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Review

Enhancing Turnaround Maintenance in Process Plants through On-Stream Phased Array Corrosion Mapping: A Review

by
Jan Lean Tai
1,
Mohamed Thariq Hameed Sultan
2,3,*,
Andrzej Łukaszewicz
4,*,
Farah Syazwani Shahar
2,
Zbigniew Oksiuta
5 and
Renga Rao Krishnamoorthy
6,7
1
Department of Aerospace Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia
2
Laboratory of Biocomposite Technology, Institute of Tropical Forest and Forest Product (INTROP), Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia
3
Aerospace Malaysia Innovation Centre (944751-A), Prime Minister’s Department, MIGHT Partnership Hub, Jalan Impact, Cyberjaya 63600, Selangor, Malaysia
4
Institute of Mechanical Engineering, Faculty of Mechanical Engineering, Bialystok University of Technology, 45C Wiejska St., 15-351 Bialystok, Poland
5
Institute of Biomedical Engineering, Faculty of Mechanical Engineering, Bialystok University of Technology, 45C Wiejska St., 15-351 Bialystok, Poland
6
Smart Manufacturing Research Institute (SMRI), Universiti Teknologi MARA (UiTM), Shah Alam 40450, Selangor, Malaysia
7
School of Civil Engineering, College of Engineering, Universiti Teknologi MARA (UiTM), Shah Alam 40450, Selangor, Malaysia
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2024, 14(15), 6707; https://doi.org/10.3390/app14156707
Submission received: 7 June 2024 / Revised: 23 July 2024 / Accepted: 29 July 2024 / Published: 1 August 2024
Browse Figures
Figure 1
<p>Visualization of interaction between RCM and maintenance strategies.</p> "> Figure 2
<p>The common process flow of PACM.</p> "> Figure 3
<p>Phased array ultrasonic testing data presentation (adapted from [<a href="#B75-applsci-14-06707" class="html-bibr">75</a>]).</p> "> Figure 4
<p>Research objective/aim summaries.</p> ">
Versions Notes

Abstract

:
This review paper aims to understand the current processing plant maintenance systems and further identify on-stream phased array corrosion mapping (PACM) to reduce turnaround maintenance (TAM) activity during plant operations. Reducing the TAM duration and extending the TAM interval are common goals of most researchers. Thus, a detailed review was performed to understand the maintenance systems and the problems faced. Furthermore, a review of the current PACM application and the possibility of applying it during on-stream inspection was also performed. PACM has better detectability for localized corrosion, and the results can be obtained for a range of thicknesses, which is the main advantage of this method. However, applying PACM during on-stream inspections at elevated temperatures presents challenges owing to the limitations of the ultrasonic properties and increased probe contact. Future research is needed to evaluate the effectiveness of PACM on piping systems that can be utilized for inspection during plant operation at elevated temperatures. This will enable the detection of general and localized corrosion in common materials, thereby reducing the TAM duration and extending TAM intervals. Detecting and monitoring corrosion growth without shutdown is critical for ensuring the safety and reliability of the processing plants. This literature review provides a more precise direction for future research to address these challenges and to advance the field of on-stream corrosion monitoring.

1. Introduction

Process plants are one of the topics that many researchers have devoted their time to exploring, with process plants covering a wide range of industries, such as petrochemicals, oil and gas, power plants, and food production. These studies address vital areas, such as the design, fabrication, and material selection of plant equipment, as well as construction procedures, testing requirements, safe operating practices, maintenance strategies, and overall operational efficiency [1,2,3,4,5].
This review explores the potential application of phased array corrosion mapping (PACM) during plant operations to restrain the total turnaround maintenance (TAM) duration. The goal of redistributing the inspection workload during TAM and ensuring the availability of spare parts prior to scheduled maintenance is to reduce downtime. This review was structured into two categories to provide a holistic perspective. The first category elucidates the diverse traditional maintenance strategies prevalent in process plants, thereby enhancing the understanding of maintenance terminology. This section also encapsulates the challenges researchers face in TAM and introduces diverse methodologies for enhancing TAM systems. Techniques, such as assessment tools for optimization [6,7], questionnaire surveys [8,9], software aid development, and Risk-based Inspection methods [10,11], have emerged as effective approaches in this context.
The second focus area of the review deals with PACM, which is currently applied in plant inspection and maintenance. It also addressed the advantages and limitations of the proposed method.
An important insight from the reviewed literature is that researchers mainly share the common goal of minimizing TAM duration and extending TAM intervals, thus reducing TAM costs. A common solution that has attracted considerable attention is strategically shifting the TAM activity to on-stream inspections.
The PACM is gaining prominence because it can swiftly detect localized corrosion and cover larger scan areas in less time. However, it is challenging to apply this technique during on-stream inspections where elevated temperatures often exceed the recommended ultrasonic testing (UT) temperature limit of 52 °C [12]. Yet, successful articles related to PACM applications exist and have been successfully applied in various industries, such as oil and gas, power generation, and marine, to detect and monitor corrosion growth without shutdown. For example, in the oil and gas industry, PACM has been used to inspect pipelines and storage tanks, enabling operators to identify and address corrosion issues before they lead to failures or leaks [13,14,15]. Most of these applications have been conducted at ambient temperatures. However, there are some exceptions, such as Turcu et al. [16], who introduced the use of a dual linear probe to conduct PACM experiments at temperatures of up to 150 °C.
This review underlines the growing inclination toward integrating on-stream inspections with normal plant operations to curtail the overall TAM duration. The efficacy of PACM in swiftly detecting localized corrosion reinforces its potential and underscores the need for further investigation.
Future research aims to evaluate phased array corrosion mapping on piping typically joined by multi-bolt systems [17,18,19] that can perform inspection during the plant’s operation (on-stream) up to 400 °C to effectively detect general and localized corrosion in the most common materials. Furthermore, it reduces TAM duration and extends TAM interval.

2. The Process Plant Inspection and Maintenance System

The world relies heavily on operational processing plants, encompassing vital sectors, such as petrochemicals and refineries, which provide essential petroleum derivatives for modern society. These plants form the backbone of many economies and serve as strategic reserves for certain nations, underlining their significance [20].
Optimizing efficiency, augmenting output, and mitigating power disruptions in refineries have been focal points of discussion among researchers. Maintenance is a critical factor for ensuring the seamless functionality of these plants. Various maintenance strategies can be deployed within petrochemical plants and refineries to improve equipment performance. These strategies encompass diverse methodologies, including preventive, predictive, condition-based, and proactive maintenance. Each strategy presents a distinct approach to equipment upkeep, encompassing regular maintenance routines, condition monitoring, and targeted interventions. Table 1 shows the interrelationships between the different maintenance strategies applied in the TAM. The selection of a maintenance strategy depends on the specific demands and prerequisites of a given plant or refinery. A judicious evaluation of the array of maintenance options is pivotal for determining the most optimal and resource-efficient path for maintaining equipment in its prime operational state.

2.1. Classification of Current Plant Maintenance Strategies

The criticality of operational efficiency and reliability cannot be overstated in the realm of processing plants encompassing domains such as petrochemicals and refineries. Maintaining the functionality of these plants is a pivotal challenge due to their role as economic pillars and strategic reservoirs. Researchers have delved into diverse facets of this landscape, exploring maintenance strategies, equipment optimization, and the intricate dynamics of operations [20].
A pivotal aspect of plant management is the maintenance strategy that is employed. Aghaee et al. [21] conducted a comprehensive study using fuzzy decision-making trial evaluation and laboratory (DEMATEL), dissecting various maintenance strategies. They identified corrective maintenance (CM), synonymous with breakdown maintenance, as an approach to address equipment failures. In contrast, preventive maintenance (PM) has emerged as a cornerstone strategy aimed at forestalling equipment malfunctions and forming a cornerstone of enhancement in maintenance endeavors [22,23]. The total productive maintenance (TPM) strategy has garnered considerable attention within this spectrum. It extends beyond equipment to encompass assets and personnel, thus portraying the evolution of PM [24]. TPM’s core thrust lies in waste reduction, in alignment with the insights of Bataineh et al. and Chaabane et al. [25,26], Sahoo [27] explored the fusion of TPM and total quality management (TQM), and Kundu et al. [28] highlighted TPM’s role in achieving world-class manufacturing objectives.
In the realm of strategy frameworks, reliability-centered maintenance (RCM) is a tried-and-true plan analysis approach. It establishes equipment requirements aligned with design and inherent reliability factors [29]. Risk-based maintenance (RBM) and condition-based maintenance (CBM) strategies are nested within the RCM [30]. CBM based on real-time condition monitoring has emerged as a linchpin approach for averting breakdown and preserving equipment integrity [31,32].
By seamlessly interweaving with various maintenance strategies, RCM demonstrates robust compatibility. Table 2 elucidates the nuanced interactions of the RCM with different maintenance strategies, whereas Figure 1 visually articulates the relationships between this methodology and renowned maintenance strategies.
Predictive maintenance (PdM) is an essential component of plant operation. This is an effective preemptive measure in the manufacturing industry, where operational failures can lead to market exit [33]. Aghaee et al. [21] underscore its strategic value, positioned ahead of RCM, CBM, TPM, PM, and CM. This sentiment resonates with Tiddens et al. [34], who posited PdM as a preemptive replacement operation to thwart unforeseen breakdowns.
PdM, a form of CBM, tackles aging equipment before failure using sophisticated methods such as vibration analysis [35]. This approach curbs failure probabilities and minimizes inspection and repair costs by eliminating redundant maintenance. Although PM, PdM, and CM present opportunities for many processes, the complex nature of TAM occasionally necessitates comprehensive shutdowns [36].
Among these strategies, effective communication is crucial for orchestrating successful plant turnarounds. Ensuring clear and consistent information exchange across stakeholders (management, maintenance teams, suppliers, and contractors) guards against misunderstandings, enables timely equipment delivery, and ensures coordinated execution [37]. Table 3 lists the advantages and limitations of the maintenance strategies.
Considering the realm of condition monitoring, Dhandha [38] underscored the value of non-destructive testing (NDT) methods in assessing petrochemical equipment, accounting for damage type, defect size, location, and inspection method sensitivity. Meanwhile, Laza [39] drew attention to the underemphasized significance of piping systems. These systems, which are often overlooked, can yield catastrophic disruptions if left unchecked, thus necessitating a nuanced understanding of their complexities, inspection frequencies, and vulnerabilities.
Essentially, the intricate landscape of processing plants has been unraveled through these studies, spanning maintenance strategies, condition monitoring, and the imperative role of communication. As researchers have shed light on these domains, a holistic perspective emerges, guiding the path toward optimized, reliable, and efficient plant operations.

2.2. Improve TAM Efficiency by Conducting a Survey Questionnaire

TAM is a pivotal endeavor in the operation of diverse industries involving the convergence of resources, skills, and planning for repairs, inspections, and equipment enhancements [6]. Al-Turki et al. [20] explored over 80 pieces of literature comprehensively, aimed at unraveling trends and optimizing TAM strategies to curtail duration, human resources, and costs. Their study mapped out the four stages of TAM—initiation, preparation, execution, and termination–illustrating its complex and resource-intensive nature. In petrochemical plants, an eight-week process involving up to 4000 workers during peak periods is typical, whereas refinery TAM spans 40 days every four years, requiring approximately 300,000 man-hours. Efficient planning, scheduling, and adept coordination have emerged as linchpin factors for this intricate landscape [20].
Scheduled shutdowns are paramount for mitigating the risks of unplanned plant mishaps. The ability to accomplish turnaround activities within predetermined timelines is pivotal because of the potential for significant financial ramifications in cases of delays. Hlophe et al. [40] probed the role of risk management in ensuring the success of plant turnarounds using a questionnaire-based approach. The study’s insights underline the direct link between effective shutdown risk management and the avoidance of considerable cost and time overruns. Project risk management is a widely acknowledged success factor for orchestrating turnaround shutdowns.
Delving into core challenges and crucial maintenance activities, Iheukwumere-Esotu et al. [41] conducted interviews and frequency analyses across diverse industries. Their findings illuminated the recurrent nature of labor-intensive and capital-demanding shutdowns, which often lead to delayed cost overruns and unfulfilled objectives. The demographic mappings of inspections, overhauls, replacements, and repairs complemented these observations.
Within petrochemical plants, the significance of turnaround activities is undeniable and is driven by the need for improvement, modification, repair, and maintenance. However, a sobering statistic reveals that a staggering 80% of turnaround endeavors fail to meet performance indicators encompassing time, cost, safety, quality, and environment. Akbar et al. [42] elucidate the intricacies of these situations, wherein a dense workforce environment can cause conflicts, accidents, confusion, and errors. Their study underscores the pivotal role of coordination and robust human resource management in achieving performance benchmarks.
Musah et al. [8] conducted a questionnaire-based exploration of TAM employees to examine their cultural values and their impact on ethics and conflict management. The findings revealed that successful conflict resolution is often tied to job compatibility and overshadowing of individual temperament. Similarly, Waratimi et al. [9] used survey-based journeys to assess TAM performance. Their study highlighted that many TAM activities frequently miss deadlines, with inappropriate contractor selection emerging as a leading cause of overruns.
Wongthong et al. [43] further emphasize the importance of astute contractor selection. This model, which involves factors such as cost, time, quality, flexibility, reliability, and human resources, underpins the need for reliability in TAM. Similarly, Mazumder et al. [44] surveyed company employees to determine the extended inspection and testing times during TAM activities. Their efforts aimed to identify areas for optimization to enhance efficiency and curb costs.
In the dynamic realm of turnaround maintenance, these studies collectively paint a vivid picture of the challenges, successes, and strategies, providing insights to guide efficient and effective plant operations.

2.3. Improve TAM Efficiency with Assessment Tools

In the realm of TAM, continuous efforts are underway to enhance efficiency, streamline processes, and optimize outcomes. Wenchi et al. [45] embarked on a case study using a value stream map (VSM) to dissect TAM processes, seeking to unveil inefficiencies and root causes. Their exploration revealed inherent uncertainty in the turnaround process of the oil and gas industry, which leads to workflow fluctuations and waste accumulation. Nonetheless, this case study underscores the potential of VSM as a tool to pinpoint waste and enhance value, thus promoting TAM efficiency.
Unveiling the core factors influencing TAM performance, the Analytic Hierarchy Process (AHP) method revealed the significance of labor skills, communication proficiency, supervision gaps, transportation idling, and safety concerns [6]. A unique challenge in the TAM realm is the influx of new workers, who often lack comprehensive training or task familiarity, a situation exacerbated by the transient nature of TAM teams. This scenario often triggers simultaneous work within confined areas, exposing communication gaps, idling workers, and spare part shortages, culminating in budget overrun.
Fabić et al. [46] introduced a logistic regression approach to dissect intricate factors affecting success. This analytical method dissected the roles of leadership, teamwork, policy, safety, and strategy, highlighting their role in orchestrating efficient and productive turnaround processes. This study provides a profound understanding of the management elements that contribute to improved turnaround outcomes, fostering a blueprint for refining future turnaround management strategies.
The integration of technology into maintenance practices was the focus of Yin et al. [4]. Their innovative methodology gathered global data on maintenance activities, seeking to identify opportunities for technological solutions to streamline processes and reduce maintenance time. Their approach aimed to foster continuous improvement by implementing technology-based solutions, which showed the potential for automating tasks and increasing maintenance efficiency.
Turning the spotlight to plant shutdown turnaround, Muralidharan et al. [7] introduced an activity-analysis approach tailored to site conditions. Their analysis revealed that turnaround personnel expended substantial waiting time beyond direct work, whether for permits, instructions, materials, or QA/QC inspections. This discovery prompted the adjustment of work cycles to enhance direct work percentage and improve overall productivity.
Similarly, Krishnankutty et al. [1] embraced a decision matrix to expand the scope of improvement for contractors and plant owners during the TAM. Their study revealed that auxiliary activities, including preparation, transportation, and material handling, absorbed substantial on-site work hours. The pursuit of efficiency has led to advancements in quality assurance/quality control (QA/QC) inspection assessment and NDT tracking systems, propelling documentation for real-time automated systems.
These studies reflect a dynamic landscape of TAM optimization efforts, unveiling factors, methodologies, and technologies that are pivotal in enhancing turnaround processes, reducing waste, and ensuring efficient and productive plant operations.

2.4. Improve TAM Efficiency with Software Development

Efforts to refine and optimize TAM processes encompass a range of innovative approaches, each striving to enhance efficiency and minimize disruptions. Shou et al. [47] introduced a cutting-edge approach involving four-dimensional building information modeling, enabling the simulation of TAM activities before execution. This strategy aids in familiarizing all parties involved with the process sequence, particularly scaffold and heavy equipment plans, effectively reducing the safety risks, TAM duration, and costs.
To comprehensively evaluate TAM’s impact on equipment conditions, Khasanah et al. [48] recommended statistical analysis based on prior-year data, specifically examining plant downtime loss and availability in fertilizer production. Al-Turki et al. [49] devised a TAM performance measurement system, emphasizing the need for efficient agreements with external suppliers and contractors to ensure timely delivery of equipment and parts. This intricate process, often outsourced to specialized companies, requires strategic planning to minimize downtime.
A nonlinear numerical model such as the finite element method is widely used [50,51,52]. Altehmazi et al. [53] employed a nonlinear mathematical model to optimize schedules through labor distribution adjustments to address resource underutilization during TAM. This approach targets optimizing the task start and completion times, thereby minimizing the TAM length.
Ostadi et al. [54] introduced a maintenance management system that leveraged interviews and questionnaires with plant stakeholders to ensure a holistic understanding of the equipment life cycles. Chen et al. [55] harnessed an in-service inspection platform to streamline nuclear power plant inspections. The platform’s core functions, database integration, and paperless reporting significantly enhance data tracking and management efficiency.
In response to the complexity of accident cost assessments, Vianello et al. [10] developed inspection manager software based on the RBI methodology. This software aids in selecting response actions for unforeseen hazardous events, considering intricate cost implications.
Pursuing optimal maintenance planning led Tak et al. [56] to create a model by using a mixed-integer nonlinear programming algorithm. This model enhances inspection and replacement decisions for refinery piping, maximizing inspection intervals while ensuring safe operation based on the wall thickness, inspection frequency, and inspection time.
Lee et al. [57] devised an inspection framework and work-aid tool based on API 570 by further optimizing piping inspections. This comprehensive tool assists in calculating the remaining life and corrosion rates and generating inspection reports. Through its data-driven approach, the framework empowers efficient and informed inspection decision-making.
In summary, these diverse approaches collectively represent a dynamic landscape of strategies to enhance TAM efficiency, from simulation and modeling to statistical analysis, software development, and innovative inspection frameworks.

2.5. Improve TAM Efficiency with the Risk-Based Inspection Method

Various methodologies and approaches have been explored to enhance the efficiency and effectiveness of TAM to minimize downtime, reduce costs, and optimize asset management. Gunawan [58] interviewed TAM experts and leveraged risk-based identification from historical data to enhance the TAM interval and reduce its duration. Defective parts found during turnaround execution pose challenges, necessitating the rapid arrangement of human resources, spare parts, and logistics. An RBI and decision-making model were formulated to mitigate the turnaround delays caused by such contingencies. This model aims to strategically shift maintenance activities to routine schedules while addressing high-risk units during shutdowns. The implementation of this model yielded promising results: a substantial reduction in TAM duration from 30 to 21 days, attesting to the efficacy of risk-based strategies in bolstering turnaround efficiency [59].
Wagh [60] highlighted the benefits of RBI for assessing aboveground storage tanks and process piping in petrochemical plants. Unlike traditional remaining life calculations, this approach focuses on the risk, incident consequences, and probability. RBI assessments prioritize high-risk components by optimizing inspection resources, maximizing efficiency, and ensuring resource allocation.
Building upon this risk-based paradigm, researchers have sought to refine the maintenance schedules for plant heat exchangers [36,61]. By evaluating factors such as vibration, fouling, corrosion, and other failure modes, this approach aimed to extend TAM intervals while minimizing the maintenance duration. The advantages of this strategy include reduced downtime, increased plant reliability, and cost savings.
Elwerfalli et al. [11] applied a framework rooted in RBI to optimize the TAM for pressure drums. By balancing the TAM interval against risk exposure, this study aimed to determine an optimal interval that minimizes downtime and maintenance costs without compromising safety.
Elwerfalli et al. [62] proposed a comprehensive methodology to curtail TAM duration in four steps: selecting noncritical equipment for standard maintenance, applying RBI to critical static equipment, utilizing risk-based failure analysis for rotating equipment, and generating probability failure distributions to fine-tune the TAM plan.
Acknowledging the dual nature of plant shutdowns and reducing operational risk while potentially generating new risks, Hameed et al. [63] examined human errors introduced during shutdowns. Using maintenance optimization and RBI, they strived to define optimal shutdown intervals that balanced maintenance needs with the associated risks.
Priyanta et al. [64] conducted a systematic assessment focusing on equipment classification and failure modes. The vast amount of piping equipment used in refining and processing plants poses management challenges, emphasizing the need for structured maintenance strategies. Metal loss due to corrosion is a primary failure mode in carbon steel and stainless steel piping systems, although stainless steel has higher corrosion resistance [65], prompting the application of RBI methods to assess failure consequences and probabilities, as recommended by the American Petroleum Institute (API).
These studies reflect a concerted endeavor to revolutionize TAM practices, leveraging risk-based insights to fine-tune intervals, durations, and strategies, fostering enhanced efficiency, resource allocation, and asset management.

2.6. Potential Non-Destructive Testing Techniques for On-Stream Inspection

The Ultrasonic Thickness Gauging (UTG) technique serves as a primary method for on-stream inspections in plants and effectively detects corrosion [66]. Its notable advantage lies in its ability to operate at elevated temperatures, with some equipment functioning even at 500 °C [12]. However, UTG is not without limitations, such as reduced productivity in spot thickness measurements and limited sensitivity to localized corrosion. Addressing these limitations is crucial for further enhancing UTG’s effectiveness in corrosion detection during plant inspections [67].
Phased Array Ultrasonic Testing (PAUT) has emerged as a compelling alternative, notably surpassing conventional Ultrasonic Testing (UT) and Radiography Testing (RT) techniques, both of which fall under volumetric inspection methods [68,69].
Figure 2 illustrates the common process of the PACM. The first step in the process is to calibrate the PACM equipment based on the selected material and thickness. The variation in ultrasonic velocity in different materials is a key factor in obtaining accurate results, so it is important to calibrate the equipment accordingly.
During on-site data collection, it is crucial to ensure that the test surface is smooth and free of obstructions to improve the accuracy of the results. Sufficient couplant should also be applied to the surface to improve the acoustic contact between the probe and the surface. This can help to enhance the signal-to-noise ratio and improve the overall quality of the data collected [70].
Once the data have been successfully collected, they can be transferred to a computer for further analysis. This may involve the use of specialized software to identify defect dimensions, depth, and location. The results can then be used to assess the severity of corrosion and determine appropriate maintenance or repair actions.
The UT is an NDT technique that uses ultrasonic waves to measure the thickness of a material. They are widely used for inspecting welds and detecting corrosion in thin-walled structures. However, the UT has limitations in detecting localized corrosion and is less suitable for high-temperature environments.
RT is a NDT technique that uses radiation to inspect materials for defects. They are widely used for inspecting welds and detecting corrosion in thick-walled structures [71]. However, RT has some limitations. Radiation exposure can pose a risk to human health, which may require other trade workers in the area to stop working, thereby causing inconvenience. Additionally, RT requires that both surfaces be accessible for inspection, which may not always be the case in industrial settings.
The PAUT circumvents radiation hazards, making it safe for simultaneous on-site use with other trades. This technique offers rapid result processing, three-dimensional defect information, and permanent records, similar to RT [72]. Its superiority over UT is evident in its multiple-element probe, which permits flexible beam steering, rendering superior scan coverage and speed [69,73,74]. The proficiency of PAUT is reflected in its ability to provide three-dimensional defect information through S-scan, C-scan, D-scan, and A-scan data, as shown in Figure 3 [75].
However, it is important to compare PACM with other NDT techniques in order to understand its strengths and limitations in a broader context. One such technique is eddy current testing (ET), which is commonly used to detect surface and subsurface defects in conductive materials [76,77]. While ET can be sensitive to small defects, it is not as effective at detecting localized corrosion as other techniques such as PAUT or acoustic emission testing (AET). ET is less suitable for high-temperature environments and requires the material to be electrically conductive, which may not be the case for all the materials used in processing plants.
Another technique that is often used for corrosion detection is AET, which is based on the principle of the generation and propagation of elastic waves in materials [78,79]. AET can provide information about the location and size of defects, making it suitable for detecting localized corrosion. However, AET is less suitable for high-temperature environments and requires materials that can generate and propagate elastic waves, which may not be the case for all materials used in processing plants.
Table 4 provides a comparison of different NDT techniques, including their advantages, limitations, and suitability for process-plant applications and high-temperature surfaces. This table allows the reader to easily compare and contrast the different techniques.
Comparative studies have affirmed the advantages of PAUT over traditional UT. Tangadi et al. [14] found that PAUT’s scanning speed covered a 30 mm width in a single scan. Similarly, Jamil et al. [13] reported that corrosion mapping using PAUT offers good detectability and comprehensive results through A-, B-, and C-scan displays. These studies underscore the superiority of PACM in terms of efficiency and accuracy for corrosion detection and analysis.
Further comparisons by researchers such as Turcotte et al. [80] highlighted PAUT’s capacity to display a range of thicknesses simultaneously, distinguishing it from UT, which provides thickness information simultaneously. In pursuit of increased productivity, Mohan et al. [15] adopted a HydroFORM scanner, which yielded favorable results compared to UTG. However, while several studies have lauded UTG’s high-temperature capabilities of UTG and explored PAUT for enhanced scanning speed and result display, most experiments have been conducted at ambient temperatures. Turcu et al. [16] extended the PAUT corrosion mapping experiment to 150 °C by using a Dual Linear Probe. This effort revealed challenges, such as couplant selection, scanner choice, velocity alteration due to temperature changes, and the risk of probe damage from prolonged contact with hot surfaces.
The field of corrosion detection and analysis benefits from the high-temperature functionality of the UTG technique, and the advancements introduced by PAUT overcome the limitations of the traditional UT methods. The capacity of the latter for rapid scanning, three-dimensional defect representation, and enhanced coverage makes it a promising solution for improved corrosion assessment, even in challenging environments.

2.7. Literature Review Summary

The literature review explores various strategies and techniques employed in TAM for petrochemical plants and refineries. This review covers a range of topics, including maintenance strategies, risk management, communication, inspection techniques, and optimization methods. This review provides a clear narrative that guides the reader through the complexities of TAM in these industries.
The review begins by highlighting the significance of the TAM and its impact on plant operations, emphasizing the need for efficient maintenance strategies to ensure safe and uninterrupted production. The review then delves into different maintenance strategies, such as PM, PdM, TPM, and RCM. Each strategy is discussed in detail, focusing on its unique equipment maintenance approach and its interactive nature with other strategies.
The interaction of these strategies is closely related to RCM, and has been discussed in detail, as well as visually illustrated in Figure 1 and Table 2. Each maintenance strategy has advantages and limitations. For instance, the advantage of the RCM is that it employs data-driven approaches to optimize maintenance strategies, providing systematic and accurate data for engineers to make maintenance decisions. However, this also requires substantial investment in data collection and analysis.
In the course of conducting a literature review, various methods were identified to enhance and optimize TAM activities, which can be categorized into four main themes. These include assessment tools for optimization, questionnaire surveys, software aid development, and Risk-based Inspection methods. A critical analysis of these studies reveals several common themes and patterns. For instance, many studies have found that risk-based inspection methods can be effective in identifying high-risk components and optimizing maintenance schedules. However, these methods can be time-consuming and require significant data collection and analysis. In contrast, software development approaches can streamline TAM processes and improve efficiency but may be less effective in identifying high-risk components. Table 5 presents a comprehensive overview of the techniques utilized by other researchers to investigate ways to improve process equipment maintenance systems and the existing research gaps.
Subsequently, the focus of the review shifted towards examining the inspection methods employed in process plants. Here, the utilization of UTG and PAUT to identify corrosion is presented. This highlights the benefits and limitations of each technique and provides an in-depth analysis comparing conventional UT and advanced PAUT. It is evident that a variety of NDT techniques are effective during plant shutdowns at ambient temperature. However, some techniques, such as RT, require both side access and pose radiation hazards. ET can assess specific materials, whereas AET requires the generation and propagation of elastic waves. UT and UTG face challenges in detecting localized corrosion and require extended inspection times for larger areas of corrosion. On the other hand, PACM offers several advantages and can detect localized corrosion effectively. Nevertheless, there are still challenges in determining the future research directions.
Figure 4 presents a visual representation of the distribution of research objectives among the studies reviewed in this literature review. The chart shows the percentage of studies that focused on various research objectives, including reducing the duration of the TAM, extending examination intervals, defining TAM strategies, determining optimal turnaround intervals, mitigating production loss, evaluating TA performance, and reducing TA costs.
The chart reveals that the dominant themes among the studies were centered on reducing the TAM duration and extending examination intervals. Approximately 28% of the studies aimed to reduce the duration of TAM, emphasizing the importance of minimizing operational downtime and improving efficiency. For example, Al-Turki et al. [20] explored over 80 pieces of literature to unravel trends and optimize TAM strategies with the goal of curtailing duration, human resources, and costs. Similarly, Hlophe et al. [40] probed the role of risk management in ensuring the success of plant turnarounds using a questionnaire-based approach, highlighting the direct link between effective shutdown risk management and avoidance of considerable cost and time overruns.
Another 11% of the studies focused on extending examination intervals, which can help identify potential issues earlier and reduce the likelihood of unplanned downtime. For instance, Wagh [60] highlighted the benefits of RBI for assessing aboveground storage tanks and process piping in petrochemical plants, focusing on the risks, incident consequences, and probability. This approach optimizes inspection resources, maximizes efficiency, and ensures resource allocation by prioritizing high-risk components.
The remaining research objectives represented in Figure 4 include defining TAM strategies, determining optimal turnaround intervals (9%), mitigating production loss, evaluating TA performance (7%), and reducing TA costs (3%). For example, Krishnankutty et al. [1] expanded the scope of improvement for contractors and plant owners during TAM by using a decision matrix. Their study revealed that auxiliary activities, including preparation, transportation, and material handling, absorbed substantial on-site work hours. By optimizing these activities, this study aimed to reduce TAM duration and costs.

3. Conclusions

Numerous researchers have dedicated their efforts to minimizing the TAM duration and extending TAM intervals. Despite some studies proposing the integration of certain TAM activities into on-stream maintenance, none of these initiatives have explored corrosion assessment during plant operation.
Currently, on-stream inspection in high-temperature environments predominantly relies on UTG, with capabilities reaching material surface temperatures up to 500 °C [12]. However, UTG’s drawback lies in its time-intensive grid-by-grid measurement approach and the limitation of detecting localized corrosion. PAUT has garnered traction in various industries, expanding from weld inspection to corrosion mapping, and even encompassing diverse materials. While multiple studies have presented PACM, most have been conducted during equipment cool-down and at ambient temperatures. Notably, Turcu et al. [16] utilized a Dual Linear Probe to experiment with corrosion mapping on test samples up to 150 °C, although the accuracy of this technique has not been explored extensively.
The reviewed literature proposed a pivotal shift in TAM practices by incorporating on-stream inspections into regular plant operations. This would entail assessing piping systems and preparing for TAM without necessitating a complete plant shutdown. PACM has emerged as a promising technique for on-stream inspections because it can detect localized corrosion within a notably shorter inspection timeframe than other methods. Consequently, this technique warrants further exploration as a research subject. The potential benefits of integrating on-stream inspection and PACM include reduced TAM duration and extended intervals between TAM, thereby yielding diminished production downtime, heightened plant reliability and availability, and diminished maintenance expenses.

4. Future Work

The integration of on-stream inspections and PACM into regular plant operations has been suggested as a potential solution to minimize TAM duration and extend TAM intervals. To better understand the potential benefits and limitations of this approach, future research should focus on several key areas.
First, the accuracy and reliability of PACM for on-stream inspections in high-temperature environments must be investigated. This can be done by conducting experiments on test samples up to 500 °C, primarily focusing on assessing the accuracy of the corrosion detection technique.
Second, it is essential to explore the feasibility and potential impact of integrating on-stream inspections and PACM into existing maintenance strategies. This includes developing a framework for incorporating these techniques and conducting case studies to evaluate their effects on TAM duration, maintenance expenses, and plant reliability and availability. An important aspect of this research is the incorporation of Life Cycle Cost (LCC) analysis, which depends on various factors such as plant size, region, and on-stream inspection coverage. To conduct a comprehensive LCC analysis, future research should collect data on cost components, including initial investment in PACM equipment and software, operational costs (labor, consumables, energy consumption), maintenance costs, and training costs for personnel. These cost components should be compared with the savings from reduced plant downtime and improved plant reliability. It is crucial to note that the LCC analysis requires extensive data collection and may vary depending on the specific plant and context. The variation in plant sizes, regions, and on-stream inspection coverage necessitates a case-by-case approach to LCC analysis. Therefore, future research should aim to develop a methodology for conducting LCC analyses that can be adapted to different plants and contexts, enabling a more accurate assessment of the cost-effectiveness of integrating on-stream inspections and PACM into regular plant operations.
Lastly, future research should investigate the potential of automating on-stream inspections and PACM to enhance efficiency and reduce maintenance costs. This can be achieved by developing autonomous inspection systems capable of navigating piping systems and equipment and integrating machine learning algorithms to analyze inspection data and provide maintenance recommendations. By focusing on these key areas, future research can contribute to the advancement and optimization of integrating on-stream inspections and PACM into regular plant operations.

Author Contributions

Conceptualization, data curation, formal analysis, investigation, methodology, visualization, and writing of the first draft were performed by J.L.T. Supervision and funding acquisition were performed by M.T.H.S., A.Ł. and Z.O. Project administration was performed by F.S.S., M.T.H.S., Z.O., A.Ł. and R.R.K. Reviewed and edited the previous versions of the manuscript M.T.H.S., A.Ł., Z.O. and R.R.K. All authors have read and agreed to the published version of the manuscript.

Funding

The authors would like to thank Universiti Putra Malaysia for the financial support through Geran Inisiatif Putra Siswazah (GP-IPS) with grant number 9739200. This research was partially financed by the Ministry of Science and Higher Education of Poland with allocation to the Faculty of Mechanical Engineering, Bialystok University of Technology, for the WZ/WM-IIM/5/2023 and WZ/WM-IIB/4/2023 academic projects in the mechanical and biomedical engineering discipline.

Data Availability Statement

No new data were created in this study.

Acknowledgments

The authors would also like to express their gratitude to the Department of Aerospace Engineering, Faculty of Engineering, Universiti Putra Malaysia, and the Laboratory of Biocomposite Technology, Institute of Tropical Forestry and Forest Product (INTROP), Universiti Putra Malaysia (HICOE) for their close collaboration in this study.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Krishnankutty, P.; Hwang, B.G.; Caldas, C.H.; Muralidharan, S.; de Oliveira, D.P. Assessing the Implementation of Best Productivity Practices in Maintenance Activities, Shutdowns, and Turnarounds of Petrochemical Plants. Sustainability 2019, 11, 1239. [Google Scholar] [CrossRef]
  2. Nageswaran, C. Maintaining the Integrity of Process Plant Susceptible to High Temperature Hydrogen Attack. Part 1: Analysis of Non-Destructive Testing Techniques; TWI Ltd.: Cambridge, UK, 2018. [Google Scholar]
  3. Ghazali, Z.; Lim, M.R.T.; Jamak, A.B.S.A. Maintenance Performance Improvement Analysis Using Fuzzy Delphi Method a Case of an International Lube Blending Plant in Malaysia. J. Qual. Maint. Eng. 2019, 25, 162–180. [Google Scholar] [CrossRef]
  4. Yin, Z.; Caldas, C.; De Oliveira, D.; Hwang, B.-G.; Shan, M. Mechanization Level Assessment and Technology Identification for Productivity Improvement in Petrochemical Facility Maintenance. J. Qual. Maint. Eng. 2019, 27, 1–20. [Google Scholar] [CrossRef]
  5. Čepin, M. Evaluation of the Importance Factors of the Power Plants within the Power System Reliability Evaluation. Eksploat. Niezawodn. Maint. Reliab. 2019, 21, 631–637. [Google Scholar] [CrossRef]
  6. Al-Marri, A.N.; Nechi, S.; Ben-Ayed, O.; Charfeddine, L. Analysis of the Performance of TAM in Oil and Gas Industry: Factors and Solutions for Improvement. Energy Rep. 2020, 6, 2276–2287. [Google Scholar] [CrossRef]
  7. Muralidharan, S.; Krishnankutty, P.; Hwang, B.G.; Caldas, C.; Mulva, S. Enhancing Labour Productivity in Petrochemical Construction and Maintenance Projects. In Proceedings of the 26th Annual Conference of the International Group for Lean Construction, Chennai, India, 18–22 July 2018; Volume 2, pp. 829–839. [Google Scholar] [CrossRef]
  8. Musah, A.A.; Ghazali, Z.; Nizam Shahrul Isha, A. Turnaround Maintenance Workers Cultural Values and Conflict Management Style Preference: Moderating Role of Temperament. SHS Web Conf. 2018, 56, 02004. [Google Scholar] [CrossRef]
  9. Waratimi, E.; Wordu, A.A.; Nkoi, B. Statistical Model to Evaluate Turn-Around-Maintenance of Port Harcourt Refinery in Nigeria. Am. J. Eng. Res. 2018, 7, 166–178. [Google Scholar]
  10. Vianello, C.; Milazzo, M.F.; Maschio, G. The Management of Industrial Safety in Chemical and Petrochemical Industry by Comparing Costs and Benefits. Chem. Eng. Trans. 2018, 67, 379–384. [Google Scholar] [CrossRef]
  11. Elwerfalli, A.; Alsadaie, S.; Mujtaba, I.M. Estimation of Shutdown Schedule to Remove Fouling Layers of Heat Exchangers Using Risk-Based Inspection (RBI). Processes 2021, 9, 2177. [Google Scholar] [CrossRef]
  12. Jory, C. Tips for Internal Corrosion Using the Echo to Echo Technique with Compression. NDT Tech. 2019, 18, 8–11. [Google Scholar]
  13. Jamil, J.; Yahya, S.Y.S. Corrosion Assessment Using Advanced Ultrasonic Measurement Technique. IOP Conf. Ser. Mater. Sci. Eng. 2019, 554, 012004. [Google Scholar] [CrossRef]
  14. Tangadi, S.; Telidevara, N.K.S.P.; Maddi, H.K. PAUT as Tool for Corrosion Damage Monitoring. In Proceedings of the Indian National Seminar & Exhibition on Non-Destructive Evaluation NDE 2015, Hyderabad, India, 26–28 November 2015. [Google Scholar]
  15. Mohan, B.C.; Jeyasekhar, M.C.; Duhan, K.; Land, I.; Belait, K.; Manager, C. Oil and Gas Assets Condition Monitoring by High Sensitive PAUT Hydroform Corrosion Monitoring Technique for Integrity Assessment. In Proceedings of the NDE 2019—Conference & Exhibition, Bengaluru, India, 5–7 December 2019. [Google Scholar]
  16. Turcu, F.; Jedamski, T.; Treppmann, D.R.D. In-Service Corrosion Mapping—Challenges for the Chemical Industry. In Proceedings of the 12th European Conference on Non-Destructive Testing (ECNDT 2018), Gothenburg, Sweden, 11–15 June 2018; pp. 1–8. [Google Scholar]
  17. Grzejda, R. Modelling Nonlinear Preloaded Multi-Bolted Systems on the Operational State. Eng. Trans. 2016, 64, 525–531. [Google Scholar]
  18. Grzejda, R. Study of the Distribution of Bolt Forces in a Multi-Bolted System under Operational Normal Loads. AIP Conf. Proc. 2019, 2078, 020011. [Google Scholar] [CrossRef]
  19. Grzejda, R. Impact of Nonlinearity of the Contact Layer between Elements Joined in a Multi-Bolted System on Its Preload. Mech. Mech. Eng. 2017, 21, 541–548. [Google Scholar] [CrossRef]
  20. Al-turki, U.; Duffuaa, S.; Bendaya, M. Trends in Turnaround Maintenance Planning: Literature Review. J. Qual. Maint. Eng. 2019, 25, 253–271. [Google Scholar] [CrossRef]
  21. Aghaee, A.; Aghaee, M. A Novel Fuzzy Hybrid Multi-Criteria Decision-Making Approach for Evaluating Maintenance Strategies in Petrochemical Industry. J. Qual. Maint. Eng. 2020, 27, 351–365. [Google Scholar] [CrossRef]
  22. Alsyouf, I.; Hamdan, S.; Shamsuzzaman, M.; Haridy, S.; Alawaysheh, I. On Preventive Maintenance Policies: A Selection Framework. J. Qual. Maint. Eng. 2020, 27, 225–252. [Google Scholar] [CrossRef]
  23. Jiang, A.; Qingxia, L.; Jinyi, Y.; Leqing, H.; Haining, W. An Operating Environment-Based Preventive Maintenance Decision Model Maintenance. J. Qual. Maint. Eng. 2019, 26, 592–610. [Google Scholar] [CrossRef]
  24. Oleghe, O.; Salonitis, K. The Application of a Hybrid Simulation Modelling Framework as a Decision-Making Tool for TPM Improvement. J. Qual. Maint. Eng. 2019, 25, 476–498. [Google Scholar] [CrossRef]
  25. Bataineh, O.; Al-Hawari, T.; Alshraideh, H.; Dalalah, D. A Sequential TPM-Based Scheme for Improving Production Effectiveness Presented with a Case Study. J. Qual. Maint. Eng. 2019, 25, 144–161. [Google Scholar] [CrossRef]
  26. Chaabane, K.; Schutz, J.; Dellagi, S.; Trabelsi, W. Analytical Evaluation of TPM Performance Based on an Economic Criterion. J. Qual. Maint. Eng. 2020, 27, 413–429. [Google Scholar] [CrossRef]
  27. Sahoo, S. Assessment of TPM and TQM Practices on Business Performance: A Multi-Sector Analysis. J. Qual. Maint. Eng. 2019, 25, 412–434. [Google Scholar] [CrossRef]
  28. Kundu, K.; Cifone, F.; Costa, F.; Portioli-Staudacher, A. An Evaluation of Preventive Maintenance Framework in an Italian Manufacturing Company Company. J. Qual. Maint. Eng. 2020, 28, 37–57. [Google Scholar] [CrossRef]
  29. Salonen, A.; Gopalakrishnan, M. Practices of Preventive Maintenance Planning in Discrete Manufacturing Industry. J. Qual. Maint. Eng. 2019, 27, 331–350. [Google Scholar] [CrossRef]
  30. Uchida, S.; Chimi, Y.; Kasahara, S.; Hanawa, S.; Okada, H.; Naitoh, M.; Kojima, M.; Kikura, H.; Lister, D.H. Improvement of Plant Reliability Based on Combining of Prediction and Inspection of Crack Growth Due to Intergranular Stress Corrosion Cracking. Nucl. Eng. Des. 2019, 341, 112–123. [Google Scholar] [CrossRef]
  31. Wang, L.; Lu, Z.; Han, X.; Lu, Z. Joint Optimal Production Planning and Proactive Maintenance Policy for a System Subject to Degradation. J. Qual. Maint. Eng. 2019, 25, 236–252. [Google Scholar] [CrossRef]
  32. Acernese, A.; Del Vecchio, C.; Tipaldi, M.; Battilani, N.; Glielmo, L. Condition-Based Maintenance: An Industrial Application on Rotary Machines Rotary Machines. J. Qual. Maint. Eng. 2020, 27, 565–585. [Google Scholar] [CrossRef]
  33. Khalid, W.; Albrechtsen, S.H.; Sigsgaard, K.V.; Mortensen, N.H.; Hansen, K.B.; Soleymani, I. Predicting Maintenance Work Hours in Maintenance Planning Work Hours. J. Qual. Maint. Eng. 2019, 27, 366–384. [Google Scholar] [CrossRef]
  34. Tiddens, W.; Braaksma, J. Exploring Predictive Maintenance Applications in Industry Applications. J. Qual. Maint. Eng. 2020, 28, 68–85. [Google Scholar] [CrossRef]
  35. Braglia, M.; Castellano, D.; Gallo, M.; Braglia, M. A Novel Operational Approach to Equipment Maintenance: TPM and RCM Jointly at Work. J. Qual. Maint. Eng. 2019, 25, 612–634. [Google Scholar] [CrossRef]
  36. Elwerfalli, A.; Al-Maqespi, S. Selection of Appropriate Maintenance Strategy for Oil and Gas Equipment Using Analytical Hierarchy Process (AHP). In Proceedings of the 2nd South American International Conference on Industrial Engineering and Operations Management, Sao Paulo, Brazil, 5–8 April 2021; pp. 3578–3584. [Google Scholar]
  37. Mhlanga, M.Z.; Munapo, E.; Mavetera, N. Investigating Causes of Delays and Cost Escalation in Project Execution during Turnarounds. Investig. Manag. Financ. Innov. 2016, 13, 334–348. [Google Scholar] [CrossRef]
  38. Dhandha, K.H. Shut down Inspection Requirements in Oil and Gas Refineries. In Proceedings of the NDE 2020—Virtual Conference & Exhibition, Virtual, 10–12 December 2020. [Google Scholar]
  39. Laza, K. The Piping Integrity Management Challenge. Insp. J. 2017, 23, 1–11. [Google Scholar]
  40. Hlophe, S.C.; Visser, J.K. Risk Management during Outage Projects at Power Plants. S. Afr. J. Ind. Eng. 2018, 29, 82–91. [Google Scholar] [CrossRef]
  41. Iheukwumere-Esotu, L.O.; Yunusa-Kaltungo, A. Knowledge Management and Experience Transfer in Major Maintenance Activities: A Practitioner’s Perspective. Sustainability 2022, 14, 52. [Google Scholar] [CrossRef]
  42. Akbar, J.u.D.; Ghazali, Z. The Influence of Coordination on the Performance of Plant Turnaround Maintenance through Team Alignment in Malaysian Process-Based Industry. SHS Web Conf. 2018, 56, 02007. [Google Scholar] [CrossRef]
  43. Wongthong, T.; Paoprasert, N. Factors Affecting the Petrochemical Maintenance Supplierselection in Thailand. Int. J. Mech. Eng. Technol. 2019, 10, 46–60. [Google Scholar]
  44. Mazumder, L.K.; Mubashar, S. A Study on Plant Shut down Process and Its Impact on Time and Cost. Int. J. Res. IT Manag. 2016, 6, 1–5. [Google Scholar]
  45. Wenchi, S.; Wang, J.; Wang, X.; Chong, H.Y. An Application of Value Stream Mapping for Turnaround Maintenance in Oil and Gas Industry: Case Study and Lessions Learned. In Proceedings of the 31st Annual ARCOM Conference, Lincoln, UK, 7–9 September 2015; pp. 813–822. [Google Scholar]
  46. Fabić, M.; Pavletić, D.; Šterpin Valić, G. Factors in Turnaround Refinery (TAR) Project Management Process. Teh. Vjesn. 2020, 27, 1367–1377. [Google Scholar] [CrossRef]
  47. Shou, W.C.; Wang, J.; Wang, X.Y. 4D BIM for Improving Plant Turnaround Maintenance Planning and Execution: A Case Study. In Proceedings of the ISARC 2018—35th International Symposium on Automation and Robotics in Construction and International AEC/FM Hackathon: The Future of Building Things, Berlin, Germany, 20–25 July 2018. [Google Scholar] [CrossRef]
  48. Khasanah, R.; Jamasri; Yuniarto, H.A. Evaluation of Turnaround Maintenance Practice Effects in the Process Industry. IOP Conf. Ser. Mater. Sci. Eng. 2019, 673, 012097. [Google Scholar] [CrossRef]
  49. Al-Turki, U.; Duffuaa, S. Performance Measures for Turnaround Maintenance. In Proceedings of the International Conference on Industrial Engineering and Operations Management, Pilsen, Czech Republic, 23–26 July 2019; pp. 534–539. [Google Scholar]
  50. Łukaszewicz, A. Nonlinear Numerical Model of Friction Heating during Rotary Friction Welding. J. Frict. Wear 2018, 39, 476–482. [Google Scholar] [CrossRef]
  51. Sidun, P.; Łukaszewicz, A. Verification of Ram-Press Pipe Bending Process Using Elasto-Plastic FEM Model. Acta Mech. Autom. 2017, 11, 47–52. [Google Scholar] [CrossRef]
  52. Łukaszewicz, A. Temperature Field in the Contact Zone in the Course of Rotary Friction Welding of Metals. Mater. Sci. 2019, 55, 39–45. [Google Scholar] [CrossRef]
  53. Altehmazi, M.M.; Suliman, S.M.A.; Alalawi, Y. An Optimization Approach to the Preventive Maintenance Planning Process. Mod. Appl. Sci. 2017, 11, 20–29. [Google Scholar] [CrossRef]
  54. Ostadi, B.; Saifpanahi, H. A Practical Self-Assessment Framework for Evaluation of Maintenance Management System Based on RAMS Model and Maintenance Standards. J. Ind. Syst. Eng. 2017, 10, 125–143. [Google Scholar]
  55. Chen, Y.H.; Li, T.; Xue, D.L. Research and Application of Nuclear Power Plant In-Service Inspection Information Management Platform. E3S Web Conf. 2021, 257, 3–6. [Google Scholar] [CrossRef]
  56. Tak, K.; Kim, J. A Planning Model for Inspection and Replacement of Pipes in a Refinery Plant. Chem. Eng. Trans. 2017, 57, 991–996. [Google Scholar] [CrossRef]
  57. Lee, J.C.; Aziz, H.A.; Osman, H.; Tan, L.S.; Manaf, N.A. In-Service Piping Inspection Work-Aid Tool for Oil & Gas Industries. Curr. Sci. Technol. 2021, 1, 32–43. [Google Scholar] [CrossRef]
  58. Gunawan, N. Identification of Variables Causing Delays to Turnaround Maintenance Project. IOP Conf. Ser. Mater. Sci. Eng. 2021, 1098, 022072. [Google Scholar] [CrossRef]
  59. Elwerfalli, A.; Khan, M.K.; Munive-Hernandez, J.E. Developing Turnaround Maintenance (TAM) Model to Optimize TAM Performance Based on the Critical Static Equipment (CSE) of GAS Plants. Int. J. Ind. Eng. Oper. Manag. 2019, 1, 12–31. [Google Scholar] [CrossRef]
  60. Wagh, P.V. Risk Based Inspection Approach for Effective Monitoring Remaining Life for Integrity of Refinery Equipment. In Proceedings of the NDE 2018 Conference & Exhibition, Mumbai, India, 19–21 December 2018. [Google Scholar]
  61. Elwerfalli, A. A Methodology to Increase Interval between Turnarounds Maintenance for Methanol Plants Based on the Heat Exchangers. In Proceedings of the 2nd International Conference on Research in Science, Engineering and Technology, Oxford, UK, 8–10 November 2019. [Google Scholar]
  62. Elwerfalli, A.; Khan, M.K.; Munive, J.E. A New Methodology for Improving TAM Scheduling of Oil and Gas Plants. In Proceedings of the World Congress on Engineering, London, UK, 29 June–1 July 2016; Volume 2224, pp. 807–812. [Google Scholar]
  63. Hameed, A.; Khan, F.; Ahmed, S. A Risk-Based Shutdown Inspection and Maintenance Interval Estimation Considering Human Error. Process Saf. Environ. Prot. 2016, 100, 9–21. [Google Scholar] [CrossRef]
  64. Priyanta, D.; Zaman, M.B. Semin the Development of a Risk-Based Maintenance Flowchart to Select the Correct Methodology to Develop Maintenance Strategies of Oil and Gas Equipment. IOP Conf. Ser. Mater. Sci. Eng. 2021, 1052, 012042. [Google Scholar] [CrossRef]
  65. Romanczuk-Ruszuk, E.; Krawczyńska, A.; Łukaszewicz, A.; Józwik, J.; Tofil, A.; Oksiuta, Z. Bioactivity, Cytotoxicity, and Tribological Studies of Nickel-Free Austenitic Stainless Steel Obtained via Powder Metallurgy Route. Materials 2023, 16, 7637. [Google Scholar] [CrossRef] [PubMed]
  66. Nisbet, R.T. Ultrasonic Thickness Measurements at High Temperature. NDT Tech. 2004, 10, 1–4. [Google Scholar]
  67. Cheong, Y.M.; Kim, K.M.; Kim, D.J. High-Temperature Ultrasonic Thickness Monitoring for Pipe Thinning in a Flow-Accelerated Corrosion Proof Test Facility. Nucl. Eng. Technol. 2017, 49, 1463–1471. [Google Scholar] [CrossRef]
  68. Njelle, V.Z.; Ikeh, O.D.; Ikpe, A.E.; Usuoyibo, A. Study of Corrosion Rate of Low and Medium Carbon Steel Pressure Vessel in Nigeria Oil and Gas Industry Using Ultrasonic Testing (UT) and Phase Array Ultrasonic Testing (PAUT) Method. J. Sci. Technol. Res. 2019, 1, 161–170. [Google Scholar]
  69. Choi, Y.M.; Kang, D.; Kim, Y.L.; Cho, S.; Park, T.; Park, I.K. Reliability Assessment of PAUT Technique in Lieu of RT for Tube Welds in Thermal Power Plant Facilities. Appl. Sci. 2022, 12, 5867. [Google Scholar] [CrossRef]
  70. Brizuela, J.; Camacho, J.; Cosarinsky, G.; Iriarte, J.M.; Cruza, J.F. Improving Elevation Resolution in Phased-Array Inspections for NDT. NDT E Int. 2019, 101, 1–16. [Google Scholar] [CrossRef]
  71. Ghose, B. Evaluation of Probability of Detection (POD) and Minimum Number of Exposures Required for Detection of Planar Flaw in Cylindrical Object by Radiographic NDE Method. In Proceedings of the Asia Pacific Conference on NDT (APCNDT—2013), Mumbai, India, 18–22 November 2013; Volume 19. [Google Scholar]
  72. Sengottuvelappan, E. Phased Array Ultrasonic Inspection Of 25%Cr Super Duplex Stainless Steel on Subsea Manifold Piping Girth Welds in Lieu of Radiography. E-J. Nondestruct. Test. 2015, 20, 1–8. [Google Scholar]
  73. Tai, J.L.; Grzejda, R.; Sultan, M.T.H.; Łukaszewicz, A.; Shahar, F.S.; Tarasiuk, W.; Rychlik, A. Experimental Investigation on the Corrosion Detectability of A36 Low Carbon Steel by the Method of Phased Array Corrosion Mapping. Materials 2023, 16, 5297. [Google Scholar] [CrossRef]
  74. Lei, X.; Wirdelius, H.; Rosell, A. Experimental Validation of a Phased Array Probe Model in Ultrasonic Inspection. Ultrasonics 2020, 108, 106217. [Google Scholar] [CrossRef]
  75. Tai, J.L.; Sultan, M.T.H.; Tarasiuk, W.; Napiórkowski, J.; Łukaszewicz, A.; Shahar, F.S. Ultrasonic Velocity and Attenuation of Low-Carbon Steel at High Temperatures. Materials 2023, 16, 5123. [Google Scholar] [CrossRef] [PubMed]
  76. Weekes, B.; Almond, D.P.; Cawley, P.; Barden, T. Eddy-Current Induced Thermography—Probability of Detection Study of Small Fatigue Cracks in Steel, Titanium and Nickel-Based Superalloy. NDT E Int. 2012, 49, 47–56. [Google Scholar] [CrossRef]
  77. Yusa, N.; Tomizawa, T.; Song, H.; Hashizume, H. Probability of Detection Analyses of Eddy Current Data for the Detection of Corrosion. Nondestruct. Test. Diagn. 2018, 4, 3–7. [Google Scholar] [CrossRef]
  78. Barat, V.; Marchenkov, A.; Ivanov, V.; Bardakov, V.; Elizarov, S.; Machikhin, A. Empirical Approach to Defect Detection Probability by Acoustic Emission Testing. Appl. Sci. 2021, 11, 9429. [Google Scholar] [CrossRef]
  79. Pollock, A. Probability of Detection for Acoustic Emission. J. Acoust. Emiss. 2007, 25, 231–237. [Google Scholar]
  80. Turcotte, J.; Rioux, P.; Lavoie, J. Comparison Corrosion Mapping Solutions Using Phased Array, Conventional UT and 3D Scanners. In Proceedings of the 19th World Conference on Non-Destructive Testing 2016, Munich, Germany, 13–17 June 2016; pp. 1–10. [Google Scholar]
  81. Kurian, M.C.; Shalij, P.R.; Pramod, V.R. Maintenance Strategy Selection in a Cement Industry Using Analytic Network Process. J. Qual. Maint. Eng. 2019, 26, 509–525. [Google Scholar] [CrossRef]
  82. Adjoul, O.; Benfriha, K. Design for Maintenance: New Algorithmic Approach. J. Qual. Maint. Eng. 2018, 27, 129–143. [Google Scholar] [CrossRef]
  83. Nwadinobi, C.P.; Nwankwojike, B.N.; Abam, F.I. Improved Markov Stable State Simulation for Maintenance Planning. J. Qual. Maint. Eng. 2019, 25, 199–212. [Google Scholar] [CrossRef]
  84. Hatsey, N.H.; Birkie, S.E. Total Cost Optimization of Submersible Irrigation Pump Maintenance Using Simulation. J. Qual. Maint. Eng. 2018, 27, 187–202. [Google Scholar] [CrossRef]
  85. Elwerfalli, A. A Framework to Prolong Interval of Turnaround Maintenance (TAM) of Processing Plants: Pressure Drums Case Study. In Proceedings of the 3rd European International Conference on Industrial Engineering and Operations Management, Pilsen, Czech Republic, 23–26 July 2019; pp. 1819–1824. [Google Scholar]
  86. Zhang, M.; Liang, W.; Qiu, Z.; Lin, Y. Application of Risk-Based Inspection Method for Gas Compressor Station. J. Phys. Conf. Ser. 2017, 842, 012064. [Google Scholar] [CrossRef]
  87. Eskandari, D.; Charkhand, H.; Gholami, A. A Semi-Quantitative Approach Development for Risk-Based Inspection in a Petrochemical Plant. J. Med. Sci. 2020, 8, 425–433. [Google Scholar] [CrossRef]
  88. Siswantoro, N.; Priyanta, D.; Ramadhan, J. Implementation of Risk-Based Inspection (RBI) in Condensate Separator and Storage Vessel: A Case Study. Int. J. Mar. Eng. Innov. Res. 2021, 6, 1–10. [Google Scholar] [CrossRef]
  89. Saisandhiya, N.R. Hazard Identification and Risk Assessment in Petrochemical Industry. Int. J. Res. Appl. Sci. Eng. Technol. 2020, 8, 778–783. [Google Scholar] [CrossRef]
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Figure 1. Visualization of interaction between RCM and maintenance strategies.
Figure 1. Visualization of interaction between RCM and maintenance strategies.
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Figure 2. The common process flow of PACM.
Figure 2. The common process flow of PACM.
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Figure 3. Phased array ultrasonic testing data presentation (adapted from [75]).
Figure 3. Phased array ultrasonic testing data presentation (adapted from [75]).
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Figure 4. Research objective/aim summaries.
Figure 4. Research objective/aim summaries.
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Table 1. Description of maintenance strategies applied in TAM.
Table 1. Description of maintenance strategies applied in TAM.
TAM
RCMIntegration: TAM employs Reliability-centered maintenance (RCM) principles during turnarounds to optimize maintenance strategies. RCM identifies essential assets and failure modes, which subsequently impact the planning and execution of TAM tasks.
CBMStrategic Use: Condition-based maintenance (CBM) is implemented at TAM to evaluate the current status of critical assets in real time. The information derived from CBM is utilized to make informed decisions and perform maintenance actions that are precisely targeted.
TPMEfficiency Goals: Total productive maintenance (TPM) principles are applied within TAM to maximize equipment efficiency during production. Turnarounds allow the implementation of TPM strategies, contributing to overall plant productivity.
PMScheduled Tasks: Preventive maintenance (PM) tasks are scheduled during TAM to prevent potential failures. The integration ensures that planned maintenance is executed efficiently, minimizing disruptions during production.
CMUnplanned Maintenance: While TAM primarily focuses on planned maintenance, Corrective maintenance (CM) is included to address unforeseen issues discovered during the turnaround. CM tasks are executed efficiently to minimize downtime.
PdMPredictive Insights: Predictive maintenance (PdM) techniques are employed within TAM to predict potential issues before they become critical. Predictive insights guide the planning of maintenance tasks, which optimizes resource allocation.
RBMRisk Assessment: Risk-based maintenance (RBM) principles are crucial in TAM for prioritizing maintenance tasks based on risk assessments. Identifying high-risk components ensures that resources are allocated to address critical areas during turnarounds.
Table 2. Interaction of RCM with different maintenance strategies.
Table 2. Interaction of RCM with different maintenance strategies.
RCM
CBMData Synergy: CBM data can complement RCM analyses by providing real-time condition data for assets identified as critical through RCM. This synergy enhances the precision of maintenance decision-making.
TPMOptimizing Strategies: RCM principles optimize TPM strategies by identifying the most effective maintenance tasks for enhancing equipment reliability. The collaboration ensures a proactive approach to asset management.
PMTask Optimization: RCM influences the optimization of PM tasks during both routine operations and turnarounds. PM tasks are selected based on RCM analyses, ensuring a targeted preventive approach.
CMReducing Unplanned Downtime: RCM aims to reduce the need for CM by proactively addressing potential failure modes. CM tasks become more focused and efficient, minimizing unplanned downtime.
Table 3. Advantages and limitations of different maintenance strategies.
Table 3. Advantages and limitations of different maintenance strategies.
Maintenance StrategyAdvantagesLimitation
PdM
  • Uses data and analytics to predict equipment failure before it occurs
  • Allows for targeted maintenance, reducing downtime and costs
  • Can improve equipment reliability and safety
  • Requires significant investment in data collection and analytics infrastructure
  • May not be accurate in predicting all types of equipment failure
PM
  • Reduce the likelihood equipment failure by performing regular maintenance tasks
  • Can extend the lifespan of equipment
  • Cost-effective compared to corrective maintenance
  • May result in unnecessary maintenance tasks if equipment is in good condition
  • Difficult to predict when maintenance is needed without prior knowledge of failure modes
CBM
  • May result in unnecessary maintenance tasks if equipment is in good condition
  • Difficult to predict when maintenance is needed without prior knowledge of failure modes
  • Requires significant investment in monitoring equipment and infrastructure
  • May not be effective in predicting all types of equipment failure
RCM
  • Focuses on identifying critical equipment and failure modes
  • Uses data-driven approaches to optimize maintenance strategies
  • Can improve equipment reliability and safety
  • Requires significant investment in data collection and analysis
  • May not be effective in predicting all types of equipment failure
CM
  • Addresses equipment failures as they occur
  • Can improve equipment reliability and safety
  • Allows for immediate response to equipment failures
  • Can result in unplanned downtime and increased costs
  • May not be effective in preventing future equipment failures
TPM
  • Focuses on improving overall equipment effectiveness and productivity
  • Involves all employees in maintenance activities
  • Can improve equipment reliability and safety
  • Can reduce maintenance costs and increase productivity
  • Requires significant cultural change and employee buy-in
  • May not be effective in predicting equipment failure
Table 4. Advantages and limitations of different NDT techniques.
Table 4. Advantages and limitations of different NDT techniques.
TechniqueAdvantagesLimitationsSuitable for Process PlantSuitable for High-Temperature Surfaces
Phased Array Corrosion Mapping (PACM)
  • Rapid and accurate results
  • Three-dimensional defect information
  • Safe for simultaneous on-site use with other trades
  • Requires specialized equipment and training
  • More expensive than other NDT techniques
YesYes
(with limitations)
Ultrasonic Thickness Gauging (UTG)
  • Ability to operate at elevated temperatures
  • Effective in detecting general corrosion
  • Reduced productivity in spot thickness measurements
  • Limited sensitivity to localized corrosion
YesYes
Ultrasonic Testing (UT)
  • Widely used for inspecting welds and detecting corrosion in thin-walled structures
  • Limited sensitivity to localized corrosion
  • Less suitable for high-temperature environments
YesNo
Radiography Testing (RT)
  • Effective in detecting corrosion in thick-walled structures
  • Permanent record
  • Radiation exposure risks
  • Requires both surfaces to be accessible for inspection
YesNo
Eddy Current Testing (ET)
  • Sensitive to small defects
  • Can provide depth information
  • Less suitable for high-temperature environments
  • Selective material
Yes (for conductive materials)No
Acoustic Emission Testing (AET)
  • Can provide information about the location and size of defects
  • Less suitable for high-temperature environments
  • Requires the material to be able to generate and propagate elastic waves
YesNo
Table 5. Summary of plant maintenance studies by researchers.
Table 5. Summary of plant maintenance studies by researchers.
Improving MethodMethodsDescriptionResearch/Knowledge GapsJournal Articles
Survey QuestionnaireQuestionnaire-based approachConducting a survey questionnaire to identify the underlying causes of maintenance issues and to collect potential solutions from stakeholdersMore research is needed to determine the most effective survey design and to validate the results against other data sources. Additionally, more research is needed to investigate the impact of cultural values on ethics and conflict management during TAM.[8,9,40,41,42,43,44]
Assessment ToolsValue Stream Map, Analytic Hierarchy Process (AHP) method, and logistic regression approachUsing assessment tools such as the value stream map, AHP method, and logistic regression approach to dissect intricate factors affecting success and to orchestrate efficient and productive turnaround processesMore research is needed to compare the effectiveness of different assessment tools and to determine the most appropriate tool for different types of maintenance strategies. Additionally, more research is needed to investigate the impact of new workers and transient teams on communication gaps, idling workers, and spare part shortages during TAM.[1,4,6,7,9,36,45,46,81,82]
Software DevelopmentFour-dimensional building information modeling, maintenance management system, inspection manager software, in-service inspection platform, and optimization modelDeveloping software tools such as four-dimensional building information modeling, maintenance management system, inspection manager software, in-service inspection platform, and optimization model to streamline processes and enhance data trackingMore research is needed to evaluate the usability and effectiveness of the software tools, and to determine their impact on maintenance outcomes. Additionally, more research is needed to investigate the use of technology-based solutions for automating tasks and increasing maintenance efficiency.[10,47,48,49,53,54,55,56,57,83,84]
Risk-Based InspectionRisk-based identification, decision-making model, risk-based failure analysis, probability failure distributions, risk-based optimization, and risk-based maintenance strategiesEmploying a risk-based inspection approach to assess the risk, incident consequences, probability of failure modes, and to optimize maintenance schedules, intervals, and strategiesMore research is needed to determine the most appropriate risk assessment methodology for different types of maintenance strategies, and to evaluate the accuracy and reliability of the results. Additionally, more research is needed to investigate the impact of human errors introduced during shutdowns on maintenance optimization and risk-based strategies.[11,58,59,60,61,62,63,64,85,86,87,88,89]
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MDPI and ACS Style

Tai, J.L.; Sultan, M.T.H.; Łukaszewicz, A.; Shahar, F.S.; Oksiuta, Z.; Krishnamoorthy, R.R. Enhancing Turnaround Maintenance in Process Plants through On-Stream Phased Array Corrosion Mapping: A Review. Appl. Sci. 2024, 14, 6707. https://doi.org/10.3390/app14156707

AMA Style

Tai JL, Sultan MTH, Łukaszewicz A, Shahar FS, Oksiuta Z, Krishnamoorthy RR. Enhancing Turnaround Maintenance in Process Plants through On-Stream Phased Array Corrosion Mapping: A Review. Applied Sciences. 2024; 14(15):6707. https://doi.org/10.3390/app14156707

Chicago/Turabian Style

Tai, Jan Lean, Mohamed Thariq Hameed Sultan, Andrzej Łukaszewicz, Farah Syazwani Shahar, Zbigniew Oksiuta, and Renga Rao Krishnamoorthy. 2024. "Enhancing Turnaround Maintenance in Process Plants through On-Stream Phased Array Corrosion Mapping: A Review" Applied Sciences 14, no. 15: 6707. https://doi.org/10.3390/app14156707

APA Style

Tai, J. L., Sultan, M. T. H., Łukaszewicz, A., Shahar, F. S., Oksiuta, Z., & Krishnamoorthy, R. R. (2024). Enhancing Turnaround Maintenance in Process Plants through On-Stream Phased Array Corrosion Mapping: A Review. Applied Sciences, 14(15), 6707. https://doi.org/10.3390/app14156707

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