- Volumes 84-95 (2024)
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Volumes 72-83 (2023)
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Volume 83
Pages 1-258 (December 2023)
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Volume 82
Pages 1-204 (November 2023)
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Volume 81
Pages 1-188 (October 2023)
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Volume 80
Pages 1-202 (September 2023)
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Volume 79
Pages 1-172 (August 2023)
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Volume 78
Pages 1-146 (July 2023)
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Volume 77
Pages 1-152 (June 2023)
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Volume 76
Pages 1-176 (May 2023)
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Volume 75
Pages 1-228 (April 2023)
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Volume 74
Pages 1-200 (March 2023)
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Volume 73
Pages 1-138 (February 2023)
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Volume 72
Pages 1-144 (January 2023)
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Volume 83
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Volumes 60-71 (2022)
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Volume 71
Pages 1-108 (December 2022)
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Volume 70
Pages 1-106 (November 2022)
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Volume 69
Pages 1-122 (October 2022)
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Volume 68
Pages 1-124 (September 2022)
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Volume 67
Pages 1-102 (August 2022)
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Volume 66
Pages 1-112 (July 2022)
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Volume 65
Pages 1-138 (June 2022)
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Volume 64
Pages 1-186 (May 2022)
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Volume 63
Pages 1-124 (April 2022)
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Volume 62
Pages 1-104 (March 2022)
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Volume 61
Pages 1-120 (February 2022)
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Volume 60
Pages 1-124 (January 2022)
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Volume 71
- Volumes 54-59 (2021)
- Volumes 48-53 (2020)
- Volumes 42-47 (2019)
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- Volumes 30-35 (2017)
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- Volume 11 (2013)
- Volume 10 (2012)
- Volume 9 (2011)
- Volume 8 (2010)
- Volume 7 (2009)
- Volume 6 (2008)
- Volume 5 (2007)
- Volume 4 (2006)
- Volume 3 (2005)
- Volume 2 (2004)
- Volume 1 (2003)
An initial prediction of the particulate mode of flow in pneumatic conveying systems is beneficial as this knowledge can provide clearer direction to the pneumatic conveying design process. There are three general categories of modes of flow, two dense flows: fluidised dense phase and plug flow, and dilute phase only. Detailed in this paper is a review of the commonly used and available techniques for predicting mode of flow. Two types of predictive charts were defined: basic particle parameter based (e.g. particle size and density) and air-particle parameter based (e.g. permeability and de-aeration). The basic particle techniques were found to have strong and weak areas of predictive ability, on the basis of a comparison with data from materials with known mode of flow capability. It was found that there was only slight improvement in predictive ability when the particle density was replaced by loose-poured bulk density in the basic parameter techniques. The air-particle-parameter-based techniques also showed well-defined regions for mode of flow prediction though the data set used was smaller than that for the basic techniques. Also, it was found to be difficult to utilise de-aeration values from different researchers and subsequently, an air-particle-based technique was developed which does not require any de-aeration parameter in its assessment.