John T. McCoy

583 total citations
14 papers, 434 citations indexed

About

John T. McCoy is a scholar working on Mechanical Engineering, Control and Systems Engineering and Water Science and Technology. According to data from OpenAlex, John T. McCoy has authored 14 papers receiving a total of 434 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Mechanical Engineering, 7 papers in Control and Systems Engineering and 3 papers in Water Science and Technology. Recurrent topics in John T. McCoy's work include Mineral Processing and Grinding (8 papers), Fault Detection and Control Systems (7 papers) and Minerals Flotation and Separation Techniques (3 papers). John T. McCoy is often cited by papers focused on Mineral Processing and Grinding (8 papers), Fault Detection and Control Systems (7 papers) and Minerals Flotation and Separation Techniques (3 papers). John T. McCoy collaborates with scholars based in South Africa, Canada and Australia. John T. McCoy's co-authors include Lidia Auret, Steve Kroon, Chris Aldrich, B. M. Herbst, João B. P. Soares, C. Dorfling, Gregorio Cadenas‐Pliego, Kevin Brooks, J.D. le Roux and Margret Bauer and has published in prestigious journals such as Computers & Chemical Engineering, Minerals Engineering and Macromolecular Symposia.

In The Last Decade

John T. McCoy

13 papers receiving 418 citations

Peers

John T. McCoy

ME

AI

CSE

WST

BE

Heikki Hyötyniemi Finland

A. Jahedsaravani Iran

Runda Jia China

Zhong-Sheng Chen China

Zeyu Yang China

Javier Bonilla Spain

Kai Bian China

Yujie Zhou China

Sridharakumar Narasimhan India

Huajun Wang China

Heikki Hyötyniemi Finland

John T. McCoy

285 ×1.2

ME

102 ×0.8

AI

252 ×2.3

CSE

202 ×2.0

WST

58 ×1.1

BE

Citations per year, relative to John T. McCoy John T. McCoy (= 1×) peers Heikki Hyötyniemi

Countries citing papers authored by John T. McCoy

Since Specialization

Citations

This map shows the geographic impact of John T. McCoy's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by John T. McCoy with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John T. McCoy more than expected).

Fields of papers citing papers by John T. McCoy

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by John T. McCoy. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by John T. McCoy. The network helps show where John T. McCoy may publish in the future.

Co-authorship network of co-authors of John T. McCoy

This figure shows the co-authorship network connecting the top 25 collaborators of John T. McCoy. A scholar is included among the top collaborators of John T. McCoy based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with John T. McCoy. John T. McCoy is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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14 of 14 papers shown

1.

Bauer, Margret, et al.. (2020). The Use of a Semi-Rigorous SAG Mill Model for a Hands-On Workshop. IFAC-PapersOnLine. 53(2). 11889–11894. 3 indexed citations

2.

McCoy, John T., et al.. (2018). Fault diagnosis and economic performance evaluation for a simulated base metal leaching operation. Minerals Engineering. 123. 128–143. 11 indexed citations

3.

McCoy, John T., Steve Kroon, & Lidia Auret. (2018). Variational Autoencoders for Missing Data Imputation with Application to a Simulated Milling Circuit. IFAC-PapersOnLine. 51(21). 141–146. 80 indexed citations

4.

McCoy, John T., et al.. (2018). Monitoring of a simulated milling circuit: Fault diagnosis and economic impact. Minerals Engineering. 120. 132–151. 21 indexed citations

5.

McCoy, John T. & Lidia Auret. (2018). Machine learning applications in minerals processing: A review. Minerals Engineering. 132. 95–109. 219 indexed citations

6.

McCoy, John T., et al.. (2018). Fault Diagnosis for an Industrial High Pressure Leaching Process with a Monitoring Dashboard. IFAC-PapersOnLine. 51(21). 117–122. 2 indexed citations

7.

McCoy, John T., et al.. (2018). Economic Assessment of Condition-based Maintenance with Statistical Process Monitoring. IFAC-PapersOnLine. 51(24). 867–873.

8.

Auret, Lidia, et al.. (2017). Performance of Convolutional Neural Networks for Feature Extraction in Froth Flotation Sensing. IFAC-PapersOnLine. 50(2). 13–18. 68 indexed citations

9.

McCoy, John T., et al.. (2017). Spiral Concentrator Interface Monitoring Through Image Processing: A Statistical Learning Approach. IFAC-PapersOnLine. 50(2). 53–58. 7 indexed citations

10.

McCoy, John T., et al.. (2013). Analysis of Slurry‐Phase Co‐Polymerization of Ethylene and 1‐Butene by Ziegler–Natta Catalysts Part 1: Experimental Activity Profiles. Macromolecular Reaction Engineering. 7(8). 350–361. 6 indexed citations

11.

Soares, João B. P., et al.. (2013). Heterogeneous Ethylene and Alpha‐Olefin Copolymerization Using Zirconocene Aluminohydride Complexes. Macromolecular Symposia. 325-326(1). 71–76. 4 indexed citations

12.

McCoy, John T., et al.. (2012). Another Look at Site Heterogeneity in Ziegler‐Natta Catalysts for Polyolefin Production. Macromolecular Symposia. 313-314(1). 19–32. 1 indexed citations

13.

McCoy, John T., et al.. (2011). A more efficient simulator of particle size distribution in slurry phase polyolefin systems. Computers & Chemical Engineering. 36. 68–78. 4 indexed citations

14.

McCoy, John T., et al.. (2005). An Electronic Vision System For Sorting Cotton Boll Worm Pupae By Sex. 2. 538–542. 8 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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