Advanced control implementation optimization

Advanced Control |mplementation Optimization

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David Gianamore Westinghouse Electric Cc.--~psny

INTRODUCTION The thinking of business today with regard to process control is consistent with what we have all seen in the past few decades. Such lauded advantages as "accuracy/" "productivity/" and "safety" very often lead the progression. New management terms appear now and then, the latest and hottest being "Quality," re~rring to the ability to produce on- spec product every time. This characteristic has been taken to the pinnade of honor in the U.S. Governmentsponsored Malcolm Baldridge awards for improved quality in industry. In response to these requirements, operations engineers have sought to produce repeatable grade product in the safest and most cost-effective numner. They have been joined by design engineers of analog and digital hardware, who have developed products to accomplish the same goals through the implementation of standard tools. Control vendors have developed and implemented packages of tools in their computer products to provide an environment that makes implementation easier for the operations engineer. Such standard tools as the single teedback loop have been enhanced with links to exterior algorithms and tuning modes to make the 0019-0578/91/02/0069/6/$2.50 © ISA 1991

front-end control more intelligent; and they can be combined with algorithms to tie the single loop to previously independent variables. As the speed and power of controllers have increased, the ability to define and relate variables to one another in coordinated control has become reality. This hardware and software capability has ushered in the era of advanc~d control ir~ process control. No longer does a small or even large plant consist of independent areas of control, each of

advanced control, as a way of demonstrating how the development of the D ~ has facilitated advanced control in the following ways: 1. It provides a guaranteed network communications rate. 2. High-speed individual controllers and CKT screens are available. 3. Standard algorithms accommodate advanced control at the controller level.

As the speed and power of controllers have increased, the ability to define and relate variables to one another in coordinated control has become reality. A u t o m a t i c linking of controllers promotes coordinated supervisory intellifinal product (what business regence. ally needs) being optimized, as It is hoped the reader will see well as all subfuncfions. Building that the DCS is required for true on this platform, an array of prod- advanced control, and, most imucts has been developed that portantly, it underlines the fact could not have been developed that this is truly a new level of without such an environment: statistical process control, artificial control for our industry, the outintelligence, and intelligent diag- come of which will help our businesses to produce higher quality nosis of process alarms. This paper offers three experi- product in the safest and most ences in the application of DCS for cost-effectivemanner. which is to be optimized to meet its own standard. The entire process operates as a unit with the

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VOLUME 30 = NUMBER 2 • 1991

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OCS:SELECTION,IMPLEMENTATION,AND MAXIMIZATION

EJ(AMPLE1. AN ASSORIMEHT OF OPERATiOHS Certain plant operations are normally associated with continuous loop controllers and others with PLCs. All process plants have a variety of types of control areas, and conventional thinking was to buy independent controllers for the single-loop needs and independent PLCs where they w e r e a p r o p o s . Just as y o u wouldn't purchase a jackhammer to break old concrete as well as mix new, why would you buy one device to Perform two types of control? However, once control hardware is selected that is appropriate for a unique loop, an integrated plant process requires some form of coordinated supervisory intelligence between separate areas. This problem was addressed in the example shown in Figure 1, which features a petroleum industly application. The plant located in Bakersfield, California, performs steam-enhanced oil recov-

ery, in which pressurized steam is injected into well heads. The steam liberates crude from deposits underground, which is then pumped up into a well header. The crude is subsequently transported to a dehydration stage in which the condensed steam is extracted and recycled into the plant process, and the crude is stored for refining at a different installation. Plant operations decided to produce the steam itself on-site using a set of heat recovery steam generators (HSRG), which obtain their heated air from gas turbine generators. In this way, the electricity produced by the turbines could be used on site or sold to a nearby utility and the steam produced from the exhaust gas directly injected into the wells. Several wells on the plant site would be the recipients of steam, but they do not need the steam at all times. The applicatic.~n of steam and removal of crude is a cyclic operation based upon the conditions and output of each well. Groups of the wells feed into a di-

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Why a DCS? At first glance, each operation of the plant appears to be quite independent and should be fitted with suitable controllers. The turbines could be sequenced by PLC, as could the series of header valves that divert flow. The prep stage and HRSGs, being continuous, were best serviced by process controllers or banks of loop controllers. However, the interrelationship between each operation is so acute that demand at the prep stage can require dynamic

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vetting header, which then supplies constant flow to the storage area. Recovered crude must have water extracted prior to storage and the water recycled back into wells. This process is a combination of continuous flow, tempcrature, and pressure operations that extracts water from the crude. The product is conditioned to required characteristics and p u m p e d to storage. It is later transferred to a separate site for further refinemerit.

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ADVANCED CONTROLIMPLEMENTATIONOPTIMIZATION

response at the turbine stage cany terminated to the selected prepare it for storage. Indi~du,d within one minute. With tl-.~ in- well, and steam is diverted to it sequence and continuous funcherent mechanical and fluid de- for a predefined time period; sub- tions are p e r f o m ~ in this group lays, it was clear that reliance on sequently, pumping is resumed. of conlxoflers, which work in conto perform the prep function. independent controllers would The controller performs this well not meet the performance require- testing, as well as sequence con- Flow demand is determined and trol on the well outflow diverter signalled back to the outflow dements for the plant. A complete distributed control into which seven wells feed. This liverers and pumps. In addition, system was selected instead, so diverter selects only the pumping quality is measured and decisions that inference intelligence could wells while inhibiting those under are made to adjust for well performance or shut down. be built in to foresee demand and steam flush. The result of this consolidation adjust interrelated variables. The of controls onto a single network DCS chosen had both sequential WorthwhileInformation with identical controllers wa~ and continuous capabilities as ® coordinated super~risory well as guaranteed network upThe typical DCS consists of incontrol of various dissimilar date time of one-tenth second, in dividual modules whose function which variables from unique conand otherwise unrelated trollers would be transferred to all is unaffected by the failure of any functions in the complete other module on the network. chain of product synthe~s; others. Each drop has a specific operation Each of the three turbines had a that it performs either singularly ° intelligent diagnosis and dedicated controller mounted in or redundantly, but which is not alarms using a knowlo,tge its local turbine control panel that duplicated in any other drop. base of functions and performed the sequencing to re- More sophisticated controls such diate access to active p i n t s spond to demand. It monitored as this plant, which requires coornetwork wide; power output and alarm condi- dinated supervisory intelligence ® savings in spare parts and tions providing both a local indi- distributed in the steam generaconfiguration training excation and, via the network, tion and pumping areas, have pense due to the use of idenalarms and production figures in pushed this DCS to provide multitical controllers on all the central control room. Start-up ple levels of control at each drop. functions; and and s h u t d o w n could be per- The hardware and software re® ultimate high quality prodformed by the operator at the tur- quirements include faster processuct output and safe operabine itself, through the local panel, ing and a c o m p r e h e n s i v e tion by problem avoidance or at the central control room configuration language at each due to awareness of operausing the operator station. drop, with automatic external tion characteris~cs. Control of the HRSG is per- linking to others. It is on this platThis example mixed a series of formed with a separate but identi- form that advanced control in the dissimilar operations that had an cal controller positioned in the form of coordinated supervisory indirect but S~:luential depencontrol room and monitored at intelligence is facilitated. dance u p o n each other, from the operator station. This controlData derived by crude quality which the controllers inferred to ler automatically adjusts to steam is evaluated by one controller to perform a control decision. The demand at the well sites and per- decide the health and efficiencyof second example features a quite forms supplemental duct firing. If the well itself. Forecasting of pro- parallel operation that, due to the power production exc~ds steam duction and inference of possible same speed and flexibility characd e m a n d , the controller vents problems are derived from on-line teristics of the distributed control steam until turbine rates can be data base trends. Management re- system, yields similar savings in reduced to requiredheat rateout- porting information is gathered engineering and operation, with a put. As steam d e m a n d is in- assess the performance of each high-quality result. creasedat the well sitecontrollers, well and its impact on productivthe H R S G controllerresponds by ity. Decisions can be made as to furthering operation or shutting EXAMPLE 2. increasingoutput. AN APPROPRIATEFIT FOR Well head selectionof steam in- down a well. The crude flow is channeled PROCESS MODEUHG jectionand pumping rates is determined by well product into the dehydration stage. There, Ethylene plants have been fitcharacteristics,which indicatethe a series of controllers work in conted with advanced controlsand need for additional steam. When junction to extract condensed optimizers since the middle 1950s. this occurs, pumping is automati- water from the crude and further VOLUME 30 ®NUMBER 2 = 1991

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DCS: SELECTION,IMPLEMENTATION,AND MAXIMIZATION

They represent the largest integrated process in the world. The typical plant processes over 500 billion pounds per year of hydrocarbon and contains over twenty major unit operations. This particular installation, located near Chicago, m., is a retrofit to an existing plant that started in 1987 and included controls on both hot and cold sides on a single network. The scope of the project was to replace individual loop controllers and implement advanced control and optimization. Since the ethylene plant is a single-train operation providing feedstock to other processes at the site, its production availability is critical. The DCS was selected to provide high reliability through redundancy, powerful operator interface, and advanced control configuration capability at the controllers. A total of sixteen fully redundant controllers were installed for 14 furnaces, columns, and compressors. Seven operator stations were also installed.

soft target control, constraint control, and energy optimization. The process modeling and adv a n c e d control functions ,are shared between the DCS controllers and a DEC MicroVAX 3000TM computer. Due to the comprehensive instruction set and high speed of the controllera, a majority of the algorithms are resident there, with the added advantage that they are automa~caUy backed up by the redundant processors. Data is transferred from the IX~ controllers to the VAX through two high speed interfaces on the DCS highway. Calculated variables and set points are passed to the DCS in time-critical cycles. The time critical nature of process model data as well as alarms emphasizes the need for guaranteed network communications. In scientific or MIS networks, the baud rate might be high, but the philosophy is demo~atic, yielding transmission priority arbitrarily. In effect, a n y user or, ce

The entire system can be viewed from any of the operator screen, yielding total operator station redundancy. Process~

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The advanced controls and local optimizers were engineered and commissioned by Setpoint, Inc., of Houston, TX. Setpoint has d e v e l o l ~ controls and optimizers for over 30 ethylene plants during the past 15 years. Controls and opeimization were installed in two modes: 1. For the cracking furnaces...feed rate, steam rate, outlet temperature, balancing, and constraint controls. 2. For the distillation columns...hand target control, "~'2

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obtaining control of the network keeps it until he's done since no one else has a priority higher or lower. But this DCS has a realtime, broadcast philosophy that guarantees acce~ by all users by limiting the time in which any user can maintain control of the network. In this way, it insures the transfer of model points between controllers and the VAX, so that the advanced control model is current with process conditions. Additionally, preventive maintenance programs are employed on a series of steam turbo pumps. Signals are taken into the controllers from Bently-NevadaTM vibration monitors from which pump performance is inferred. The con-

trollers alarm suspect conditions at the operator stations and can aitomate pumps to free the questionable unit for servicing. Finally, the entire system can be viewed from any of the operator screen, yielding total operator station redundancy. This includes variables generated by the process model in the VAX, as well as those at the controllers.

Resultsof this implementation The use of the optimization package was facilitated, first, by ~he speed of each controller and resident configuration software that made programming large portions of the model in the controllers possible. This had a direct impact on reliability since mere control modules were resident in ~ l u n d a n t hardware. Second, the network performance in terms of speed and access philosophy permired the distribution of controls network-wide; this, while guaranteeing critical time access between controllers. The network speed had a corollary effect in the speed at which operator stations update and display new variables.

EXAMPL|"3: HIGH-SPEED CONTROLAND OPERATORINTERFACE We haw,. shown how the DCS is used to perform control when the process demands complex, mathintensive algorithms that work in coordinated control as a unit operation. In some cases the response time of the process requires such complex control to occur at high speed as well. In this example, the DCS is s h o ~ to provide a balanced plant control approach, inc l u d i n g high speed complex control and operator interface, as applied to an aerodynamic wind tunnel for flight models. The Icing Research Tunnel (Figure 2) at NASA Lewis Re-

ADVANct-u CONTROLIMPLEMENTATIONOPTIMIZATION

search Center in Cleveland, OH, was constructed in the early 1940s and tested its first model in June 1944. Model subjects are wing and tail sections, air inlets, cowls, spinners, antennas, and idng probe inslrumentation. From the outside, the IRT appears to be a conventional, single-return closed throat subsonic wind tunnel witha 6 foot by 9 foot test section. However, a 2100-ton refrigeration system that can cool the air to -20 degrees F, and a spray bar system that atomizes water into supercooled droplets, force test m o d e l s to fly through a cold, supersaturated cloud of air with known liquid water content and drop size that duplicates natural icing conditions. This simulated icing cloud results in a rapid ice buildup on the model; anti-icing and deicing systems are then tested or the model's performance degradation is measured. As part of the aircraft test procedures, the control system manipulates a 5000 liP drive motor to produce the desired test section velocity. Over 80 loops of control

are required to control the facility systems. These systems consist of three heated windows using 440 V power; a small AC motor with dynarrdc breaking to drive a turntable so models can be positioned to an angle of attack; electric and gas air heaters to provide up to 900 degrees F air to the model if required. Also, an altitude exhaust to provide v a c u u m conditions to simulate engine pumping action so correct model inlet pressures can be achieved along with the necessary start-stop commands for doors and valves throughout the facility. To provide a uniform cloud in the test section is the most demanding function for the control system. This requires intricate and high speed con~o! of the spray bar for the emission of moisture into the tunnel and is addressed using more than 30 control loops distributed between two controllers. The system operates over a wide range of air pressures and water pressure~ There are eight spray bars to be used in concert, each of which also requires coor-

dinaled control of air and water pressures. The combination resuits in that, upon commemd, a continuous cloud of condensate entem the subsonic air mass, contributing to reliaHe h ~ results. The spray bar system and, in fact, all other plant areas of control are referenced and manipulated through the operator station graphics. Each operator station can access all areas of the plant, making them totally redundant, and screen display occurs within two seconds of a request. The processing and CRT access speeds were major factors in the seie~ion of the DCS and remains a productivity and reliability advantage at this installation.

CONCLUSIONS Years ago, if an op~'ations engineer had a control problem to

solve, he would start first by designing a relay bank and hardwiring a panel that operated in the desired fashion. As the industry has progr'~,ed, standardized packages have ap-

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Figure2-EthyleneDCS VOLUME 30 ®NUMBER 2 ® 1991

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DCS:SELECTION,IMPLEMENTATION,AND MAXIMIZATION

Years ago, if an operations engineer had a control problem to solve, he would start first by designing a relay bank and hardwiring a panel that operated in the desired fashion. peared and each new product is more powerful. The ultimate desire for operations engineering is to be provided with a hardware and software platform that has the power and flexibility to implement inference intelligence packages that will maximize the productivity of the plant. The distributed control system has provided this environment because of its distributed data base concept, high-speed network

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communications, and independent controller flexibility. What began as a method of obtaining higher reliability to a centrally controlled plant has developed into a tool that encourages the use of advanced control techniques in the following ways: ® Through performance.....in terms of controller and CRT

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T h r o u g h reliability ..... in terms of hardware redun-

dancy and guaranteed network access • Through configuration-by providing an environment of compilers and algorithms to facilitate coordinated supervisory and complex functions at the controller level

AC~O~E~MENTS The author would like to extend his thanks to the following engineers, whose efforts on two of the examples above have made this presentation possible. R. J. Freedman, NASA Lewis Research J. Poje, Setpoint, Houston