Method and system for performing model-based multi-objective asset optimization and decision-making

A computerized method for performing multi-objective asset optimization and decision-making using predictive modeling, comprising:

• building, via a process manager application executing on a processor, at least two predictive models for an asset, the asset comprising a physical machine that is communicatively coupled to the processor, the building comprising:
• categorizing operational historical data of the asset that is retrieved from a storage device, the operation historical data categorized by at least one of:
• controllable variables;
• uncontrollable variables;
• output objectives;
• and
• constraints;
• selecting at least two output objectives or constraints;
• and
• identifying at least one controllable or uncontrollable variable suitable for achieving the at least two output objectives or constraints;
• inputting, via the process manager application, the at least one controllable or uncontrollable variable to each of the at least two predictive models;
• validating, via the process manager application, each predictive model;
• if results of the validating indicate a confidence level above a specified threshold, applying, via the process manager application, a live data stream of inputs from the asset to the predictive models;
• if results of the validating indicate a confidence level at or below a specified threshold, selecting, via the process manager application, at least one alternative controllable or uncontrollable variable for input to the predictive models;
• performing, via the process manager application, multi-objective optimization using the predictive models, comprising:
• specifying search constraints, comprising:
• upper and lower bounds for each input variable;
• and
• tolerance levels representing a range of values for achieving optimal output objectives, and constraints;
• applying a multi-objective optimization algorithm;
• and
• generating a Pareto Frontier, the Pareto Frontier including optimal input-output vectors;
• using results of the multi-objective optimization, selecting, via the process manager application, from the Pareto Frontier, a Pareto optimal input-output vector for deployment to the asset, the selected Pareto optimal input-output vector specifying an optimal operational state for the asset;
• and
• re-configuring the asset, via the process manager application, using the Pareto optimal input-output vector to realize the optimal operational state.