Optimization Solution White Paper. Layered Optimization: A low-risk, scaleable approach to driving sustainable plant wide benefits

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1 Optimizatin Slutin White Paper Layered Optimizatin: A lw-risk, scaleable apprach t driving sustainable plant wide benefits

2 Optimizatin Slutin White Paper Layered Optimizatin 2 Table f Cntents Intrductin...3 Advanced Cntrl and Real-Time Optimizatin...3 Layered Optimizatin Slutin...5 Overview f Optimizatin Technlgies...6 Optimizatin Slutin Cmpnents...7 Unit Optimizatin...7 Multi-Unit Dynamic Optimizatin...8 Nn-Linear Dynamic Optimizatin...10 Cnverging n an Uncnstrained Optimum...11 Nn-Linear Steady-State Optimizatin...13 Dynamic Nn-Linear Cntrl and Optimizatin...13 Recmmended Optimizatin Apprach and Benefits...13 Optimizatin Benefit Estimatin...14 Summary...14 Table f Figures Figure 1: Advanced Cntrl and Optimizatin Layers...5 Figure 2: Optimizatin Cnfiguratins...6 Figure 3: Layered ptimizatin slutin cmpnents...7 Figure 4: Prduct Value Optimizatin within the cntrller...7 Figure 5: Cmparisn f dynamic and steady-state ptimizatin...9 Figure 6: Multiple unit ptimizatin with dynamic ptimizer...10 Figure 7: DGEM architecture...11 Figure 8: Cnverging n an uncnstrained ptimum with gain updating...12

3 Optimizatin Slutin White Paper Layered Optimizatin 3 Intrductin Advanced Cntrl and Real-Time Optimizatin Advanced cntrl and real-time ptimizatin (RTO) tls have becme necessary technlgies fr tday s prcess perating cmpanies t cmpete and maintain prfitable peratins. It is widely accepted that the benefits f advanced prcess cntrl (APC) technlgy include imprved prfitability thrugh enhanced prcess stability, increased thrughput and yield, decreased perating csts, imprved prduct quality, and increased perating flexibility. Anther benefit f advanced prcess cntrl is that APC frms the fundatin fr n-line ptimizatin, which typically adds an additinal 20 percent f the advanced cntrl benefits with prject paybacks ften in less than six mnths. The fllwing table shws typical APC and ptimizatin benefits that several industrial plants and mills have experienced. Petrchemicals Ethylene VCM Armatics (50KBPD) Chemicals Ammnia Plylefins Oil & Gas Upstream prductin Industrial Utilities Cgeneratin/Pwer Systems Pulping Bleaching TMP (Therm Mechanical Pulping) Refining Benefits (/yr) 2-4% increase in prductin 3-5% increased capacity / 1-4% yield imprvement 3.4M - 5.3M US$ Benefits (/yr) 2-4% increased capacity / 2-5% less energy/tn 2-5% increase in prductin/up t 30% faster grade transitin Benefits (/yr) 1-5% increase in prductin Benefits (/yr) 2-5% decrease in perating csts Benefits (/yr) 10-20% reductin in chemical usage $1M-$2M Benefits ($0.01/bbl) Crude Distillatin (150 KBPD) 5-13 Cking (40 KBPD) Hydrcracking (70 KBPD) Catalytic Cracking (50 KBPD) Refrming (50 KBPD) Alkylatin (30 KBPD) Ismerizatin (30 KBPD) 3-17

4 Optimizatin Slutin White Paper Layered Optimizatin 4 In the past, many cmpanies underestimated the cst and cmplexity f implementing and maintaining real-time ptimizatin systems. Traditinally, cmpanies invlved in ptimizatin prjects adpted a tp dwn apprach using detailed nn-linear steady-state prcess mdels. These traditinal, steady-state ptimizers sent targets t multivariable cntrllers which drve the plant tward ptimum peratin. Hwever, unless plant persnnel were cmmitted t understanding and maintaining such systems, mst f these real-time ptimizatin systems went ut f service within the first year. Althugh there is a need fr ptimizatin based n detailed mdeling in sme cases, a large prtin f the ptimizatin prblems in the prcess industries tday can be addressed mre directly with mre leverage f plant data and less extensive, targeted mdeling that fcuses n key units that exhibit significant nn-linearity. Even s, detailed prcess mdeling has prven invaluable t perating cmpanies as a tl fr prcess design, peratr training, and peratins planning, mnitring and ptimizatin. Detailed mdeling is essential fr perfrming tests which are nt feasible n the real plant. Fr example, a mdel may be used t evaluate different reactr catalysts r t evaluate a prcess retrfit. Fr use in n-line ptimizatin, hwever, the trade-ff between prcess benefit versus added cst and nging maintenance effrt is very real and must be cnsidered carefully. In many cases, a less extensive mdeling apprach may be used fr n-line ptimizatin because prcess measurements are available t crrect fr mdel inaccuracies n an nging basis. The challenge fr the prcess industries is t prvide a level f ptimizatin which realizes the best return n investment (ROI) fr the custmer. This ROI includes nt nly the cst f implementing a prject and the expected ptimizatin benefits, but als the cst f maintaining the system with the custmer s available resurces. T meet this challenge, a layered apprach t ptimizatin is necessary and includes: Cntrller-based ptimizatin Distributed dynamic ptimizatin Nn-linear gain updating Traditinal steady-state nn-linear ptimizatin Dynamic nn-linear ptimizatin A cmpany psitined t prvide the level f ptimizatin needed fr a particular applicatin, frm rbust multivariable cntrl and dynamic ptimizatin t detailed first-principles mdeling and nn-linear ptimizatin, is necessary. In additin, prcess and ptimizatin cnsultants that can analyze the prcess t determine which technlgy is mst apprpriate is beneficial. This paper describes the cmpnents f layered ptimizatin, the advantages and benefits f this apprach, as well as methds fr determining ptimizatin benefits f a particular applicatin.

5 Optimizatin Slutin White Paper Layered Optimizatin 5 Layered Optimizatin Slutin As shwn in the figure belw, advanced cntrl and ptimizatin are dependent n ther enabling technlgies such as advanced regulatry cntrl and inferential mdeling tls. In additin, ptimizatin ccurs at a number f layers in the cntrl and ptimizatin hierarchy, frm unit cntrl and ptimizatin t multi-unit ptimizatin t plant-wide ptimizatin. These layers represent increasing ptimizatin scpe. The layered ptimizatin apprach als cnsists f different ptimizatin technlgy layers, frm linear dynamic ptimizatin, t nnlinear steady-state ptimizatin, t nn-linear dynamic ptimizatin. Figure 1: Advanced Cntrl and Optimizatin Layers Optimizatin technlgy can be divided int fur categries: Linear mdel-based predictive cntrl Nn-linear mdel-based predictive cntrl Dynamic ptimizatin Steady-state ptimizatin We will discuss the differences and the relatinship (and relative benefits) f these in this paper.

6 Optimizatin Slutin White Paper Layered Optimizatin 6 Overview f Optimizatin Technlgies Optimizatin technlgies are ften used in cmbinatin with APC technlgies. These advanced cntrl technlgies als cntain embedded ptimizatin t enable ptimizatin and cntrl t be perfrmed simultaneusly. The advantage f a layered ptimizatin strategy is that the apprpriate cmbinatin f technlgies can be selected t slve specific custmers prblems, rather than using the same technlgy fr each prblem. Figure 2 shws six ptimizatin cnfiguratins that have been successfully implemented fr custmers. SS-Real Time Optimizer Dynamic Optimizer Dynamic Optimizer SS-Real Time Optimizer Mdel Predictive Cntrller Mdel Predictive Cntrller Mdel Predictive Cntrller Mdel Predictive Cntrller SS-Real Time Optimizer Dynamic Nnlinear Cntrller DCS DCS DCS DCS DCS DCS I II III IV V VI Figure 2: Optimizatin Cnfiguratins The apprpriate ptimizatin slutin fr a specific custmer s applicatin is based n the fllwing criteria: Expected ROI: Initial investment Maintenance csts (e.g. rigrus mdels versus data mdels) Ptential benefits (e.g. tangible and intangible) Cmplexity f slutin (affects applicatin uptime) Prcess changes (magnitude, frequency, type) Prcess flexibility (degrees f freedm t adapt t changes) Level f custmer technical expertise (less expertise may require a less cmplex slutin) Implementatin requirements Maintenance requirements The gal in selecting the apprpriate ptimizatin technlgy is t maximize the ROI and achieve an ptimizatin slutin that has sustained benefits ver the lng term with minimized lifecycle csts. Accunting fr bth the magnitude f ptential benefits, as well as a custmer s ability t maintain the slutin, ensure that the benefits are sustainable.

7 Optimizatin Slutin White Paper Layered Optimizatin 7 Optimizatin Slutin Cmpnents Briefly, a layered ptimizatin slutin includes the fllwing technlgy cmpnents: Dynamic Nn-linear Prcess Mdels Steady State, Nn-linear Prcess Mdels Multi-unit Crdinatin & Glbal Optimizatin Multivariable Cntrl & Unit Optmizatin Dynamic, Nn-linear Prcess Mdels Figure 3: Layered ptimizatin slutin cmpnents Unit Optimizatin The first level f ptimizatin used with APC is the Prduct Value Optimizatin (PVO) cmpnent built directly int the multivariable cntrl applicatin. The PVO ptin prvides ecnmic ptimizatin fr a prcess unit (r sub-unit) based n a quadratic (QP) bjective functin. The user specifies prduct values and assciated prductin csts, which are then used t maximize the mst valuable prducts, subject t unit cnstraints. The ptimizer uses the cntrller s dynamic prcess mdels and is fully integrated with the cntrller t prvide dynamic ptimizatin. Therefre, bth the ptimum steady-state slutin and the best path t that ptimum are calculated at each iteratin as part f the ptimizatin prcess. PVO is illustrated in Figure 4. Figure 4: Prduct Value Optimizatin within the cntrller PVO is ideally suited fr small-scale ptimizatin prblems that invlve pushing unit cnstraints. In mst prcess plants, distillatin clumns are ideal candidates fr PVO. Fr large-scale ptimizatin invlving multiple units r highly nn-linear r uncnstrained ptimum peratins, PVO is elegantly integrated with the ptimizatin system using external glbal ptimum targets (ideal resting values), a feature inherent in the QP bjective functin.

8 Optimizatin Slutin White Paper Layered Optimizatin 8 Multi-Unit Dynamic Optimizatin The secnd level f ptimizatin in the cntrl and ptimizatin structure cnsists f multi-unit dynamic ptimizatin based n distributed quadratic prgramming. When prcess units interact with ther units, it is imprtant t cnsider the dynamics f their interactins and shared cnstraints when frmulating the ptimizatin prblem. Fr example, ne may want t maximize feed rate t an upstream unit but be limited by the prductin capacity f a dwnstream unit. Multiple unit ptimizatin cnsiders multiple prcess units, their multivariable cntrllers and any dynamic interactins between these units. Traditinal attempts t slve this prblem were t either: 1. Include the multiple units int ne large multivariable cntrller (MVC) and use the MVC ptimizer n the cmbined units r 2. Use a detailed mdel t simulate the cmbined units, calculate glbal ptimum targets, and pass these targets t multiple MVCs The first slutin, cmbined cntrl, is relatively easy t implement but has several limitatins: Time delays degrade perfrmance. Prcesses with significant time delay between units are nt well suited fr cmbined cntrl, thus degrading cntrl perfrmance. In additin, prcess mdels are extremely difficult t identify between units with lng delays. Crss-unit cntrl is ften undesirable. Cntrl may nt be acceptable if the cntrller attempts t manipulate variables in ne prcess unit t dynamically cntrl variables in a different unit. Distributed hardware may be a prblem. Cntrl acrss multiple DCS data highways and/r cmputers is nt always feasible. Optimizatin ften is all r nthing. The cmbined cntrller cannt break the ptimizatin prblem int smaller sub-ptimizatin prblems if part f the prcess is ff-line. Bigger is nt better. As the size f the cntrller increases, tuning becmes mre cmplex, peratr acceptance and percent nline time declines, maintenance is cstly and difficult, and verall benefit ges dwn. The secnd slutin, detailed mdeling fr ptimizatin, allws fr multiple independent multivariable cntrllers but has its wn disadvantages: Detailed mdeling is expensive. Detailed prcess mdeling is a cmplicated and cstly prcess which can be difficult t maintain by in-plant persnnel. Matching plant peratins and mdel predictins is difficult. The prcess must be in steady-state befre the mdels can be updated t match actual plant results. As ptimizatin ften ccurs under nn-steady-state cnditins, the parameter estimatin fr mdel updating is disabled r simplified dynamic predictins are used t augment mre detailed steady-state mdels. The end result in either case, hwever, is mre system cmplexity as well as reduced benefits during nn-steady-state peratins. Extensive data input increases slutin risk. Detailed mdeling f the prcess requires many inputs and increases the prbability f prblems resulting frm errneus instrument r manually entered data. Plant dynamics are a prblem. Steady-state mdels d nt accunt fr prcess dynamics. This can be prblematic if ptimum targets determined frm steady-state mdels are dwnladed simultaneusly t many units with lng delays between units. Althugh much f this can be avided thrugh careful implementatin, an ver-simplified transitin t the dynamics f the real plant can result in dwnstream prcesses ging ff-spec r vilating cnstraints until the upstream mves take effect. Optimizers and cntrls can fight. Careful crdinatin between the ptimizatin system and MVC is necessary t prevent cnflicting bjectives. Otherwise, the tendency f the tw systems is t fight each ther. Multiple prcess mdels, LP r QP bjective functins, and different executin frequencies must be effectively managed.

9 Optimizatin Slutin White Paper Layered Optimizatin 9 Extensive mdeling is ften nt a requirement fr success. Optimizatin via detailed mdeling is ften an verkill slutin fr many ptimizatin prblems which can ften be slved much mre simply and cst-effectively thrugh the practical applicatin f linear prcess mdels and apprpriate real-time plant feedback. Many times, hwever, extensive prcess mdeling effrts are unnecessarily placed ahead f simpler but mre effective appraches that leverage plant data, withut the verkill effrt that can be assciated with detailed mdeling. T address these prblems and t prvide the best return n investment fr custmers, cmpanies shuld ffer multiple unit ptimizatin with the distributed quadratic prgramming ptimizatin cmpnent. The advantages f this include: Reliability - prvides a multiple unit ptimizatin slutin which is highly reliable and easy t maintain and perate. Dynamic ptimizatin - prvides a rbust slutin which handles nn-steady-state peratin and prcess dynamics between prcess units, and crdinates implementatin f the ptimizatin slutin acrss multiple units, resulting in higher benefits cmpared t traditinal steady-state ptimizatin as shwn in Figure 5. Wrks with cntrllers - integrates multiple unit ptimizatin with unit-level cntrller ptimizatin (uses same mdels and ecnmics as lcal ptimizatin thrugh the PVO ptin). Leverages cntrl investments - capitalizes n the investments f predictive cntrl mdels. Much time and expense is put int the develpment f dynamic MVC mdels. Using the same mdels significantly reduce implementatin and maintenance csts. High return n investment - Dynamic ptimizatin csts significantly less t implement and maintain than a detailed mdeling ptimizatin apprach and can ften return the same ptimizatin benefit, especially when nn-linear cntrl sftware is added (see next sectin). Figure 5: Cmparisn f dynamic and steady-state ptimizatin Dynamic ptimizatin is an extensin f the lwer-level ptimizatin technlgy fund in mst cmmercial mdel-based, predictive cntrllers (MPC + PVO); hwever, multi-unit dynamic ptimizatin cmbines the QP bjective functins frm tw r mre PVO applicatins. This type f technlgy can be quickly cnfigured and implemented n tp f existing cntrller-based applicatins. By using existing MVC mdels and a set f sensible graphical user interface (GUI) tls, large-scale ptimizatin is feasible in a much shrter time and at a significantly lwer cst than cmpared t traditinal steady-state RTO slutins. Figure 6 illustrates a typical Prfit Optimizer installatin.

10 Optimizatin Slutin White Paper Layered Optimizatin 10 Dynamic Prfit Optimizer Optimizer Distributed Dynamic Optimizatin Distributed Dynamic Optimizatin Mdel- Based Prfit Cntrller Mdel- Based Prfit Cntrller Mdel- Based Cntrller Prduct Value Optimizatin Mdel-Based Algrithm Figure 6: Multiple unit ptimizatin with dynamic ptimizer Dynamic ptimizatin technlgy is ideally suited fr mid- t large-scale ptimizatin prblems n mst prcesses. It applies t bth linear and nn-linear prcesses, uniquely leveraging the cntinuus prcess feedback design t cmbine step-wise linear mdeling with quadratic ptimizatin at each executin cycle (typically nce per minute). Fr nn-linear systems with lcal ptima in the ptimizatin search space, nn-linear gain updating r traditinal steady-state nn-linear ptimizatin is recmmended. Nn-Linear Dynamic Optimizatin The next layer f the cmplete ptimizatin adds the nn-linear cntrl and ptimizatin aspect in the heirarchy via a set f dynamic gain extractin and gain mapping (DGEM) sftware. By updating the linear mdels embedded in the base cntrl and ptimizatin applicatins with infrmatin frm user-supplied nn-linear prcess mdels, DGEM technlgy represents a high perfrmance alternative t large-scale, rigrus ptimizatin systems. Generally speaking, DGEM sftware is capable f autmatically detecting the nset f prcess nnlinearities, extracting gain infrmatin frm nn-linear mdels and regularly updating the cntrl and ptimizatin mdels t reflect this infrmatin. The result is imprved cntrl and ptimizatin benefits, since varying cnditins that affect the ptimum, such as changing feeds, ecnmics and envirnmental factrs, can be accunted fr autmatically. DGEM sftware (as shwn in Figure 7) integrates nn-linear prcess mdels with cntrller-based and/r multi-unit dynamic ptimizatin applicatins t deliver enhanced cntrl and ptimizatin benefits. The sftware autmatically extracts gain infrmatin frm these mdels and updates the linear mdels with the infrmatin. This gain-updating feature prvides superir cntrl and ptimizatin capability since the cntrl and ptimizatin mdels are cnstantly updated t reflect the current perating cnditins.

11 Optimizatin Slutin White Paper Layered Optimizatin 11 Dynamic Optimizer DGEM Flwsheet Mdels Mdel - Based Cntrller Mdel - Based Cntrller Mdel - Based Cntrller 3rd Party Mdel Figure 7: DGEM architecture DGEM sftware emplys existing prcess mdels develped fr ff-line use in prcess design and analysis, thus leveraging the investment made t create these mdels, and ensuring cnsistent mdels fr bth ff-line and n-line use. The sftware is nt limited t a specific type f prcess mdel; it can be easily cnfigured t use mdels prvided by mst mdeling systems r it can use custm user-written mdels within dynamic flwsheet simulatin packages. Anther advantage f DGEM sftware is that it allws smaller scale mdels t be used. Rather than mdeling the entire prcess, the sftware allws selective use f nn-linear mdels when and where they are needed. Smaller scale mdels translate int lwer installatin and maintenance csts, higher executin speeds, and higher service factrs, all f which add t the benefits achieved frm imprved prcess perfrmance. DGEM sftware shuld be used where nn-linear prcess behaviur culd result in an uncnstrained ptimum (i.e. FCC unit vercracking regin), r a change in the ptimal cnstraint set (i.e. ethylene plant ptimizatin with varying feeds). Cnverging n an Uncnstrained Optimum Typically, mst units perate in a cnstrained mde where there are few available degrees f freedm. Hwever, in sme cases there is the ptential fr an uncnstrained ptimum, such as in FCC peratin, where the ptimal Riser Outlet Temperature (ROT) is chsen t maximize prductin f a specific cmpnent, such as naphtha. The use f DGEM sftware fr gain updating f multivariable cntrl applicatins is ideally suited fr such an applicatin and enables cnvergence f the slutin t an uncnstrained ptimum. An LP slutin with static gains will always reside at the intersectin f cnstraint limits. When an uncnstrained ptimum is encuntered using gain updating, the slutins f subsequent ptimizatin runs will switch frm ne cnstraint intersectin (i.e. crner) t anther, and the multivariable cntrl applicatin will mve the prcess t the slutin subject t the cnfigured ptimizatin speed. By using the cncept f MV sft limits (essentially a mre restrictive set f limits hnred nly by the ptimizer) t define MV step sizes, the distance between subsequent ptimizatin slutins can be reduced by limiting the search space that the cntrl applicatin has fr its ptimizatin calculatins at each ptimizatin interval. Sme multivariable cntrl applicatins have the unique ability t separate the cntrl hrizn (time ver which cnstraints are met) frm the ptimizatin hrizn (time ver which the ptimizatin targets are reached) and ensures that the cntrller des nt scillate when an uncnstrained ptimum is reached. Thugh minr scillatins ccur as the slutin switches between sft limits, these fluctuatins are nrmally nt bservable and are clse t the magnitude f the nise f the measurements. Figure 8 illustrates hw this technique will cnverge n an uncnstrained ptimum with respect t, fr example the ROT in an FCC unit. Lcatin 1 represents the initial perating cnditins and 2-5 represent subsequent perating pints with 4 being the ptimum. The bunds arund lcatin 4 are the sft limits (say 0.5 ºF). A sufficiently small step size is used t avid having the slutin drift frm the ptimum.

12 Optimizatin Slutin White Paper Layered Optimizatin 12 Figure 8: Cnverging n an uncnstrained ptimum with gain updating The main benefits f using DGEM sftware in cnjunctin with multi-variable cntrl applicatins and distributed dynamic ptimizatin cmpared t traditinal steady-state RTO are: 1. Lw cst Typical prject cst ($ K) f a nn-linear cntrl sftware prject is significantly less than the typical $1M fr full-scale, rigrus, mdel-based slutins 2. Quick delivery f benefits Implementatin time f 3-6 mnths cmpared with abut 12 mnths fr full-scale, rigrus, mdel-based slutins 3. Sustainable benefits Much easier t maintain Typically requires 5-10 percent f an engineer s time t maintain On-line availability typically > 95 percent Lw training time 4. High ROI Similar benefits result in typical payback < six mnths

13 Optimizatin Slutin White Paper Layered Optimizatin 13 Nn-Linear Steady-State Optimizatin The next level f ptimizatin includes a nn-linear steady-state ptimizatin (ften mistakenly referred t as Real Time Optimizatin [RTO]) slutin. This technlgy is typically used where multiple lcal ptima r significant nn-linear behaviur is bserved. In additin t n-line ptimizatin, this technlgy can be used fr predictive simulatin, prcess mnitring and trubleshting, engineering studies, develpment f regressed mdels fr cntrl, planning and scheduling, and peratr training. Certain nn-linear steady state technlgies can be run in n-line r ff-line mdes with varying levels f practical difficulty. Additinally, nn-linear steady-state ptimizatin shuld integrate with simulatin sftware t determine ptimum steady-state targets that can be dwnladed t the APC applicatin. This allws the applicatin the flexibility t carry ut the steps required fr traditinal RTO such as data validatin, steady-state detectin, data recnciliatin and parameter estimatin, ptimizatin, etc. Dynamic Nn-Linear Cntrl and Optimizatin Dynamic nn-linear cntrl and ptimizatin (DNLCO) technlgies allw the plant t carry ut bth cntrl and ptimizatin simultaneusly, primarily fr plymer applicatins. Delivering rbust cntrl and ptimizatin, such technlgy is designed t cntrl nnlinear prcesses in bth prcess gains and prcess dynamics. The use f a rigrus prcess mdel that describes prcess equipment gemetry and chemical kinetics remves the need fr step testing the plant. This mdel als cmbines the advantages f reliable multivariable cntrl and ptimizatin f n-line prcess and dynamic ff-line simulatin fr new prduct grades in plymer applicatins. Each f these ptimizatin technlgies have merit depending n the applicatin, and as a result, each f these technlgies frm part f the layered ptimizatin slutin. Recmmended Optimizatin Apprach and Benefits In general, the recmmended RTO slutin is t use cntrller-based ptimizatin fr single unit ptimizatin, distributed dynamic ptimizatin in cnjunctin with cntrller-based ptimizatin fr multi-unit ptimizatin, and then add DGEM sftware as necessary t accunt fr significant nn-linearities that culd result in an uncnstrained ptimizatin slutin. This slutin apprach is the mst practical ptimizatin slutin available and results in significant, sustainable benefits with lw maintenance csts. Extensive experience with this technlgy reveals significant benefits cmparable t traditinal RTO, but with less maintenance requirements and higher n-line time (>95 percent). Prject implementatin times f 3-6 mnths fr this recmmended apprach is significantly less than traditinal RTO (typically 6-12 mnths), and training time is lw fr engineers and peratrs because the slutin leverages existing advanced cntrl technlgy and user interface(s) withut the need fr anther level f end-user cmplexity. In sme cases, traditinal RTO is still necessary and therefre traditinal steady-state nn-linear slutins are apprpriate. Fr example, mixed integer nn-linear prgramming (MINLP) prblems that might ccur with utility systems when determining the ptimal driver selectin (steam vs. electric) is an example where this type f slutin is required. Als, traditinal RTO has sme additinal features such as the ability t d prcess mnitring, hwever the drawback is that the large mdels are ften difficult t maintain fr cntrl engineers. Dynamic nn-linear cntrl and ptimizatin (DNLCO) is recmmended fr thse cases where step testing is difficult (r prhibited), significant nn-linearities exist in the prcess and there are frequent changes due t prduct specificatin changes. An example where this slutin has been successful is in cntrl and ptimizatin f plyethylene prductin where frequent prduct transitins may ccur.

14 Optimizatin Slutin White Paper Layered Optimizatin 14 Optimizatin Benefit Estimatin Generally speaking, there are n easy ways t estimate ptimizatin benefits since ptimizatin benefits are affected by equipment cnstraints, prduct cnstraints, prcess cnstraints (i.e. degrees f freedm) and changing ecnmic cnditins. Hwever, the fllwing three appraches can be used t assist in estimating ptimizatin benefits during prject justificatin. 1. Use typical industry standard estimates as indicated in the benefit table presented earlier. Take 20 percent f the benefits in the table and assume that ptimizatin can prvide that amunt. This methd is subject t a significant amunt f errr, but can prvide sme rule-f-thumb benefits. 2. If advanced cntrl benefits have been estimated r are knwn fr the particular applicatin, then estimate the ptimizatin benefits as 20 percent f the knwn r estimated APC benefits. Again, this is a rule-f-thumb estimate, but is mre accurate than methd 1 since it accunts fr the specific applicatin. 3. Undertake a benefit study t estimate benefits cntact cnsultant t determine if necessary. Typically such a study requires a site visit and 3-6 weeks f effrt, and invlves running a mdel f the prcess (supplied by the custmer) t determine the nn-linear behaviur f the plant ver the expected perating regin and bservatin f the active cnstraint sets under varying prcess and ecnmic cnditins. The study can mre accurately identify the anticipated benefits and als whether the nn-linearity f the prcess justifies using DGEM sftware t supply gain updating r ptentially nn-linear steady-state ptimizatin sftware. In many cases a gd cmmercial dynamic flwsheet simulatin sftware package can expedite this apprach. Summary In summary, a layered ptimizatin slutin can slve all types f ptimizatin prblems. The result f a layered apprach is a slutin that achieves significant benefits with lw risk, requires lw lifecycle maintenance csts and sustains benefits in the lng term. Fr Mre Infrmatin T learn mre abut Hneywell s Optimizatin Slutins, visit ur website r cntact yur Hneywell accunt manager. Autmatin & Cntrl Slutins Prcess Slutins Hneywell 2500 W. Unin Hills Dr. Phenix, AZ Tel: r April 2008 Printed in USA 2008 Hneywell Internatinal Inc.