Remote Optimization in Petrochemistry

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1 Proeedings of the 9th WSEAS International Conferene on Automati Control, Modeling & Simulation, Istanbul, Turkey, May 7-9, Remote Optimization in Petrohemistry Cornel Resteanu, Marin Andreia, Cristina Andreia and Ioan Milaus National Institute for Researh and Development in Informatis, 8-0 Averesu Avenue, 0455, Buharest, Romania Eonomi Studies Aademy, 6 Romana Square, 0057, Buharest, Romania Commerial Aademy 5 Mihai Eesu, 900, Satu Mare, Romania Abstrat: - In order to extend worldwide the proessing business of the petrohemial plant, its potential business partners an themselves onurrently simulate prodution plans with high quality tehnial and eonomi features. In the simulation proess, a large number of divergent goals are under attention. Therefore, the plant omputer will use the multi-obetive linear programg as a tool for negotiations. The dialog between a partner and the plant omputer onsists in two steps, namely proessing demand and plant response, performed repeatedly until the business makes sense or it shows unaeptable. In the first ase, an be signed the proessing ontrat. Key-Words: - E-Business, Large Sale Systems, Mathematial Modeling, Web Enabled Optimization, Multi- Obetive Linear Programg, Internet Computing. Introdution As opposed to disrete proess plants that an adopt, beside new tehnology, the reorganization of their internal funtions and an better realize the horizontal integration with their business partners, possibly by onsidering the onept of extended enterprise, ontinuous proess plants, like petrohemial ones, an only resort, at this stage of tehnologial development, to reengineering the business proesses [], [], []. Obviously, the approahing of the business proesses reengineering are linked to the development of new software for prodution ontrol [4], [5], [6]. As it is known, e-business is now the most important way to detere hanges in the ating manner of businesspersons. Beause of sarity of raw materials, a onsequene of shrinking finane, the plant must proess the raw materials belonging to various partners in order to avoid working below its atual apaities. Petrohemial plants yield a good deal of their output under proessing regime. This signifies that the raw materials are not purhased by the plant, but are owned by a business partner whose intention is to squeeze maximum out of their treatment. The drawbak in this ase is bombarding the plant with lots of proessing proposals, not to be finalized ever beause of their overrating by business people. In order to negotiate a mutual advantageous business, the solution ould be a dialogue via Internet between potential partners and the plant omputer. One founds that business makes sense, the ontrat signing an be done. The simulating of optimal plans in these irumstanes must be very fast and it must allow the onurrent work for a large number of plant partners. For this purpose, ICI has developed the software named Tele-PROCESSING. Plant mathematial model The software designed for prodution planning in petrohemial industry ahieves both strutural and funtional desription of the plant [7]. The plant anatomy is given by desribing the struture direted multi-graph, verties being the individual prodution ells (IPCs) and ars being the pipelines (Ps) between these. The plant physiology is given by desribing the IPCs and Ps behavior. From this, it is possible to generate and solve planning problems as Multi- Obetive Linear Programg (MOLP) problems.. Syntheti plant model The planning model is: x F f ( x ), F = { x g( x ) b, x 0 }, where: (0) f ( x ) = ( f ( x ),.., f ( x )), g( x ) = ( g ( x ),.., g ( x )), o m x = ( x,.., x ), b = ( b,..,b ), 0 = ( 0,.., 0 ), n m n

2 Proeedings of the 9th WSEAS International Conferene on Automati Control, Modeling & Simulation, Istanbul, Turkey, May 7-9, n n f ( x ) = x, l,h, g ( x ) = a x,, m, l i= li i i= i i li,a i, b R, ( )l, o, i, n,, m. In onlusion, there exist: n deision variables, namely the yields of prodution system, m onstraints ( m = m + m, m are tehnologial onstraints and m are ommerial onstraints), o obetives ( o = o + o, o are plant obetives and o are partner obetives).. Analytial plant model Let C = Υ 6 C k be the set of IPCs, deomposed into six k= lasses: ) system inputs, ) effiieny-type relations based IPCs, ) blending-type relations based IPCs, 4) speifi onsumptions relations based IPCs, 5) flow merging / distribution nodes, 6) system outputs. In the multi-graph terology all the elements of C are verties, while for C and C 6 only outgoing and inog ars respetively exist, for the remaining C (( )k =, 5 ) both ar types being possible. For a k ertain vertex, let id () be the indegree and od () be the outdegree. Some harateristi features of the model, whih represents the industrial omplex, an be identified: ) id ( ) = 0 ( ) C, od ( ) = 0 ( ) C6, and ),od( ) 0, ( ) Υ 5 C k ; ) no k= (self)loops are present; ) all iruits have more than two ars; 4) dimensions are rather large, the number of verties, ars and iruits typially amounts to hundreds, thousands and tens, respetively and so, suh a kind of plant is a large-sale system (LSS). Eah IPCs lass has its own behavior, whih is presented in the following: and and quantities ( ),, outputs O i available Q, Q max, fulfill: C AVA - Q AVA Q max (limiting of the available quantity relation) () od( ) - O AVA i= i (relation for the distribution of the available quantity) () and ( ),, inputs I, outputs O i, apaities CAP and CAP max, inputs I O i effiieny transformation oeffiients in outputs R i, quantities Q, Q max and weights k and outputs respetively, fulfill: id ( ) p, q k for inputs - Oi = R i I i ( ) i, od( ) (effiieny relations) = id ( ) where R i fulfills R = ( ) i, od( ) () - CAP id ( ) = I = i CAP max (refining apaity relation) (4) - ( ) i {,,......, od ( )} suh that Q O i Q max (limitation relations for some inputs) (5) ISUB ({,,..., )} \ { }) - ( ) pk R + ( ) k ISUB for fixed suh that I = pk I k (6) k ISUB OSUB ({,,..., od( )} \ { i}) & ( ) qk R + ( ) k OSUB that Oi = qko k k OSUB for fixed i suh (the inputs and outputs interondition relation) and ( ),, inputs I, output (7) O, apaities CAP and CAP max, blending oeffiients r, max qualities QUALl, QUALl of output and qualities QUAl l whih define the ontribution of input to the orresponding quality of the output QUAL l, fulfill: id ( ) - O = I (free blend relation) (8) = - I = r O, ( ), ) (blending reipe relation) with 0 r ( ), ) and r = (9) l id ( ) id ( ) = - O QUAL QUAL I,( ) l, l( O ) max l = id ( ) & O QUAL QUAL I,( ) l, l( O) = l l (0) () (quality standards relations) - CAP O CAP (apaity relation) () max 4 and ( ), 4 inputs I, prinipal / seondary outputs PRI _ and SEC_ O is, apaities O i p CAP and CAP max, quantities Q i p, Q i p speifi onsumptions for obtaining the prinipal outputs and the ratios for obtaining the SC i p max,

3 Proeedings of the 9th WSEAS International Conferene on Automati Control, Modeling & Simulation, Istanbul, Turkey, May 7-9, seondary outputs from the first prinipal output RA is, fulfill: id ( ) - I = PRI _ O + SEC _ O i i = i PRI OSUB p i SEC OSUB s p _ s _ (inputs - outputs balane relation) () where: PRI _ OSUB, SEC_ OSUB {,,..., od ( )} PRI _ OSUB, SEC_ OSUB PRI _ OSUB Ι SEC_ OSUB = PRI _ OSUB ΥSEC_ OSUB = {,,..., od ( )} ard ( PRI _ OSUB ) << ard ( SEC_ OSUB ) ip PRI _ OSUB - PRI _ O = CS I, ( ) i p i p (speifi, id ( ) onsumptions relations) (4) - SEC _ O = RA PRI _ O, ( ) is SEC _ OSUB (ratio i s i s relations) (5) id ( ) I CAPmax (apaity relation) (6) = - ( ) i p PRI _ OSUB suh that Q Q Q i p i p i p max - CAP (limitation for prinipal outputs) (7) 5 and ( ), 5, inputs I, outputs O i and weights p k, q k for inputs and outputs respetively, fulfill: id ( ) od ( ) O i = i= - I = (inputs-outputs balane relation) (8) ISUB ({,,.., id ( )} \ { }) - ( ) for fixed suh that pk R + k ( ) ISUB I = pk I k ISUB k (9) OSUB ({,,.., od ( )} \ { i}) &( ) for fixed i suh that qk R + k ( ) OSUB Oi = qko k OSUB k (0) (the inputs and outputs interondition relations) 6 and ( ), 6, inputs I, deliverable DEL, imum needed quantity O and maximum quantity that an be stored / transported S / T _ O max, fulfill: id ( ) - DEL = I = (deliverable quantity aumulation relation) () - Q DEL S / T _ Qmax (relation that limits the deliverable quantity) () Remarks: For the Ps with aumulation buffers, input in aumulation buffer I, output from aumulation buffer O, apaity CAP max, initial stok I _ S, final stok F S _ and quantities before / after the aumulation buffer A_ Q and P_ Q, fulfill: - A_ Q + O = P_ Q + I (flow balane relation) () - I + I _ S = O + F _ S (aumulation buffer balane relation) (4) - I + I _ S CAP O I _ S max, (aumulation buffer apaity relations) (5) therefore, the links have their own behavior; The IPCs belonging to C and C 4 an have several running modes. Thus, the relations (), (), (4), (5), (6) and (7) should be written adding a supersript r that denotes the running mode. As a result, the following relations must be added: r( ) r - I = I, ( ), ) r= r( ) r - O = O,( ), ) r= (6) (7) At any given moment, the IPCs belonging to C an funtion aording to only one set of the relations: (8) / (9) / (0) & (); The deision maker an define a set of linear obetive funtions, denoted by f = f ( x), f ( ),... ( x n..., f l ( x)), where x R+. For instane, the value of final produts is: - PRICE DEL _ DEL (8) C7 ' ' '' C = C7Υ C7 7 where, ' '' C7 Ι C7 = ' C 7 being deliverable outputs and '' C 7 being remaining outputs (side effets). The parameters and variables desribing the IPCs and Ps behavior are in R +.. Model Charateristis In this field, MOLP problems have the following harateristis: huge dimensions (thousands of rows and olumns), predoane of equality relations (following from Kirkhoff s first law), emphasized

4 Proeedings of the 9th WSEAS International Conferene on Automati Control, Modeling & Simulation, Istanbul, Turkey, May 7-9, sparsity, wide differenes in the order of magnitude of the various oeffiients of the model (leading to internal saling), tight feasibility domains (sometimes leading to the absene of a feasible solution). This is true mainly for the tehnologial onstraints. Nevertheless, the problem also ontains ommerial onstraints that are usually stated by the business partner. They are limits for the end produts. Main obetives inluded in the plant obetives set are the market value of the end produts, the value of the net prodution, proessing osts, labor osts, stoks of end produts, on flow stoks, profit and pollution prevention osts. The partner obetives set usually ontains profit, rude oil value, shipping osts, ingredients ost, proessing ost and end produts value. To generate and solve suh kind of problems is a very diffiult task by itself. In nowadays, many welltrained teams in prodution departments of petrohemial plants are designated to simulate diverse variants of prodution plans using the PIMS software. However, to aomplish this task from remote loations, worldwide situated, by people without a speial training, it is a true hallenge. In the following, it is shown how this thing is possible. Tele-PROCESSING Software Like any advaned software working from a plant eletroni site, Tele-PROCESSING has two funtional bloks. The first blok addresses the Prodution Department in plant, therefore, works on the Intranet and is written in MSVC 6.0. The seond blok addresses the partners of proessing business, therefore, works on the Internet and is written in ASP. An automation blok, whose role is to exlude plant speialists intervention in the simulation proess, links these two bloks. It is a supervisor program, developed in the dormant tehnique that automatially runs, at a given tat, the optimizations / re-optimizations as well as other ations suh as optimization data management. Obviously, there an be some exeptions that must be treated by human intervention, the omputer breakdowns only! It is to notie that Tele-PROCESSING works as a shell over the PIMS software whose database, ontain rude oils and their harateristis, raw materials, ingredients, on-flow produts, end produts, apaities, tehnologial data (regarding effiieny transformation oeffiients, speifi onsumptions, blending reipes, inputs / outputs inter-onditions relations, stoking apaities, funtioning regime et.), repairs, system timetable, is available. Ones must onsider a proessing demand only for a rude oil that the prodution system an proess it and only for the free apaities in a ertain time level (deade, month, quarter, and year). Even for the same time level, in various time horizons, free apaities map an be very different beause the regular prodution plans an differ. Adding, to this bak-offie, strutured information about plant partners (users, ompanies and their banks) and assoiated simulated prodution plans one obtains the information framework for Tele-PROCESSING software well funtioning. The supplementary information is managed using SQL Server 7.0 Enterprise Edition. This is the ensemble of information neessary to start simulating the proessing business, i.e. defining an optimization planning problem, solving it, reading the result and the assoiated eonomi indiators, (repeat the proess if is neessary) and deiding on bussines opportunity.. Partners Registration Using the Registration site setion, the potential partners must register in the system. Only thus, they beome benefiiaries of the mehanism that is able to make a remote plan simulation and an estimation of the profit that an be obtained in the proessing business. The seurity is a very serious problem, solved in an elaborate manner. For exterior, there is the PKI omponent that assures aess ontrol, user authentiation, non-repudiation, integrity of data transfer, digital signature, et. For interior, a speial omponent manages the database integrity. Every unauthorized aess, even those made by programmers diretly on the database tables, provoke system blokage until the system automatially regains its integrity. The supervisor also runs this funtion. Therefore, the partners an trust that they work in an environment haraterized by orretness and loyalty. The trust s problem was very well solved. The partners risk is imized beause, from the very beginning, they are not finanially involved. The organizer risk involves the ompetitors earnestness. The business is based, as always, on partners honor. The size of the business is not a limitation.. Proessing Proposals It is now possible to aess the e-proessing site using Mirosoft Internet Explorer or Netsape Navigator browsers. As a rule, the mathematial model data are the plant know-how and are seret. To protet them, the plant makes publi on the Internet only the data referring the ingredients and the end produts. For a partner it is normal to give the quantity of rude oil to be proessed with its physial-hemial properties, the data levels assoiated with the desired produts and the

Proeedings of the 9th WSEAS International Conferene on Automati Control, Modeling & Simulation, Istanbul, Turkey, May 7-9, 007 97 proessing time horizon.

Aessing the site setion named Proessing proposals, the business partner an make a proessing demand by speifying: proessing demand s name, rude oil name, measurement unit, prie and quantity.

5 Proeedings of the 9th WSEAS International Conferene on Automati Control, Modeling & Simulation, Istanbul, Turkey, May 7-9, proessing time horizon. These are the data that the business partners find to be of paramount importane and only they are taken into onsideration. Aessing the site setion named Proessing proposals, the business partner an make a proessing demand by speifying: proessing demand s name, rude oil name, measurement unit, prie and quantity. To desribe the rude oil it is also of outer importane. Ones an do this ob by filling up the table ontaining: attribute name, measurement unit and level for eah physial-hemial harateristi. Shipping ost is information of low importane. It is neessary to ompute the partner profit (Capture ). A simple pushing on submitting button triggers the taking over of the proessing demand by the Tele- PROCESSING supervisor. In order to deide whether the rude oil an be proessed or not, the supervisor heks the rude oil pattern. If it is tehnologially unaeptable, the partner is invited to give another rude oil with harateristis that fits to the plant prodution system. If it is aeptable, the harateristis are frozen, they annot be afterworlds hanged, and the proessing proposal is passed, for optimizing, to the sheduling mehanism. This inserts the demand into the waiting queue. The insertion is made aording to a priority oeffiient omputed taking into aount the value of the business and the number of the previous optimizations. Even a powerful omputer annot effiiently solve more than five optimizations at the same time. So, the launhings in exeutions are made gradually in time. The partner is informed about the plant response by onsulting the optimization status. One the optimization is finished, one offers three ategories of information: a) Inputs (ingredient name, measure unit, quantity, prie and value); b) Outputs (end produt name, measure unit, lower limit, optimal quantity, upper limit, prie and value); ) Eonomi indiators (rude oil value, shipping ost, ingredients ost, proessing ost, end produts value and profit) (Capture ). At this moment, the business partner an establish some onditions for the end produts, maybe influened by the market and ask for a new optimization The number of the optimizations, as well as the time allowed for negotiations, is limited. As it is shown before, the business partner analyzes the results of any optimization work, i.e. the end produts levels attained through prodution optimization and the values of obetive funtions. The partner may onsider it neessary that the plant omputer must make the iterations of the business optimization for as many times as it takes to reah the best result, or otherwise he may wish to get out of business.. Best Proposals Taking into aount that a multitude of potential proessing partners an onurrently make tempting proposals for the same time level and horizon, Tele- PROCESSING software arranges the proposals, for eah time level and horizon, into three lasses: big, medium and small businesses. Every business lass benefit from a Multi-Attribute Deision Making (MADM) type optimization. If in the MLP problem there exist only ten obetives, in the MADM problem are stated over two hundred attributes, expressed by more than one expert (tehnologists, eonomists, bankers, eologists et.) in more than one state of nature (eonomi stability, inflation, deflation et.) Thus, the plant managers an appreiate on solid basses, whih is the best proposal. The pratie shows that it is possible to exist one or many winners for every business lass. They are invited for a last disussion and ontrat signing. Capture. Proessing proposal Capture. Input and output data of an optimized proessing

6 Proeedings of the 9th WSEAS International Conferene on Automati Control, Modeling & Simulation, Istanbul, Turkey, May 7-9, The prodution department staff, to whom some IT people will oin, will take the responsibility for wathing the system and answering to the partners eletroni letters. The plant omputer automatially solves even the proessing demands that present problems, like void feasibility domain, aused by overrating of plant apaities. The funtion that ensures the man-mahine interfae on the Intranet is analogous with the funtion that works on the Internet. In addition, also a funtion manages the partners setion of the database. The main goal of this funtion is to ompute, in time, based on statistial data, the partners allowane. As ones an see in this paper, the designer preoupation was the optimization proess automation. Only the eonomi ontrat signing is out of the system, mainly beause of the bureauray aepted as neessary in this field. Thus, the plant will be able to improve the quality of the proessing business. Extending worldwide the business of a plant is one of the globalization ommandments. Another, very important too, is to promote mutual advantageous business. Tele-PROCESING will be apable to support suh kind of business. This is possible by making use of advaned simulation and optimization tehniques. Web enabled simulation and optimization is a new trend in treating the omplex industrial problems. Moreover, the designing team preoupation is to extend the failities of treating the onurrent optimizations by using GRID solutions. 4 Conlusion The Internet reated the support for a new range of appliations. In our days, e-appliations undergo a fast development. In the authors opinion, elaborate e- business appliations will soon beome fats on the IT market. Tele-PROCESSING software is under final testing; launhing it is thought to be a real event and to have a signifiant impat at level of petrohemial industry. A series of Romanian petrohemial plants have supported testing in real onditions and the onlusions have been enouraging. The software arhiteture is general for a advaned e-appliations but, as Tele- PROCESSING is not business to onsumer but business-to-business software and its appliability is with petrohemial plants, some partiularities individualizes the manner of doing eletroni business with this software. It really ontributes to the modernization of the proessing business. Its apabilities related to genuine e-business make it suitable to ontribute to the invigoration of the business. With this software, petrohemial plants may beome more ative on the world market via Internet. The strong point of Tele-PROCESSING software is the fat that the proessing business is optimal for both the plant and its business partners. Please remark that although the mathematial model shows high omplexity, Internet developed interfae is a narrow one, beause it onerns reasonable demands. Its interfae is very human beause people without a speial training in mathematis and informatis an easily use the software. Tele-PROCESSING delivery onditions and servies inluded are software and doumentation on CD, autodemonstration, on-all / on-site assistane in use and short time training ourse (6-40 hours) for initiation in eletroni business. Further developments refer to extensions for other ontinuous proess industries. Ones suppose that the Internet modules and the simulation mehanism will be the same, only the Intranet modules and the optimization will hange. Therefore, a lot of programg work will be save with good onsequenes for delivery time and software ost. Referenes: [] Limam Mansar, S. and H.A. Reiers (005). Best Praties in Business Proess Redesign: Validation of a Redesign Framework. Computers in Industry, 56(5) pp [] Jossey-Bass (00). The New Proet Management: Tools for an Age of Rapid Change, Complexity and Other Business Realities. Business & Management Series by Frame, J. Davidson. San Franiso, CA John Wiley & Sons, In, US. [] Dennis, A. R., Carte, T.A. and G. G. Kelly (00). Breakingthe rules:suess and failure in Groupwaresupported business proess reengineering. Deision Support Systems, v. 6, n., September, pp -4. [4] Draman, M., A. Kuban, N. Bagori, A. T. Unal and B. Birgoren (00). A lone-based graphial modeler and mathematial model generator for optimal prodution planning in proess industries. European Journal of Operational Researh Volume 7, Issue, pp [5] Resteanu, C., F.G. Filip, S. Stanesu and C. Ionesu (000). A ooperative prodution planning method in the field of ontinuous proess plants. Elsevier Siene, International Journal of Prodution Eonomis 64, pp [6] Resteanu, C., N. Andrei and E. Mitan (000). Continuous proess plant as a system open to business. In: Dietmar P.F. Moller Ed., Proeedings of the ESS 000, -th European Simulation Symposium, Simulation in Industry. (Hamburg, Germany, September 8-0), pp [7] Xiong, G. and T.R. Nyberg (000). Push/pull prodution plan and shedule used in modern refinery CIMS. Robotis and Computer-Integrated Manufaturing, Volume 6, Issue 6, pp

Remote Optimization in Petrochemistry

Remote Optimization in Petrochemistry Remote Optimization in Petrohemistry Cornel Resteanu and Marin Andreia Abstrat In order to extend worldwide the proessing business of the petrohemial plant, its potential business partners an themselves