iii

The Northern Alberta Institute of Technology

Edmonton, Alberta

A simplified model implementation of DeltaV Predict MPC for distillation process

optimization

Prepared for

Mr. Stephen Au, Instructor

Instrumentation Engineering Technology

School of Information and Engineering Technologies

Mr. H Cartmell, Instructor

English and Communications

Prepared by

Mr. Jeff Kuzub, Student

Instrumentation Engineering Technology

School of Information and Engineering Technologies

iv

November 21, 2014

62 Ormsby Road West NW

Edmonton, AB. T5T5V1

Mr. Stephen Au, Instructor

Mr. H Cartmell, Instructor

Instrumentation Engineering Technology

School of Information and Engineering Technologies, NAIT

11762 106 Street

Edmonton, AB. T5G2R1

Dear Sirs:

I am pleased to provide for your evaluation the enclosed report entitled A

simplified model implementation of DeltaV Predict MPC control for distillation process

optimization, for your consideration and in partial fulfillment of the requirements of the

Industrial Engineering Technology 490 curriculum.

This report will examine an advanced supervisory control scheme for one of

Alberta’s most commonly automated industrial operations within the energy sector,

hydrocarbon “upgrading” through the use of distillation columns. In order to optimize the

profitability of the process, Model Predictive Control is implemented through a

simplified model utilizing automated features. Recommendations for overcoming the

challenges inherent in this type of control system migration are suggested.

I would like to kindly acknowledge Mr. Saul Mtaulka, a Systems Integration

Engineer from Spartan Controls, for the contribution of his time and expertise in the

implementation of the Predict MPC software. In addition, the value of the contributions

of Mr. Zul Bandali, Instructor at the Northern Alberta Institute of Technology, towards

the author’s interest in this topic, as well as the further discussion on MPC

implementation as a project management challenge cannot be understated.

Should further information be required or should any clarification be needed, I

can be contacted at <jeffkuzub@shaw.ca>.

Regards,

Mr. Jeff Kuzub, Industrial Engineering Technology 490 Student

Enclosure: two copies of the technical report described above

iii

Abstract - Alberta’s oil-sands produce heavy crude oil which requires fractionation

and refinement to allow it to be marketed. This stream of refinement relies on

distillation as a primary unit step. The ubiquity of this process and its industrial

scale make optimization of its energy usage and through-put important to the

profitable operation of the process. Model Predictive Control (MPC) can provide

tight control and allow the process to operate closer to constraints reducing over-

head costs, where regulatory (PID) control cannot.

Traditional MPC implementation has been complex and costly. Modern

MPC software allows for automation of configuration and commissioning, speeding

migration time and reducing time-to-profit. A simplified implementation approach

is suggested, inverse of the former methods, using DeltaV Predict MPC software as

an example of available MPC controllers. This approach is modelled on a

distillation process and the resulting controller performance compared against pure

PID control. While some improvements in control quality are achieved through the

feed-forward control scheme, multivariable optimization is explored as the primary

benefit of MPC implementation. Generalized recommendations for applying this

simplified implementation approach to other processes are made.

Index Terms - Model Predictive Control, DeltaV Predict MPC, Implementation, Multivariable Control,

MPC for Distillation

iv

Table of Contents

Abstract………..………………………………………………………...………..iii

List of figures..…………………………………………..……………...…………v

1 Introduction

1.1 Problem….……………..………………….……………….………….3

1.2 Purpose…………….……………………………………..….………...5

1.3 Scope and Relevance……………….………………………..………..6

2 Model Predictive Control

2.1 Model Predictive Control Fundamentals……………………..……….8

2.2 The Multivariable Control Matrix…………………………………….8

2.3 The MPC Cost-Minimizing Algorithm………………………………..9

3 Simplified Implementation Strategy

3.1 Automated Data Collection….…………………………….……..…..13

3.2 Model and Controller Generations………………………..…….……15

3.3 Model Verification and Offline Simulation………………………….16

4 Model Implementation of DeltaV Predict MPC controller

4.1 Distillation Process Overview………...……………………………...19

4.2 Configuration of the MPC Function Block…………………………..20

4.3 Selection of Inputs and Outputs……… ……………………….…….21

4.4 Controller Tuning…………...……………………………………......22

4.5 Results………………………………………………………………..25

5 Conclusion…..……...……………………………………………………..…27

6 Appendix A, List of References….…………..……………..……………..…29

v

List of Figures

Figure Number Description Page Number

1 October 2014 Crack-Spread margins 2

2

Project Flow Diagram of simplified

implementation approach versus traditional

approach

13

3 Overview of proposed MPC implementation

project phases 14

4 Distillation Process Flow Diagram 18

5 MPC Function-Block visualization 20

6 Distillation Multivariable Control Matrix 22

7 Tuning of output dynamics 24

8 MPC versus PID control comparison 26

1

1 Introduction

Nearly 1.9 million barrels of heavy crude oil was extracted each day from the

Alberta oil-sands in 2012. Fifty four percent was sent for further upgrading within

Alberta, primarily to form synthetic crude oil for export [1]. Synthetic crude from

primary-upgrading is an important diluent for heavy bitumen, bringing its density up to a

transportable specific gravity so it can be brought to market. Alberta has a further daily

capacity for secondary-refining of synthetic and heavy crude oil into consumer-level

products of almost half a million barrels per day [1]. These products range from lighter-

fluid through gasoline, diesel, jet fuel and heavy oils, and also form feed-stocks for

fertilizer, polymer and other petrochemical manufacturing. Both these streams of

refinement rely on distillation as a primary unit-operation for fractionation, and both

refinement operations begin to add economic value to their through-put starting at this

primary step of upgrading.

A refining business must rely on the margin of profitability between the price of

feed-stock and the price of marketable output streams. This is known as the “Crack-

Spread”. Gasoline, for example, has a typical high-season market price more than three

times that of the diluted bitumen from which it was made, making this a highly profitable

output [2]. The cost of construction and operation of refinement capacity is huge, and the

actual margin of profitability can be small and highly variable. Still, natural resource

companies continue to invest millions in refinement capacity, with soaring distillation

columns up to 157 feet (48 m) tall [3]. Only by operating within the narrow crack-spread

can these operators achieve economic viability. The most efficient operations maximize

2

1 Introduction (continued)

through-put and minimize overhead costs through effective process control to optimize

profitability and return on investment.

Figure 1. October 2014 crack-spread margins. A week-over-week comparison of the market price-

gap between Western Canadian Select heavy crude oil and the market price of gasoline

(raw stocks for blending) October, shows only a narrow gap of profitability for operators.

Model Predictive Control (MPC) for industrial process control was pioneered in

the early1970s by a project-team of engineers from Shell who developed the first DMC1

and IDCOM software algorithms [4]. Before this, the concept of MPC was restricted to

the academic community. Further generational improvements in the technology have

refined it to its current state: a practical and easily implemented control methodology

which is ideally suited for multi-variable, non-linear and other complex processes.

3

1 INTRODUCTION (continued)

MPC is a supervisory-level control system which integrates multiple inputs and

outputs to direct an entire process towards a set of desired goals. It cascades instructions

into single-variable control-loops within its control hierarchy to direct not just one single

control element, but an entire collection of related manipulated variables (MVs)

simultaneously. MPC can control several diverse output variables allowing for much

more generalized process goals to be achieved. MPC controllers typically achieve all

former control objectives as their PID-based controllers did before, but also allow for the

optimization of the operating conditions to minimize energy inputs and operate closer to

physical limits. For an operator of a refinery in Alberta, this means achieving the

designed maximum volumetric through-put volume from the distillation column,

maintaining the purity of the tops-product and simultaneously using as little heat as

possible without allowing the column to vary below effective pressures and temperatures.

Achieving this optimized state, across all inputs and outputs, represents the promise of

MPC.

1.1 PROBLEM

Heavy crude oil feedstock requires fractionation, using continuous a distillation

process. Traditional Proportional-Integral-Derivative gain (PID) loop controls and

compound control schemes have been typically employed to achieve the stable conditions

at which the process can operate at maximum through-put and achieve accurate product

specifications. However the complex interactive nature of the process, coupled with its

4

1.1 PROBLEM (CONTINUED)

importance as the primary unit-step, and its energy-intensiveness, make it a prime target

for control optimization using MPC.

Large and capitally-intensive refineries are typically conservative and risk-

adverse in adopting new technology. Fully digital distributed-control-systems (DCS)

have been available for over thirty years, but are only now becoming common-place.

MPC has been no exception, and it has suffered considerably form the lack of

widespread, integrated DCS use, forcing MPC controllers to be built from ad-hoc

software components and custom-written data exchange protocols. The amount of

specialized engineering experience and maintenance involved in such a system was

prohibitive, both economically and technically.

The MPC controller must be mapped to the entire process, including many

physical input and output variables. It must also operate within a processor that is robust

and powerful enough to perform many complex iterative calculations with high-speed. A

generally advanced control methodology such as MPC cannot be easily implemented

within a control network that is generally un-complex and de-centralized or lacking in

significant processing power.

The business-case driving implementation of MPC has, until recently, been weak

and showed long time to recoup investment. Newer DCS systems provide an ideal

environment within which MPC can easily be implemented, and quickly begin showing

5

1.1 PROBLEM (CONTINUED)

economic benefits. Current MPC controllers now automate much of their own

configuration and commissioning which reduces costs and speeds implementation, but

lack precedence. A model MPC implementation program for a distillation process needs

to be modelled to facilitate adoption of the technology to drive profitability for Alberta’s

crude oil refinement industry.

1.2 PURPOSE

By modelling a typical implementation project for MPC, a generalized

methodology for its implementation may be derived. This methodology takes full

advantage of the evolved state of a fourth-generation MPC controller, and its automated

features. A significant portion of this stream-lined implementation strategy rests upon

inverting the traditional project phases. This allows the controller to be physically

installed first, so that it can begin automated set-up and commissioning early on. Previous

programs installed the actual MPC controller last, only after all of the control systems

parameters had been established. This approach may be extended from the generalized

distillation process example here to other diverse industrial processes. Promoting MPC

usage will enhance the profitability of Alberta’s crude oil refinement sector and reduce

the carbon footprint of production, and can yield similar benefits across the entire oil and

gas industry.

6

1.3 Scope and Relevence

MPC operational algorithms have been well discussed by Allgower, Badgwell,

Qin, Rawlings and Wright (1999) [5], and Garcia, Prett and Woraly (1989) [6] in their

notable reviews of MPC theory; and thus they will be only noted here summarily to

provide necessary background for controller implementation and historical significance.

Utilization of the automated features typical to a modern MPC solution, such as

Emerson Process Control’s DeltaV Predict MPC, can significantly speed the

implementation and commissioning phases of a migration project. These features are

investigated within the context of a sample distillation process control migration.

An emphasis on the properties of the distillation process which challenge loop

control schemes is presented as the focus of an overview of distillation. The process’

variables are examined and their mapping to the MPC controller through population of

the Multivariable Control Matrix is discussed. Tuning of the static process model and

dynamic behaviours are demonstrated. The MPC controller’s generalized strategy to

solve for the optimized process state is demonstrated.

7

1.3 Scope and Relevence (continued)

Recommendations appropriate to the current economic state of crude oil

refinement in Alberta concerning the use of MPC can be made. No specific product

recommendations will be made although the Predict MPC software is used as an example

of available modern MPC features.

2 Model Predictive Control

MPC as a technology resembles human experience more closely than any other

control system. It relies on a mathematical model to predict the consequences of its

control actions, and thus anticipates process disturbances in the same way a human might

see a crack in the sidewalk and recognize it as a tripping hazard to be avoided before a

fall occurs. For the human brain, stating a process goal implies that the necessary

conditions to produce that goal are also met, and many supporting elements are thus also

controlled. The goal of walking from one point to another is the goal from supervisory

viewpoint, but this also entails avoiding tripping hazards and navigating the shortest path

to the objective as well. Properly tuned MPC also behaves with a supervisory knowledge

of the overall process objectives, and acts with foresight, akin to elements of human

behaviour.

8

2.1 Model Predictive Control Fundamentals

Two fundamental disadvantages exist for traditional feed-back based control

systems. The first is that they use controllers which respond to an error value generated

by the controller. This error represents a deviation in some controlled variable (CV) from

the ideal state. Feed-forward control systems have long existed where less complex

processes could be mathematically modelled using energy or mass-balances. These

control systems attempt to make control element changes before deviations from set-

points occur. MPC control schemes extend upon feed-forward modelling to continuously

move an entire multivariable system towards an idealized operational state.

The second disadvantage comes from the single-variable scope of typical PID

control loops; a controller can only consider and maintain one variable. This problem is

exacerbated in multi-variable processes where one control-loop’s operation can affect

another and this disturbance may go unmeasured. MPC considers the control problem of

a matrix of desired outcomes and available inputs to compute the most efficient state

using a mathematical model of the actual process.

2.2 The Multivariable Control Matrix

MPC’s ability to effectively and efficiently control multi-variable processes arises

from the variables interdependencies. These relationships can be solved for

9

2.2 The Multivariable Control Matrix (continued)

mathematically or be experimentally determined and used to create the mathematical

model of the process. The purpose of this model is to allow for an optimum state of all

control elements to be found. The model consists of a first-order differential relationship

between input and output variables, placed within a matrix, called the Multivariable

Control Matrix.

The Multivariable Control Matrix determines the steady-state output of the model

and is populated with all relevant process-inputs and output variables. With each cell of

the matrix describing a complex differential function which solves for the effect of any

input variable and all of the output variables, an optimization algorithm can be used to

calculate a set of control element changes which moves the entire system towards optimal

states of all output variables [7]. This model allows the algorithm to test control actions

and direct the process with intelligence.

2.3 The MPC Cost-Minimizing Algorithm

Early MPC research was focussed on ways to express the problem of multivariable

control mathematically, so that a cost-minimizing function could be used to solve for an

optimized position of all inputs to achieve desired outputs [8]. The process model, in the

10

2.3 The MPC Cost-Minimizing Algorithm (continued)

form of the control matrix, represents the multivariable process, and provides the

environment to simulate controller actions. All possible process states or combinations of

inputs are considered, and are then evaluated for their relative degree of success in

obtaining the desired outputs. This methodology is not unlike a chess program’s method

of selecting its next move from all possible moves, after evaluating each for their ability

to move closer to capturing the opponent’s king.

Adapting iterative solution finders to MPC requires adapting them to an un-

ending continuous process. No finite time horizon, such as the end of a game of chess,

exists for continuous process control, making handling of the time horizon an important

and distinguishing feature of MPC. As early as the 1960’s, Kalman et al. [9] approached

the problem of a system in constant dynamic response by providing the controller an

artificial finite time horizon that was equal to the slowest dynamic variables response

time within the process. After this slowest responding variable reaches steady-state, the

entire process can also be said to be at steady-state. This time horizon is used to compare

all available process states and select the best available one to control outputs. Then

controller actions that shift the entire process towards the best available state are

cascaded to loop controllers. Upon the next scan cycle of the controller, a new time

horizon is defined and the selection of a new idealized process sate is repeated. In this

way steady state outputs are targeted, despite the process never reaching steady-state.

11

2.3 The MPC Cost-Minimizing Algorithm (continued)

Predict MPC, like most modern MPC controllers, uses a hybrid of several

algorithms, including a derivative of the first DMC algorithm developed by Shell in the

early 1970s. This fourth-generation algorithm is highly efficient in its use of

computational resources and can calculate control matrices of up to 3200 cells [10]. As

with most complex computations, the ideal state is solved for using a brute-force iterative

approach.

3 Simplified Implementation Approach

Traditional MPC implementations typically required outside engineering

consultants who would first need to perform operational readiness testing and process

observation. From this data, inputs and outputs of the process would be selected, and a

step-testing regiment would be determined. Data collected would be processed and

screened using separate off-line methods to reveal the multivariable relationships and

provide model data. Once a model with suitable accuracy is developed it is inverted to

provide a controller strategy. The controller would still require extensive system-

integration work to allow it to network with the field devices. The common use of

proprietary software to perform the controller generation and integration made MPC even

more specialized and cost prohibitive. Data servers had to be installed and configured, as

well as graphical user interfaces. Significant expense and time would be spent and little

12

3 Simplified Implementation Approach (continued)

apparent progress would be seen. This methodology also pushed further back the time-to-

profitability of an MPC implementation and increased maintenance costs dramatically

[11].

Within a DCS system, many of the challenges inherent to non-networked control

devices disappear, allowing MPC implementation to focus on effective control, not on

building sufficient architecture to support the necessary data input/output mapping to the

controller. This can also allow a well-integrated MPC controller to be introduced early on

to a DCS system where it can automate a large portion of its own implementation and

commissioning. The simplified approach presented here relies heavily on this strategy to

reduce operator input to a minimum.

13

Figure 2. Project Flow Diagram of simplified implementation approach versus traditional

approach. Installing the MPC controller first allows taking advantage of its ability to

automate its own integration and shortens the duration of each task and the overall

project.

3.1 Automated Data Collection

The simplified approach begins with installation of MPC software and its basic

configuration. The next phases of the project can be completed automatically with only

minimal operator direction required, although full customization is also possible. Data

collection, the next phase of implementation, takes full advantage of automation and the

DCS process historian.

14

3.1 Automated Data Collection (continued)

Figure 3. Overview of proposed MPC implementation project phases. Implementation project

phases suggested for using a fourth-generation MPC controller with automated

implementation tools and features, such as DeltaV Predict MPC.

Data Collection involves first providing the MPC controller access to the process

historian data base from which it will begin to construct its process model. Statistical

analysis of historian data reflects long-term trends, and traditional regression-certainty

analysis is applied to discover the relationships between input and output variables.

During this process, it is often found that some unexpected MVs will show influence on

the choses CVs. These may include ambient temperature effects, upstream disturbances

and downstream loading.

15

3.2 Automated Data Collection (continued)

Finally the process model is completed with assimilation of step-test data. MPC

controllers can also automate this stage of model creation allowing users to strike a

balance between the current operations of the plant and the required minor process upsets

caused by this testing. Step-tests provide data that tests the process model and completes

missing data fields. These tests can even use data generated by daily operational process

changes instead of deliberate steps in controller’s output, minimizing their impact while

still providing good data to the controller.

3.3 Model and Controller Generation

Model creation proceeds by means of system identification. This refers to the

process of creating models of dynamic processes using statistical information. It is a

determining early step in MPC implementation: if accurate, the model’s outputs will

accurately mirror those of the actual plant and MPC can begin to yield its operational

benefits quickly. After automated creation, the model’s accuracy in predicting the process

outputs reaction to input changes is generally quite good, even without any optimization

or feedback trimming which occurs later, once live process data is being fed into the

MPC controller [11].

16

3.3 Model and Controller Generation (continued)

Controller generation begins once the model has reached a minimum level of

sophistication for its accuracy to be of acceptable tolerance. By inverting the process

model, a complimentary, but negative controller scheme can be derived [11]. The

controller is next mapped to its input/output points within the controller network and also

integrated with other supervisory safety controls. Some MPC manufacturers may supply

additional controller templates for customization and quick-start up, including suggesting

variables to populate the multivariable control matrix, and providing tuning guidelines.

3.4 Model Verification and Offline Simulation

Once the MPC controller has completed its integration, it can operate this model

in an off-line mode where it provides simulated controller outputs while existing process

control is unaffected. This is a useful feature that serves to verify the model’s outputs and

the control strategy as well. Root-mean–squared error regression techniques quantify

inaccuracies in the model to produce a confidence index. When sufficiently accurate, the

model alerts the user to proceed with further implementation. DeltaV Predict includes

trouble-shooting tools which can suggest causes of model inaccuracies and remedial

methods of data acquisition or re-configuration [11].

17

3.4 Model Verification and Offline Simulation (continued)

Continuous feedback trimming of the model can be used to further refine it, even

in an off-line mode. This feature is especially helpful when minor process changes occur

which upset the dynamics of the process slightly. The installation of a new sensor well,

for example, may alter flow patterns in a section of pipe. Feedback trimming of the

model will quickly allow for tailoring of the model to reflect this change.

A fully developed model is also highly useful as a simulation tool for process

engineering and development as well as a training tool for operations. Simulations can

allow development of start-up and shut-down procedures, as well as operational

procedures. The most important use of the model is for testing the control schemes

themselves and playing out scenarios including failures and emergency conditions. At

this point, with testing and training completed, the MPC controller can be brought online.

4 Model Implementation of DeltaV Predict MPC controller

Below is a typical continuous distillation column process, which poses many

challenges to control engineers. Visible are the multiple outputs and inputs to the system,

although their interactive and non-linear nature cannot be displayed by this simple

process flow diagram. Compounded by long-lag times, even well-tuned PID control

18

4 Model Implementation of DeltaV Predict MPC controller

(continued)

often yields sub-optimal performance, and some controller actions may even push the

process beyond its efficient operating conditions in the singular pursuit of one controlled

variable. MPC’s ability to simultaneously control all the variables within the process

makes it an ideal strategy for this type of process.

Figure 4. Distillation Process Flow Diagram. Noted are inputs and outputs from the process.

Temperature and pressure are outputs (controlled variables), although they do form inputs

into the separation process by influencing purity of output streams and separation

efficiency.

19

4.1 Distillation Process Overview

Exploiting the differences in vapor pressure (boiling point temperatures) of

hydrocarbons forms the scientific principle behind distillation. As heavy oil is heated, the

lighter fractions with smaller carbon-chains and lower molecular weights boil into vapor

before the heavier ones. Capturing the vapors and condensing them back to liquid

(distillate or “tops”) separates the lighter fractions from the residue of heavier

hydrocarbons (residium or “bottoms”).

The distillation column, a large pressure vessel with a height several times greater

than its diameter, is plumbed for feedstock input near its midpoint. Recirculation of

residue from a reboiler enters the column at the bottom, and vapor extraction at the top

leads to a condenser. Column pressure is controlled by varying the flow rate of vapor out

of the column to the condenser. Distillate is recirculated back into the top of the column

forming reflux which has a cooling effect, establishing a temperature gradient, essential

to efficient separation. Purity of the distillate is also increased with an increase in reflux

flow. Likewise the purity of the residue is also increased by recirculation from the re-

boiler to the bottom of the column. Multiple individual trays or packing exists within the

column to provide localized conditions of equilibrium, improving the separation. The

distillate and residue streams are sent for further refinement or to sales as finished

products from the distillation operation.

20

4.2 Configuration of the MPC function block

This simplified model implementation begins with the installation of an MPC

module containing the controller and its associated implementation tools. The Predict

MPC controller is visualized as a function block and is easily and rapidly connected to

input and outputs, including automatically self-assigning all process historian connections

necessary. The MPC controller also begins monitoring of normal process changes and

operator actions to begin to accrue the data for the process model. When appropriate,

step-testing to populate any gaps in the process model data is automatically triggered and

can be performed strategically to minimize process disturbance, while still providing

quality response data.

Figure 5. MPC Function-Block visualization. Predict MPC controller showing sample connections

to process inputs and outputs. The simplicity of function-block programming

environments makes integration of this controller strategy rapid and straight-forward.

21

4.3 Selection of Inputs and Outputs

For a typical distillation column operation, the maximum flow rate and the

maximum purity of both distillate and residue sent to sales or for further refinement is the

primary operating goal [12]. The flow rates of these streams should be maximized for

economic benefit, but are reduced by refluxing and recirculation of residue, although this

promotes purity [13].

Column pressure and temperature are outputs of the control of the reflux and

reboiler flow rates, but are inputs in achieving the purity and flow rates of output streams.

This makes them a special type of variable known as a constraint or Limiting Variable

(LV) which is a special case of a CV. LVs can range somewhat about their set-points,

while true CVs must be controlled as close as possible to the set-point [14].

Inputs or MVs available to control the system includes the flow rate of feedstock,

flow rate of heated bottoms from the reboiler, and the flow rate of reflux back into the

process. The condenser and reboiler control net energy addition to the system and the

cooling/ heating medium flow rates are considered as inputs to allow for energy

optimization.

When data from the process historian is analyzed, additional input or output variables

may be suggested, especially if the distillation column is within a series of unit operations

such as in a refinery. Disturbances to the individual column may be caused by

22

4.3 Selection of Inputs and Outputs (continued)

control loop actions further upstream or downstream, and thus may be modelled

accurately by taking that control loops output as input to the control matrix [15].

Figure 6. Distillation Multivariable Control Matrix. Shown are MVs, CVs and LVs selected for the

distillation process controller. The matrix is populated with transfer functions relating

each of the variables, forming the steady-state process model.

4.4 Controller Tuning

Static tuning the MPC controller is achieved by assigning relative priorities to

each of the CVs. This is achieved within the control matrix by attaching a coefficient

which relates the CV’s priority to the magnitude of error between set-point and process

conditions. This coefficient is a multiplier, typically called a “penalty” or “cost”

23

4.4 Controller Tuning (continued)

(“Penalty on Error” in the Predict MPC system), which weights the relative significance

of error in multiple CVs. In the specific case of the distillation column, distillate purity is

prioritized over flow rate slightly as product that is off-specification is often useless,

regardless of the output volume. Error from desired purity is responded to more

aggressively than error from desired flow rate, by increasing the magnitude of the

penalty-on-error for that CV. Initially all penalties are set at a value 1.0, and incremental

changes of 10-20% are suggested [16]. Integrating variables, with no final steady state,

should be given a penalty of 0.5 or less to avoid instability.

In a similar fashion multiple MVs can be prioritized by attaching a penalty

coefficient to them which relates the costs of their manipulation to the magnitude of the

change. This coefficient is often a cost multiplier, as processes must have an energy flow

stream and its associated costs. It is termed a “Penalty on Move” in the DeltaV Predict

MPC system. These values are set by default by Predict MPC although they are

configurable. For example, an operator may have an adaptive penalty-on-move

configuration where real-time energy cost data is fed to an MPC controller to vary the

cost-function of moving the heating medium control valve. Increments similar to those

used for tuning penalty-on-error gain should be used for tuning [16].

24

4.4 Controller Tuning (continued)

Dynamic tuning of the movements of both input and outputs to target a specific

response pattern is also possible. By selecting the desired dynamics process disturbances

can be avoided and process equipment’s limitations can be respected. In our distillation

process, it is useful to force the heating medium flow valve to move towards a new set-

point by targeting a first-order response with a relatively long time constant. In this way,

the steam-valve’s relatively fragile graphite packing may have its wear reduced by slow

movement of this control element. Any specific transfer function can be selected,

although often a first-order response with variable time constant is generally used [13].

Figure 7. Tuning of output dynamics. A sample control matrix showing detail of the dynamics of

each transfer function, and the steady-state output. Various user-selectable and

customizable dynamics are available with DeltaV Predict.

25

4.5 Results

MPC cascades controller set-point into existing PID control loops, and so there is

no dramatic shift in operation of the process when MPC is brought on-line. Well-tuned

PID controllers already minimize error well, and moderate improvements in error

minimizations and disturbance rejection may be the only way to know an MPC controller

is operational. Comparisons of PID and MPC control are only somewhat useful in

illustrating these moderate control improvements.

A simulated distillation column, created by Manimaran et al. (2013) [17] for the

express purpose of comparing MPC and PID control methodologies showed reductions in

total error of approximately 13.7% averaged across several step-tests. The models

parameters were developed using Sundaresan-Krishnaswamy methods, and PID tuning

was completed using Ziegler-Nichols tuning methods. This experiment demonstrates

improved control with MPC; however caution should be taken in the interpretation of

these results, as the translation from simulation to operational environments will erode

these gains somewhat.

26

4.5 Results (continued)

Figure 8. MPC versus PID control comparison. An example step-test comparing total error (using

integrated area) after step-testing with both MPC and PID controllers, tuned

appropriately. Enhanced disturbance rejection and improved control owes primarily to

the predictive process model [17].

In practice, MPC may show moderate control improvements over PID control, but

this is only a small portion of the MPC control methodology. The desired feature is the

ability to optimize the process state across all variables simultaneously. This allows

industrial processes to proceed with tight control, while minimizing energy inputs,

maximizing through-put and operating in a safe manner for both personnel and

equipment. These gains in efficiency may be moderate, but continuous distillation

operation accrues small cumulative benefits of reduced overhead. This translates to

bottom-line profit, reduced downtime, increased production, improved profitability and

enhanced product specifications. Cases exist of MPC bringing operations closer to

optimal process states of energy use such that implementation costs were recovered in as

little as ten days [15]. These savings, coupled with the reduced costs of implementation

27

4.5 Results (continued)

using the proposed simplified approach can keep operations within the crack-spread

margin despite volatile markets and feedstock.

Ongoing maintenance of the MPC controller is minimal, as the Predict module

continually re-evaluates its own process model by way of feed-back trimming. MPC may

also lengthen equipment maintenance intervals and may even be a predictor of

preventative maintenance needs. In the case of sensor failure, the process model can even

simulate these inputs to continue operation and prevent downtime. Predict proves to be a

robust control methodology due to its total integration into the DeltaV DCS, and similar

experiences are available with most DCS manufacturers providing MPC controllers also.

5 Conclusion

High risk accompanies the operation of capitally-intensive refinement capacity in

Alberta. A marketplace rife with hazards from fluctuating demand and prices, regulatory

interventions and international competition necessitates maximum operational efficiency

to guarantee profitability. MPC, in its most evolved current state, represents the best tool

for achieving an optimally efficient process state to achieve profitability. With automated

implementation tools, not only are migration costs reduced, but so is down-time and

time-to-profitability. The simplified approach to MPC implementation outlined here

follows the familiar template of any DCS function programming, by representing the

28

5 Conclusion (continued)

MPC controller as a function block. This type of integration saves the costs of hiring

outside consulting engineers, and takes full advantage of DCS usage.

Modern MPC controllers make this advanced control methodology available to

more and more operators as its costs fall and its usability is improved. Following the

implementation example described here will allow MPC migration to be streamlined.

This approach may be adapted to many other multivariable processes, appropriate for

MPC control.

Alberta’s heavy-oil producers face unique and significant challenges. Daily

operational profitability may be secured with MPC, and the larger goals of reduction of

energy usage (and thus carbon emissions) can also be achieved incrementally. Value

adding at the distillation column is maximized with MPC, in ways that other regulatory

control cannot reproduce. With Alberta’s economic outlook so dependent on the profit

margins of oil and gas producers, MPC is a technology with far-reaching implications.

The simplified model implementation presented here should be copied and adapted to suit

other industrial process control applications so that the economy, the environment, and

the population of Alberta all can benefit.

29

Appendix A – List of References

[1] Alberta Energy, (2014, February). “Upgrading and Refining”. , Government of

Alberta. Edmonton, Alberta, Canada. [Industry Report]. Available:

<http://www.energy.alberta.ca/Oil/pdfs/FSRefiningUpgrading.pdf>

[2] G. R. Crandall, R. A. McKetta, G. A. Houlton, et al., “Phase II – refined products and

petrochemicals from bitumen”. Houston, Texas: Purvin & Gertz, Inc. (With Assistance

from CMAI), page 48.

[3] FLUOR Constructors. (2003). Shell Canada - Athabasca Oil Sands Dry Bitumen

Plant. Available:

<http://www.fluor.com/canada/projects/ProjectInfoPage.aspx?PrjID=74>

[4] S. Joe. Qin and Thomas A. Badgwell, “A survey of industrial model predictive

control technology”, Control Engineering Practice, volume 11, pages 733 – 764, July

2003.

[5] Allgower, F., & Zheng, A., (Eds.). (2000). “Nonlinear model predictive control,

progress in systems and control theory”. Vol. 26. Basel,Boston, Berlin: Birkhauser

Verlag.

[6 Garcia, C. E., Prett, D. M., & Morari, M. (1989). “Model predictive control: Theory

and practice—a survey”. Automatica, 25(3), 335–348.

[7] Vojtech Vesely and Danica Rosinova (2010). “Robust Model Predictive Control

Design”, Model Predictive Control, Tao Zheng (Ed.), ISBN: 978-953-307-102-2, InTech,

August 2010. Available from: <http://www.intechopen.com/books/model-predictive-

control/robust-model-predictive-control-design>

[8] David A. Hokanson and James G. Gerstle, “Dynamic Matrix Control Multivariable

Controllers” in Practical Distillation Control, 1st ed. New York, NY, Van Nostrand

Reinhold, 1992, ch. 12, Sections 12.1 – 12.7, pp. 248–271.

[9] Kalman, R. E. (1960b). “A new approach to linear filtering and prediction problems”.

Transactions of ASME, Journal of Basic Engineering, 87, 35–45.

[10] DeltaVpredict and DeltaVPredictPRO, First ed., Emerson Process Management,

Austin, Texas, 2013, pp 4 – 6.

[11] Vasiliki Tzovla and Ashish Mehta, “A Simplified and Integrated Approach to Model

Predictive Control Implementation”, Instrument Society of America and Fisher-

Rosemount Systems, Austin, Texas, Technical Report, 2000.

30

[12] J.W. Ponton, “Degrees of Freedom Analysis in Process Control”, Chemical

Engineering Science, Volume 49, No. 13, pages 2089 – 2095, July 1994.

[13] Dr. M.J. Willis, “Selecting a Distillation Column Control Strategy a basic guide”.

Callaghan, NSW, Australia: Department of Chemical and Process Engineering,

University of Newcastle, 2000, pages 2-12. Available:

<http://lorien.ncl.ac.uk/ming/control/gen/column1.pdf>

[14] J.W. Ponton, “The ECOSSE Control HyperCourse, Part 3 - Control Systems For

Complex Processes”. Edinburgh, Scotland, U.K.: The University of Edinburgh, School of

Engineering, 2007, section 3.1, Available

<http://www.see.ed.ac.uk/~jwp/control06/controlcourse/restricted/course/fourth/course/m

odule3-1.html>

[15] Umesh Mathur, P.E., Robert. D Rounding, Daniel R. Webb, Robert J. Conroy, “Use

Model Predictive Control to Improve Distillation Operations”, American Institute of

Chemical Engineers, volume 1, pages 3 – 41, January 2008.

[16] Beall, J. (2012).” Master the Mystery and Marvels of DeltaV Model Predictive

Control”. Emerson Process Soln. Houston, Tx. Tech Rep. October 15, 2012, 7-9.

[17] Manimaran M. “Optimization and composition control of Distillation column using

MPC”, IJET, vol. 5 no. 2, pp. 1224-1230, May 2013.