Prediction of methanol to aromatics products based on virtual sample generation technology

Abstract Aiming at the difficulty of high data acquisition, high data repetition rate, small sample size, and large data information interval in two-stage methanol to aromatics (MTA) process, an extreme learning machine modeling method based on virtual sample generation technology and genetic algorithm (GA) optimization is proposed to predict the total yield of benzene, toluene and xylene (BTX), which are important products of the process. First, based on the data samples collected from the two-stage methanol to aromatics pilot plant, the input data for the model are expanded using the multi-distribution mega-trend-diffusion technology, and then the virtual sample set is obtained using the limit learning machine model optimized by GA. After verifying the rationality of the data, the BTX yield prediction model is established using the fusion of the original data set and the virtual data set. Through the verification of the MTA experimental data and the comparative analysis of the other two modeling methods, the results show that the modeling method based on virtual samples has the best precision performance, and the method has good stability, which is suitable for predicting BTX yield in methanol to aromatics process.

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	GAO Tun
	YANG Chen
	YU Feng
	ZHANG Wei
	ZHANG Kan

Keywords： methanol to aromatics product content prediction multi-distribution mega-trend-diffusion virtual sample extreme learning machine

Received 2023-03-08;

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GAO Tun, YANG Chen, YU Feng, ZHANG Wei, ZHANG Kan.Prediction of methanol to aromatics products based on virtual sample generation technology[J] Chemcial Industry and Engineering, 2025,V42(2): 145-153

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