Help with k_kin in SMA isotherm

Hi, I’m new to CADET-Process and chromatography modelling in general. I’m attempting to replicate the results of GoSilico modelling software by Cytiva for a CEX Column Chromatography using CADETProcess.

The isotherm parameters in GoSilico are:

Equilibrium (SMA) = 7e-7
Charge (SMA) = 10.11
Kinetic (SMA) = 1.8e-8
Shielding = 0

It seems like they are using this SMA derivation with k_kinetic being the inverse of k_d

My current CADET-Process snippet for the binding model is like below. I’m setting rapid equliibrium is_kinetic=False, but not sure how to incorporate the k_kinetic parameter in there. Any help would be appreciated!

Warm regards,
Daniel

component_system = ComponentSystem(['Salt', 'Protein'])
binding_model = StericMassAction(component_system)
binding_model.is_kinetic = False
binding_model.adsorption_rate = [0, 7E-07]
binding_model.desorption_rate = [0, 1]
binding_model.capacity =1400
binding_model.characteristic_charge = [0, 10.11]
binding_model.steric_factor = [0, 0] #shielding
binding_model.reference_liquid_phase_conc = 1
binding_model.reference_solid_phase_conc = 1
# k_Kinetic (SMA) = 1.8e-8 

Hi Dan,

Welcome to the forum. It seems like you already have the answer. It can sometimes be beneficial to apply parameter transformations before executing specific optimization tasks. Using k_{kin} and k_{eq} instead of k_a and k_d is a classic of such transformations. In the end it only matters that the original parameters are uniquely determined by the transformd ones. However, I would prefer to use k_{kin} = k_d as then larger values indicate faster processes.

Thanks for your response! I think my question was unclear. What I’m trying to ask is, where do I put the k_Kinetic = 1.8e-8 into my current binding model? Currently, it’s commented out, and the binding model is set to use k_{eq} only with is_kinetic = False.

When I tried to set k_{d} = \frac{1}{k_{kin}} and k_a = \frac{k_{eq}}{k_{kin}} for is_kinetic = True, I cannot complete the simulation.

The full code (non-working due to is_kinetic=True) is like below:

c_salt_low = 10
c_salt_high = 335.735
c_protein = 0.00667

flow_rate = 2.312e-8
load_time = 0
wash_time = 713.22
elution_time = 2448.12
cycle_time = 12293.77

k_Kinetic = 1.8e-8
k_eq = 7e-7
########### This doesn't work :( ###########
component_system = ComponentSystem(['Salt', 'Protein'])
binding_model = StericMassAction(component_system)
binding_model.is_kinetic = True
binding_model.adsorption_rate = [0, k_eq/k_Kinetic]
binding_model.desorption_rate = [0, 1/k_Kinetic]
binding_model.capacity =1400
binding_model.characteristic_charge = [0, 10.11]
binding_model.steric_factor = [0, 0]
binding_model.reference_liquid_phase_conc = 1
binding_model.reference_solid_phase_conc = 1
################################################

# ########### This works, but no k_Kinetic ###########
# component_system = ComponentSystem(['Salt', 'Protein'])
# binding_model = StericMassAction(component_system)
# binding_model.is_kinetic = False
# binding_model.adsorption_rate = [0, k_eq]
# binding_model.desorption_rate = [0, 1]
# binding_model.capacity =1400
# binding_model.characteristic_charge = [0, 10.11]
# binding_model.steric_factor = [0, 0]
# binding_model.reference_liquid_phase_conc = 1
# binding_model.reference_solid_phase_conc = 1
# ################################################

# Column
column = LumpedRateModelWithPores(component_system, 'column')
column.binding_model = binding_model
column.length = 0.2
column.diameter = 0.0113
column.bed_porosity = 0.89
column.particle_radius = 43e-5
column.particle_porosity = 0.83
column.axial_dispersion = 4e-07
column.film_diffusion = [5e-06, 5e-06]
column.c = [c_salt_low, 0]
column.q = [binding_model.capacity, 0]

# Unit Operations
inlet = Inlet(component_system, name='inlet')
inlet.flow_rate = flow_rate
outlet = Outlet(component_system, name='outlet')

# Flow Sheet
flow_sheet = FlowSheet(component_system)
flow_sheet.add_unit(inlet)
flow_sheet.add_unit(column)
flow_sheet.add_unit(outlet, product_outlet=True)
flow_sheet.add_connection(inlet, column)
flow_sheet.add_connection(column, outlet)

# Process
process = Process(flow_sheet, 'lwe')
process.cycle_time = cycle_time
c_load = np.array([c_salt_low, c_protein])
c_wash = np.array([c_salt_low, 0])
concentration_difference = np.array([c_salt_high, 0.0]) - np.array([c_salt_low, 0.0])
gradient_duration = process.cycle_time - elution_time
gradient_slope_ = concentration_difference/gradient_duration

process.add_event('load', 'flow_sheet.inlet.c', c_load)
process.add_event('wash', 'flow_sheet.inlet.c', c_wash, wash_time)
process.add_event('grad_start', 'flow_sheet.inlet.c', 
                  [[c_salt_low, gradient_slope_[0]], 
                   [0, gradient_slope_[1]]], 
                  elution_time)

# Simulation 
process_simulator = Cadet()
process_simulator.__dict__['timeout']=10.0
simulation_results = process_simulator.simulate(process) #time out 10 sec
outputs = simulation_results.solution.column.outlet

from CADETProcess.plotting import SecondaryAxis
sec = SecondaryAxis()
sec.components = ["Salt"]
sec.y_label = '$c_{salt}$'

_ = simulation_results.solution.column.outlet.plot(secondary_axis=sec)

Hey Dan,

I can reproduce the error and will look into it. It’s afaik unexpected behavior and your code looks correct.

Thanks Ron - I look forward to hearing from you. On a side note, I did try removing the timeout 10 seconds, but the code still doesn’t work, so I put it back.

Just wondering, would setting some of the zeros to a negligibly small number solve this problem?

I tried multiple non-zero values (setting every 0 to 1e-5, 1e-4, 1e-3), but it didn’t work for any of them :frowning:

I think the k_d = 1/k_Kinetic = 55555555.55 is way to big compared to k_a = k_eq/k_Kinetic = 38.88; but not sure how to address it…

Any suggestion on changing reference_liquid_phase_conc and reference_solid_phase_conc ?

I can reproduce this in CADET-Core so it’s not a CADET-Process problem. I did some investigations and I think this is a numerical issue. The solver repeatedly failed to converge when switching to the wash phase, meaning the parameter values you provided are giving the solver a hard time. Regarding the reference solution, please find the document here. The reference solution is designed to minimize problems like this. However, I also tried a couple of reference solutions for you but the error still persists. Therefore, I would suggest

  1. double check the parameter values, do all these parameters make sense, are they close to those reported in the literature? For instance, 10.11 seems to be big and 7e-7 seems to be small. I can produce the same results switching between is_kinetic=True and is_kinetic=False when 10.11 is changed to 5.
  2. continue trying other reference values.
  3. switch to a different unit system for all parameters and test other reference solutions.

Hope it helps,
Flynn.

Hey Flynn,

thanks for your input!
I agree, it’s a numerical issue. As you said, the simulation fails at the first section switch, during the re-initialization. Apparently, NaN’s get computed, but I don’t yet know why. Here’s the debug log:

[Debug: integrate::1253] Solve from 713.22 to 713.22 => IDA_SUCCESS
[Debug: integrate::1279] === #Steps: 202
=== #Residual evals: 203
=== #Error test fails: 0
=== #Newton iters: 203
=== #Conv test fails: 0
=== Last step size: 3.87311
=== Next step size: 7.74621
[Debug: integrate::1253] Solve from 713.22 to 714 => IDA_TSTOP_RETURN
[Debug: integrate::1279] === #Steps: 202
=== #Residual evals: 203
=== #Error test fails: 0
=== #Newton iters: 203
=== #Conv test fails: 0
=== Last step size: 3.87311
=== Next step size: 7.74621
[Debug: integrate::1091]  ###### SECTION 1 from 713.22 to 2448.12
[Debug: notifyDiscontinuousSectionTransition::138] Switching from valve configuration 0 to 1 (sec = 1 wrapSec = 1)
[Debug: notifyDiscontinuousSectionTransition::151] Unit op 0 (INLET) port -1 comp -1 => 1 (LUMPED_RATE_MODEL_WITH_PORES) port -1 comp -1
[Debug: notifyDiscontinuousSectionTransition::151] Unit op 1 (LUMPED_RATE_MODEL_WITH_PORES) port -1 comp -1 => 2 (OUTLET) port -1 comp -1
[Debug: integrate::1104] ---====--- CONSISTENCY ---====---
[Debug: updateDynamicModelFlowRates::226] Flow out unit 0 port 0: 2.312e-08
[Debug: updateDynamicModelFlowRates::215] Flow in unit 1 port 0: 2.312e-08
[Debug: updateDynamicModelFlowRates::226] Flow out unit 1 port 0: 2.312e-08
[Debug: updateDynamicModelFlowRates::215] Flow in unit 2 port 0: 2.312e-08
[Debug: residualNorm::479] Residual: 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[Debug: integrate::1106]  ==========> Consistency error prev: 7.93019e+07
[Debug: updateDynamicModelFlowRates::226] Flow out unit 0 port 0: 2.312e-08
[Debug: updateDynamicModelFlowRates::215] Flow in unit 1 port 0: 2.312e-08
[Debug: updateDynamicModelFlowRates::226] Flow out unit 1 port 0: 2.312e-08
[Debug: updateDynamicModelFlowRates::215] Flow in unit 2 port 0: 2.312e-08
[Debug: consistentInitialConditionAlgorithm::513] Residual post state: 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[Debug: updateDynamicModelFlowRates::226] Flow out unit 0 port 0: 2.312e-08
[Debug: updateDynamicModelFlowRates::215] Flow in unit 1 port 0: 2.312e-08
[Debug: updateDynamicModelFlowRates::226] Flow out unit 1 port 0: 2.312e-08
[Debug: updateDynamicModelFlowRates::215] Flow in unit 2 port 0: 2.312e-08
[Debug: residualNorm::479] Residual: 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[Debug: integrate::1117]  ==========> Consistency error post Full: 1.27983e-08
[Debug: integrate::1132] y = 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[Debug: integrate::1133] yDot = 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[Debug: integrate::1134] Contains NaN: y = 0 yDot = 0
[Trace: residualDaeWrapper::217] ==> Residual at t = 713.22 sec = 1
[Debug: updateDynamicModelFlowRates::226] Flow out unit 0 port 0: 2.312e-08
[Debug: updateDynamicModelFlowRates::215] Flow in unit 1 port 0: 2.312e-08
[Debug: updateDynamicModelFlowRates::226] Flow out unit 1 port 0: 2.312e-08
[Debug: updateDynamicModelFlowRates::215] Flow in unit 2 port 0: 2.312e-08
[Trace: linearSolveWrapper::313] ==> Solve at t = 713.22 alpha = 1e+06 tol = 0.33

My current theory is, that the kinetic constant is too small (i.e. too fast), and that CADET overshoots, but I don’t really know. I’ll put this onto the agenda for our next team meeting.

But regarding your current issue @danPh :

  1. For the reproduction of your GoSilico results: that’s all rapid equilibrium anyways with a kKin that fast. With that combination of parameters for charge, capactiy etc. the kKin parameter only starts getting noticeable above 1e25, so some 30 orders of magnitude above your GoSilico values. So you can run your simulations with is_kinetic=False or set kKin to anything higher than 1e-6 (e.g. 1) and you should get the same results you would get if CADET didn’t crash with kKin=1e-8.
  2. I think 10.11 for the characteristic charge is reasonable.
  3. For your own simulations/ parameter estimations etc moving forward, I would recommend using reference concentrations as much as you can as they stabilize the numerics of the SMA isotherm. The values we recommend are the stationary phase capacity and the maximum salt concentration for bound and solved reference concentrations respectively. But note that the ka and kd CADET takes are the transformed ka and kd, so changing the reference concentrations is going to change the simulation results. If you want to have an exchange about reference concentrations feel free to join us at the CADET Office hour and we can talk about it :slight_smile:

Thanks for confirming that the characteristic charge is reasonable. I think I have some ideas on what’s going on now.

The problem is k_{kin}. In ChromX:

k_{kin,i} \frac{\partial q_i}{\partial t} = k_{eq,i} \bar{q}_{salt}c_{p,i} - c_{salt}^{\nu_i} q_i

k_{kin} = 1.8e-8, the left hand side is practically 0. @danPh How did you get this number? Have you tried 1.8e-7, 1.8e-6 or 1.8e-9 in ChromX? Does it give you the same result? My guess is that such a small number in ChromX is used to denote equilibrium as the time derivative part is practically 0. Anyway, for such a small k_{kin} you should look for is_kinetic=False .

Regarding the CADET simulation using is_kinetic=True, it fails because the rate constants are too big, possibly making the problem stiffer when switching phases. Therefore, this problem can be addressed by loosening some constraints in IDA to help the solver to further adapt its order and step sizes at ‘difficult times’. For instance, changing either max_errtest_fail to 10 or init_step_size to 1e-15 in the /solver/time_integrator group can solve this problem.

I can produce the following result using is_kinetic=True even without using reference concentrations:
image

I still recommend using reference concentrations and setting is_kinetic=False in this case.

3 Likes

Hi Flynn & Ron,

Thank you for your support. I’m also discussing with my colleagues about the small value of k_{kin} from GoSilico - I suspected that as Flynn said, it’s just an indicator of rapid equilibrium.

I decided to perform inverse fitting of both k_eq and charge with CADET, and the newly fitted values are different from the GoSilico’s values, but the quality of the fit as well as the generalizability is very good. I think the underlying binding model implemented by GoSilico might have some differences with CADET’s one.

Again, much appreciated!