An Online Inverse Problem for the Simulation of Extraction Columns using Population Balances

Jildeh, Hanin B.; Attarakih, Menwer; Mickler, Matthias

doi:10.1016/b978-0-444-59520-1.50067-1

Cited by 5 publications

(48 citation statements)

References 7 publications

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“…It requires solving the inverse population balance equation by using a nonlinear optimisation algorithm that is programmed in MATLAB. The CM model (extended fixed pivot technique) and OPOSPM was solved using the above discussed correlations . The unknown coalescence model parameters were obtained by minimising the square sum of errors of holdup or Sauter mean diameter data or both data (holdup, Sauter diameter) (tolerance value = 1.00 × 10 −6 ) with simple bounds constraining the coalescence parameters .…”

Section: Resultsmentioning

confidence: 99%

“…For online prediction and reduction purposes, the model has been simplified and adjusted by introducing two learning parameters ( K b and K c ) for the breakage and coalescence kernels, respectively . Based on this modification the source term in Equation is given by:

S = K_{b} (ϑ (d_{30}) - 1) Γ (d_{30}) N - \frac{1}{2} K_{c} ω (d_{30}, d_{30}) N^{2}

…”

Section: Droplet Population Balancementioning

confidence: 99%

“…An alternative approach, which is faster and computationally less expensive, is the use of learning parameters, during online experiments . In the learning phase, data is used for parameter optimisation.…”

Section: Introductionmentioning

confidence: 99%

“…In the learning phase, data is used for parameter optimisation. This requires a fast and simple PBM, as is the One Primary and One Secondary Particle Method (OPOSPM), which was proposed by Attarakih et al The so retrieved optimised learning parameters can be used for column simulation and control …”

Section: Introductionmentioning

confidence: 99%

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Parameter optimisation and validation for droplet population balances

et al. 2013

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Single droplet experiments in Kühni compartment for five different liquid systems were used to evaluate Schmidt breakage model parameters. Based on these optimised parameters, Coulaloglou and Tavlarides coalescence model parameters were obtained by an inverse solution of population balance model (PBM) using the extended fixed‐pivot technique. The estimated interaction parameters were used to study the hydrodynamics of a pilot Kühni column. For online prediction purposes the One Primary and One Secondary Particle Model (OPOSPM), which is essentially a reduced population balance model (PBM), was used as an alternative to the computational expensive detailed PBM. Different test systems and column geometries were used for steady and transient state validation where OPOSPM was found to produce remarkable good results.

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Section: Resultsmentioning

confidence: 99%

S = K_{b} (ϑ (d_{30}) - 1) Γ (d_{30}) N - \frac{1}{2} K_{c} ω (d_{30}, d_{30}) N^{2}

…”

Section: Droplet Population Balancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Parameter optimisation and validation for droplet population balances

et al. 2013

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“…Secondly, parameter validation of the models was performed offline using existing experimental data and then online by online monitoring and a simulation tool (OMST). OMST is an inhouse tool based on optical particle online analysis and a conductivity probe (droplet size distribution and holdup) [2,3]. The mass transfer profiles (GC analysis) are then applied for a model comparison.…”

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confidence: 99%

A Comparison of Droplet Interaction Models

Jildeh

Mickler

Attarakih

et al. 2013

Chemie Ingenieur Technik

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Online monitoring, simulation and prediction of multiphase flows

et al. 2013

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An online monitoring and simulation tool (OMST) is used to determine, analyse, simulate and predict the multiphase flow behaviour in an extraction column within the context of model predictive control (MPC). The simulation was done with the droplet population model OPOSPM using an adapted moments method for online simulation. For validation, steady‐state and transient experiments are performed, where rotor speed and dispersed volume flow rate are changed using the EFCE system toluene–water. OMST deviations are between 3% (droplet sizes in steady state) and 20% (hold‐up in dynamic step‐changes) which are highly dependent on the quality of the parameter estimation.

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An Online Inverse Problem for the Simulation of Extraction Columns using Population Balances

Cited by 5 publications

References 7 publications

Parameter optimisation and validation for droplet population balances

Parameter optimisation and validation for droplet population balances

A Comparison of Droplet Interaction Models

Online monitoring, simulation and prediction of multiphase flows

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