Process Modelling Method and System for Non-Linear Continuous-Like Process and Application in Pulp and Paper Industry

Abstract

A method, system, and computer program product are described capable of controlling an industrial based chemical process through accessing sensor data, pre-processing accessed sensor data through an automated process, forming an initial prediction model of the chemical process, and automatically determining linearity or non-linearity of the initial prediction model. As a function of quantitatively measured linearity, being non-linear, quasilinear, or linear, automatically train the initial prediction model, then deploy the trained prediction model in a manner that controls the subject chemical process at the industrial plant, including optimizing consumption of a certain resource. Data-driven modeling of non-linear, continuous-like industrial or chemical processes results.

What is claimed is: 
     
         1 . A computer-implemented method of controlling an industrial based chemical process, the method comprising:
 obtaining working data representative of a subject chemical process of an industrial plant, said obtaining being automatically performed by a digital processor;   responsively in computer memory, forming an initial model of the subject chemical process based on the working data, the working data having empirical values of dependent variables and independent variables of the subject chemical process, different characteristics of the subject chemical process being represented by different mathematical relationships of respective dependent variables and independent variables, including a certain resource consumption being indicated by one or more independent variables;   said forming including the processor automatically determining non-linearity of the initial model as a function of non-linearity of the different mathematical relationships in the working data, and automatically training the initial model based on results of the determining, such that: (a) where the different relationships are determined to be substantially linear, then training the initial model as a linear predictive model and resulting in a trained prediction model, and (b) where the different relationships are determined to be quasi-linear or non-linear, then (i) training the initial model as a non-linear predictive model, (ii) combining the trained non-linear predictive model with one or more local linear models dynamically adjusting control variables of the certain resource consumption in respective windows of time, and (iii) producing a resulting trained prediction model based on the non-linear predictive model combined with the one or more local linear models;   and   deploying the resulting trained prediction model in a manner controlling the subject chemical process at the industrial plant, including optimizing consumption of the certain resource.   
     
     
         2 . The method of  claim 1 , wherein the subject chemical process is continuous, semi-continuous, or continuous in one or more parts. 
     
     
         3 . The method of  claim 1 , wherein the subject chemical process is a Kraft process or similar, and the industrial plant is of a pulp and paper industry. 
     
     
         4 . The method of  claim 3 , wherein the working data represents quality of production of the subject chemical process as a function of the certain resource consumption, and the quality of production is determined by any one or combination of: a measurement of completeness of a pulping process, a Kappa number measurement or equivalent, total alkaline charge, and amount of residual alkali. 
     
     
         5 . A method as claimed in  claim 3 , wherein the certain resource is a reagent utilized in the Kraft process. 
     
     
         6 . A method as claimed in  claim 5 , wherein the reagent is white liquor. 
     
     
         7 . A method as claimed in  claim 3 , wherein output of the deployed prediction model further controls any one or combination of: total H factor, a black liquor stream after extraction, digester discharge consistency of a digester at the industrial plant, and liquor temperature in the digester. 
     
     
         8 . A method as claimed in  claim 3 , wherein the prediction model is further configured to manipulate control parameters of the subject chemical process at the industrial plant in a manner that minimizes any one or more of: toxic waste of the industrial plant, waste water, and chemicals in a drying process. 
     
     
         9 . A method as claimed in  claim 1 , wherein obtaining working data includes accessing sensor output data indicative of the subject chemical process; and the method further comprising automatically adjusting the prediction model over time based on additional sensor output data. 
     
     
         10 . A method as claimed in  claim 1  wherein training the initial model as a linear predictive model employs partial least squares (PLS) regression, and number of components for the PLS regression is an estimated rank among the independent variables and dependent variables. 
     
     
         11 . A method as claimed in  claim 1  wherein training the initial model as a non-linear predictive model employs extreme gradient boosting (XGBoost), and number of estimators for XGBoost is based on an estimated rank among the independent variables and dependent variables. 
     
     
         12 . A method as claimed in  claim 1  wherein obtaining working data includes:
 accessing sensor output data indicative of the subject chemical process, the accessed sensor output data including one or more time periods of operating states of the subject chemical process; and 
 pre-processing the accessed sensor output data in a manner that: (i) groups data based on time, and (ii) removes outlier data from groups of data, said pre-processing resulting in the working data, the accessing and pre-processing being automatically performed by the digital processor. 
 
     
     
         13 . A system for controlling an industrial based chemical process, the system comprising:
 a digital processor; and   a process modeler executable by the digital processor such that during execution the digital processor:   automatically obtains working data representative of a subject chemical process in an industrial plant;   responsively forms in computer memory, an initial model of the subject chemical process based on the working data, the working data having empirical values of dependent variables and independent variables of the subject chemical process, different characteristics of the subject chemical process being represented by different mathematical relationships of respective dependent variables and independent variables, including a certain resource consumption being indicated by one or more independent variables;   said forming including the digital processor automatically determining non-linearity of the initial model as a function of non-linearity of the different mathematical relationships in the working data, and automatically training the initial model based on results of the determining, such that: (a) where the different relationships are determined to be substantially linear, then training the initial model as a linear predictive model and resulting in a trained prediction model, and (b) where the different relationships are determined to be quasi-linear or non-linear, then (i) training the initial model as a non-linear predictive model, (ii) combining the trained non-linear predictive model with one or more local linear models dynamically adjusting control variables of the certain resource consumption in respective windows of time, and (iii) producing a resulting trained prediction model based on the non-linear predictive model combined with the one or more local linear models; and   deploys the resulting trained prediction model in a manner controlling the subject chemical process at the industrial plant, including optimizing consumption of the certain resource.   
     
     
         14 . The system of  claim 13 , wherein the subject chemical process is continuous, semi-continuous, or continuous in one or more parts. 
     
     
         15 . The system of  claim 13 , wherein the subject chemical process is a Kraft process or similar, and the industrial plant is of a pulp and paper industry. 
     
     
         16 . The system of  claim 15 , wherein the working data represents quality of production of the subject chemical process as a function of the certain resource consumption, and the quality of production is determined by any one or combination of: a measurement of completeness of a pulping process, a Kappa number measurement or equivalent, total alkaline charge, and amount of residual alkali. 
     
     
         17 . A system as claimed in  claim 15 , wherein the certain resource is a reagent utilized in the Kraft process that is white liquor. 
     
     
         18 . A system as claimed in  claim 15 , wherein output of the deployed prediction model further controls any one or combination of: total H factor, a black liquor stream after extraction, digester discharge consistency of a digester at the industrial plant, and liquor temperature in the digester. 
     
     
         19 . A system as claimed in  claim 15 , wherein the prediction model is further configured to manipulate control parameters of the subject chemical process at the industrial plant in a manner that minimizes any one or more of: toxic waste of the industrial plant, waste water, and chemicals in a drying process of the Kraft process. 
     
     
         20 . A system as claimed in  claim 13  wherein the digital processor obtaining working data includes accessing sensor output data indicative of the subject chemical process; and the process modeler when executed further comprising the digital processor automatically adjusting the prediction model over time based on additional sensor output data. 
     
     
         21 . A system as claimed in  claim 13 , wherein the digital processor obtaining working data includes:
 automatically accessing sensor output data indicative of the subject chemical process, the accessed sensor output data including one or more time periods of operating states of the chemical process at the industrial plant; and   automatically pre-processing the accessed sensor output data in a manner that: (i) groups or otherwise classifies data based on time, and (ii) removes outlier data from the groups of data, said pre-processing resulting in the working data.   
     
     
         22 . A non-transitory computer program product controlling an industrial based chemical process, the computer program product comprising a computer-readable medium with computer code instructions stored thereon, the computer code instructions being configured, when executed by a processor, to cause an apparatus associated with the processor to:
 obtain working data representative of a subject chemical process in an industrial plant;   responsively in computer memory, form an initial model of the subject chemical process based on the working data, the working data having empirical values of dependent variables and independent variables of the subject chemical process, different characteristics of the subject chemical process being represented by different mathematical relationships of respective dependent variables and independent variables, including representing quality of production of the subject chemical process as a function of a certain resource consumption, quality of production corresponding to one or more dependent variables, and the certain resource consumption being indicated by one or more independent variables;   said responsively form including the apparatus automatically determining non-linearity of the initial model as a function of non-linearity of the different mathematical relationships in the working data, and automatically training the initial model based on results of the determining, such that: (a) where the different relationships are determined to be substantially linear, then training the initial model as a linear predictive model and resulting in a trained prediction model, and (b) where the different relationships are determined to be quasi-linear or non-linear, then (i) training the initial model as a non-linear predictive model, (ii) combining the trained non-linear predictive model with one or more local linear models dynamically adjusting control variables of the certain resource consumption in respective windows of time, and (iii) producing a resulting trained prediction model based on the non-linear predictive model combined with the one or more local linear models; and   deploy the resulting trained prediction model in a manner controlling the subject chemical process at the industrial plant, including optimizing consumption of the certain resource.   
     
     
         23 . The computer program product of  claim 22 , wherein obtaining the working data includes:
 accessing sensor output data indicative of the subject chemical process, the accessed sensor output data including one or more time periods of operating states of the chemical process at the industrial plant; and   pre-processing the accessed sensor output data in a manner that: (i) groups or otherwise classifies data based on time, and (ii) removes outlier data from the groups of data, said pre-processing resulting in working data.   
     
     
         24 . The computer program product of  claim 22  wherein the apparatus automatically determining non-linearity of the initial model includes quantitatively measuring non-linearity of the working data. 
     
     
         25 . A computer-implemented method of modeling an industrial-based process, the method comprising:
 obtaining working data based on the past collected data and real-time collected data indicative of a subject industrial-based process, said obtaining being automatically performed by one or more digital processors;   as a function of linearity of the obtained working data, automatically selecting between linearly modeling the subject industrial-based process and non-linearly modeling the subject industrial-based process, said selecting being automatically performed by the one or more digital processors, the working data having empirical values of dependent variables and independent variables of the subject industrial-based process, different characteristics of the subject industrial-based process being represented in the working data by different mathematical relationships of respective dependent variables and independent variables;   the one or more digital processors automatically selecting being by: (a) testing linearity of the different mathematical relationships in the obtained working data, and (b) where the different relationships are determined to be substantially linear, then selecting and training a linear predictive model as representative of the subject industrial-based process, and where the different relationships are determined to be quasi-linear or non-linear, then selecting and training a non-linear predictive model as representative of the subject industrial-based process; and   generating a resulting model of the subject industrial-based process based on one of: (i) the selected and trained linear predictive model, and (ii) a combination of the selected and trained non-linear predictive model and one or more local linear models dynamically adjusting certain variables of the subject industrial-based process.   
     
     
         26 . A computer-implemented method as claimed in  claim 25  wherein the testing linearity of the different mathematical relationships in the obtained working data includes the one or more digital processors quantitatively measuring non-linearity of the different mathematical relationships in the obtained working data. 
     
     
         27 . A computer-implemented method as claimed in  claim 25  wherein the industrial-based process is a chemical process, an industry plant process, or the like.