Real time corrosion prediction in the refining industry – impact on asset

integrity and corrosion management

Slawomir KUS1, Pierre CONSTANTINEAU2, Sridhar SRINIVASAN3

1Honeywell Process Solutions, UK, slawomir.kus@honeywell.com

2Honeywell Process Solutions, Canada, pierre.constantineau@honeywell.com 3Honeywell Process Solutions, US, sridhar.srinivasan@honeywell.com

Abstract

The development of kinetic and thermodynamic models which correlate corrosion rates to process

variables has facilitated introduction of corrosion prediction into design of refinery units. Corrosion

caused by naphthenic acids, ammonium bisulfide or amine can be modelled with respect to materials of

construction and expected process fluctuations. The standard approach for corrosion prediction assumes

“static” modelling based on a given set of operating scenarios. The continuous push for improving

refinery profitability can shift operating regimes outside their designed safe boundaries. Consequently,

unexpected corrosion failures may occur. In order to reduce the likelihood of such failures, API RP584

introduced the concept of Integrity Operating Window (IOW) where critical variables are identified,

safe limits quantied and monitored. The integration of models with on-line real time corrosion

monitoring brings a new level of fidelity to Integrity Operating Windows. Calculating corrosion based

on “live” DCS data helps overcome limitations arising from traditional off-line monitoring and off-line

modelling. This paper discusses “real-time” corrosion prediction, its integration with on-line corrosion

monitoring and asset integrity management systems. An example of real-time corrosion prediction from

application in a crude distillation unit is also presented.

Keywords: corrosion prediction, real-time, asset integrity, refinery corrosion management, IOW.

Introduction

Corrosion management, together with Risk Based Inspection (RBI), Alarm and Process

Safety management are foundational to maintenance of safe and reliable Plant Operations. Over

the last decade, Asset Integrity Management (AIM) has evolved from a generic, conceptual

approach into a concrete operational framework designed to help plant operators and managers

proactively manage plant asset reliability and integrity. Recently published API1 RP584 -

Integrity Operating Windows (IOW) together with RBI guideline documents (API RP

580/581), API RP 571 and other industry guidelines provide a broad, implementable set of rules

for managing corrosion and integrity. However, detailed analysis of those methodologies and

guidelines reveals specific gaps and deficiencies that may lead to over/under-estimation of

corrosion risk. Indeed, accurate quantification of degradation rate and consequent estimation of

risk of failure remains the most difficult and incompletely addressed elements of AIM.

Traditionally, determination of corrosion rate is based on time-lagging, non-real-time,

techniques like periodic ultrasonic thickness (UT) inspections, exposition of corrosion coupons,

off-line electrical resistance (ER) or off-line linear polarization resistance (LPR). Those

techniques can provide important and useful cumulative corrosion data but under dynamic

process conditions, may not fully reflect the actual corrosion corresponding to altered process

states. Recently developed corrosion monitoring systems like Super-LPR, High Resolution ER

or High Resolution UT, facilitate on-line, real-time corrosion measurement, but also have

limitations e.g. requirement for conductive fluid for electrochemical monitoring or impact of

conductive scales or corrosion deposits on ER measurement. Moreover, the high cost of

implementation for these new technologies has diminished / limited the number of working

real-time corrosion monitoring points in process applications.

Some deficiencies of on-line corrosion monitoring can be addressed with the help of

modern kinetic-thermodynamic models of corrosion processes. Primary concept of corrosion

prediction originated from the Oil&Gas industry, where there was a need to evaluate / quantify

corrosion in difficult-to-monitor tubing or equipment applications. A number of joint industry

research and data development programs led to numerous corrosion prediction models

encapsulating laboratory data, empirical correlations and field experience. These prediction

models have found wide application in the upstream Oil&Gas production applications.

However, development of similar prediction models for refinery applications lagged, due to

significant complexity of potential interactions between various process parameters and

corrosion reactions.

The first serious effort towards development of a reliable, comprehensive, data based

corrosion prediction model for refining applications stemmed from the work done as part of the

Sour Water JIP [1], for predicting and quantifying alkaline sour water corrosion or ammonium

bi-sulfide corrosion (NH4HS). The sour water corrosion in Hydroprocessing units (REAC,

high/low pressure separators etc.) was one of the key-issues in refineries since intensification

of low sulfur fuels boosted throughput of hydro-desulfurization units. Over a decade ago, based

on multi-year research effort within Joint Industry Program by Honeywell2, the first

sophisticated sour water corrosion model was released. Field applications of sour water

corrosion model proved its accuracy, matching calculated corrosion rate with refinery-

inspection data [2]. Further extensive, joint-research programs lead to development of several

prediction models focusing on corrosion prediction / quantification for key-refinery processes

such as H2SO4-alkylation, amine unit corrosion or crude distillation units [3-6]. All those

prediction models utilize real laboratory data from simulated experiments to predict corrosion

rates for carbon steel or other commonly used high materials of construction (MOC), based on

specific sets of process parameters integrated with multiphase flow and process modeling.

1 American Petroleum Institute 2 Honeywell is a trademark of Honeywell International Inc.

Historically, corrosion prediction models were used at design stage for verification of

MOC or estimation of expected service life upon potential process changes or modifications.

Such so-called “off-line” modelling approaches provided useful and important information

about corrosion degradation but still remained a few steps behind the “live” process

environment. In order to overcome those limitations, a new corrosion prediction concept was

proposed – on-line, real-time prediction models functioning in tandem with real time

monitoring systems.

Real-time corrosion modelling is conceptually parallel to real-time corrosion monitoring

where the main purpose is to provide on-time and accurate information about process

corrosivity directly to key-plant personnel ranging from operator to unit or plant manager. In a

similar way real-time corrosion models are designed to provide information about estimated

corrosion rate based on current process conditions. The advantage of on-line prediction is that

estimation of corrosion rates can be performed for multiple locations whereas hardware based,

traditional corrosion monitoring suffer from operational obstacles on the basis of resource

expenditure, intrusive implementation or inadequate infrastructure support (high temperature,

lack of access fittings etc.). Hence, real-time modelling may be considered as an efficient

complement to sensor-based monitoring, providing plant personnel predictive, quantified

corrosion data, and can be of significant assistance in ensuring safe, reliable unit operations.

This paper describes the overall concept of real-time corrosion prediction through

models and applications relevant to corrosion and integrity management. Field example of real-

time modelling application in crude distillation unit for prediction of high temperature

naphthenic (Nap) acid and sulfidic corrosion have also been provided.

Real-time corrosion modelling and integrity management

Integrity management comprises number of co-dependent elements like RBI, inspection

data management, Performance Monitoring, Process Safety and Corrosion monitoring. Those

elements fundamentally work under IOW umbrella, enabling accurate quantification of the risk

of corrosion failures. Defining critical parameters, establishing limits and their prioritization

according to a specified risk prioritization schema are essential to the IOW process according

API RP584 (Figure 1).

Figure 1 IOW work process per API RP584

Despite availability of a clear and simple step-wise framework for establishing integrity

boundaries, application of IOW in operating plants is significantly complex. Revealed gaps and

deficiencies in practical applications of IOW concept lead to fundamental question about

uncertainty in quantification of corrosion rate. Some of the challenges to operational

implementation of IOWs are given below:

a) Accurate corrosion knowledge / data base

IOWs, risk analysis and risk ranking should stem from the most recent, state-of-the-art

knowledge about interactions between materials and processes. Real-life corrosion processes

are defined by multi-parametric interactions and an adequate methodology to quantify

parametric interactions into corrosion rates is essential. Most industry guidelines combine

interactions of two or three major parameters based on approximate assessments (and not real

operational or lab corrosion data), e.g. for high temperature sulfidation and Nap Acids corrosion

typically pointed key-variables are TAN, Total Sulphur and temperature. In some process

scenarios such approaches may yield acceptable results; often, such generic rules have been

shown to result in serious over- or under-estimation of corrosion rates [7]. Consequently, IOW

criticality ranking, limits and mitigation actions become irrelevant because they do not reflect

the actual corrosion threat.

Furthermore, process flow, which often entails multiphase flow regimes and

interactions, is handled through simplified, generic, linear liquid phase velocity boundaries.

Such simplification of multiphase flow (ignoring two phase pressure drops, wall shear stress

and consequent mass transfer effects) means corrosion characterization is also correspondingly

flawed. It has been documented by a number of papers that characteristic wall shear stress

(WSS), not liquid phase velocity, determines behaviour of surface deposits/protective layers

and hence corrosion rate. Figure 2 shows the impact of WSS on corrosion rate of carbon steel

under mixed-mode damage mechanism (sulfidic-naphthenic) in HVGO side cut piping, while

linear velocity remains constant.

Figure 2 Simulation of corrosion rate versus temperature at different WSS; HVGO side cut,

carbon steel pipe – straight and with weld protrusion 5mm, TAN=3, High active sulphur

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

280 290 300 310 320 330 340

Co

rro

sio

n r

ate,

mm

/yea

r

Temperature, degC

Straight pipe. V=3.46m/s,WSS=27Pa

Weld Protrusion, V=3.46m/sWSS=94Pa

b) Process and corrosion data quality

One of the key challenges in overall corrosion and integrity managing process is the

quality of parametric process and measured / inspection corrosion data. Historically, ultrasonic

(UT) wall thickness surveys, mostly conducted off-line during turnarounds or shutdowns have

been the primary source for corrosion data. Major deficiencies of UT monitoring include highly

localized and time-delayed measurements. Superior results are possible through ‘real-time’

corrosion monitoring such as high resolution electrical resistance (ER) or

multi=electrochemical technique (e.g. Super-LPR) methods where actual corrosion rate is

measured within seconds or minutes [8-10]. With capabilities for direct linkage of real-time

corrosion data with plant’s DCS, the operator and process/corrosion engineers have a unique

opportunity to see corrosion processes in “live” mode. That option is however limited due to

the intrusive nature of implementation of monitoring sensors and the relatively high cost of

novel, on-line, real time monitoring techniques. Moreover, integration of corrosion and process

data, necessary for proper parametric correlation of process to corrosion, has hitherto remained

unrealized due to separation of so called “Corrosion Servers” from plant’s DCSs. Consequently,

analyses of corrosion-process interactions, an essential component of IOW limit management,

has remained elusive.

c) Information exchange

Lack of collaboration and information exchange between key parties (operation-

inspection-maintenance) involved in plant’s operation is a serious limitation for maintaining

integrity and corrosion management. Often, corrosion-inspection data are managed without any

critical metrics to assess effectiveness of applied corrosion mitigation strategies in a

quantified/measureable mode. Corrosion Key Performance Indicators (KPIs) based on

cumulative data from UT measurements, corrosion coupons or other traditional, offline, non-

real-time techniques distort any efforts to correlate real time process to corrosion with data that

may be deemed irrelevant or spurious. Standard operation/integrity dashboards are exclusively

reserved for production data (throughput, conversion rate, selectivity etc.) while critical

information about potential integrity threats originated from high corrosion rates are likely

abandoned.

Corrosion modelling in crude distillation unit – sulfidic and naphthenic acids corrosion

High temperature sulfidation and naphthenic acids corrosion – general information

High temperature sulfidation and naphthenic acid corrosion have been sources of

significant damage and well-publicized failures in hot sections of CDU/VDU units. Main

heaters and transfer lines, parts of distillation towers and side cut pipelines in both vacuum and

atmospheric units are primarily exposed to mentioned damage mechanisms (See Figure 3).

Naphthenic acids are carboxylic acids containing one or more cyclopentane and/or

cyclohexane rings. Low molecular naphthenic acids start to boil at about 200-210ºC, and hence

is usually considered as the lowest limiting temperature at which naphthenic acid attack may

occur. Here, corrosion entails naphthenic acid reaction with iron to form oil-soluble

naphthenates which can be schematically described by reaction (1).

𝐅𝐞 + 𝟐𝐑𝐂𝐎𝐎𝐇 → 𝐅𝐞(𝐑𝐂𝐎𝐎)𝟐 + 𝐇𝟐 ↑ (1)

Naphthenic acids are distilled and partitioned alongside crude fractions. Consequently, certain

fractions are particularly enriched with naphthenic acids due to similarity in boiling

temperatures e.g. vacuum gas oil (VGO) with typical distillation range 360-565ºC, leading to

potential for significant naphthenic acid corrosion during fractionation.

Figure 3 Schematic of CDU/VDU Units with potential areas of sulfidic and naphthenic acids

corrosion

High temperature sulfidic corrosion is driven by reactive sulfur species present in the

crude oil. Mercaptans, thiols, thiophenes and other organic-sulfur species contribute in sulfidic

corrosion based on their thermal stability and capability to generate hydrogen sulfide. Sulfidic

corrosion may be captured as the following chain of reactions and in some cases, may work in

synergy with naphthenic acid corrosion (equations 6-8):

𝐑 − 𝐒 𝐇 ∆→ 𝐑 ∗ + ∗ 𝐒 − 𝐇 (2)

𝐑 − 𝐒 − 𝐑 ∆→ 𝐑 ∗ + ∗ 𝐒 − 𝐑 (3)

∗ 𝐒𝐇 + 𝐑𝐇 → 𝐑 ∗ + 𝐇𝟐𝐒 ↑ (4)

𝐅𝐞 + 𝐇𝟐𝐒 → 𝐅𝐞𝐒 + 𝟐𝐇𝟐 ↑ (5)

𝐅𝐞 + 𝟐𝐑𝐂𝐎𝐎𝐇 → 𝐅𝐞(𝐑𝐂𝐎𝐎)𝟐 + 𝐇𝟐 ↑ (6)

𝐅𝐞(𝐑𝐂𝐎𝐎)𝟐 + 𝐇𝟐𝐒 → 𝐅𝐞𝐒 + 𝟐𝐑𝐂𝐎𝐎𝐇 (7)

𝐅𝐞(𝐑𝐂𝐎𝐎)𝟐 + 𝐇𝟐𝐒 ↔ 𝐅𝐞𝐒 + 𝟐𝐑𝐂𝐎𝐎𝐇 (8)

Depending on specific composition and types of sulphur species (different bond dissociation

energies for primary, secondary and tertiary sulphur compounds in straight chain or ring

structures) as well as on types and concentration of certain naphthenic acids in given crude, one

may expect either acceleration or reduction of corrosion rate [11].

Naphthenic acids and sulfidic corrosion prediction

The generic assessment of sulfidic-dominant corrosion is usually based on well-known

McConomy/modified McConomy curves that are also incorporated in referencing corrosion

tables shown in API RP581 Section G guidelines. However, the real-life reactivity of different

sulphur species is far more complicated than those captured through the McConomy approach.

Presence of naphthenic acids and synergistic interactions with sulphur compounds will further

complicate overall corrosion rate prediction process by adding multiple reaction paths and

consecutive corrosion scenarios [12]. It is commonly accepted that corrosion in sulfidic/nap

acid regime is at least determined by the following parameters:

- Specific nap acids composition that is reflecting by TAN/NAN

- “Reactive/active” sulphur that is NOT captured by total sulphur value

- Temperature

- Process flow dynamics driven by Wall Shear Stress, NOT linear velocity

Extensive joint-research studies (Crude Corrosivity JIP Phase I and II) conducted over last

decade by the authors’ organization [4] has yielded laboratory-data based prediction model for

sulfidic and Naphthenic acid corrosion. Simplified calculation flow chart for this model is

presented on Figure 4.

Figure 4 Sulfidic/Nap acid corrosion prediction flow chart based on JIP model – red lines

marked new elements to existing McConomy/APIRP581 rules.

Newly developed iso-cor diagrams for

sulfidic/Nap acids systems

Traditional, “static” modelling approach

Traditional approach to corrosion modelling is based on point-to-point analysis assuming

fixed limits for key corrosion parameters including temperature, flow rate and TAN value

(Figure 5). Calculations are usually performed in a step-sequence mode and incorporate

different configurations of the pipeline including the impact of flow restriction areas (elbows,

tees, weld protrusions) (Figure 6).

This approach, while shown to be quite accurate, assumes a “static” view of corrosion

and doesn’t reflect the impact of ‘real-time’, time-dependent fluctuations of process variables.

Using multipoint sensitivity analysis to simulate typical scenarios reflecting impact of selected

variables (Table 1) is one approach to addressing this limitation. However, assumptions of

“constant” changes to process variables may often not coincide with reality. In real-life

operational situations, process variables fluctuate within minutes/hours. In order to capture such

dynamic changes and calculate actual corrosion rate, an on-line, real-time modelling concept

has been described herein.

Figure 5 Typical corrosion modelling approach for CDU/VDU side cut line.

Figure 6 Isometric view of single pipe spool divided on several modelling sections and

example corrosion results for single element.

Corrosion rate

Damage mechanism

Flow regime

WSS/Velocity

Service life

Temperature

Flow rate

Pipe ID

Pipe configuration

Material of costruction

Active sulfur (as H2S)

TAN/NAT

Viscosity

Density

Surface roughness

Fraction type

Set of Parameters

Pre

dic

tio

n E

ngin

e

Results

Tag P

bar(g) T, C

H2S Levels ( Active Sulfur)

TAN mg/g

WSS Pa

Type of Flow

CS mmpy

12 Cr mmpy

Predicted Mechanism

Tag_0 5.88 235 Very High 0 20 Horizontal 0.18 0.078 Sulfidic

Dominated

Tag_1 5.88 235 Very High 0.1 20 Horizontal 0.19 0.10 Sulfidic

Dominated

Tag_7 5.88 235 Very High 0.7 20 Horizontal 0.23 0.11 Sulfidic

Dominated

Tag_8 5.88 235 Very High 0.8 20 Horizontal 0.24 0.11 Sulfidic

Dominated

Tag_9 5.88 235 Very High 0.9 20 Horizontal 0.25 0.11 Sulfidic

Dominated

Tag_10 5.88 235 Very High 1 20 Horizontal 0.26 0.11 Mixed

(Acidic/Sulfidic)

Tag_11 5.88 235 Very High 1.1 20 Horizontal 0.26 0.11 Mixed

(Acidic/Sulfidic)

Tag_12 5.88 235 Very High 1.2 20 Horizontal 0.26 0.11 Mixed

(Acidic/Sulfidic)

Table 1 Sensitivity analysis table with TAN as a major changing variable

On-line, Real time corrosion prediction approach

The on-line, real-time corrosion modelling concept introduced herein addresses,

remediates several deficiencies of traditional, “off-line” models. Typical gaps of standard

(“static”) prediction models are listed below:

- Static modeling is based on single-set of process data with a limited number of corrosion

scenarios

- Static prediction is usually outdated in comparison to real process conditions e.g.

original design assumptions may not be valid after years of operation

- Feedback/calculations from static prediction models are not available to key-personnel

from operation/maintenance and management

Incorporating the modelling engine into plant’s IT systems and “live” linkage with

DCS/Process Historian data, allows alleviation of many of these limitations. Example of typical

configuration for real-time prediction/monitoring system with separate prediction server is

shown on Figure 7. Specific architecture for networking of real time modelling (as well as real

time corrosion monitoring) with plant/unit information system will depend on certain IT

security policy and local business requirements. The real-time corrosion prediction server

downloads key-process parameters directly from DCS using simple tag-linking communication

– example of configuration window for sour water prediction engine is shown in Figure 8.

Currently, live, real-time variables from Historian are limited to temperature, flow rate or pH.

Other information required for corrosion rate computation, including Ammonium Bisulfide

levels, dissolved H2S, TAN or active sulphur levels can come from LIMS systems or pre-

defined crude properties / measurements. In a similar manner, mechanical properties of

analyzed process line e.g. pipe orientation, flow restrictions or corrosion allowance are also

often predefined, though vapor and liquid phase flow rates are often available as real time

values.

The results from on-line, real time calculations (corrosion rate, remaining life etc.) are

stored in process Historian using typical tag-based assignments. In the next step, data from the

Historian is made available, in real time, to integrity/corrosion management server for trending,

globalization and detailed analysis with respect to specific KPIs and IOWs.

In the real-time approach described here, real time corrosion models represent a

paradigm shift, in that they offer “virtual” corrosion monitoring of critical process streams, even

in equipment and pipes where sensor based monitoring is infeasible. They also complement

sensor-based corrosion monitoring, providing a validation basis for model based predictions at

selected corrosion “hot spots”.

Figure 8 Screenshot of configuration window for real time prediction module – ammonium

bisulfide corrosion prediciton engine

Prediction Engine

KPI Dashboard

Super LPR High Res ER High Res UT

4-20mA / HART / Modbus

Inspection / RBI server Lab server Integrity server

Operator Console

Figure 7 Schematic integration of real-time monitoring and prediction into plant’s

IT network.

Prediction of high temperature sulfidation and naphthenic acids corrosion in real time mode

Prediction and control of high temperature sulfidic / naphthenic acid corrosion processes

in CDU/VDU has been a significant challenge for operators for the last several decades of

refinery operations. Control of high temperature corrosion has been difficult and challenging,

stemming from absence of reliable intrusive or non-intrusive monitoring techniques that could

bring a realistic picture of corrosion degradation. Electrochemical systems are inapplicable due

to absence of a conductive medium; electrical resistance is often inadequate due to presence of

conductive corrosion deposits that significantly impact accuracy; high-sensitivity UT provides

location specific, spot data. Corrosion coupons provide reasonable results but in a form of

historical, cumulative metal loss which is irrelevant given the need for correlating real time

process data to corrosion damage as a means to process control.

Application of real time corrosion prediction models offers an accurate and adoptable

framework to derive / obtain a complete view of high temperature Sulfidic and Naphthenic acid

corrosion

Typical field application of real-time modelling requires the following elements:

o Corrosion prediction model integrated with plant DCS – typically installed on

separate server-type PC.

o Open Platform Communications (OPC) server to facilitate data-interchange

between plant’s DCS/Historian and Corrosion Prediction Server. Alternatively

the OPC server may act as a Historian independent of the Plant’s data collection

systems.

o Optionally, corrosion/integrity database-server that collects all available

corrosion and integrity data with a high-level corrosion or integrity Dashboards.

Figures 9 & 10 present screenshots from field application of real time prediction models

deployed in crude distillation (HVGO side cut lines). Each corrosion prediction point comprises

options for incorporation of real-time corrosion monitoring – if required due to observed high

corrosion rate or for overall verification purposes, hardware monitoring may be installed and

results compared with prediction in real time.

Figure 9 Screenshot from the operation panel showing corrosion modelling and monitoring

points (not active) in vacuum distillation

Calculated corrosion rates and respective corrosion KPIs – e.g., remaining service life

– are immediately delivered to operator dashboards and to other relevant AIM management /

engineering personnel e.g. corrosion engineer, RBI/integrity engineer/manager etc.

Figure 10 Screenshot from KPI panel showing major corrosion parameters

Summary & Conclusions

Appropriate corrosion management and implementation of IOWs are critical

components of plant asset integrity and reliability. Traditional, off-line determination of

corrosion rates through monitoring or modelling are deficient because of the absence of real-

time correlation. IOW establishment, qualification and risk categorization will not reflect actual

unit’s integrity status when such characterization is based on out-dated information.

Novel, real-time, on-line corrosion modelling approach has been described and shown

to overcome deficiencies of traditional corrosion risk assessment methods. Integration of real

time variables from the plant’s DCS with prediction models demonstrate in the capability to

model simultaneous model corrosion across multiple process streams. In this sense real-time

prediction acts as a “virtual” monitoring tool and helps focus traditional hardware based

monitoring to necessary locations.

Transition of existing, off-line corrosion models into the real time prediction framework

has been described and was seen to be particularly useful in assessment of high temperature

naphthenic acid and sulfidic corrosion in CDU/VDU, were high temperatures and low

conductance reduced the number of locations where hardware sensors could be implemented.

Plant implementation case study described herein showed the ease with which

naphthenic and sulfidic corrosion processes were monitored and correlated with major KPIs

(corrosion rate, remaining service life and remaining corrosion allowance). Data were provided

correlating KPIs to real time process parameters and presented in real-time mode to plant

management via standard integrity Dashboards. Benefits from real-time corrosion modelling

supported by real-time monitoring also included:

o reduction in cost for implementation of traditional corrosion monitoring which

can be targeted specifically to locations of high corrosion or criticality,

o ready access to Corrosion expertise for quick turnaround,

o quantified basis for proactive process management and identification of process

hot spots

o “Visibility” of corrosion degradation (via corrosion KPIs) to plant management

and operators as well as key functional managers responsible for plant reliability

and integrity (RBI, inspection, operation, maintenance).

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