Forty years of Computers and Chemical Engineering: Analysis of the field via text mining techniques

Abstract Since its launch in 1977, Computers & Chemical Engineering has published numerous papers on the application of computing technology to chemical engineering problems. In this paper, we present a topic analysis of the journal using various text mining techniques. In particular, we examine the dramatic growth of the journal’s topic coverage since the 1970s. We found that the journal exhibits a similar popularity across all 18 topics covered today. Certain topics have grown rapidly in the last decade, and are now among the journal’s most covered topics. We also studied the relationship between topic coverage and citation count. According to our results, the highest cited articles have similar characteristics and are often unique.

[1]  Thomas Hansmann,et al.  Big Data - Characterizing an Emerging Research Field Using Topic Models , 2014, 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT).

[2]  Michael L. Mavrovouniotis,et al.  Hierarchical neural networks , 1992 .

[3]  Margaret E. Roberts,et al.  A Model of Text for Experimentation in the Social Sciences , 2016 .

[4]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .

[5]  Sanjeev Arora,et al.  Learning Topic Models -- Going beyond SVD , 2012, 2012 IEEE 53rd Annual Symposium on Foundations of Computer Science.

[6]  E. F. Vogel,et al.  A plant-wide industrial process control problem , 1993 .

[7]  Ramaswamy Vaidyanathan,et al.  Process fault detection and diagnosis using neural networks , 1990 .

[8]  J. Savković-Stevanović,et al.  Neural networks for process analysis and optimization: Modeling and applications , 1994 .

[9]  N. Meinshausen,et al.  High-dimensional graphs and variable selection with the Lasso , 2006, math/0608017.

[10]  B. Ydstie.  Forecasting and control using adaptive connectionist networks , 1990 .

[11]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[12]  N. V. Bhat,et al.  Use of neural nets for dynamic modeling and control of chemical process systems , 1990 .

[13]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[14]  Derek Greene,et al.  An analysis of the coherence of descriptors in topic modeling , 2015, Expert Syst. Appl..

[15]  Mark Stevenson,et al.  Evaluating Topic Coherence Using Distributional Semantics , 2013, IWCS.

[16]  Venkat Venkatasubramanian,et al.  Signed Digraph based Multiple Fault Diagnosis , 1997 .

[17]  Chong Wang,et al.  Reading Tea Leaves: How Humans Interpret Topic Models , 2009, NIPS.

[18]  John D. Lafferty,et al.  A correlated topic model of Science , 2007, 0708.3601.

[19]  Petr Sojka,et al.  Software Framework for Topic Modelling with Large Corpora , 2010 .

[20]  T. Landauer,et al.  Indexing by Latent Semantic Analysis , 1990 .

[21]  Mark A. Kramer,et al.  Improvement of the backpropagation algorithm for training neural networks , 1990 .

[22]  Mark Steyvers,et al.  Finding scientific topics , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[23]  David M. Mimno,et al.  Computational historiography: Data mining in a century of classics journals , 2012, JOCCH.

[24]  H. Sebastian Seung,et al.  Learning the parts of objects by non-negative matrix factorization , 1999, Nature.

[25]  R. Sargent,et al.  A general algorithm for short-term scheduling of batch operations */I , 1993 .

[26]  S. Joe Qin,et al.  A unified geometric approach to process and sensor fault identification and reconstruction : The unidimensional fault case , 1998 .

[27]  Li Gao,et al.  Evolutionary polymorphic neural network in chemical process modeling , 2001 .

[28]  Daniel Jurafsky,et al.  Studying the History of Ideas Using Topic Models , 2008, EMNLP.

[29]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part III: Process history based methods , 2003, Comput. Chem. Eng..

[30]  Jeffrey Dean,et al.  Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.

[31]  Lyle H. Ungar,et al.  Adaptive networks for fault diagnosis and process control , 1990 .

[32]  Sen Han,et al.  Design of CO2 hydrogenation catalyst by an artificial neural network , 2001 .

[33]  Andrew McCallum,et al.  Topics over time: a non-Markov continuous-time model of topical trends , 2006, KDD '06.

[34]  D. Depeyre,et al.  Some practical insights into neural network implementation in metallurgical industry , 1994 .

[35]  Derek Greene,et al.  Exploring the Political Agenda of the European Parliament Using a Dynamic Topic Modeling Approach , 2016, Political Analysis.

[36]  José A. Romagnoli,et al.  Topological preservation techniques for nonlinear process monitoring , 2015, Comput. Chem. Eng..

[37]  David Buttler,et al.  Exploring Topic Coherence over Many Models and Many Topics , 2012, EMNLP.

[38]  Mark A. Kramer,et al.  Diagnosis using backpropagation neural networks—analysis and criticism , 1990 .