diff --git a/applications/Bioconversion/Models_classes.py b/applications/Bioconversion/Models_classes.py
index e3a5c0c..4cea9aa 100644
--- a/applications/Bioconversion/Models_classes.py
+++ b/applications/Bioconversion/Models_classes.py
@@ -200,19 +200,39 @@ def acetylfrac(self):
     @acetylfrac.setter
     def acetylfrac(self,a):
         if not 0 < a < 1:
-            raise ValueError(f"Value {a} is outside allowed interval (0, 1)")
+            raise ValueError(f"Value {a} is outside allowed interval (0, 0.025)")
         self.ve.pt_in['acetylfrac'] = float(a)
     
     @property
-    def DAtemp(self):
+    def DACtemp(self):
         return self.ve.pt_in['deacetylation_temperature']
     
-    @DAtemp.setter
-    def DAtemp(self,a):
-        if not 200 < a < 600:
-            raise ValueError(f"Value {a} is outside allowed interval (200, 600)")
-        self.ve.pt_in['deacetylation_temperature'] = float(a)
+    @DACtemp.setter
+    def DACtemp(self,a):
+        if not 277.15 < a < 373.15:
+            raise ValueError(f"Value {a} is outside allowed interval (277.15, 373.15) degrees K")
+        self.ve.pt_in['acetylfrac'] = float(a)
+
+    @property
+    def DACretention(self):
+        return self.ve.pt_in['deacetylation_retention_time']
+    
+    @property
+    def DACvolume(self):
+        return self.ve.pt_in['deacetylation_volume']
+    
+    @property
+    def DACpH(self):
+        return self.ve.pt_in['deacetylation_inital_pH']
     
+    @property
+    def DACbm(self):
+        return self.ve.pt_in['deacetylation_biomass_loading']
+
+    @property
+    def DACNaOH(self):
+        return self.ve.pt_in['deacetylation_NaOH_loading']   
+
     @property
     def model_type(self):
         return self.ve.pt_in['model_type']
diff --git a/applications/Bioconversion/models/pretreatment_model/deacetylation_model/deacetylation.py b/applications/Bioconversion/models/pretreatment_model/deacetylation_model/deacetylation.py
index e3daaa0..b9be6ba 100644
--- a/applications/Bioconversion/models/pretreatment_model/deacetylation_model/deacetylation.py
+++ b/applications/Bioconversion/models/pretreatment_model/deacetylation_model/deacetylation.py
@@ -94,58 +94,7 @@
 Lig_P = 0.153 # weight percentage of lignin in biomass
 # Xylan Groups
 Xy_P = 0.213 # corn stover
-# Acetyl Groups
-C_acy = W_BM*Acy_P/V_DA # acetyl groups -g/L-2
-Molar_acy = C_acy/M_acy
-C_ace_max = C_acy*(M_ace/M_acy)
-# Lignin Groups
-C_lig = W_BM*Lig_P/V_DA # -g/L-
-C_lig_max = C_lig
-Molar_lig = C_lig/M_Lig
-# Xylan Groups
-C_xy = W_BM*Xy_P/V_DA # acetyl groups -g/L-2
-C_xyo_max = C_xy*(M_X/M_Hem)
-Molar_xy = C_xy/M_X
-
-#######################################
-# Set UP Matrices
-#######################################
-# Calculation Matrix -row 0 g/L- -row 1 mol/L-
-loops=(1,12)
-DAC = np.zeros(loops)
-M_DAC = np.zeros(loops)
-dM_DAC = np.zeros(loops)
-DAC[0,0] = 0                                               # Acetate
-DAC[0,1] = 0                                               # Acetic Acid
-DAC[0,2] = 0                                               # Sodium Acetate
-DAC[0,3] = 0                                               # Cellulose
-DAC[0,4] = C_xy                                            # Hemicellulose
-DAC[0,5] = C_lig                                           # Lignin
-DAC[0,6] = C_acy                                           # Acetyl Groups
-DAC[0,7] = 0                                               # Deacetylated Xylan
-DAC[0,8] = C_NaOH                                          # Sodium Hydroxide
-DAC[0,9] = 0                                               # Sodium
-DAC[0,10] = 10**(-pOHi_DA)*M_OH                            # Hydroxide
-DAC[0,11] = 10**(-pHi_DA)*M_H                              # Hydrogen
-# Convert
-M_DAC[0,:] = DAC[0,:]/const[0,:] # -mol/L-
-# NaOH Dissociates Completely
-M_DAC[0,9] = M_DAC[0,9] + (1*M_DAC[0,8]) # Sodium
-M_DAC[0,10] = M_DAC[0,10] + (1*M_DAC[0,8]) # Hydroxide
-M_DAC[0,8] =M_DAC[0,8] - M_DAC[0,8] # Sodium Hydroxide
-# Convert
-DAC[0,:] = M_DAC[0,:]*const[0,:] # -g/L-
-
-# Free Acids
-M_DAC[0,10] = M_DAC[0,10] - 0.02
 
-# Arrehnius Rate Kinetics
-# Lignin
-kT_arh_L = Arc_L*np.exp(-Ea_L/(R*Tf)) # -L/(mol-s)-
-# Acetate
-kT_arh = Arc*np.exp(-Ea/(R*Tf)) # -L/(mol-s)-
-# Xylan
-kT_arh_X = Arc_X*np.exp(-Ea_X/(R*Tf)) # -L/(mol-s)-
 
 ###############################################
 ### DEFINE FUNCTION
@@ -160,6 +109,64 @@ def deacetylate(ve_params,verbose=True,show_plots=True):
     Acy_P = ve_params.pt_in['acetylfrac']
     fis_in = float(ve_params.pt_in['initial_solid_fraction'])
     fis_dac = float(ve_params.pt_in.get('deacetylation_solid_fraction', fis_in))
+    t_final = ve_params.pt_in['deacetylation_retention_time']
+    V_DA = ve_params.pt_in['deacetylation_volume']
+    pHi_DA = ve_params.pt_in['deacetylation inital pH']
+    W_BM = ve_params.pt_in['deacetylation_biomass_loading']
+    C_NaOH = float(ve_params.pt_in['deacetylation_NaOH_loading'])
+
+    # Acetyl Groups
+    C_acy = W_BM*Acy_P/V_DA # acetyl groups -g/L-2
+    Molar_acy = C_acy/M_acy
+    C_ace_max = C_acy*(M_ace/M_acy)
+    # Lignin Groups
+    C_lig = W_BM*Lig_P/V_DA # -g/L-
+    C_lig_max = C_lig
+    Molar_lig = C_lig/M_Lig
+    # Xylan Groups
+    C_xy = W_BM*Xy_P/V_DA # acetyl groups -g/L-2
+    C_xyo_max = C_xy*(M_X/M_Hem)
+    Molar_xy = C_xy/M_X
+
+    #######################################
+    # Set UP Matrices
+    #######################################
+    # Calculation Matrix -row 0 g/L- -row 1 mol/L-
+    loops=(1,12)
+    DAC = np.zeros(loops)
+    M_DAC = np.zeros(loops)
+    dM_DAC = np.zeros(loops)
+    DAC[0,0] = 0                                               # Acetate
+    DAC[0,1] = 0                                               # Acetic Acid
+    DAC[0,2] = 0                                               # Sodium Acetate
+    DAC[0,3] = 0                                               # Cellulose
+    DAC[0,4] = C_xy                                            # Hemicellulose
+    DAC[0,5] = C_lig                                           # Lignin
+    DAC[0,6] = C_acy                                           # Acetyl Groups
+    DAC[0,7] = 0                                               # Deacetylated Xylan
+    DAC[0,8] = C_NaOH                                          # Sodium Hydroxide
+    DAC[0,9] = 0                                               # Sodium
+    DAC[0,10] = 10**(-pOHi_DA)*M_OH                            # Hydroxide
+    DAC[0,11] = 10**(-pHi_DA)*M_H                              # Hydrogen
+    # Convert
+    M_DAC[0,:] = DAC[0,:]/const[0,:] # -mol/L-
+    # NaOH Dissociates Completely
+    M_DAC[0,9] = M_DAC[0,9] + (1*M_DAC[0,8]) # Sodium
+    M_DAC[0,10] = M_DAC[0,10] + (1*M_DAC[0,8]) # Hydroxide
+    M_DAC[0,8] =M_DAC[0,8] - M_DAC[0,8] # Sodium Hydroxide
+    # Convert
+    DAC[0,:] = M_DAC[0,:]*const[0,:] # -g/L-
+
+    # Free Acids
+    M_DAC[0,10] = M_DAC[0,10] - 0.02
+
+    # Arrehnius Rate Kinetics
+    # Lignin
+    kT_arh_L = Arc_L*np.exp(-Ea_L/(R*Tf)) # -L/(mol-s)-
+    # Acetate
+    kT_arh = Arc*np.exp(-Ea/(R*Tf)) # -L/(mol-s)-
+    # Xylan
+    kT_arh_X = Arc_X*np.exp(-Ea_X/(R*Tf)) # -L/(mol-s)-
 
     # Initial Conditions
     Cacetyl = M_DAC[0,6]
diff --git a/applications/Bioconversion/virtual_engineering_notebook.ipynb b/applications/Bioconversion/virtual_engineering_notebook.ipynb
index 42257ca..e74bab9 100644
--- a/applications/Bioconversion/virtual_engineering_notebook.ipynb
+++ b/applications/Bioconversion/virtual_engineering_notebook.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "d2f02adc",
+   "id": "0",
    "metadata": {},
    "source": [
     "# Bioconversion GUI using Widgets\n",
@@ -12,34 +12,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "97091f4a",
+   "execution_count": null,
+   "id": "1",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "It looks like you're running this notebook on a laptop.\n",
-      "Operations requiring HPC resources will be disabled.\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {
-      "image/png": {
-       "width": 800
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from ipywidgets import *\n",
     "\n",
@@ -55,7 +31,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a6c2868e",
+   "id": "2",
    "metadata": {},
    "source": [
     "---\n",
@@ -70,53 +46,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "08d383e5",
+   "execution_count": null,
+   "id": "3",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "021448ab0f8c4725be0e7ac18ca78787",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(BoundedFloatText(value=0.263, description='Initial $X_X$', layout=Layout(width='350px'), max=1.…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "42900a82d2634ca19294b986bdc966f6",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(BoundedFloatText(value=0.4, description='Initial $X_G$', layout=Layout(width='350px'), max=1.0,…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c1a54e89b5424630b60a176f51fa4517",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(BoundedFloatText(value=0.8, description='Initial Porosity', layout=Layout(width='350px'), max=1…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "#================================================================\n",
     "# Create the collection of widgets for feedstock options\n",
@@ -154,7 +87,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1132ecc0",
+   "id": "4",
    "metadata": {},
    "source": [
     "---\n",
@@ -166,123 +99,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "7b0046aa",
+   "execution_count": null,
+   "id": "5",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8643f201880846469082d6179e55d430",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(HTMLMath(value='', layout=Layout(display='flex', justify_content='flex-end', padding='0px 5px 0…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fb799d37a1934977b23133c1d972c51b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(BoundedFloatText(value=0.0001, description='Acid Loading', layout=Layout(width='350px'), max=1.…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "af231d173bb94485b4df8663e771a2f7",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(BoundedFloatText(value=150.0, description='Steam Temperature', layout=Layout(width='350px'), ma…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "29b27dc8392d48979094905de05bae83",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(BoundedFloatText(value=0.745, description='Initial FIS$_0$', layout=Layout(width='350px'), max=…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "081e467156ac4b868e3308028482b3dc",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(BoundedFloatText(value=8.3, description='Final Time', layout=Layout(width='350px'), max=1440.0,…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ff1a25cfc09f4ea5a322c1f02344cc70",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(BoundedFloatText(value=0.025, description='Acetyl group fraction', layout=Layout(width='350px')…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8b95e6e824f84faba4b67136c82ce6ca",
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     "# Create the collection of widgets for pretreatment options\n",
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-     "text": [
-      "Initializing Pretreatment Model\n",
-      "Initializing Enzymatic Hydrolysis Model\n",
-      "Initializing Bioreactor Model\n",
-      "\n",
-      "Running Pretreatment\n",
-      "Mass of xylan initial (g): 0.1315\n",
-      "Mass of xylan (g): 0.13149999999999984\n",
-      "Unadjusted mass of xylog (g): 0.0\n",
-      "Unadjusted mass of xylose (g): 0.0\n",
-      "Unadjusted mass of furfural (g): 0.0\n",
-      "Unadjusted mass of products (g): 0.0\n",
-      "Unadjusted mass of products / initial xylan (g): 0.0\n",
-      "Adjusted mass of xylog (g): nan\n",
-      "Adjusted mass of xylose (g): nan\n",
-      "Adjusted mass of furfural (g): nan\n",
-      "Adjusted mass of products (g): nan\n",
-      "Adjusted mass of products / initial xylan (g): nan\n",
-      "Fractional conversion of xylog nan\n",
-      "Fractional conversion of xylose nan\n",
-      "Fractional conversion of furfural nan\n",
-      "Time (s): 0\n",
-      "Xylan (g/g): 0.2629999999999995\n",
-      "Xylog (g/L): 0.0\n",
-      "Xylose (g/L): 0.0\n",
-      "Furfural (g/L): 0.0\n",
-      "FIS (g/g): 0.4444444444444445\n",
-      "rho_x (g/g): 0.0\n",
-      "X_G (g/g): 0.40000000000000013\n",
-      "conv: 1.2664140965305975e-15\n",
-      "frac_conv_xylog: nan\n",
-      "frac_conv_xylose: nan\n",
-      "frac_conv_furfural: nan\n",
-      "\n",
-      "Mass of xylan initial (g): 0.1315\n",
-      "Mass of xylan (g): 0.10591540188050036\n",
-      "Unadjusted mass of xylog (g): 0.006914596220493513\n",
-      "Unadjusted mass of xylose (g): 0.019020281681723535\n",
-      "Unadjusted mass of furfural (g): 1.4157500890530782e-05\n",
-      "Unadjusted mass of products (g): 0.025949035403107577\n",
-      "Unadjusted mass of products / initial xylan (g): 0.1973310677042401\n",
-      "Adjusted mass of xylog (g): 0.006817485224855481\n",
-      "Adjusted mass of xylose (g): 0.018753154226623034\n",
-      "Adjusted mass of furfural (g): 1.3958668021136184e-05\n",
-      "Adjusted mass of products (g): 0.02558459811949965\n",
-      "Adjusted mass of products / initial xylan (g): 0.1945596815171076\n",
-      "Fractional conversion of xylog 0.015207571604240364\n",
-      "Fractional conversion of xylose 0.12549639330363704\n",
-      "Fractional conversion of furfural 0.00014595565421340117\n",
-      "Time (s): 30.0\n",
-      "Xylan (g/g): 0.22785431712490348\n",
-      "Xylog (g/L): 9.106715375412543\n",
-      "Xylose (g/L): 25.0502395385376\n",
-      "Furfural (g/L): 0.018645822102394793\n",
-      "FIS (g/g): 0.37973144421443683\n",
-      "rho_x (g/g): 34.156954913950145\n",
-      "X_G (g/g): 0.42157147345392815\n",
-      "conv: 0.1945596815171076\n",
-      "frac_conv_xylog: 0.015207571604240364\n",
-      "frac_conv_xylose: 0.12549639330363704\n",
-      "frac_conv_furfural: 0.00014595565421340117\n",
-      "\n",
-      "Mass of xylan initial (g): 0.1315\n",
-      "Mass of xylan (g): 0.08785441459503089\n",
-      "Unadjusted mass of xylog (g): 0.01168341512858848\n",
-      "Unadjusted mass of xylose (g): 0.03250388936539956\n",
-      "Unadjusted mass of furfural (g): 4.782120525736733e-05\n",
-      "Unadjusted mass of products (g): 0.04423512569924541\n",
-      "Unadjusted mass of products / initial xylan (g): 0.33638878858741755\n",
-      "Adjusted mass of xylog (g): 0.011527705296544804\n",
-      "Adjusted mass of xylose (g): 0.03207069623666546\n",
-      "Adjusted mass of furfural (g): 4.7183871758831415e-05\n",
-      "Adjusted mass of products (g): 0.043645585404969094\n",
-      "Adjusted mass of products / initial xylan (g): 0.33190559243322504\n",
-      "Fractional conversion of xylog 0.025714526390264707\n",
-      "Fractional conversion of xylose 0.2146175869830084\n",
-      "Fractional conversion of furfural 0.0004933674803680091\n",
-      "Time (s): 60.0\n",
-      "Xylan (g/g): 0.20003911824116213\n",
-      "Xylog (g/L): 13.225758755178415\n",
-      "Xylose (g/L): 36.794772300769935\n",
-      "Furfural (g/L): 0.054134148034181984\n",
-      "FIS (g/g): 0.3320703852424122\n",
-      "rho_x (g/g): 50.020531055948354\n",
-      "X_G (g/g): 0.43825586781598264\n",
-      "conv: 0.3319055924332251\n",
-      "frac_conv_xylog: 0.025714526390264707\n",
-      "frac_conv_xylose: 0.2146175869830084\n",
-      "frac_conv_furfural: 0.0004933674803680091\n",
-      "\n",
-      "Mass of xylan initial (g): 0.13149999999999998\n",
-      "Mass of xylan (g): 0.07433432037902826\n",
-      "Unadjusted mass of xylog (g): 0.015171345272917278\n",
-      "Unadjusted mass of xylose (g): 0.04264473247027711\n",
-      "Unadjusted mass of furfural (g): 9.168324919519883e-05\n",
-      "Unadjusted mass of products (g): 0.05790776099238959\n",
-      "Unadjusted mass of products / initial xylan (g): 0.44036320146303876\n",
-      "Adjusted mass of xylog (g): 0.01497692620864263\n",
-      "Adjusted mass of xylose (g): 0.042098245073545426\n",
-      "Adjusted mass of furfural (g): 9.05083387836607e-05\n",
-      "Adjusted mass of products (g): 0.05716567962097172\n",
-      "Adjusted mass of products / initial xylan (g): 0.43471999711765574\n",
-      "Fractional conversion of xylog 0.03340860599139548\n",
-      "Fractional conversion of xylose 0.28172209630965767\n",
-      "Fractional conversion of furfural 0.0009463799682702167\n",
-      "Time (s): 90.0\n",
-      "Xylan (g/g): 0.17722885291230994\n",
-      "Xylog (g/L): 15.356746625854617\n",
-      "Xylose (g/L): 43.16587222113059\n",
-      "Furfural (g/L): 0.09280366390705863\n",
-      "FIS (g/g): 0.29802452984780514\n",
-      "rho_x (g/g): 58.52261884698521\n",
-      "X_G (g/g): 0.451636178128238\n",
-      "conv: 0.43471999711765574\n",
-      "frac_conv_xylog: 0.03340860599139548\n",
-      "frac_conv_xylose: 0.28172209630965767\n",
-      "frac_conv_furfural: 0.0009463799682702167\n",
-      "\n",
-      "Mass of xylan initial (g): 0.1315\n",
-      "Mass of xylan (g): 0.06377682690456683\n",
-      "Unadjusted mass of xylog (g): 0.017828509732206916\n",
-      "Unadjusted mass of xylose (g): 0.050605598714248815\n",
-      "Unadjusted mass of furfural (g): 0.00014149444268878674\n",
-      "Unadjusted mass of products (g): 0.06857560288914451\n",
-      "Unadjusted mass of products / initial xylan (g): 0.5214874744421635\n",
-      "Adjusted mass of xylog (g): 0.017606892243874028\n",
-      "Adjusted mass of xylose (g): 0.04997654525711247\n",
-      "Adjusted mass of furfural (g): 0.00013973559444668696\n",
-      "Adjusted mass of products (g): 0.06772317309543319\n",
-      "Adjusted mass of products / initial xylan (g): 0.51500511859645\n",
-      "Fractional conversion of xylog 0.039275196893812786\n",
-      "Fractional conversion of xylose 0.33444380096014426\n",
-      "Fractional conversion of furfural 0.0014611136301459662\n",
-      "Time (s): 120.0\n",
-      "Xylan (g/g): 0.15801962686411836\n",
-      "Xylog (g/L): 16.578350009723906\n",
-      "Xylose (g/L): 47.05706424922819\n",
-      "Furfural (g/L): 0.13157265697244322\n",
-      "FIS (g/g): 0.2728857209704647\n",
-      "rho_x (g/g): 63.635414258952096\n",
-      "X_G (g/g): 0.462666484882276\n",
-      "conv: 0.5150051185964499\n",
-      "frac_conv_xylog: 0.039275196893812786\n",
-      "frac_conv_xylose: 0.33444380096014426\n",
-      "frac_conv_furfural: 0.0014611136301459662\n",
-      "\n",
-      "Mass of xylan initial (g): 0.13149999999999998\n",
-      "Mass of xylan (g): 0.055286712236933135\n",
-      "Unadjusted mass of xylog (g): 0.01990887545334024\n",
-      "Unadjusted mass of xylose (g): 0.05704525083234631\n",
-      "Unadjusted mass of furfural (g): 0.00019503281089425348\n",
-      "Unadjusted mass of products (g): 0.0771491590965808\n",
-      "Unadjusted mass of products / initial xylan (g): 0.5866856205063179\n",
-      "Adjusted mass of xylog (g): 0.019667367366441257\n",
-      "Adjusted mass of xylose (g): 0.05635325346526845\n",
-      "Adjusted mass of furfural (g): 0.0001926669313571599\n",
-      "Adjusted mass of products (g): 0.07621328776306688\n",
-      "Adjusted mass of products / initial xylan (g): 0.5795687282362502\n",
-      "Fractional conversion of xylog 0.04387144051322765\n",
-      "Fractional conversion of xylose 0.37711682927327944\n",
-      "Fractional conversion of furfural 0.0020145781795900754\n",
-      "Time (s): 150.0\n",
-      "Xylan (g/g): 0.141547967070928\n",
-      "Xylog (g/L): 17.336543113583414\n",
-      "Xylose (g/L): 49.67470175791504\n",
-      "Furfural (g/L): 0.16983353693461886\n",
-      "FIS (g/g): 0.2537984864324628\n",
-      "rho_x (g/g): 67.01124487149845\n",
-      "X_G (g/g): 0.47193551431641595\n",
-      "conv: 0.5795687282362499\n",
-      "frac_conv_xylog: 0.04387144051322765\n",
-      "frac_conv_xylose: 0.37711682927327944\n",
-      "frac_conv_furfural: 0.0020145781795900754\n",
-      "\n",
-      "Mass of xylan initial (g): 0.13149999999999998\n",
-      "Mass of xylan (g): 0.04830884574050471\n",
-      "Unadjusted mass of xylog (g): 0.021569208971308828\n",
-      "Unadjusted mass of xylose (g): 0.06237194787744904\n",
-      "Unadjusted mass of furfural (g): 0.0002510431697139226\n",
-      "Unadjusted mass of products (g): 0.08419220001847179\n",
-      "Unadjusted mass of products / initial xylan (g): 0.6402448670606221\n",
-      "Adjusted mass of xylog (g): 0.021312750948351004\n",
-      "Adjusted mass of xylose (g): 0.0616303450462114\n",
-      "Adjusted mass of furfural (g): 0.00024805826493287387\n",
-      "Adjusted mass of products (g): 0.08319115425949528\n",
-      "Adjusted mass of products / initial xylan (g): 0.6326323517832342\n",
-      "Fractional conversion of xylog 0.04754175116488944\n",
-      "Fractional conversion of xylose 0.4124312063928977\n",
-      "Fractional conversion of furfural 0.0025937651276251076\n",
-      "Time (s): 180.0\n",
-      "Xylan (g/g): 0.12723738796542813\n",
-      "Xylog (g/L): 17.837554797023177\n",
-      "Xylose (g/L): 51.58107743037736\n",
-      "Furfural (g/L): 0.2076105944426184\n",
-      "FIS (g/g): 0.23895802865596372\n",
-      "rho_x (g/g): 69.41863222740054\n",
-      "X_G (g/g): 0.47983626557799897\n",
-      "conv: 0.6326323517832341\n",
-      "frac_conv_xylog: 0.04754175116488944\n",
-      "frac_conv_xylose: 0.4124312063928977\n",
-      "frac_conv_furfural: 0.0025937651276251076\n",
-      "\n",
-      "Mass of xylan initial (g): 0.1315\n",
-      "Mass of xylan (g): 0.04247716657880298\n",
-      "Unadjusted mass of xylog (g): 0.022912470618381052\n",
-      "Unadjusted mass of xylose (g): 0.06685481306264658\n",
-      "Unadjusted mass of furfural (g): 0.0003087787904468668\n",
-      "Unadjusted mass of products (g): 0.0900760624714745\n",
-      "Unadjusted mass of products / initial xylan (g): 0.6849890682241406\n",
-      "Adjusted mass of xylog (g): 0.022644562819052565\n",
-      "Adjusted mass of xylose (g): 0.0660731022580613\n",
-      "Adjusted mass of furfural (g): 0.0003051683440831487\n",
-      "Adjusted mass of products (g): 0.08902283342119702\n",
-      "Adjusted mass of products / initial xylan (g): 0.6769797218341979\n",
-      "Fractional conversion of xylog 0.05051258626303234\n",
-      "Fractional conversion of xylose 0.44216220522504907\n",
-      "Fractional conversion of furfural 0.0031909237499188553\n",
-      "Time (s): 210.0\n",
-      "Xylan (g/g): 0.11468158892566835\n",
-      "Xylog (g/L): 18.184685077778212\n",
-      "Xylose (g/L): 53.05991404098797\n",
-      "Furfural (g/L): 0.24506501967836639\n",
-      "FIS (g/g): 0.22718160046140778\n",
-      "rho_x (g/g): 71.24459911876619\n",
-      "X_G (g/g): 0.48664504080581544\n",
-      "conv: 0.6769797218341979\n",
-      "frac_conv_xylog: 0.05051258626303234\n",
-      "frac_conv_xylose: 0.44216220522504907\n",
-      "frac_conv_furfural: 0.0031909237499188553\n",
-      "\n",
-      "Mass of xylan initial (g): 0.1315\n",
-      "Mass of xylan (g): 0.03753886927614635\n",
-      "Unadjusted mass of xylog (g): 0.024009519909941008\n",
-      "Unadjusted mass of xylose (g): 0.07067966490454951\n",
-      "Unadjusted mass of furfural (g): 0.00036777733448924724\n",
-      "Unadjusted mass of products (g): 0.09505696214897975\n",
-      "Unadjusted mass of products / initial xylan (g): 0.7228666323116331\n",
-      "Adjusted mass of xylog (g): 0.02373273443495109\n",
-      "Adjusted mass of xylose (g): 0.06986485874865324\n",
-      "Adjusted mass of furfural (g): 0.00036353754024933885\n",
-      "Adjusted mass of products (g): 0.09396113072385368\n",
-      "Adjusted mass of products / initial xylan (g): 0.7145333134893815\n",
-      "Fractional conversion of xylog 0.05293993993094286\n",
-      "Fractional conversion of xylose 0.4675366973293905\n",
-      "Fractional conversion of furfural 0.00380124804443225\n",
-      "Time (s): 240.0\n",
-      "Xylan (g/g): 0.10358245223431574\n",
-      "Xylog (g/L): 18.434116863364274\n",
-      "Xylose (g/L): 54.26669119587129\n",
-      "Furfural (g/L): 0.28237342475408345\n",
-      "FIS (g/g): 0.2176799071412333\n",
-      "rho_x (g/g): 72.70080805923556\n",
-      "X_G (g/g): 0.4925636808036236\n",
-      "conv: 0.7145333134893813\n",
-      "frac_conv_xylog: 0.05293993993094286\n",
-      "frac_conv_xylose: 0.4675366973293905\n",
-      "frac_conv_furfural: 0.00380124804443225\n",
-      "\n",
-      "Mass of xylan initial (g): 0.13149999999999998\n",
-      "Mass of xylan (g): 0.03331283850552668\n",
-      "Unadjusted mass of xylog (g): 0.024910982507703235\n",
-      "Unadjusted mass of xylose (g): 0.07397959191434413\n",
-      "Unadjusted mass of furfural (g): 0.00042774044806563415\n",
-      "Unadjusted mass of products (g): 0.099318314870113\n",
-      "Unadjusted mass of products / initial xylan (g): 0.755272356426715\n",
-      "Adjusted mass of xylog (g): 0.024627267042021592\n",
-      "Adjusted mass of xylose (g): 0.07313702561394125\n",
-      "Adjusted mass of furfural (g): 0.0004228688385104394\n",
-      "Adjusted mass of products (g): 0.09818716149447328\n",
-      "Adjusted mass of products / initial xylan (g): 0.7466704296157665\n",
-      "Fractional conversion of xylog 0.05493534853480103\n",
-      "Fractional conversion of xylose 0.4894340877586944\n",
-      "Fractional conversion of furfural 0.004421632341839197\n",
-      "Time (s): 270.0\n",
-      "Xylan (g/g): 0.09371405044340829\n",
-      "Xylog (g/L): 18.611913645809494\n",
-      "Xylose (g/L): 55.27288118147163\n",
-      "Furfural (g/L): 0.3195806620535998\n",
-      "FIS (g/g): 0.20985292969212352\n",
-      "rho_x (g/g): 73.88479482728113\n",
-      "X_G (g/g): 0.4977441754819617\n",
-      "conv: 0.7466704296157666\n",
-      "frac_conv_xylog: 0.05493534853480103\n",
-      "frac_conv_xylose: 0.4894340877586944\n",
-      "frac_conv_furfural: 0.004421632341839197\n",
-      "\n",
-      "Mass of xylan initial (g): 0.1315\n",
-      "Mass of xylan (g): 0.029666959378235047\n",
-      "Unadjusted mass of xylog (g): 0.025653835695860876\n",
-      "Unadjusted mass of xylose (g): 0.07685154625654109\n",
-      "Unadjusted mass of furfural (g): 0.0004884390651750931\n",
-      "Unadjusted mass of products (g): 0.10299382101757706\n",
-      "Unadjusted mass of products / initial xylan (g): 0.7832229735176963\n",
-      "Adjusted mass of xylog (g): 0.025364706996110452\n",
-      "Adjusted mass of xylose (g): 0.07598539945859663\n",
-      "Adjusted mass of furfural (g): 0.0004829341670578819\n",
-      "Adjusted mass of products (g): 0.10183304062176496\n",
-      "Adjusted mass of products / initial xylan (g): 0.7743957461731176\n",
-      "Fractional conversion of xylog 0.056580334997660825\n",
-      "Fractional conversion of xylose 0.5084954488484033\n",
-      "Fractional conversion of furfural 0.005049691860871388\n",
-      "Time (s): 300.0\n",
-      "Xylan (g/g): 0.08490444058911752\n",
-      "Xylog (g/L): 18.7295902616478\n",
-      "Xylose (g/L): 56.10848956171211\n",
-      "Furfural (g/L): 0.3566041221659421\n",
-      "FIS (g/g): 0.20325377269553718\n",
-      "rho_x (g/g): 74.83807982335992\n",
-      "X_G (g/g): 0.5023018492350889\n",
-      "conv: 0.7743957461731176\n",
-      "frac_conv_xylog: 0.056580334997660825\n",
-      "frac_conv_xylose: 0.5084954488484033\n",
-      "frac_conv_furfural: 0.005049691860871388\n",
-      "\n",
-      "Mass of xylan initial (g): 0.1315\n",
-      "Mass of xylan (g): 0.026501313546173584\n",
-      "Unadjusted mass of xylog (g): 0.026266102278982052\n",
-      "Unadjusted mass of xylose (g): 0.07936874775784959\n",
-      "Unadjusted mass of furfural (g): 0.0005496871649772514\n",
-      "Unadjusted mass of products (g): 0.1061845372018089\n",
-      "Unadjusted mass of products / initial xylan (g): 0.8074869749186988\n",
-      "Adjusted mass of xylog (g): 0.025972766941701097\n",
-      "Adjusted mass of xylose (g): 0.07848237115937934\n",
-      "Adjusted mass of furfural (g): 0.0005435483527459962\n",
-      "Adjusted mass of products (g): 0.10499868645382643\n",
-      "Adjusted mass of products / initial xylan (g): 0.7984690985081857\n",
-      "Fractional conversion of xylog 0.057936717132311695\n",
-      "Fractional conversion of xylose 0.5252052214467972\n",
-      "Fractional conversion of furfural 0.005683490380423914\n",
-      "Time (s): 330.0\n",
-      "Xylan (g/g): 0.07701720823606935\n",
-      "Xylog (g/L): 18.793175947198833\n",
-      "Xylose (g/L): 56.78767353752638\n",
-      "Furfural (g/L): 0.393296557578727\n",
-      "FIS (g/g): 0.19755917097495876\n",
-      "rho_x (g/g): 75.5808494847252\n",
-      "X_G (g/g): 0.506327430165928\n",
-      "conv: 0.7984690985081857\n",
-      "frac_conv_xylog: 0.057936717132311695\n",
-      "frac_conv_xylose: 0.5252052214467972\n",
-      "frac_conv_furfural: 0.005683490380423914\n",
-      "\n",
-      "Mass of xylan initial (g): 0.1315\n",
-      "Mass of xylan (g): 0.02373847446231967\n",
-      "Unadjusted mass of xylog (g): 0.026769569636844417\n",
-      "Unadjusted mass of xylose (g): 0.08158784221355742\n",
-      "Unadjusted mass of furfural (g): 0.0006113223934123473\n",
-      "Unadjusted mass of products (g): 0.10896873424381417\n",
-      "Unadjusted mass of products / initial xylan (g): 0.8286595759985869\n",
-      "Adjusted mass of xylog (g): 0.02647300330752698\n",
-      "Adjusted mass of xylose (g): 0.08068397236393152\n",
-      "Adjusted mass of furfural (g): 0.0006045498662218347\n",
-      "Adjusted mass of products (g): 0.10776152553768034\n",
-      "Adjusted mass of products / initial xylan (g): 0.8194792816553638\n",
-      "Fractional conversion of xylog 0.059052580255066524\n",
-      "Fractional conversion of xylose 0.5399383701920892\n",
-      "Fractional conversion of furfural 0.00632133890536139\n",
-      "Time (s): 360.0\n",
-      "Xylan (g/g): 0.06994138518348918\n",
-      "Xylog (g/L): 18.807886554012235\n",
-      "Xylose (g/L): 57.32235898283659\n",
-      "Furfural (g/L): 0.4295056804873612\n",
-      "FIS (g/g): 0.1925461694332083\n",
-      "rho_x (g/g): 76.13024553684883\n",
-      "X_G (g/g): 0.5098938860451155\n",
-      "conv: 0.8194792816553637\n",
-      "frac_conv_xylog: 0.059052580255066524\n",
-      "frac_conv_xylose: 0.5399383701920892\n",
-      "frac_conv_furfural: 0.00632133890536139\n",
-      "\n",
-      "Mass of xylan initial (g): 0.13149999999999998\n",
-      "Mass of xylan (g): 0.021316683568877583\n",
-      "Unadjusted mass of xylog (g): 0.027181638376733916\n",
-      "Unadjusted mass of xylose (g): 0.08355397553098351\n",
-      "Unadjusted mass of furfural (g): 0.0006732078868768905\n",
-      "Unadjusted mass of products (g): 0.11140882179459431\n",
-      "Unadjusted mass of products / initial xylan (g): 0.8472153748638352\n",
-      "Adjusted mass of xylog (g): 0.02688263832375734\n",
-      "Adjusted mass of xylose (g): 0.08263487555754147\n",
-      "Adjusted mass of furfural (g): 0.0006658025498235976\n",
-      "Adjusted mass of products (g): 0.1101833164311224\n",
-      "Adjusted mass of products / initial xylan (g): 0.8378959424419956\n",
-      "Fractional conversion of xylog 0.059966341508001175\n",
-      "Fractional conversion of xylose 0.5529938440352586\n",
-      "Fractional conversion of furfural 0.006961813733896935\n",
-      "Time (s): 390.0\n",
-      "Xylan (g/g): 0.06358338745891846\n",
-      "Xylog (g/L): 18.781181650741765\n",
-      "Xylose (g/L): 57.7317073511001\n",
-      "Furfural (g/L): 0.4651536981291458\n",
-      "FIS (g/g): 0.18807784918608297\n",
-      "rho_x (g/g): 76.51288900184187\n",
-      "X_G (g/g): 0.5130616734228657\n",
-      "conv: 0.8378959424419956\n",
-      "frac_conv_xylog: 0.059966341508001175\n",
-      "frac_conv_xylose: 0.5529938440352586\n",
-      "frac_conv_furfural: 0.006961813733896935\n",
-      "\n",
-      "Mass of xylan initial (g): 0.1315\n",
-      "Mass of xylan (g): 0.019185634131302403\n",
-      "Unadjusted mass of xylog (g): 0.02751647101149009\n",
-      "Unadjusted mass of xylose (g): 0.08530391867090562\n",
-      "Unadjusted mass of furfural (g): 0.0007352324277991894\n",
-      "Unadjusted mass of products (g): 0.11355562211019489\n",
-      "Unadjusted mass of products / initial xylan (g): 0.8635408525490106\n",
-      "Adjusted mass of xylog (g): 0.027215693377126496\n",
-      "Adjusted mass of xylose (g): 0.08437147675823935\n",
-      "Adjusted mass of furfural (g): 0.0007271957333317737\n",
-      "Adjusted mass of products (g): 0.11231436586869761\n",
-      "Adjusted mass of products / initial xylan (g): 0.854101641587054\n",
-      "Fractional conversion of xylog 0.06070927800220866\n",
-      "Fractional conversion of xylose 0.5646152056825142\n",
-      "Fractional conversion of furfural 0.007603757667917786\n",
-      "Time (s): 420.0\n",
-      "Xylan (g/g): 0.05786236361184584\n",
-      "Xylog (g/L): 18.72006328549342\n",
-      "Xylose (g/L): 58.034140909752544\n",
-      "Furfural (g/L): 0.5001948677285147\n",
-      "FIS (g/g): 0.18405770812534872\n",
-      "rho_x (g/g): 76.75420419524596\n",
-      "X_G (g/g): 0.5158818960319367\n",
-      "conv: 0.854101641587054\n",
-      "frac_conv_xylog: 0.06070927800220866\n",
-      "frac_conv_xylose: 0.5646152056825142\n",
-      "frac_conv_furfural: 0.007603757667917786\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Mass of xylan initial (g): 0.13149999999999998\n",
-      "Mass of xylan (g): 0.017303818768587706\n",
-      "Unadjusted mass of xylog (g): 0.027785728694856043\n",
-      "Unadjusted mass of xylose (g): 0.08686802151280779\n",
-      "Unadjusted mass of furfural (g): 0.000797307169358644\n",
-      "Unadjusted mass of products (g): 0.11545105737702248\n",
-      "Unadjusted mass of products / initial xylan (g): 0.8779548089507414\n",
-      "Adjusted mass of xylog (g): 0.02748371631905158\n",
-      "Adjusted mass of xylose (g): 0.08592382394121871\n",
-      "Adjusted mass of furfural (g): 0.0007886409711419849\n",
-      "Adjusted mass of products (g): 0.11419618123141227\n",
-      "Adjusted mass of products / initial xylan (g): 0.8684120245734775\n",
-      "Fractional conversion of xylog 0.06130714920346107\n",
-      "Fractional conversion of xylose 0.5750035366408555\n",
-      "Fractional conversion of furfural 0.008246245895971326\n",
-      "Time (s): 450.0\n",
-      "Xylan (g/g): 0.05270767279156585\n",
-      "Xylog (g/L): 18.629690381630404\n",
-      "Xylose (g/L): 58.24300534353154\n",
-      "Furfural (g/L): 0.5345760720306583\n",
-      "FIS (g/g): 0.18040591693815375\n",
-      "rho_x (g/g): 76.87269572516195\n",
-      "X_G (g/g): 0.5183981865144879\n",
-      "conv: 0.8684120245734775\n",
-      "frac_conv_xylog: 0.06130714920346107\n",
-      "frac_conv_xylose: 0.5750035366408555\n",
-      "frac_conv_furfural: 0.008246245895971326\n",
-      "\n",
-      "Mass of xylan initial (g): 0.1315\n",
-      "Mass of xylan (g): 0.01563671957653142\n",
-      "Unadjusted mass of xylog (g): 0.027999081828672372\n",
-      "Unadjusted mass of xylose (g): 0.0882715396166711\n",
-      "Unadjusted mass of furfural (g): 0.0008593613817024473\n",
-      "Unadjusted mass of products (g): 0.11712998282704593\n",
-      "Unadjusted mass of products / initial xylan (g): 0.8907223028672694\n",
-      "Adjusted mass of xylog (g): 0.027696285709401122\n",
-      "Adjusted mass of xylose (g): 0.0873169268975263\n",
-      "Adjusted mass of furfural (g): 0.0008500678165411564\n",
-      "Adjusted mass of products (g): 0.11586328042346858\n",
-      "Adjusted mass of products / initial xylan (g): 0.8810895849693428\n",
-      "Fractional conversion of xylog 0.06178132173453229\n",
-      "Fractional conversion of xylose 0.5843262028123434\n",
-      "Fractional conversion of furfural 0.008888541807939849\n",
-      "Time (s): 480.0\n",
-      "Xylan (g/g): 0.048057355336731736\n",
-      "Xylog (g/L): 18.516292739334048\n",
-      "Xylose (g/L): 58.37554524449582\n",
-      "Furfural (g/L): 0.568311025691788\n",
-      "FIS (g/g): 0.17707467608479585\n",
-      "rho_x (g/g): 76.89183798382987\n",
-      "X_G (g/g): 0.5206479615395219\n",
-      "conv: 0.8810895849693429\n",
-      "frac_conv_xylog: 0.06178132173453229\n",
-      "frac_conv_xylose: 0.5843262028123434\n",
-      "frac_conv_furfural: 0.008888541807939849\n",
-      "\n",
-      "Mass of xylan initial (g): 0.1315\n",
-      "Mass of xylan (g): 0.01472728607909724\n",
-      "Unadjusted mass of xylog (g): 0.02810367768774305\n",
-      "Unadjusted mass of xylose (g): 0.08904553038173652\n",
-      "Unadjusted mass of furfural (g): 0.0008965601979441652\n",
-      "Unadjusted mass of products (g): 0.11804576826742373\n",
-      "Unadjusted mass of products / initial xylan (g): 0.897686450702842\n",
-      "Adjusted mass of xylog (g): 0.02780059601392519\n",
-      "Adjusted mass of xylose (g): 0.0880852265847048\n",
-      "Adjusted mass of furfural (g): 0.000886891322272783\n",
-      "Adjusted mass of products (g): 0.11677271392090277\n",
-      "Adjusted mass of products / initial xylan (g): 0.8880054290562948\n",
-      "Fractional conversion of xylog 0.06201400378264681\n",
-      "Fractional conversion of xylose 0.5894676760041082\n",
-      "Fractional conversion of furfural 0.00927357846482948\n",
-      "Time (s): 498.0\n",
-      "Xylan (g/g): 0.045486271199255454\n",
-      "Xylog (g/L): 18.438722060859284\n",
-      "Xylose (g/L): 58.42245288013397\n",
-      "Furfural (g/L): 0.5882299279261713\n",
-      "FIS (g/g): 0.175208185131814\n",
-      "rho_x (g/g): 76.86117494099325\n",
-      "X_G (g/g): 0.5218835069033171\n",
-      "conv: 0.8880054290562948\n",
-      "frac_conv_xylog: 0.06201400378264681\n",
-      "frac_conv_xylose: 0.5894676760041082\n",
-      "frac_conv_furfural: 0.00927357846482948\n",
-      "\n",
-      "Pretreatment wallclock time: 0.35 s.\n",
-      "Finished Pretreatment\n",
-      "\n",
-      "Running Enzymatic Hydrolysis Model\n",
-      "\n",
-      "FINAL OUTPUTS (at t =   24 hours)\n",
-      "Conversion Rate = 0.4937\n",
-      "rho_g = 24.53 g/L\n",
-      "rho_x = 23.25 g/L\n",
-      "rho_sL = 3.242 g/L\n",
-      "rho_f = 0.1679 g/L\n",
-      "Finished Enzymatic Hydrolysis\n",
-      "\n",
-      "Running Bioreactor\n",
-      "\n",
-      "INPUTS\n",
-      "Gas velocity = 0.08 m/s\n",
-      "Column height = 40.00 m\n",
-      "Column diameter = 5.00 m\n",
-      "Bubble diameter = 0.0060 m\n",
-      "OUR_max = 87.69 mol/m^3/hr\n",
-      "t_final = 100.0 s\n",
-      "\n",
-      "FINAL OUTPUTS (at t = 100.0 seconds)\n",
-      "OUR = 68.3978 mol/m^3/hr\n",
-      "Finished Bioreactor\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "#================================================================\n",
     "run_button = widgets.Button(\n",
@@ -1282,7 +384,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f1b17bcb",
+   "id": "12",
    "metadata": {},
    "source": [
     "---"
@@ -1290,35 +392,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "130891c9",
+   "execution_count": null,
+   "id": "13",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<script>\n",
-       "code_show=true; \n",
-       "function code_toggle() {\n",
-       " if (code_show){\n",
-       " $('div.input').hide();\n",
-       " } else {\n",
-       " $('div.input').show();\n",
-       " }\n",
-       " code_show = !code_show\n",
-       "} \n",
-       "$( document ).ready(code_toggle);\n",
-       "</script>\n",
-       "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Toggle code visibility (hidden by default).\"></form>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "a = HTML('''<script>\n",
     "code_show=true; \n",
@@ -1341,7 +418,17 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "60905d23",
+   "id": "14",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "! echo hey"
+   ]
+  },
+  {
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diff --git a/applications/Bioconversion/virtual_engineering_notebook.md b/applications/Bioconversion/virtual_engineering_notebook.md
new file mode 100644
index 0000000..3e309ee
--- /dev/null
+++ b/applications/Bioconversion/virtual_engineering_notebook.md
@@ -0,0 +1,375 @@
+---
+jupyter:
+  jupytext:
+    formats: ipynb,md
+    text_representation:
+      extension: .md
+      format_name: markdown
+      format_version: '1.3'
+      jupytext_version: 1.18.1
+  kernelspec:
+    display_name: Python 3 (ipykernel)
+    language: python
+    name: python3
+---
+
+# Bioconversion GUI using Widgets
+
+The first step is to select "Cell" > "Run All" from the toolbar.  This will initialize all the widgets and allow you to interact with the unit operation options via the GUI controls.
+
+```python
+from ipywidgets import *
+
+from virteng.WidgetFunctions import WidgetCollection
+from virteng.Utilities import get_host_computer
+from Models_classes import make_models_list
+
+hpc_run = get_host_computer()   # See if we're running on HPC or on a laptop
+
+from IPython.display import Image, HTML, clear_output
+Image(os.path.join(os.path.dirname("__file__"), 'figures', 'three_unit_flow.png'), width=800)
+```
+
+<!-- #region -->
+---
+
+## Set Virtual Engineering Options
+
+
+### 0. Feedstock properties
+
+Set the feedstock properties.
+
+<!-- #endregion -->
+
+```python
+#================================================================
+# Create the collection of widgets for feedstock options
+fs_options = WidgetCollection()
+
+fs_options.xylan_solid_fraction = widgets.BoundedFloatText(
+    value = 0.263,
+    max = 1,
+    min = 0,
+    description = r'Initial $X_X$',
+    tooltip = 'The initial fraction of solids that is xylan (kg/kg).  Must be in the range [0, 1]'
+)
+
+fs_options.glucan_solid_fraction = widgets.BoundedFloatText(
+    value = 0.40,
+    max = 1,
+    min = 0,
+    description = r'Initial $X_G$',
+    tooltip = 'The initial fraction of solids that is glucan (kg/kg).  Must be in the range [0, 1]'
+)
+
+fs_options.initial_porosity = widgets.BoundedFloatText(
+    value = 0.8,
+    max = 1,
+    min = 0,
+    description = r'Initial Porosity',
+    tooltip = 'The initial porous fraction of the biomass particles.  Must be in the range [0, 1]'
+)
+
+#================================================================
+# Display the widgets
+fs_options.display_all_widgets()
+#================================================================
+```
+
+---
+
+### 1. Pretreatment Operation
+
+Set the options for the pretreatment operation below.
+
+```python
+#================================================================
+# Create the collection of widgets for pretreatment options
+pt_options = WidgetCollection()
+
+pt_options.model_type = widgets.RadioButtons(
+    options = ['dilute acid', 'deacetylation'],
+    #value = 'Deacetylation',
+    #description = 'Model Type',
+    #disabled = False,
+    #tooltip = 'Specifies the solver to use for the EH step, "CFD Simulation" requires HPC resources.'
+)
+
+#### this needs to be changed to g acid / g bone-dry biomass (then converted in the run function) ####
+pt_options.initial_acid_conc = widgets.BoundedFloatText(
+    value = 0.0001,
+    max = 1.0,
+    min = 0.0,
+    description = 'Acid Loading',
+    tooltip = 'The initial concentration of acid (mol/mL).  Must be in the range [0, 1]'
+)
+
+pt_options.steam_temperature = widgets.BoundedFloatText(
+    value = 150.0,
+    max = 250.3,
+    min = 3.8,
+    description = 'Steam Temperature',
+    tooltip = r'The fixed temperature of the steam ($^\circ$C).',
+)
+
+pt_options.initial_solid_fraction = widgets.BoundedFloatText(
+    value = 0.745,
+    max = 1,
+    min = 0,
+    description = r'Initial FIS$_0$',
+    tooltip = 'The initial fraction of insoluble solids (kg/kg).  Must be in the range [0, 1]'
+)
+
+pt_options.final_time = widgets.BoundedFloatText(
+    value = 8.3,
+    max = 1440,
+    min = 1,
+    description = 'Final Time',
+    tooltip = r'Total simulation time (min).  Must be $\geq$ 1'
+)
+
+pt_options.acetylfrac = widgets.BoundedFloatText(
+    value = 0.025,
+    max = 0.1,
+    min = 0.0,
+    description = 'Acetyl group fraction',
+    tooltip = 'The initial fraction of acetyl groups. Must be in the range [0, 1]'
+)
+
+pt_options.DAtemp = widgets.BoundedFloatText(
+    value = 365,
+    max = 600,
+    min = 200.0,
+    description = 'Deacetylation temperature',
+    tooltip = 'Fluid temperature for deacetylation. Must be in the range [200, 600]'
+)
+
+pt_options.show_plots = widgets.Checkbox(
+    value = False,
+    tooltip = 'Show Plots'
+)
+
+#================================================================
+# Display the widgets
+pt_options.display_all_widgets()
+#================================================================
+```
+
+---
+
+### 2. Enzymatic Hydrolysis Operation
+
+Set the options for the enzymatic hydrolysis operation using either a two-phase reaction rate model or high-fidelity CFD below.
+
+
+```python
+#================================================================
+# Create the collection of widgets
+eh_options = WidgetCollection()
+
+eh_options.model_type = widgets.RadioButtons(
+    options = ['Lignocellulose Model', 'CFD Surrogate', 'CFD Simulation'],
+    value = 'CFD Surrogate',
+    description = 'Model Type',
+    disabled = False,
+    tooltip = 'Specifies the solver to use for the EH step, "CFD Simulation" requires HPC resources.'
+)
+
+eh_options.lambda_e = widgets.BoundedFloatText(
+    value = 30.0,
+    max = 1000.0,
+    min = 0.0,
+    description = 'Enzymatic Load',
+    tooltip = 'Ratio of the enzyme mass to the total solution mass (mg/g).  Must be in the range [0, 1000]'
+)
+
+eh_options.fis_0 = widgets.BoundedFloatText(
+    value = 0.05,
+    max = 1.0,
+    min = 0.0,
+    description = r'FIS$_0$ Target',
+    tooltip = 'The target value for initial fraction of insoluble solids *after* dilution (kg/kg).  Must be in the range [0, 1]'
+)
+
+eh_options.t_final = widgets.BoundedFloatText(
+    value = 24.0,
+    min = 1.0,
+    max = 24.0,
+    description = 'Final Time',
+    tooltip = r'The total time of the simulation (h).  Must be $\geq$ 1'
+)
+
+eh_options.show_plots = widgets.Checkbox(
+    value = False,
+    tooltip = 'Show Plots',
+    disabled = True,
+)
+
+#================================================================
+# Display the widgets
+eh_options.display_all_widgets()
+#================================================================
+
+def model_type_action(change):
+        
+    if eh_options.model_type.value == 'Lignocellulose Model':
+        # Lignocellulose Model
+        eh_options.show_plots.value = False
+        eh_options.show_plots.disabled = False
+        eh_options.show_plots.description_tooltip = 'Show Plots'
+
+    else:
+        # Surrogate Model, CFD Simulation
+        eh_options.show_plots.value = False
+        eh_options.show_plots.disabled = True
+        eh_options.show_plots.description_tooltip = 'Show Plots (Only available for lignocellulose model)'
+
+eh_options.model_type.observe(model_type_action, names='value')
+```
+
+---
+
+### 3. Bioreaction Operation
+
+Set the options for the bubble column bioreaction operation below.
+
+
+```python
+#================================================================
+# Create the collection of widgets
+br_options = WidgetCollection()
+
+br_options.model_type = widgets.RadioButtons(
+    options = ['CFD Surrogate', 'CFD Simulation'],
+    value = 'CFD Surrogate',
+    description = 'Model Type',
+    disabled = False,
+    tooltip = 'Specifies the solver to use for the bioreaction step, "CFD Simulation" requires HPC resources.'
+)
+
+br_options.gas_velocity = widgets.BoundedFloatText(
+    value = 0.08, 
+    min = 0.01,
+    max = 0.1,
+    description = 'Gas velocity',
+    tooltip = r'Gas velocity in the simulation (m/s).  Must be in $[0.01, 0.1]$'
+)
+
+br_options.column_height = widgets.BoundedFloatText(
+    value = 40., 
+    min = 10.,
+    max = 50.,
+    description = 'Column height',
+    tooltip = r'Column height (m).  Must be in $[10, 50]$'
+)
+
+br_options.column_diameter = widgets.BoundedFloatText(
+    value = 5.0, 
+    min = 1.,
+    max = 6.,
+    description = 'Column diameter',
+    tooltip = r'Column diameter (m).  Must be in $[1, 6]$'
+)
+
+br_options.bubble_diameter = widgets.BoundedFloatText(
+    value = 0.006,
+    min = 0.003,
+    max = 0.008,
+    description = 'Bubble diameter',
+    tooltip = r'Bubble diameter (m).  For surragate model must be in $[0.003, 0.008]$, for cfd must be $>=0$'
+)
+
+br_options.t_final = widgets.BoundedFloatText(
+    value = 100.0, # default 500
+    min = 1.0,
+    max = 1e16,
+    description = 'Final Time',
+    tooltip = r'The total time of the simulation (h).  Must be $\geq 1$'
+)
+
+#================================================================
+# Display the widgets
+br_options.display_all_widgets()
+#================================================================
+```
+
+---
+
+## Run Model
+
+When finished setting options for all unit operations, press the button below to run the complete model.
+
+
+```python
+#================================================================
+run_button = widgets.Button(
+    description = 'Run All.',
+    tooltip = 'Execute the model start-to-finish with the properties specified above.',
+    layout =  {'width': '200px', 'margin': '25px 0px 100px 170px'}, 
+    button_style = 'success'
+)
+#================================================================
+
+# run_button_output = widgets.Output()
+display(run_button)
+
+#================================================================
+# Define a function to be executed each time the run button is pressed
+def run_button_action(b):
+    clear_output()
+    display(run_button)
+    verbose = True
+    # Initialize models
+    models_list = make_models_list([fs_options, pt_options, eh_options, br_options], n_models=4, hpc_run=hpc_run)
+    for model in models_list:
+        model.run(verbose=verbose)
+    
+run_button.on_click(run_button_action)
+#================================================================
+```
+
+---
+
+```python
+a = HTML('''<script>
+code_show=true; 
+function code_toggle() {
+ if (code_show){
+ $('div.input').hide();
+ } else {
+ $('div.input').show();
+ }
+ code_show = !code_show
+} 
+$( document ).ready(code_toggle);
+</script>
+<form action="javascript:code_toggle()"><input type="submit" \
+value="Toggle code visibility (hidden by default)."></form>''')
+
+display(a)
+```
+
+```python
+! echo hey
+```
+
+```python
+
+```
+
+```python
+
+```
+
+```python
+
+```
+
+```python
+
+```
+
+```python
+
+```