diff --git a/examples/run_pomato_fbmc.py b/examples/run_pomato_fbmc.py
new file mode 100644
index 0000000..1f2775b
--- /dev/null
+++ b/examples/run_pomato_fbmc.py
@@ -0,0 +1,45 @@
+"""IEEE Test Case."""
+from pathlib import Path
+import numpy as np
+import pandas as pd
+import pomato
+
+
+mato = pomato.POMATO(wdir=self.wdir, options_file="profiles/nrel118.json")
+mato.load_data('data_input/nrel_118_original.zip')
+
+mato.options["model_horizon"] = [0, 168]
+mato.options["redispatch"]["include"] = False
+mato.options["redispatch"]["zones"] = list(mato.data.zones.index)
+mato.options["infeasibility"]["electricity"]["bound"] = 200
+mato.options["infeasibility"]["electricity"]["cost"] = 1000
+mato.options["redispatch"]["cost"] = 20
+
+# %% Nodal Basecase
+mato.data.results = {}
+mato.options["type"] = "opf"
+mato.create_grid_representation()
+mato.update_market_model_data()
+mato.run_market_model()
+result_name = next(r for r in list(mato.data.results))
+basecase = mato.data.results[result_name]
+
+mato.options["fbmc"]["minram"] = 0.2
+mato.options["fbmc"]["lodf_sensitivity"] = 0.1
+mato.options["fbmc"]["cne_sensitivity"] = 0.2
+fb_parameters = mato.create_flowbased_parameters(basecase)
+fbmc_domain = pomato.visualization.FBDomainPlots(mato.data, fb_parameters)
+fbmc_domain.generate_flowbased_domains(("R1", "R2"), ["R1", "R3"], timesteps=["t0001"])
+mato.visualization.create_fb_domain_plot(fbmc_domain.fbmc_plots[0], show_plot=False)
+
+# %% FBMC market clearing
+mato.options["redispatch"]["include"] = True
+mato.options["redispatch"]["zones"] = list(mato.data.zones.index)
+mato.create_grid_representation(flowbased_parameters=fb_parameters)
+mato.update_market_model_data()
+mato.run_market_model()
+mato.visualization.create_generation_overview(list(mato.data.results.values()), show_plot=False)
+mato._join_julia_instances()
+
+# Join all subprocesses. 
+mato._join_julia_instances()
diff --git a/examples/run_pomato_ieee.py b/examples/run_pomato_ieee.py
index f873242..90ee471 100644
--- a/examples/run_pomato_ieee.py
+++ b/examples/run_pomato_ieee.py
@@ -6,9 +6,11 @@
 
 # Init POMATO with the options file and the dataset
 wdir = Path("/examples/") # Change to local copy of examples folder
+
 mato = pomato.POMATO(wdir=wdir, options_file="profiles/ieee118.json")
 mato.load_data('data_input/pglib_opf_case118_ieee.m')
 
+
 # %% Access the data from the main pomato instance in the data object.
 nodes = mato.data.nodes
 lines = mato.grid.lines
@@ -17,9 +19,9 @@
 plants = mato.data.plants
 
 # %% Run uniform pricing
-mato.options["type"] = "dispatch"
+mato.options["type"] = "uniform"
 mato.create_grid_representation()
-mato.run_market_model()
+mato.run_market_model(update_data=True)
 
 # Obtain the market result by name and its instance
 dispatch_result_name = mato.market_model.result_folders[0].name
@@ -32,9 +34,9 @@
 print("Number of overloaded lines N-1 (Dispatch): ", len(df3))
 
 # %% Run nodal-prcing market clearing
-mato.options["type"] = "nodal"
+mato.options["type"] = "opf"
 mato.create_grid_representation()
-mato.run_market_model()
+mato.run_market_model(update_data=True)
 
 # Obtain the market result by name and its instance
 nodal_result_name = mato.market_model.result_folders[0].name
@@ -48,12 +50,11 @@
 print("Number of overloaded lines N-1 (nodal): ", len(df3))
 
 # Create geoplot for nodal result, including price layer and saving it as .html
-mato.visualization.create_geo_plot(nodal_result, 
-                                   show_prices=True,
-                                   filepath=mato.wdir.joinpath("geoplot_nodal.html"))
+mato.visualization.create_geo_plot(
+    nodal_result, show_prices=True, filepath=mato.wdir.joinpath("geoplot_nodal.html"))
 
 # %% Rerun the model as SCOPF
-mato.options["type"] = "cbco_nodal"
+mato.options["type"] = "scopf"
 mato.options["grid"]["redundancy_removal_option"] = "conditional_redundancy_removal"
 # Optionally set a line rating factors
 # mato.options["grid"]["short_term_rating_factor"] = 1
@@ -61,10 +62,7 @@
 
 # SCOPF requires to presolve the network with the RedundancyRemoval Algorithm
 mato.create_grid_representation()
-
-# Update the model data
-mato.update_market_model_data()
-mato.run_market_model()
+mato.run_market_model(update_data=True)
 
 # Check for overloaded lines (Should be none for N-0 and N-1)
 scopf_result_name = mato.market_model.result_folders[0].name
@@ -77,9 +75,11 @@
 print("Number of overloaded lines N-1 (SCOPF): ", len(df3))
 
 # Create geoplot for scopf result, including price layer and saving it as .html
-mato.visualization.create_geo_plot(scopf_result, 
-                                   show_prices=True,
-                                   filepath=mato.wdir.joinpath("geoplot_scopf.html"))
+mato.visualization.create_geo_plot(
+    scopf_result, show_prices=True,
+    filepath=mato.wdir.joinpath("geoplot_scopf.html")
+)
 
 # Join all subprocesses. 
 mato._join_julia_instances()
+
diff --git a/pomato/data/worker.py b/pomato/data/worker.py
index 9e62961..892ce9f 100644
--- a/pomato/data/worker.py
+++ b/pomato/data/worker.py
@@ -440,17 +440,18 @@ def process_matpower_case(self, casefile, m_type):
         ### Make ieee case ready for the market model
         self.data.nodes["name"] = ["n" + str(int(idx)) for idx in self.data.nodes.index]
         self.data.nodes.rename(columns={"baseKV": "voltage"}, inplace=True)
-        self.data.nodes.set_index("name", drop=False, inplace=True)
+        self.data.nodes = self.data.nodes.set_index("name", drop=False).rename_axis(index="index")
+
         self.data.nodes.zone = ["z" + str(int(idx)) for idx in self.data.nodes.zone]
 
         self.data.lines.node_i = ["n" + str(int(idx)) for idx in self.data.lines.node_i]
         self.data.lines.node_j = ["n" + str(int(idx)) for idx in self.data.lines.node_j]
         self.data.lines["voltage"] = self.data.nodes.loc[self.data.lines.node_i, "voltage"].values
         self.data.lines["technology"] = "ac"
-        condition_tranformer = (self.data.nodes.loc[self.data.lines.node_i, "voltage"].values 
+        condition_transformer = (self.data.nodes.loc[self.data.lines.node_i, "voltage"].values 
                                 != self.data.nodes.loc[self.data.lines.node_j, "voltage"].values)
         
-        self.data.lines.loc[condition_tranformer, "technology"] = "transformer"
+        self.data.lines.loc[condition_transformer, "technology"] = "transformer"
 
         self.data.zones = pd.DataFrame(index=set(self.data.nodes.zone.values))
         self.data.plants.node = ["n" + str(int(idx)) for idx in self.data.plants.node]
diff --git a/pomato/market_model/market_model.py b/pomato/market_model/market_model.py
index a6e4bcf..131db7e 100644
--- a/pomato/market_model/market_model.py
+++ b/pomato/market_model/market_model.py
@@ -4,7 +4,7 @@
 the market model written in julia. This is done by saving the relevant data as csv,
 run the model in a threaded julia program which provides the results in folder as csv files.
 
-The Modes is initionaled empty and then filled with data seperately. This makes it
+The model is initialized empty and then filled with data separately. This makes it
 easy to change the data and rerun without re-initializing everything again.
 
 """
@@ -107,6 +107,10 @@ def run(self):
 
         if not self.julia_model:
             self.julia_model = tools.JuliaDaemon(self.logger, self.wdir, self.package_dir, "market_model", self.options["solver"]["name"])
+        elif not self.julia_model.julia_daemon.isAlive():
+            self.logger.info("Joining previous market model thread.")
+            self.julia_model.join()
+            self.julia_model = tools.JuliaDaemon(self.logger, self.wdir, self.package_dir, "market_model", self.options["solver"]["name"])
 
         self.logger.info("Start-Time: %s", t_start.strftime("%H:%M:%S"))