Machine Learning Optimization System for Caustic Soda Production
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Solution Overview
Problem
Current optimization methods for caustic soda production, such as bipolar membrane electrodialysis and direct electrosynthesis, are inefficient in managing complex energy consumption due to reliance on human judgment and inability to handle high-order polynomial functions, leading to high electricity costs.
Innovation Solution
A machine learning-based optimization system that solves multivariant third-degree polynomial equations to optimize electricity and production costs by managing electrolyte load, coal blend, and current densities, using a cloud database and AI to provide customized recommendations for electrolyzer load and production planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional optimization methods (human judgment, Excel, Gurobi, PuLP, Baron) are used, then implementation is simple, but they cannot handle complex multivariate high-order polynomial functions
Solution Approach 1:
The patent replaces traditional mechanical/mathematical optimization tools (Excel, Gurobi, PuLP, Baron) with a machine learning-based optimization system. The ML model learns optimal solutions from historical data and automatically handles complex multivariate polynomial functions without requiring explicit mathematical modeling, thus achieving high adaptability while maintaining system simplicity.
Solution Approach 2:
The patent transforms the optimization problem from solving complex polynomial equations directly to training a machine learning model on historical operational data. By changing the approach from mathematical computation to statistical learning, the system can handle high-order polynomial relationships without the computational complexity of traditional optimization solvers.
2Use of energy by moving object
If bipolar membrane electrodialysis or direct electrosynthesis processes are used, then energy consumption is reduced, but the optimization is still based on human judgment which is not perfect and optimal
Solution Approach 1:
The patent implements a feedback-based optimization system where the machine learning model continuously learns from historical operational data and performance outcomes. The system analyzes past decisions and their results, automatically adjusting optimization strategies to improve both energy efficiency and optimization accuracy without relying on human judgment.
Solution Approach 2:
The optimization system is self-learning and self-improving through machine learning algorithms. It automatically processes historical data, identifies optimal patterns, and generates optimization recommendations without human intervention, thereby achieving both energy reduction and high optimization precision simultaneously.
3Measurement precision
If machine learning-based optimization system is implemented, then complex multivariate polynomial functions can be solved accurately, but implementation complexity increases
Solution Approach 1:
The patent performs preliminary training of the machine learning model using historical operational data before deployment. This pre-training phase captures complex polynomial relationships in advance, allowing the system to make accurate optimization decisions during operation without requiring complex real-time computations, thus balancing accuracy with implementation simplicity.
Data Source
AI summary
The present invention technically relates to an optimization system that is designed to solve the multivariate polynomial equation based on machine learning. The optimization system comprises a front-end framework to receive a plurality of optimizing queries and a back end to execute the received query. A serve database stores a plurality of data entered on the input interface of the front end and a communication network transmit various recommendation executed by the back-end framework. The optimization system helps to minimize the electricity cost in caustic soda production by executing the optimizing constraints.