Ainul Akmar Mokhtar

Semi-Quantitative Risk Assessment Matrix for Rotating Equipment

In petrochemical, power generation, oil and gas industries and in variety of other sectors rotating equipments are in use to fulfill production requirements. Failure of rotating equipment, especially in such industries can result to risk related issues. A well implemented rotating equipment risk assessment strategy is most needed to achieve desired plant availability and efficiency. In this research semi-quantitative risk assessment approach is proposed to evaluate the risk of rotating equipment and categorize their associated failure risks. Borda ranking is adopted to evaluate the risk in order to minimize risk ties which exist in risk matrix. Compressor is taken as case study to show the applicability of the proposed method for rotating equipment. It was observed that risks of selected failure modes of gas turbine compressor fall in the categories of serious and medium levels based on risk matrix. Rotor bend distortion, blade failure or inlet guide vane failures needed more attention for treatment based on Borda ranking.

Probabilistic Life Cycle Costing: A Case Study of Centrifugal Pumps

Life cycle costing (LCC) has been gaining attention in industries as a decision-making tool for the management of assets. LCC is generally recognized as a valuable tool in making an optimal decision considering the total cost of ownership rather than just the initial acquisition cost. The deterministic LCC model is commonly used in many plants; however, the deterministic model only inherently encompasses uncertainty factors, i.e., the economic issue alone, and cannot practically and effectively handle the ambiguous other uncertainties such as changes in interest rate, cost of production loss per hour due to unexpected failure, and labor cost per failure, to name a few. The life cycle cost of a repairable system is closely linked to and highly influenced by its maintenance cost which includes its reliability, maintainability, and maintenance support. Thus, to incorporate these uncertainties into LCC, one needs to consider the application of the reliability engineering principles to evaluate the probabilistic nature of the equipment failures and repairs.

Continuous Life Cycle Cost Model for Repairable System

Traditionally, the estimation of maintenance cost of a repairable system was evaluated using discrete approach based on estimated number of system failure, cost of repair as well as the interest rates. As maintenance cost represents a significant portion of overall life cycle cost (LCC), accurate estimation of maintenance cost would influence LCC analysis. However, in actuality the failure of the repairable system occurs in a continuous probabilistic manner thus the assumption of discrete occurrence is rather inaccurate. This paper presents an alternative continuous LCC model to better represent the actual operating phenomena of repairable system. The model was established based on the widely used Weibull distribution probability density function and continuous combined interest method. The result of the developed LCC model was then validated using Monte Carlo method. The result indicates that the continuous LCC model is able to accurately estimate LCC for any given time that can be useful in decision making based on life cycle cost.

Assessment of Insulated Piping System Inspection Using Logistic Regression

Corrosion under insulation (CUI) is a common problem not only in chemical process plants but also in utility and power plants. According to empirical study, CUI is mainly driven by the operating temperature where CUI is more susceptible when the equipment or piping system is operating between −12 and 121 °C. Other factors such as insulation type and equipment or pipe location are also seen to be the contributing factors to CUI. However, to date, it is not clear which factors are more important in contributing to CUI occurrence. This paper presents a methodology for predicting the likelihood of CUI occurrence for insulated piping system using a logistic regression model. Logistic regression, a special case of linear regression, requires binary data and assumes a Bernoulli distribution. Using historical data, the variables of operating time in year, pipe operating temperature, type of insulation and pipe size are modelled as factors contributing to CUI. The outcome of this model does not produce the probability of failure to be used in quantitative risk-based inspection (RBI) analysis. However, the result rather uses the historical inspection data to provide the decision makers with a means of evaluating which pipe to be inspected for future planning of scheduled inspection, based on the likelihood of CUI occurrence.

A Neural Based Fuzzy Logic Model to Determine Corrosion Rate for Carbon Steel Subject to Corrosion under Insulation

One of the most common external corrosion failures in petroleum and power industry is due to corrosion under insulation (CUI). The difficulty in corrosion monitoring has contributed to the scarcity of corrosion rate data to be used in Risk-Based Inspection (RBI) analysis for degradation mechanism due to CUI. Limited data for CUI presented in American Petroleum Institute standard, (API 581) reflected some uncertainty for both stainless steels and carbon steels which limits the use of the data for quantitative RBI analysis. The objective of this paper is to present an adaptive neural based fuzzy model to estimate CUI corrosion rate of carbon steel based on the API data. The simulation reveals that the model successfully predict the corrosion rates against the values given by API 581 with a mean absolute deviation ( MAD ) value of 0.0005, within that the model is also providing its outcomes for those values even for which API 581 has not given its results. The results from this model would provide the engineers to do necessary inferences in a more quantitative approach.

Identification of Critical Factors to System Performance Using Availability Modeling and Simulation Analysis

Availability analysis presents a means to understand the impact of existing maintenance system and maintenance resources to the overall system operational availability. The practical method for conducting availability analysis of a plant system at operation phase is illustrated and discussed via a case study of an acid gas removal system of gas processing plant. This study demonstrates that the availability modeling and simulation is effective in assessing the existing and future system configurations and determining possible impacts and critical factors to systems availability. These findings can significantly assist management to make right actions in improving plant system performances.

Reliability assessment framework for repairable system

System reliability assessment serves as one of the decision tools in selecting the right maintenance strategy. However, selecting the right reliability model can be a formidable task given the vast number of available reliability prediction models This paper presents a framework of selecting the right model based on system failure data with special emphasis on generalized renewal process (GRP) for system that exhibits failure trending The results indicate a better fit for the data with GRP compared with life data analysis approach.

Reliability assessment of multi-state repairable system subject to minimal repairs and constant demand

Effective maintenance management is essential to reduce the adverse effect of equipment failure to operation. This can be accomplished by accurately predicting the equipment failure such that appropriate actions can be planned and taken in order to minimize the impact of equipment failure to operation. This paper presents a model to assess system reliability for a degraded multi-state system based on discrete time Markov process and continuous time Markov process. The selection of which model to use is based on the type of available data. The system degradation was quantified by discrete level of systems performance rate with system states ranging from perfect functioning state to complete failure. At any point in time, the system can experience random failures from any degraded state upon which general repair will be performed. This research also explored a method of estimating of transition probabilities as well as definition of states for the Markov process by utilizing system performance data and data clustering method. The results proved the applicability of both discrete time Markov chain and continuous time Markov process in assessing the reliability of multi-state systems using the systems performance data. The results are then utilized to perform equipment replacement analysis due to deterioration based on the expected demand.

Development of spreadsheet based decision support system for product distributions

Product distribution is a complex process as it involves meeting requirements from several stake holders including the distributors and customers. The primary objective of product distribution process is meeting the customers’ demand as well as minimizing the cost incurred by the distributor. For distributor that supports large number of customers, the available commercial softwares for optimizing and scheduling of product distribution are typically being used. However, these systems are complex, costly and require long processing time on a dedicated computer system. Thus, these commercial softwares are not practical for distributors that support small number of customers and as such the optimization and scheduling activities are usually done manually based on rule-of-thumb. This process is time consuming and the results may not be optimal. This paper presents a decision support system employing a two-step sequential approach for product deliveries. First is to determine the optimum carrier required to meet customers demand utilizing linear programming with the objective function to minimize the total distribution cost. Premium Solver Platform (PSP) is utilized to model the optimization problem. Second is to use multi-criteria decision making approach applying various physical and logistic rules to generate the carrier assignment and scheduling. Both approaches are developed using spreadsheet due to its ease of implementation and lowest cost of ownership. The outcome indicates that this decision support system gives a better result compared to the manual assignment of carrier while minimizing the distribution cost. Furthermore, the system requires only a few minutes to generate the results and thus can be applied to practical usage. It is also shown that the system could be used as a viable planning tool for strategic decision concerning investment on the number of carrier required to meet future demand.