Hi,
here’s what I’m trying to achieve. I want to align the sum of my three species with my experimental curve. I have two experiments. Experiment A (single step) looks like this:
Experiment B (multiple step) looks like this:
I want the red curve of the species sum to align better with the orange curve of my experimental data. For simplicity, let’s just say I want to optimize the film diffusion of the species ‘H2IA’.
Up until now, in CADET-Process, I was only able to fit each experiment separately. This means I get 2 results for the film diffusion of the species - one for experiment A, one for experiment B. But I would like to variate the film diffusion in both experiments simultaneously, and find an optimum. Meaning, that I will end up with only one value for the film diffusion - that yields the best result when both experiments were viewed “like one single entity”. Basically, I’m trying to do what this user here did:
But in my case, I have a component system where one component is made up of three species, and I want to fit the sum of the species (=“the component”) to my experimental curve.
I have tried to find out a way to do it in CADET-Process. That sure would be much easier. As far as I’m trying to understand, I would need two references, two processes, and then it all would somehow need to be “unified” in one single difference metric. So that for example I get only one RMSE, which would then be minimized by the optimizer. And this is the step, where I don’t know, if it can possibly be done. Unfortunately, everything fitting-related in CADET is a bit harder to understand for me, and I don’t find every part of it as intuitive as the rest of modeling in CADET.
If it can be done, I would be very glad to receive some hints on how to set up this fitting. This would already solve my problem.
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Now, if it can NOT be done in CADET-Process, the question for me would be:
“How do I tell CADET-match to evaluate the difference -of the sum- of my 3 species (in the CADET solution) to my experimental curve?”
I see, I would need to set up a “transform”, for example “auto”, specify a variable path and the index of my component. Here, I have no option like “use_total_concentration_components=True”.
If I specify my component with the three species here, will it automatically use the sum?
In theory, I could install CADET-match and test it all… but I’m in my Bachelor’s thesis currently, and it seemed better to just ask :).
I’m not sure what code examples I could give you to further clarify what I’m trying to do. Code-wise, I have my process model with my LRMP column, and then the implementation of the parameter fitting. I basically just copied that from the “CADET workshop” example, added my own reference and variables, and specified the difference metric arguments like in my initial post. For completeness, I will give you the implementation of my parameter fitting, and an example of my experimental data:
import LaborversucheLoader as LabLoad fit_components=[3,4,5] component_names=['H2O','H3O+','OH-','H2IA','HIA-','IA2-'] experimental_data= LabLoad.read_laborversuche(pH, Versuchstyp, current_system) #Reference #Reference muss in mM umgerechnet werden from CADETProcess.reference import ReferenceIO experimental_data_average = experimental_data[0].sum(1)/3 #c-Mittelwert bilden experimental_solution= np.zeros([len(experimental_data_average.index),6]) #Leere Lösung experimentelle Daten in der richtigen Shape erzeugen experimental_solution[:,3]=ER.gL_to_mM(experimental_data_average) # in mM umrechnen und in H2IA-Spalte einfügen reference = ReferenceIO('experimental data',experimental_data[0].index*60,experimental_solution,component_system=component_system) from CADETProcess.comparison import Comparator comparator = Comparator() comparator.add_reference(reference) #Difference metrics comparator.add_difference_metric('RMSE', reference, 'column.outlet',components='IA',use_total_concentration_components=True) comparator.plot_comparison(simulation_results) metrics = comparator.evaluate(simulation_results) from CADETProcess.optimization import OptimizationProblem optimization_problem = OptimizationProblem('film_diffusion') optimization_problem.add_evaluation_object(process) #Optimization Problem ###Variablen### for comp in fit_components: optimization_problem.add_variable( name='film_diffusion_'+component_names[comp], parameter_path='flow_sheet.column.film_diffusion', lb=1e-9, ub=1e-3, transform='auto', indices=[comp] ) #Evaluator und Objectives optimization_problem.add_evaluator(process_simulator) optimization_problem.add_objective( comparator, n_objectives=comparator.n_metrics, requires=[process_simulator] ) #Optimizer-Einstellungen from CADETProcess.optimization import U_NSGA3 optimizer = U_NSGA3() optimizer.n_cores = 4 optimizer.pop_size = 32 optimizer.n_max_gen = 8 optimization_results = optimizer.optimize( optimization_problem, use_checkpoint=False ) print(optimization_results.x) print(optimization_results.f) optimization_results.plot_convergence('objectives') optimization_results.plot_objectives(autoscale=False)
Explanation: The function “LaborversucheLoader” selects the correct file for my experimental data, and reads it into my variable “experimental data”. experimental_data[0] adressses the excel file of which I share one example with you:
Next, the average of three individual experiments is calculated and converted from g/L to mM. And then, the matrix is created, where every column is zero, except that of the first species. This way, it serves as experimental data for the sum of the species (=the “mother” component).
I add the fitting variables in a for-loop, because specifying a list for “indices”, when trying to fit multiple components simultaneously, did not work. I add variables for indices 3,4,5, which means fitting all three species simultaneously.
The above code does not contain any attempts at mulitple-experiment-fitting.
I hope it is a little bit clearer now. If not, please let me know.
Greetings, AN_IA