Machine Learning Optimization for Sustainable Multi-Process Systems
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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 machine learning (ML) to optimize inputs and outputs of multiple processes within a complex system, incorporating a trained ML model to predict sustainability constraints and process constraints, and output recommendations for optimal operation while minimizing waste and risk of failure.
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, but the tools cannot optimize parameters for future states of multiple processes
Solution Approach 1:
The patent replaces conventional deterministic LCA calculation methods with machine learning models that can predict sustainability outcomes and enable optimization. The ML models learn from historical LCA data and process parameters to generate optimized process configurations that meet sustainability constraints, transforming the reactive LCA approach into a proactive optimization system.
2Object-generated harmful factors
If waste metrics are minimized for sustainability constraints, then environmental performance improves, but the complexity of optimizing multiple interconnected processes increases
Solution Approach 1:
The patent segments the complex multi-process optimization problem into individual process optimizations, where each process is optimized separately using ML models while considering its specific waste metrics and sustainability constraints. This segmentation allows the system to handle complexity by breaking down the overall optimization into manageable process-level sub-problems that can be solved independently and then integrated.
3Loss of substance
If process parameters are optimized for sustainability, then waste is reduced, but the risk of failure to operate processes reliably increases without proper constraints
Solution Approach 1:
The patent applies preliminary anti-action by incorporating process constraints and risk assessments into the optimization framework before determining optimal parameters. The ML models are trained to recognize feasible operating regions that balance waste reduction with reliability requirements, and the system predicts potential failures or constraint violations in advance, allowing corrective actions to be taken before actual operational problems occur.
Data Source
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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.