Machine Learning for Waste-Constrained Multi-Process Operations
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Solution Overview
Problem
Existing life cycle assessment (LCA) tools are inadequate for optimizing parameters in a system of multiple processes, lacking the ability to predict and optimize sustainability metrics across interconnected processes.
Innovation Solution
A computer-implemented method using a trained machine learning model to optimize inputs and outputs of multiple processes within a complex system, considering sustainability constraints and process constraints, while minimizing waste and avoiding operational risks, through a system that includes a data-driven AI framework and dynamic optimization techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional LCA tools are used to study existing processes, then environmental sustainability can be quantified and reported, but the tools cannot optimize parameters for future states of multiple processes
Solution Approach 1:
The patent transforms static LCA assessment into dynamic optimization by implementing a machine learning model that continuously learns from process data and dynamically adjusts parameters to optimize sustainability outcomes for multiple processes, enabling the system to adapt to changing conditions and optimize future states rather than merely assessing past performance
Solution Approach 2:
The patent replaces conventional mechanical LCA calculation methods with a machine learning-based optimization system that uses algorithms to automatically determine optimal parameters for multiple processes, substituting manual or rule-based sustainability assessment with intelligent automated optimization capable of handling complex multi-process interactions
2Object-generated harmful factors
If tools simulate waste vectors for simulated processes, then waste metrics can be identified, but the tools lack capability to optimize parameters across multiple interconnected processes
Solution Approach 1:
The patent creates a universal optimization platform that handles multiple waste metrics and multiple processes simultaneously through a single machine learning model, enabling the system to optimize parameters across interconnected processes while considering various waste outputs, thereby providing multi-functional capability that conventional single-process tools lack
3Productivity
If parameters are optimized for single simulated process, then that process efficiency improves, but system-wide sustainability across multiple processes cannot be optimized
Solution Approach 1:
The patent merges multiple single-process optimization capabilities into a unified multi-process optimization system, where the machine learning model simultaneously considers and optimizes parameters across interconnected processes, ensuring that improvements in one process do not negatively impact system-wide sustainability and that all processes are optimized in an integrated manner
Data Source
AI summary
A method is provided that includes processing, using a trained machine learning model, a set of inputs and a set of outputs associated with actual, simulated, or twinned performance of multiple processes of a complex system on one or more physical machines to generate objective functions for the respective multiple processes, optimizing the set of inputs and the set of outputs of the multiple processes for meeting sustainability constraints for the complex system in view of waste metrics associated with the set of outputs and further for meeting process constraints for the respective multiple processes, and outputting a recommendation for actually operating the multiple processes on the one or more physical machines based on the optimized set of inputs and set of outputs for avoiding a risk of failure to operate the multiple processes while meeting the sustainability constraints for the complex system and the process constraints for the respective, multiple processes.