1

Paper T9a05 - F

Advanced Process Control and Optimization of AcetyleneHydrogenation Reactors

By

Mario Lesieur

Petromont

Varennes, Quebec, Canada

Sanjay Sharma and Ravi Nath

Honeywell Hi-Spec Solutions

Houston, TX, USA

Presented at the 2003 AIChE Spring National Meeting

15th Annual Ethylene Producers’ Conference

Session T9a05 - Ethylene Plant Process Control

New Orleans , Louisiana

April 2, 2003

UNPUBLISHED

AIChE shall not be responsible for statements or opinions contained in papers or printedin its publications

2

Advanced Process Control and Optimization of AcetyleneHydrogenation Reactors

Mario LesieurPetromont

Varennes, Quebec, Canada

Sanjay Sharma and Ravi NathHoneywell Hi-Spec Solutions

Houston, TX, USA

Paper presented at the 15th Annual Ethylene Producers ConferenceNew Orleans, LA, April 2003

ABSTRACT

Polymer grade Ethylene has stringent specifications of acetylene impurity, around 1 PPMmaximum. Since acetylene stays with ethylene-ethane fraction of the cracked gas all through theseparation process, typical ethylene processes rely on catalytic hydrogenation of acetylene tomeet product specification.

In addition to the desired reaction of acetylene hydrogenation, ethylene hydrogenation also takesplace. Obviously, ethylene hydrogenation must be minimized as it contributes directly to productloss. In addition to these two main reactions, a number of other side reactions also take place thatlead to the formation of low order polymers (green oil) that build up on the catalyst surface andslowly reduce its activity. For this reason reactors must be regenerated on a frequent basis.Reactor run-length varies, typically between 60 to 90 days, depending on the operating conditionsencountered by the reactor.

Typically 3 catalytic reactors are employed with two reactors in service at any given time. Foreach reactor there typically are two manipulated variables (MV): hydrogen flow to the reactor andreactor inlet temperature. Carbon Monoxide, which acts as an inhibitor, is sometimes added toimprove reaction selectivity and could be an additional MV. During normal operation there aredegrees of freedom in the operation of the reactor system; that is there are more MVs than neededfor control, and that presents us with an opportunity for economic optimization.

Control of acetylene reactors however is a challenging problem because of the inherent difficultyin controlling to an impurity level that is nearly zero. Similarly, economic optimization is alsochallenging as it requires a careful tradeoff between reactor activity, selectivity and run-length.

For Petromont, these challenging problems have been solved by using Honeywell’s robust multi-variable predictive controller that works in conjunction with and gets guidance from a rigorous,on-line, non-linear, multi-time period, kinetic based optimizer. These advanced applications werefirst commissioned in October 2001. These applications were modified in May 2002 after theprocess turn around in which some instrumentation changes were made. Use of these advancedapplications has resulted in a much smoother operation of the process and a net improvement inthe ethylene yield by about half a percent.

This paper highlights the overall project and details the control and optimization applications.

3

INTRODUCTION

The Petromont Olefins Plant in Varennes is a joint venture between Dow, Canada and SGF whichis a Quebec Government Crown Corporation. The nominal capacity of the Olefins Plant is 292thousand metric tones per year. Olefins products for the plant are completely consumed bydownstream polymer units which are fed by connecting pipelines.

Petromont Olefins Plant at Varennes has advanced process control applications (APC) for most ofthe units. Ten Profit Controllers and a plant wide Profit Optimizer were commissioned byHoneywell in November 1998 [1]. Profit Controllers were for the following units:

· 6 Furnaces,· Demethanizer, Cold Box, Ethylene Refrigeration,· Deethanizer,· Ethylene Fractionator, Propylene Refrigeration, and· Depropanizer including Condensate Stripper.

Scope of Profit Optimizer comprised all of the units above. In addition, important operatingconstraints in process units that did not have APC were included in simplified form as CombinedConstraints with Profit optimizer. The Profit Optimizer had Combined Constraints for theQuench, the CGC and the Acetylene Hydrogenation Unit (AHU).

A primary process constraint for the Acetylene Hydrogenation Unit is the maximum outletacetylene concentration that is governed by the Ethylene product specification. Since the ProfitOptimizer lacked a model for the AHU, a Combined Constraint for the AHU effluentconcentration was not possible. Instead, a Combined Constraint for the inlet acetyleneconcentration was added as a proxy for the outlet concentration constraint. The upper limit forthis Combined Constraint was manually specified which for practical reasons had to beconservative, and often limited Profit Optimizer moves and thus the performance by the process.

To obtain maximum optimization benefits, AHU model needs to be incorporated in the plant-wide Profit Optimizer, which is the motivation for the current effort. This work is planned in twophases. Phase I, which is discussed in this paper comprises a Profit Controller and a stand-alonekinetic based optimizer for the AHU. In this framework, the AHU Optimizer sends MV desiredvalues to the AHU Controller that continuously moves the reactor operation towards the mosteconomical operating point. Scope for phase 2 of the project comprises integration of the AHUProfit Controller and the AHU Optimizer to the existing plant-wide Profit Optimizer. At the timeof this writing in late 2002, the phase 2 of the project is in the final stages of completion.

PROCESS DESCRIPTION:

Petromont Olefins plant at Varennes, Quebec, Canada has a back-end Acetylene HydrogenationUnit (AHU) designed to reduce effluent Acetylene down to sub PPM level. AHU comprisesthree reactors: one large and two identical smaller units. At any given time, two reactors are inservice while the third one is either regenerating or is in stand-by mode. The large reactor alwaystakes the lead position and the guard position is always taken by one of the smaller reactors. Thereactors are operated adiabatically with an intercooler between the two reactors. Overhead vaporstream from the de-ethanizer tower mixes with a hydrogen rich stream from de-methanizeroverhead and is preheated before introduction in the first acetylene converter (lead bed). The

4

effluent stream from the lead bed mixes with additional hydrogen and is pre-cooled beforeintroduction in the second acetylene reactor (guard bed). Effluent stream from the guard bed goesto the ethylene fractionator where high purity ethylene product is drawn in the overhead and sentto polymer units. There are analyzers to measure hydrogen purity and acetylene concentration ineach of the two reactor feed streams. There also are on-line measurements of hydrogen andacetylene concentration in the effluent streams. Typically, these analyzers are on 0 – 3 minutecycle time. There are several measurements of temperature along the process flow. Figure 1 is asimplified process flow diagram.

PROCESS CHEMISTRY

There are two main reactions in the acetylene reactors, a) the desirable reaction of conversion ofacetylene to ethylene and b) the undesirable reaction of conversion of ethylene to ethane.

1) Acetylene Hydrogenation:C2H2 + H2 � C2H4 (Reaction 1)

2) Ethylene Hydrogenation:C2H4 + H2 � C2H6 (Reaction 2)

Both reactions are exothermic. Acetylene Converters at Petromont are adiabatic so that the heatof reaction is absorbed by the process fluid that increases the process fluid temperature graduallyalong the reactor length. The two reactions proceed in parallel. The extent to which each of thereaction proceeds depends on the rate of reaction that is discussed next.

Reaction RatesThe reaction rate equation gives a relationship between rate of reaction and the variables thataffect it. Such variables include various compositions, temperature, pressure, surface propertiesof catalyst and reactor geometry.

Rate equations can take on many forms. For heterogeneous catalysis where the number of activesites on the catalyst surface is rate limiting, such as the case here, the Langmuir-Hinshelwoodformulation is generally applicable [2, 3, 4]. It takes the following form for the abovementionedreactions:

K1 * TEe /1− * [C2H2]X1 * [H2]Y1

r1 = (1 + A1[H2] + B1 * [C2H2] + C1 * [C2H4])Z1

K2 * TEe /2− * [C2H4]X2 * [H2]Y2

r2 = (1 + A2[H2] + B2 * [C2H2] + C2 * [C2H4]) Z2

Catalyst DeactivationCatalyst in the larger lead reactor deactivates rather rapidly and is typically regenerated every 8 to12 weeks. The catalyst in the smaller guard bed deactivates somewhat slower and is also

5

regenerated every 8 to 12 weeks. Although the mechanism of catalyst deactivation is not verywell understood, it is generally believed that deactivation is caused by polymerization ofacetylene, for the sake of simplicity these reactions will be lumped as;

Acetylene � Coke (Reaction 3)

Again for the sake of simplicity we can assume the following form of rate equation for the cokingreaction;

r3 = K3 * TEe /3− * [C2H2]X3

The effect of coking on the main reactions is that of slowing down their reaction rates. This canbe adequately represented by multiplying each of the hydrogenation reaction rate by anexponential decay factor such as Cdie− , where C is the coke level on the catalyst and di is anappropriate coefficient.

ADVANCED PROCESS CONTROL (APC)

The primary objective of APC is to maintain all of the controlled variables within the operatorspecified limits. A secondary objective is to implement the optimum operating targets for each ofthe two reactors that are sent by the rigorous optimization application while honoring the operatorspecified limits. Honeywell’s Profit Controller was utilized to attain these objectives.

Honeywell’s Profit Controller is a dynamic model based multivariable controller. This controlleralso includes an optimization layer to exploit any extra degrees of freedom available to thecontroller. The control layer of the Profit Controller utilizes a proprietary technology calledRange Control Algorithm (RCA) [5]. This technology allows the Profit Controller to toleratesignificant model mismatch. This was an important requirement for this application because oneof the important variables normally operates at or near zero that creates a model mismatch.

The optimization layer within Profit Controller contains an objective function that includes bothlinear as well as quadratic terms. For many applications the optimization layer is adequate todefine the optimum operating point within the operating domain. The operating domain isdefined by the operator specified bounds on the controlled and manipulated variables. In the caseof AHU optimization, there are significant challenges (such as time dependency of variables,non-linearities of the process etc.) that needed to be addressed by a rigorous, multi-periodoptimization function external to Profit Controller. Therefore, the Profit Controller’soptimization layer in this case was primarily used as a conduit to pass the optimum targets fromthe external rigorous optimizer to the Profit Controller control layer.

Manipulated Variables (MVs): There are 2 independent handles in the operation of eachreactor: inlet temperature and inlet hydrogen stream flow. For the two reactors in AHU there are4 MVs.

Controlled Variables (CVs): The single most important CV in the AHU controller is theacetylene concentration in the guard bed effluent stream. Other important CVs are there tomaintain viable reaction (prevention of quenching and run away conditions) and to maintaincontrollability (avoidance of valve saturation). There are additional CVs for informationalpurposes, an example being estimated ethylene gain in the AHU.

6

Disturbance Variables (DVs): There are three measured disturbances to the process: reactorfeed flow, reactor hydrocarbon feed composition and the inlet hydrogen purity. Thesedisturbances add to the process non-linearity. From the control perspective, these disturbancevariables were combined into a single DV that was linear for the controller. Therefore, the ProfitController had a single disturbance variable.

ChallengesProfit Controller is a dynamic model based multivariable controller. The dynamic models used inthe Profit Controller are identified from plant step testing. It is very difficult to obtain a dynamicprocess model for a variable that normally operates at or near zero. This is the case for theacetylene concentration in the second (or guard) reactor effluent. The second challenge is tocontrol a concentration that normally operates at or near zero because the process becomesdiscontinuous when the concentration reaches zero. Other minor technical challenges includeddetecting which reactor is in service, in what position (lead or guard), and for how long (run-length). The reactor in-service and position detection are important so that appropriate reactor’sconstraint can be addressed by the controller.

OpportunitiesThe good news is that most of the time there are degrees of freedom in the controller, meaningthere are more MVs than are needed for control purpose. These extra degrees of freedom areexploited by the multi-period, rigorous kinetic model based optimization application (discussed innext section). The optimum operating conditions are supplied to the Profit Controller in terms ofdesired manipulated variable values by the optimization application. A quadratic function that ispart of the Profit Controller optimization layer then passes these desired values to the controllayer to move the process towards the optimum conditions.

OPTIMIZATION

In general, the objective of optimization is to determine the values of independent variables thatmaximize a given objection function that is consistent with prescribed process constraints.Usually the objective function is defined as an economic profit function. Implied in optimizationis a process model that relates the various variables. These relations usually are manifestations ofmass and energy conservation equations. For a steady state process the optimization problemsimplifies to maximizing the instantaneous value the objective function;

Max P(x)S/t

M(x) = 0LL < C(x) < HL

For an unsteady state process, where the variables are function of time, the objective functionbecomes an integral and the model and constraints could include time dependent variables;

Max �max

0

),(t

dttxP

S/t M( x′ ,x, t) = 0LL < C( x′ ,x, t) < HL

7

For AHU, the problem is still more complex as two differential variables are involved. Althoughan assumption of pseudo steady state for reactors is adequate, nonetheless the reaction rateschange along the reactor length. And by including the catalyst decay over time, the reaction ratesbecome time dependent;

Max �max

0

)),((t

dttVxP

S/tM( x′ (t,v), x(t,v), t, v) = 0

LL < C( x′ (t,V), x(t,V), t, V) < HL

This is indeed a difficult problem to solve. Engineering judgment is required to make theproblem tractable without losing the essence of the problem. The details of approach taken forthe current project are discussed next.

Objective functionTime is discretized into N equal intervals over the entire time horizon, so our objective functionsimplifies to a summation;

Max �=

N

iitVET

1),(

Process ModelThe rate equations need to include catalyst deactivation;

K1 * TEe /1− * [C2H2]X1 * [H2]Y1

r1 = * e-D1*[Coke] (1)(1 + A1[H2] + B1 * [C2H2] + C1 * [C2H4])Z1

K2 * TEe /2− * [C2H4]X2 * [H2]Y2

r2 = * e-D2*[Coke] (2)(1 + A2[H2] + B2 * [C2H2] + C2 * [C2H4]) Z2

r3 = K3 * TEe /3− * [C2H2]X3 (3)

Note that the net effect of inclusion of catalyst deactivation is that the original ODEs becomePDEs. As we noticed earlier, inclusion of catalyst deactivation also leads to a multi time periodobjective function. Indeed the optimization problem formulation with deactivation is morecomplex but it is also more realistic. Solution to this problem will give realistic operations policythat will be consistent with desired reactor run length.

Differential heat and mass balances for each time period are;

∂ [H2] / ∂ v = – (r1 + r2) / F (4)∂ [C2H2] / ∂ v = – r1 / F (5)∂ [C2H4] / ∂ v = (r1 – r2) / F (6)∂ [C2H6] / ∂ v = r2 / F (7)

8

∂ [Coke] / ∂ t = r3 (8)∂ T / ∂ v = (r1·∆Hr1 + r2· ∆Hr2) / (F * CP) (9)

Above 9 equations represent the model for a single reactor in a single time period. The secondreactor will have similar equations. And this model will basically be repeated for each of the timeperiods. Multi-time period model is obtained by connecting successive time period models by acommon thread of progressive coke buildup.

There will be auxiliary algebraic equation to represent additional heat and material balancerelationships for example for mixing of hydrogen and hydrocarbon streams.

ConstraintsConstraints for the optimizer are the same as those for the AHU controller as described earlier.

Solution technologyThe model described above is numerically stiff as acetylene concentration changes by more thanfour orders of magnitude. Traditional numerical integration techniques such as Runge-Kutta andpredictor-corrector are computationally too slow for on-line use even with today’s fast computers.The least squares kinetic parameter estimation problem to identify coefficients of the rateequations are two point boundary value problems that require iterative solution of the modelequations, compute time for which will be impractical with traditional numerical integrationtechniques.

Orthogonal collocation is a finite element technique that converts differential equations intoalgebraic equations [6, 7]. These algebraic equations can then be solved very efficiently usingmodern SQP solvers such as NOVA [8, 9].

Honeywell’s acetylene reactor models utilize the orthogonal collocation technology and arewritten in “open” form. In “open” formulation, all model equations, constraints and optimalityconditions are solved simultaneously. “Open” formulations also provide a complete flexibility inchanging the specification of variables. The end result is that a single model can be used forparameter estimation, for simulation and for optimization simply by changing the specification ofthe variables. For example, for parameter estimation case, reactor operating conditions andreactor inlet and outlet streams are ‘fixed’ while the kinetic parameter are ‘optimized’; forsimulation case, the kinetic parameters, reactor inlet stream and rector operating conditions are‘fixed’ while the reactor outlet streams are ‘calculated’; and for the optimization case, the kineticparameters and inlet streams are ‘fixed’ while the reactor operating conditions are ‘optimized’and the reactor outlet streams are ‘calculated’.

Optimization cycleOn-line optimization is a cyclical activity [10]. A typical optimization cycle is shown in figure 2.It comprises following 6 steps;

1. Steady state detectionHere we are dealing with a pseudo state reactor model. It is important to ensure thatprocess data used for optimization calculations is representative of a steady operation.

2. Data Reconciliation & Parameter Update (DRPU)Despite the best efforts of all parties, neither the model nor the process measurements areperfect. Purpose of this step is to fit the model to the current operation of the process; this

9

way the constraints can be meaningfully applied. This is usually accomplished byadjusting the lead coefficients in the rate equations and by biasing model variables tomatch process measurements. A single time period model is adequate for the DRPUcase.

3. SimulationFor nonlinear optimization, it is helpful to have a reasonably consistent initialization.Purpose of this step is to perform a multi time period simulation to generate a consistentinitialization for the optimization step.

4. OptimizationOptimization is performed with current CV and MV limits. In addition, it is usual toapply step limits to keep the optimization from making very large moves.

5. Consistency checkPurpose of this step is to ensure that the state of the process has not changed since thestart of optimization.

6. Setpoint implementationResults of optimization are downloaded as desired values to the AHU Profit Controllerfor implementation.

In addition, an independent watchdog functionality is also required to ensure that only freshoptimization results are implemented by the controller. If for some reason fresh optimizationresults are not available then the watchdog inactivates the implementation of stale optimizationresults.

PROJECT DETAILS

ScopeScope of the current project comprises a Profit Controller for the AHU and a stand alone, kineticbased, non-linear optimizer for the AHU. The project was kicked off in Dec. 2000.

ObjectivesThe objective of AHU Profit Controller is to maintain control of the AHU process. The objectiveof the AHU Optimizer is to determine MV desired values that maximize ethylene production andare consistent with desired reactor run-lengths and controller constraints. The AHU ProfitController implements the MV desired values determined by the AHU Optimizer. It is expectedthat APC and Optimization will significantly reduce average hydrogen to acetylene ratio.

Systems OverviewPetromont Varennes has recently upgraded to Honeywell TPS system. The exiting ProfitControllers and Profit Optimizer resided on two Honeywell Application Nodes (APP-nodes).AHU Profit Controller resides on one of the existing APP-nodes. AHU Optimizer is installed ona new personal computer with Microsoft’s NT operating system.

AHU Profit Controller Design & Commissioning6MVs, 16CVs and 1 DV for the AHU Profit Controller were defined, plant step tests wereconducted and dynamic models were identified. Profit Controller was installed and

10

commissioned on the cold-side APP-node in October 2001. Controller execution frequency isonce every 30 seconds.

AHU Optimizer Design & CommissioningNonlinear Optimizer and Optimizer Watchdog were configured for Petromont reactors. Severalsets of plant data over the entire reactor run length were collected. This plant data was used toregress the parameters for the kinetic model. AHU Optimizer was installed and commissioned onthe new NT computer in October 2001. Optimizer execution frequency averages once every 5minutes.

In May 2002 some instrumentation changes were made in the AHU that required changes to theAHU Optimizer model. These changes were subsequently made and the Optimizer was restartedin September 2002.

BenefitsUse of AHU Profit Controller and AHU have led to a smoother operation of the AcetyleneHydrogenation Unit. Working together, the controller and the optimizer have reduced theaverage Hydrogen to Acetylene ratio that has resulted in a net improvement in the ethylene yieldby about half a percent that translates to close to a million dollars per year. Figure 3 showstypical moves by AHU Controller and Optimizer.

ChallengesPetromont Varennes is a very dynamic plant. Disturbances in feed flow rate and feedcompositions to the olefins plant are common; as a consequence the AHU is subjected to manychanges in it’s feed flow rate and feed composition. Because of the fast reactor dynamics andbecause of finite analyzer dead time and because of the severity of impact of AHU effluentacetylene composition on the Ethylene product quality; the controller has been tuned to take fairlyaggressive action. By design, the optimizer is designed to take somewhat slower actions so as notto interfere with the controller actions. Slower response of the optimizer implies lost profitopportunity. There is a tradeoff between control and optimization functions; a careful tuning ofthe control actions and the optimization actions are required to obtain the maximum benefits fromthe AHU applications.

CONCLUSION

Honeywell’s Profit Controller and Optimizer technology has been successfully applied to theAcetylene Hydrogenation Unit at Petromont Varennes Olefins Plant. Application of theseapplications has resulted in a smoother operation and a net improvement in the ethylene yield byabout half a percent.

ACKNOWLEDGEMENTS

We would like to thank S. Tatlock, R. Dinello, Z. Alzein, S. Norris and AppTech for their support of thiswork. We would also like to thank the management of Petromont and Honeywell for permission to publishthis work.

11

NomenclatureC Constraint equationsCP heat capacity, J/mole/°KET amount of ethylene in AHU effluent, moles/secF feed flow rate, mole/secHL high limiti index variableLL lower limitM Model equationsN number of time intervalsODE ordinary different equationP objective functionPDE partial differential equationr rate of reaction, moles/sec/M3s/t subject tot time, secT temperature, °Kv reactor volume integration variable, M3V reactor volume, M3x generic variable[X] concentration of species X, mole fraction or moles/M3

Ai, Bi, Ci, Di, Ei, Ki, Xi, Yi and Zi refer to kinetic parameters

∆Hr heat of reaction, J/mole

∂ differential symbol∂ / ∂ v partial derivative with respect to reactor volume∂ / ∂ t partial derivative with respect to time

superscipts‘ derivative

12

REFERENCES

[1] Nath, R., Z. Alzein, R. Pouwer and M. Lesieur, On-line Dynamic Optimization of anEthylene Plant using Profit Optimizer, 12th Annual Ethylene Producers Conference, Atlanta, GA(2000).

[2] Nasi, M., M. Alikoski and D. C. White, Advanced Control of Acetylene HydrogenationReactors, Hydrocarbon Processing, June (1985).

[3] Malik, S. A. and D. A. Fortin, Non-linear Constrained Computer Control of HydrogenationReactions in Acetylene Converter, AIChE Annual Meeting, San Francisco, (1989).

[4] Weiss, G., Modelling and Control of an Acetylene Converter, J. Proc. Cont., 6(1), 7, (1996).

[5] Anonymous, RMPCT Concepts, Honeywell Internal Technology Document, (1998).

[6] Villadsen, J. and M. L. Michaelsen, Solution of Differential Equation Models byPolynomial Approximation. Prentice Hall, Englewood Cliffs, New York (1971).

[7] Finlayson, J., Nonlinear Analysis in Chemical Engineering. McGraw Hill, New York (1980).

[8] Anonymous, Users Manual for NOVA Optimization System, Dot Products Inc. , (1998).

[9] Anonymous, Users Manual for NOVA DAE System, Dot Products Inc., (1998).

[10] Anonymous, ProfitMax Configuration Guide, Honeywell Training Document, (1997).

13

Figure 1: Acetylene Hydrogenation Unit: PFD

Figure 2: AHU Optimization Cycle

SS detection

DRPU

SIMULATION

OPTIMIZATION

ConsistencyCheck

Download

AHU PROCESS

AUH Controller

Data collection SS detection

DRPU

SIMULATION

OPTIMIZATION

ConsistencyCheck

Download

AHU PROCESS

AUH Controller

Data collection

Lead Bed

Guard Bed

Feed Heater

Inter-cooler

H from DC12

FromDC2

ToC2S

Lead Bed

Guard Bed

Feed Heater

Inter-cooler

H from DC12

FromDC2

ToC2S

14

Figure 3: Sample AHU Controller and Optimizer moves