Water Management in Refineries-Presentation

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Unformatted text preview: Water Management Water in Refineries M. Colin Arnold M. 1. Image -- http://www.ge.com/research/grc_2_6.html & Joshi Samuel Joshi Overview Overview Background Background Project Goals Project Unit Operations Unit Water Treatments Water Results Results Conclusion Conclusion 1. http://pubs.usgs.gov/circ/2004/circ1268/htdocs/text-in.html Background Background Some water uses in refinery 1 Caustic treatment Caustic Distillation Distillation Sweetening Sweetening Desalting Desalting 1. http://pubs.usgs.gov/circ/2004/circ1268/htdocs/text-in.html Background Background Traditionally • Only fresh water feed sources • No recycle • Collected into a sink • Disposed after clean up 1. Koppol, A.P., et al. Adv. in Env. Res., V(8), 2003, 151-171. Background Recently • Water reuse • Minimal or zero discharge • Minimizing Cost Reasons Reasons • Stricter EPA regulations • Water scarcity • Purchase Cost 1. 2. Koppol, A.P., et al. Adv. in Env. Res., V(8), 2003, 151-171. Image -- http://en.wikipedia.org/wiki/Image:Water_pollution.jpg Project Goals Project Reduce 1. Fresh water intake 2. Total operation cost • Optimizing Waste water treatment • Minimizing Total discharge • Maximizing Water reuse Project Goals Project • In other words, we want to change this: Project Goals Project • In other words, we want to change this: Project Goals Project • In other words, we want to change this: to this: Unit-1 Unit-2 Unit-3 Unit Operations Six Units: Six 1:: Caustic Treating 1 2:: Distillation 2 3:: Amine Sweetening 3 4:: Merox I Sweetening 4 5: Hydrotreating Hydrotreating 6:: Desalting 6 Unit Operations Unit Six Units: 1:: Caustic Treating 1 Figure - http://www.pall.com/chemical_5582.asp Unit Operations Unit Six Units: 2:: Distillation 2 1. http://www.cluin.org/download/toolkit/petrefsn.pdf Unit Operations Six Units: 3:: Amine Sweetening 3 4:: Merox I Sweetening 4 Amine or Merox -I Refined Fuel Steam Amine or Merox -1 With Absorbed Acid gases Absorption Unit Sour Feed Sweetening PFD 1 - http://www.newpointgas.com/amine_treating.php Steam Stripping Unit Amine or Merox -I Unit Operations Unit Six Units: 5: Hydrotreating Hydrotreating 1- http://www.hghouston.com/refining.html Unit Operations Unit Six Units: 6:: Desalting 6 1- http://www.hghouston.com/refining.html Unit Operations Unit Process Contaminant Salts Each unit has an Each inherent values inherent 300 Cout,max (ppm) 500 Mass Load (kg/h) 0.18 50 500 1.2 H2S 5000 11000 0.75 Ammonia (1) Caustic Treating Organics Cin,max (ppm) 1500 3000 0.1 10 200 3.61 1 4000 100 Salts (2) Distillation Organics 0 500 0.25 Ammonia • Cin,max H2S 0 1000 0.8 0.6 Salts • Cout,max 1000 1 3500 30 H2S 0 2000 1.5 Ammonia (3) Amine Sweetening 10 Organics 0 3500 1 Salts • Mass Load (4) Merox I Sweetening 100 400 2 Organics 200 6000 60 50 2000 0.8 1000 3500 1 3.8 H2S Ammonia Salts 350 200 1800 45 H2S 300 6500 1.1 Ammonia (5) Hydrotreating 85 Organics 200 1000 2 Salts (6) Desalter From: Koppol, A.P., et al. Adv. in Env. Res., V(8), 2003, 151-171. 1000 9500 120 Organics 1000 6500 480 H2S 150 450 1.5 Ammonia 200 400 0 Unit Operations Unit Each unit has an Each inherent values inherent FPROCESS FPROCESS Process Stream Process Stream • Cin,max • Cout,max FWATER FWATER Extraction Stream Extraction Stream • Mass Load Cin, max Cout, max Water Treatment Water Tables - Cartwright, Peter. Process Water Treatment – Challenges and Solutions. Chemical Engineering Magazine. March 2006. Water Treatment Water Three options for treatment1 1. Option 1: API separator followed by ACA 2. Option 2: Reverse Osmosis Treatment 3. Option 3: Chevron waste water treatment 1. Koppol, A.P., et al. Adv. in Env. Res., V(8), 2003, 151-171. Water Treatment Water [2] Option 1: API separator Option followed by ACA followed Reduces Organics to 50 ppm ppm $0.12 per ton $0.12 1. 2. http://www.monroeenvironmental.com/clarifier-apiseparator.htm Koppol, A.P., et al. Adv. in Env. Res., V(8), 2003, 151-171. [1] Water Treatment Water [2] Option 1: API separator Option followed by ACA followed Figures - Cartwright, Peter. Process Water Treatment – Challenges and Solutions. Chemical Engineering Magazine. March 2006. J= − kA ∆P × µ δ Water Treatment Water Option 2: Reverse Osmosis Option Treatment Treatment [2] Reduces Salts to 20 ppm ppm $0.56 per ton $0.56 [3] 1. 2. http://www.aquatechnology.net/commercialro.html http://ag.arizona.edu/region9wq/pdf/nv_ROhow.pdf J= − kA ∆P × µ δ Water Treatment Water [2] Option 2: Reverse Osmosis Option Treatment Treatment Reduces Salts to 20 ppm ppm $0.56 per ton $0.56 [3] − kA ∆P J= × µ 1. 2. δ http://www.aquatechnology.net/commercialro.html http://ag.arizona.edu/region9wq/pdf/nv_ROhow.pdf where “J” - Volumetric flux across membrane “k” – permeability “A” – flux area “∆P” – Pressure drop “ ” – viscosity “ ” – membrane thickness Water Treatment Water Option 3: Chevron waste Option water Treatment water Reduces H2S Reduces to 5 ppm ppm Reduces Ammonia to 30 ppm ppm $1.00 per ton $1.00 http://www.chevron.com/products/prodserv/refiningtechnology/waste_wtr_treat_6a.shtm Unit Operations Unit Assumptions 1. Parallel Operation 2. Outlets from a unit may be split and Outlets fed to any unit fed 3 a) Outlets can be combined, treated, Outlets and recycled OR and b) Outlets can be treated separately Outlets and recycled and 4. No water loss during treatment GAMS Model GAMS • The Backbone of the Program Set u water using units w freshwater source s wastewater sink c Contaminant Alias(u,ua); / 1*6 / /1/ /1/ / 1*4 /; Parameters CFW(w) Cost of freshwater in $ per ton / 1 .32 / CWW(s) Cost of wastewater treatment $ per ton / 1 1.68 /; Table ConFW(w,c) Freshwater source concentration in ppm Table ConFW(w,c Freshwater ppm 1 2 3 4 10 0 0 0; GAMS Model GAMS • The Backbone of the Program Table Cinmax(u,c) Table Cinmax(u,c 1 2 1 300 50 2 10 1 3 10 1 4 100 200 5 85 200 6 1000 1000 maximum inlet concentration in the units in ppm maximum ppm 3 4 5000 1500 0 0 0 0 50 1000 300 200 150 200; Table Coutmax(u,c) maximum outlet concentration in the units in ppm Table Coutmax(u,c maximum ppm 1 2 3 4 1 500 500 11000 3000 2 200 4000 500 1000 3 1000 3500 2000 3500 4 400 6000 2000 3500 5 350 1800 6500 1000 6 9500 6500 450 400; Table ConWW(s,c) Concentration limits at the sink in ppm Table ConWW(s,c Concentration ppm 1 2 3 4 1 10000000000 10000000000 10000000000 10000000000; GAMS Model GAMS • The Backbone of the Program Table ML(u,c) 1 1 180 2 3610 3 600 4 2000 5 3800 6 120000 Variable FW(w,u) FW(w,u) F(u,u) F(u,u) FS(u,s) FS(u,s) Cout(u,c) Cost Cost Consu Mass Load (transfered to water at unit) in g per hour 2 3 4 1200 750 100 100000 250 800 30000 1500 1000 60000 800 1000 45000 1100 2000 480000 1500 0; Flowrates between freshwater sources and units in ton per hour Flowrates Flowrates between units in ton per hour Flowrates Flowrates between units and sinks in ton per hour Flowrates Outlet concentration in the units in ppm ppm Cost in $Mil per year Cost Consumption in ton per hour; Positive variable FW,F,FS,Cout; GAMS Model GAMS • The Backbone of the Program Equations waterbalance(u) Balance of water Balance iinlet(u,c) nlet(u,c) Limit of inlet concentration of the units Limit outlet(u,c) Calculation of outlet concentration of the units outlet(u,c) Calculation maxout(u,c) Limit of outlet concentration of the units Limit sink(s,c) Limit of inlet concentration of the sinks sink(s,c) Limit ObjCost Objective function that minimizes cost ObjConsu Objective function that minimizes cosumption Objective cosumption **Linear - Starting points **Linear inletl(u,c) Limit of inlet concentration of the units Limit outletl(u,c) Calculation of outlet concentration of the units Calculation maxoutl(u,c) Limit of outlet concentration of the units; Limit waterbalance(u) inlet(u,c) outlet(u,c) maxout(u,c) (u,c) sink(s,c) sink(s,c) ..sum(w,FW(w,u))+sum(ua,F(ua,u))=e=sum(ua,F(u,ua))+sum(s,FS(u,s)); ..sum(w,FW(w,u)*ConFW(w,c))+sum(ua,F(ua,u)*Cout(ua,c))=L=(sum(ua,F(u,ua))+sum(s,FS(u,s)))*Cinmax(u,c); ..sum(w,FW(w,u)*ConFW(w,c))+sum(ua,F(ua,u)*Cout(ua,c))+ML(u,c)=E=(sum(ua,F(u,ua))+sum(s,FS(u,s)))*Cout(u,c); ..Cout(u,c)=L=Coutmax(u,c); ..Cout(u,c)=L= ..sum(u,FS(u,s)*(Cout(u,c)-ConWW(s,c)))=L=0; ..sum(u,FS(u,s)*( GAMS Model GAMS • The Backbone of the Program ObjCost ..Cost=e=(sum(w,sum(u,FW(w,u))*cfw(w))+sum(s,sum(u,FS(u,s))*cww(s)))*0.008760; **This cost assumes constant operation **The "*0.008760" term comes from 8760 hours opperated per year and divided by 1E6 to get units **The opperated per of millions of dollars of ObjConsu ..Consu=e=sum(w,sum(u,FW(w,u))); inletl(u,c) outletl(u,c) maxoutl(u,c) (u,c) ..sum(w,FW(w,u)*ConFW(w,c))+sum(ua,F(ua,u)*Coutmax(ua,c))=L=(sum(ua,F(u,ua))+sum(s,FS(u,s)))*Cinmax(u,c); ..sum(w,FW(w,u)*ConFW(w,c))+sum(ua,F(ua,u)*Coutmax(ua,c))+ML(u,c)=E=(sum(ua,F(u,ua))+sum(s,FS(u,s)))*Coutmax(u,c); ..Cout(u,c)=e=Coutmax(u,c); ..Cout(u,c)=e= MODEL Reuse / waterbalance,inlet,outlet,maxout,sink,ObjCost,ObjConsu/; MODEL waterbalance,inlet,outlet,maxout,sink,ObjCost,ObjConsu MODEL Start / waterbalance,inletl,outletl,maxoutl,ObjCost,ObjConsu/; MODEL waterbalance,inletl,outletl,maxoutl,ObjCost,ObjConsu *SOLVE Start using MIP minimizing cost; SOLVE Start using MIP minimizing consu; SOLVE consu *SOLVE Reuse using MINLP minimizing cost; SOLVE Reuse using MINLP minimizing consu; SOLVE consu DISPLAY Cost.l,Consu.l,FW.l,F.l,FS.l,Cout.l; DISPLAY Cost.l,Consu.l,FW.l,F.l,FS.l,Cout.l GAMS Model GAMS In other words… • Amount of freshwater was calculated Amount of waste water was calculated and checked to assure a zero mass balance zero • The cost of fresh water and treatment was calculated as the total cost cost • The program minimizes either the total cost or freshwater The required required First estimates solution by solving the problem linearly First Then uses a non-linear algorithm to find a solution Then The initial linear guess is necessary because of nature of nonlinear systems nonlinear GAMS Model GAMS Important Equations • waterbalance(u) ..sum(w,FW(w,u))+sum(ua,F(ua,u))=e=sum(ua,F(u,ua))+sum(s,FS(u,s)); ∑ FW w, u w + ∑ Fu , u = ∑ Fu , u + ∑ FSu , s j uj i i uj j s Water balance around each unit Water “FW” Flow rate of streams from fresh water to units Flow “F” Flow rate of streams between units Flow “FS” Flow rate of streams from units to sinks Flow “w” Fresh water source Fresh “s” Waste water sink Waste “u”, “ui”, and “uj” Any unit and Any GAMS Model GAMS Important Equations • inlet(u,c) ..sum(w,FW(w,u)*ConFW(w,c))+sum(ua,F(ua,u)*Cout(ua,c))=L=(sum(ua,F(u,ua))+sum(s, FS(u,s)))*Cinmax(u,c); in ( FWw, u ×Cw, c ) + ∑ ( Fu , u ×Cu , c ) ≤ (∑ Fu , u + ∑ FSui , s ) × Cu ,max ∑ j w uj i j i uj j i s Sets the mixed inlet concentration of a contaminant less than its allowed maximum its “C” Concentration Concentration GAMS Model GAMS Important Equations • outlet(u,c) ..sum(w,FW(w,u)*ConFW(w,c))+sum(ua,F(ua,u)*Cout(ua,c))+ML(u,c)=E=(sum(ua,F(u,ua ))+sum(s,FS(u,s)))*Cout(u,c); out (FWw, u ×Cw, c) + ∑(Fu , u ×Cu , c) + MLu, c = (∑ Fu , u + ∑ FSui, s) × Cu ∑ j w uj i j i uj j i s Finds the outlet concentration of a contaminant once it has picked up a mass load in a unit picked “ML” Mass Load Mass GAMS Model GAMS Important Equations • maxout(u,c) (u,c) ..Cout(u,c)=L=Coutmax(u,c); ..Cout(u,c)=L= out u C ≤C out , max u Sets each outlet concentration from a unit to less than or equal to the allowed maximum equal GAMS Model GAMS Important Equations • sink(s,c) ...sum(u,FS(u,s)*(Cout(u,c)-ConWW(s,c)))=L=0; . ∑ (F u, s ×(C u , c − C s , c )) ≤ 0 s Sets the concentration of contaminants going to a sink to not exceed the limits allowed at the sink exceed GAMS Model GAMS Important Equations • ObjCost ..Cost=e=(sum(w,sum(u,FW(w,u))*cfw(w))+sum(s,sum(u,FS(u,s))*cww(s)))*0.0 08760; Cost = ∑ (∑ ( FWw, u × Pw) + ∑ ( FWu , s × Ps )) × 0.008760 w u s Determines the total cost of a given setup Determines Pw Price of purchasing fresh water Price Ps Price of treating waste water Price Multiplied by 0.008760 to convert from $/hr to $Mil/yr Multiplied GAMS Model GAMS Important Equations • ObjConsu ..Consu=e=sum(w,sum(u,FW(w,u))); Consu = ∑ ∑ FWw, u w u Calculates the fresh water requirement of a setup Calculates GAMS Model • The maximum number of streams available are shown schematically as: GAMS Model 2.616 • And the results of the And GAMS model minimizing consumption are: consumption 25.0 8.571 8.239 24.959 49.947 Caustic Treating System 21.718 Distillation System Amine Sweetening System Merox-I Sweetening System 10.345 Hydrotreating System Desalting System 87.273 End-ofPipe Treatment GAMS Model • Result (all flowrates in ton/hour) Result flowrates Origin Destination Fresh Water Source Caustic Treating Distillation Amine Sweetening Merox I Sweetening Hydrotreating Desalting Sum to Destination Caust. Dist. Am.Sw. M1S Hydr. Desalt Sink Sum from Source 2.616 25 8.571 8.239 24.959 49.947 n/a 119.332 0 0 0 1.815 0.057 0.794 0 2.666 0 0 0 0.29 0 2.995 21.715 25 0.025 0 0 0 0 8.547 0 8.572 0 0 0 0 0 0 10.345 10.345 0.026 0 0 0 3.109 24.99 0 28.125 0 0 0 0 0 0 87.273 87.273 2.667 25 8.571 10.344 28.125 87.273 119.333 • Can compare to published results1 Origin Destination Fresh Water Source Caustic Treating Distillation Amine Sweetening Merox I Sweetening Hydrotreating Desalting Sum to Destination Caust. Dist. Am.Sw. M1S Hydr. Desalt Sink Sum from Source 2.4 25 8.571 8.388 24.445 50.518 n/a 119.322 0 0 0 1.645 0.775 0 0 2.42 0 0 0 0.312 0 2.97 21.718 25 0 0 0 0 0 8.571 0 8.571 0 0 0 0 0 0 10.345 10.345 0 0 0 0 0 25.21 0 25.21 0 0 0 0 0 0 87.269 87.269 2.4 25 8.571 10.345 25.22 87.269 119.332 • Very similar for the most part • Only significant differences are that F1,6, F3,1, F5,1, and F5,5 are all 5,5 zero in the published results zero 1. Koppol, A.P., et al. Adv. in Env. Res., V(8), 2003, 151-171. GAMS Model Problems GAMS • Minimum flowrates Minimum flowrates Very low flowrates may be physically unrealizable flowrates As an example, a minimum of 0.1 ton per hour is set As • One possible solution is use of a binary marker Yi,i One • Set so that Yi,i*Fmin ≤ Fi,i≤Yi,i*Fmax If Fi,i ≤ Fmin, the model should automatically set Yi,i to to zero and reset Fi,i to zero Maximum must be included as well so that Yi,i isn’t always zero Fmax is an arbitrarily large number GAMS Model Problems GAMS Binary Marker, Yi,i • Using this marker works fundamentally Origin Destination Fresh Water Source Caustic Treating Distillation Amine Sweetening Merox I Sweetening Hydrotreating Desalting Sum to Destination Caust. Dist. Am.Sw. M1S Hydr. Desalt Sink Sum from Source 79.343 3.E+01 8.571 9.828 0 0 n/a 122.742 0 0 0 0 80.478 0 0 80.478 0.1 0 0 0.517 2.847 0 21.536 25 1.035 0 0 0 0 0 7.536 8.571 0 0 0 0 0 0 10.345 10.345 0 0 0 0 0.1 83.325 0 83.425 0 0 0 0 0 0 83.325 83.325 80.478 25 8.571 10.345 83.425 83.325 122.742 • However, the solver did not guarantee the solution to be the However, absolute optimum in this case absolute • Solution still is very close to previous results Consumption = 122.761 ton/hr, Cost = $Mil 2.151/yr Consumption GAMS Model Problems GAMS Binary Marker, Yi,i • Results can be compared to published results Origin Destination Fresh Water Source Caustic Treating Distillation Amine Sweetening Merox I Sweetening Hydrotreating Desalting Sum to Destination Caust. Dist. Am.Sw. M1S Hydr. Desalt Sink Sum from Source 79.343 3.E+01 8.571 9.828 0 0 n/a 122.742 0 0 0 0 80.478 0 0 80.478 0.1 0 0 0.517 2.847 0 21.536 25 1.035 0 0 0 0 0 7.536 8.571 0 0 0 0 0 0 10.345 10.345 0 0 0 0 0.1 83.325 0 83.425 0 0 0 0 0 0 83.325 83.325 80.478 25 8.571 10.345 83.425 83.325 122.742 • Published Results: Origin Destination Fresh Water Source Caustic Treating Distillation Amine Sweetening Merox I Sweetening Hydrotreating Desalting Sum to Destination Caust. Dist. Am.Sw. M1S Hydr. Desalt Sink Sum from Source 2.4 25 8.571 8.388 24.445 50.518 n/a 119.322 0 0 0 1.645 0.775 0 0 2.42 0 0 0 0.312 0 2.97 21.718 25 0 0 0 0 0 8.571 0 8.571 0 0 0 0 0 0 10.345 10.345 0 0 0 0 0 25.21 0 25.21 0 0 0 0 0 0 87.269 87.269 2.4 25 8.571 10.345 25.22 87.269 119.332 GAMS Model Problems GAMS • Minimum flowrates Minimum flowrates Using a binary marker uses very many resources Using Thus, a different process may be desired Thus, • An alternate solution is to set individual flowrates lless than the An flowrates ess minimum to zero minimum F3,1 = 0.025 → F3,1 = 0 0.025 • If another flowrate iis less than the minimum after doing this, it is If flowrate s set to zero also set • This process is repeated until none are below the minimum • Six different combinations of streams set to zero meet the Six minimum flowrate standards flowrate GAMS Model Problems 2.616 • New Results (flowrates in ton per hour) 25.0 Before: Before: 8.571 8.239 24.959 Origin Destination Fresh Water Source Caustic Treating Distillation Amine Sweetening Merox I Sweetening Hydrotreating Desalting Caust. Dist. Am.Sw. M1S Hydr. Desalt Sink Sum from Source 2.616 25 8.571 8.239 24.959 49.947 n/a 119.332 0 0 0 1.815 0.057 0.794 0 2.666 0 0 0 0.29 0 2.995 21.715 25 0.025 0 0 0 0 8.547 0 8.572 0 0 0 0 0 0 10.345 10.345 0.026 0 0 0 3.109 24.99 0 28.125 0 0 0 0 0 0 87.273 87.273 • Published Results (flowrates in ton per hour) Sum to Destination 2.667 25 8.571 10.344 28.125 87.273 119.333 49.947 Caustic Treating System 21.718 Distillation System Amine Sweetening System Merox-I Sweetening System 10.345 Hydrotreating System Desalting System 87.273 End-ofPipe Treatment GAMS Model Problems 2.616 • New Results (flowrates in ton per hour) 25.0 After: After: 8.571 • All flowrates are now greater than the All flowrates are arbitrary minimum (0.1) arbitrary 24.959 Origin Destination Fresh Water Source Caustic Treating Distillation Amine Sweetening Merox I Sweetening Hydrotreating Desalting Sum to Destination Caust. Dist. Am.Sw. M1S Hydr. Desalt Sink Sum from Source 2.4 25 8.571 8.388 24.445 50.518 n/a 119.322 0.267 0 0 1.645 0.775 0 0 2.687 0 0 0 0.312 0 2.974 21.715 25.001 0 0 0 0 0 8.571 0 8.571 0 0 0 0 0 0 10.345 10.345 0 0 0 0 2.905 25.21 0 28.115 0 0 0 0 0 0 87.273 87.273 2.667 25 8.571 10.345 8.239 28.125 87.273 119.333 • Fresh water requirements do not change Fresh by use of minimum flowrates flowrates 49.947 Caustic Treating System 21.718 Distillation System Amine Sweetening System Merox-I Sweetening System 10.345 Hydrotreating System Desalting System 87.273 End-ofPipe Treatment GAMS Model Problems 2.616 • New Results (flowrates in ton per hour) 25.0 Caustic Treating System 21.718 Distillation System Origin Destination Fresh Water Source Caustic Treating Distillation Amine Sweetening Merox I Sweetening Hydrotreating Desalting Sum to Destination Caust. Dist. Am.Sw. M1S Hydr. Desalt Sink Sum from Source 2.4 25 8.571 8.388 24.445 50.518 n/a 119.322 0.267 0 0 1.645 0.775 0 0 2.687 0 0 0 0.312 0 2.974 21.715 25.001 0 0 0 0 0 8.571 0 8.571 0 0 0 0 0 0 10.345 10.345 0 0 0 0 2.905 25.21 0 28.115 0 0 0 0 0 0 87.273 87.273 2.667 25 8.571 10.345 28.125 87.273 119.333 8.571 8.239 24.959 • Published Results (flowrates in ton per hour) 49.947 Origin Destination Fresh Water Source Caustic Treating Distillation Amine Sweetening Merox I Sweetening Hydrotreating Desalting Sum to Destination Caust. Dist. Am.Sw. M1S Hydr. Desalt Sink Sum from Source 2.4 25 8.571 8.388 24.445 50.518 n/a 119.322 0 0 0 1.645 0.775 0 0 2.42 0 0 0 0.312 0 2.97 21.718 25 0 0 0 0 0 8.571 0 8.571 0 0 0 0 0 0 10.345 10.345 0 0 0 0 0 25.21 0 25.21 0 0 0 0 0 0 87.269 87.269 2.4 25 8.571 10.345 25.22 87.269 119.332 Amine Sweetening System Merox-I Sweetening System 10.345 Hydrotreating System Desalting System 87.273 End-ofPipe Treatment GAMS Model Robustness GAMS • The GAMS model can be used to test The other scenarios other • Scenario 1: The Amine sweetening unit (unit 3) is aging early and another has been ordered to replace it; unfortunately, the unit will not arrive and be in operation for another year. A consequence of the early aging of the unit is that it can only handle Cin,max one fifth of its previous operation one capacity, the Cout,max are cut in half and the are mass loads triples. mass Origin Destination Fresh Water Source Caustic Treating Distillation Amine Sweetening Merox I Sweetening Hydrotreating Desalting Sum to Destination Caust. Dist. Am.Sw. M1S Hydr. Desalt Sink Sum from Source 2.4 25 8.571 8.091 24.371 19.394 n/a 87.827 0.267 0 0 1.529 0.871 0 0 2.667 0 0 0 0 0 0 25 25 0 0 0 0 0 0 8.571 8.571 0 0 0 0 0 0 10.345 10.345 0 0 0 0.725 1.349 24.242 0.275 26.591 0 0 0 0 0 0 43.636 43.636 2.667 25 8.571 10.345 26.591 43.636 87.827 GAMS Model Robustness GAMS • A more valuable check of robustness more is to fix the streams between units as either existent or non-existent once an either existent initial minimization is run initial • The mass load can then be changed to The test that new solutions are reasonable test GAMS Model Robustness GAMS Caustic Treating System 33.417 0.484 • New Scenario: Mass loads double, network fixed Mass 25 Amine Sweetening System 8.571 Merox-I Sweetening System 9.828 8.571 10.345 83.516 83.316 122.357 Cost = $Mil 2.144 / yr Consumption = 122.357 ton/hr Consumption 83.316 0.2 25 45.541 33.901 33.901 3.674 Caust. Dist. Am.Sw. M1S Hydr. Desalt Sink Sum from Source 33.417 25 8.571 9.828 45.541 0 n/a 122.357 0 0 0 0 33.901 0 0 33.901 0 0 0 0.517 0.2 0 24.283 25 0.484 0 0 0 3.674 0 4.413 8.571 0 0 0 0 0 0 10.345 10.345 0 0 0 0 0.2 83.316 0 83.516 0 0 0 0 0 0 83.316 83.316 0.2 Origin Destination Sum to Destination 4.413 0.517 • Initial Results: Fresh Water Source Caustic Treating Distillation Amine Sweetening Merox I Sweetening Hydrotreating Desalting 24.283 Distillation System 10.345 Hydrotreating System Desalting System 83.316 End-ofPipe Treatment GAMS Model Robustness GAMS Caustic Treating System 13.8 0.2 • New Scenario: Mass loads double, network fixed Mass 50 Amine Sweetening System 17.143 Merox-I Sweetening System 19.655 17.143 20.689 166.85 166.65 244.671 Cost = $Mil 4.287 / yr Consumption = 244.67 ton/hr Consumption 166.65 14 50 144.072 0.2 14 8.378 Caust. Dist. Am.Sw. M1S Hydr. Desalt Sink Sum from Source 13.8 50 17.143 19.655 144.072 0 n/a 244.67 0 0 0 0 14 0 0 14 0 0 0 1.034 0.2 0 48.766 50 0.2 0 0 0 8.378 0 8.565 17.143 0 0 0 0 0 0 20.69 20.69 0 0 0 0 0.2 166.65 0 166.85 0 0 0 0 0 0 166.65 166.65 0.2 Origin Destination Sum to Destination 8.565 1.034 • Final Results: Fresh Water Source Caustic Treating Distillation Amine Sweetening Merox I Sweetening Hydrotreating Desalting 48.766 Distillation System 20.69 Hydrotreating System Desalting System 166.65 End-ofPipe Treatment GAMS Model Robustness GAMS • Other modes of the GAMS program can be used find Other solutions that: solutions GAMS Model Robustness GAMS • Other modes of the GAMS program can be used find Other solutions that: solutions Minimize cost Minimize Origin Destination Fresh Water Source Caustic Treating Distillation Amine Sweetening Merox I Sweetening Hydrotreating Desalting Sum to Destination Caust. Dist. Am.Sw. M1S Hydr. Desalt Sink Sum from Source 16.68 25 8.571 0 24.774 44.307 n/a 119.332 0 0 0 9.852 0.243 7.062 0 17.157 0 0 0 0 0 3.285 21.715 25 0 0 0 0 0 8.571 0 8.571 0 0 0 0 0 0 10.345 10.345 0.477 0 0 0.493 3.109 24.047 0 28.126 0 0 0 0 0 0 87.273 87.273 17.157 25 8.571 10.345 28.126 87.272 119.333 GAMS Model Robustness GAMS • Other modes of the GAMS program can be used find Other solutions that: solutions Minimize cost, then minimize the consumption with the initial solutions fixed initial Origin Destination Fresh Water Source Caustic Treating Distillation Amine Sweetening Merox I Sweetening Hydrotreating Desalting Sum to Destination Caust. Dist. Am.Sw. M1S Hydr. Desalt Sink Sum from Source 2.4 25 8.571 8.422 25.572 49.367 n/a 119.332 0.265 0 0 1.606 0.794 0 0 2.665 0 0 0 0.316 0.937 2.032 21.715 25 0 0 0 0 0 8.571 0 8.571 0 0 0 0 0 0 10.344 10.344 0 0 0 0 0.822 27.303 0 28.125 0 0 0 0 0 0 87.273 87.273 2.665 25 8.571 10.344 28.125 87.273 119.332 GAMS Model Robustness GAMS • Other modes of the GAMS program can be used find Other solutions that: solutions Minimize consumption, then minimize the cost with the initial solutions fixed initial Origin Destination Fresh Water Source Caustic Treating Distillation Amine Sweetening Merox I Sweetening Hydrotreating Desalting Sum to Destination Caust. Dist. Am.Sw. M1S Hydr. Desalt Sink Sum from Source 2.4 25 8.571 8.388 26.058 48.914 n/a 119.331 0.267 0 0 1.645 0.755 0 0 2.667 0 0 0 0.312 1.312 1.662 21.715 25.001 0 0 0 0 0 8.571 0 8.571 0 0 0 0 0 0 10.345 10.345 0 0 0 0 0 28.125 0 28.125 0 0 0 0 0 0 87.273 87.273 2.667 25 8.571 10.345 28.125 87.272 119.333 GAMS Model & Regeneration GAMS • Treatment units can be added mid-process to regenerate process streams streams • Equations must be updated to include flow from units to Equations regenerators and cost of regeneration regenerators • Calculation of outlet concentrations Mixing Streams: Cmix = Σi(Cin,unit-i * Fin,unit-i)/Fout,total Outlet Concentration: Cout = Cmix * X + Cregen If contaminant is not reduced in regenerator, Cregen = 0 and and X = 1;; 1 If contaminant is reduced in regenerator Cregen iis its s is reduced specified outlet concentration and X = 0 specified GAMS Model & Regeneration GAMS • Results: Destination Caust. Dist. 0 0 0 0 0 0 0 0 1.333 1.333 25 0 0 0 0 0 0 0 0 0 Sum to Destination Origin Fresh Water Source Caustic Treating Distillation Amine Sweetening Merox I Sweetening Hydrotreating Desalting API and ACA Reverse Osmosis Chevron WWT 2.666 25 Am.Sw. M1S Hydr. Desalt API/ACA 8.571 0 0 0 n/a 0 0.014 0 0 2.55 0 0 0.016 0.013 19.814 0 0 0 0 8.471 0 0 0 0 0 0 0 0 0 29.893 0 0 0 0 56.396 0 0 0 0 n/a 0 2.187 10.905 74.413 n/a 0 7.885 19.072 0 0 8.571 10.086 29.993 74.426 117.124 RO n/a 0 0 0 0 0 0 88.838 n/a n/a 88.838 CWWT Sink Sum from Source n/a n/a 33.571 0 0.1 2.664 0 5.156 24.999 0 0.1 8.571 0 10.085 10.085 0 0.1 29.993 0 18.03 74.426 28.289 n/a 117.127 0 n/a 88.838 n/a n/a 28.29 28.289 33.571 • Consumption = 33.571 ton/hr, Cost: = $Mil 1.301/yr • Published consumption is the same: Consumption = 33.571 ton/hr, Cost = $Mil 1.110/yr Cost is somewhat higher Cost GAMS Summary GAMS • Conclusions GAMS model values are very close to published results GAMS Model maintains effectiveness with use of regeneration Model Model is robust enough to predict results in other cases Model Future Work Future • Include price of piping in GAMS model Extensive piping networks may save in water cost compared to simpler networks, but cost more to construct compared Cost of network, length and type of pipe required could all be variables in model be • Include maximum flowrates Include flowrates Need to include minimum flowrates previously explained flowrates Reasoning for using maximum flowrates is similar flowrates • Continue study with fixed initial setup and changing mass Continue loads loads This setup better models the cost over time, so extending time period and increasing number of periods makes model more useful more Thank You! 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