Machine Learning Waste Modeling for Sustainability-Constrained Process Tuning
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Solution Overview
Problem
Existing life cycle assessment (LCA) tools are unable to optimize process parameters for sustainability, lacking the ability to predict and adjust inputs to minimize waste and meet sustainability constraints proactively.
Innovation Solution
A computer-implemented method using machine learning (ML) to optimize process inputs and outputs, incorporating a trained ML model that predicts waste metrics and adjusts parameters to meet sustainability and process constraints, employing techniques like ML regression, Jacobian matrix optimization, and stochastic gradient descent to minimize waste and costs.
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 compared, but the tools cannot optimize parameters for future processes proactively
Solution Approach 1:
The patent applies preliminary action by using machine learning models to predict waste metrics and optimize process parameters before actual process execution. The system performs simulations and generates optimization recommendations in advance, allowing proactive parameter adjustment rather than reactive analysis of existing processes.
Solution Approach 2:
The patent replaces conventional mechanical LCA simulation methods with machine learning-based predictive modeling. Instead of using traditional simulation tools that require extensive data processing and cannot optimize parameters, the system employs trained ML models that can rapidly predict waste metrics and provide optimization recommendations.
2Measurement precision
If machine learning models process multiple waste metrics, then prediction accuracy improves, but computational complexity and data dimensionality increase
Solution Approach 1:
The patent applies the extraction principle by isolating and reconstructing the waste vector separately from the main process model. The waste vector is extracted as a distinct component that can be optimized independently, reducing the complexity of the overall system while maintaining prediction accuracy for multiple waste metrics.
Solution Approach 2:
The patent segments the prediction model into distinct components: the main process model and the waste metric prediction model. This segmentation allows each component to be optimized independently, reducing computational complexity while maintaining overall prediction accuracy for multiple waste metrics.
3Loss of substance
If process parameters are optimized for sustainability constraints, then waste metrics are minimized, but the risk of failing to meet process constraints increases
Solution Approach 1:
The patent applies feedback by implementing an iterative optimization process where the ML model predicts waste metrics, the system generates optimization recommendations, and the process parameters are adjusted accordingly. This closed-loop feedback mechanism ensures that sustainability constraints are met while maintaining compliance with process constraints through continuous refinement.
Solution Approach 2:
The patent uses preliminary action by performing simulations and generating optimization recommendations before actual process execution. This allows potential constraint violations to be identified and corrected in advance, ensuring both waste minimization and process constraint compliance when the optimized parameters are implemented.
Data Source
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AI summary
A method of performing sustainability optimization includes processing a set of inputs using a trained machine learning model to generate a set of outputs, wherein the set of inputs correspond to configuration parameters of a process configured to be performed on a physical machine, and wherein the set of outputs includes a plurality of predicted waste metrics resulting from performance of the process on the physical machine. The method further includes optimizing the set of inputs and the set of outputs for meeting sustainability constraints in view of prospcess constraints and outputting a recommendation for operating the process on the physical machine based on the optimized set of inputs and set of outputs, for avoiding a risk of failure to operate the process, while meeting the sustainability constraints and the process constraints.