Knowledge Center: Asset Optimization

Risk Based Inspection (RBI) is a structured methodology that facilitates incorporation of the current state of a piece of equipment (inspection data) and inferences about likelihood of failure derived from inspection and process data into decisions about asset integrity, maintenance and resource allocaiton. Risk of failure, often due to corrosion-related phenomena, has been seen to be a catastrophic event, typically leading to substantive personnel / economic loss and resource degradation in the plant. Part of the challenge with RBI is the issue of tyring to correlate current plant performance data with potential for corrosion failure. To this end, traditional methods of qualitative assessment of risk of corrosion failure through utilization of rules of thumb and approximations, have yielded very conservative approaches to inspection and maintenance.

However, advances in corrosion prediction technology, including development of sophisticated prediction models that incorporate complex multphase fluid dynamic analyses with process modeling and corrosion modeling have given rise to an accurate basis for assessing risk of corrosion failure in plants. This article describes development of application of one such prediction model for prediction of sour water corrosion in refineries, and provides a framework for utilizaiton of such models as a basis for Real Time Risk Based Management and Inspection (RBMI). The Predict-SW system, developed as a consequence of six years of a Joint Industry Project led by Honeywell, has imparted a paradigm-shift to the critical issues of corrosion characterization due to Ammonium Bisulfide and Sour Water, as well as evaluation and determination of appropriate materials of construction for applications corrosive to carbon steel. The model encapsulates a complex numerical model, reflecting in-depth laboratory evaluation fourteen materials of construction in representative NH4HS sour water environments, and has shown, in multiple real-life plant application case studies, to be an accurate predictor of corrosion rates.

This article details case studies that demonstrate the efficacy of the Predict-SW prediction model for quantifying sour water corrosion, and proposes a real time framework for utilization of the model Risk-based Engineering. It is proposed that such quantificaiton of corrosion damage is an essential and valuable building block for assessing corrosion risk in refineries plants in real time, so as to facilitate quick and effective remedial measures. A real time RBMI framework, it is proposed, will lead to substantive cost savings and failure prevention in inspecting and maintaining plants, while providing a safe and optimized operating environment for critical plant functionality.

Corrosion Challenges The failure of critical operational units in refineries due to corrosion presents a formidable challenge from a stand point of asset integrity management and risk-management. The API Risk-Based Inspection (RBI) program has, over the last twenty years, provided a methodology to characterize the risk of failure, through appropriate determination of both the probability of failure and consequence of failure. While consequence of failure computation, often represented in terms of economic and environmental effects of a failure has well set quantifiable criteria, the probability of failure represents a more fundamental and complex task from an RBI stand point. Risk analysis is meant to be a quantifiable basis to provide advance notice about state of critical equipment. However, traditionally, most risk analyses has incorporated qualitative and quantitative methods to compute likelihood of failure and consequence of failure. The likelihood of failure often includes characterizing multiple corrosion mechanisms, such as localized corrosion and general corrosion, as well as relevant forms of cracking such as Sulfide Stress Cracking and Stress Corrosion Cracking. The API RBI methodology requires generation of a risk ranking matrix based on likelihood values and consequence values based on a given data set.

Three levels of RBI have been proposed by the API sponsor group in charaterizing risk for different types of pressure equipment :1. Level I RBI, in which most of the likelihood of failure are determined on the basis of qualitative rules by domain experts, utilizing heuristics or rules of thumb. Typical Level I RBI will generate a simple 5 x 5 matrix designating the risk rank. This tends to be typically conservative, since there is not a quantifiable basis for supporting the risk rankings (in terms of rigorous corrosion rate predictions or quantified material behavior assessments). Figure 1 shows typical risk ranking derived from Level I RBI.

2. Level II RBI is semi-quantitative RBI, which is an intermediate framework between the qualitative bases of Level I RBI and the quantified bases of Level III RBI. Even though Level II RBI does utilize a 5 X 5 matrix as in Level I, it utilizes a more exhaustive data collection framework to support the risk components

3. Level III RBI, also known as quantitative RBI, is the most accurate way to perform RBI. Here, the equipment or plant is characterized for corrosion damage utilizing complex ionic and prediction models ; Level III RBI will generate accurate numerical values for both likelihood of failure and consequence of failure, and often involves mechanistic and thermodynamic characterization of process data.

Risk-based analysis in the refining industry, most often, utilizes Level I RBI. RBI and corrosion consultants (typically engineering personnel with significant plant experience) will evaluate process data and inspection data to come up with numerical rankings (typically on a scale of 1- 10 or 1-100) for likelihood of failure for each relevant mechanism. Since these methods involve human judgement based on historical data, the RBI rankings suffer from significant flaws:

1. The risk ranking may not have accounted, accurately, for contributions of critical parameters to corrosion damage. Such methods also are not guaranteed to account for effect of every relevant corrosion-causing parameter or parameter interaction.

2. The rules of thumb utilized by the RBI consultant may not always be applicable for the unit under consideration. Another RBI consultant, working with the same process data and unit, may generate an entirely different set of RBI ranknigs, which makes the process highly inaccurate and questionable.

3. Level I analyses necessarily has to involve past data, and not real time data, since the data collection often is performed during unit shut-downs. This implies that the risk analyses is relevant, not to the current operating state of the equipment, but only to a past condition.

In recent years, Honeywell International, Inc, has been involved in the development of engineering data to support rigorous predicitons of corrosion in refineries, based on multiphase flow modeling, ionic / thermodynamic modeling and integration with numerical models dervied from comprehesive laboratory data. Predictive modeling applications, which quantify corrosion, have been developed or being developed in the following domains:

- Sour water (NH4HS) corrosion seen in multiple refinery units (hydroprocessing, sour water strippers etc)- Corrosion in Amine Units (MEA, DEA, DGA, MDEA) - Naphthenic acid corrosion in Crudes - Corrosion in Sulfuric Acid Alkylation

Current RBI methodologies are confined to utilizing historical inspection data, since absence of models / applications for corrosion quantification makes real time asset integrity and likelihood of failure analyses difficult. This difficulty is removed with the availability of models for corrosion quantification that may deployed in real-time. Figure 2 shows a framework for real time asset integrity management through real time RBMI.

The rest of this paper describes a corrosion quantification application for sour water systems in refineries, a key component of the Real Time RBI framework shown in Figure 2. Such quantification facilitates adoption of real time inspection and process data in making decisions about asset integrity and maintenance prioritization.

Corrosion Quantification for Refinery Sour Water Systems: Overview Hydroprocessing units serve several purposes in petroleum refining. Hydrotreating/ hydrodesulfurizing units are used to remove undesirable sulfur and/or nitrogen, while hydrocracking units are used to crack heavier feeds into lower boiling point products. Both of these units use hot, high-pressure hydrogen in the reactor sections of the process. Byproducts of these reactions include ammonia (NH3) and hydrogen sulfide (H2S). The product of these two gases can form a salt, ammonium bisulfide (NH4HS) as the reactor effluent stream cools. Formation of NH4HS salts can lead to reactor effluent air cooler (REAC) tube plugging, and if wet, can lead to rapid under-deposit corrosion. These salts are readily dissolved in water, and hence these REAC systems are generally water washed ahead of the salt formation point to minimize plugging and underdeposit corrosion. The resulting sour water solutions containing the dissolved NH4HS salts in combination with dissolved H2S and ammonia (and in some cases cyanide) can be highly corrosive.

Perhaps the most notable effort was attempted by R. L. Piehl that involved a survey conducted by NACE Group Committee T-8 (now Specific Technology Group [STG] 34) covering corrosion in the reactor effluent air coolers and associated piping in 42 hydrocrackers and hydrotreaters. Damin and McCoy studied laboratory corrosion data developed over the NH4HS concentration range of 10 – 45 wt%. However, there has never been a comprehensive effort to develop a quantitative understanding of corrosion in refinery sour water systems that could be used to assess and optimize unit operation, operational boundary monitoring, better maintenance planning and asset management.

The API Recommended Practice (RP 932-B) , Design, Materials, Fabrication, Operation, and Inspection Guidelines for Corrosion Control in Hydroprocessing Reactor Effluent Air Cooler (REAC) Systems, details critical parameters relevant to ammonium Bisulfide corrosion (Section 4.1), including ammonium Bisulfide concentration, fluid velocity and wash water quality. Sections 5.1 and 5.2 provide strategies to promote system reliability based on individual unit operating envelope and process parameters.

THE PREDICT-SW PREDICTION MODEL Honeywell International, Inc., in collaboration with Shell Global Solutions (US) Inc. and a consortium of major refinery operators and service companies initiated a program entitled “Prediction and Assessment of Ammonium Bisulfide Corrosion under Refinery Sour Water Service Conditions.” . Phase I of this program, commonly referred to as the Sour Water JIP, was conducted from March 2000 to February 2003. The final program report was issued in June 2003. The goal of this program was to develop a quantitative engineering database and rule-base to provide automated corrosion prediction and assessment in H2S-dominated alkaline sour water systems as a function of NH4HS concentration, velocity (wall shear stress), H2S partial pressure, temperature, chloride concentration, hydrocarbon content and chemical treatments. In a subsequent phase of this work, the effort was extended to NH4-dominated alkaline sour water systems that also included the effects of free cyanide. This program utilized extensive process and flow modeling and experimental simulation to obtain corrosion rates for commonly used alloys over a wide range of flow induced wall shear stress conditions.

The results of this effort showed that NH4HS corrosion was dependent upon a number of factors including NH4HS concentration, velocity (wall shear stress), H2S partial pressure, temperature and many other secondary variables including hydrocarbon presence and type, flow regime and use of inhibitors. The key findings of the program and an overview of the influence of these variables on NH4HS corrosion has been covered in recent publications by Srinivasan, Horvath, Cayard, Kane and Giesbrecht .

These data were used as a basis for the development of an accurate and comprehensive corrosion prediction software tool, including assessment methodologies for control of NH4HS corrosion of a wide range of materials of construction to help attain safe and reliable operation of process units handling NH4HS. These data and the associated software tool have been successfully used to provide significant economic benefit to refinery operators.

PREDICT-SW – SYSTEM DESCRIPTION The Predict®-SW(1) software system was developed to incorporate the JIP program data, industry experience and streamline the prediction of unit corrosion rates based on process and flow conditions including the incorporation of the shear stress acting on the tube/pipe wall associated with the process stream (including multiphase effects of liquid hydrocarbon, sour water and gas phases). This software system provides an easy interface for data input relevant to the environment, application and process stream variables. The program utilizes the input data and a sophisticated numerical model to quantify corrosion rates for 14 materials commonly utilized in refining applications. It even corrects corrosion rates for the effect of H2S partial pressure, temperature, hydrocarbon protection, and chemical treatment.

The Predict-SW system performs rigorous fluid dynamic characterization and utilizes widely known flow maps from Taitel-Dukler and Mendhane et. al. and provides the end user the ability to assess pressure drops, liquid hold up, dimensionless factors and wall shear stress for both vertical and horizontal, single-phase and multiphase fluid systems based on widely recognized flow analysis methodologies . The Predict-SW software system facilitates evaluation of up to twenty (20) process streams simultaneously, and provides an easy path to assess the most critical systems / locations from a sour water corrosion perspective. The system also provides a sensitivity analysis module to study effects of changes to critical system parameters, as well as the ability to work with SI/American units interchangeably. A notable aspect of the Predict-SW system is access to all the JIP program data through a secure electronic interface, through which the user can see and utilize all of the corrosion data points, reports and presentations generated in the JIP effort.

Several case studies have been published that show the efficacy of the Predict-SW model as a basis for corrosion quantification in refineries. The functionality and efficacy of the Predict-SW system has been extensively detailed elsewhere.

Prediction Accuracy Risk-Based Management and Inspection of plants and refineries represents a complex task that has traditionally been achieved through qualitative methods. New developments in corrosion prediction technology have facilitated quantification of corrosion risk, and have promoted the creation of a real-time corrosion risk management and RBMI framework. One such prediction model that supports quantification of corrosion risk damage has been described. A framework for real time RBMI has been provided, and is believed to represent the foundation for future implementations of Level III RBMI, wherein corrosion risk quantification becomes the cornerstone of accurate and effective risk and integrity analyses.

The Predict-SW system, with a rigorous numerical model integrated into an easy to use interface has developed an extensive track record of excellent prediction accuracy, validated through actual corrosion rate measurements on plant equipment. The program continues to find extensive use at leading operating companies and refineries. The Predict-SW model has now been integrated with Honeywell’s process modeling tools as a precursor to implementation of real time RBMI framework.

 

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