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RCS for process control: Is there anything new under the sun?

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RCS for process control: Is there anything new under the sun?
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  18 th  European Symposium on Computer Aided Process Engineering – ESCAPE 18Bertrand Braunschweig and Xavier Joulia Editors!" #$$8 Elsevier B%&%'(td% All rights reserved% RCS for process control: Is there anything new under the sun? )anuel *odr+gue,- *icardo San,  ASLab-UPM, José Gutiérrez Abascal, Madrid 28006, Spain Abstract .he purpose o/ this paper is to e0plore the potential use in process control o/ cognitivearchitectures used in other domains% A well2nown cognitive architecture- used tocontrol comple0 systems o/ di//erent areas has 3een selected% 4t has 3een compared withthe current control strategy used in process plants% Conclusions on its applica3ility- itsstrengths and wea2nesses are also presented in the paper% Keywords 5 comple0 control- cognitive architecture- process control% 1. Introduction .he process industry is 6uite mature in many aspects% 7ne o/ these is process control%Although decentrali,ed control 3ased on P4 controllers still is e0tensively used-signi/icant advances and research! have 3een made5 multivaria3le predictive control-use o/ simulation models- realtime optimi,ation- change in communication protocolshy3rid or digital!% Even the implementation o/ the control architecture may change and/latten! in the /uture i/ standard protocols li2e 4ndustrial Ethernet apply to all the levelso/ the plant 91:% But still the classic hierarchical organi,ation in /our levels remains% ;owadays many /ields loo2 into other domains to see i/ the ideas developed /or thesrcinal domains can 3e success/ully applied to their domains% 4n this paper a cognitivearchitecture success/ully applied to implement comple0 controllers in di//erent areas isconsidered and its possi3le application to the process industry studied%.he paper is organised as /ollows5 ne0t section presents the *CS cognitive architecture-its components and organi,ation- section three descri3es how process control iscurrently implemented in most industrial plants- section /our compares 3oth approachesand /inally section /ive draws some conclusions out o/ the presented ideas% 2. RCS: the cognitie architecture *CS *ealtime Control System! 9#<: is a cognitive architecture designed to ena3leany level o/ intelligent 3ehavior% 4nitially 3ased on a theoretical model o/ thecere3ellum- it has 3een evolving over the last three decades% .oday it is a realtimecognitive control architecture with di//erent applications% 4t has 3een used /or intelligentmachine tools9=:- /actory automation systems9>: and intelligent autonomous systems9?:among others%*CS is a multilayered multiresolutional hierarchy o/ computational agents or nodes%*CS nodes have vertical hierarchical! as well as hori,ontal relationships% Each node/ollows a common design pattern- 3eing composed o/ the /ollowing elements5 sensory processing SP!- @orld )odeling @)!- value udgment &J!- 3ehavioral generationB! and 2nowledge data3ase !%  #  M !odri"uez et al Digure 1 shows the 3asic control node with its elements and relationships%  Dig 1% *CS Computational control! node% A 3rie/ description o/ the di//erent elements o/ each o/ the control agents /ollows% Sensory !rocessing:  .his element gets sensory input and compares these o3servationswith e0pectations generated 3y an internal world model% "orld #odel:  4t is the systems 3est estimate o/ the state o/ the world% .he worldmodel includes a data3ase o/ 2nowledge ! a3out the world% 4t also contains asimulation capa3ility which generates e0pectations and predictions% 4t can provideanswers to re6uests /or in/ormation a3out the past- present and pro3a3le /uture states o/ the world  #$at i%   and #$at is  6ueries!% .his in/ormation goes to the tas2 decomposition element in the B! and to the sensory processing element% $alue %udg&ent:  4t determines what is good and 3ad% 4t evaluates the o3served and predicted state o/ the world% 4t computes costs- ris2s and 3ene/its o/ o3served situationsand o/ planned activities% 'ehaior (eneration:  Behavior is generated in a tas2 decomposition element that plans and e0ecutes tas2s 3y decomposing them into su3tas2s- and y se6uencing thesesu3tas2s so as to achieve goals% oals are selected and plans generated 3y a loopinginteraction 3etween tas2 decomposition hypothesi,e plans!- world modeling predictresults! and value udgment evaluate the results!% Behavior generation is typically donevia Dinite State )achines or rules governing costs /or nodes and edges in a graphsearchmethod% .his node contains three su3elements5 the Jo3 Asigner- the Planner and the   !&S %or process control' (s t$ere an)t$in" ne* under t$e sun+ FE0ecutor% *CS systems are 3uilt /ollowing the *CS methodology that has several steps%.he /irst one is to gather domain 2nowledge with e0perts- and to generate with the helpo/ the e0perts! the hierarchical tas2 decomposition% Knowledge )atabase:  4t stores in/ormation a3out space- time- entities- events- states o/ the system environment and a3out the system itsel/- parameters o/ the algorithms-models /or simulation- etc% *. +he process control hierarchy .he process industry comprises mainly continuous 3ut 3atch processes also% .heindustries involved are chemical- petrochemical- pharmaceuticals- re/ineries- etc% .heseare usually very large and comple0 /acilities% .he main goal o/ any process plant is toget the ma0imum 3ene/it which means the demanded amount with the speci/ied 6ualityusing the less resources! assuring sa/ety and sta3ility o/ the plant% 4n order to achievethis goal- control strategies have 3een applied and evolved over the years as newcapa3ilities were availa3le% Drom the initial manual control to the current digitaldistri3uted control system CS!%.o handle the comple0ity o/ the plant and to still achieve the overall goal- a controlhierarchy has 3een developed and used /or many years% .his architecture gets thecompany policy several wee2s time resolution! and re/ines it to the current action to 3eapplied on any actuator o/ the plant mssec resolution time!% .he procedure is too3serve the state o/ the plant through thousands o/ sensors and evaluate the ne0t action/or any resolution time% 4mplicit- e0plicit- heuristic and /irst principles models are usedin order to generate the ade6uate action% .he common process control architecture has/our control levels% .he lower level o/ the architecture is the 3asic regulatory control-this control is achieved 3y single decentrali,ed loops% )ost o/ these loops are controlled 3y standard P4 controllers% .he actuating hori,on at this level is ust one% .he second level is the advanced and predictive control% .hese are two di//erent controlschemes that wor2 at the same level% 4n/ormation is transmitted hori,ontally andvertically in this and upper! level% )ore ela3orated control strategies as selectivecontrol- ratio control- /eed/orward control are implemented% 4n this second level implicitas well as e0plicit heuristic and /irst principles 3ased! models are used to generate theaction% .he action is the set point goal! to achieve at the lowest level% Predictionhori,on is in the case o/ model predictive control! o/ tens o/ movements% Gpper levels o/ control deals with optimi,ation- scheduling and planning% Gnitoptimi,ation can 3e made online with continuous in/ormation /low /rom and to thelower levels% Site optimi,ation- scheduling and planning are done o//line% &erydi//erent types o/ models are used in these levels% As commented- in/ormation /lowsvertically and hori,ontally through the architecture and each upper level is o/ lower timeresolution% ,. RCS s )CS )any similarities e0ist 3etween the two architectures- as can 3e o3served in /igure #% 7/ course in the process control system there is no a common identi/ied computationalagent with so well de/ined elements as in the *CS architecture- 3ut at any level a good  <  M !odri"uez et al matching can 3e esta3lished- as it is shown in the /ollowing comparison 3etween the process control and the *CS elements Dig #% CS vs% *CS architecture Regulatory control node. .his is the simplest node% 4t implements the simplest *CSnode- one in which the 3ehavior is purely reactive% 4t has a @orld model the P4algorithm is a model- an empirical or heuristic model- 3ut a model o/ the system under control!- 3ut this model does not predict the 3ehavior% 4t only reacts to the current valueso/ the plant and decides an action to 3e per/ormed there is no plan- it is ust an action/or the ne0t time!% 4t can 3e considered to have a  where the model parameters arestored% &ery simple preprocessing is per/ormed 3ut some it is done as signal /ailure-%%%! #odel predictie control node. )PC has several components% 4t has a model usuallyan identi/ied linear! o/ the world% 4t has a  where past values o/ the manipulatedvaria3les )&s! and controlled varia3les C&s! are stored% 4n this  other in/ormationis stored as )&s and C&s limitations- weighting /actors- etc% .he model uses the inputsto predict the /uture% .his state is used in Behavior eneration module% 4n this modulean optimi,ation is per/ormed to select the 3est action plan% .his plan a set o/ movements /or the )&s along with C&s values! is set and sent to the regulatory level%Some preprocessing is implemented as well% .he )PC module implements also a/eed3ac2 loop to correct model errors due to model mismatch with the actual plant!% Real +i&e -pti&iation. .his module receives the values o/ the varia3les o/ the plant- per/orms reconciliation on these values% .his node has a steady state mathematical- physically 3ased! model o/ the plant% An optimi,ation is made using that model everyhour or so% .he optimi,ation results are sent to the lower level- the supervisory control%.hese results are the new set points o/ the controlled varia3les% .he 3est operating pointo/ the plant which means a set o/ set points values! is calculated in each optimi,ation%.he optimi,ation ta2es into account constraints on the varia3les limited change inmanipulated varia3les- sa/ety- 6uality- etc% constraints in controlled varia3les!% .he nodeuses as well a historian module with past data o/ the plant%   !&S %or process control' (s t$ere an)t$in" ne* under t$e sun+ = !lanning and scheduling. .his module corresponds with the 3usiness part o/ thecontrol hierarchy% 4t has a 3usiness model and 3ased on plant data current and pastvalues!- on e0ternal data mar2et data- e0ternal plant in/o- etc%! and using the company 3usiness goals derives a production plan /or the plant% 4t gives capacity productionvalues as well as 6uality values to the lower- optimi,ation- level% .he resolution time atthis level is days or wee2s% Dig F% Process control levels as *CS agents .he control levels introduced a3ove are presented in the /igure F% /ollowing thestructure o/ a *CS node% 4t can 3e o3served that the node in any level complies with the*CS node% As a preliminary conclusion it can 3e said that the conventional controlstructure is *CS compliant- or can 3e considered as an implementation o/ it% So is t$erean)t$in" ne* under t$e sun+, *$ats t$e bene%it o% usin" !&S or ot$er t)pe o% co"niti.earc$itecture/ %or process control+ .he answer is that it depends on the application and on the point o/ view% 4n spite o/ thisand 2nowing that CS is *CS compliant some di//erences or capa3ilities must 3estressed5
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