HONEYWELL PROCESS CONTROL &

OPTIMIZATION TECHNOLOGY
 HONEYWELL FORGE
Advanced Process Control

PORTFOLIO OVERVIEW

Honeywell Forge Advanced Process Control maximizes operating profitability by optimizing production tradeoffs,
managing product specifications and pushing operating envelopes to constraints.

KEY FEATURES

Optimize operations Combined APC + lifecycle Sustain Performance

through advanced management tool +Cloud through cloud-paired
multivariable control and based monitoring into a application.
optimization Single software package

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 Introduction

Multivariable Predictive Control

(MPC)

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Levels of Process Control

Optimization Local LP/QP Multi-unit LP/QP Rigorous model-based Gate to

Gate
Optimization Optimization Optimization
Optimization

Model
E.g.
Smith Multivariable
Based Forge APC or
predictors predictive
Control etc. control [DMC] + PACE+SMOC

Advanced Feedforward Dynamic On-stream Constraint

Regulatory control decoupling Analyzers Control
Control

Basic Single Cascade

Regulatory PID
Control

Different technologies available at each level

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Levels of Process Control

• Basic regulatory
▪ Advanced regulatory (intermediate regulatory)
• Multivariable, model based
• Constrained economic optimization
Increasing complexity and costs and
diminishing benefits

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What is MPC ?
▪ Process Control
• Regulatory control, you look at one input (measurement) and control it to a
target (setpoint) by adjusting one output/valve (e.g Flow, Level, etc.):

▪ SISO = Single Input Single Output

• Advanced control, you look at many inputs and then control one or more
outputs:

▪ MIMO = Multiple Input Multiple Output

▪ Multivariable (digestion, filtration, washing)

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What is MPC ?
▪ Multivariable Process Control (MPC) that is model-based software and is used

to direct the process operation and is commonly referred to as Advanced

Predictive Control (APC) or Model Predictive Control.

▪ MPC requires that the process model created accurately represents the process

dynamics. Improved economics of the operation or production improvements are

typical driving forces for using MPC

▪ MPC’s ability to co-ordinate variables to more stably and push toward a known

constraint is key here

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What is MPC ?
MPC will include:
▪ Offline and online model building, model verification, and data analysis tools that
ensure model accuracy
▪ Driver software to collect historical data from control systems or online process
historian systems
▪ Operator guidance to advise where the MPC is taking the process
▪ Prediction of controlled variables based on future planned moves of manipulated
variables
▪ Run time MPC tuning to adjust for changes in process dynamics without having to
rebuild the model

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What is Multivariable Control (MVC) or APC ?

Single-Input-Single-Output
SP

PV PID OP

Multi-Input-Multi-Output
SPs

PV1 OP1
MPC OP2
PV2 OP3
PNn OPm

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MPC Overview

Model Predictive Control (MPC) technologies use process relationships to better control and
optimize complex industrial processes.

Set points, Ranges, Optimization Objectives

CV’s MPC
DV’s MV’s

Process Models

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What is a CV?

CV's are Controlled Variables. The MPC controller is designed to "control" these variables

(within a range or to a setpoint.) Representative examples would be product qualities,

valve outputs, level %, etc.

Setpoints, Ranges, Optimization Objectives

Controlled Variables
(CV’s)
MPC Manipulated Variables
(MV’s)
Disturbance Variables
(DV’s)
Process Models

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What is a MV?

MV's are Manipulated Variables. The MPC controller can move these values (within a range) to

control the CV's. Representative examples would be flow, pressure, and temperature controller

setpoints.
Setpoints, Ranges, Optimization Objectives

Controlled
Variables(CV’s)
MPC Manipulated
Disturbance Variables (MV’s)
Variables(DV’s)

Process Models

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What is a DV?
DV's are Disturbance Variables. These variables impact the process but the controller is not

allowed to move the value. DV's are included so that changes to these variables can be

accounted for by the controller. A typical example of a DV would be a unit feed rate.

Setpoints, Ranges, Optimization Objectives

Controlled
Variables(CV’s)
Manipulated
MPC
Variables(MV’s)
Disturbance
Variables(DV’s)

Process Models

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What is a Process Model?
A Process Model is simply a time varying relationship between the process variables.

Setpoints, Ranges,
Optimization Objectives

CV’s MPC MV’s

DV’s

Process Models

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Simple Process Example for APC
• Imagine a shower…
- Two taps (hot & cold stream), one mixer head
- Two handles

▪ What are the objectives ? ( CVs)

▪ Correct water flow
CVs
▪ Correct water temperature
▪ Minimise hot water usage? (Optimization)
Model
▪ Keep temperature within a range?

MVs
▪ Two controlled variables (MVs)
▪ Two taps (hot & cold stream) DVs
MVs
▪ Uncontrollable factor (DVs) DVs
▪ Sudden changes in water pressure
▪ Water temperature of hot stream drop

▪ One mixer head ( Process Model)

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Model Identification

Top quality
Reflux
Step test signals Process Pressure
Process responses

Reboil Bottom draw

Step Test Data Process Control Models

Reflux Reboil
Reflux
Top quality
Reboil
Model
Top quality Identification Pressure

Pressure
Bottom draw
Bottom draw

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 HOW DOES CONTROLLER COMPUTE CONTROL MOVE ?

Setpoints, Ranges, • Control Calculations:

Optimization Objectives
The prediction calculations estimate the control adjustments
necessary to the process to meet the control objectives of the
Controlled Variable
(CVs)
operator.
Process Manipulated Variable
controller (MVs)
Disturbance Variable
(DVs) • Prediction Calculations:
These calculations track the effect of disturbance variables in

Process Model the process as well as the results of previous control moves to
predict the future state of the process (actions).

• Optimization Calculations:
These calculations push a process according to economic
considerations after the control objectives are met. Imposing
optimization calculations on a process is optional.

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Model Based Predictive Control2
CV Targets
Optimizer

MVs
Prediction Controller Process
CVs

Model
-

Prediction Model Error

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(SISO) Prediction Models2
2.5

1.97 1.99 1.9

2 1.95
1.90 1.98 1.999 2.00
1.81 1.93 1.96
1.86
1.73
1.62 Finite step response model
1.5 1.47 MV
Value

CV
1.26

1 0.97

Deadtime =2
0.5
0.57 Gain =2
Unit MV Change at t = 1

0
0 1 5 10 Time Intervals15 20 25

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(SISO) Prediction Models5
12
Now
10 Unforced Response

8
Value

MV
CV
4

2
History Settling Time
0
0 5 10 15 20 25 30 35
Time Intervals

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What Does the Model Look Like?
APC Control Matrix

MV 1 MV 2 MV 3 DV 1

CV 1

CV 2

CV 3

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APC Model
Process Relationships between CVs, MVs and DVs

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Model Reconciliation1
CV Targets
Optimizer

MVs
Prediction Controller Process
CVs

Model
-

Prediction Model Error

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Model Reconciliation2
12
Now
10
Bias update
8
Value

6
MV
Reconciled CV trajectory CV
4 CV process

2
History Settling Time
0
0 5 10 15 20 25 30 35
Time Intervals

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Control
CV Targets
Optimizer

MVs
Prediction Controller Process
CVs

Model
-

Prediction Model Error

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Desired CV Trajectory
12
Now SP Reconciled CV trajectory
10

Error Vector MV
Value

Now/SS
6 CV
Target
Error

History Settling Time

0
0 5 10 15 20 25 30 35 40
Time Intervals

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Horizon With Control (one MV move)
12
Now SP Forced CV trajectory
10

Error Vector MV
Now/SS
Value

6 CV
Target
Error

4
Future MV move

History Settling Time

0
0 5 10 15 20 25 30 35 40
Time Intervals

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Horizon With Control
12
Now SP Forced CV trajectory
10

Error Vector MV
Now/SS
Value

6 CV
Target
Error

4
Future MV moves

History Settling Time

0
0 5 10 15 20 25 30 35 40
Time Intervals

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Move Calculations

• Planning multiple future MV moves allows

- Improved control performance
▪ Multivariable system (dynamic decoupling)
▪ Complex dynamics and dead-times
- Game plan of where MV’s will move
▪ Can defer MV movement
• Least squares method is very aggressive / unstable
- No mechanism to tune controller performance
- Alternatives
▪ Modify reference trajectory
▪ Use movement suppression for MV’s
▪ Use alternative solution strategy

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Optimization1
CV Targets
Optimizer

MVs
Prediction Controller Process
CVs

Model
-

Prediction Model Error

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Optimization2

• Starting point for optimizer

- Current values of the MVs
- Steady state (unforced) values for the CVs
▪ Based on MV and DV moves until current time
• Optimizer solves for a set of new MV steady state values (one step controller) taking into
account
- MV constraint limits
- CV constraint limits
- An objective function (direction of steepest decent)
▪ Based on economics for the process

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Optimization3
MV 2

MV 1 Low MV 1 High

MV 2 High

Current position

MV 2 Low

Direction: from economics

MV 1

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Debutanizer Column1
Interaction: Cooling water

CV1
Increase in reflux (MV1)
causes: TI
LC

 in top temperature F
MV1 FC
C
CV3
 in distillate
DV1
CV4 Distillate
 in base temperature Feed

 in base product
MV2
F
C
Steam
CV5
LC
CV Controlled variable Base
DV Disturbance (FF) variable product
CV2 TI
MV Manipulated variable

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Debutanizer Column2

Interaction: Cooling water

Increase in steam (MV2) CV1

causes: LC
TI
Reflux
 in top temperature F
MV1 FC
C CV3
 in distillate
DV1
CV4 Distillate
 in bottom temperature Feed

 in base product MV2

F
C
Both steam and reflux CV5
Steam
affect most column LC
variables Base
product
CV2
TI

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How Does APC Achieve The Benefits?

APC Improves Process Stability and pushes the process to the most profitable
operating point

Profit
Specification Limit

Operator Target

Poor Control Improved Control Improved Profit by

Changing Targets

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 37

Incentive for Advanced Control

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 Incentives for Advanced Control

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INCENTIVE FOR SOLUTION IN THIS INITIATIVE
❑ Closer Optimal Condition

MV2

CV1 CV2
MV2 High Limit High
BEST Operating Region

MV2 Low Limit Low

MV1 Low Limit MV1 High Limit MV1

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How Does APC Gain Its Benefit?

Optimum
Manipulated Variable Increases process
e.g. Feed Rate capacity Advanced Control

$ Limits the
Production Capacity
$ Costly operation
beyond constraint

$
Conservative Delay in responding Overcompensation
operation to extra capacity for violation

Worst Operator Best Operator

(Do nothing) (Chase the optimum)

Time

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How Does MPC Deliver Benefits?
Every minute, twenty-four hours throughout the year!

Consistent
Operation

Normal Operation
Days per Year

Plant Capacity Limit

< 60% Daily Production 100%

$
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Why Consider APC?
Advanced Process Control (APC) and Optimization benefits are field proven and
generate high rates of return on investment.

Typical Benefits: Typical Payback Period

▪ Increased Throughput & Yields Oil and Gas 1 to 2 months

▪ Reduced energy usage Refining 3 to 6 months

▪ Decreased Operating Costs Petrochem 4 to 6 months

▪ Improved Quality Consistency Pulping/Paper 6 to 8 months

▪ Increased Operating Flexibility
Industrial Power 10 to 12 months
▪ Improved Process Stability
“ Give A worthwhile Return On Investment ”

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Why Consider APC?
The benefits of multivariable predictive control and optimization strategies can
also be intangible.

Intangible Benefits of APC:

▪ Improved Process Safety

▪ Improved Operator Effectiveness

▪ Reduced Downstream Variability

▪ Better Process Information

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 Honeywell’s Advanced Control Solutions

• Process Controller • Inferentials

Past Future
Optimal Response
Predicted
Setpoint Unforced
CV Response
Control Funnel
• Control Performance Monitor & Analytics
MV Assumed Values

Minimum Effort Move

• Profit Online Modeler • Profit Optimizer: Real-time,

Dynamic Optimization
Optimum
*

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TECHNOLOGY
No. Honeywell Technology General Function

▪ Model Predictive Control

▪ APC Engineering Studio: Offline Engineering Tool, Modeling, Converting Tool
1 Process Controller
and Simulation
▪ APCOS / Web-Viewer : Operator interface

2 Inferentials ▪ Soft Sensing, Statistical Modeling and Lab/Analyzer Management

3 Control Modeler ▪ Automated Testing, Identification, Online Modeler and model maintenance

▪ Real Time Optimization

4 Optimizer ▪ Muti Unit Optimizer
▪ Site Wild Optimizer

5 Control Performance Analytics ▪ Monitoring and Analysis for APC, PID and financial impact.

▪ Custom Toolkit Development, Customizable function blocks, URT Software Developers

6 Toolbox
Kit, Python

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Honeywell Technology

Honeywell Application and Tool

for

Process Control & Optimization Technology

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PROCESS CONTROLLER
• Forge APC Controller is a 2nd generation multivariable
predictive controller (MVPC) that employs the patented Robust
Multivariable Predictive Controller Technology (RMPCT ). Past Future
• Inherently More Stable Optimal Response

- Minimum MV move – no overcorrections

Setpoint Predicted
Predicted
- Optimal response trajectories CV Unforced
Unforced
Response
Response
- Less sensitive to model error Control Funnel

- Built in robustness and stability with On-line Dynamic

MV Assumed Values
matrix conditioning protection
• Easier to Tune
- “One-knob” tuning T=0
Minimum Effort Move

- No need for move suppression

- Independent CV by CV tuning (MVs too)
- >90% of applications work with default tuning
• Easier to Commission and keep running
- Fully supports closed-loop identification
- Warm mode enables validation

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APC ENGINEERING STUDIO
• Project organization tools
• Integrated help and documentation
• Direct data import & export
• Data analysis, manipulation, vector
calculations, transformations
• Data statistics
• Trending, advanced visualization options
• Multiple dynamic modeling options
• 3rd Party model convertor
• Statistical correlation analysis, model
confidence, and statistical summaries to
gauge model quality
• Offline simulation & testing
• Auto-documentation and back-build
• Benefit Calculation Module

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SMOC Conversion
▪ The conversion of SMOC controllers to Process Controller
models is done using the models documented in the report
generated by the SMOC tool. The document is generated in
.htm format. The HTML and other files are documentation of
the Graphical Model Builder (GMB) tool of SMOC.
▪ The transfer functions which are directly relating the MV/DV to
CV can be plugged in the submodel in Process Controller
model matrix
▪ The values of the function block parameters:
▪ Dead Time, Gain, Beta, Time Constants (Tau1, Tau2)
along with the transfer function model type, first order,
second order, ramp etc.
▪ In APC Engineering Studio tool, Model files can be
populated manually with equivalent Laplace models.

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APC OPERATOR STATION
❑ Engages operator in proactive monitoring and
control

▪ Provide operator tools for problem analysis

▪ Maximum flexibility in layout and

personalization

❑ Multiple environments to suit wide variety of user

needs

▪ Standard .net-based windows interface

▪ HMIweb shape-based interface

❑ Embedded Performance Metrics

❑ Online Tunning

❑ Operator Guidance

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WEB VIEWER
❑ Web Viewer is an Application Dashboard
solution to allow user to work with APC
applications from business network (L4)
❑ Application Dashboard provides an interface
to:
▪ Monitor the applications
▪ Activate or Deactivate the applications
▪ Turn the applications ON, OFF, or
WARM
▪ Set CV, MV high and low limits
▪ Launch the themes and migrating
APCOS Themes into APC Web Viewer
▪ Lab Update
▪ The Event Log display
▪ Specify user access limit

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INFERENTIALS
❑ Soft Sensor Development
❑ Extensive Data Pre-processing, Modeling, Data
Validation, and Model Qualification Capabilities
❑ Linear and non-linear modeling capability
▪ Ordinary, Weighted, and Partial Least
❑ Regressed and User-entered
▪ Regress to custom equation forms
▪ User-Entered Online Transformations
❑ Steady-state and dynamic
▪ Dynamic Subspace Inferential
▪ Automated dynamic compensation for asynchronous
property data
❑ Lab/Analyzer Bias Update
❑ Tightly Integrated into Forge APC Engineering Studio

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CONTROL MODELER
❑ Automates Key Tasks:
▪ Data collection, Plant Testing
▪ Model identification
▪ Visualization of test progress
▪ End of test indication
❑ Applicable to all Development Scenarios
▪ Open & Closed-loop testing with constraint management
▪ ID algorithm common to runtime and offline environments
Forge APC Engineering Studio
▪ Immediate visualizations on model quality
❑ Results
▪ 40% to 60% reduction in plant test time
▪ 60% to 99% reduction in post-test model ID
▪ Improved controller performance

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SITE WIDE OPTIMIZER
A multilayer solution which inherits a business planning
model, acquires underlying operating constraints
through a unit level APC model and delivers an
end-to-end optimization solution in real time. Honeywell’s
patented techniques dynamically optimize the plant,
always ensures a feasible solution driving decision
making in real time
❑ Pure simulation-based optimizers are limited by lack
of real-time, dynamic constraint information
▪ Require every CV from APC to be defined in the
optimizer
▪ Dynamic variability causes solution to be unstable
and violate APC constraints
❑ Solution: Proxy Limits
▪ Plant-wide Optimizer Proxy Limits represent the
constraints in lower- level APC
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Control Performance Analytics-Unified (CPA-U)
(CPA-Unified) is a solution that monitors, identifies, diagnoses
and remedies control asset issues and their economic impact
across all levels of plant control. It ties instruments, PIDs,
analyzers and advanced process control (APC).

❑ Providing quantified performance metrices and

recommendations.

❑ Identifying and tracking constraint violations for main CVs,

with analysis of probable causes, clamped MVs and CVs,
MVs dropped by operators due to instrumentation issues,
Controller Off

❑ Key Process Variables – Summarizes the lost opportunity

❑ Lost Opportunity Costs – Summarizes, by category the lost

❑ Prioritizing issues by financial impact.

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UOP TOOLKIT
Why Honeywell Forge APC for UOP units?
UOP Inside Optimization • Varying Feedstocks
• Rate, Composition

Leveraging • Significant unmeasured unit loads/disturbances

• APC layered optimization • Operator skill set issues
• UOP licensor knowledge • Considerable operating knowledge embedded in APC
UOP Performance Toolkit Calculations configuration
• Proprietary process calculation packages • Analyzer maintenance issues
• Unique value drivers
• Inferential property estimation
APC Solutions for:
• CCR Platforming
• Changing economics
• Aromatics (Isomar and Tatoray) • Ability to switch operating modes more effectively
• Hydroprocessing (Unicracking and Unifining) • Keeping track of where you maximize profit
• Oleflex • Benefit
Delivering • Optimize production, reduce off-spec, increase yields
and reduce energy
• Better overall performance with complex catalytic process
• More sustainable solution for long-term success
• Real-time access to UOP technology and knowledge

No Other Licensor or Vendor Can Match the Potential!

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HONEYWELL SERVICE SUPPORT
The Benefits Guardianship Program (BGP) is Honeywell’s premium maintenance and
support offering. That’s designed its BGP program to allow our customers to gain maximum
benefits from Honeywell products.

1. BGP

The support of software: Free download software updates(patches) and upgrades(new

releases). And combination of consulting via telephone and email from support
specialists.

2. BGP- Plus

BGP Plus extends from the BGP offering by providing a combination of remote
supporting and on-site visits support services.

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What Are the Main Features Of APC ?

Helps coordinate and

decouple the effects of Uses dynamic models
multiple process variable to predict process
interactions behavior into the future.
This information is then
used to proactively
control the process.

APC
Integrated
optimization Monitors and maintains
Manipulated Variables
capabilities to drive
and Controlled Variables
applications toward within limits while it is
specified design objectives. controlling the process

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TRAINING
Honeywell provides training courses

Customers can
choose courses of
interest and
advanced courses.

Honeywell Worldwide Automation College

(https://www.honeywellprocess.com/en-US/training/Pages/default.aspx)

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