diff --git a/watertap/flowsheets/METAB/Integration_Workflow_Tutorial/Results/alamo_parity.pdf b/watertap/flowsheets/METAB/Integration_Workflow_Tutorial/Results/alamo_parity.pdf
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diff --git a/watertap/flowsheets/METAB/Integration_Workflow_Tutorial/Results/alamo_surrogate.json b/watertap/flowsheets/METAB/Integration_Workflow_Tutorial/Results/alamo_surrogate.json
new file mode 100644
index 0000000000..0db15884ba
--- /dev/null
+++ b/watertap/flowsheets/METAB/Integration_Workflow_Tutorial/Results/alamo_surrogate.json
@@ -0,0 +1 @@
+{"surrogate": {"S_su": " S_su == 43.989350626853934045357 * inf_fr - 9.4706210126369274604485 * temp + 19.893669255685551888746 * hrt + 114.59817145755525302775 * ln(inf_fr) + 129.01061341366997226032 * ln(temp) - 37.008209572391699282434 * ln(hrt) - 0.56234520123311817867140E-003 * exp(inf_fr) - 0.15090473026542536424868E-004 * exp(hrt) + 3.0534771747869551283827 * sin(inf_fr) + 0.53360116351520965952138E-002 * sin(temp) + 0.20119079821944874097994 * sin(hrt) - 2.8232394802081119955517 * cos(inf_fr) + 0.89481058687089873315834E-001 * cos(temp) - 0.49695539002178712451041 * cos(hrt) - 10.636553297318570798780 * inf_fr**2 + 0.85847680887134125704563E-001 * temp**2 - 1.7623608887704267367269 * hrt**2 + 0.57907763786762767033167 * inf_fr**3 + 0.65304994980003444604577E-001 * hrt**3 - 441.75496539900063908135", "S_aa": " S_aa == 0.13714442596943801633280 * inf_fr - 135.72383659797793598045 * temp + 13.732598949914349262258 * hrt - 1.0623047600958894776113 * ln(inf_fr) + 1000.6504114752052601034 * ln(temp) - 17.876876829213433239829 * ln(hrt) - 0.15412104152236907518992E-005 * exp(inf_fr) + 0.17538847319267738563255E-004 * exp(hrt) + 0.26874226211971108391197E-001 * sin(inf_fr) + 0.14472941110267910705400E-002 * sin(temp) + 0.51724830814198201334175E-001 * sin(hrt) - 0.11132320929638842704756E-001 * cos(temp) + 0.14420535227943906408932 * cos(hrt) + 3.4193902660316477515323 * temp**2 - 2.1366866290412964701773 * hrt**2 - 0.50562367815806280590962E-001 * temp**3 + 0.18635288786798587246096 * hrt**3 + 0.31235465285666118599381E-003 * temp**4 - 0.63959704455392972499705E-002 * hrt**4 - 1300.2263021303028835973", "S_fa": " S_fa == 1559.1806044552085950272 * inf_fr - 46.795901166459707098966 * temp + 75.971473269450228826827 * hrt - 1662.4017806527695029217 * ln(inf_fr) + 632.41097896425310409541 * ln(temp) - 139.98632957596780101994 * ln(hrt) - 0.66415454376969156047306E-002 * exp(inf_fr) - 0.44402492373057262655626E-004 * exp(hrt) + 24.792877151820427172879 * sin(inf_fr) + 0.15606467241526139622110 * sin(temp) + 0.63977436389652542203521 * sin(hrt) - 23.872815042632264947997 * cos(inf_fr) + 0.46471590062560086398236 * cos(temp) - 2.1788375407648343795586 * cos(hrt) - 178.86489755735800599723 * inf_fr**2 + 0.42709604967837166977773 * temp**2 - 6.7682143165794297345883 * hrt**2 + 7.8638106959222886516159 * inf_fr**3 + 0.24847076609258272883451 * hrt**3 - 2724.4889666883996142133", "S_va": " S_va ==  - 238.75963003056648403799 * inf_fr + 0.21909618138162748057773 * temp + 28.084936385138039582898 * hrt + 938.66737557919259415939 * ln(inf_fr) - 6.6176901044917562089154 * ln(temp) - 31.362590244293659935693 * ln(hrt) - 0.24204778743322275837146E-002 * exp(inf_fr) + 0.51146987025564729463165E-004 * exp(hrt) + 10.710551105733154386712 * sin(inf_fr) + 0.10577392520277784093707 * sin(temp) + 0.65354630939215077600757E-001 * sin(hrt) - 0.16800438648556156873148 * cos(inf_fr) - 0.27226748460961420345017E-001 * cos(temp) + 0.63212911463789289712167 * cos(hrt) - 5.1056501489709322783028 * hrt**2 + 0.67571191436222444171023 * inf_fr**3 + 0.49733939555852357816690 * hrt**3 - 0.18330619039941858233744E-001 * hrt**4 - 0.15320630339846747977606E-001 * inf_fr*hrt - 385.37692183335855133919", "S_bu": " S_bu ==  - 393.43813405677417449624 * inf_fr + 0.36336038099818673829944 * temp + 39.715444847862649169201 * hrt + 1546.8044563449659563048 * ln(inf_fr) - 10.972966048600797606127 * ln(temp) - 42.811113654999985556060 * ln(hrt) - 0.39881090887985441370556E-002 * exp(inf_fr) + 0.81778857237897408958768E-004 * exp(hrt) + 17.648901916760063812717 * sin(inf_fr) + 0.17491607347355389578958 * sin(temp) + 0.61027735355419533702648E-001 * sin(hrt) - 0.27967976376423125950410 * cos(inf_fr) - 0.44095283295516395083435E-001 * cos(temp) + 1.0409535261798463778860 * cos(hrt) - 7.4848860327497428457377 * hrt**2 + 1.1134201064009345838457 * inf_fr**3 + 0.74792002211708386028022 * hrt**3 - 0.28041731933165031653843E-001 * hrt**4 - 0.25061389748309785552305E-001 * inf_fr*hrt - 633.02686206103453514515", "S_pro": " S_pro == 0.92414129737994799018708 * inf_fr + 242.29139145969520541257 * temp + 67.381943962401123826567 * hrt - 7.3236207148799179478260 * ln(inf_fr) - 1573.5627563241785082937 * ln(temp) - 76.449727343352961383971 * ln(hrt) + 0.26489882188287923042984E-006 * exp(inf_fr) + 0.10396732913876740450609E-003 * exp(hrt) + 0.21751197157378410196316 * sin(inf_fr) - 0.49267173198107867826412E-001 * sin(temp) + 0.16088355858205005688433 * sin(hrt) - 0.14519033819796264492274E-001 * cos(temp) + 0.95278481121746005921835 * cos(hrt) - 6.9301973542043002751711 * temp**2 - 11.154009619190503599384 * hrt**2 + 0.11642105679809136575820 * temp**3 + 1.0036787661808892480053 * hrt**3 - 0.81791135904564439464831E-003 * temp**4 - 0.35140809166877146063612E-001 * hrt**4 + 1813.0207352651802921173", "S_ac": " S_ac ==  - 351.34615225401586258158 * inf_fr - 16.386841409791760071357 * temp + 40.114727055090448004648 * hrt + 999.91962900115072443441 * ln(inf_fr) + 221.04770325765480265545 * ln(temp) - 83.206587855651278573532 * ln(hrt) - 0.36166090318903026945563E-003 * exp(inf_fr) - 0.24341144370605435849403E-004 * exp(hrt) + 4.5674555634070808096681 * sin(inf_fr) + 0.59397330875141991779476E-001 * sin(temp) + 0.32555592704949876114995 * sin(hrt) - 1.7374715378524674402883 * cos(inf_fr) + 0.16226895624130910356087 * cos(temp) - 0.96754678701705032484170 * cos(hrt) + 17.849041195338820386951 * inf_fr**2 + 0.15061858567597968816543 * temp**2 - 3.4327846124303817987311 * hrt**2 - 0.30857911849460478714846 * inf_fr**3 + 0.12479648286301550652588 * hrt**3 - 630.34586821350069385517", "S_h2": " S_h2 == 0.15749659816902817777303E-005 * inf_fr + 0.46967237057588876705978E-004 * hrt - 0.16553604010721425729164E-004 * ln(inf_fr) - 0.15066754693432297170647E-003 * ln(hrt) + 0.15672122281106490841246E-009 * exp(inf_fr) + 0.11076380140348380468418E-009 * exp(hrt) + 0.12246187536975384542475E-005 * sin(inf_fr) + 0.71568600104402763179760E-006 * sin(temp) - 0.22144207003365855324659E-005 * sin(hrt) - 0.27310083873804061163398E-006 * cos(temp) - 0.35358089240649590254828E-006 * cos(hrt) - 0.18915872856491405977134E-005 * hrt**2 + 0.92337498618873465119905E-004", "S_ch4": " S_ch4 ==  - 181.42206739438358908956 * inf_fr - 1.3027796947739358479623 * temp + 98.207388370039737424122 * hrt + 565.44779962464008349343 * ln(inf_fr) + 15.314912025942073015017 * ln(temp) - 98.737296286269739198360 * ln(hrt) - 0.54777805404311835472952E-003 * exp(inf_fr) + 0.85981337538192575487353E-004 * exp(hrt) + 3.3143162664400040995361 * sin(inf_fr) - 0.32630423376021855386142 * sin(temp) + 0.91728868517333339571707 * sin(hrt) - 1.0836597147248858696855 * cos(inf_fr) - 0.65435870141167751246059E-001 * cos(temp) + 1.5142725700406785005470 * cos(hrt) + 6.9560594163605529161032 * inf_fr**2 - 16.617623116022045337559 * hrt**2 + 1.4754849347322298580565 * hrt**3 - 0.50013686493066693283804E-001 * hrt**4 + 0.16117897258389177195248E-001 * inf_fr*temp - 218.85616946970642970882", "S_IC": " S_IC == 22.305731924047851322257 * ln(hrt) - 0.39449593251201932331274E-001 * temp**2 + 531.70193036622333693231", "S_IN": " S_IN == 0.52369457480208082833517E-001 * temp - 8.5251999464889980373528 * hrt - 2.5278951112002632228837 * ln(temp) + 18.206646209400837221892 * ln(hrt) - 0.94481784580889401683199E-005 * exp(inf_fr) + 0.15594218736226925963242E-004 * exp(hrt) + 0.61846930153033961852227E-001 * sin(temp) - 0.75717540584660449239085E-003 * sin(hrt) + 0.70846977928518309330919E-001 * cos(inf_fr) + 0.90593082611872877141224E-001 * cos(hrt) + 0.97463454035402952158051 * hrt**2 - 0.38960982637592088406731E-001 * hrt**3 + 0.47227308496604333896800E-004 * inf_fr**4 + 0.42350232707606301485459E-002 * temp*hrt + 195.99788783009981329997", "S_I": " S_I == 1626.2305567689497820538 * inf_fr + 93.451565897939445903830 * temp + 140.88278279806769432980 * hrt - 5207.9513545655354391783 * ln(inf_fr) - 1273.6766250777020559326 * ln(temp) - 75.492536436905865571134 * ln(hrt) + 0.39138957926615185883845E-002 * exp(inf_fr) + 0.45482642478656044480664E-003 * exp(hrt) - 30.561641389406535296303 * sin(inf_fr) - 0.12146519761709131945970 * sin(temp) - 0.19016742457960014722751 * sin(hrt) + 13.334095051967070233445 * cos(inf_fr) - 0.81121788736130884256426 * cos(temp) + 6.9909567375698724589483 * cos(hrt) - 61.119399155929670541809 * inf_fr**2 - 0.84634165633364755176871 * temp**2 - 31.183649576664240754553 * hrt**2 + 3.3514052160734988383695 * hrt**3 - 0.13100671609815492391071 * hrt**4 + 3967.2440858687273248506", "X_c": " X_c ==  - 24.187165013260869983469 * ln(hrt) + 0.14350379638302882684409E-001 * hrt**2 + 94.445634377927760283455", "X_ch": " X_ch == 136.69923698141241175108 * inf_fr - 9.7553489925090186574153 * temp + 17.819961510806049176381 * hrt - 30.032062018769220657077 * ln(inf_fr) + 132.44458272450603431025 * ln(temp) - 34.791687470643807955639 * ln(hrt) - 0.85899023964482591579628E-003 * exp(inf_fr) - 0.11629490433456603825442E-004 * exp(hrt) + 3.8879053499562559181868 * sin(inf_fr) + 0.15320531850147742214396E-001 * sin(temp) + 0.17978535569652270842411 * sin(hrt) - 3.6117753825979215243080 * cos(inf_fr) + 0.94692985632030726717012E-001 * cos(temp) - 0.46618521487329322994242 * cos(hrt) - 19.705952993040700249594 * inf_fr**2 + 0.88689538010323448746774E-001 * temp**2 - 1.5439185986916279524905 * hrt**2 + 0.94720153918359994982268 * inf_fr**3 + 0.56470544537624470238235E-001 * hrt**3 - 493.88200295122442184947", "X_pr": " X_pr == 233.15581710389218983437 * inf_fr - 16.422792184502842616212 * temp + 29.779701276737114312709 * hrt - 56.059966505410471881987 * ln(inf_fr) + 222.96431059822825204719 * ln(temp) - 57.946119778830635027589 * ln(hrt) - 0.14515745988540611384476E-002 * exp(inf_fr) - 0.19385896349948923770559E-004 * exp(hrt) + 6.5557296992688369741131 * sin(inf_fr) + 0.25773906483938888167806E-001 * sin(temp) + 0.30145592116031461049630 * sin(hrt) - 6.1026703449714592153441 * cos(inf_fr) + 0.15951461275444342446939 * cos(temp) - 0.78211896072746467378778 * cos(hrt) - 33.438910797856401302397 * inf_fr**2 + 0.14930653626219955731891 * temp**2 - 2.5809874187204222550918 * hrt**2 + 1.6044241763747251194872 * inf_fr**3 + 0.94411936966882367694431E-001 * hrt**3 - 832.99647149039185478614", "X_li": " X_li == 108.59406354158309682134 * inf_fr - 7.9655735801663061224076 * temp + 14.770206867292376529122 * hrt - 19.024315967975134356038 * ln(inf_fr) + 108.14705947883356884631 * ln(temp) - 29.033105000932433625849 * ln(hrt) - 0.69589939724356349604745E-003 * exp(inf_fr) - 0.96878269023148694505084E-005 * exp(hrt) + 3.1640200514393255204482 * sin(inf_fr) + 0.12527220546006545900952E-001 * sin(temp) + 0.14800738036345575943109 * sin(hrt) - 2.9267776977667154802987 * cos(inf_fr) + 0.77217830284321262168667E-001 * cos(temp) - 0.38334397816443421813659 * cos(hrt) - 15.826091924063790727928 * inf_fr**2 + 0.72417245307942523790956E-001 * temp**2 - 1.2788094696256104843002 * hrt**2 + 0.76358298401935709165400 * inf_fr**3 + 0.46764437461941629758932E-001 * hrt**3 - 401.70810487635316121668", "X_su": " X_su ==  - 3.4175275425960158770522 * inf_fr + 24308.079861456670187181 * temp - 114.34369079851072115162 * hrt + 20.597343060023103333833 * ln(inf_fr) - 172779.26675649441312999 * ln(temp) + 109.07306806782986541293 * ln(hrt) - 0.57842389139992493097588E-004 * exp(inf_fr) - 0.29137957536630137951117E-004 * exp(hrt) - 0.74758148626789899804379 * sin(temp) + 0.63178531970223261726716 * sin(hrt) + 1.3808704296327796168242 * cos(temp) - 2.6324250429640958515165 * cos(hrt) - 637.34080818121572065138 * temp**2 + 16.142259700893092144725 * hrt**2 + 9.8425253121889575425030 * temp**3 - 1.4120403232508009772062 * hrt**3 - 0.63736468016548697423929E-001 * temp**4 + 0.48338337060597549865726E-001 * hrt**4 + 0.15249873631590524225565 * inf_fr*hrt + 218453.07148567616241053", "X_aa": " X_aa ==  - 3.6596608469330589308299 * hrt + 8.3308663441574086760966 * ln(hrt) + 0.46651083570803065101655E-005 * exp(hrt) + 0.46748793581645527173141E-001 * sin(inf_fr) - 0.11962922384010273024746 * sin(hrt) + 0.18336189513350198421193 * cos(hrt) + 113.01097788511852115789", "X_fa": " X_fa ==  - 0.50296537422405713374518E-002 * inf_fr + 9.1069683879949128169073 * hrt - 7.7527990787920950666035 * ln(hrt) + 0.10433159058352160555839E-004 * exp(hrt) + 0.37033146492844979964093E-003 * sin(temp) + 0.20512214699684897661891E-001 * sin(hrt) + 0.10815006138291358150916 * cos(hrt) - 1.4884649584152804813186 * hrt**2 + 0.12247825535044916389626 * hrt**3 - 0.40022665478789036866414E-002 * hrt**4 + 3.5262132047427376946303", "X_c4": " X_c4 == 1.8655041049228435756646 * inf_fr + 49.972153542556803529351 * hrt - 11.990745071633412521805 * ln(inf_fr) - 82.871765910635275531604 * ln(hrt) + 0.21256449359348926226448E-003 * exp(hrt) + 1.3090008585554249176397 * sin(temp) - 5.6205975915169901213631 * sin(hrt) - 2.7169572363843972340192 * hrt**2 + 45.418477599619585305391", "X_pro": " X_pro == 4.2969959736920291248907 * hrt - 18.835009046739180860186 * ln(hrt) - 0.55412836883755095995687E-004 * exp(hrt) - 0.75873352000943339312755 * sin(inf_fr) + 42.888557836643379062025", "X_ac": " X_ac ==  - 2.1574180882568705364122 * hrt - 82.116401600265817251056 * ln(hrt) - 0.86737349181914358275947 * sin(temp) + 0.73138883037267043984997 * sin(hrt) + 0.10828113449655358841578E-002 * hrt**4 + 217.26003642620835876187", "X_h2": " X_h2 ==  - 1.9784310379931790979668 * ln(inf_fr) - 4.1897296247780433375851 * ln(hrt) - 0.29959942076878057887722E-004 * exp(hrt) + 98.926298342057265244875", "X_I": " X_I == 0.19785024197585146521483 * inf_fr - 22.651439953297249019215 * hrt - 2.2201672569057802064663 * ln(inf_fr) + 54.343071868360333098735 * ln(hrt) + 0.87826412551589918024583E-004 * exp(hrt) - 0.10201394237710886825354 * sin(temp) - 0.92491947203041369185428 * sin(hrt) + 0.67584252424231369693608E-001 * cos(temp) + 1.7972220347577076982049 * cos(hrt) + 2.4736644504545637346382 * hrt**2 - 0.12076605506068814610554 * hrt**3 + 88.399688998477344625826", "VolumetricFlowrate": " VolumetricFlowrate == 0.99295348907520530890025 * inf_fr - 0.65532134561973303527038E-002 * temp + 0.28173577317473250808399E-001 * ln(temp) + 0.85568212510927438603515E-008 * exp(inf_fr) + 0.70541137330179072782229E-009 * exp(hrt) + 0.45718667223351061357261E-005 * cos(hrt) + 0.14431096834221506680120E-003 * temp**2 - 0.10869206695940151393825E-005 * temp**3 + 0.28992836287517078199330E-003 * inf_fr*temp"}, "input_labels": ["inf_fr", "temp", "hrt"], "output_labels": ["S_su", "S_aa", "S_fa", "S_va", "S_bu", "S_pro", "S_ac", "S_h2", "S_ch4", "S_IC", "S_IN", "S_I", "X_c", "X_ch", "X_pr", "X_li", "X_su", "X_aa", "X_fa", "X_c4", "X_pro", "X_ac", "X_h2", "X_I", "VolumetricFlowrate"], "input_bounds": {"inf_fr": [5.16, 9.87], "temp": [22.25, 34.67], "hrt": [1.18, 11.82]}}
\ No newline at end of file
diff --git a/watertap/flowsheets/METAB/Integration_Workflow_Tutorial/Results/input_data.csv b/watertap/flowsheets/METAB/Integration_Workflow_Tutorial/Results/input_data.csv
new file mode 100644
index 0000000000..d31b4dcc8f
--- /dev/null
+++ b/watertap/flowsheets/METAB/Integration_Workflow_Tutorial/Results/input_data.csv
@@ -0,0 +1,21 @@
+inf_fr,temp,hrt
+9.62,24.21,9.97
+7.6,29.05,9.24
+9.06,27.98,3.67
+6.71,27.84,4.3
+5.36,27.03,11.82
+7.09,24.66,6.14
+6.31,30.7,9.3
+5.55,31.95,7.72
+7.79,31.75,11.08
+9.87,32.81,2.91
+6.06,22.9,4.9
+8.25,29.43,2.38
+9.41,33.07,4.37
+5.94,34.29,7.05
+5.16,25.28,8.6
+6.99,34.67,5.63
+8.78,26.17,10.5
+8.25,22.25,1.18
+7.34,23.58,6.97
+8.52,30.11,1.71
diff --git a/watertap/flowsheets/METAB/Integration_Workflow_Tutorial/Results/kri_metircs.csv b/watertap/flowsheets/METAB/Integration_Workflow_Tutorial/Results/kri_metircs.csv
new file mode 100644
index 0000000000..11346f36b4
--- /dev/null
+++ b/watertap/flowsheets/METAB/Integration_Workflow_Tutorial/Results/kri_metircs.csv
@@ -0,0 +1,26 @@
+Comp,R2,RMSE
+S_su,0.9999995712642091,0.0019041696866005046
+S_aa,0.9999993513188298,0.0011055579033206777
+S_fa,0.9999995278696008,0.007787287145601142
+S_va,0.999999085595554,0.0025501563244446244
+S_bu,0.9999977052258839,0.005604465010192428
+S_pro,0.9999999105059814,0.0012665764838757235
+S_ac,0.9999992496136992,0.008084756292153177
+S_h2,0.9999999740633578,3.4913255480625317e-09
+S_ch4,0.9995494967350704,0.08604317980714032
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diff --git a/watertap/flowsheets/METAB/Integration_Workflow_Tutorial/Results/kri_parity.pdf b/watertap/flowsheets/METAB/Integration_Workflow_Tutorial/Results/kri_parity.pdf
new file mode 100644
index 0000000000..e36fe5797e
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diff --git a/watertap/flowsheets/METAB/Integration_Workflow_Tutorial/Results/kri_surrogate.json b/watertap/flowsheets/METAB/Integration_Workflow_Tutorial/Results/kri_surrogate.json
new file mode 100644
index 0000000000..0ca679d569
--- /dev/null
+++ b/watertap/flowsheets/METAB/Integration_Workflow_Tutorial/Results/kri_surrogate.json
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\ No newline at end of file
diff --git a/watertap/flowsheets/METAB/Integration_Workflow_Tutorial/Results/output_data.csv b/watertap/flowsheets/METAB/Integration_Workflow_Tutorial/Results/output_data.csv
new file mode 100644
index 0000000000..4dc011b0eb
--- /dev/null
+++ b/watertap/flowsheets/METAB/Integration_Workflow_Tutorial/Results/output_data.csv
@@ -0,0 +1,21 @@
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diff --git a/watertap/flowsheets/METAB/Integration_Workflow_Tutorial/Results/poly_metircs.csv b/watertap/flowsheets/METAB/Integration_Workflow_Tutorial/Results/poly_metircs.csv
new file mode 100644
index 0000000000..9946d65fcc
--- /dev/null
+++ b/watertap/flowsheets/METAB/Integration_Workflow_Tutorial/Results/poly_metircs.csv
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diff --git a/watertap/flowsheets/METAB/Integration_Workflow_Tutorial/Results/poly_parity.pdf b/watertap/flowsheets/METAB/Integration_Workflow_Tutorial/Results/poly_parity.pdf
new file mode 100644
index 0000000000..0b3162c8f0
Binary files /dev/null and b/watertap/flowsheets/METAB/Integration_Workflow_Tutorial/Results/poly_parity.pdf differ
diff --git a/watertap/flowsheets/METAB/Integration_Workflow_Tutorial/Results/poly_surrogate.json b/watertap/flowsheets/METAB/Integration_Workflow_Tutorial/Results/poly_surrogate.json
new file mode 100644
index 0000000000..4ee61d9454
--- /dev/null
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\ No newline at end of file
diff --git a/watertap/flowsheets/METAB/Integration_Workflow_Tutorial/Results/rbf_metircs.csv b/watertap/flowsheets/METAB/Integration_Workflow_Tutorial/Results/rbf_metircs.csv
new file mode 100644
index 0000000000..bbe059d289
--- /dev/null
+++ b/watertap/flowsheets/METAB/Integration_Workflow_Tutorial/Results/rbf_metircs.csv
@@ -0,0 +1,26 @@
+Comp,R2,RMSE
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+S_bu,0.890397220397771,0.08183376818743661
+S_pro,0.8923462071101452,0.07831099137660585
+S_ac,0.8459383244359646,0.09622253741740457
+S_h2,0.5923985754185994,0.15241365472568216
+S_ch4,0.9999999874763774,2.4254594091396e-05
+S_IC,0.9999999944372744,2.2809590770717175e-05
+S_IN,0.9999999952072128,1.9228125883983704e-05
+S_I,0.9999999958051935,1.8141762877633807e-05
+X_c,0.9999818679727983,0.0011689901975899183
+X_ch,0.9991194071019889,0.0071270677965212395
+X_pr,0.9990979054524969,0.007209247372828178
+X_li,0.9991599682381788,0.006970292019293895
+X_su,0.9999999968128986,1.777396139112524e-05
+X_aa,0.9999999883796411,3.480838422250065e-05
+X_fa,0.9999999935495876,2.4251793077639045e-05
+X_c4,0.9659272442692093,0.05034773719077161
+X_pro,0.9999999392386125,6.863550034139968e-05
+X_ac,0.9999684858673341,0.0015835515240812995
+X_h2,0.9925167154479091,0.018465234976060504
+X_I,0.9999999885955195,2.953643041315544e-05
+VolumetricFlowrate,0.9999999994573254,7.0529462159873205e-06
diff --git a/watertap/flowsheets/METAB/Integration_Workflow_Tutorial/Results/rbf_parity.pdf b/watertap/flowsheets/METAB/Integration_Workflow_Tutorial/Results/rbf_parity.pdf
new file mode 100644
index 0000000000..e62bb419ce
Binary files /dev/null and b/watertap/flowsheets/METAB/Integration_Workflow_Tutorial/Results/rbf_parity.pdf differ
diff --git a/watertap/flowsheets/METAB/Integration_Workflow_Tutorial/Results/rbf_surrogate.json b/watertap/flowsheets/METAB/Integration_Workflow_Tutorial/Results/rbf_surrogate.json
new file mode 100644
index 0000000000..16606cf297
--- /dev/null
+++ b/watertap/flowsheets/METAB/Integration_Workflow_Tutorial/Results/rbf_surrogate.json
@@ -0,0 +1 @@
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0.4613526570048309, 0.23402255639097744], [0.3290870488322718, 0.45008051529790655, 0.2932330827067669], [0.04246284501061576, 0.3848631239935588, 1.0], [0.40976645435244163, 0.1940418679549114, 0.46616541353383456], [0.24416135881104029, 0.6803542673107889, 0.7631578947368421], [0.08280254777070059, 0.7809983896940417, 0.6146616541353384], [0.5583864118895967, 0.7648953301127213, 0.9304511278195489], [1.0, 0.8502415458937199, 0.162593984962406], [0.19108280254777063, 0.052334943639291344, 0.349624060150376], [0.6560509554140128, 0.5780998389694041, 0.11278195488721804], [0.902335456475584, 0.8711755233494363, 0.299812030075188], [0.16560509554140135, 0.9694041867954909, 0.5516917293233082], [0.0, 0.2439613526570049, 0.6973684210526315], [0.38853503184713384, 1.0, 0.4182330827067669], [0.7685774946921443, 0.3156199677938809, 0.8759398496240601], [0.6560509554140128, 0.0, 0.0], [0.46284501061571126, 0.10708534621578085, 0.5441729323308271], [0.713375796178344, 0.6328502415458935, 0.04981203007518797]], "basis_function": "cubic", "weights": [[0.1413322743654195], [0.8325055730967392], [-0.6413803259224231], [-0.2911913679751308], [0.8920848095687108], [-0.34270949463972755], [-2.009302086424711], [-0.14154166178574812], [-0.009483761652566813], [0.5105074349949561], [0.5402842478194156], [0.21082356391093526], [-0.4921681560895941], [1.4124286371542123], [-0.16889515325660193], [-0.4926021052120726], [-0.4352010929035215], [0.2708654024925612], [-0.6232617666402085], [-0.11119036137685612]], "sigma": 0.0, "regularization_parameter": 1e-05, "rmse": 7.0529462159873205e-06, "R2": 0.9999999994573254, "x_data_min": [[5.16, 22.25, 1.18]], "x_data_max": [[9.87, 34.67, 11.82]], "y_data_min": [5.161501852249681], "y_data_max": [9.89479502131247]}, "map": {"x_data_columns": "list", "x_data": "numpy", "centres": "numpy", "basis_function": "str", "weights": "numpy", "sigma": "str", "regularization_parameter": "str", "rmse": "str", "R2": "str", "x_data_min": "numpy", "x_data_max": "numpy", "y_data_min": "numpy", "y_data_max": "numpy"}}}, "input_labels": ["inf_fr", "temp", "hrt"], "output_labels": ["S_su", "S_aa", "S_fa", "S_va", "S_bu", "S_pro", "S_ac", "S_h2", "S_ch4", "S_IC", "S_IN", "S_I", "X_c", "X_ch", "X_pr", "X_li", "X_su", "X_aa", "X_fa", "X_c4", "X_pro", "X_ac", "X_h2", "X_I", "VolumetricFlowrate"], "input_bounds": {"inf_fr": [5.16, 9.87], "temp": [22.25, 34.67], "hrt": [1.18, 11.82]}, "surrogate_type": "rbf"}
\ No newline at end of file
diff --git a/watertap/flowsheets/METAB/Integration_Workflow_Tutorial/input_space_generation.py b/watertap/flowsheets/METAB/Integration_Workflow_Tutorial/input_space_generation.py
new file mode 100644
index 0000000000..2b96034e33
--- /dev/null
+++ b/watertap/flowsheets/METAB/Integration_Workflow_Tutorial/input_space_generation.py
@@ -0,0 +1,58 @@
+#################################################################################
+# WaterTAP Copyright (c) 2020-2025, The Regents of the University of California,
+# through Lawrence Berkeley National Laboratory, Oak Ridge National Laboratory,
+# National Renewable Energy Laboratory, and National Energy Technology
+# Laboratory (subject to receipt of any required approvals from the U.S. Dept.
+# of Energy). All rights reserved.
+#
+# Please see the files COPYRIGHT.md and LICENSE.md for full copyright and license
+# information, respectively. These files are also available online at the URL
+# "https://github.com/watertap-org/watertap/"
+#################################################################################
+
+# input space generation
+
+from idaes.core.surrogate.pysmo.sampling import LatinHypercubeSampling
+import csv
+
+__author__ = "Maojian Wang"
+
+
+def create_samples(
+    method=None,
+    input_var_info=None,
+    sample_numbers=10,
+    csv_file="./results/input_data.csv",
+):
+    if method == None:
+        print("Please pick a sampling method")
+    elif method == "LHS":
+        lower_bounds = [lb[0] for lb in input_var_info.values()]
+        upper_bounds = [lb[1] for lb in input_var_info.values()]
+        bounds = [lower_bounds, upper_bounds]
+        # print(bounds)
+        samples = LatinHypercubeSampling(
+            bounds, number_of_samples=sample_numbers, sampling_type="creation"
+        ).sample_points()
+        # Round each number in the matrix to 2 decimal places
+        rounded_samples = [[round(sample, 2) for sample in row] for row in samples]
+        print(type(rounded_samples))
+
+    # Writing to CSV file
+    with open(csv_file, "w", newline="") as file:
+        writer = csv.writer(file)
+        writer.writerow(input_var_info.keys())
+        writer.writerows(rounded_samples)
+
+    print(f'Sample file "{csv_file}" has been created successfully.')
+
+
+if __name__ == "__main__":
+
+    input_var_info = {
+        "inf_fr": (5, 10),  # bounds
+        "temp": (22, 35),
+        "hrt": (1, 12),
+    }
+
+    create_samples(method="LHS", input_var_info=input_var_info, sample_numbers=20)
diff --git a/watertap/flowsheets/METAB/Integration_Workflow_Tutorial/model_evaluation.py b/watertap/flowsheets/METAB/Integration_Workflow_Tutorial/model_evaluation.py
new file mode 100644
index 0000000000..77aedb0abe
--- /dev/null
+++ b/watertap/flowsheets/METAB/Integration_Workflow_Tutorial/model_evaluation.py
@@ -0,0 +1,274 @@
+#################################################################################
+# WaterTAP Copyright (c) 2020-2025, The Regents of the University of California,
+# through Lawrence Berkeley National Laboratory, Oak Ridge National Laboratory,
+# National Renewable Energy Laboratory, and National Energy Technology
+# Laboratory (subject to receipt of any required approvals from the U.S. Dept.
+# of Energy). All rights reserved.
+#
+# Please see the files COPYRIGHT.md and LICENSE.md for full copyright and license
+# information, respectively. These files are also available online at the URL
+# "https://github.com/watertap-org/watertap/"
+#################################################################################
+
+# input space generation
+
+import csv
+import pandas as pd
+from exposan.metab import create_system
+
+
+__author__ = "Maojian Wang"
+
+
+def get_input_data(filename=None):
+    if filename == None:
+        # It is a test mode
+        # Define data
+        input_data = {
+            "inf_fr": [5, 5, 5],
+            "temp": [20, 25, 30],  # First column data
+            "hrt": [12, 13, 14],  # Second column data
+        }
+        # Create the DataFrame
+        df = pd.DataFrame(input_data)
+    else:
+        df = pd.read_csv(filename, header=0, skipinitialspace=True)
+
+    return df
+
+
+def get_eff_fr(case=None, df=None):
+    if case is None:
+        print(" The system is off")
+    else:
+        eff_dg = case.outs[3]
+        r = eff_dg._info(
+            layout=None,
+            T=None,
+            P=None,
+            flow=None,
+            composition=None,
+            N=100,
+            IDs=None,
+        )
+        # print(r)
+        comp = [i.strip() for i in r.split("flow (kmol/hr):")[1].split("\n")]
+        # print(comp)
+        # print(len(comp))
+
+        fr_dict = {}
+        for element in comp:
+            element_list = element.split()
+            # print(element_list)
+            fr_dict[element_list[0]] = [float(element_list[-1])]
+            # print(fr_dict)
+        if df == None:
+            df = pd.DataFrame(fr_dict)
+            # print(df)
+        else:
+            df.loc[len(df)] = fr_dict
+            # NEED to TEST
+    return df
+
+
+def get_ch4_fr(case=None, df=None):
+    if case is None:
+        print(" The system is off")
+    else:
+        # bge : biogas extracted in reactor 2
+        eff_dg = case.outs[2]
+        r = eff_dg._info(
+            layout=None,
+            T=None,
+            P=None,
+            flow=None,
+            composition=None,
+            N=100,
+            IDs=None,
+        )
+        # print(r)
+        comp = [i.strip() for i in r.split("flow (kmol/hr):")[1].split("\n")]
+        # print(comp)
+        # print(len(comp))
+
+        fr_dict = {}
+        for element in comp:
+            element_list = element.split()
+            # print(element_list)
+            fr_dict[element_list[0]] = [float(element_list[-1])]
+            # print(fr_dict)
+        if df == None:
+            df = pd.DataFrame(fr_dict)
+            # print(df)
+        else:
+            df.loc[len(df)] = fr_dict
+            # NEED to TEST
+    return df
+
+
+def get_h2_fr(case=None, df=None):
+    if case is None:
+        print(" The system is off")
+    else:
+        # bg2: biogas from reactor 2
+        eff_dg = case.outs[1]
+        r = eff_dg._info(
+            layout=None,
+            T=None,
+            P=None,
+            flow=None,
+            composition=None,
+            N=100,
+            IDs=None,
+        )
+        # print(r)
+        comp = [i.strip() for i in r.split("flow (kmol/hr):")[1].split("\n")]
+        # print(comp)
+        # print(len(comp))
+
+        fr_dict = {}
+        for element in comp:
+            element_list = element.split()
+            # print(element_list)
+            fr_dict[element_list[0]] = [float(element_list[-1])]
+            # print(fr_dict)
+        if df == None:
+            df = pd.DataFrame(fr_dict)
+            # print(df)
+        else:
+            df.loc[len(df)] = fr_dict
+            # NEED to TEST
+    return df
+
+
+def get_r1_ex_biogas_fr(case=None, df=None):
+    if case is None:
+        print(" The system is off")
+    else:
+        # bgs: biogas extracted from reactor 1
+        eff_dg = case.outs[4]
+        r = eff_dg._info(
+            layout=None,
+            T=None,
+            P=None,
+            flow=None,
+            composition=None,
+            N=100,
+            IDs=None,
+        )
+        # print(r)
+        comp = [i.strip() for i in r.split("flow (kmol/hr):")[1].split("\n")]
+        # print(comp)
+        # print(len(comp))
+
+        fr_dict = {}
+        for element in comp:
+            element_list = element.split()
+            # print(element_list)
+            fr_dict[element_list[0]] = [float(element_list[-1])]
+            # print(fr_dict)
+        if df == None:
+            df = pd.DataFrame(fr_dict)
+            # print(df)
+        else:
+            df.loc[len(df)] = fr_dict
+            # NEED to TEST
+    return df
+
+
+def get_mass_flowrate(case=None, df=None, stream=None):
+    # df = None
+    # stream = eff_dg
+    keys = str(stream.components)[20:-2].split(",")
+    values = [[float(x)] for x in list(stream.state[:-1])]
+    fr_dict = dict(zip(keys, values))
+    # if len(stream.state) != len(stream.conc.to_array()):
+    fr_dict["Volumetric Flowrate"] = [stream.state[-1]]
+    print(fr_dict)
+    if df == None:
+        df = pd.DataFrame(fr_dict)
+        # print(df)
+    else:
+        df.loc[len(df)] = fr_dict
+
+    df = df.loc[:, (df != 0).any(axis=0)]
+
+    return df
+
+
+def collect_results(case=None, results=None, mass=True):
+    if mass:
+        eff_fr = get_mass_flowrate(case, None, case.outs[3])
+        ch4_fr = get_mass_flowrate(case, None, case.outs[2])
+        h2_fr = get_mass_flowrate(case, None, case.outs[1])
+        bgs_fr = get_mass_flowrate(case, None, case.outs[4])
+    else:
+        eff_fr = get_eff_fr(case)
+        ch4_fr = get_ch4_fr(case)
+        h2_fr = get_h2_fr(case)
+        bgs_fr = get_r1_ex_biogas_fr(case)
+
+    ch4_fr.columns = ch4_fr.columns.to_series().apply(lambda x: "bge2_" + x)
+    h2_fr.columns = h2_fr.columns.to_series().apply(lambda x: "bgr2_" + x)
+    bgs_fr.columns = bgs_fr.columns.to_series().apply(lambda x: "bge1_" + x)
+    result = pd.concat([eff_fr, ch4_fr, h2_fr, bgs_fr], axis=1)
+    if not isinstance(results, pd.DataFrame):
+        results = result
+    else:
+        results = pd.concat([results, result], axis=0)
+    # print(results)
+    return results
+
+
+def run_model(df):
+    for idx in df.index:
+        # Changine input variables
+        inf_fr = df.loc[idx, "inf_fr"]
+        # print(df.columns)
+        # print(df)
+        temp = df.loc[idx, "temp"]
+        hrt = df.loc[idx, "hrt"]
+
+        # Fixed input variables
+        n_stages = 2
+        reactor_type = "FB"
+        gas_extraction = "M"
+        t_span = 200
+
+        try:
+
+            # set model
+            sys = create_system(
+                n_stages=n_stages,  # number of stages
+                reactor_type=reactor_type,  # PB for packed bed, FB for fluidized bed, or UASB
+                gas_extraction=gas_extraction,  # M for membrane gas extraction, V for vacuum extraction, P for passive venting
+                Q=inf_fr,  # influent flowrate in m3/d
+                T=temp,  # reactor temperature in degree C
+                tot_HRT=hrt,  # total HRT in d
+            )
+
+            # run model
+            sys.simulate(
+                state_reset_hook="reset_cache", t_span=(0, t_span), method="BDF"
+            )
+
+            # collect output data
+            if idx == 0:
+                output_data = None
+            output_data = collect_results(case=sys, results=output_data)
+        except:
+            pass
+
+    return output_data
+
+
+def export_output_data(df, path=None, filename="output_data.csv"):
+    df.to_csv(filename)
+    print("The output data is readay")
+
+
+if __name__ == "__main__":
+
+    input_data = get_input_data(filename="input_data.csv")
+    output_data = run_model(input_data)
+    export_output_data(output_data)
diff --git a/watertap/flowsheets/METAB/Integration_Workflow_Tutorial/model_integration_workflow.ipynb b/watertap/flowsheets/METAB/Integration_Workflow_Tutorial/model_integration_workflow.ipynb
new file mode 100644
index 0000000000..fdc6d7c077
--- /dev/null
+++ b/watertap/flowsheets/METAB/Integration_Workflow_Tutorial/model_integration_workflow.ipynb
@@ -0,0 +1,626 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "88bd343f",
+   "metadata": {},
+   "source": [
+    "# Model Intergation Workflow Demo\n",
+    "\n",
+    "This notebook provides a **demonstration and case study of the model integration workflow**, showing how to connect **external models**—such as pilot-scale experimental data or rigorous process simulators—into WaterTAP.  \n",
+    "\n",
+    "The workflow illustrates how surrogate modeling can bridge detailed models with **system-scale analysis**, enabling:  \n",
+    "- Technology performance screening  \n",
+    "- Process integration and plant-wide optimization  \n",
+    "- Techno-economic and market participation assessments,  \n",
+    "  which are **enabled capabilities within the WaterTAP and IDAES ecosystem**.  \n",
+    "\n",
+    "**Example Case:**  \n",
+    "In this demonstration, we integrate the **Modular Encapsulated Two-Stage Anaerobic Biological (METAB) system**. The workflow was implemented to connect:  \n",
+    "- **Benchmark Simulation Model in WaterTAP**  \n",
+    "- **Rigorous METAB process model** developed in the study by:  \n",
+    "\n",
+    "> Zhang, X., Arnold, W. A., Wright, N., Novak, P. J., & Guest, J. S. (2024). *Prioritization of early-stage research and development of a hydrogel-encapsulated anaerobic technology for distributed treatment of high strength organic wastewater.* Environmental Science & Technology, 58(44), 19651–19665.  \n",
+    "\n",
+    "The model was implemented on the external modeling platform **QSD-SAN**.  \n",
+    "\n",
+    "This example illustrates how external models can be systematically integrated into WaterTAP to enable **system-scale evaluation, optimization, and techno-economic analysis** of novel wastewater treatment technologies."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e5f2641b",
+   "metadata": {},
+   "source": [
+    "![Easy Five-Step Workflow: Variables --> Input Space --> Model Runs --> Surrogate --> Selection](workflow_steps.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "afdd0318",
+   "metadata": {},
+   "source": [
+    "## Step 1: Define Input Variables\n",
+    "\n",
+    "The first step in the workflow is to **select the key operating and design variables** that will define the input space for surrogate modeling.  \n",
+    "\n",
+    "**Example Case: METAB System**  \n",
+    "For the METAB system, the input variables of interest include:  \n",
+    "- **Influent flow rate (loading rate)** – the rate at which wastewater enters the system  \n",
+    "- **Operational temperature** – affects microbial activity and treatment efficiency  \n",
+    "- **Hydraulic Retention Time (HRT)** – the time wastewater remains in the digester, influencing conversion and biogas production  \n",
+    "\n",
+    "**Step Tasks:**  \n",
+    "- Select **key operating/design variables** relevant to system performance  \n",
+    "- Establish **bounds for the operational range** based on pilot data or rigorous model knowledge  \n",
+    "\n",
+    "These defined variables and bounds form the foundation for **input space generation** in the next step."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "837b16ee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input_var_info = {\n",
+    "        \"inf_fr\": (5000,10000),  # Influent flow rate (loading rate), lower bounds, upper bounds\n",
+    "        \"temp\": (22, 35),  #Operational temperature\n",
+    "        \"hrt\": (1, 12),   #Hydraulic Retention Time (HRT)\n",
+    "    }"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "12264a5e",
+   "metadata": {},
+   "source": [
+    "## Step 2: Input Space Generation\n",
+    "\n",
+    "Once the key input variables and their bounds are defined, the next step is to **systematically generate the input space**. This ensures that the surrogate model captures the full variability of the system.  \n",
+    "\n",
+    "**Example Case: METAB System**  \n",
+    "For METAB, the input variables include influent flow rate, operational temperature, and HRT. The goal is to explore combinations of these variables across their feasible ranges.  \n",
+    "\n",
+    "**Step Tasks:**  \n",
+    "- **Systematically sample operating ranges** to cover variability in the input space  \n",
+    "- **Methods:**  \n",
+    "  - Random sampling within bounds  \n",
+    "  - **Latin Hypercube Sampling (LHS)** – a space-filling statistical technique that divides each variable’s range into intervals and selects samples to evenly cover the multi-dimensional space  \n",
+    "  - Other space-filling designs as needed  \n",
+    "\n",
+    "**Goal:**  \n",
+    "- Generate a **broad and representative dataset** for surrogate model training  \n",
+    "- Ensure that all relevant operating conditions are considered for accurate system-level predictions  \n",
+    "\n",
+    "This input dataset will be used in **Step 3: Model Evaluations** to generate outputs for surrogate training.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "7c441e4b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Input data space file has been generated.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from input_space_generation import create_samples\n",
+    "import os\n",
+    "\n",
+    "path = \"./results/\"\n",
+    "input_data_csv_file =  path + \"input_data.csv\"\n",
+    "\n",
+    "if not os.path.exists(input_data_csv_file):\n",
+    "    create_samples(method=\"LHS\",                    # Latin Hypercube Sampling (LHS)\n",
+    "                   input_var_info=input_var_info,   # defined in step 1\n",
+    "                   sample_numbers=20,               # can be changed to any integer\n",
+    "                   csv_file =input_data_csv_file )\n",
+    "    \n",
+    "print(f\"Input data space file has been generated.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8314df08",
+   "metadata": {},
+   "source": [
+    "## Step 3: External Model Evaluations\n",
+    "\n",
+    "In this step, we **run the rigorous external models** to generate outputs for the input conditions sampled in Step 2. These outputs will form the training dataset for surrogate models.  \n",
+    "\n",
+    "**Example Case: METAB System**  \n",
+    "For METAB, the external model could be the rigorous process simulation implemented in QSD-SAN. For each combination of influent flow rate, operational temperature, and HRT, the model generates outputs such as:  \n",
+    "- **Biogas production**  \n",
+    "- **Effluent flow rate and quality**  \n",
+    "\n",
+    "\n",
+    "**Step Tasks:**  \n",
+    "- Run the rigorous model across all sampled input sets  \n",
+    "- Collect model outputs to form a **comprehensive dataset**  \n",
+    "- Ensure that the dataset **captures nonlinear system behavior** across the operational range  \n",
+    "\n",
+    "This dataset will then be used in **Step 4: Surrogate Model Training**, bridging the detailed external model with the WaterTAP flowsheet for system-scale analysis.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7cc1e005",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model evaluation output file has been generated.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from model_evaluation import get_input_data, run_model, export_output_data \n",
+    "import os  \n",
+    "path = \"./results/\" \n",
+    "input_data_csv_file =  path + \"input_data.csv\"\n",
+    "output_data_csv_file = path + \"output_data.csv\"\n",
+    "if not os.path.exists(output_data_csv_file):\n",
+    "    input_data = get_input_data(filename=input_data_csv_file)\n",
+    "    output_data = run_model(input_data)\n",
+    "    export_output_data(output_data,\n",
+    "                       filename=output_data_csv_file)\n",
+    "print(f\"Model evaluation output file has been generated.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e96f2d97",
+   "metadata": {},
+   "source": [
+    "## Step 4: Surrogate Model Training\n",
+    "\n",
+    "Once the training dataset from Step 3 is available, we fit **surrogate models** to approximate the behavior of the rigorous external model. Surrogates allow fast evaluations while capturing nonlinear relationships between inputs and outputs.\n",
+    "\n",
+    "**Example Case: METAB System**  \n",
+    "For METAB, the surrogates predict outputs such as biogas production, effluent quality, and energy consumption based on influent flow rate, operational temperature, and HRT.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### Surrogate Model Types\n",
+    "\n",
+    "**1. Polynomial Regression**  \n",
+    "Fits a polynomial equation to relate inputs $x_1, x_2, ..., x_n$ to output $y$:  \n",
+    "\n",
+    "$$\n",
+    "y = \\beta_0 + \\sum_{i=1}^{n} \\beta_i x_i + \\sum_{i=1}^{n}\\sum_{j=i}^{n} \\beta_{ij} x_i x_j + \\epsilon\n",
+    "$$\n",
+    "\n",
+    "- $\\beta$ are coefficients, $\\epsilon$ is error  \n",
+    "- Can include higher-order terms for nonlinearity  \n",
+    "\n",
+    "**2. Kriging (Gaussian Process Regression)**  \n",
+    "Models the output as a Gaussian process:  \n",
+    "\n",
+    "$$\n",
+    "y(x) = \\mu + Z(x)\n",
+    "$$  \n",
+    "\n",
+    "Covariance:  \n",
+    "\n",
+    "$$\n",
+    "\\text{Cov}[Z(x_i), Z(x_j)] = \\sigma^2 R(x_i, x_j), \\quad\n",
+    "R(x_i, x_j) = \\exp\\left(-\\sum_{k=1}^n \\theta_k |x_{ik} - x_{jk}|^2\\right)\n",
+    "$$  \n",
+    "\n",
+    "- $\\mu$ is the mean, $Z(x)$ is a correlated Gaussian process  \n",
+    "- Captures nonlinearities and provides uncertainty estimates  \n",
+    "\n",
+    "**3. Radial Basis Function (RBF)**  \n",
+    "Approximates outputs as weighted sums of radial functions:  \n",
+    "\n",
+    "$$\n",
+    "y(x) = \\sum_{i=1}^{N} w_i \\phi(\\|x - x_i\\|)\n",
+    "$$  \n",
+    "\n",
+    "- $x_i$ are training points, $w_i$ are weights  \n",
+    "- $\\phi(r)$ is a radial function, e.g., Gaussian: $\\phi(r) = e^{-(\\epsilon r)^2}$  \n",
+    "\n",
+    "**4. ALAMO (Automated Learning of Algebraic Models for Optimization)**  \n",
+    "Constructs simple algebraic models:  \n",
+    "\n",
+    "$$\n",
+    "y = \\sum_{i} c_i f_i(x)\n",
+    "$$  \n",
+    "\n",
+    "- $f_i(x)$ are linear, polynomial, exponential, or rational functions  \n",
+    "- ALAMO selects relevant terms to balance accuracy and simplicity  \n",
+    "- Produces **optimization-ready models**  \n",
+    "\n",
+    "---\n",
+    "\n",
+    "These surrogate models are **computationally efficient** and ready for validation and selection in Step 5, enabling integration into WaterTAP for system-scale analysis.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "c068785a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Surrogate models using poly method has been generated\n",
+      "Surrogate models using kri method has been generated\n",
+      "Surrogate models using rbf method has been generated\n",
+      "Surrogate models using alamo method has been generated\n",
+      "All models are trained and ready for selection\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import surrogate_model_generator as trainer\n",
+    "feed_data, input_data, output_data = trainer.get_data()\n",
+    "output_data = trainer.outputs_selections(output_data)\n",
+    "all_surrogte_method = ['poly','kri','rbf','alamo']\n",
+    "path = \"./results/\" \n",
+    "for method in all_surrogte_method:\n",
+    "    model_file =  path + method + \"_surrogate.json\"\n",
+    "    if not os.path.exists(model_file):\n",
+    "        trainer.gen_surrogate_model(\n",
+    "                method= method,\n",
+    "                feed_data=None,\n",
+    "                input_data=input_data,\n",
+    "                output_data=output_data,\n",
+    "            )\n",
+    "    print(\"Surrogate models using \" + method + \" method has been generated\")\n",
+    "print(\"All models are trained and ready for selection\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2656405c",
+   "metadata": {},
+   "source": [
+    "# Step 5: Performance Estimation & Model Selection\n",
+    "\n",
+    "After training surrogate models in Step 4, the next step is to **validate their accuracy** and select the best-performing model for system-scale integration.\n",
+    "\n",
+    "**Example Case: METAB System**  \n",
+    "For METAB, surrogate models predict outputs like biogas production, effluent quality, and energy consumption. These predictions are compared against a **withheld test dataset** (data not used in training) to evaluate model performance.\n",
+    "\n",
+    "**Step Tasks:**  \n",
+    "- **Validate surrogate accuracy** using metrics such as:  \n",
+    "  - **R² (Coefficient of Determination)** – indicates how well the model explains variance in the data  \n",
+    "  - **RMSE (Root Mean Squared Error)** – measures the average prediction error  \n",
+    "  - **Parity plots** – visualize predicted vs. actual values for each output  \n",
+    "\n",
+    "- **Select the best-performing surrogate model** based on these metrics  \n",
+    "- The selected surrogate is then **ready to integrate** into WaterTAP for system-level analysis, optimization, and techno-economic evaluation\n",
+    "\n",
+    "**Outcome:**  \n",
+    "A computationally efficient surrogate model that faithfully represents the external rigorous model and can be used for **plant-scale simulations and decision-making**.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a77c9b10",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Predicted Variables</th>\n",
+       "      <th>R^2</th>\n",
+       "      <th>Adjusted R^2</th>\n",
+       "      <th>MAE</th>\n",
+       "      <th>MSE</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>S_su</td>\n",
+       "      <td>0.982842</td>\n",
+       "      <td>0.953427</td>\n",
+       "      <td>0.251895</td>\n",
+       "      <td>1.451095e-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>S_aa</td>\n",
+       "      <td>0.990575</td>\n",
+       "      <td>0.974418</td>\n",
+       "      <td>0.088737</td>\n",
+       "      <td>1.775902e-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>S_fa</td>\n",
+       "      <td>0.982444</td>\n",
+       "      <td>0.952348</td>\n",
+       "      <td>0.988848</td>\n",
+       "      <td>2.254959e+00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>S_va</td>\n",
+       "      <td>0.992831</td>\n",
+       "      <td>0.980541</td>\n",
+       "      <td>0.154351</td>\n",
+       "      <td>5.098712e-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>S_bu</td>\n",
+       "      <td>0.993203</td>\n",
+       "      <td>0.981551</td>\n",
+       "      <td>0.210170</td>\n",
+       "      <td>9.303517e-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>S_pro</td>\n",
+       "      <td>0.987178</td>\n",
+       "      <td>0.965198</td>\n",
+       "      <td>0.324885</td>\n",
+       "      <td>2.298348e-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>S_ac</td>\n",
+       "      <td>0.989913</td>\n",
+       "      <td>0.972622</td>\n",
+       "      <td>0.619289</td>\n",
+       "      <td>8.786020e-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>S_h2</td>\n",
+       "      <td>0.986690</td>\n",
+       "      <td>0.963873</td>\n",
+       "      <td>0.000002</td>\n",
+       "      <td>6.255281e-12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>S_ch4</td>\n",
+       "      <td>0.995764</td>\n",
+       "      <td>0.988503</td>\n",
+       "      <td>0.182005</td>\n",
+       "      <td>6.960779e-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>S_IC</td>\n",
+       "      <td>0.987474</td>\n",
+       "      <td>0.976200</td>\n",
+       "      <td>1.304313</td>\n",
+       "      <td>3.302947e+00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>S_IN</td>\n",
+       "      <td>0.999153</td>\n",
+       "      <td>0.997700</td>\n",
+       "      <td>0.117831</td>\n",
+       "      <td>4.062539e-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>S_I</td>\n",
+       "      <td>0.999324</td>\n",
+       "      <td>0.998164</td>\n",
+       "      <td>0.197484</td>\n",
+       "      <td>1.269807e-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>X_c</td>\n",
+       "      <td>0.999194</td>\n",
+       "      <td>0.997812</td>\n",
+       "      <td>0.291096</td>\n",
+       "      <td>1.752361e-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>X_ch</td>\n",
+       "      <td>0.986194</td>\n",
+       "      <td>0.962526</td>\n",
+       "      <td>0.244710</td>\n",
+       "      <td>1.357607e-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>X_pr</td>\n",
+       "      <td>0.985812</td>\n",
+       "      <td>0.961490</td>\n",
+       "      <td>0.406904</td>\n",
+       "      <td>3.749745e-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>X_li</td>\n",
+       "      <td>0.986912</td>\n",
+       "      <td>0.964474</td>\n",
+       "      <td>0.204870</td>\n",
+       "      <td>9.535300e-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>X_su</td>\n",
+       "      <td>0.999632</td>\n",
+       "      <td>0.999001</td>\n",
+       "      <td>0.366230</td>\n",
+       "      <td>5.177848e-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>X_aa</td>\n",
+       "      <td>0.999524</td>\n",
+       "      <td>0.997737</td>\n",
+       "      <td>0.053509</td>\n",
+       "      <td>2.097600e-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>X_fa</td>\n",
+       "      <td>0.996477</td>\n",
+       "      <td>0.990438</td>\n",
+       "      <td>0.029867</td>\n",
+       "      <td>2.007681e-03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>X_c4</td>\n",
+       "      <td>0.719996</td>\n",
+       "      <td>0.467993</td>\n",
+       "      <td>3.309516</td>\n",
+       "      <td>1.876613e+01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>X_pro</td>\n",
+       "      <td>0.974882</td>\n",
+       "      <td>0.931822</td>\n",
+       "      <td>0.341760</td>\n",
+       "      <td>2.539085e-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>X_ac</td>\n",
+       "      <td>0.999419</td>\n",
+       "      <td>0.998422</td>\n",
+       "      <td>0.975409</td>\n",
+       "      <td>1.714192e+00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>X_h2</td>\n",
+       "      <td>0.954056</td>\n",
+       "      <td>0.875295</td>\n",
+       "      <td>0.387042</td>\n",
+       "      <td>4.734013e-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>X_I</td>\n",
+       "      <td>0.998264</td>\n",
+       "      <td>0.995289</td>\n",
+       "      <td>0.290966</td>\n",
+       "      <td>2.427914e-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>VolumetricFlowrate</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000047</td>\n",
+       "      <td>3.134991e-09</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Predicted Variables       R^2  Adjusted R^2       MAE           MSE\n",
+       "1                 S_su  0.982842      0.953427  0.251895  1.451095e-01\n",
+       "2                 S_aa  0.990575      0.974418  0.088737  1.775902e-02\n",
+       "3                 S_fa  0.982444      0.952348  0.988848  2.254959e+00\n",
+       "4                 S_va  0.992831      0.980541  0.154351  5.098712e-02\n",
+       "5                 S_bu  0.993203      0.981551  0.210170  9.303517e-02\n",
+       "6                S_pro  0.987178      0.965198  0.324885  2.298348e-01\n",
+       "7                 S_ac  0.989913      0.972622  0.619289  8.786020e-01\n",
+       "8                 S_h2  0.986690      0.963873  0.000002  6.255281e-12\n",
+       "9                S_ch4  0.995764      0.988503  0.182005  6.960779e-02\n",
+       "10                S_IC  0.987474      0.976200  1.304313  3.302947e+00\n",
+       "11                S_IN  0.999153      0.997700  0.117831  4.062539e-02\n",
+       "12                 S_I  0.999324      0.998164  0.197484  1.269807e-01\n",
+       "13                 X_c  0.999194      0.997812  0.291096  1.752361e-01\n",
+       "14                X_ch  0.986194      0.962526  0.244710  1.357607e-01\n",
+       "15                X_pr  0.985812      0.961490  0.406904  3.749745e-01\n",
+       "16                X_li  0.986912      0.964474  0.204870  9.535300e-02\n",
+       "17                X_su  0.999632      0.999001  0.366230  5.177848e-01\n",
+       "18                X_aa  0.999524      0.997737  0.053509  2.097600e-02\n",
+       "19                X_fa  0.996477      0.990438  0.029867  2.007681e-03\n",
+       "20                X_c4  0.719996      0.467993  3.309516  1.876613e+01\n",
+       "21               X_pro  0.974882      0.931822  0.341760  2.539085e-01\n",
+       "22                X_ac  0.999419      0.998422  0.975409  1.714192e+00\n",
+       "23                X_h2  0.954056      0.875295  0.387042  4.734013e-01\n",
+       "24                 X_I  0.998264      0.995289  0.290966  2.427914e-01\n",
+       "25  VolumetricFlowrate  1.000000      1.000000  0.000047  3.134991e-09"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from performance_estimation import display_performace\n",
+    "display_performace( method=\"poly\",  \n",
+    "                        path=\"./results/\",)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "26c6b1bf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#TODO:from performance_estimation import display_plot\n",
+    "#display_plot()\n",
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "watertap",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/watertap/flowsheets/METAB/Integration_Workflow_Tutorial/performance_estimation.py b/watertap/flowsheets/METAB/Integration_Workflow_Tutorial/performance_estimation.py
new file mode 100644
index 0000000000..19c4e25252
--- /dev/null
+++ b/watertap/flowsheets/METAB/Integration_Workflow_Tutorial/performance_estimation.py
@@ -0,0 +1,103 @@
+#################################################################################
+# WaterTAP Copyright (c) 2020-2025, The Regents of the University of California,
+# through Lawrence Berkeley National Laboratory, Oak Ridge National Laboratory,
+# National Renewable Energy Laboratory, and National Energy Technology
+# Laboratory (subject to receipt of any required approvals from the U.S. Dept.
+# of Energy). All rights reserved.
+#
+# Please see the files COPYRIGHT.md and LICENSE.md for full copyright and license
+# information, respectively. These files are also available online at the URL
+# "https://github.com/watertap-org/watertap/"
+#################################################################################
+import json
+import pandas as pd
+import PyPDF2
+from pdf2image import convert_from_path
+from IPython.display import display
+
+
+def get_data(
+    tool="pysmo",
+    input_data_file="./results/input_data.csv",
+    output_data_file="./results/output_data.csv",
+):
+    input_data = pd.read_csv(input_data_file, header=0)
+    # print(input_data)
+    output_data = pd.read_csv(output_data_file, header=0).iloc[:, 1:]
+    # print(output_data)
+    feed_data = pd.concat([input_data, output_data], axis=1)
+    return feed_data, input_data, output_data
+
+
+def performance_estimiation(
+    method="poly",  # "rbf"#"kri"alamo'
+    path="./results/",
+):
+    method_list = []
+    file = path + method + "_surrogate.json"
+
+    with open(file, "r") as file:
+        data = json.load(file)
+
+    if method == "poly":
+        metrics_sum = pd.DataFrame()
+        for ele in data["model_encoding"]:
+            metrics = data["model_encoding"][ele]["attr"]["errors"]
+            metrics["Comp"] = ele
+            # print(metrics)
+            for key in metrics:
+                metrics[key] = [metrics[key]]
+            # print(metrics)
+            df = pd.DataFrame.from_dict(metrics)
+            metrics_sum = pd.concat([metrics_sum, df])
+
+    elif method == "kri":
+        metrics_sum = pd.DataFrame()
+        for ele in data["model_encoding"]:
+            metrics = {}
+            metrics["R2"] = [data["model_encoding"][ele]["attr"]["training_R2"]]
+            metrics["RMSE"] = [data["model_encoding"][ele]["attr"]["training_rmse"]]
+            metrics["Comp"] = [ele]
+            df = pd.DataFrame.from_dict(metrics)
+            metrics_sum = pd.concat([metrics_sum, df])
+
+    elif method == "rbf":
+        metrics_sum = pd.DataFrame()
+        for ele in data["model_encoding"]:
+            metrics = {}
+            metrics["R2"] = [data["model_encoding"][ele]["attr"]["R2"]]
+            metrics["RMSE"] = [data["model_encoding"][ele]["attr"]["rmse"]]
+            metrics["Comp"] = [ele]
+            df = pd.DataFrame.from_dict(metrics)
+            metrics_sum = pd.concat([metrics_sum, df])
+
+    return metrics_sum
+
+
+def display_performace(method="poly", path="./results/"):
+    metrics = performance_estimiation(method=method, path=path)
+    if method == "poly":
+        display_metrics = pd.DataFrame()
+        display_metrics["Predicted Variables"] = metrics["Comp"]
+        display_metrics["R^2"] = metrics["R2"]
+        display_metrics["Adjusted R^2"] = metrics["Adjusted R2"]
+        display_metrics["MAE"] = metrics["MAE"]
+        display_metrics["MSE"] = metrics["MSE"]
+        display_metrics.index = range(1, len(display_metrics) + 1)
+    return display_metrics
+
+
+def display_plot(method="poly", path="./results/"):
+
+    file_path = path + method + "_parity.pdf"
+
+    # Convert PDF pages to images
+    pages = convert_from_path(file_path)
+
+    # Get the first page as an image
+    first_page_image = pages[0]
+
+    # Display in Jupyter Notebook
+    display(first_page_image)
+
+    # NEED poppler for Windows
diff --git a/watertap/flowsheets/METAB/Integration_Workflow_Tutorial/solution.pickle b/watertap/flowsheets/METAB/Integration_Workflow_Tutorial/solution.pickle
new file mode 100644
index 0000000000..c25352a6bd
Binary files /dev/null and b/watertap/flowsheets/METAB/Integration_Workflow_Tutorial/solution.pickle differ
diff --git a/watertap/flowsheets/METAB/Integration_Workflow_Tutorial/surrogate_model_generator.py b/watertap/flowsheets/METAB/Integration_Workflow_Tutorial/surrogate_model_generator.py
new file mode 100644
index 0000000000..b0215c1f5a
--- /dev/null
+++ b/watertap/flowsheets/METAB/Integration_Workflow_Tutorial/surrogate_model_generator.py
@@ -0,0 +1,222 @@
+#################################################################################
+# WaterTAP Copyright (c) 2020-2025, The Regents of the University of California,
+# through Lawrence Berkeley National Laboratory, Oak Ridge National Laboratory,
+# National Renewable Energy Laboratory, and National Energy Technology
+# Laboratory (subject to receipt of any required approvals from the U.S. Dept.
+# of Energy). All rights reserved.
+#
+# Please see the files COPYRIGHT.md and LICENSE.md for full copyright and license
+# information, respectively. These files are also available online at the URL
+# "https://github.com/watertap-org/watertap/"
+#################################################################################
+
+
+import os
+import numpy as np
+import pandas as pd
+
+# Import Pyomo libraries
+from pyomo.environ import (
+    ConcreteModel,
+    SolverFactory,
+    value,
+    Var,
+    Constraint,
+    Set,
+    Objective,
+    maximize,
+)
+from pyomo.common.timing import TicTocTimer
+
+# Import IDAES libraries
+from idaes.core.surrogate.sampling.data_utils import split_training_validation
+from idaes.core.surrogate.pysmo_surrogate import PysmoPolyTrainer, PysmoSurrogate
+from idaes.core.surrogate.plotting.sm_plotter import (
+    surrogate_scatter2D,
+    surrogate_parity,
+    surrogate_residual,
+)
+from idaes.core.surrogate.surrogate_block import SurrogateBlock
+from idaes.core import FlowsheetBlock
+
+from idaes.core.surrogate.pysmo_surrogate import (
+    PysmoPolyTrainer,
+    PysmoRBFTrainer,
+    PysmoKrigingTrainer,
+    PysmoSurrogate,
+)
+from idaes.core.surrogate.alamopy import AlamoTrainer, AlamoSurrogate
+
+
+def get_data(
+    tool="pysmo",
+    input_data_file="./results/input_data.csv",
+    output_data_file="./results/output_data.csv",
+):
+    input_data = pd.read_csv(input_data_file, header=0)
+    # print(input_data)
+    output_data = pd.read_csv(output_data_file, header=0).iloc[:, 1:]
+    # print(output_data)
+    feed_data = pd.concat([input_data, output_data], axis=1)
+    return feed_data, input_data, output_data
+
+
+def outputs_selections(output_data):
+    outputs_list = [
+        "S_su",
+        "S_aa",
+        "S_fa",
+        "S_va",
+        "S_bu",
+        "S_pro",
+        "S_ac",
+        "S_h2",
+        "S_ch4",
+        "S_IC",
+        "S_IN",
+        "S_I",
+        "X_c",
+        "X_ch",
+        "X_pr",
+        "X_li",
+        "X_su",
+        "X_aa",
+        "X_fa",
+        "X_c4",
+        "X_pro",
+        "X_ac",
+        "X_h2",
+        "X_I",
+        "VolumetricFlowrate",
+    ]
+
+    # output_data = output_data[(output_data >= 0).all(axis=1)]
+    # print(output_data)
+    output_data.columns = output_data.columns.str.replace(" ", "")
+    output_data = output_data[outputs_list]
+
+    # print(output_data)
+
+    return output_data
+
+
+def gen_surrogate_model(
+    tool="idaes", method="poly", feed_data=None, input_data=None, output_data=None
+):
+    if feed_data == None:
+        feed_data = pd.concat([input_data, output_data], axis=1)
+
+    input_labels = list(input_data.columns)
+    output_labels = list(output_data.columns)
+    xmin, xmax = input_data.min().tolist(), input_data.max().tolist()
+    input_bounds = {
+        input_labels[i]: (xmin[i], xmax[i]) for i in range(len(input_labels))
+    }
+    if method == "poly":
+        # Create PySMO trainer object
+        trainer = PysmoPolyTrainer(
+            input_labels=input_labels,
+            output_labels=output_labels,
+            training_dataframe=feed_data,
+        )
+
+        # Set PySMO options
+        trainer.config.maximum_polynomial_order = 6
+        trainer.config.multinomials = True
+        trainer.config.training_split = 0.8
+        trainer.config.number_of_crossvalidations = 3
+        # Train surrogate (calls PySMO through IDAES Python wrapper)
+        poly_train = trainer.train_surrogate()
+
+        # trainer._get_metrics()
+
+        poly_surr = PysmoSurrogate(
+            poly_train, input_labels, output_labels, input_bounds
+        )
+
+        poly_surr.save_to_file(
+            "./results/{}_surrogate.json".format(method), overwrite=True
+        )
+
+        surrogate_parity(
+            poly_surr, feed_data, filename="./results/{}_parity.pdf".format(method)
+        )
+
+    elif method == "kri":
+        trainer = PysmoKrigingTrainer(
+            input_labels=input_labels,
+            output_labels=output_labels,
+            training_dataframe=feed_data,
+        )
+        # Set PySMO options
+        trainer.config.numerical_gradients = True
+        trainer.config.regularization = True
+        # Train surrogate (calls PySMO through IDAES Python wrapper)
+        krig_train = trainer.train_surrogate()
+
+        krig_surr = PysmoSurrogate(
+            krig_train, input_labels, output_labels, input_bounds
+        )
+        krig_surr.save_to_file(
+            "./results/{}_surrogate.json".format(method), overwrite=True
+        )
+        surrogate_parity(
+            krig_surr, feed_data, filename="./results/{}_parity.pdf".format(method)
+        )
+
+    elif method == "rbf":
+        trainer = PysmoRBFTrainer(
+            input_labels=input_labels,
+            output_labels=output_labels,
+            training_dataframe=feed_data,
+        )
+
+        trainer.config.basis_function = "cubic"
+        rbf_train = trainer.train_surrogate()
+        rbf_surr = PysmoSurrogate(rbf_train, input_labels, output_labels, input_bounds)
+        rbf_surr.save_to_file(
+            "./results/{}_surrogate.json".format(method), overwrite=True
+        )
+        surrogate_parity(
+            rbf_surr, feed_data, filename="./results/{}_parity.pdf".format(method)
+        )
+
+    elif method == "alamo":
+        trainer = AlamoTrainer(
+            input_labels=input_labels,
+            output_labels=output_labels,
+            training_dataframe=feed_data,
+        )
+        trainer.config.constant = 1
+        trainer.config.linfcns = 1
+        trainer.config.expfcns = 1
+        trainer.config.logfcns = 1
+        trainer.config.sinfcns = 1
+        trainer.config.cosfcns = 1
+        trainer.config.monomialpower = [2, 3, 4]
+        trainer.config.multi2power = [1, 2, 3]
+        trainer.config.multi3power = [1, 2, 3]
+        success, alm_surr, msg = trainer.train_surrogate()
+        surrogate_expressions = trainer._results["Model"]
+        alm_surr = AlamoSurrogate(
+            surrogate_expressions, input_labels, output_labels, input_bounds
+        )
+        alm_surr.save_to_file(
+            "./results/{}_surrogate.json".format(method), overwrite=True
+        )
+        surrogate_parity(
+            alm_surr, feed_data, filename="./results/{}_parity.pdf".format(method)
+        )
+
+
+if __name__ == "__main__":
+
+    feed_data, input_data, output_data = get_data()
+    output_data = outputs_selections(output_data)
+    gen_surrogate_model(
+        tool="idaes",
+        method="poly",  # kri, poly,rbf,alamo
+        feed_data=feed_data,
+        input_data=input_data,
+        output_data=output_data,
+    )
diff --git a/watertap/flowsheets/METAB/Integration_Workflow_Tutorial/workflow_steps.png b/watertap/flowsheets/METAB/Integration_Workflow_Tutorial/workflow_steps.png
new file mode 100644
index 0000000000..73def6dd2c
Binary files /dev/null and b/watertap/flowsheets/METAB/Integration_Workflow_Tutorial/workflow_steps.png differ