Recommending optimum configurations in industrial control systems for improving quality of product

A system comprising:

• an input/output interface, to receive a historical batch data of an industrial manufacturing unit comprising a plurality of operating parameters, one or more quality values, end product quality score and associated quality indicators, and one or more predefined constraints associated with the industrial manufacturing unit;
• a memory in communication with the one or more hardware processors, wherein the one or more hardware processors are configured to execute programmed instructions stored in the memory to:
• pre-process the received historical batch data for data sufficiency check and noise estimation, wherein the data sufficiency is an indicator of a probability of improvement on the available data;
• build a multilayer perceptron (MLP) model based on the pre-processed historical batch data to learn a relationship among the plurality of operating parameters, the one or more quality values, and end product quality score and associated quality indicators, while adhering to the one or more predefined constraint values, wherein the historical batch data is annotated with a domain specific label;
• analyze the learnt relationship among the plurality of operating parameters and the end product quality score and associated quality indicators as a maximization problem of a quality;
• determine a minimization problem of the quality by converting the maximization problem of quality using a gradient decent technique to achieve a global minima;
• select a predefined set of data with maximum quality to train a regression model for each of the plurality of controllable parameters, wherein the trained regression model predicts value of each controllable parameter based on the plurality of non-controllable parameters;
• determine a candidate point of the minimization problem by using the values corresponding to the plurality of controllable parameters and the plurality of uncontrollable parameters;
• create a soft ball of a predefined radius around a candidate key to overcome noisy data around the determined optimal starting point of the minimization problem and to overcome a local minima and converge to a global minima, wherein a spherical gaussian distribution technique is used to simulate the created soft ball;
• perform a geometric decay with a predefined decay factor for a momentum of the soft ball in the minimization problem to predict an optima, wherein the predicted optima is an optimal quality for the values of the plurality of controllable parameters;
• converge the predicted optima to the global minima through a gradient descent through iterative optimization of the soft ball from the candidate point in the minimization problem using a cost function in terms of the quality values and the constraint values, wherein the cost function is resultant sum of a mean squared error (MSE) of negative loss function of quality score and soft plus function of defined constraints;
• recommend the values of the plurality of controllable parameters to obtain the optimal quality from the industrial manufacturing unit;
• send the recommended values of the plurality of controllable parameters as set points for the industrial manufacturing unit, to an operator of the industrial manufacturing unit for a next batch run;
• receive after the next batch run actual set points for the industrial manufacturing unit along with an updated quality score;
• and
• retrain the MLP model based on the received actual set points and the updated quality score fed back as a new data set after getting a minimum number of feedbacks, thereby improving the MLP model.

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

• receiving, via an input/output interface, a historical batch data of an industrial manufacturing unit comprising a plurality of operating parameters, one or more quality values, end product quality score and associated quality indicators, and one or more predefined constraints associated with the industrial manufacturing unit, wherein the plurality of operating parameters comprise a plurality of controllable parameters and a plurality of uncontrollable parameters;
• pre-processing, via one or more hardware processors, the received historical batch data for data sufficiency check and noise estimation, wherein the data sufficiency is an indicator of a probability of improvement on available data;
• building, via the one or more hardware processors, a multilayer perceptron (MLP) model based on the pre-processed historical batch data to learn a relationship among the plurality of operating parameters, the one or more quality indicator values, and end product quality score and associated quality indicators, while adhering to the one or more predefined constraint values;
• analyzing, via the one or more hardware processors, the learnt relationship among the plurality of operating parameters and the end product quality score and associated quality indicators as a maximization problem of a quality;
• determining, via the one or more hardware processors, a minimization problem of the quality by inverting the maximization problem of the quality using a gradient decent technique to achieve a global minima;
• selecting, via the one or more hardware processors, a predefined set of data with maximum quality to train a regression model for each of the plurality of controllable parameters, wherein the trained regression model predicts value of each controllable parameter based on the plurality of non-controllable parameters;
• determining, via the one or more hardware processors, a candidate point of the minimization problem by using the values corresponding to the plurality of controllable parameters and the plurality of uncontrollable parameters;
• creating, via the one or more hardware processors, a soft ball of a predefined radius around a candidate key to overcome noisy data around the determined optimal starting point of the minimization problem and to overcome a local minima and converge to a global minima;
• performing, via the one or more hardware processors, a geometric decay with a predefined decay factor for a momentum of the soft ball in the minimization problem to predict an optima;
• converging, via the one or more hardware processors, the predicted optima to the global minima through a gradient descent through iterative optimization of the soft ball from the candidate point in the minimization problem using a cost function in terms of the quality values and the constraint values;
• recommending, via the one or more hardware processors, the values of the plurality of controllable parameters to obtain the optimal quality from the industrial manufacturing unit;
• sending, via the one or more hardware processors, the recommended values of the plurality of controllable parameters as set points for the industrial manufacturing unit, to an operator of the industrial manufacturing unit for a next batch run;
• receiving after the next batch run, via the one or more hardware processors, actual set points for the industrial manufacturing unit along with an updated quality score;
• and
• retraining, via the one or more hardware processors, the MLP model based on the received actual set points and the updated quality score fed back as a new data set after getting a minimum number of feedbacks, thereby improving the MLP model.