decision problem. STEP 1: Structuring of the decision problem I. INTRODUCTION

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1 Criticality Analysis for Assets Priority Setting of Abadan Oil Refinery Using AHP and Delphi Techniques Fereshteh Jaderi, Elham sa idi, Bagher Anvaripour and Nader Nabhani Faculty of Environmental Studies, University Putra Malaysia, Malaysia Petroluem University of Technology (PUT), Abadan Institute of Technology (AIT), Abadan, Iran technical and economic issues. The AHP is built on three Abstract Regarding to the quick development of oil and basic principles: decompositions, comparative judgment gas industries and on the other hand, losing the time and and hierarchy composition of synthesis priorities []. A wiring of assets, it seems necessary to determine the complex problem can be structured into a hierarchy of approaches for decision making of engineering units. There clusters, sub- clusters and so on. To determine the are many different approaches for achieving the safety and decreasing the direct and indirect costs of asset's failure. priorities of the element in each cluster and as a smallest The analytical hierarchy process (AHP) method is use to in decisions, a pairwise comparison should be taken prioritize assets according to their criticality. This paper between the two of them with respect to their parents. propose the prioritize procedure due to criticality analysis of Then the alternative comparison and criteria weighting is oil refinery assets by using AHP and Delphi techniques. This done []. The essential elements for understanding the method applied for Abadan Oil Refinery in Iran. This theory and application of AHP that should be covered in approach allows the decision makers to model a problem in a any class on the AHP are listed as following: hierarchical structure showing the relationship of the goal, The AHP Fundamental Pairwise Comparison Scale. objectives (criteria), sub-objectives and alternatives. The result Inconsistency and Sensitivity Analysis. showed that the Boiler asset located at the first rank. So Ratio Scales. decision maker and experts in maintenance management at The Ratings Model. first should repair and maintain the boilers failure for reducing the cost and improve the safety and environmental The Team Approach for Solving the AHP. risk. The AHP and Resource Allocation [0]. Index Terms: AHP, Criticality Analysis, Maintenance Four steps are involved in application of AHP to a Management, MCDM, Oil Industries, decision problem. STEP : Structuring of the decision problem I. INTRODUCTION STEP : Making pair-wise comparisons and obtaining the Critically analysis for a particular industry means the judgmental matrix degree of importance of a certain piece of equipment to STEP : Computing local weights and consistency of that industry []. This kind of analysis generally can be comparisons achieved by determining the consequences of failure. STEP : Aggregation of local weights []. Thus, the decisions of the businesses are made by taking Due to this fact that AHP is somewhat scale independent, the damage of a certain amount of risk into account. The this scale is extremely useful because of its applications ability to public explanation of the basis for decisions in giving the relative preference between two alternatives about the regulation of risks now in wide areas of public and capturing much information []. Combination of policy making is an urgent issue []. So the use of Multi synthesis means the multiplication of the local properties Criteria Decision Making (MCDM) methods is required of elements in a cluster by the global priority of the for making decisions that satisfy all the relevant criteria parent element that product the global properties for the for all s. Decision making within the oil and gas alternatives as a lowest element []. Given the industry is a complex process involving extensive growing number of AHP applications, a number of papers analysis of multiple objectives based on diverse criteria surveying its applications have been published regularly variety. Different multi criteria decision making []. Some advantages of AHP over conventional scoring methodologies include the Multi Attribute Utility method are its more accuracy and consistency, Theory(MAULT), the Simple Multi Attribute Rating quantification of subjective consideration in a structured technique (SMART), Analytic Hierarchy Process (AHP), framework and the most important, its effort required to etc. []. AHP proposed by Thomas Saaty. It is one of the make all pair- wised comparison []. The number of useful and popular MCDM techniques for formulating required pair- wised comparison is an exponential and analyzing decisions [] - []. AHP allows decision function of the size of the hierarchy. About more than makers to model a problem in a hierarchical structure 0% of AHP applications are integrated with some other determining the relationships between the goals, methodologies []. One of these methodologies is Delphi objectives (criteria), sub- objectives and alternatives []. that incorporation of AHP with this softer methodology Using this method is useful especially in complex can facilitate reaching to the goals. There are many

2 numbers of papers in the huge variety of application fields which have applied the combination of AHP and Delphi method []-[]. This paper presents a comprehensive AHP application in Abadan oil refinery and develops a framework for asset priority with criticality analysis using AHP and Delphi techniques. In the next section of this paper, detail of the procedure has been explained in order to carry out an assets criticality analysis following AHP and Delphi methods in the Abadan Oil Refinery. II. PROCEDURE The AHP can be applied when problems require considerations of both quantitative and qualitative factors. The existing review has found that significant research gap exists in the application of AHP in the forecasting, layout of facilities and managing stocks issues []. Decision making process for the oil and gas industry, in addition to the priority assets, shows the AHP and related mechanisms for determining proper use of weights and priorities the assets []. The processes for model the problem Goals are include a variety of major equipment failures of a plant according to their criticality. The criteria are failure frequency (FF), failure detection (FD), failure severity (FS) and failure costs (FC) []. The alternatives each industry are some of the assets of that industry creating main failure. As a case study, the Abadan oil refinery was considered. The alternatives of it were reactor, generator, gas compressor, pressure control valve (PV) and boiler. The alternatives each industry are some of the assets of that industry creating main failure. There are currently operating petroleum refineries within the Iran. As a case study, the Abadan oil refinery was considered. The alternatives of it were reactor, generator, gas compressor, pressure control valve (PV) and boiler. Fig.. Equipment Criticality Decision Hierarchy These facilities transform crude oils into refined products. The Abadan refinery located in Abadan city near the Iran-Iraq border, (km) from the coast of the Persian Gulf. It is the oldest refinery in the Middle East completed in. It was one of the world s largest oil refinery. This refinery produce various products and containes differnt production units. The hierarchical tree for information description of goal, criteria and alternatives for Abadan refinery assets can be arranged as a tree shown in the Fig.. A. Define the scale for each criteria To define the scales for each criterion, the equipment historical data may be need []. In the literature, the Delphi survey group size appears to be very different, usually between the to participants. At the Abadan refinery s case study, the Delphi group is organized with nine participator, five experts from the departments of research and development, HSE, operation, maintenance and process and four being academics. However, participants should able to draw some relevant conclusions and avoid at the same time difficulty reaching consensus among experts. It have been argue at this paper that an experts categorization should be made properly before undertaking the Delphi survey in order to build up the most representative group[]. As for them, different aspects for the participant s selection in the Delphi survey group were required: Sufficient knowledge and experience about the survey issues, Capacity, willingness and time to participate, Good communication skills []. All of members reviewed the main failures of refinery, then they discussed on the scales and confirmed the Marquez (00) scales and eventually five alternatives have been identified for assessment. The criteria description was an important issue for using its specialist in the panel. For example, The FF criteria require review of the equipment failure records. Attention to table I proves this requirement of FF. Table I. FF criteria [] FF failure frequency Level identification 0 Very high: almost unavoidable High: continuously Moderate: occasionally Low: few failures Remove: failure very unlikely An occurrence each week An occurrence each month An occurrence every months An occurrence every months An occurrence every months An occurrence every year An occurrence every to years An occurrence every to years An occurrence every to years An occurrence every 0or more years By having the information regarding aspects related to instrumentation, control, safety and protection system, certain equipment can be scored according to the failure detection criteria (FD). The score of FD criteria for the

3 refinery equipments has been defined as those mentioned in the table II. For considering the impact of equipment failures on safety, environment and operations, the FS scale is needed. The Delphi group established the classification and scale of FC as explained in table III. Table II. FD criteria scale [] FD Failure detection Level definition 0 FS 0 Absolutely uncertain Low Moderate High Very high Totally controlled Equipment is not inspected nor controlled. Failure event are not detected Control is reduced to visual inspection of the equipment Equipment is statistically controlled ( % automated inspection) Equipment is statistically controlled ( % automated inspection) Equipment is statistically controlled ( %00 automated inspection) Total inspection and permanent test equipment calibration ( %00 automated inspection) Table III. FC criteria scale [] failure severity Level definition Dangerously high High Low Very low Non- existent Failures may cause loss of human life Failures may create complications with existing laws and regulations Failure producing function loss and inoperable equipment Failure causing important decrease in customer satisfaction Failures impacting a production subsystem decreasing service quality Failures causing efficiency loss and customer complaint Failures that can be avoided with minimal modifications and with low service impact Failures creating small inefficiencies to the customer that the some customer could correct Failures difficult to be recognized by the customer and whose effects are not significant for the process Failure that con not be detected by the customer and not impacting process efficiency For description the economic impact of equipment failures to safety, environment and operations, the FC scale would be identified. This scale has been defined as in table IV. The team studied the Marquez scales and after matching those to the case study area, use them. Table IV. FC criteria scale [] FC failure cost Level definition 0 Dangerously high Very high High Moderate Very low Failure may cause high indemnification cost Failure causing total production loss Failures causing high maintenance direct cost repair cost Failures causing significant operation or maintenance cost Failure causing no significant cost to production process B. Alternative evaluation Using the scales defined at the table II to V the alternative evaluation for each of the criteria FF, FD, FS and FC for Abadan oil refinery s assets has been present in the table VII. The n-by-n judgments matrix on pairs of criteria alternatives, C i and C, (criteria in our case study are FF, FD, FS and FC) are represented. This judgments matrix can be represented by A = (a ij ) Where the entries a ij are defined by the following entry rules: Rule. If a ij = α, then a ij = / α, α 0, Rule. If the criteria Ci be of equal relative importance as criteria Cj, then a ij = a ji =. Thus the matrix A has the form A= (I) Where aij represents how many times the criteria C is more important for equipment criticality than criteria Cj. In other words, the entries of this Matrix are set according to the table I and according to it, the weight of each criterion could be found. These works has been done with the help of the member of Delphi panel. C. Criteria weighting and its consistency It has been showed mathematically that change of scale has no effect on consistency of a consistent input matrix []. However, a complex impact on the consistency of the input matrix by the change of scale in inconsistent input matrices is considerable []. The assumption is that reader is familiar to the AHP approach in determining weight and its consistency. As a hint the consistency index (CI) and consistency rating (I R ) formulas as detailed in [] are: CI= I R = CI/ RI (II) (III) 0

4 Where for a n by-n matrix, RI is the random average value of CI. When the CI has been calculated, the result is compared with that of the same index of a randomly generated reciprocal matrix from the scale of to as table I [], [ ]. Values of RI for by to 0 by 0 matrix are shown in Table V. that the using the Delphi and AHP is carried out for prioritize the assets. The Marquez procedure for critical analysis is developed []. The scales of the criteria were validate by the experts group and were applicable for this research. This research is applicable for the oil and petrochemical industries. Table V. RI values for matrices of different order [0] N 0 R I 0 An acceptable consistency rating (IR) is 0.0 or less [], []. III. RESULT The weight values were obtained using Delphi method and have been shown in Table VII (which is shown in Appendix). Asset evaluation for each criterion has been calculated and results have been inserted in table VIII(Shown in Appendix). The consistency rankings using the formula (II) and (III) are done. W i is the average weight, CI is calculated according to the formula (II) and RI is the mean of CI value where RI for Matrix in, is 0.. So I R =0.0. () () () () () W W W W W W i FF F D FS F C Table VI. The relative weight for Abadan oil refinery s assets The table VII presents the relative weights for the five assets. The weight calculated separately based on failures criteria for the assets. Then the average weight calculated for the each criterion. The results from the table VIII indicate the FF, FD, FC and FS for the five assets. The data calculated according to the experts group. Table IX indicate the final asset hierarchy by using the output of table VII and VIII. The final assets critically ranking are carried out in table IX. Table IX. Final assets critically ranking Assets Final hierarchy Ranking Boiler 0. Regenerator 0. Pressure control valve 0. Reactor 0. Compressor 0. the assets priority result illustrate that in Abadan oil refinery the Boiler and Regenerator assets were in the first and second priority with 0. and 0. rate. The Compressor asset was located in the last priority of the ranking. Comparing to the literature review can mention IV. CONCLUSION In this study the AHP and Delphi method was applied for prioritize the Abadan Oil Refinery assets. Among the main failures prioritized based on AHP and Delphi technique, the boiler and gas compressor that arranged in the table X, had the highest failures and lowest failures, respectively. This work showed the effectiveness of AHP in prioritization and as a result the facilities in decision making of assets in oil and gas refineries. This critical analysis can be help the decision makers and experts by prioritize the assets improve the maintenance management, reducing the cost maintenance and increase the production. ACKNOWLEDGMENT The Authors thanks to head and related experts of Abadan oil refinery for their useful comments and would like to acknowledgement especially to R & D department management as the facilitator for data collection. REFERENCES [] JD. Campbell, A. Jardine, Maintenance excellence, New York: Marcel Dekker, 00. [] I Tope, Shell U.K. Exploration and Production, Risk Decision Making in the Offshore Business, the Offshore Europe Conference, Aberdeen, Scotland, September. [] Lev Virine SPE, Derek Murphi SPE, Schlumberger Ltd., Analysis of multicriteria decision-making methodologies for the petroleum industry, International Petroleum Conference held in Dubai U.A.E., -, December 00. [] S.I. Gass, T. Rapcsák, Singular value decomposition in AHP, European Journal of Operational Research, pp., 00 [] A. Altuzarra, M.J. José Maria, M. Salvador, A Bayesian periodization procedure for AHP-group decision making, European Journal of Operational Research, pp., 00. [] N. Subramanian, R. Ramanathan, A review of applications of Analytic Hierarchy Process in operations management, Int. J. Production Economics, pp. -, 0. [] TL. Saaty, The Atlantic Hierarchy Process, New York, McGraw Hill, 0. [] A. Crespo Marquez, The Maintenance Management Framework Models and Methods for Complex Systems Maintenance, Springer-Verlag London Limited 00 pp. -, 00. [] TL. Saaty, LG. Vargas, Logic of Priorities. Boston: Kluwer-Nijhoff Publishing,.

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6 APPENDIX Table VII. Abadan refinery s Asset evaluation for each criterion Asset FF FF i / i FF i FC FD FD i / i FD i FS FS i / i FS i FC i / i FC i Reactor regenerator Gas compressor Pressure control valve Boiler Total Table VIII. Final hierarchy provided by Delphi panel Asset Reactor FF i / i FF i W FF () FD i / i FD i W FD () FC i / i FC i W FC () FS i / i FS i W FS () Final asset hierarchy =()+ ()+ ()+() regenerator Gas compressor Pressure control valve Boiler