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Optimal Hydrocarbon Piping Replacement Decisions Using the Markov Decision Process

AbstractPiping is an essential component of processing plants that transfers process fluids from one piece of equipment to another. To detect the unpredictable deterioration of piping systems, periodic or continuous pipe inspection for wall loss is conducted, which determines when preventative or co...

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Published in:Journal of pipeline systems 2024-08, Vol.15 (3)
Main Authors: Asare Bediako, Eric Kwaku, Xiang, Yisha, Alaswad, Suzan
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description AbstractPiping is an essential component of processing plants that transfers process fluids from one piece of equipment to another. To detect the unpredictable deterioration of piping systems, periodic or continuous pipe inspection for wall loss is conducted, which determines when preventative or corrective maintenance activities, usually replacements, should be done. Therefore, it is necessary to develop an optimal maintenance strategy to replace piping systems to reduce the overall maintenance expenditure and avert the effects of pipe failure. Most of the literature in this field concentrates on maintenance activities executed during decision-making. This paper investigates creating a system and tool to assist decision-makers in evaluating and selecting the optimal moment to replace a deteriorating pipe based on the current state of the piping system. Considering that preventative replacement of the pipeline is done during a scheduled shutdown, also referred to as a turnaround, this paper investigates making a preventative replacement ruling two turnaround cycles ahead. We account for the varying thickness at different monitoring locations of two piping systems in a refinery by applying a nonstationary gamma process with random effects. We employ the Markov decision process (MDP) and the value iteration algorithm to determine the most effective maintenance replacement policy. The outcome of our analysis is an optimal maintenance policy that minimizes the cost of maintenance. Practical ApplicationsThis paper’s practical use is to aid in maintenance planning in refineries, midstream or pipelines, chemical plants, and gas facilities by planning piping replacement in two shut-down cycles ahead of time. The pipe wall is classified into states ranging from the present thickness reading to the retirement thickness. The likelihood of transiting from one state to another is calculated based on the corrosion rate. Given that the pipe wall thickness is classified into states, the current wall thickness, transitional probabilities, and preventative and corrective maintenance costs, including lost profit opportunity, can be inputted into the model to help decision-makers decide if it is optimal to replace the piping in the subsequent two shut-down cycles. This policy will assist in reducing the risk of a pipe failure, which has financial, safety, and environmental consequences. It is also critical not to replace a still-in-good-shape pipe. This model may be customize
doi_str_mv 10.1061/JPSEA2.PSENG-1515
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To detect the unpredictable deterioration of piping systems, periodic or continuous pipe inspection for wall loss is conducted, which determines when preventative or corrective maintenance activities, usually replacements, should be done. Therefore, it is necessary to develop an optimal maintenance strategy to replace piping systems to reduce the overall maintenance expenditure and avert the effects of pipe failure. Most of the literature in this field concentrates on maintenance activities executed during decision-making. This paper investigates creating a system and tool to assist decision-makers in evaluating and selecting the optimal moment to replace a deteriorating pipe based on the current state of the piping system. Considering that preventative replacement of the pipeline is done during a scheduled shutdown, also referred to as a turnaround, this paper investigates making a preventative replacement ruling two turnaround cycles ahead. We account for the varying thickness at different monitoring locations of two piping systems in a refinery by applying a nonstationary gamma process with random effects. We employ the Markov decision process (MDP) and the value iteration algorithm to determine the most effective maintenance replacement policy. The outcome of our analysis is an optimal maintenance policy that minimizes the cost of maintenance. Practical ApplicationsThis paper’s practical use is to aid in maintenance planning in refineries, midstream or pipelines, chemical plants, and gas facilities by planning piping replacement in two shut-down cycles ahead of time. The pipe wall is classified into states ranging from the present thickness reading to the retirement thickness. The likelihood of transiting from one state to another is calculated based on the corrosion rate. Given that the pipe wall thickness is classified into states, the current wall thickness, transitional probabilities, and preventative and corrective maintenance costs, including lost profit opportunity, can be inputted into the model to help decision-makers decide if it is optimal to replace the piping in the subsequent two shut-down cycles. This policy will assist in reducing the risk of a pipe failure, which has financial, safety, and environmental consequences. It is also critical not to replace a still-in-good-shape pipe. 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To detect the unpredictable deterioration of piping systems, periodic or continuous pipe inspection for wall loss is conducted, which determines when preventative or corrective maintenance activities, usually replacements, should be done. Therefore, it is necessary to develop an optimal maintenance strategy to replace piping systems to reduce the overall maintenance expenditure and avert the effects of pipe failure. Most of the literature in this field concentrates on maintenance activities executed during decision-making. This paper investigates creating a system and tool to assist decision-makers in evaluating and selecting the optimal moment to replace a deteriorating pipe based on the current state of the piping system. Considering that preventative replacement of the pipeline is done during a scheduled shutdown, also referred to as a turnaround, this paper investigates making a preventative replacement ruling two turnaround cycles ahead. We account for the varying thickness at different monitoring locations of two piping systems in a refinery by applying a nonstationary gamma process with random effects. We employ the Markov decision process (MDP) and the value iteration algorithm to determine the most effective maintenance replacement policy. The outcome of our analysis is an optimal maintenance policy that minimizes the cost of maintenance. Practical ApplicationsThis paper’s practical use is to aid in maintenance planning in refineries, midstream or pipelines, chemical plants, and gas facilities by planning piping replacement in two shut-down cycles ahead of time. The pipe wall is classified into states ranging from the present thickness reading to the retirement thickness. The likelihood of transiting from one state to another is calculated based on the corrosion rate. Given that the pipe wall thickness is classified into states, the current wall thickness, transitional probabilities, and preventative and corrective maintenance costs, including lost profit opportunity, can be inputted into the model to help decision-makers decide if it is optimal to replace the piping in the subsequent two shut-down cycles. This policy will assist in reducing the risk of a pipe failure, which has financial, safety, and environmental consequences. It is also critical not to replace a still-in-good-shape pipe. This model may be customized to pipe systems in the various petrochemical sectors with known retirement thicknesses to estimate the risk of operating near the retirement thickness.</description><subject>Cost analysis</subject><subject>Decision making</subject><subject>Fluids</subject><subject>Hydrocarbons</subject><subject>Inspection</subject><subject>Iterative algorithms</subject><subject>Maintenance</subject><subject>Maintenance costs</subject><subject>Markov analysis</subject><subject>Markov processes</subject><subject>Pipes</subject><subject>Piping systems</subject><subject>Refineries</subject><subject>Technical Papers</subject><issn>1949-1190</issn><issn>1949-1204</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNp1UE1PwzAMjRBITGM_gFskzh1xmqbNcRpjAw02DXaOsjaBjq0pSYe0f09K-Tjhg23p-T3bD6FLIEMgHK7vl0-TER2G_DiNIIHkBPVAMBEBJez0pwdBztHA-y0JEQOjDHpotaibcq92eHYsnM2V29gKL8u6rF7wStc7leu9rhp8o_PSl7byeO1brHnV-EG5N_vxC-FlENDeX6Azo3ZeD75rH61vJ8_jWTRfTO_Go3mkKGdNlFDGUsbCIYUpEsILLTJuCsh0rIThMYk5T2OmMzCaGM4z4LEqmKKbNOcbIeI-uup0a2ffD9o3cmsPrgorZSAnjAmapGEKuqncWe-dNrJ24WF3lEBk657s3JNf7snWvcAZdhzlc_2n-j_hE5sycNA</recordid><startdate>20240801</startdate><enddate>20240801</enddate><creator>Asare Bediako, Eric Kwaku</creator><creator>Xiang, Yisha</creator><creator>Alaswad, Suzan</creator><general>American Society of Civil Engineers</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FD</scope><scope>F1W</scope><scope>FR3</scope><scope>H96</scope><scope>KR7</scope><scope>L.G</scope></search><sort><creationdate>20240801</creationdate><title>Optimal Hydrocarbon Piping Replacement Decisions Using the Markov Decision Process</title><author>Asare Bediako, Eric Kwaku ; Xiang, Yisha ; Alaswad, Suzan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a264t-5244744314dfd506de986fd18e3a9f630366734e81fe0f668163ad4a2b7c6b993</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Cost analysis</topic><topic>Decision making</topic><topic>Fluids</topic><topic>Hydrocarbons</topic><topic>Inspection</topic><topic>Iterative algorithms</topic><topic>Maintenance</topic><topic>Maintenance costs</topic><topic>Markov analysis</topic><topic>Markov processes</topic><topic>Pipes</topic><topic>Piping systems</topic><topic>Refineries</topic><topic>Technical Papers</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Asare Bediako, Eric Kwaku</creatorcontrib><creatorcontrib>Xiang, Yisha</creatorcontrib><creatorcontrib>Alaswad, Suzan</creatorcontrib><collection>CrossRef</collection><collection>Technology Research Database</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>Aquatic Science &amp; Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy &amp; Non-Living Resources</collection><collection>Civil Engineering Abstracts</collection><collection>Aquatic Science &amp; Fisheries Abstracts (ASFA) Professional</collection><jtitle>Journal of pipeline systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Asare Bediako, Eric Kwaku</au><au>Xiang, Yisha</au><au>Alaswad, Suzan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Optimal Hydrocarbon Piping Replacement Decisions Using the Markov Decision Process</atitle><jtitle>Journal of pipeline systems</jtitle><date>2024-08-01</date><risdate>2024</risdate><volume>15</volume><issue>3</issue><issn>1949-1190</issn><eissn>1949-1204</eissn><abstract>AbstractPiping is an essential component of processing plants that transfers process fluids from one piece of equipment to another. To detect the unpredictable deterioration of piping systems, periodic or continuous pipe inspection for wall loss is conducted, which determines when preventative or corrective maintenance activities, usually replacements, should be done. Therefore, it is necessary to develop an optimal maintenance strategy to replace piping systems to reduce the overall maintenance expenditure and avert the effects of pipe failure. Most of the literature in this field concentrates on maintenance activities executed during decision-making. This paper investigates creating a system and tool to assist decision-makers in evaluating and selecting the optimal moment to replace a deteriorating pipe based on the current state of the piping system. Considering that preventative replacement of the pipeline is done during a scheduled shutdown, also referred to as a turnaround, this paper investigates making a preventative replacement ruling two turnaround cycles ahead. We account for the varying thickness at different monitoring locations of two piping systems in a refinery by applying a nonstationary gamma process with random effects. We employ the Markov decision process (MDP) and the value iteration algorithm to determine the most effective maintenance replacement policy. The outcome of our analysis is an optimal maintenance policy that minimizes the cost of maintenance. Practical ApplicationsThis paper’s practical use is to aid in maintenance planning in refineries, midstream or pipelines, chemical plants, and gas facilities by planning piping replacement in two shut-down cycles ahead of time. The pipe wall is classified into states ranging from the present thickness reading to the retirement thickness. The likelihood of transiting from one state to another is calculated based on the corrosion rate. Given that the pipe wall thickness is classified into states, the current wall thickness, transitional probabilities, and preventative and corrective maintenance costs, including lost profit opportunity, can be inputted into the model to help decision-makers decide if it is optimal to replace the piping in the subsequent two shut-down cycles. This policy will assist in reducing the risk of a pipe failure, which has financial, safety, and environmental consequences. It is also critical not to replace a still-in-good-shape pipe. This model may be customized to pipe systems in the various petrochemical sectors with known retirement thicknesses to estimate the risk of operating near the retirement thickness.</abstract><cop>Reston</cop><pub>American Society of Civil Engineers</pub><doi>10.1061/JPSEA2.PSENG-1515</doi></addata></record>
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subjects Cost analysis
Decision making
Fluids
Hydrocarbons
Inspection
Iterative algorithms
Maintenance
Maintenance costs
Markov analysis
Markov processes
Pipes
Piping systems
Refineries
Technical Papers
title Optimal Hydrocarbon Piping Replacement Decisions Using the Markov Decision Process
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