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C4 olefin production conditions optimizing based on a hybrid model.
Yancong Zhou1, Chenheng Xu2, Yongqiang Chen1
1School of Information Engineering, Tianjin University of Commerce, Tianjin 300134, China.
Mathematical Biosciences and Engineering : MBE
|July 28, 2023
Summary
Artificial intelligence optimizes ethanol reaction conditions for higher C4 olefin yields. The hybrid GXGB-SSA model, enhanced by SHAP values, achieved a 25.46% increase in C4 olefin production compared to manual experiments.
Area of Science:
- Chemical Engineering
- Catalysis
- Artificial Intelligence
Background:
- Low C4 olefin yields are a challenge due to complex product mixtures and resource-intensive manual optimization of ethanol reaction conditions.
- Developing efficient methods to determine optimal reaction parameters is crucial for maximizing C4 olefin production.
Purpose of the Study:
- To introduce an artificial intelligence-based approach for optimizing ethanol reaction conditions to maximize C4 olefin yield.
- To develop a hybrid model combining GXGB and SSA for complex optimization problems.
- To utilize SHAP values for interpreting the influence of reaction conditions on C4 olefin yield.
Main Methods:
- A Gaussian noise-based eXtreme Gradient Boosting tree (GXGB) was developed to establish the objective function for optimization.
- The Sparrow Search Algorithm (SSA) was integrated with GXGB to create the GXGB-SSA hybrid model for enhanced optimization efficiency.
- SHAP (SHapley Additive exPlanations) values were employed to analyze the impact of individual reaction parameters on C4 olefin yield.
Main Results:
- The GXGB-SSA model successfully identified optimal ethanol reaction conditions yielding a maximum C4 olefin output of 5611.46%.
- Optimized conditions included specific Co loading, Co/SiO2 and HAP mass ratio, ethanol concentration, catalyst mass, and reaction temperature.
- The AI-driven approach resulted in a 25.46% higher yield compared to the best results from manual experimentation (4472.81%).
Conclusions:
- The proposed GXGB-SSA hybrid model effectively optimizes complex reaction conditions for C4 olefin production.
- The integration of SHAP values provides valuable insights into parameter influence, enabling informed adjustments for yield maximization.
- This AI-driven methodology offers a significant advancement over traditional experimental approaches for chemical process optimization.
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