula (
The structural importance.
Basic events  The structural importance 


0.043209876543 

0.043209876543 

0.037037037037 



0.018518518519 

0.049382716049 



0.100823045267 
The order for structural importance of basic events is
The value of basic events has no effect on the order of structural importance in terms of the definition of the structural importance. If we only consider the position of basic events in the fault tree structure,
The probability importance and the critical importance can be figured out in a similar way. In Tables
Probability importance.
Basic events  Probability importance 


0.338029738618 

0.338029738618 

0.324165292257 



0.155671058198 

0.377330394618 



0.331552854357 
Critical importance degree.
Basic events  Critical importance degree 


0.013441370697 

0.040698971035 

0.039029683879 



0.035019597445 

0.156258014109 



0.252488627038 
The order for probability importance of basic events is
According to the order above, it can be seen that reducing the occurrence probability of basic event
The order for probability importance of basic events is
Compared with probability importance, the importance degree of
Importance degree calculation through Bayesian network can reflect the actual status of things in a more comprehensive way. For example, the value of probability
It is rarely seen to assess the risk of oil wharf handling through Bayesian network method. Taking advantage of Bayesian network, this paper maps the fault tree model onto Bayesian network. The problem of quantitative risk assessment of oil wharf handling has been solved effectively both by Bayesian network and ANP decisionmaking methods, which expands the range of Bayesian network’s and ANP’s application greatly. Main conclusions and prospects are as follows:
Replacing the hierarchical relationships with network structural relationships via Saaty’s ANP decisionmaking methods is the way to calibrate the fuzzy value of relative probability of basic events, which will be the probability value of basic events. This has solved the problem that there are interdependent relationships and feedback among basic events, which is impossible for traditional methods to deal with. This method makes the calculation results more accurate and credible. In the meantime, it makes up for the lack of database of oil wharf handling accidents in China.
Building the Bayesian network of oil wharf handling risk assessment model will not only make the tree causality more clear in logic, but also make the expression of conditional probability relations in different layers accurate. Taking advantage of the reasoning algorithm with Bayesian network, it is easy to figure out the importance degree of basic events so as to acquire the importance extent that basic events mean to the occurrence of accidents from various aspects.
In the safety assessment of oil wharf handling, a combination of ANP decisionmaking methods and Bayesian network will give the analysis result more realistic significance and pertinence in finding the weak link in the process of oil wharf handling so as to improve the working condition.
Oil wharf handling is a dynamic process. The Bayesian network built in this paper is based on a static logic and static accident mechanism. How to build a dynamic Bayesian network in order to make the analysis of the risk more accurate in oil wharf handling will be the key problem of researches in this area in the future.
The authors declare that they have no competing interests.