The methodology of this study is divided into two parts. First, data collection and analysis is performed. Then, the risk models were constructed accordingly using Python programming language software based on the methodology of this study approach including collection of data, corrosion rate calculation, remaining life of piping calculation, likelihood of failure, consequence of failure and risk evaluation.
Implementation of RBI in this study because of its hybrid technique which is comprehensive qualitative and quantitative approach that applicable and suitable to all types of assets that will provide essential and high performance of inspection and maintenance.
Thus, this can optimized the inspection services and operational cost of the refinery that can evaluate safety, environmental and business interruption risks of the industry.
The general risk model planning process for this study based on API RP 580. Essentially, API RP 580 defines the required features of an RBI system, providing guidance on the design of an RBI program for static equipment and piping in the service of the oil and gas industry (Horrocks & Adair, 2010).
API 581 is an API 580 development to the extent that it provides a recommended practice for the practical application of the principles set out in API 580. Nevertheless, it should be noted that API 581 is a recommended method and does not claim to be the only solution capable of achieving the goals set out in API 580 satisfactorily. This distinction has resulted in a number of subtly different general use methods, many of which are incorporated in software applications (Horrocks & Adair, 2010). Almost all RBI code claims compliance with API 580 and only code created by the API itself complies with API 581 (Sitton, 2005).
In Malaysia, enforcement exercises on the installation and operation of machinery requiring a fitness certificate as stated in the Factories and Machinery Act, 1967. In order to obtain each extension and exemption, the owner must prove that the integrity of each pressurized machinery is satisfactory and safe to operate and explain its impact on economic and public safety if regular inspection for the pressurized machinery is to be carried out.
On April 8, 2014 new regulations called the Factories and Machinery (Special Scheme Inspection) 2014 (Risk-Based Inspection) were implemented, with the purpose of changing from a time-based fitness certificate to a risk-based fitness certificate and to enable to determine the safety and usability of the machinery during operation. Additionally, the objective of the new regulation is to encourage industry competitiveness to focus its efforts on enhancing the product to meet customer demand by reducing the number of inspections carried out by the Asset Integrity Department (DOSH, 2014).
Python programming language software will be used as a tool for constructing this risk model to generate outputs related to detailed assessment. The reason behind selecting Python is because it provides a simple but powerful syntax, unlike other popular languages such as C, C++, Java, and C#. In addition, Python is accessible to beginner programmers and allows them to tackle interesting problems more quickly than many other, more complex languages that have a steeper learning curve (Halterman, 2019). Figure 3.1 presented the flowchart of this study and the details of each procedure are described accordingly in the following sections.
In this section, the data regarding to piping is collected. There is two ways of data collecting; primary and secondary. The primary data is collected from journal of previous research while secondary data is through industrial visit. The secondary data is needed as a support to the primary data.
The collected data are corrosion rate, the actual piping thickness for a given location, the required thickness of the piping at the same location and remaining life calculation of piping.
In this step, sub-objective to study and analyse the risk level of piping based on corrosion is obtained. Corrosion in the crude inlet feed piping is primarily due to corrosion of sulfidic and naphthenic acid at high temperatures. The corrosion rate data is obtained through previous research of (Baby et al., 2016) and industrial visit. However, the fundamental of corrosion rate can be calculated as Equation (3.2).
Cr=87.6 ?(W/DAT) (3.2)
Where,
Cr – Corrosion rate in mm/y
W – Weight reduction in mg
D – Metal thickness in g/cm3
A – Region of test in cm2
T – Time of introduction of the metal example in hours
Remaining Piping life is calculated using data such as corrosion rate, actual thickness and retiring thickness. Actual piping thickness is obtained through previous research of (Baby et al., 2016) and industrial visit. The formula for calculating the rest of life as shown in Equation 3.3.
Lr=(Ta-Tr)/Cr (3.3)
Where,
Lr – The remaining life of the piping in years
Ta – The actual piping thickness measured for a given location
Tr – The required thickness of the piping at the same location
In this step, sub-objective to evaluate the risk level for piping considering remaining life of piping as Likelihood of Failure is obtained. Likelihood of failure (LoF) been applied in this study is quantitative approach which is through the calculation piping remaining life based on RBI recommended maximum interval as shown in Table 3.2.
In this step, sub-objective to evaluate the risk level for piping considering environment & reputation as Consequence of Failure is obtained. Consequence of Failure (CoF) been applied in this study is qualitative approach where the environmental and reputation is analysed.
Environmental CoF is been analysed qualitatively based on impact to environment (permanent or temporary), size of affected area, recovery and clean-up effort and compliance to statutory requirement.
Meanwhile, reputation CoF is done qualitatively based on public awareness, media attention, involvement of government or action groups and potential impact to future business and activities. Table 3.3 shows the estimation of CoF category for environment and reputation.
To evaluate and develop the risk model system, Python programming language is used as it has easy to be used and handle.
The evaluation of risk is obtained by multiplying the LoF and CoF category. The LoF, which is remaining life of piping is calculated using the formula with consideration of the actual piping thickness for a given location, the required thickness of the piping at the same location and the corrosion rate.
Next, environmental and reputation CoF which is a qualitative been estimate accordingly to the LoF which is the remaining life of the piping through the CoF category based on (PETRONAS Technical Standard, 2019).
The result of the multiplication will be categorized according to the risk level through risk assessment matrix table. If the risk cannot be evaluate, reassessment need to be repeated until the result of risk level of piping been obtained
All the data collection as in Table 3.3 including risk assessment matrix table will be implemented and display in the Python programming language for risk evaluation. The risk level of piping based on risk assessment matrix table will be displayed by the Python after all the data been input and evaluate.
The risk assessment matrix will provide the risk ranking of the piping between very low, low, medium and high. Risk assessment matrix is a tabulated table of determined CoF and LoF. As shown in Table 3.4, very low level of risk of piping which in light blue colour are varies from 25 to 36. Next, the green colour which indicate low level of risk of piping that varies from 15 to 26. Meanwhile, medium level of risk of piping which in yellow varies from 7 to 14 and high level in the red colour varies from 1 to 6.
A Resource Planning table is included in this Research Methodology Chapter to summarise resources that be used or consumed as prove that the resources are within the study means and affordability. Most of the resource came from reference book through internet surfing. It is not a big deal in completing this research study as there is accessible to the needed resource. However, the mileage expenses need to be considered as industrial visit will take place for secondary data collection. Resource planning table as shows in Table 3.6.
(Hardware/Software/Consumable materials/etc.) Source Type of sourcing Quantity Unit Cost Total Cost
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