diff --git a/src/bayesvalidrox/post_processing/post_processing.py b/src/bayesvalidrox/post_processing/post_processing.py
index fcb51945f56ff4a5b9ec2d138d843f3bcb935fa2..5110af0b3c17120461bc93275c5c0b1b2df8338b 100644
--- a/src/bayesvalidrox/post_processing/post_processing.py
+++ b/src/bayesvalidrox/post_processing/post_processing.py
@@ -334,14 +334,14 @@ class PostProcessing:
                 # Extract the values from dict
                 sorted_seq_opt = {}
                 # Number of replications
-                n_reps = PCEModel.ExpDesign.nReprications
+                n_reps = PCEModel.ExpDesign.n_replication
 
                 # Get the list of utility function names
                 # Handle if only one UtilityFunction is provided
-                if not isinstance(PCEModel.ExpDesign.UtilityFunction, list):
-                    util_funcs = [PCEModel.ExpDesign.UtilityFunction]
+                if not isinstance(PCEModel.ExpDesign.util_func, list):
+                    util_funcs = [PCEModel.ExpDesign.util_func]
                 else:
-                    util_funcs = PCEModel.ExpDesign.UtilityFunction
+                    util_funcs = PCEModel.ExpDesign.util_func
 
                 for util in util_funcs:
                     sortedSeq = {}
@@ -393,12 +393,12 @@ class PostProcessing:
                         if ref_BME_KLD is not None:
                             if plot == 'BME':
                                 refValue = ref_BME_KLD[0]
-                                plot_label = r'$BME/BME^{Ref.}$'
+                                plot_label = r'BME/BME$^{Ref.}$'
                             if plot == 'KLD':
                                 refValue = ref_BME_KLD[1]
-                                plot_label = '$D_{KL}[p(\theta|y_*),p(\theta)]'\
-                                    ' / D_{KL}^{Ref.}[p(\theta|y_*), '\
-                                    'p(\theta)]$'
+                                plot_label = '$D_{KL}[p(\\theta|y_*),p(\\theta)]'\
+                                    ' / D_{KL}^{Ref.}[p(\\theta|y_*), '\
+                                    'p(\\theta)]$'
 
                             # Difference between BME/KLD and the ref. values
                             all_errors = np.divide(all_errors,
@@ -461,7 +461,7 @@ class PostProcessing:
                         if ref_BME_KLD is not None:
                             if plot == 'BME':
                                 refValue = ref_BME_KLD[0]
-                                plot_label = r'$BME/BME^{Ref.}$'
+                                plot_label = r'BME/BME$^{Ref.}$'
                             if plot == 'KLD':
                                 refValue = ref_BME_KLD[1]
                                 plot_label = '$D_{KL}[p(\\theta|y_*),p(\\theta)]'\
diff --git a/src/bayesvalidrox/surrogate_models/sequential_design.py b/src/bayesvalidrox/surrogate_models/sequential_design.py
index e7fc9261fb839cc915af5b363ae0961d4634f11c..e1c6f46896c97f918c82860d5c7d52f0f04117b1 100644
--- a/src/bayesvalidrox/surrogate_models/sequential_design.py
+++ b/src/bayesvalidrox/surrogate_models/sequential_design.py
@@ -101,11 +101,6 @@ class SeqDesign():
         # TotalSigma2 = np.empty((0, 1))
         if len(obs_data) != 0 and hasattr(PCEModel, 'Discrepancy'):
             TotalSigma2 = PCEModel.Discrepancy.parameters
-        #     # ------ Prepare diagonal enteries for co-variance matrix ---------
-        #     for keyIdx, key in enumerate(Model.Output.names):
-        #         # optSigma = 'B'
-        #         sigma2 = np.array(PCEModel.Discrepancy.parameters[key])
-        #         TotalSigma2 = np.append(TotalSigma2, sigma2)
 
             # Calculate the initial BME
             out = self.__BME_Calculator(initPCEModel, obs_data, TotalSigma2)