AI Reactor Input Control for On-Spec Chemical Production
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Solution Overview
Problem
Chemical process control is challenging due to varying reactivity conditions and the difficulty in understanding and measuring chemical processes, leading to high rates of defective products and energy wastage, with manual corrections often involving trial and error.
Innovation Solution
An AI model-based process control system that includes a data storage unit, data correction unit, data derivation unit, and output unit to derive optimal reactor input conditions for composition, temperature, flow rate, and pressure, using machine learning algorithms like linear regression and neural networks to predict and adjust reactor conditions for on-spec product production.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual trial and error method is used to correct defective product conditions, then user can attempt to adjust production parameters, but the process becomes time-consuming and inefficient
Solution Approach 1:
The patent replaces manual trial-and-error adjustment with an AI-based automated system that uses machine learning models to predict optimal production conditions and automatically adjust parameters, eliminating the need for human operators to manually test and correct defective product conditions
Solution Approach 2:
The AI system enables the production process to self-correct by automatically analyzing process data, predicting optimal conditions, and adjusting parameters without human intervention, allowing the system to service itself in correcting defective product conditions
2Productivity
If AI model is implemented for automated process control, then productivity and accuracy improve, but system complexity increases
Solution Approach 1:
The AI-based process control system is designed to handle multiple functions including data collection from various sensors, data preprocessing, model training, prediction, and automatic control adjustment within a single integrated platform, reducing the need for separate systems for each function
Solution Approach 2:
The patent introduces an AI model as an intermediary layer between raw process data and control decisions, which learns optimal mappings from historical data and automatically translates process conditions into control actions, simplifying the overall control architecture
Data Source
AI summary
The present invention relates to a method for generating an artificial intelligence model for process control, a process control system based on the artificial intelligence model, and a reactor comprising same, the present invention may easily derive an optimal reactor input condition for achieving the target operation condition of the reactor and the target physical property value of the product by using the AI model.