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

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.

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.

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.