Tracing the technological trajectory of coal slurry pipeline transportation technology: An HMM-based topic modeling approach

Jinfeng Wang · Kang Li · Lijie Feng

10 citations0/0 files storedFrontiers in Energy Research

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Intellectual Property and Patents Computational and Text Analysis Methods Coal Combustion and Slurry Processing

Abstract

Coal slurry pipeline transportation is an important way to realize green coal logistics. However, there are still challenges in understanding the cognitive aspects of coal slurry pipeline transportation technology development trajectory. This study attempts to trace and predict the technology trend from patent texts through the stochastic process analysis of topic evolution. It helps understand the challenges in the development process of coal slurry pipeline transportation technology. And capture trends and development characteristics of the technology to improve research and development (R&D) efficiency and sustainability. As a result, this study extracts potential technology topics from patent text by using the Latent Dirichlet Distribution method. Then, a Word2vec-based topic word vector model is applied to calculate the cosine similarity between topics. And the HMM-based topic evolution trend model is constructed by introducing the Hidden Markov Model (HMM) which can portray a dual stochastic process. Finally, it is used to analyze and predict trends in the technological evolution of this field. It was found that the advancement of technology related to pulping is fundamental to promoting the development of coal slurry pipeline transportation technology, which is also a common research topic. Finally, technologies related to pipeline transportation capacity enhancement and the industrial application of coal slurry will be the focus of future R&D in this field with broad research and application prospects. This study is intended to provide directions for sustainable R&D activities in coal slurry pipeline transportation technology, facilitate interdisciplinary discussions, and provide objective data for future decisions making for scientists and R&D managers in this field.

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References

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