Deep Learning Control of Toluene Chlorination Parameters
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Solution Overview
Problem
The existing continuous toluene chlorination process relies on manual adjustment of reaction parameters, which is costly and unable to make timely adjustments, limiting the efficiency and stability of the reaction.
Innovation Solution
An automatic optimization system based on deep learning is introduced, including data acquisition, preprocessing, division, and a deep belief network (DBN) for predicting toluene mass fraction, with a validation module and expert solution database to adjust parameters automatically, ensuring the reaction stays within a preset standard interval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual adjustment of reaction parameters is used, then operation flexibility is maintained, but adjustment timeliness and reaction stability deteriorate
Solution Approach 1:
The system uses self-service by implementing automatic parameter adjustment through the DBN model and actuator, eliminating the need for manual intervention. The system monitors toluene mass fraction in real-time and automatically adjusts reaction parameters based on predicted values, making the system self-regulating and responsive without human operation.
Solution Approach 2:
The patent replaces the mechanical manual adjustment system with an intelligent automated system. The DBN model predicts toluene mass fraction, and the actuator automatically adjusts reaction parameters, substituting human manual operations with an automated control system that provides timely and consistent adjustments.
2Device complexity
If manual adjustment of reaction parameters is used, then system complexity is reduced, but manufacturing precision and yield deteriorate
Solution Approach 1:
The system implements feedback control by continuously monitoring the toluene mass fraction in the reaction mixture, comparing it with predicted values from the DBN model, and automatically adjusting reaction parameters to maintain the mass fraction within the standard interval, thereby improving control precision through closed-loop feedback.
Solution Approach 2:
The patent applies parameter changes by using the DBN model to predict toluene mass fraction based on reaction parameters and dynamically adjusting these parameters through the actuator to optimize the reaction process and maintain product quality within specified intervals.
3Productivity
If deep learning automatic optimization system is implemented, then reaction stability and productivity are improved, but device complexity increases
Solution Approach 1:
The system achieves multi-functionality by integrating data acquisition, preprocessing, DBN model construction, prediction, validation, and automatic parameter adjustment into a single unified platform. This universal system handles multiple tasks simultaneously, improving productivity while consolidating complexity into one integrated solution rather than separate systems.
Data Source
AI summary
The present disclosure provides an automatic optimization system for a toluene chlorination parameter based on deep learning. The automatic optimization system for a toluene chlorination parameter based on deep learning includes a toluene chlorination data acquisition module, a toluene chlorination data preprocessing module, a toluene chlorination data division module, a toluene chlorination deep belief network (DBN) construction module, a toluene mass fraction prediction module, a toluene mass fraction validation module, and a toluene chlorination parameter optimization module. The automatic optimization system for toluene chlorination parameter provided by the present disclosure can realize a complete process including automatic detection, information processing, analysis and determination, operation control and expected goal implementation. While reducing manpower, the present disclosure improves the stability of the chemical reaction process, and improves the yield and purity of the target component.