diff --git a/docs/source/Tutorials/10 - Special case evaporation.ipynb b/docs/source/Tutorials/10 - Special case evaporation.ipynb
index a3a9da0..19d5447 100644
--- a/docs/source/Tutorials/10 - Special case evaporation.ipynb	
+++ b/docs/source/Tutorials/10 - Special case evaporation.ipynb	
@@ -5,12 +5,30 @@
    "metadata": {},
    "source": [
     "# Special case: evaporation of water\n",
-    "\n"
+    "**TL;DR:** Evaporation of water from the bioreactor will bias the estimated yields and rates. Using the pseudo batch transformation one can estimate the correct even values when evaporation is significant. This requires that the volume data is the true volume of the bioreactor. In cases where the volume data does not account for evaporation you need to either assume that evaporation is insignificant or do some correction of the volume data before applying the Pseudo batch transformation.\n",
+    "\n",
+    "## Why care about evaporation?\n",
+    "Evaporation of water from a bioreactor will bias the estimated titer, rates and yields if it is not properly accounted for. As a motivating example, imagine a batch bioreactor with an initial volume of 100 ml. In this scenario it is common to assume that any change in concentration of species is caused by the metabolic activity of the organism in the reactor. However, if let say 10 ml water evaporated during the process this would cause a significant increase in concentration which is unrelated to the metabolic activity. As result the production rates will appear larger causing the strains to look more attractive, than really. \n",
+    "\n",
+    "Evaporation can very a lot between different bioreactor equipment and scales, thus evaporation could very well take a part of the blame for the changing performance of cell factories at different scales. The cause the varying evaporation is multi-facetted and can originate from dryness of process air, presence of condensers, differences in agitation method, etc [1]. \n",
+    "\n",
+    "Miniaturised bioreactor systems can exhibit particularly large evaporation rates. With the increased popularity of miniturised bioreactor systems for high-throughput cell factory and bioprocess development evaporation becomes a important issue [1]. \n",
+    "\n",
+    "For fed-batch fermentations, evaporation adds an additional source of volume change. The Pseudo batch transformation can handle scenarios where evaporation is significant, however to get accurate estimates of rates and yields the transformation method needs the true volume of the bioreactor, which may not always be accessible. Through this tutorial we will show that the Pseudo batch transformation method works when water evaporation is significant and illustrate a common scenario where the evaporation is not accounted for in the volume data.\n",
+    "\n",
+    "[1] M. Ask and S. M. Stocks, “Aerobic bioreactors: condensers, evaporation rates, scale-up and scale-down,” Biotechnol Lett, vol. 44, no. 7, pp. 813–822, Jul. 2022, doi: 10.1007/s10529-022-03258-7.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Before we start we will first load the necessary Python packages and functions."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 106,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -18,22 +36,145 @@
     "import pandas as pd\n",
     "import numpy as np\n",
     "\n",
-    "from pseudobatch import pseudobatch_transform_pandas, hypothetical_concentration, metabolised_amount\n",
+    "from pseudobatch import pseudobatch_transform_pandas\n",
     "from pseudobatch.datasets import load_evaporation_fedbatch"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pseudo batch transformation works when evaporation is significant\n",
+    "The `pseudobatch` package holds an example dataset which is produced to mimic a fed-batch process with an exponential feeding profile where water evaporates from the bioreactor at a constant rate. This data is loaded in the code block below. We also show some of the data columns to give an overview of the structure of this data frame."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 107,
+   "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>timestamp</th>\n",
+       "      <th>sample_volume</th>\n",
+       "      <th>c_Glucose</th>\n",
+       "      <th>c_Biomass</th>\n",
+       "      <th>c_Product</th>\n",
+       "      <th>c_CO2</th>\n",
+       "      <th>v_Volume</th>\n",
+       "      <th>v_Feed_accum</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.075000</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1000.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.06006</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.075004</td>\n",
+       "      <td>0.503014</td>\n",
+       "      <td>0.002474</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>999.995704</td>\n",
+       "      <td>0.055765</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.12012</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.075009</td>\n",
+       "      <td>0.506047</td>\n",
+       "      <td>0.004964</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>999.991745</td>\n",
+       "      <td>0.111865</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.18018</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.075013</td>\n",
+       "      <td>0.509097</td>\n",
+       "      <td>0.007469</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>999.988123</td>\n",
+       "      <td>0.168303</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.24024</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.075016</td>\n",
+       "      <td>0.512166</td>\n",
+       "      <td>0.009988</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>999.984842</td>\n",
+       "      <td>0.225082</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   timestamp  sample_volume  c_Glucose  ...  c_CO2     v_Volume  v_Feed_accum\n",
+       "0    0.00000            0.0   0.075000  ...    0.0  1000.000000      0.000000\n",
+       "1    0.06006            0.0   0.075004  ...    0.0   999.995704      0.055765\n",
+       "2    0.12012            0.0   0.075009  ...    0.0   999.991745      0.111865\n",
+       "3    0.18018            0.0   0.075013  ...    0.0   999.988123      0.168303\n",
+       "4    0.24024            0.0   0.075016  ...    0.0   999.984842      0.225082\n",
+       "\n",
+       "[5 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 107,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fedbatch_df = load_evaporation_fedbatch()\n",
+    "fedbatch_df[['timestamp','sample_volume', 'c_Glucose', 'c_Biomass','c_Product', 'c_CO2', 'v_Volume', 'v_Feed_accum']].head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "fedbatch_df = load_evaporation_fedbatch()"
+    "To show the effect of the evaporation, we will calculate the expected volume if no volume evaporated. If is simply done by staring with the initial reactor volume, then adding the volume of feed and subtracting the sample volume. Below this is calculated and stored in a column named `expected_volume`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 108,
    "metadata": {},
    "outputs": [
     {
@@ -42,13 +183,13 @@
        "<Axes: xlabel='timestamp'>"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 108,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -59,12 +200,21 @@
    ],
    "source": [
     "initial_volume = fedbatch_df['v_Volume'].iloc[0]\n",
-    "fedbatch_df['expected_volume'] = initial_volume + fedbatch_df['v_Feed_accum'] - fedbatch_df['sample_volume'].cumsum()\n",
-    "# accum_evaporation = expected_volume - fedbatch_df['v_Volume']\n",
-    "# accum_evaporation\n",
+    "\n",
+    "# Calculated the expected volume by tracking known volume changing \n",
+    "# events. For the Pseudo batch transform the volume has to be the \n",
+    "# volume BEFORE sampling. Therefore the sampling volume data is \n",
+    "# shifted one position before the cumsum.\n",
+    "fedbatch_df['expected_volume'] = (\n",
+    "    initial_volume \n",
+    "    + fedbatch_df['v_Feed_accum']\n",
+    "    - fedbatch_df['sample_volume'].shift(1, fill_value=0).cumsum()\n",
+    ")\n",
+    "\n",
     "fedbatch_df.plot(\n",
     "    x='timestamp',\n",
-    "    y=['v_Volume', 'expected_volume']\n",
+    "    y=['v_Volume', 'expected_volume'],\n",
+    "    label=['True volume', \"Expected volume\"]\n",
     ")"
    ]
   },
@@ -72,12 +222,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now, we can transform the hypothetical CO2 concentration and the biomass concentration measurements."
+    "This plot clearly shows that the expected volume (orange line) diverge from the true volume (blue line) of the bioreactor. This divergence is caused by volume evaporation. To estimate the correct rates and yields using the pseudo batch transformation the TRUE bioreactor volume is required. In real world scenarios the true volume may not be known, this is discussed further in the next section. For now we will use the simulated true volume for the Pseudo batch transformation to validate the method. \n",
+    "\n",
+    "In the following code block the pseudo batch transformed biomass and glucose concentrations are calculated."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 109,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -89,12 +241,19 @@
     "    accumulated_feed_colname='v_Feed_accum',\n",
     "    concentration_in_feed=[0,substrate_in_feed],\n",
     "    sample_volume_colname='sample_volume'\n",
-    ")\n"
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will use standard linear modelling procedures to estimate the specific growth rate and the substrate yield coefficient from the Pseudo batch transformed data."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 110,
    "metadata": {},
    "outputs": [
     {
@@ -114,21 +273,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 111,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Yxs_hat = -1.8500000000000008\n",
+      "Yxs_hat = 1.8500000000000008\n",
       "true Yxs = 1.85\n"
      ]
     }
    ],
    "source": [
     "Yxs_hat, intercept = np.polyfit(fedbatch_df['pseudo_Biomass'], fedbatch_df['pseudo_Glucose'], 1)\n",
-    "print(f\"Yxs_hat = {Yxs_hat}\")\n",
+    "print(f\"Yxs_hat = {abs(Yxs_hat)}\")\n",
     "print(f\"true Yxs = {fedbatch_df['Yxs'].iloc[-1]}\")"
    ]
   },
@@ -136,132 +295,187 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Consumed gaseous species, Oxygen\n",
-    "The amount of metabolised $O_2$ has to be calculated from the mass balance. When the oxygen enters the bioreactor some will go straight through and into the off-gas analyzer, and some will be solubilized in the aqueous phase. From the aqueous phase the $O_2$ can either be metabolised or removed through sampling (or evaporate again). Thus, we need to solve the mass balance to find the amount of metabolised $O_2$.\n",
-    "\n",
-    "$$\n",
-    "\\int_0^t Metabolism=M_{species}(t)-\\int_0^t In_{gas}-\\int_0^t In_{liquid}+\\int_0^t Out_{gas}+\\int_0^t Sampled\n",
-    "$$\n",
-    "\n",
-    "Some of these terms are obtained from measurements and some can be estimated, e.g. the oxygen content in the liquid feed medium is rarely measured, but is likely insignificant. In this example, we will assume that $\\int_0^t In_{liquid}=0$ for all $t$.\n",
-    "\n",
-    "You can calculate the metabolized amount of $O_2$ on your own, but it does require special care to use the correct volume and mass' regarding before or after sampling. To ease the processes we have made a function which calculates the metabolized amount for you, `metabolised_amount()`."
+    "We see that both of the estimated parameters match the true simulated parameters, thus showing that the Pseudo batch transformation can be used in situations where the evaporation is present."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "At the sampling time points this dataset contains the values just before the sample was taken. However, the `metabolised_amount()` function requires the values from just after the sample withdrawal. In the following we calculate the $O_2$ mass after sample withdrawal."
+    "## What happens volume data does not account for evaporation\n",
+    "In some cultivation systems the volume is not actually measured. Instead the reactor volume is inferred by keeping track of the liquid going in and out of the reactor. This method does not account for the evaporation of water from the system and will overestimate the actual volume of the reactor. In the simulated example used above, this would mean that the volume timeseries from the instrument would be the \"Expected volume\" and NOT the \"True volume\" (See plot above).\n",
+    "\n",
+    "Let's investigate what happens if we estimate the specific growth rate and substrate yield using the biassed volume data."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "fedbatch_df['m_O2_after_sample'] = fedbatch_df['m_O2'] - fedbatch_df['c_O2'] * fedbatch_df['sample_volume']"
+    "fedbatch_df[['pseudo_Biomass_wrong_volume', 'pseudo_Glucose_wrong_volume']] = pseudobatch_transform_pandas(\n",
+    "    df=fedbatch_df,\n",
+    "    measured_concentration_colnames=[\"c_Biomass\", \"c_Glucose\"],\n",
+    "    reactor_volume_colname='expected_volume',\n",
+    "    accumulated_feed_colname='v_Feed_accum',\n",
+    "    concentration_in_feed=[0,substrate_in_feed],\n",
+    "    sample_volume_colname='sample_volume'\n",
+    ")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Here, we will use the `metabolised_amount()` function to calculate the mass of consumed $O_2$."
+    "Using the regular linear model, we can estimate the rate and yield from the Pseudo batch concentration based on the wrong volume."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 115,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mu0_hat = 0.09959\n",
+      "true mu0 = 0.1\n"
+     ]
+    }
+   ],
    "source": [
-    "fedbatch_df['m_O2_consumed'] = metabolised_amount(\n",
-    "    off_gas_amount=fedbatch_df['m_O2_gas'].to_numpy(),\n",
-    "    dissolved_amount_after_sampling=fedbatch_df['m_O2_after_sample'].to_numpy(),\n",
-    "    inlet_gas_amount=fedbatch_df['m_O2_in'].to_numpy(),\n",
-    "    sampled_amount=(fedbatch_df['c_O2'] * fedbatch_df['sample_volume']).cumsum().to_numpy(),\n",
-    "    inlet_liquid_amount=np.zeros_like(fedbatch_df['m_O2_in'].to_numpy()),\n",
-    ")"
+    "mu0_hat, intercept = np.polyfit(fedbatch_df['timestamp'], fedbatch_df['pseudo_Biomass_wrong_volume'].transform(np.log), 1)\n",
+    "print(f\"mu0_hat = {round(mu0_hat, 5)}\")\n",
+    "print(f\"true mu0 = {fedbatch_df['mu0'].iloc[-1]}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Yxs_hat = 1.6852002355943905\n",
+      "true Yxs = 1.85\n"
+     ]
+    }
+   ],
+   "source": [
+    "Yxs_hat, intercept = np.polyfit(fedbatch_df['pseudo_Biomass_wrong_volume'], fedbatch_df['pseudo_Glucose_wrong_volume'], 1)\n",
+    "print(f\"Yxs_hat = {abs(Yxs_hat)}\")\n",
+    "print(f\"true Yxs = {fedbatch_df['Yxs'].iloc[-1]}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we see that both the specific growth rate and the substrate yield estimates are biassed. However, the bias of the growth rate is very small. It is important to note that the size of these biasses are dependent on the specific evaporation rate, bioprocess and strain."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now, that we have calculated the metabolised amount of $O_2$ we can calculate the hypothetical concentration and run the pseudo batch transformation on the hypothetical concentration data."
+    "To improve these the estimated rates and yields, we need to estimate the true bioreactor volume. To estimate the true bioreactor volume we need a function which can calculate/estimate how much water has evaporated from the bioreactor at each time point. Below we will implement a Python function to calculate the amount of volume evaporated. For this illustration case, we will assume that the water evaporates at a constant rate. Therefore the accumulated volume over a given time period is calculated as\n",
+    "\n",
+    "$$\n",
+    "V_{evaporated} = (t_1 - t_0) * evaporation\\_rate\n",
+    "$$\n",
+    "\n",
+    "\n",
+    "Here the evaporated volume of water ($V_{evaporated}$) is a function of three parameters: the initial timepoint ($t_0$), the final timepoint ($t_1$) and the evaporation rate ($evaporation\\_rate$). One could easily imagine modelling the evaporation rate as a function of other factors such as temperature, stirring speed, etc. We leave it up to the user to decide how they will model evaporation in their setup.\n",
+    "\n",
+    "**IMPORTANT NOTE:** the authors of the Pseudo batch transformation paper are not experts in evaporation, and thus this evaporation function is purely for the sake of the example. We do not recommend to use this function for volume correction, instead please refer to the literature on this topic.\n",
+    "\n",
+    "Now we implement the evaporation function described above."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [],
    "source": [
-    "fedbatch_df['hypothetical_c_O2'] = hypothetical_concentration(\n",
-    "    metabolised_amount=fedbatch_df['m_O2_consumed'].to_numpy(),\n",
-    "    reactor_volume=fedbatch_df['v_Volume'].to_numpy(),\n",
-    "    sample_volume=fedbatch_df['sample_volume'].to_numpy(),\n",
-    ")\n",
-    "\n",
-    "fedbatch_df['pseudo_O2'] = pseudobatch_transform_pandas(\n",
-    "    df=fedbatch_df,\n",
-    "    measured_concentration_colnames=['hypothetical_c_O2'],\n",
-    "    reactor_volume_colname='v_Volume',\n",
-    "    accumulated_feed_colname='v_Feed_accum',\n",
-    "    concentration_in_feed=[0],\n",
-    "    sample_volume_colname='sample_volume',\n",
-    ")"
+    "def evaporated_volume(t1, t0, evaporation_rate):\n",
+    "    \"\"\"Calculates the liquid phase volume of evaporated water during a \n",
+    "    timespan t0 to t1, assuming that the water evaporates at a known \n",
+    "    fixed rate.\"\"\"\n",
+    "    return (t1 - t0) * evaporation_rate "
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We are now ready to estimate the oxygen yield coefficient using a linear model."
+    "As this is a simulated data set we know the true evaporation rate, which is stored in the `evap_rate` column of the data frame."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Yxo2_hat = -0.010000000000000162\n",
-      "true Yxo2 = 0.01\n"
+      "Simulated evaporation rate 1.0 µL/h\n"
      ]
     }
    ],
    "source": [
-    "Yxo2_hat, intercept = np.polyfit(fedbatch_df['pseudo_Biomass'], fedbatch_df['pseudo_O2'], 1)\n",
-    "print(f\"Yxo2_hat = {Yxo2_hat}\")\n",
-    "print(f\"true Yxo2 = {fedbatch_df['Yxo2'].iloc[-1]}\")"
+    "simulated_evaporation_rate = fedbatch_df.evap_rate.iloc[0]\n",
+    "print(f\"Simulated evaporation rate {simulated_evaporation_rate} µL/h\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We see that the estimated oxygen yield coefficient match the one used for simulating the data show the the method works."
+    "Now we will estimate the volume of evaprorated water for each time step using the the function we defined above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fedbatch_df['estimated_evaporated_volume'] = fedbatch_df['timestamp'].apply(evaporated_volume, t0=0, evaporation_rate=simulated_evaporation_rate)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Volatile product\n",
-    "Finally, we simulated a generic volatile product which is produced in the liquid and then evaporates through first order kinetics. We will assume that we have measurements both of the evaporating amount and the liquid concentration of the product. For the mass balance we will assume that both $\\int_0^t In_{gas}=0$ and $\\int_0^t In_{liquid}=0$.\n",
-    "\n",
-    "Let's first inspect the raw simulated data."
+    "Then we can estimate the true volume of the bioreactor by subtracting the evaporated volume from the \"measured\" volume."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fedbatch_df['estimated_true_volume'] = fedbatch_df['expected_volume'] - fedbatch_df['estimated_evaporated_volume']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, we can visualize the \"measured\", estimated and the true volume to see that the estimated true volume is similar to the true volume."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
@@ -270,13 +484,13 @@
        "<Axes: xlabel='timestamp'>"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 129,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -288,7 +502,10 @@
    "source": [
     "fedbatch_df.plot(\n",
     "    x='timestamp',\n",
-    "    y=['m_Product', 'm_product_gas']\n",
+    "    y=['v_Volume', 'expected_volume', 'estimated_true_volume'],\n",
+    "    label=['True volume', \"Expected volume\", \"Estimated true volume\"],\n",
+    "    style=['-', '-', '--'],\n",
+    "    color=['C0', 'C1', 'C2']\n",
     ")"
    ]
   },
@@ -296,94 +513,80 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Some of the dissolved product (m_Product) is removed when the bioreactor is sampled, while the product that has evaporated is not removed through sampling. We need to calculate the total production of the product, i.e. combining the two curves above. We can use the `metabolised_amount()` to calculated the total production."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# again first we need to calculate the mass of product after sampling\n",
-    "# to obey the requirements of the metabolised_amount function\n",
-    "fedbatch_df['m_Product_after_sample'] = fedbatch_df['m_Product'].to_numpy() - (fedbatch_df['c_Product'] * fedbatch_df['sample_volume']).to_numpy()\n",
+    "We see that the estimated true volume (green dashed line) follows the true volume (blue solid line) showing that we successfully estimated the true volume. In a real world scenario the true volume will rarely be know and one will have to live with the uncertainty that this introduces into the rate and yield estimates.\n",
     "\n",
-    "fedbatch_df['m_Product_produced'] = metabolised_amount(\n",
-    "    off_gas_amount=fedbatch_df['m_product_gas'].to_numpy(),\n",
-    "    dissolved_amount_after_sampling=fedbatch_df['m_Product_after_sample'].to_numpy(),\n",
-    "    inlet_gas_amount=np.zeros_like(fedbatch_df['m_Product']),\n",
-    "    sampled_amount=(fedbatch_df['c_Product'] * fedbatch_df['sample_volume']).cumsum().to_numpy(),\n",
-    ")\n",
-    "fedbatch_df['hypothetical_c_Product'] = hypothetical_concentration(\n",
-    "    metabolised_amount=fedbatch_df['m_Product_produced'].to_numpy(),\n",
-    "    reactor_volume=fedbatch_df['v_Volume'].to_numpy(),\n",
-    "    sample_volume=fedbatch_df['sample_volume'].to_numpy()\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now that we have the hypothetical concentration we can perform pseudobatch transformation on the data."
+    "To make the example complete we will estimate the specific growth rate and substrate yield using the estimated true volume for the Pseudo batch transformation."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [],
    "source": [
-    "fedbatch_df['pseudo_Product'] = pseudobatch_transform_pandas(\n",
+    "fedbatch_df[['pseudo_Biomass_estimated_volume', 'pseudo_Glucose_estimated_volume']] = pseudobatch_transform_pandas(\n",
     "    df=fedbatch_df,\n",
-    "    measured_concentration_colnames='hypothetical_c_Product',\n",
-    "    reactor_volume_colname='v_Volume',\n",
+    "    measured_concentration_colnames=[\"c_Biomass\", \"c_Glucose\"],\n",
+    "    reactor_volume_colname='estimated_true_volume',\n",
     "    accumulated_feed_colname='v_Feed_accum',\n",
-    "    concentration_in_feed=[0],\n",
+    "    concentration_in_feed=[0,substrate_in_feed],\n",
     "    sample_volume_colname='sample_volume'\n",
     ")"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 133,
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mu0_hat = 0.1\n",
+      "true mu0 = 0.1\n"
+     ]
+    }
+   ],
    "source": [
-    "Finally, we can estimate the product yield coefficient using a linear model."
+    "mu0_hat, intercept = np.polyfit(fedbatch_df['timestamp'], fedbatch_df['pseudo_Biomass_estimated_volume'].transform(np.log), 1)\n",
+    "print(f\"mu0_hat = {round(mu0_hat, 5)}\")\n",
+    "print(f\"true mu0 = {fedbatch_df['mu0'].iloc[-1]}\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Yxp_hat = 0.8215102466751055\n",
-      "true Yxp = 0.8215102466751038\n"
+      "Yxs_hat = 1.850000000000001\n",
+      "true Yxs = 1.85\n"
      ]
     }
    ],
    "source": [
-    "Yxp_hat, intercept = np.polyfit(fedbatch_df['pseudo_Biomass'], fedbatch_df['pseudo_Product'], 1)\n",
-    "print(f\"Yxp_hat = {Yxp_hat}\")\n",
-    "print(f\"true Yxp = {fedbatch_df['Yxp'].iloc[-1]}\")"
+    "Yxs_hat, intercept = np.polyfit(fedbatch_df['pseudo_Biomass_estimated_volume'], fedbatch_df['pseudo_Glucose_estimated_volume'], 1)\n",
+    "print(f\"Yxs_hat = {abs(Yxs_hat)}\")\n",
+    "print(f\"true Yxs = {fedbatch_df['Yxs'].iloc[-1]}\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Again, we show that the estimated yield coefficient matches the coefficient that was used for the simulation."
+    "As expected the calculated rate and yield match the simulated parameters. This accuracy is off course only possible because we here work with simulated data."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Through out this tutorial we have shown that pseudobatch transformation is capable of handling measurements of gaseous compounds. And how to use the two helper functions `hypethetical_concentration` and `metabolised_amount` to prepare the measurements for pseudo batch transformation."
+    "## Conclusion\n",
+    "During this tutorial, we have showed that the Pseudo batch transformation is applicable, when significant amounts of water evaporated from the bioreactor. We futhere hope that this tutorial has illustrated some of the considerations about evaporations from bioreactor and an overall approach account for water evaporation when applying the Pseudo batch transformation."
    ]
   }
  ],