Borealis AS N-3960 Stathelle Norway Phone: +47 35 57 7000 Fax: +47 35 57 7200 Model Predictive Control of a BORSTAR Polyethylene Process. Date 26.04.2000 Pages 5 Ketil Strand Andersen 1, Borealis R&D, Stathelle, Norway Magne Hillestad 2, HILLESTAD AS, Trondheim, Norway Tomi Koskelainen 3, Borealis Polymers O.Y, Porvoo, Finland ABSTRACT The introduction of model predictive control (MPC) in the chemical industry has been very successful. MPC tools like QDMC, IDCOM and many other systems have been applied to solve many industrial control problems with great success. One of the main reasons for the success is that the MPC systems are able to handle constraints on both output and input variables and resolves the problem with time delays, inverse response and coupling between variables in a natural way. Perhaps the most important factor of its success is the fact that MPC is a unified way of doing control - constraints, delays, inverse response, feedforward and feedback action etc, are all handled in MPC. Borealis has developed it s own system for MPC, OnSpot, which takes into account and solves the control problems related to the characteristics of a polymer process such as frequent grade transitions, non-linearities, etc. The controller is a non-linear MPC (NMPC) controller. In addition to OnSpot, there is a thermodynamic package that is used for calculating fluid densities, dew point, bubble point, instantaneous production rates based on steady-state mass and energy balances etc. These software packages are now parts of what is called BorAPC. OnSpot, is a special implementation of the MPC algorithm. One of the things that make OnSpot special is the type of model applied. Most of the commercial MPC systems available are based on black-box models that are general model structures, such as step response and impulse response models, generated from plant data. OnSpot, on the other hand, is based on a non-linear state space model formulation, which is the most natural formulation when physical and chemical knowledge of the process are to be imbedded into the model. Product quality models, such as MFR and polymer density, are formulated based on chemical and physical knowledge and is a natural part of the overall model. First principle models can be tuned against lab or pilot plant data and not necessarily against plant data. The reuse of the 1 ketil.andersen@borealisgroup.com 2 magne.hillestad@online.no 3 tomi.koskelainen@borealisgroup.com 1 (5)
model becomes very convenient and easy. The model can be identified without doing plant experiments at all. Since the first principle models have been made for similar processes earlier in Borealis, most of the model was quite swiftly made. The major part of the modeling work was associated with the reaction kinetics and the product quality models. The reaction kinetics including activation-deactivation was verified and tuned based on laboratory data. As setpoints, both component concentration ratios and product quality parameters may be specified. The operators can switch between ratio control and polymer property control. The operators have a very simple DCS display as interface to the OnSpot applications and do not need to handle model or tuning parameters and it has been well acknowledged among operators. Figure 1 shows how the operator DCS display for an application might look like. Available Program Reset Speed(1-5) LRE_AVAIL LRE_PROG LRE_RESET LRE_SPEED ONSPOT LOOP REACTOR CONTROLLER MV SP MIN MAX PV MODE C2= feed (t/h) FIC8307.BSP MIN_UE2 MAX_UE2 FIC8307 UE2_MODE C3+ feed (t/h) FIC8311.BSP MIN_UP2 MAX_UP2 FIC8311 UP2_MODE Catalyst feed HIC8307 MIN_UCAT MAX_UCAT HIC8307 UCAT_MODE H2/C3+ feed (kg/t) FF8309A.BSP MIN_UH2 MAX_UH2 FF8309A UH2_MODE C4/C2 feed (kg/t) FF8310A.BSP MIN_UB2 MAX_UB2 FF8310A UB2_MODE CV SP MIN MAX PV MODEL MODEL PRED. MODE LRE_RATE (t/h) LRE_RATE.OSP MIN_LRE_RATE MAX_LRE_RATE LRE_RATE.BC LRE_RATE.ML LRE_RATE.MLP UE2_MODE LRE_SOLID (t) LRE_SOLID.OSP MIN_LRE_SOLID MAX_LRE_SOLID LRE_SOLID.BC LRE_SOLID.ML LRE_SOLID.MLP UP2_MODE LRE_XC2 (%) LRE_XC2.OSP MIN_LRE_XC2 MAX_LRE_XC2 AI8307C1_C LRE_XC2.ML LRE_XC2.MLP UCAT_MODE LRE_H2/C2 (mol/kmol) LRE_H2C2.OSP MIN_LRE_H2C2 MAX_LRE_H2C2 AI8307H2C2 LRE_H2C2.ML LRE_H2C2.MLP LRE_H2C2_MODE LRE_MFR (g/10min) LRE_MFR2.OSP MIN_LRE_MFR2 MAX_LRE_MFR2 LAB_LREMFR LRE_MFR2.ML LRE_MFR2.MLP LRE_H2C2_MODE LRE_C4/C2 (mol/kmol) LRE_C4C2.OSP MIN_LRE_C4C2 MAX_LRE_C4C2 AI8307C4C2 LRE_C4C2.ML LRE_C4C2.MLP LRE_C4C2_MODE LRE_DENS2 (kg/dm^3) LRE_DENS2.OSP MIN_LRE_DENS2 MAX_LRE_DENS2 LAB_LREDENS LRE_DENS2.ML LRE_DENS2.MLP LRE_C4C2_MODE [Figure 1: DCS Operator Display] The Future graph plot is a very informative display that shows the current future predicted controlled and manipulated variables. This has been useful to understand the behavior of the controller and also the plant. Since this is a multivariable predictive controller, the control actions may at first sight look strange, however, when looking at the future graph the logic behind the control actions becomes more evident. Figure 2 shows a typical future graph plot. 2 (5)
MV CV <-------past future ------------> <-------past future ------------> [Figure 2: Future Graph] OnSpot is applied in several process control applications in Borealis, the last ones that have been put in operation are two applications for Borealis Borstar Polyethylene plant in Porvoo, Finland. The Borstar Polyethylene process consists of a prepolymerization reactor, a loop reactor and a fluidized bed reactor. The loop reactor is operated at supercritical conditions where the fluid medium consists of propane, ethylene, hydrogen, butylene and cocatalyst. A reaction kinetics module is implemented to calculate the effect of the process conditions on the reaction rates as well as a polymer properties module is used to calculate the polymer quality parameters from the reaction rates for quality control purpose. Using the OnSpot control system with the first principle models developed for the process, the solids concentration loop has become very stable as shown in Figure 3. Also the production rate of the two reactors, the split and the product quality have become very stable and reproducible. Figure 4 shows the control of the production rate and Figure 5 shows how the ethylene concentration is controlled expressed as deviations from the setpoint in %units. 3 (5)
Solids in the loop reactor 10.5 Solids in the loop reactor (tons) 10 9.5 9 8.5 8 7.5 Solids in loop Solids setpoint 7 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 Time (days) Fig.3 : Solids in the loop reactor 8.000 Production rate in the loop reactor (t/h) 7.500 Production rate (t/h) 7.000 6.500 6.000 5.500 5.000 RATE MASSBAL RATE MODEL RATE SP 4.500 4.000 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 time (days) Fig.4 : Production rate in the loop reactor 4 (5)
Deviation from setpoint for C2 conc (%units) Dev from sp in %units 1.00 0.80 0.60 0.40 0.20 0.00-0.20-0.40-0.60-0.80 C2 conc GC C2 conc SP C2 conc MODEL -1.00 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 time (days) Fig.5 : Deviation from setpoint (%-units) for ethylene concentration 5 (5)