Industrial Process Circularity Optimization for Nonlinear Variables
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Solution Overview
Problem
Achieving circularity in industrial processes is challenging due to the complexity of optimizing multiple input variables and their non-linear relationships across different components, leading to inefficient and time-consuming brute force calculations.
Innovation Solution
A process circularity optimization system utilizing a non-linear attribute optimizer with a mathematical model based on sequential quadratic programming and the APOPT solver, which generates and filters multiple combinations of input variable values to optimize target attributes within predefined constraints, prioritizing solutions based on probability of success and user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If brute force calculations are used to optimize multiple input variables and their non-linear relationships, then comprehensive optimization coverage is achieved, but processing time and computational resources increase significantly
Solution Approach 1:
The patent transforms the optimization problem by changing parameters from direct brute force evaluation of all input variable combinations to an objective function-based approach. The system defines an objective function that represents the target attribute to be optimized, and uses mathematical optimization algorithms (such as gradient descent, genetic algorithms, or other iterative methods) to search for optimal input variable values. This parameter transformation reduces computational complexity from exponential to polynomial time, enabling real-time optimization while maintaining comprehensive coverage of the solution space.
2Object-affected harmful factors
If comprehensive understanding of entire value chain is implemented to achieve circularity, then sustainability and environmental friendliness are improved, but system complexity and implementation difficulty increase
Solution Approach 1:
The patent introduces an intermediary optimization system that acts as a mediator between the complex value chain processes and the circularity objectives. This system includes: (1) a data collection module that aggregates information from various stages of the value chain, (2) an objective function formulation module that translates sustainability goals into mathematical criteria, and (3) an optimization engine that computes optimal operating parameters. This intermediary layer simplifies the complex value chain analysis by providing structured methodologies and automated computations, making circularity achievement more manageable despite the inherent system complexity.
Data Source
AI summary
A process circularity optimization system improves the circularity of industrial processes via a non-linear analysis of process attributes. A process input provides one or more target attributes to be optimized along with an objective function for improving the circularity of a manufacturing process. The input variables to be set to optimize the target attributes along with any constraints are accessed. Multiple input variable value combinations are generated via non-linear processing of the input variables and a combination of input variable values with the highest probability of success is implemented in the manufacturing process to optimize circularity.