PROCESS OPTIMIZATION AND INTEGRATION STRATEGIES FOR MATERIAL RECLAMATION AND RECOVERY. A Dissertation HOUSSEIN A. KHEIREDDINE

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1 PROCESS OPTIMIZATION AND INTEGRATION STRATEGIES FOR MATERIAL RECLAMATION AND RECOVERY A Dssertaton by HOUSSEIN A. KHEIREDDINE Submtted to the Offce of Graduate Studes of Texas A&M Unversty n partal fulfllment of the requrements for the degree of DOCTOR OF PHILOSOPHY May 2012 Major Subject: Chemcal Engneerng

2 PROCESS OPTIMIZATION AND INTEGRATION STRATEGIES FOR MATERIAL RECLAMATION AND RECOVERY A Dssertaton by HOUSSEIN A. KHEIREDDINE Submtted to the Offce of Graduate Studes of Texas A&M Unversty n partal fulfllment of the requrements for the degree of DOCTOR OF PHILOSOPHY Approved by: Co-Chars of Commttee, Mahmoud El-Halwag Nmr Elbashr Commttee Members, M. Sam Mannan Hsham Nasreldn Head of Department, Charles Glover May 2012 Major Subject: Chemcal Engneerng

3 ABSTRACT Process Optmzaton and Integraton Strateges for Materal Reclamaton and Recovery. (May 2012) Houssen A. Khereddne, B.A., Texas A&M Unversty-Kngsvlle Co-Chars of Advsory Commttee: Dr. Mahmoud El-Halwag Dr. Nmr Elbashr Industral facltes are characterzed by the sgnfcant usage of natural resources and the massve dscharge of waste materals. An effectve strategy towards the sustanablty of ndustral processes s the conservaton of natural resources through waste reclamaton and recycles. Because of the numerous number of desgn alternatves, systematc procedures must be developed for the effectve synthess and screenng of reclamaton and recycle optons. The objectve of ths work s to develop systematc and generally applcable procedures for the synthess, desgn, and optmzaton of resource conservaton networks. Focus s gven to two mportant applcatons: materal utltes (wth water as an example) and spent products (wth lube ol as an example). Tradtonally, most of the prevous research efforts n the area of desgnng drectrecycle water networks have consdered the chemcal composton as the bass for process constrants. However, there are many desgn problems that are not componentbased; nstead, they are property-based (e.g., ph, densty, vscosty, chemcal oxygen demand (COD), basc oxygen demand (BOD), toxcty). Addtonally, thermal

4 v constrants (e.g., stream temperature) may be requred to dentfy acceptable recycles. In ths work, a novel approach s ntroduced to desgn materal-utlty (e.g., water) recycle networks that allows the smultaneous consderaton of mass, thermal, and property constrants. Furthermore, the devsed approach accounts for the heat of mxng and for the nterdependence of propertes. An optmzaton formulaton s developed to embed all potental confguratons of nterest and to model the mass, thermal, and property characterstcs of the targeted streams and unts. Soluton strateges are developed to dentfy stream allocaton and targets for mnmum fresh usage and waste dscharge. A case study on water management s solved to llustrate the concept of the proposed approach and ts computatonal aspects. Next, a systematc approach s developed for the selecton of solvents, solvent blends, and system desgn n n extracton-based reclamaton processes of spent lube ol Property-ntegraton tools are employed for the systematc screenng of solvents and solvent blends. The proposed approach dentfes the man physcal propertes that nfluence solvent(s) performance n extractng addtves and contamnants from used lubrcatng ols (.e. solublty parameter (), vscosty (), and vapor pressure (p)). The results of the theoretcal approach are valdated through comparson wth expermental data for sngle solvents and for solvent blends. Next, an optmzaton formulaton s developed and solved to dentfy system desgn and extracton solvent(s) by ncludng techno-economc crtera. Two case studes are solved for dentfcaton of feasble blends and for the cost optmzaton of the system.

5 v ACKNOWLEDGEMENTS Frst of all, I would lke to express my deepest thanks, regards, and apprecaton to my advsor Dr El-Halwag. I had the honor to work under hs supervson. He supported me wth any decson that helped me to become a better engneer. I want to thank hm for hs countless advce toward my academc research. I very much apprecate hs effort n provdng me wth the ultmate workng envronment. Also, I want to thank hm for hs assstance to work n teams and collaborate wth other research groups around the world. He has provded me wth varous opportuntes and consstently exposed me to dfferent research areas n order to broaden my thnkng. Through hs gudance and support, I was able to present my work and research n top natonal conferences. I owe hm a great deal of apprecaton, and I wsh hm all the best. I would also want to take the opportunty to thank my co-char, Dr Elbashr.I want to thank hm for hs valuable nput and feedback toward my research. He consstently provded me wth dfferent nsghts and prospectve. I want to thank hm for sharng hs sold experence n the re-refnng of used lubrcatng ol. Also, I apprecate hs efforts n makng hmself avalable for dscusson regardless of the dstance and tme zone dfference. In addton, I would lke to gve specal thanks to Dr. Mannan, and Dr. Nasreldn for ther precous tme n revewng my thess and ther valuable advce to produce qualty work.

6 v Partcularly, I want to thank Dr Jose Mara Ponce-Ortega, Dr Denny Ng, and Douglas Tay for ther frutful collaboraton. It has been a great successful experence. Through ther help and support, I was able to make my research lfe more meanngful. I would lke to extend my grattude to both the former and present members of process optmzaton and ntegraton group, Mohamed Noureldn, Kerron Gabrel, Rene Elms, Chun Deng, Grace Pokoo-Akns, Bowlng Ian, Bupng Bao, Eman Tora, Vet Pham, Abdullah Bn Mahfouz, Mng-Hao Chou, Erc Pennaz for ther help and collaboraton. They have truly enrched my research. Fnally, I am deeply ndebted to my famly. They have supported and nspred me uncondtonally throughout my entre academc career. They have been magnfcent role models. Through ther love and boundless support, I was able to strve all the way toward achevng my goals and dreams.

7 v TABLE OF CONTENTS Page ABSTRACT... ACKNOWLEDGEMENTS... v TABLE OF CONTENTS... v LIST OF FIGURES...x LIST OF TABLES...x 1 INTRODUCTION TO PROCESS OPTIMIZATION AND INTEGRATION Preface and Motvaton Key Strateges Process Integraton Introducton Mass Integraton Property Integraton Optmzaton Dssertaton Goals OBJECTIVES OF WORK Objectves Overvew Water Conservaton and Drect Recycle Network Lube Ol Reclamaton and Property Integraton Solvent Selecton Systematc Approach Optmzaton formulaton for Solvent Extracton n the Lube Ol Applcaton OPTIMIZATION OF DIRECT RECYCLE NETWORKS WITH THE SIMULTANEOUS CONSIDERATION OF PROPERTY, MASS, AND THERMAL EFFECT Introducton Problem Statement Approach and Mathematcal Formulaton Case Study Data Extracton (Scenaro 1)... 30

8 v Data Extracton (Scenaro2) Soluton and Results Conclusons Nomenclatures A PROPERTY-INTEGRATION APPROACH TO SOLVENT SCREENING AND CONCEPTUAL DESIGN OF SOLVENT-EXTRACTION SYSTEMS FOR RECYCLING USED LUBRICATING OIL Introducton and Lterature Revew Problem Statement Selecton of Prncpal Propertes and Constructon of Property Clusters Desgn Approach Case Study Conclusons A SYSTEMATIC TECHNO-ECONOMICAL ANALYSIS FOR THE SUPERCRITICAL SOLVENT FISCHER TROPSCH SYNTHESIS Introducton Solvent Extracton Process Descrpton and Problem Statement Problem Statement Mass Balance Component Materal Balance Heat Balance Equlbrum Equatons Optmzaton Formulaton Case Study and Results Data Collecton Results and Dscussons Conclusons CONCLUSIONS AND RECOMMENDATION FOR FUTURE WORK REFERENCES...98 APPENDIX A VITA

9 x LIST OF FIGURES Page Fgure 1.2: Snk and source composte dagram for materal recycle pnch analyss (El-Halwag, 2006)... 6 Fgure 1.3: Ternary dagram representaton of Intra-Stream of clusters (Shelly and El-Halwag, 2000) Fgure 1.4: Lever arm addton for clusters (Shelly and El-Halwag, 2000) Fgure 3.1: Source/snk allocaton wth drect reuse/recycle Fgure 3.2: Process flowsheet of the producton of phenol from cumene Fgure 3.3: Optmal property-based water network wth/wthout heat of mxng (scenaro 1) Fgure 3.4: Optmal property-based water network wth/wthout heat of mxng (scenaro 2) Fgure 3.5 The retroftted process flow sheet based on the optmzed results Fgure 4.1: A smplfed solvent extracton process Fgure 4.2: The ternary dagram for vapor pressure, solublty parameter, and vscosty Fgure 4.3: Ternary dagram that represents the three propertes and dentfes the feasble regon for approprate solvent extracton system Fgure 4.4: Schematc Dagram for our Solvent Desgn Approach Fgure 4.5: The ternary-cluster representaton of the feasblty regon for the three propertes for solvents and blends to be employed n re-refnng of used lubrcatng ol Fgure 4.6: Sngle-solvent representaton n the ternary dagram and ther postons relatve to the feasble regon Fgure 4.7: Representaton of methanol/hexane mxture on the ternary dagram

10 x Fgure 4.8: Propanol-hexane mxture representaton of Case Study 3 on the ternary dagram Fgure 5.1: Common Used ol dsposal methods (Project Rose 1990) Fgure 5.2: Smplfed block flow dagram for the solvent extracton process of recyclng used lubrcatng ol Fgure 5.3: Process Flow Dagram for the solvent extracton process of recyclng used lubrcatng ol Fgure 5.4: Effect of solvent to ol rato (SOR) and Temperature (T) on percent ol loss and percent sludge removal (POL/PSR) usng 1- Butanol as a solvent (Katyar 2010) Fgure 5.5: Effect of solvent to ol rato (SOR) and Temperature (T) on percent ol loss and percent sludge removal (POL/PSR) usng MEK as a solvent (Katyar, 2010) Fgure 5.6: Butanol Ftted K value versus atmospherc column temperature Fgure 5.7: Butanol Ftted K value versus vacuum column temperature Fgure 5.8: MEK Ftted K value versus atmospherc column temperature Fgure 5.9: MEK Ftted K value versus vacuum column temperature

11 x LIST OF TABLES Page Table 3.1 Sources and fresh water (scenaro 1) Table 3.2 Snk data and constrants (scenaro 1) Table 3.3 Sources and fresh water (scenaro 2) Table 3.4 Snk data and constrants (scenaro 2) Table 3.5 Ppng costs for the case study (32) Table 3.6 Comparson for the optmal results wth/wthout property constrants Table 4.1: Lst of Common addtves used n lubrcatng ols (Kopelovch, 2011) Table 4.2: Solublty parameters of base ol, addtves and number of alcohol solvents Table 4.3: Expermentally reported performance of number of solvent(s) used n re-refnng used ols Table 5.1: Solvents and ol costs Table 5.2: SOR values at 5% and 10% solvent losses Table 5.3: Solvent selecton and extracton operatng condtons at dfferent total % sludge removed takng nto account 5% of solvent s lost at each stage Table 5.4: Solvent selecton and extracton operatng condtons at dfferent total % sludge removed takng nto account 10% of solvent s lost at each stage Table 5.5: Result summary of the effect of base ol qualty takng nto account 5 % solvent loss n each stage Table 5.6: Result summary of the effect of base ol qualty takng nto account 10% solvent loss n each stage

12 1 1 INTRODUCTION TO PROCESS OPTIMIZATION AND INTEGRATION 1.1 Preface and Motvaton Mass, heat, and property ntegraton have been used commonly n the ndustry n order to acheve core objectves of any process. Process ntegraton has been used manly for resource conservaton, emsson reducton, and sustanablty performance mprovement. These objectves have been targeted for decades now. However, they are more mportant than ever before due to: - The escalaton of raw materal prces: Natural Gas, crude ol, and utltes such as fresh water and hydrogen prces keep ncreasng year after year. Energy s no longer cheap. Regardless of the poltcal nfluence on these prces, optmzaton and ntegraton of exstng processes and nventon of newer ones can help reduce and conserve these resources. - Depleton of natural resources: processng facltes use tremendous amount of fresh materals. Such usage can lead to depleton of natural resources f not recycled and managed properly. Ths dssertaton follows the style of Educaton for Chemcal Engneers Journal.

13 2 - The rresponsble usage of utltes: Ths apples especally to fresh water. It s a major factor n the lack of fresh water n major parts of the world. Dumpng waste water stream rresponsbly back to the sea or nto water ways has caused major envronmental ssues. - Increasng envronmental regulatons: Envronmental Protecton Agency has been a major polcy maker toward reducton of emssons and ncrease safety applcatons. Advances n technology and scence allowed scentst better understand the effect of polluton on the envronment and the human beng. A lot of work could be done n that matter n order to help create safer world. 1.2 Key Strateges 1. Recycle and reuse: Not only from economc stand pont, but recyclng waste streams can have a major contrbuton to the conservaton of these precous resources. 2. Process Modfcaton/alteraton: Addton of extra unts to the process that helps purfy toxc emssons s one way to help mprove envronmental performance and conserve natural resources. Another way s to change the process as a whole and reduce total utlty usage whle reachng the same output product. 3. Materal substtuton: Substtute toxc and unrecyclable resources by safer and recyclable alternatves.

14 3 1.3 Process Integraton Introducton Process ntegraton s a holstc approach to process desgn, retrofttng, and operaton whchemphaszes the unty of the process (El-Halwag, 1997). It nvolves fve man actvtes (El-Halwag, 2006): 1. Task Identfcaton: It s the expresson of the goal that we are amng for, and ts descrpton n actonable task. 2. Targetng: It s a very powerful tool that allows us to benchmark process performance wthout specfyng the means of achevng these targets. 3. Generaton of Alternatves: It s the generaton of enormous number of possble solutons and confguratons n order to acheve the goal/target. 4. Selecton of Alternatve(s): It s necessary to choose a feasble alternatve. However, t s more mportant to choose an optmum one. 5. Analyss of Selected Alternatve(s): It s mportant to evaluate the selected alternatve. Ths evaluaton may nclude economc analyss, safety analyss and assessment, etc Mass Integraton Mass Integraton s a holstc approach to the generaton, separaton, and routng of speces and streams throughout the process (El-Halwag, 1997). It requres full understandng of the mass flow wthn the process (Fgure 1.1). Ths mass ntegraton could be done wth the use of mass nterceptors for purfcaton purposes or t could be

15 4 done wthout the purchase of any peces of equpment (Drect Recycle). A source s a process stream that contans our target spece, and a snk s a process unt/equpment that can accept a source (El-Halwag, 2006) Fgure 1.1: Mass ntegraton schematc representaton (El-Halwag, Et Al 1996, Garrson Et Al 1996) Over the past two decades a huge amount of effort has been done on development of mass ntegraton strateges. Pnch analyss and mathematcal programmng methods were developed for targetng.

16 5 For example, Pnch analyss s a graphcal method that targets mnmum fresh materals, mnmum waste dscharged and maxmum allowable recycle. The steps are as follow: 1, rank the sources and snks n ascendng order of compostons. Note that a common mstake s to rank them n ascendng order of mass load. 2, In order to form the source and snk composte curve, plot the source and snk wth the load of mpurty versus flowrate. Each source s connected from the arrow of the prevous source wth superposton arrow startng from the snk wth lowest composton. The same apples to snks. After that, the source composte curve could be moved horzontally untl touched by the snk composte curve. The pont of ntersecton between the snk and source composte curves called the pnch pont (Fgure 1.2). The mass transfer flows from the process source to the snk upward on the graph. The flowrate that could not be transferred upward from a process source to process snk wll be consdered waste, and the flowrate that could not be suppled by a process source to a process snk upward, t wll be suppled by a fresh source The thumb rule appled here s that, there should be no fresh feed to snk above the pnch, no waste from the source below the pnch, and no flowrate passed n the pnch (El-Halwag, 2006).

17 6 Fgure 1.2: Snk and source composte dagram for materal recycle pnch analyss (El-Halwag, 2006) Note that these targets are not theoretcal, and they are achevable by followng the correct procedures. The mathematcal programmng model ncludes economc assessment of the process where The objectve s to decde the mnmum cost for the system, ncludng the cost of the nterceptors and the cost of the fresh feed cost and waste cost (El-Halwag, 2006). (1)

18 7 Subject to the number of constrants: splttng of sources to all nterceptores could be expressed as follow: (2) For purfcaton purposes, the removal of pollutant n uth nterceptor s: ( ) (3) After ntercepton, the source splt could be presented n equaton (1.4): (4) The mass balance around snk j at the feed mxng pont can be shown as follow: (5) Component Materal balance for each snk j when mxng s expresses below: (6) Snk j materal composton upper and lower bounds can be presented as follow: (7) All the process flows that could not be allocated to a process snk wll be sent to waste: (8) where s the separaton effcency of the nterceptor u s the cost of the nterceptor u s the fresh feed cost s the waste treatment cost s the flowrate of fresh feed s the flowrate of waste stream

19 8 s the composton from each source to unt u s the flowrate to each unt u s the flowrate comng out of each unt u to dfferent snk j s the flowrate nto each snk j s the flowrate of each source s the composton of streams nto each snk j Property Integraton Mass ntegraton s very powerful targetng tools. It was to batch and contnuous water networks. However, a major feature of mass ntegraton s that t s chemo-centrc. Ths means that the chemcal composton of a stream s the only parameter beng tracked. Because of the heavy dependence of the system desgn on propertes, a more mportant approach for optmal desgn s the framework of property ntegraton whch s defned by El-Halwag et al. (2004) as a functonalty-based holstc approach for the allocaton and manpulaton of streams and processng unts, whch s based on functonalty trackng, adjustment and assgnment throughout the process. Several graphcal and algebrac technques have been developed for desgnng and optmzng recycle/reuse systems based on property ntegraton (e.g., Shelley and El-Halwag,2000; El-Halwag et al.,2004; Qn et al.,2004; and Ng et al.,2009). Ths dependency of the system desgn on propertes poses major property constrants. The dentfcaton of the upper and lower property bounds values are not as

20 9 smple as they sound. They could be extracted from expermental results or va smulaton runs. Ths could be presented as followng: p mn p p max (9) One of the major challenges of property ntegraton s the dentfcaton of the mxng rule expresson. Dfferent propertes can have dfferent mxng rules. Some may be lnear, and others can be non-lnear. A generc mxng rule expresson s shown below (El-Halwag, 2004): F ( P) F ( p ) r (10) wth, P s the property of the mxture ( P ) s the property mxng operator of property r F s the flowrate of mxture and can be expressed as follow: F F (11) Note that the multplcaton of the flowrate by the property operator s consdered the property load. Thus, composton can be consdered as a specal case of property, and the same mass ntegraton pnch analyss that was appled to mass can be appled to any property. Ths s very mportant when t comes to the allocaton of process sources and snks takng nto account composton and property constrants.

21 10 In order to normalze the property operator nto a dmensonless operator, ts dvson by a reference value ref. s needed: r ( pr, ) r, ref r (12) Then, an AUgmented Property (AUP) ndex for each stream s the summaton of the dmensonless operators: AUP r1 r, (13) Then, the cluster for property r n stream can be defned as follow: C r, r, AUP (14) Now, through clusterng, every stream can be presented n a ternary dagram by a sngle pont. Ths could be llustrated n fgure 1.3. A very mportant characterstc of clusterng s that the summaton of clusters s equal to 1.

22 11 Fgure 1.3: Ternary dagram representaton of Intra-Stream of clusters (Shelly and El-Halwag, 2000) Another mportant characterstc of clusterng s that t s consstent wth leverarm addtve rule. Ths means that the resultng mxng stream les on the lne that connects both ponts n the ternary dagram. Ths could be llustrated n fgure 1.4.

23 12 Fgure 1.4: Lever arm addton for clusters (Shelly and El-Halwag, 2000) 1.4 Optmzaton Optmzaton s the recognton of the best soluton among all avalable alternatves (El-Halwag, 2006). The man objectve of an optmzaton formulaton s to maxmze or mnmze an objectve functon. Ths objectve functon s usually subject to a number of constrants. These constrants could be lnear or non-lnear. The more the non-lnear constrants are present n the formulaton, the more complex t gets to fnd the optmal soluton. Also, the varables could be decmals or ntegers. A specal case of nteger varable s the bnary ntegers where the varable could be only hold a value of zero or one. Otherwse, t wll be called mxed nteger varable.

24 13 The mathematcal formulaton of an optmzaton model entals the followng steps (El-Halwag, 2006): 1- Determne the objectve functon: Identfy the quantty/value that needs to be maxmzed or mnmze (maxmze proft or mnmze cost) Identfy the varables that should be ncluded n the functon Express the objectve functon mathematcally 2- Develop the game plan to tackle the problem: Identfy how to address the problem What s the valdaton, motvaton, and reasonng What are the key concepts that can help develop a formulaton that reflects your nput and thoughts 3- Develop the constrants: Convert the approach to a mathematcal framework Determne mathematcally all the relatons and restrctons Determne the regon(s) where the soluton can be accepted Include subtle constrants 4- Improve formulaton: Avod non-lnearty Smplfy the formulaton as much as possble Enhance clarty for debuggng purposes

25 Dssertaton Goals Water and used lubrcatng ol are the two waste streams that have been consdered n ths work. The mshandlng of these two speces has led to major ndustral and envronmental ssues. Process ntegraton s an mportant tool that helped not only target water recycle and lube ol reclamaton for resource conservaton and envronmental purposes, but t also made t economcal to do so. Ths economcal drve has posed an mportant task on ndustry decson makers to benchmark performance and make the modfcatons needed to reach those targets. The followng sectons wll reveal n detals the objectve of the proposed approach and how t contrbutes to the resource conservaton and reclamaton (Secton 2). Then, a full descrpton of the water drect recycle network problem statement s presented, and followed by the proposed approach. Ths s followed by case study n order to llustrate the applcablty of the proposed approach (Secton 3). Then, problem statement descrbng the need for a systematc approach for solvent selecton n the rerefnng of used lubrcatng ol s descrbed. Then a valdaton of the proposed approach usng expermental result s shown (Secton 4). After formng a solvent consderaton set usng the proposed approach n secton 4, an optmzaton formulaton that maxmzes proft s developed n secton 5. Ths s done n order to obtan clear optmal results for solvent selecton based on economc assessment.

26 15 Fnally, a case study and senstvty analyss s presented (Secton 5). Fnally, secton 6 wll nclude an overall concluson and recommendatons for future work.

27 16 2 OBJECTIVES OF WORK 2.1 Objectves Overvew Sustanablty s the satsfacton of the present generaton wthout deprvng the future generaton from the ablty to meet ther needs. It has socal, economc, and envronmental dmensons. Therefore, n order to operate n a sustanable matter, there s a need for effcent and responsble usage of natural resources. The ntent here s to focus on the development of systematc and generally applcable tools for the desgn, ntegraton, and optmzaton of resource-conservaton networks that reduce the consumpton of fresh natural resources and the dscharge of waste materals to the envronment. Ths focus s gven to two mportant applcatons: water conservaton and lube-ol reclamaton. For water conservaton, an optmzaton approach wll be developed to enable the recycle of process streams whle consderng economc ssues as well process requrements nvolvng mass, thermal, and property constrants. Next, lube ol reclamaton wll be addressed to conserve the use of fresh base ol and to reduce the dscharge of spent ol. Two approaches wll be developed. The frst one s ntended to dentfy mportant bounds for the selecton of solvents and solvent blends that can be effectvely used n extractng the base ol and rejectng contamnants and sludge. The second approach nvolves the development of an optmzaton program that screens solvents and blends and optmzes process desgn for lube-ol reclamaton.

28 Water Conservaton and Drect Recycle Network There s a growng need to develop systematc and cost-effectve desgn strateges for drect recycle strateges that lead to the reducton n the consumpton of fresh materals and n the dscharge of waste streams. Drect recycle network s defned as the case when reroutng of waste streams does not requre the purchase of any new peces of equpment (El-Halwag, 2006). These equpments are usually mass nterceptor such as strpper, scrubber, etc. In that case, mass separatng agents wll be requred to purfy or modfy the mpurty composton. Tradtonally, most of the prevous research efforts n the area of desgnng drect-recycle networks have consdered the chemcal composton as the bass for process constrants. However, there are many desgn problems that are not component based, but they are property based (e.g., ph, densty, vscosty, COD, BOD, toxcty). Addtonally, thermal constrants (e.g., stream temperature) may be requred to dentfy acceptable recycles. In ths work, we ntroduce a novel approach to the desgn of recycle networks whch allows the smultaneous consderaton of mass, thermal, and property constrants. Furthermore, the devsed approach also accounts for the heat of mxng and for the nterdependence of propertes. An optmzaton formulaton s developed to embed all potental confguratons of nterest and to model the mass, thermal, and property characterstcs of the targeted streams and unts. Soluton strateges are developed to dentfy stream allocaton and targets for mnmum fresh usage and waste dscharge. A case study s solved to llustrate the concept of the proposed approach and ts computatonal aspects.

29 Lube Ol Reclamaton and Property Integraton Solvent Selecton Systematc Approach When thnkng along the sustanablty lnes, one of the man areas that come to mnd s lube ol reclamaton. It s used for many dfferent applcatons (refer to secton 4). It s composed of base ol and addtves. Because of ts stablty characterstc, the base ol molecules stay almost ntact after usage. However, the man made addtves wear out. The need for sustanable re-refnng technque s necessary. Also, the sgnfcant quanttes of used and dscharged lubrcatng ols pose a major envronmental problem around the world. Recently, there has been a growng nterest n the sustanable usage of lubrcatng ols by adoptng recovery, recycle, and reuse strateges. In ths work, a property-ntegraton framework s used n the optmzaton of solvent selecton for rerefnng of used lubrcatng ols. Property-ntegraton tools are employed for the systematc screenng of solvents and solvent blends. The proposed approach dentfes the man physcal propertes that nfluence solvent(s) performance n extractng addtves and contamnants from used lubrcatng ols (.e. solublty parameter (), vscosty (), and vapor pressure (p)). To dentfy a feasblty regon for an effectve solvent or solvent blends for ths process, we construct a ternary dagram utlzng the propertyclusterng technque. The results of the theoretcal approach are valdated through comparson wth expermental data for sngle solvents and for solvent blends.

30 Optmzaton Formulaton for Solvent Extracton n the Lube Ol Applcaton As dscussed above, he lube ol reclamaton s necessary. Ths could be done by dfferent technologes. Secton 4 brefly descrbes the advantages and dsadvantages of each process. As shown n secton 4, solvent extracton s ultmate opton for many reasons. Ths could be done through the use of organc solvents. Ths recyclng should not be done for envronmental purposes only, but for economcal drve as well. The selecton of solvent s not an easy task. After the applcaton of screenng method proposed and justfed n secton 4, feasble solvent consderaton set could be developed. However, the selecton of optmal solvent should not be valued based on expermental performance only. Therefore, an optmzaton formulaton based on maxmzng proft was formulated. Ths formulaton takes nto account the captal cost as well as the operatng cost assocated wth each solvent. A case study excludng the captal cost was addressed to compare two major sngle solvents MEK and Butanol. Expermental results and Aspen Plus Smulaton were used to collect the data requred. Butanol performs better from PSR pont of vew. MEK performs better from POL stand pont. In most cases, MEK was favored due to ts lower cost and hgher ablty to preserve our valuable base ol product. Fnally senstvty analyss was performed n order to gve better nsght on the results obtaned.

31 20 3 OPTIMIZATION OF DIRECT RECYCLE NETWORKS WITH THE SIMULTANEOUS CONSIDERATION OF PROPERTY MASS AND THERMAL EFFECT 3.1 Introducton The effcent use of natural resources s a key challenge to ndustral facltes seekng to operate n a sustanable manner. One of the promsng means to accomplsh the sustanablty objectves s materal recovery and effectve allocaton of resources. Over the past two decades, sgnfcant progress has been made n developng systematc process ntegraton technques for conservaton of mass. Ths effort n the feld of mass ntegraton has emerged as an effectve technque to dentfy performance targets for the maxmum extent of materal recovery wthn ndvdual processes (El-Halwag, 1997, 1998, and 2006; Dunn et al., 2003). Drect recycle s recognzed as an effectve savng tool n reducng the consumpton of raw materals, generaton of ndustral wastes, and cost. Much research has been performed to desgn cost-effectve materal (e.g., water, hydrogen, solvent) recycle networks. Recent surveys can be found n lterature (Foo, 2009; Fara et al., 2010; Jezowsk, 2010). Three general approaches have been developed: graphcal (Wang et al., 2004; Dhole et al., 1996; Alves et al., 2002; Hallale, 2002; El-Halwag et al., 2003), algebrac (Feng et al., 2007; Sorn et al., 1999; Manan et al., 2004; Foo et al., 2006), and mathematcal programmng (El-Halwag et al., 1996; Savelsk et al., 2003; Hernandez-Suarez, 2004).

32 21 Early mass ntegraton methodologes were based on stream compostons. Nonetheless, there are many wastewater streams that are characterzed by propertes n addton to concentratons. These problems can be effectvely addressed by the propertyntegraton framework whch s defned as a functonalty-based holstc approach for the allocaton and manpulaton of streams and processng unts, whch s based on functonalty trackng, adjustment and assgnment throughout the process (El-Halwag, 2004). Usng the property-based approach, several methodologes have been developed for the desgn of recycle/reuse networks. These nclude graphcal (Shelly and El- Halwag, 2000; Kazantz 2005), algebrac (Qn, 2004; Foo, 2006), and optmzaton technques (Ng et al., 2009; Ng et al., 2010; Ponce Ortega et al., 2009; Ponce Ortega, 2010; Nápoles-Rvera et al., 2010). Ths paper expands the scope of recycle/reuse network by ntroducng for the frst tme a systematc approach whch accounts for the smultaneous consderaton of mass, property and operatng temperature constrants to satsfy a set of process and envronmental regulatons. The paper also addresses the dependence of propertes on composton and temperature. The problem s formulated as a nonlnear programmng NLP problem that mnmzes the total annualzed cost of the system whle satsfyng the process and envronmental constrants. 3.2 Problem Statement The problem can be expressed as follows. Gven s a set of snks wth the constrants for the nlet flowrates and allowable compostons, propertes and

33 22 temperatures. Also gven s a set of fresh and process sources, whch can be recycle/reused n snks. Each source has a known flowrate, composton, property and temperature. The fresh sources have to be purchased to supplement the use of process sources n snks. In addton, the dscharged waste has to meet the envronmental regulatons. The objectve s to fnd an optmal drect recycle/reuse network whle smultaneously consderng property, mass, and thermal effects and mnmzng the cost the overall system. Furthermore, the devsed approach should also account for the heat of mxng and for the nterdependence of propertes. 3.3 Approach and Mathematcal Formulaton A source-snk mappng dagram s used to represent the superstructure of the problem embeddng potental confguratons of nterest (Fgure 3.1). Each source s splt nto fractons that are mxed wth fractons of other streams to form the feeds to the process snks whch must meet the process constrants expressed as bounds on concentratons, temperature, and propertes.

34 Sources Snks =1 j=1 =2 j=2 =N source j=n snk r=1 Waste r=n fresh Fgure 3.1: Source/snk allocaton wth drect reuse/recycle Mass balance for the th source: F F F NSOURCE, j, waste jnsink (1) to waste: A smlar mass balance can be appled for rth fresh source wthout assgn any fresh r r, j (2) jnsink F F r NFRESH Mass balance for jth snk: (3) F F F j NSINK j r, j, j rnfresh NSOURCE

35 24 Component materal balance for cth component n jth snk: F z F z F z c NCOMP j NSINK n j j, c r, j r, c, j, c, rnfresh NSOURCE (4) It s worth notng that the component materal balances should be lmted to the key components upon whch constrants are mposed or the ones that hghly mpact the heat of mxng. as: If the heat effect of mxng s nvolved, the heat balance for the jth snk s rewrtten F Cp ( T T ) F Cp ( T T ) F Cp ( T T ) F H n mx j j j 0 r, j r r 0, j 0 j j rnfresh NSOURCE j NSINK (5) where Cp can be calculated as Cp x Cp c NCOMP (6) = c c c where x c denotes the mole fracton of component c and Cp for each component can be calculated by a temperature-dependent expresson. For example, the followng lnearzed equaton may be used: Cpc ac bct c NCOMP (7) The heat of mxng can be calculated as,

36 25 H mx RT 2 E ( G ) RT T Px, (8) For the case study, the Wlson Equaton (Wlson, 1964) s selected. For the case of bnary systems: E G x1 ln( x1 x212 ) x2 ln( x2 x1 21) RT b ln a12 T b ln a21 T (9a) where x s the mole fracton, T s the absolute temperature, and s used to represent the Wslon s equaton parameters. For mult-component systems: ( ) ( ) (9b) where s the actvty coeffcent and A j s gven as a functon of absolute temperature: (9c) Hence, Eq. 9 can be expressed as, mx 12b12 21b21 H Rx1x 2 x x x x (1) Property balance for the pth property n the jth snk, (2) F ( p ) F ( p ) F ( p ) p NPROP, j NSINK n j p j, p r, j p r, p, j p, p rfresh NSOURCE

37 26 Here the property s dependent on the temperature and other propertes operator can be consdered as a functon of temperature and other propertes: p ', the p ( p) f ( T, p) p p (3) Snks composton constrants: z z z c NCOMP j NSINK (4) mn n max j, c j, c j, c, Snks temperature constrants: T T T j NSINK (5) mn n max j j j Snks propertes constrants: p p p p NPROP j NSINK (6) mn n max j, p j, p j, p, It s mportant to pont out that one of the snks s the envronmental dscharge system wth Eqs. (13)-(15) correspond to the envronmental regulatons. Mass balance for the waste: F waste F (7) NSOURCE, waste The cth component load n the waste stream can be obtaned through the followng component mass balance: F z F z c NCOMP (8) waste waste c, waste, c NSOURCE

38 27 Consderng the heat effects of the mxng, the temperature for the waste can be calculated as: F Cp ( T T ) F Cp ( T T ) F H (9) n mx waste waste waste 0, waste 0 waste waste NSOURCE The pth property load n the waste stream s expressed through the followng property mxng rule: F ( p ) F ( p ) p NPROP (10) n waste p waste, p, waste p, p NSOURCE The objectve functon ams to mnmze the total annualzed cost, whch nvolves the cost for the fresh sources, cost for the waste dscharge, and cost for the ppelne. TAC Cost F H Cost F H rnfresh rnfresh jnsink pp r r Y waste waste Y F pp F r, j r, j, j, j NSOURCE jnsink (11)

39 Case Study Fgure 3.2 shows a schematc representaton of the phenol producton process from cumene hydroperoxde (CHP). Cumene s fed nto the reactor along wth ar and Na 2 CO 3 (whch works as a buffer soluton). In the reactor, cumene s oxdzed to CHP. The mxture of CHP and cumene s then sent to a washng operaton to remove the excess of the buffer soluton and water-soluble materals. Next, the stream leavng the washer s sent to a concentraton unt n order to ncrease the low concentraton of CHP to 80 wt.% or hgher. After that, the concentrated CHP stream s fed to the cleavage unts where the CHP s decomposed to form phenol and acetone n the presence of sulfurc acd. The resultng cleavage stream s neutralzed wth a small amount of sodum hydroxde and then t s separated nto two phases (organc and water phases). The water phase s sent to wastewater treatment and the organc phase (whch s manly a mxture of phenol, acetone and cumene) s washed wth water to remove the excess alkal and s fnally sent to dstllaton columns where t s fractonated nto the pure products phenol and acetone. Ths could be smplfed n a smple flow dagram (Fgure 3.2) that summarzes vsually the process descrbed above.

40 29 NaOH Na 2CO 3 Freshwater 2 Freshwater 1 H 2SO 4 Ar Freshwater 1 Cumene R101 Sep101 Wash101 Strp 1 Wastewater Ar R102 Acetone R103 R104 D101 Wash102 Wastewater Wastewater Phenol Acetone Cumene Fgure 3.2: Process flowsheet of the producton of phenol from cumene

41 Data Extracton (Scenaro 1) Phenol s chosen as the key pollutant due to ts envronmental hazards and carcnogenc effects. The property studed n ths case study s the vapor pressure due to ts sgnfcant contrbuton volatlty whch affects both safety and envronmental mpacts. The lower and upper bound constrants on vapor pressure guarantee the complance wth the operatonal condtons as well as the envronmental regulatons. The followng mxng rules are used for the ph and the vapor pressure: 10 x 10 ph ph (21) p x p (22) where x s the fractonal contrbuton of stream. Below s the lst of sources, snks, and avalable fresh water sources: Process snks: 1. Waterwash Cumene peroxdaton secton (Wash101) 2. Neutralzer (R104) 3. Waterwash cleavage secton (Wash102) Process sources: 1. Stream 8 from Wash Stream 22 from Decanter (D101) 3. Stream 25 from Wash102

42 31 Fresh water sources: 1. Freshwater1: 0 mpurty concentraton 2. Freshwater2: mpurty concentraton ( mass fracton) Next, the relevant data are gathered from a developed ASPEN Plus smulaton. The data are tabulated n Tables 3.1 and 3.2 for the sources and the snk data: Table 3.1 Sources and fresh water (scenaro 1) Source Flowrate (kg/hr) Impurty Concentraton z Temperature T( ) Vapor pressure (kpa) Cost ($/tonne) (Mass Fracton) Washer101 3, Decanter101 1, Washer102 1, Freshwater Freshwater

43 32 Table 3.2 Snk data and constrants (scenaro 1) Snks Water Flowrat e (kg/hr) Maxmum Inlet Impurty Concentrato n Mnmum Temperatur e T( ) Maxmum Temperatur e T( ) Mnmu m Vapor pressure (kpa) Maxmu m Vapor pressure (kpa) (Mass Fracton) max z j Wash101 2, Wash102 1, Neutralzer R104 1, Note that scenaro 1 wll be calculated wth and wthout heat of mxng consderatons Data Extracton (Scenaro2) Ths scenaro s an extenson of Scenaro 1 wth the consderaton of ph n addton to the vapor pressure, the chemcal components, and thermal effects. The lower and upper bound constrants guarantee the complance wth the operatonal condtons as well as the envronmental regulatons, and they are presented n tables 3.3 and 3.4.

44 33 Source Table 3.3 Sources and fresh water (scenaro 2) Flowrate Impurty Temperature Vapor (kg/hr) Concentraton T( ) pressure (kpa) z ph Cost (10-3 $/kg) (Mass Fracton) Washer101 3, Decanter101 1, Washer102 1, Freshwater Freshwater

45 34 Snks Table 3.4 Snk data and constrants (scenaro 2) Max. Mn. Max. Mn.V apor T( ) T( ) pressur e Water Flowrat e (kg/hr) Inlet Impurty Concentrat on (kpa) Max Vapor pressur e (kpa) Mn. ph Ma x ph (Mass Fracton) Wash101 2, Wash102 1, Neutralzer R104 1, Waste Soluton and Results Next, the proposed methodology s appled. The optmzaton software LINGO 11.0 s used to solve the developed NLP model by the embedded Global Solver. The value of the objectve functon, whch s the cost of fresh water, cost of ppng, and the cost of waste treatment, s evaluated for each case. The amount of fresh water needed wthout the drect recycle strategy s 5,838 kg/hr. However, the amount of fresh needed for all four scenaros after drect recycle are summarzed n tables 3.5 and 3.6 along wth other major results. These optmal results are llustrated n Fgures 3.3, 3.4, and 3.5. Note that

46 35 the heats of mxng values were notceable. However, snce water s the materal beng recycled coupled wth the large values of other terms n equaton (5), there were no changes n the overall optmal source snk allocaton. Ths may not be the case n other case studes. Source 1 W=3661 kg/hr z=0.016 T=75 P=38 KPa Source 2 W=1766 kg/hr z=0.024 T=65 P=25 KPa Source 3 W=1485 kg/hr z=0.022 T=40 P=7 KPa Fresh 1 W= kg/hr z=0 T=25 P=3 KPa W 2,1 = W 2,2 = W 3,waste = W 1,1 = F 1,1 = W 1,2 =988.3 F 1,2 = W 1,3 = W 3,3 = W 2,waste = Snk 1 W=2718 kg/hr z=0.013 T=64.88 P=29.95 KPa Snk 2 W=1993 kg/hr z=0.013 T=59.3 P=25 KPa Snk 3 W=1127 kg/hr z=0.1 T=61.22 P=25.24 KPa Waste W= kg/hr z=0.12 T=53.4 P=16.77 KPa Fresh 2 W=0 kg/hr z=0.012 T=35 P=6 KPa Fgure 3.3: Optmal property-based water network wth/wthout heat of mxng (scenaro 1)

47 36 Source 1 F=3661 kg/hr z=0.016 T=75 P=38 KPa ph=5.4 Source 2 F=1766 kg/hr z=0.024 T=65 P=25 KPa ph=5.1 Source 3 F=1485 kg/hr z=0.022 T=40 P=7 KPa ph=4.8 Fresh 1 F= kg/hr z=0 T=25 P=3 KPa ph=7 Fresh 2 F=0 kg/hr z=0.012 T=35 P=6 KPa ph= F 1,1 = F 1,2 = Snk 1 F=2718 kg/hr z=0.013 T=64.49 P=29.60 KPa ph=7.0 Snk 2 F=1993 kg/hr z=0.013 T=59.30 P=25 KPa ph=6.83 Snk 3 F=1127 kg/hr z=0.1 T=61.22 P=25.24 KPa ph=6.9 Waste F= kg/hr z=0.11 T=53.72 P=17.10 KPa ph=5.5 Fgure 3.4: Optmal property-based water network wth/wthout heat of mxng (scenaro 2)

48 37 Freshwater 1 NaOH Na 2CO 3 Ar H 2SO 4 Freshwater 1 Cumene R101 Sep101 Wash101 Ar R102 R103 R104 D101 Wash102 Phenol Acetone Cumene Strp 1 Acetone Wastewater Wastewater Wastewater Fgure 3.5 The retroftted process flow sheet based on the optmzed results

49 38 Table 3.5 Ppng costs for the case study (32) Sources Process, Fresh, r Snk, j *Unts n [($ h)/(kg year)]

50 39 Table 3.6 Comparson for the optmal results wth/wthout property constrants NO PH PH Mn Cost Fresh 1 (kg/hr) Fresh 2 (kg/hr) Fresh 1,1 (kg/hr) Fresh 1,2 (kg/hr) F 1,1 (kg/hr) F 1,2 (kg/hr) F 1,3 (kg/hr) F 2,1 (kg/hr) F 2,2 (kg/hr) F 3,3 (kg/hr) w 1,waste (kg/hr) w 2,waste (kg/hr) w 3,waste (kg/hr) z z z z waste T(snk1) ( ) T(snk2) ( ) T(snk3) ( ) T(waste) ( ) P(snk1) (kpa) P(snk2) (kpa) P(snk3) (kpa) P(waste) ph(snk1) ph(snk2) ph(snk3) ph(waste)

51 Conclusons Ths paper has ntroduced a systematc procedure whch addresses for the frst tme the smultaneous handlng of concentratons, temperature, and propertes to characterze the process streams and constrants. Ths has been done takng nto account the nterdependency of propertes and ther dependency on concentratons and temperature. An optmzaton formulaton has been developed to dentfy optmal allocaton of sources to snks that wll mnmze the network cost whle satsfyng all process and envronmental constrants. Fnally, a case study on water recycle n a phenol producton plant s solved. 3.7 Nomenclatures () Indces: c=ndex for the components, =ndex for the nternal sources, j=ndex for the snks, p=ndex for the propertes; r=ndex for the fresh sources, waste= ndex for waste;

52 41 () Sets: NCOMP={ c c s one of the components}, NFRESH={r r s a fresh source}, NPROP={p p s one of the propertes}; NSINK={j j s an nternal snk}, NSOURCE={ s an nternal source}, () Parameters: a c = Parameter n lnerzed temperature-dependent expresson for heat capacty of the pure component, J/(g K) for water, J/(g K) for phenol, b c = Parameter n lnerzed temperature-dependent expresson for heat capacty of the pure component, J/(g K) for water, J/(g K) for phenol, a 12 = Bnary parameter n Wlson equaton for phenol and water soluton, , a 21= Bnary parameter n Wlson equaton for phenol and water soluton, , b 12 = Bnary parameter n Wlson equaton for phenol and water soluton, K, b 21 = Bnary parameter n Wlson equaton for phenol and water soluton, K, Cp c =heat capacty of the pure component,

53 42 Cp =heat capacty dependant on temperature of process source, Cp r =heat capacty dependant on temperature of fresh source r, Cost r =unt cost of fresh source r, Cost waste = unt cost of waste; F =total mass flowrate from process source, F =total mass flowrate nlet process snk j, j T 0 = reference temperature, assumed to be 0, T r =temperature of fresh source r, T = temperature of process source, mn T j =mnmum temperature of process snk j, max T j =maxmum temperature of process snk j, p =pth property of fresh source r, r, p p =pth property of process source,, p p =mnmum property for pth property of process snk j, mn j, p p = maxmum property for pth property of process snk j, max j, p

54 43 R = deal gas constant, J/(K mol), z = composton for cth component of fresh source r, rc, z = composton for cth component of process source, c, z =mnmum composton for cth component of process snk j, mn jc, z =maxmum composton for cth component of process snk j, max jc, H y =Annual operatng hours =8000 hr/year (v) Varables: Cp = heat capacty dependent on temperature of process snk j, j Cp waste = heat capacty dependant on temperature of the waste, F r =total flowrate consumed from fresh source r, F =segregated mass flowrate from fresh source r to snk j, r, j F waste =total mass flowrate of the waste, p = nlet property for pth property of process snk j, n j, p p n waste, p =nlet property for pth property of process waste, n T j =nlet temperature of process snk j, T n waste = nlet temperature of the waste,

55 44 F =segregated mass flowrate from process source to snk j,, j F, waste =segregated mass flowrate from process source to the waste stream, E G = Excess Gbbs free energy, J/(K mol), w z = composton for cth component of the waste w, c z =nlet composton for cth component of process snk j, n jc, mx H waste =enthalpy change n the mxng node before the waste, mx H j =enthalpy change n the mxng node before process snk j, ( p) p =property operator of pth property, 12 = Bnary varable n Wlson equaton for phenol and water soluton, 21 = Bnary varable n Wlson equaton for phenol and water soluton,

56 45 4 A PROPERTY-INTEGRATION APPROACH TO SOLVENT SCREENING AND CONCEPTUAL DESIGN OF SOLVENT-EXTRACTION SYSTEMS FOR RECYCLING USED LUBRICATING OIL 4.1 Introducton and Lterature Revew Lubrcatng (lube) ols are used n sgnfcant quanttes to reduce frcton between surfaces n movng parts. Lube ol prmarly conssts of base ol (85-90%) and addtves (10-15%). The Unted States Department of Energy (DOE) reported the total natonal and global demand of lube ol to be 2.5 and 10.3 bllon gallons per year, respectvely. Base ol s a mxture of lqud hydrocarbon molecules that contan around carbon atoms. Base ol may be derved from varous sources wth crude ol beng the prmary commercal source. In order to enhance the performance of lube ol, addtves are mxed wth the base ol. Table 4.1 provdes a lst of the most common addtves used n the lube ol applcaton.

57 46 Table 4.1: Lst of Common addtves used n lubrcatng ols (Kopelovch, 2011) Common Addtves Example(s) Frcton modfers Graphte, Boron Ntrde Ant-Wear Esters,Chlornated Paraffns Rust and Corroson Inhbtors Organc acds, Alkalne compounds Ant-Oxdants Alkyl sulfdes, Hndered Phenols Detergents Phenolates, sulphonates Dspersants Hydrocarbon succnmdes Pour Pont Desperssants Co-polymers of polyalkyl methacrylates Vscosty Index Improvers Acrylate polymers Ant-Foamng Dmethylslcones Upon utlzaton, the dsposal of the used lube ols poses a major envronmental problem. In ths work, the term used ol refers to used lubrcatng ols that are collected after usage n small engnes, automotve engnes, ndustral machnes, etc. Besdes consumng the addtves durng use, lube ol also becomes contamnated as t conducts ts basc functons. Despte contamnaton, most of the base-ol porton n the used ol s not worn out. In fact, the chemcal composton of the base ol s typcally preserved to a large extent due to the hgh stablty of the heavy compounds contaned n the base ol. One gallon of lube ol yelds 0.7 gallon of re-refned ol. As ndcated earler, the major dfference between fresh lube ol and used ol s the breakdown of the addtves to form contamnants that wll mx wth other lght and heavy contamnants from the nterors of the engne. Other sources of contamnaton n used ols are generated from wear metals and road dust snce one of the functons of motor lube ol s to clean the nteror of the engne. Water s another major form of contamnaton n used ols. Durng fuel combuston, water and carbon doxde are the man byproducts that pass through the exhaust when the engne s hot. However, when the engne s cold,

58 47 condensed water may reach the lube ol lnes. Another source of contamnaton s the oxdaton of aromatcs present n the base ol va the reacton wth oxygen present n ar. Because of the relatvely hgh cost of re-refnng, used ols are normally dsposed of n landflls or llegally dumped n waterways makng t an envronmental hazard. In a number of applcatons, lube ol has been successfully recycled (Lard, 1982). DOE reported that only 17 percent of the recycled ol s beng re-refned (DOE, 2006). Examples of recycle alternatves nclude use as a fuel substtute n furnaces or as an extendng agent n road-pavng asphalt. Re-refnng s ntended for recovery of base ol for reuse for the orgnal purpose as lube ol. Because of the rsng prces of hydrocarbon fuels as well as the depleton of natural resources coupled wth the ever ncreasng envronmental regulatons, an economcally proftable and envronmentally frendly rerefnng technology that recovers the valuable base ol s essental. There are three major re-refnng technologes that have been employed ndustrally to treat used ols. One of the oldest technologes used to treat used ols s chemcal re-refnng whch s based on acd (normally sulfurc acd) followed by clay treatment. The acd-clay process nvolves atmospherc dstllaton to remove water and lght hydrocarbons. Then, the dry used ol s treated wth 5-10% by volume sulfurc acd. The sludge dssolves n the solvent (sulfurc acd), and settles down at the bottom of the decanter. The sludge contanng sulfurc acd s removed from the bottom, and the clean ol s decanted out from the top, where t undergoes a neutralzaton step wth the clay. The man advantage of ths process s ts capablty to produce hgh qualty base ol n an economcally attractve manner. However, the acdc sludge collected from ths process s envronmentally

59 48 hazardous, even more than the used ol tself, and, thus, requres qute expensve dsposal technques. The other re-refnng technque s the physcal re-refnng whch s based on dstllaton processes that nvolve atmospherc dstllaton as well as vacuum dstllaton and thn-flm evaporaton. Smlar to chemcal re-refnng, the frst step s an atmosphercs dstllaton process to recover the water and lght hydrocarbons contamnants. Ths step s followed by vacuum dstllaton and thn-flm evaporaton (10-30 mmhg) to recover addtves and other contamnants. In the last stage, the recovered base ol goes through a hydrogenaton step n a hydro reactor to completely saturate the oxdzed hydrocarbons. It s noteworthy to menton that despte the hgh cost of ths process, t s more envronmentally frendly than the chemcal re-refnng. However, ths process has many challenges. For nstance, the recovered ol s not of a hgh qualty and therefore requres addtonal treatment. Addtonally, foulng nsde the dstllaton equpment normally occurs due to carbon deposton. More mportantly, n order for ths approach to become economcally attractve t requres a steady and large volume nput. Yet, both chemcal re-refnng (acd-clay process) and physcal re-refnng have found ther way to commercalzaton scale. The thrd major re-refnng technque for recoverng used ols s the solvent extracton re-refnng process. Ths s a partcularly attractve cleaner technology snce t s amed at conservng natural resources and recoverng (nstead of destroyng) the base ol. In the solvent extracton process, the used ol and solvent are mxed n approprate proportons to assure mscblty of the base ol n the solvent and the rejecton of

60 49 addtves. A demulsfer s also used to coagulate the addtves and dspersed partcles and enhance ther aggregaton and rejecton as large partcle (flakes) that can be separated from the lqud by ether sedmentaton or centrfugaton. These solvents are referred to as extracton-flocculaton solvents (Res and Jeronmo, 1988). Fgure 4.1 s a smplfed schematc representaton of the process followed by a bref descrpton of the process. Fgure 4.1: A smplfed solvent extracton process. In the frst stage, the used ol s treated n an atmospherc dstllaton unt to remove water and lght hydrocarbons. Then, the dry used ol s ntroduced to a mxer along wth the solvent(s) n order to extract the base ol from the addtves and heavy contamnants.

61 50 The mxng step s followed by a decantaton unt whereby agglomeraton and formaton of large flakes take place and a two-phase soluton s obtaned. An organc sludge contanng the worn addtves and metals s decanted out the bottom of the decanter, and the top phase contans the base ol/solvent, whch s separated and sent to a seres of dstllaton columns for complete solvent separaton for recyclng purposes. Fnally, base ol undergoes chemcal treatment n order to adjust ts physcal propertes and hydrocarbon structure to the requred level. The major advantage of ths technology s that t overcomes most of the lmtatons encountered by the aforementoned commercalzed technologes. Compared to the acd-clay process, t produces a useful organc sludge that may be used n the asphalt or nk ndustres (Res and Jeronmo, 1982). Also, t produces hgh qualty base ol wth less lkelhood of foulng compared to the physcal re-refnng process. The process s also carred out at a lower overall operatng cost for smlar volume nput An mportant expermental measurement of the effectveness of the solvent extracton re-refnng process s normally represented by the amount of sludge removed from the used ol. Ths may be expressed as the percent sludge removal (PSR), whch s the mass of sludge removed n grams per 100 g of ol (Res and Jernmo, 1988). Another, mportant parameter s the percent ol losses (POL), whch s the mass of base ol lost n the sludge phase expressed n grams per 100 g of ol (Res and Jernmo 1988, and Elbashr et al. 2002). These two scales are key concepts n measurng the effectveness of the solvent extracton process. The man operaton parameters that control the effcency of ths process are temperature, solvent-to-ol rato, and solvent type

62 51 (normally referred to as the solvent extracton parameters or system). The characterstcs of the requred solvent for ths process have been dentfed by Res and Jernmo (1988) as follows: (1) t should be mscble n the base ol contaned n the processed used ol; (2) should have the capablty to reject addtves and dspersed partcle from the solventol mxture; and (3) should be able to aggregate the remanng addtves and contamnants to partcle szes large enough to be separated from the base ol and solvent mxture by ether sedmentaton, fltraton, or centrfugaton. Despte the commercalzaton of numerous solvent extracton processes, there s stll a need to desgn a systematc approach to quckly screen alternatves to dentfy a set of canddates that can be optmzed. Such an approach has to; smultaneously dentfy approprate solvent or solvent blends, desgn an effcent recovery process for the base ol, and establsh a regeneraton method for the recycle of the solvents, all whle optmzng the overall cost of the process. Because of the system s dependence on solvent propertes, a partcularly well-suted approach for optmal desgn s the framework of property ntegraton whch s defned by El-Halwag et al. (2004) as a functonalty-based holstc approach for the allocaton and manpulaton of streams and processng unts, whch s based on functonalty trackng, adjustment and assgnment throughout the process. Several graphcal and algebrac technques have been developed for desgnng and optmzng recycle/reuse systems based on property ntegraton (e.g., Shelley and El-Halwag, 2000; El-Halwag et al.,2004; Qn et al.,2004; and Ng et al.,2009). Optmzaton technques have been used to formulate recycle problems as property-ntegraton tasks (e.g., Ponce-Ortega et al., 2009 and 2010, Ng et

63 52 al., 2009; Nápoles-Rvera, 2010). Furthermore, a proposed model was used for the synthess of property-based resource conservaton networks n both batch and contnuous process applcatons. The framework takes nto account drect recycle network, ntercepton, and waste treatment smultaneously (Chen, 2010). Combnng process and molecular desgn has also been accomplshed through process ntegraton usng group contrbutons methods (e.g., Chemmangattuvalappl et al., 2010; Solvason, 2009; Eljack et al.,2008 and 2007; and Kazantz, 2007). 4.2 Problem Statement Consder a solvent-extracton process for the recovery and reclamaton of spent lube ol. The selecton of proper solvents and blends s on the most mportant decsons for effectve desgn and operaton. It s desred to dentfy a systematc procedure to provde gudelnes to the desgner on selectng solvents and blends wth proper propertes. A combnaton of expermental data and smulaton s to be used n defnng the feasblty ranges for the desred propertes. A property ntegraton framework s to be utlzed to generate bounds on the recommended solvents and blends Selecton of Prncpal Propertes and Constructon of Property Clusters Whle there are several successful expermental studes on the selecton and desgn of solvent extracton systems for recycle of used lube ols, there s a need to develop systematc desgn approaches that gude the desgner n selectng optmal solvents or solvent blends and desgnng the varous components of the recovery system whle accountng for the propertes of the solvents, the propertes of the used and

64 53 recovered ol, and operatonal crtera such as percent sludge removal and percent ol losses (PSR and POL). The approach should also take nto account the economc and envronmental aspects of the desgn before determnng an optmum. Addtonally, t should cover every unt of the process startng from the atmospherc dstllaton up to the hydro treatng step (see Fgure 4.1). Dfferent unts may be mpacted by dfferent propertes. For example, t s crucal to track the solublty parameter n solvent extracton applcatons, as t s mportant to track the specfc gravty and relatve volatlty n decantaton and dstllaton applcatons, respectvely. Consequently, we chose the solublty parameter, vscosty, and vapor pressure as the major propertes of concern for solvent selecton. The followng parameters are used n assessng the performance of the solvents and solvent blends: -Solublty parameter: The latent heat of vaporzaton ndcates the amount of van der Waals forces that hold lqud molecules together (Burk, 1984). For a soluton to occur, the chosen solvent must overcome these forces and fnd ther way around and between the base ol molecules. Solublty parameter s a good ndcator of such a behavor. As t has been stated earler, a good solvent for re-refnng used ols must be hghly mscble n the base ol and at the same tme facltate the mscblty of addtves and ther subsequent coagulaton through the use of a demulsfer (Res and Jernmo, 1988). These requrements have two ndcatons n terms of solublty parameters of the three major components that form the used ol (base ol, solvent, and addtves + contamnants). For dssolvng the base ol and addtves, the absolute value of the dfference between solublty parameter of base ol and solvent has to be as close to zero as possble to

65 54 facltate the complete mscblty requrement. On the other hand, f the addtves are completely mscble, t wll be dffcult to coagulate them nto flakes usng a demulsfer (such as KOH). Therefore, the absolute value of the dfference between the solublty parameter of the solvent and base ol and addtves has to be small enough for mscblty but not too small for coagulaton and flocculaton of the addtves nto separate flakes. An optmum range of solublty parameters of the solvent s needed. Another mportant factor s the polarty of the solvent whch facltates the rejecton of the spent addtves, mpurtes, carbonaceous partcles to flocculate and form large flakes that settle under gravty acton. Therefore, the optmum range of solublty parameters s selected for polar solvents (e.g. alcohols, ketones). -Vscosty: Lower vscosty solvents tend to functon more favorably n solvent extracton processes. Hgher vscosty solvents typcally experence a greater amount of tme for phase separaton to occur. Also, the mass transfer resstance decreases as the vscosty of the soluton decreases enablng hgher solvent effectveness. From an operatonal pont of vew, lower vscosty solutons are much easer to handle than hgher ones (specfcally on pumpng and tube transportaton) (Kng, 1971). -Vapor pressure: In order to complete the optmzaton loop, vapor pressure (e.g., Red vapor pressure RVP ) must also be nvestgated. In order to obtan economcal separaton, the vapor pressure dfference between the solvent and the base ol must be as great as possble so that the dstllaton process utlzes a mnmum number of stages. Whle many solvents meet ths crteron, there stll must be a careful balance. Havng too low of a vapor pressure causes the solvent to be very volatle whereby t s hard to

66 55 accomplsh approprate mass transfer durng the extracton process. Also, hgh volatlty solvent may cause solvent losses due to atmospherc leaks causng envronmentally hazardous problem and hgher operaton cost. Wth the approprate property parameters dentfed, t s possble to formulate a desgn usng property-ntegraton method. Here, we start wth the concept of clusterng. The Cluster terms were ntroduced by Shelley and El-Halwag (2000) for componentless desgn and they have been used for the trackng of propertes n the property ntegraton framework proposed by El-Halwag et al. (2004). From the above analyss, t can be concluded that there are constrants on the upper and lower bounds for each property; ths can be expressed as follows (El-Halwag, 2006): p mn p p (1) max where P s the property of nterest that can ether be the solublty, the vscosty, or the vapor pressure. The upper and lower bounds are determned ether expermentally or va smulaton. Mxng rules are used to track propertes. For nstance, the vscosty mxng rule may be expressed n the Arrhenus equaton as follow: where, ln x ln (2) x s the composton of pure component n the mxture s the vscosty of pure component n the mxture s the vscosty of the mxture Ths means that the vscosty operator ) can be expressed as follows (Shelley and El-Halwag, 2000): (

67 56 ( ) ln (3) From equaton (3), the vscosty dmensonless operator usng the followng equaton: ln can be determned,, (4) lnref where, ref s the vscosty of a chosen reference soluton The Red vapor pressure mxng rule can be expressed as follow: where, F * P F * P (5) F s the flowrate of the mxture P s the Red vapor pressure of the mxture F s the flowrate of the pure component n the mxture P s the Red vapor pressure of pure component n the mxture Therefore, the Red vapor pressure operator P) can be shown n equaton (6). Then, the Red vapor pressure dmensonless operator ( s calculated usng eqn. (7): p, 1.44 ( P ) P (6) where, p, p p reference (7) preference s the Red vapor pressure of a chosen reference soluton.

68 57 The solublty mxng rule s lnear and can be expressed n the followng equaton (Barton, 1991): where s the solublty parameter of the mxture F F * (8) * s the solublty parameter of the pure component n the mxture Therefore, the solublty operator ) can be defned as follow: ( ( ) From equaton (9), the solublty dmensonless operator can be calculated and expressed (9) n the followng equaton: where, reference (10) reference s the solublty parameter of a reference soluton. Then, the AUgmented Property ndex AUP ntroduced by Shelley and El- Halwag (2000) s defned as the summaton of the dmensonless operators as descrbed below: AUP r, r (11) where r represents the propertes beng optmzed. Ths approach also ntroduces a new parameter, whch s the cluster of a property r, the dmensonless operator dvded by AUP : C r,, that s calculated as the fracton of

69 58 C r, r, (12) AUP Ths can also be expanded to the followng form to represent all propertes of concern n ths study: C, C P, C,, (13) AUP P, AUP, AUP (14) (15) The sum of all the clusters as represented above s equal to one,.e.: C r, 1 Therefore, on a ternary dagram for the clusters of vapor pressure, solublty, and vscosty, each solvent s represented by one pont. Once the values of two clusters are determned, the thrd cluster s automatcally determned (because the sum of clusters s one) as shown n Fgure 4.2. (16)

70 59 Fgure 4.2: The ternary dagram for vapor pressure, solublty parameter, and vscosty It s also worth notng that the constrants defnng the feasblty regon for any unt (such as the constrants descrbed by Eq. 1) can be represented on the ternary dagram as shown by Fgure 4.3. Based on the upper and lower bounds of each property, sx ponts can be drawn on the ternary dagram. These ponts consttute the boundares of the feasble regon. When the sdes of the feasblty regon are extended, these lnes pass through one of the three apexes of the ternary dagram. A feasble pont (A) satsfyng the constrants of a unt must le nsde the feasblty regon of the unt. Furthermore, because lever arm rules apply for mxng, when two streams (B and C) are mxed, the resultng mxture (D) les on the straght lne connectng the two streams.

71 60 Fgure 4.3: Ternary dagram that represents the three propertes and dentfes the feasble regon for approprate solvent extracton system Desgn Approach The proposed desgn approach s shown by the flowchart llustrated by Fgure 4.3. It nvolves a combnaton of feasblty-regon determnaton, property ntegraton, and screenng and optmzaton of feasble solvents and blends. The property constrants are used to construct the ternary cluster dagrams where the feasblty regon s drawn and the canddate solvents are placed based on ther propertes. Solvents lyng outsde the cluster feasblty regons are dscarded as nfeasble but are stll consdered as canddates for blends. Straght-lne segments connectng two solvents and passng through the feasblty regon are potentally feasble (necessary but not suffcent condton). To nsure feasblty, the values of the augmented propertes (AUP) of the blends have to le wthn the feasble range of propertes. Based on the lever arms of the

72 61 lne segments lyng wthn the feasblty regon, the range of mxng ratos for blended solvents s determned. Cost data are used to screen the feasble solvents and blends. Whenever expermental data are avalable, they should be used to verfy the desgn results, especally wth regards to PSR and POL and the model results should be adjusted as needed. The result s a set of feasble solvents/blends along wth system desgn arranged n order of cost. The desgn approach s presented f fgure 4.4: Lube-Ol Recovery and Process Requrements Property Constrants from Expermental Data or Practcal Experence Select/Desgn Canddate Solvents Canddate Solvents Smulate Process to Identfy Bounds on Feasble Propertes Identfy Feasblty Regon for Solvent Propertes P mn mn mn P P max max max Solvent Data Create Ternary Property-Cluster Dagram RI Functon Determne Feasblty of Solvents and Ratos of Feasble Blends Feasble Solvents/Blends Determne Optmal Solvents/Blends Optons for Solvents/Blends Compare wth Avalable Expermental Data Economc Data Expermental Data on Solvent Propertes Revse Predcted Propertes No Solvent Characterstcs Verfed? Yes Recommend Soluton Fgure 4.4: Schematc Dagram for our Solvent Desgn Approach

73 Case Study In ths secton, three case studes are solved to demonstrate the applcablty of the devsed desgn approach. Before proceedng to the results of the case study, detals are gven on how the ranges of the three prncpal propertes were determned. a. Solublty Parameter: Upper and lower bounds for the values of the Hldebrand solublty parameter have been determned through avalable expermental data. Table 4.2 lsts the solublty parameters of major alcohols from C 1 to C 5 utlzed n ths process n addton to the solublty parameters of the base ol and typcal chemcals used as addtves (e.g. polysobutylene as descrbed by Elbashr, et al. 2002). Table 4.2: Solublty parameters of base ol, addtves and number of alcohol solvents δ (J/m3)^1/2 Base ol 15.9 Addtves 17 Methanol 29 Ethanol 26 Propanol 24.1 Butanol 22.5 Pentanol 21.1 Rncon et al (2005) concluded that methanol, ethanol, and propanol do not seem to be completely mscble n the base ol and as a result they show hgh POL despte ther capabltes n extractng the addtve and contamnates (good PSR performance). The expermental results also showed that as the number of carbon atoms n the solvent ncreases, ts ablty to remove sludge decreases despte the fact that the solvent became

74 63 more mscble n the ol (Res and Jeronmo, 1988). In other words, POL as well as PSR decreases as the number of carbons atoms ncreases (Res and Jeronmo, 1988). The expermental study suggests that butanol s a hghly effectve sngle solvent n the used ol extracton process and that t outperforms dfferent solvents (lcohols and ketones) wth carbon atoms rangng between 1 and 5. Consderng the nformaton summarzed n Table 4.2 n addton to the prevous expermental assessments of several solvents, we recommend that solvents wth solublty parameter between 21.5 and 23.5 (J/m 3 ) 1/2 should be consdered as canddates for the re-refnng of used ols. Solvent(s) wth solublty parameter less than 21.5 (J/m 3 ) 1/2 are hghly mscble n both the addtves and the base ol, and as a result, wll make t very dffcult for flocculaton of contamnants and addtves (upon the addton of a demulsfer such as KOH) and the formaton of the sludge phase. On the other hand, any solvent(s), wth a solublty parameter hgher than 23.5 (J/m 3 ) 1/2 wll be relatvely mmscble n the base ol; whch s an undesrable crteron for the solvent as t leads to losses n the base ol. b. Vapor Pressure: Settng the vapor-pressure bounds s mportant for the separaton process of the solvent-recovery unt. Therefore, a set of smulaton tests usng the software ASPEN Plus was used to determne these bounds. Based on the smulaton results, t was found that any solvent wth a vapor pressure between 5 and 150 torr should be ncluded n the consderaton set. When the vapor pressure s lower than 5 torr, the separaton process becomes very expensve due to the hgh temperature requrement. Hgh temperature operaton does not only come wth hgh utlty cost, but t wll also decrease the qualty

75 64 of the produced re-refned base ol. Any solvent wth vapor pressure hgher than 150 torr s consdered too volatle, whch s not desrable n ths extracton process snce t reduces the lqud-lqud molecule nteracton between solvent and used ol. c. Vscosty: Settng the vscosty bounds s crucal for the extracton unt. Therefore, the upper bound was extracted from publshed expermental data (Res and Jernmo, 1988). Any solvent wth a vscosty between 0.5 and 3.5 cp s ncluded n the consderaton of potental solvents. A vscosty hgher than 3.5 cp causes the soluton to become too vscous, whch results n an ncrease n mass transfer resstance, and thus causes longer extracton tme and hgher operatng cost (stemmng from a pumpng system). Solvents wth typcal vscosty less than 0.5 cp are normally n the gas phase, and as a result they should be elmnated from the consderaton set. The ternary cluster representaton of the aforementoned constrants s shown n Fgure 4.5 where the feasble regon s plotted.

76 65 Fgure 4.5: The ternary-cluster representaton of the feasblty regon for the three propertes for solvents and blends to be employed n re-refnng of used lubrcatng ol. As noted before, any solvent or blend of solvents that exsts outsde the feasble regon s elmnated. Solvent blends are constructed to le wthn the feasblty regon and the lever-arm prncple s used to calculate the relatve proportons of the mxed solvents. The solvents lsted n Table 4.3 are used along wth ther blends n the case studes. Table 4.3 provdes key data that were observed expermentally.

77 66 Table 4.3: Expermentally reported performance of number of solvent(s) used n re-refnng used ols. Solvent Observed Performance Reference Hexane PSR = 0.0 Kamal and Khan (2009) Butanol PSR = 4.9 Res and Jernmo (1988) Pentanol PSR = 3.6 Res and Jernmo (1988) Propanol+Hexane PSR = 6.0 Res and Jernmo (1990) Methanol Immscble wth base ol Res and Jernmo (1988) Ethanol Immscble wth base Res and Jernmo Propanol ol Immscble wth base ol (1988) Res and Jernmo (1988) Case Study 1: Sngle Solvents wth Expermental Verfcaton Fgure 4.6 shows the cluster representaton of the sngle solvents beng studed for lube ol reclamaton. Startng wth the elmnaton process, methanol, hexane, and pentanol are located outsde the feasble regon, and are therefore automatcally elmnated from further consderaton as sngle solvents. Although ethanol and propanol fall wthn the feasble regon, ther values of the augmented property (AUP) are outsde the feasble range for the AUP correspondng to feasble regon for the three propertes (vscosty, solublty parameter, and vapor presure). Therefore, they have been elmnated as sngle solvents as well. Fnally, butanol les wthn the feasble regon and passes the AUP feasblty test; makng t a vable canddate based on the theoretcal property-based calculatons.

78 67 The theoretcal results were compared to expermental data. As ndcated by Res and Jeronmo (1988), t was found that buatnol was the most effcent solvent for ths process at a solvent to ol rato of 3: 1 and extracton temperature of 20 C. Therefore, the selecton of butanol based on the property-ntegraton theoretcal procedure s consstent wth the expermental observaton. Fgure 4.6: Sngle-solvent representaton n the ternary dagram and ther postons relatve to the feasble regon. Case Study 2: Excluson of All Possble Infeasble Blends of Two Solvents Suppose that we are nterested n mxng hexane and methanol to get a feasble blend. As can be seen from Fgure 4.7, all blends of the two solvents le on the straght

79 68 lne connectng the two solvents. Ths straght lne les outsde the feasblty regon. Therefore, all methanol/hexane mxtures should be elmnated from further consderaton as beng nfeasble. Ths fndng reduces the desgn effort sgnfcantly by elmnatng all blends that wll not be feasble. Fgure 4.7: Representaton of methanol/hexane mxture on the ternary dagram. Case Study 3: Identfyng Ranges of Feasble Blends As ndcated earler, the elmnaton of sngle solvents lyng outsde the feasblty regon does not prevent the possblty of nvolvng these solvents n feasble blends. In ths regard, the ternary cluster dagram offers an effectve and convenent

80 69 approach to vsually determne feasble blends and the proportons of the partcpatng solvents. For nstance, as shown n Fgure 4.8, any blend of propanol and hexane s represented by the dashed lne connectng the two solvents (shown on the fgure as a dashed lne). The segment lyng outsde the feasblty regon s labeled as A, whle the segment lyng nsde the feasblty regon s labeled as B. Based on the lever-arm rule, secton A corresponds to the mxture of hexane to propane rato greater than 0.9. These are nfeasble mxtures. For secton B, we conducted an AUP test to dentfy the ratos between the two solvents and valdate ths performance wth the expermentally reported data. After runnng the AUP test on the mxture, a feasble mxture was found to exst of all mxtures wth propanol percentage between 72 and 90%. Res and Jeronmo (1989) expermentally measured the performance of a mxed solvent of 75% propanol and 25% hexane and concluded that t was an effectve blend. Ths s consstent wth the range of feasble solvents dentfed by our approach.

81 70 Fgure 4.8: Propanol-hexane mxture representaton of Case Study 3 on the ternary dagram 4.4 Conclusons A systematc approach has been developed for the selecton of feasble ranges of solvents and solvent blends for lube-ol reclamaton. A property-ntegraton framework was adopted as the bass for desgn. Specfcally, property clusters were used to graphcally represent the process and canddate solvents. Three prncpal propertes were used: solublty parameters, pressure, and vscosty. A combnaton of reverse-smulaton and expermental results was used to set the boundares on the constrants requred by the process. The feasblty of sngle solvents and solvent blends was dentfed through the ternary-cluster vsualzaton dagram. Infeasble solvents and solvent blends were

82 71 determned and removed from further consderaton. Also, all ratos of solvent blends leadng to feasble mxtures were determned. Three case studes were solved and compared to demonstrate the effectveness of the devsed approach. Expermental observatons were used to confrm the valdty of the approach and theoretcal results. In addton to ts effectveness n selectng solvents and blends and desgnng lube-ol reclamaton processes, the proposed approach also serves as the bass for gudng expermental work by dentfyng feasble and promsng solvents and blends and by sheddng lght on the nsghts on the desgn aspects. Recommended future work ncludes the development of an optmzaton approach to extend the applcablty of the procedure to more than three key propertes and to automate the decson-makng process and use t as a bass for the optmzaton of solvent selecton and process desgn.

83 72 5 A SYSTEMATIC TECHNO-ECONOMICAL ANALYSIS FOR THE SUPERCRITICAL SOLVENT FISCHER TROPSCH SYNTHESIS 5.1 Introducton Annually, 2.7 bllon gallons of lube ol are sold n the Unted States. About 2.7 bllon gallons s consumed, and the rest s consdered used ol. 500 mllon gallons are left wthout any recyclng program (Project Rose, 1990). The mshandlng of used lubrcatng ol poses a major envronmental crss. It has been manly dumped n land feld, burned as fuel, etc (Fgure 5.1). Send to recycle 14% Burn as fuel 4% Machnery use and other mscellaneous actvtes 21% dump nto trash 21% Pour on ground 40% Fgure 5.1: Common Used ol dsposal methods (Project Rose 1990)