Simplify _Q_opt in parmest with block scenario structure.

pyomo/contrib/parmest/examples/reaction_kinetics/simple_reaction_parmest_example.py

@@ -44,6 +44,8 @@ def simple_reaction_model(data):
     model.x2 = Param(initialize=float(data['x2']))
 
     # Rate constants
+    # @Reviewers: Can we switch this to explicitly defining which parameters are to be
+    # regressed in the Experiment class?
     model.rxn = RangeSet(2)
     initial_guess = {1: 750, 2: 1200}
     model.k = Var(model.rxn, initialize=initial_guess, within=PositiveReals)

pyomo/contrib/parmest/examples/reactor_design/parameter_estimation_example.py

@@ -36,9 +36,10 @@ def main():
     pest = parmest.Estimator(exp_list, obj_function='SSE')
 
     # Parameter estimation with covariance
-    obj, theta, cov = pest.theta_est(calc_cov=True, cov_n=17)
+    obj, theta, cov = pest.theta_est(calc_cov=True, cov_n=19)
     print(obj)
     print(theta)
+    print(cov)
 
 
 if __name__ == "__main__":

pyomo/contrib/parmest/examples/reactor_design/reactor_design.py

@@ -24,15 +24,11 @@ def reactor_design_model():
     # Create the concrete model
     model = pyo.ConcreteModel()
 
-    # Rate constants
-    model.k1 = pyo.Param(
-        initialize=5.0 / 6.0, within=pyo.PositiveReals, mutable=True
-    )  # min^-1
-    model.k2 = pyo.Param(
-        initialize=5.0 / 3.0, within=pyo.PositiveReals, mutable=True
-    )  # min^-1
-    model.k3 = pyo.Param(
-        initialize=1.0 / 6000.0, within=pyo.PositiveReals, mutable=True
+    # Rate constants, make unknown parameters variables
+    model.k1 = pyo.Var(initialize=5.0 / 6.0, within=pyo.PositiveReals)  # min^-1
+    model.k2 = pyo.Var(initialize=5.0 / 3.0, within=pyo.PositiveReals)  # min^-1
+    model.k3 = pyo.Var(
+        initialize=1.0 / 6000.0, within=pyo.PositiveReals
     )  # m^3/(gmol min)
 
     # Inlet concentration of A, gmol/m^3