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204 results on '"Wei, Xianyong"'

9. Synergistic CO2 mineralization using coal fly ash and red mud as a composite system.

Author

Yao, Zhenchao, Wang, Yugao, Shen, Jun, Niu, Yanxia, Yang, Jiang Feng, and Wei, Xianyong

Subjects
COAL ash, FLY ash, MINERALIZATION, MUD, INDUSTRIAL wastes, SOLID waste, CALCIUM carbonate
Abstract

CO2 mineralization plays a critical role in the storage and utilization of CO2. Coal fly ash (CFA) and red mud (RM) are widely utilized as CO2 mineralizers. However, the inert calcium species in CFA limit its carbonation capacity, meanwhile the substantial Ca2+ releasing of RM is hindered by a covering layer of calcium carbonate. In this study, CO2 mineralization in a composite system of CFA and RM was investigated to enhance the carbonation capacity. Multiple analyzers were employed to characterize the raw materials and resulting mineralization products. The results demonstrated that a synergistic effect existed in the composite system of CFA and RM, resulting in improving CO2 mineralization rate and efficiency. The produced calcium carbonate was ectopically attached the surface of CFA in the composite system, thus slowing down its coverage on the surface of RM. This phenomenon facilitated further releasing Ca2+ from the internal RM, thereby enhancing CO2 mineralization efficiency. Meanwhile, the inclusion of RM significantly improved the alkalinity of the composite system, which not only promoted the dissolution of Ca2+ of the inert CaSO4(H2O)2 in CFA, but also accelerated CO2 mineralization rate. The investigation would be beneficial to CO2 mineralization using industrial solid wastes. [ABSTRACT FROM AUTHOR]

Published
2024
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24. Sentiment Analysis of Social Network Comment Text Based on LSTM and Bert.

Author

Si, Hongying and Wei, Xianyong

Subjects
*SENTIMENT analysis, *SOCIAL network analysis, *LANGUAGE models, *DATA mining, *TASK analysis
Abstract

This paper has the aim of solving problems in research studies on the analysis tasks of text emotion; the problems are the low utilization of text, the difficulty of effective information extraction, the failure of recognizing word polysemy with effectiveness. Thus, based on LSTM and Bert, the method of sentiment analysis on text is adopted. To be precise, word embedding of dataset in view of the skip-gram model is used for training course. In each sample, the word embeddings combine matric with the two-dimensional feature to be neural network input. Next, construction of analysis model for text sentiment combines Bert pre-training language model and long short-term memory (LSTM) network, using the word vector pre-trained by Bert instead of that trained in the traditional way to dynamically generate the semantic vector according to the word context. Finally, the semantic representation of words from text is improved by effectively identifying the polysemy of words, and the semantic vector is input into the LSTM to capture the semantic dependencies, thereby enhancing the ability to extract valid information. The Accuracy, Precision, Recall and F-Measure for the method of Bert–LSTM based on analysis of text sentiment are 0.89, 0.9, 0.84 and 0.87, indicating high value than the compared ones. Thus, the proposed method significantly outperforms the comparison methods in text sentiment analysis. [ABSTRACT FROM AUTHOR]

Published
2023
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