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Quest Training

AI-Powered Predictive Maintenance for Refineries Training Course

Applies AI and advanced analytics to refinery vessels, exchangers, heaters, piping and tanks to predict corrosion-driven degradation and prevent failures.

MTM · Mechanical Maintenance, Reliability & TurnaroundAll Levels#2381ClassroomEnglish , Arabic
Duration
5 Days
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Free substitution at any time · 30+ days' notice: full refund minus 10%. Cancellation policy

Sectors:Oil & GasCategories:Mechanical Maintenance, Reliability & Turnaround

Course Overview

The AI-Powered Predictive Maintenance for Refinery Static Equipment Training Course provides static equipment maintenance leaders with a practical framework for applying artificial intelligence and advanced analytics to predict degradation and prevent failures of pressure vessels, columns, heat exchangers, fired heaters, piping systems, and storage tanks. In refineries, static equipment failures are rarely sudden; they result from corrosion, erosion, fouling, cracking, and high-temperature damage mechanisms that develop over months or years. The challenge is not a lack of data, but the ability to turn thickness measurements, inspection findings, process conditions, and maintenance history into timely and reliable decisions. This course shows how AI and machine learning can strengthen existing static equipment programmes, including risk-based inspection, integrity operating windows, corrosion monitoring, and turnaround planning. Participants will learn how data from ultrasonic thickness surveys, corrosion probes and coupons, process historians, heat exchanger performance records, fired heater tube skin temperatures, inspection reports, and CMMS work orders can be combined to forecast corrosion rates, predict fouling trends, estimate remaining life, and prioritize inspection and maintenance activities. The programme is designed around the realities of refinery static equipment. It connects AI applications to API 571 damage mechanisms, API 580 and API 581 risk-based inspection, API 579 fitness-for-service assessments, API 510, API 570, and API 653 inspection programmes, and the planning of shutdowns and turnarounds. The emphasis is on practical understanding rather than programming, enabling maintenance leaders to define use cases, assess data readiness, evaluate technology solutions, interpret model outputs, and work effectively with inspection, operations, and data teams. The course also addresses the leadership responsibilities of a static equipment maintenance lead: building a business case, selecting pilot applications, setting performance indicators, managing change within maintenance and inspection teams, and integrating predictive insights into daily decision-making and long-term asset strategies. By the end of the course, participants will be able to design a structured roadmap for AI-enabled predictive maintenance of static equipment, reduce unplanned leaks and shutdowns, optimize inspection intervals and turnaround scope, extend equipment life, and support safer and more cost-effective refinery operations.

Learning Objectives

  • Analyze the main degradation mechanisms affecting refinery static equipment and their suitability for predictive analytics during the first day of the course.
  • Evaluate static equipment data sources, including thickness measurements, corrosion monitoring, inspection records, process data, and CMMS history, for completeness and reliability.
  • Apply core machine learning concepts to corrosion rate prediction, fouling forecasting, and remaining life estimation.
  • Assess how AI can enhance risk-based inspection, integrity operating windows, and fitness-for-service decisions.
  • Identify high-value static equipment applications for predictive maintenance using criticality, risk, and data availability criteria.
  • Interpret predictive model outputs, including corrosion trends, anomaly alerts, and remaining life estimates, to support inspection and maintenance decisions.
  • Integrate predictive insights into inspection planning, maintenance scheduling, spare parts planning, and turnaround scope definition.
  • Evaluate predictive maintenance and asset integrity software solutions using structured technical and business criteria.
  • Develop static equipment reliability and integrity KPIs to measure the performance and value of the predictive programme.
  • Design an implementation roadmap and business case for an AI-powered static equipment predictive maintenance pilot by the end of the fifth day.

Who Should Attend

This course is designed specifically for static equipment maintenance leads and supervisors responsible for the reliability and integrity of pressure vessels, columns, reactors, heat exchangers, air coolers, fired heaters, piping systems, and storage tanks in refineries and petrochemical facilities. It suits professionals who lead maintenance teams, coordinate with inspection and operations departments, and plan maintenance and turnaround activities for static equipment. The programme is also highly relevant to static equipment engineers, inspection engineers, corrosion engineers, asset integrity engineers, reliability engineers, and maintenance planners who support static equipment programmes. Turnaround planners, CMMS administrators, and process engineers working on heat exchanger performance and fired heater operation will gain practical insight into how predictive analytics can improve their work. In addition, the course benefits maintenance and inspection managers, digital transformation teams, and decision-makers responsible for evaluating predictive maintenance investments and leading the adoption of data-driven maintenance practices in refining organizations.

Learning Outcomes

  • By the end of this course, participants will be able to:
  • Explain how AI-powered predictive maintenance applies to static equipment and how it differs from rotating equipment condition monitoring.
  • Link key API 571 damage mechanisms to measurable indicators that can be monitored and predicted.
  • Map available static equipment data sources and identify gaps that limit predictive capability.
  • Describe how regression, anomaly detection, and classification models are used for corrosion rate forecasting, fouling prediction, and remaining life estimation.
  • Interpret corrosion trends, integrity operating window deviations, and model alerts, and translate them into inspection and maintenance actions.
  • Use predictive insights to optimize inspection intervals within RBI programmes and support fitness-for-service decisions.
  • Apply predictive analytics to heat exchanger cleaning schedules, fired heater tube life management, and piping corrosion under insulation priorities.
  • Integrate predictions into maintenance planning, material and spare parts planning, and turnaround scope freezing.
  • Establish static equipment KPIs such as loss of containment events, inspection compliance, corrosion rate trends, and turnaround scope accuracy.
  • Prepare a practical implementation roadmap and business case for an AI-enabled static equipment predictive maintenance pilot.

Course Outline

Course Outline:

Day 1

Static Equipment Degradation and the Case for Predictive Maintenance

  • Evolution of maintenance and inspection strategies for refinery static equipment: time-based, condition-based, risk-based, and predictive
  • Key damage mechanisms under API 571: general and localized corrosion, erosion, fouling, high-temperature hydrogen attack, creep, cracking, and corrosion under insulation
  • Static equipment failure consequences: loss of containment, unplanned shutdowns, safety and environmental impacts
  • Introduction to artificial intelligence and machine learning for static equipment professionals
  • Role of digital twins, IIoT sensors, and online corrosion monitoring in modern refineries
  • Practical discussion: assessing the current maturity of participants' static equipment maintenance and inspection programmes
Day 2

Static Equipment Data Sources, Data Quality, and Integrity Operating Windows

  • Inspection and thickness data: UT surveys, condition monitoring locations, and inspection history
  • Corrosion monitoring data: probes, coupons, and process chemistry parameters
  • Process data from historians and DCS: temperatures, pressures, flow rates, and feed quality
  • Integrity operating windows (IOWs) and their role as early indicators of accelerated degradation
  • Data quality challenges: inconsistent inspection records, missing readings, and poor failure coding in CMMS
  • Practical application: evaluating a sample static equipment dataset and identifying the data required to predict corrosion or fouling
Day 3

AI and Machine Learning Applications for Static Equipment

  • Corrosion rate forecasting and remaining life estimation using regression and trend models
  • Anomaly detection for early identification of abnormal process conditions and integrity operating window deviations
  • Heat exchanger fouling prediction and optimization of cleaning schedules
  • Fired heater tube life management using tube skin temperature and operating data
  • Prioritizing corrosion under insulation and piping inspection using predictive risk ranking
  • Practical application: interpreting predictive model outputs for heat exchanger, piping, and pressure vessel case studies and recommending maintenance and inspection actions
Day 4

Integrating Predictions with RBI, Fitness-for-Service, and Turnaround Planning

  • Integrating predictive analytics with API 580 and API 581 risk-based inspection programmes
  • Supporting API 579 fitness-for-service assessments with predicted degradation trends
  • Aligning predictions with API 510, API 570, and API 653 inspection programmes
  • Using predictive insights to define turnaround scope, freeze worklists, and plan materials and resources
  • Static equipment reliability and integrity KPIs: loss of containment events, inspection compliance, corrosion trends, and turnaround scope accuracy
  • Practical application: developing a workflow that connects predictive alerts to inspection planning, maintenance execution, and turnaround preparation
Day 5

Leading Implementation, Technology Selection, and Change Management

  • Selecting pilot applications for static equipment based on value, risk reduction, and data readiness
  • Evaluating predictive maintenance and asset integrity software solutions and vendor proposals
  • Building the business case: avoided failures, optimized inspection costs, turnaround savings, and return on investment
  • Leadership role of the static equipment maintenance lead in coordinating maintenance, inspection, operations, and data teams
  • Change management, skills development, and common reasons predictive maintenance initiatives fail
  • Final workshop: developing an AI-powered predictive maintenance roadmap and pilot business case for the participant's static equipment portfolio

Upcoming Dates

Selected date

Bangkok · 15 – 19 Feb 2027 · Classroom

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LondonScheduled
2 – 6 Nov 2026€5,800—
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MadinahScheduled
9 – 13 Nov 2026€4,800—
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GenevaScheduled
16 – 20 Nov 2026€7,300—
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MadridScheduled
16 – 20 Nov 2026€6,000—
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MakkahScheduled
23 – 27 Nov 2026€4,800€4,320until 2 Nov
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MarrakeshScheduled
30 Nov – 4 Dec 2026€4,700€4,230until 9 Nov
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ManamaScheduled
30 Nov – 4 Dec 2026€5,300€4,770until 9 Nov
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MilanScheduled
7 – 11 Dec 2026€7,000€6,300until 16 Nov
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IstanbulScheduled
7 – 11 Dec 2026€5,300€4,770until 16 Nov
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MunichScheduled
14 – 18 Dec 2026€6,000€5,400until 23 Nov
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MuscatScheduled
21 – 25 Dec 2026€6,300€5,670until 30 Nov
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OnlineScheduled
27 – 31 Dec 2026€2,700€2,430until 7 Dec
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JeddahScheduled
27 – 31 Dec 2026€4,800€4,320until 7 Dec
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ParisConfirmed
4 – 8 Jan 2027€6,700€6,030until 14 Dec
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RiyadhScheduled
11 – 15 Jan 2027€4,800€4,320until 21 Dec
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RomeScheduled
11 – 15 Jan 2027€7,000€6,300until 21 Dec
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KigaliScheduled
18 – 22 Jan 2027€7,700€6,930until 28 Dec
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ViennaScheduled
18 – 22 Jan 2027€6,400€5,760until 28 Dec
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AmmanScheduled
24 – 28 Jan 2027€4,000€3,600until 4 Jan
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AmsterdamScheduled
1 – 5 Feb 2027€6,000€5,400until 11 Jan
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Kuala LumpurScheduled
1 – 5 Feb 2027€4,700€4,230until 11 Jan
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BakuScheduled
8 – 12 Feb 2027€5,300€4,770until 18 Jan
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BangkokScheduled
15 – 19 Feb 2027€6,700€6,030until 25 Jan
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SalalahScheduled
22 – 26 Feb 2027€6,300€5,670until 1 Feb
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22 – 26 Feb 2027€6,000€5,400until 1 Feb
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BerlinScheduled
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8 – 12 Mar 2027€4,700€4,230until 15 Feb
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15 – 19 Mar 2027€4,800€4,320until 22 Feb
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AbujaScheduled
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