diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml
index d7c7a42f..7497258d 100644
--- a/.github/workflows/main.yml
+++ b/.github/workflows/main.yml
@@ -28,6 +28,8 @@ jobs:
         sudo apt update
         sudo apt-get install -y -qq glpk-utils
         sudo apt-get install -y -qq coinor-cbc
+        sudo pip install idaes-pse --pre
+        idaes get-extensions --to ./solvers
 
     # Install dependencies
     - name: Set up Python 3.11
@@ -40,6 +42,7 @@ jobs:
         pip install -r requirements.txt
         pip install openpyxl
 
+
     # (optional) Cache your executed notebooks between runs
     # if you have config:
     # execute:
diff --git a/Material/Pyomo Fundamentals/1.4 Loading data from Excel.ipynb b/Material/Pyomo Fundamentals/1.4 Loading data from Excel.ipynb
index c4395342..35b8e658 100644
--- a/Material/Pyomo Fundamentals/1.4 Loading data from Excel.ipynb	
+++ b/Material/Pyomo Fundamentals/1.4 Loading data from Excel.ipynb	
@@ -33,6 +33,7 @@
     "import pandas as pd\n",
     "import pyomo.environ as pyo\n",
     "import os\n",
+    "import openpyxl\n",
     "path = os.path.abspath('knapsack_data.xlsx')\n",
     "df_items = pd.read_excel(path, sheet_name='data', header=0, index_col=0)\n",
     "W_max = 14\n",
@@ -51,7 +52,7 @@
     "model.weight_con = pyo.Constraint(\n",
     "    expr = sum( w[i]*model.x[i] for i in A ) <= W_max )\n",
     "\n",
-    "opt = pyo.SolverFactory('glpk')\n",
+    "opt = pyo.SolverFactory('cbc')\n",
     "opt_success = opt.solve(model)\n",
     "\n",
     "total_weight = sum( w[i]*pyo.value(model.x[i]) for i in A )\n",
diff --git a/Material/Pyomo Fundamentals/1.5 NLP vs MIP.ipynb b/Material/Pyomo Fundamentals/1.5 NLP vs MIP.ipynb
index f6fa2de9..f9e4ac9f 100644
--- a/Material/Pyomo Fundamentals/1.5 NLP vs MIP.ipynb	
+++ b/Material/Pyomo Fundamentals/1.5 NLP vs MIP.ipynb	
@@ -58,7 +58,7 @@
     "model.weight_con = pyo.Constraint(\n",
     "    expr = sum( w[i]*model.x[i] for i in A ) <= W_max )\n",
     "\n",
-    "opt = pyo.SolverFactory('ipopt')\n",
+    "opt = pyo.SolverFactory('ipopt', executable='/content/solvers/ipopt')\n",
     "opt_success = opt.solve(model)\n",
     "\n",
     "model.pprint()\n",