Computer surrogate model to predict the single-phase mixing quality in steady state mixing tanks

Abstract

Systems and methods of using a surrogate machine learning model, based on a CFD model, to predict the mixing quality in steady state mixing tanks are provided. An exemplary method includes generating a plurality of training CFD models for a plurality of training steady state mixing configurations based on a plurality of steady state mixing factors associated with each training steady state mixing configuration; calculating a mixing quality for each training steady state mixing configuration using each respective training CFD model; generating a training dataset that includes the steady state mixing factors associated with each training steady state mixing configuration, and the calculated mixing quality for each training steady state mixing configuration; and training a machine learning model, using the training dataset, to predict mixing qualities for steady state mixing configurations based on based on steady state mixing factors associated with the steady state mixing configurations.

1 . A method, comprising:
 generating, by one or more processors, a plurality of training computational fluid dynamic (CFD) models for a plurality of training steady state mixing configurations in which inlet streams are mixed in tanks, wherein each training CFD model is generated based on a plurality of steady state mixing factors associated with each training steady state mixing configuration;   calculating, by the one or more processors, a mixing quality for each training steady state mixing configuration using each respective training CFD model;   generating, by the one or more processors, a training dataset that includes the steady state mixing factors associated with each training steady state mixing configuration, and the calculated mixing quality for each training steady state mixing configuration;   training, by the one or more processors, a machine learning model, using the training dataset, to predict mixing qualities for steady state mixing configurations based on based on steady state mixing factors associated with the steady state mixing configurations;   recommending, by the one or more processors, one or more of a working volume or an impeller speed for a given product based on the trained machine learning model.   
     
     
         2 . The method of  claim 1 , further comprising:
 applying, by the one or more processors, the trained machine learning model to new steady state mixing factors associated with a new steady state mixing configuration; and   predicting, by the one or more processors, based on applying the trained machine learning model to the steady state mixing factors associated with the new steady state mixing configuration, a mixing quality for the new steady state mixing configuration.   
     
     
         3 . The method of  claim 1 , wherein the steady state mixing factors include one or more of: tank geometry, stirrer geometry, working volume, inlet configuration, outlet configuration, inlet flow rates for each inlet, outlet flow rates for each outlet, agitation speed, impeller speed, fluid Reynolds number for each substance, and other chemical and pharmaceutical properties for each substance. 
     
     
         4 . The method of  claim 1 , wherein the mixing quality is a measure of standard deviation of trace concentration in the tank. 
     
     
         5 . The method of  claim 1 , further comprising:
 generating, by the one or more processors, a testing computational fluid dynamic (CFD) model for a testing steady state mixing configuration in which inlet streams are mixed in tanks, wherein the testing CFD model is generated based on a plurality of steady state mixing factors associated with the testing steady state mixing configuration;   calculating, by the one or more processors, a mixing quality for the testing steady state mixing configuration using the testing CFD model;   applying, by the one or more processors, the trained machine learning model to the steady state mixing factors associated with the testing steady state mixing configuration;   predicting, by the one or more processors, based on applying the trained machine learning model to the steady state mixing factors associated with the testing steady state mixing configuration, a quality of mixing for the testing steady state mixing configuration; and   evaluating, by the one or more processors, the trained machine learning model by comparing the mixing quality calculated for the testing steady state mixing configuration using the testing CFD model and the mixing quality predicted for the testing steady state mixing configuration using the trained machine learning model.   
     
     
         6 . The method of  claim 1 , wherein the machine learning model is a deep learning model. 
     
     
         7 . A computer system, comprising:
 one or more processors; and   a non-transitory program memory communicatively coupled to the one or more processors and storing executable instructions that, when executed by the one or more processors, cause the processors to:   generate a plurality of training computational fluid dynamic (CFD) models for a plurality of training steady state mixing configurations in which inlet streams are mixed in tanks, wherein each training CFD model is generated based on a plurality of steady state mixing factors associated with each training steady state mixing configuration;   calculate a mixing quality for each training steady state mixing configuration using each respective training CFD model;   generate a training dataset that includes the steady state mixing factors associated with each training steady state mixing configuration, and the calculated mixing quality for each training steady state mixing configuration;   train a machine learning model, using the training dataset, to predict mixing qualities for steady state mixing configurations based on based on steady state mixing factors associated with the steady state mixing configurations; and   recommend one or more of a working volume or an impeller speed for a given product based on the trained machine learning model.   
     
     
         8 . The computer system of  claim 7 , wherein a first set of one or more processors, of the one or more processors, generate the plurality of training computational fluid dynamic (CFD) models, and wherein a second set of one or more processors, of the one or more processors, train the machine learning model. 
     
     
         9 . The computer system of  claim 7 , wherein the executable instructions, when executed by the one or more processors, further cause the processors to:
 apply the trained machine learning model to new steady state mixing factors associated with a new steady state mixing configuration; and   predict, based on applying the trained machine learning model to the steady state mixing factors associated with the new steady state mixing configuration, a mixing quality for the new steady state mixing configuration.   
     
     
         10 . The computer system of  claim 9 , wherein a third set of one or more processors, of the one or more processors, apply the trained machine learning model to the new steady state mixing factors associated with the new steady state mixing configuration and predict the mixing quality for the new steady state mixing configuration. 
     
     
         11 . The computer system of  claim 7 , wherein the steady state mixing factors include one or more of: tank geometry, stirrer geometry, working volume, inlet configuration, outlet configuration, inlet flow rates for each inlet, outlet flow rates for each outlet, agitation speed, impeller speed, fluid Reynolds number for each substance, and other chemical and pharmaceutical properties for each substance. 
     
     
         12 . The computer system of  claim 7 , wherein the mixing quality is a measure of standard deviation of trace concentration in the tank. 
     
     
         13 . The computer system of  claim 7 , wherein the executable instructions, when executed by the one or more processors, further cause the processors to:
 generate a testing computational fluid dynamic (CFD) model for a testing steady state mixing configuration in which inlet streams are mixed in tanks, wherein the testing CFD model is generated based on a plurality of steady state mixing factors associated with the testing steady state mixing configuration;   calculate a mixing quality for the testing steady state mixing configuration using the testing CFD model;   apply the trained machine learning model to the steady state mixing factors associated with the testing steady state mixing configuration;   predict, based on applying the trained machine learning model to the steady state mixing factors associated with the testing steady state mixing configuration, a quality of mixing for the testing steady state mixing configuration; and   evaluate the trained machine learning model by comparing the mixing quality calculated for the testing steady state mixing configuration using the testing CFD model and the mixing quality predicted for the testing steady state mixing configuration using the trained machine learning model.   
     
     
         14 . The computer system of  claim 7 , wherein the machine learning model is a deep learning model. 
     
     
         15 . A non-transitory computer readable storage medium storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to:
 generate a plurality of training computational fluid dynamic (CFD) models for a plurality of training steady state mixing configurations in which inlet streams are mixed in tanks, wherein each training CFD model is generated based on a plurality of steady state mixing factors associated with each training steady state mixing configuration;   calculate a mixing quality for each training steady state mixing configuration using each respective training CFD model;   generate a training dataset that includes the steady state mixing factors associated with each training steady state mixing configuration, and the calculated mixing quality for each training steady state mixing configuration;   train a machine learning model, using the training dataset, to predict mixing qualities for steady state mixing configurations based on based on steady state mixing factors associated with the steady state mixing configurations; and   recommend one or more of a working volume or an impeller speed for a given product based on the trained machine learning model.   
     
     
         16 . The non-transitory computer readable storage medium of  claim 15 , wherein the computer-readable instructions, when executed by the one or more processors, further cause the processors to:
 apply the trained machine learning model to new steady state mixing factors associated with a new steady state mixing configuration; and   predict, based on applying the trained machine learning model to the steady state mixing factors associated with the new steady state mixing configuration, a mixing quality for the new steady state mixing configuration.   
     
     
         17 . The non-transitory computer readable storage medium of  claim 15 , wherein the steady state mixing factors include one or more of: tank geometry, stirrer geometry, working volume, inlet configuration, outlet configuration, inlet flow rates for each inlet, outlet flow rates for each outlet, agitation speed, impeller speed, fluid Reynolds number for each substance, and other chemical and pharmaceutical properties for each substance. 
     
     
         18 . The non-transitory computer readable storage medium of  claim 15 , wherein the mixing quality is a measure of standard deviation of trace concentration in the tank. 
     
     
         19 . The non-transitory computer readable storage medium of  claim 15 , wherein the computer-readable instructions, when executed by the one or more processors, further cause the processors to:
 generate a testing computational fluid dynamic (CFD) model for a testing steady state mixing configuration in which inlet streams are mixed in tanks, wherein the testing CFD model is generated based on a plurality of steady state mixing factors associated with the testing steady state mixing configuration;   calculate a mixing quality for the testing steady state mixing configuration using the testing CFD model;   apply the trained machine learning model to the steady state mixing factors associated with the testing steady state mixing configuration;   predict, based on applying the trained machine learning model to the steady state mixing factors associated with the testing steady state mixing configuration, a quality of mixing for the testing steady state mixing configuration; and   evaluate the trained machine learning model by comparing the mixing quality calculated for the testing steady state mixing configuration using the testing CFD model and the mixing quality predicted for the testing steady state mixing configuration using the trained machine learning model.   
     
     
         20 . The non-transitory computer readable storage medium of  claim 15 , wherein the machine learning model is a deep learning model.