Method and system for estimating corrosion inhibitor concentration using a multi-electrode array sensor

1. A processor implemented method ( 300 ) for estimating corrosion inhibitor (CI) concentration, wherein the method comprises:

positioning ( 302 ), via the one or more hardware processors, a multi-electrode array sensor in a corroding environment for estimating a corrosion inhibitor (CI) concentration associated with a liquid media from the corroding environment, wherein the liquid media from the corroding environment is circulated externally into glassware by a pump, wherein the glassware contains holes to insert and fix the multi-electrode array sensor into the liquid media for in-situ estimation of CI concentration of the corroding environment;

obtaining ( 304 ), via the one or more hardware processors, from the corroding environment, a plurality of electrochemical signals using the multi-electrode array sensor, wherein the plurality of electrochemical signals are obtained using the multi-electrode array sensor having a sensing working electrode made of mild steel, a reference electrode made of a graphite stick or Ag—AgCI type electrode, and a counter electrode made of Pt mesh or graphite stick bundle, wherein the plurality of electrochemical signals is obtained by applying +0.1 V DC voltage into the working electrode with respect to the reference electrode, and a DC current is measured at the counter electrode;

analyzing ( 306 ), via the one or more hardware processors, the plurality of electrochemical signals to obtain a plurality of parameters comprising an electrochemical impedance spectra and a DC current versus time;

identifying ( 308 ), via the one or more hardware processors, a plurality of features from the plurality of parameters comprising:

a charge transfer resistance feature obtained from the electrochemical impedance spectra parameter, and

an initial DC current and an intermediate DC current obtained from the DC current versus time, wherein the initial DC current is measured at a predetermined elapsed time of 10 seconds, and the intermediate DC current is measured as an average DC current between a predetermined time range of 2000 to 3000 seconds;

estimating ( 310 ), via the one or more hardware processors, the corrosion inhibitor (CI) concentration from the plurality of features using a trained machine learning model comprising,

a regression model determining a quantitative estimate of the corrosion inhibitor (CI) concentration, wherein the regression model is trained using a training dataset utilized as a ground truth to determine the quantitative estimate of the corrosion inhibitor (CI) concentration, wherein training the regression model for determining the quantitative estimate of the corrosion inhibitor (CI) concentration comprises:

determining a reference corrosion inhibitor (CI) concentration for the regression model based on the training dataset, wherein the reference corrosion inhibitor (CI) concentration for the regression model varies between 0-0.5 mM (in milli molar unit);

computing a cost function using a pre-defined initial weightage factor θ corresponding to the plurality of features, the training dataset, and a measure of error for fitting the cost function to the reference corrosion inhibitor (CI) concentration of the regression model;

minimizing the computed cost function corresponding to the plurality of features, based on a learning rate α, a weightage factors θj for the plurality of features and the total number of iterations performed for the cost function;

determining a final weightage factors corresponding to the plurality of features; and

determining the quantitative estimate of the corrosion inhibitor (CI) of the plurality of features using the final weightage factors; and

a classification model determining a qualitative estimate of the corrosion inhibitor (CI) concentration, wherein the classification model is trained using the training dataset utilized as the ground truth to determine the qualitative estimate of the corrosion inhibitor (CI) concentration, wherein training the classification model for determining the qualitative estimate of the corrosion inhibitor (CI) concentration comprises:

determining a reference corrosion inhibitor (CI) concentration for the classification model based on the training dataset, wherein the reference corrosion inhibitor concentration for the classification model varies between 0-6.0 mM (in milli molar unit);

computing a cost function using a logistic function, a pre-defined initial weightage factors θ corresponding to the plurality of features, the training dataset and a measure of error for fitting the cost function to the reference corrosion inhibitor (CI) concentration of the classification model;

minimizing the computed cost function corresponding to the plurality of features, based on a learning rate (α), a weightage factors θj for the plurality of features and the total number of iterations performed for the cost function;

determining a final weightage factors corresponding to the plurality of features; and

determining the qualitative estimate of the corrosion inhibitor (CI) of the plurality of features using the final weightage factors.

2. The method as claimed in claim 1 , wherein the corrosion inhibitor concentration for any unknown liquid is estimated using the corrosion inhibitor concentration range of the regression model.

3. The method as claimed in claim 1 , wherein the corrosion inhibitor concentration for any unknown liquid is estimated using the corrosion inhibitor concentration range of the classification model.

4. A system ( 102 ) for electrical load disaggregation, the system ( 102 ) comprising:

a memory ( 202 ) storing instructions;

one or more Input/Output (I/O) interfaces ( 206 );

and one or more hardware processors ( 204 ) coupled to the memory ( 202 ) via the one or more I/O interfaces ( 206 ), wherein the one or more hardware processors ( 204 ) are configured by the instructions to:

position a multi-electrode array sensor in a corroding environment for estimating a corrosion inhibitor (CI) concentration associated with a liquid media from the corroding environment, wherein the liquid media from the corroding environment is circulated externally into a glassware by a pump, wherein the glassware contains holes and fix to insert the multi-electrode array sensor into the liquid media for in-situ estimation of CI concentration of the corroding environment;

obtain from the corroding environment, a plurality of electrochemical signals using the multi-electrode array sensor, wherein the plurality of electrochemical signals are obtained using the multi-electrode array sensor having a sensing working electrode made of mild steel, a reference electrode made of a graphite stick or Ag—AgCI type electrode, and a counter electrode made of Pt mesh or graphite stick bundle, wherein the plurality of electrochemical signals is obtained by applying +0.1 V DC voltage into the working electrode with respect to the reference electrode, and a DC current is measured at the counter electrode;

analyze the plurality of electrochemical signals to obtain a plurality of parameters comprising an electrochemical impedance spectra and a DC current versus time;

identify a plurality of features from the plurality of parameters comprising:

a charge transfer resistance feature is obtained from the electrochemical impedance spectra parameter,

an initial DC current and an intermediate DC current are obtained from the DC current vs time, wherein the initial DC current is measured at a predetermined elapsed time of 10 seconds, and the intermediate DC current is measured as an average DC current between a predetermined time range of 2000 to 3000 seconds; and

estimate the corrosion inhibitor (CI) concentration from the plurality of features using a trained machine learning model comprising,

a regression model determining a quantitative estimate of the corrosion inhibitor (CI) concentration, wherein the regression model is trained using a training dataset utilized as a ground truth to determine the quantitative estimate of the corrosion inhibitor (CI) concentration, wherein training the regression model for determining the quantitative estimate of the corrosion inhibitor (CI) concentration comprises:

determining a reference corrosion inhibitor (CI) concentration for the regression model based on the training dataset, wherein the reference corrosion inhibitor (CI) concentration for the regression model varies between 0-0.5 mM (in millimolar unit);

computing a cost function using a pre-defined initial weightage factor θ corresponding to the plurality of features, the training dataset, and a measure of error for fitting the cost function to the reference corrosion inhibitor (CI) concentration of the regression model;

minimizing the computed cost function corresponding to the plurality of features, based on a learning rate α, a weightage factors θj for the plurality of features and the total number of iterations performed for the cost function;

determining a final weightage factors corresponding to the plurality of features; and

determining the quantitative estimate of the corrosion inhibitor (CI) of the plurality of features using the final weightage factors; and

a classification model determining a qualitative estimate of the corrosion inhibitor (CI) concentration, wherein the classification model is trained using the training dataset utilized as the ground truth to determine the qualitative estimate of the corrosion inhibitor (CI) concentration, wherein training the classification model for determining the qualitative estimate of the corrosion inhibitor (CI) concentration comprises:

determining a reference corrosion inhibitor (CI) concentration for the classification model based on the training dataset, wherein the reference corrosion inhibitor concentration for the classification model varies between 0-6.0 mM (in millimolar unit);

computing a cost function using a logistic function, a pre-defined initial weightage factors θ corresponding to the plurality of features, the training dataset and a measure of error for fitting the cost function to the reference corrosion inhibitor (CI) concentration of the classification model;

minimizing the computed cost function corresponding to the plurality of features, based on a learning rate (α), a weightage factors θj for the plurality of features and the total number of iterations performed for the cost function;

determining a final weightage factors corresponding to the plurality of features; and

determining the qualitative estimate of the corrosion inhibitor (CI) of the plurality of features using the final weightage factors.

5. The system ( 102 ) as claimed in claim 4 , wherein the corrosion inhibitor concentration for any unknown liquid is estimated using the corrosion inhibitor concentration range of the regression model.

6. The system ( 102 ) as claimed in claim 4 , wherein the corrosion inhibitor concentration for any unknown liquid is estimated using the corrosion inhibitor concentration range of the classification model.

7. One or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors perform actions comprising:

positioning ( 302 ), via the one or more hardware processors, a multi-electrode array sensor in a corroding environment for estimating a corrosion inhibitor (CI) concentration associated with a liquid media from the corroding environment, wherein the liquid media from the corroding environment is circulated externally into a glassware by a pump, wherein the glassware contains holes to insert and fix the multi-electrode array sensor into the liquid media for in-situ estimation of CI concentration of the corroding environment;

obtaining ( 304 ), via the one or more hardware processors, from the corroding environment, a plurality of electrochemical signals using the multi-electrode array sensor, wherein the plurality of electrochemical signals are obtained using the multi-electrode array sensor having a sensing working electrode made of mild steel, a reference electrode made of a graphite stick or Ag—AgCI type electrode, and a counter electrode made of Pt mesh or graphite stick bundle, wherein the plurality of electrochemical signals is obtained by applying +0.1 V DC voltage into the working electrode with respect to the reference electrode, and a DC current is measured at the counter electrode;

analyzing ( 306 ), via the one or more hardware processors, the plurality of electrochemical signals to obtain a plurality of parameters comprising an electrochemical impedance spectra and a DC current versus time;

identifying ( 308 ), via the one or more hardware processors, a plurality of features from the plurality of parameters comprising:

a charge transfer resistance feature obtained from the electrochemical impedance spectra parameter, and

an initial DC current and an intermediate DC current obtained from the DC current versus time, wherein the initial DC current is measured at a predetermined elapsed time of 10 seconds, and the intermediate DC current is measured as an average DC current between a predetermined time range of 2000 to 3000 seconds; and

estimating ( 310 ), via the one or more hardware processors, the corrosion inhibitor (CI) concentration from the plurality of features using a trained machine learning model comprising,

a regression model determining a quantitative estimate of the corrosion inhibitor (CI) concentration, wherein the regression model is trained using a training dataset utilized as a ground truth to determine the quantitative estimate of the corrosion inhibitor (CI) concentration, wherein training the regression model for determining the quantitative estimate of the corrosion inhibitor (CI) concentration comprises:

determining a reference corrosion inhibitor (CI) concentration for the regression model based on the training dataset, wherein the reference corrosion inhibitor (CI) concentration for the regression model varies between 0-0.5 mM (in milli molar unit);

computing a cost function using a pre-defined initial weightage factor θ corresponding to the plurality of features, the training dataset, and a measure of error for fitting the cost function to the reference corrosion inhibitor (CI) concentration of the regression model;

minimizing the computed cost function corresponding to the plurality of features, based on a learning rate α, a weightage factors θj for the plurality of features and the total number of iterations performed for the cost function;

determining a final weightage factors corresponding to the plurality of features; and

determining the quantitative estimate of the corrosion inhibitor (CI) of the plurality of features using the final weightage factors; and

a classification model determining a qualitative estimate of the corrosion inhibitor (CI) concentration, wherein the classification model is trained using the training dataset utilized as the ground truth to determine the qualitative estimate of the corrosion inhibitor (CI) concentration, wherein training the classification model for determining the qualitative estimate of the corrosion inhibitor (CI) concentration comprises:

determining a reference corrosion inhibitor (CI) concentration for the classification model based on the training dataset, wherein the reference corrosion inhibitor concentration for the classification model varies between 0-6.0 mM (in millimolar unit);

computing a cost function using a logistic function, a pre-defined initial weightage factors θ corresponding to the plurality of features, the training dataset and a measure of error for fitting the cost function to the reference corrosion inhibitor (CI) concentration of the classification model;

minimizing the computed cost function corresponding to the plurality of features, based on a learning rate (α), a weightage factors θj for the plurality of features and the total number of iterations performed for the cost function;

determining a final weightage factors corresponding to the plurality of features; and

determining the qualitative estimate of the corrosion inhibitor (CI) of the plurality of features using the final weightage factors.