- Volumes 96-107 (2025)
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Volumes 84-95 (2024)
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Volume 95
Pages 1-392 (December 2024)
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Volume 94
Pages 1-400 (November 2024)
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Volume 93
Pages 1-376 (October 2024)
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Volume 92
Pages 1-316 (September 2024)
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Volume 91
Pages 1-378 (August 2024)
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Volume 90
Pages 1-580 (July 2024)
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Volume 89
Pages 1-278 (June 2024)
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Volume 88
Pages 1-350 (May 2024)
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Volume 87
Pages 1-338 (April 2024)
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Volume 86
Pages 1-312 (March 2024)
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Volume 85
Pages 1-334 (February 2024)
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Volume 84
Pages 1-308 (January 2024)
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Volume 95
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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)
- Volumes 36-41 (2018)
- Volumes 30-35 (2017)
- Volumes 24-29 (2016)
- Volumes 18-23 (2015)
- Volumes 12-17 (2014)
- 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)
• Zhang's approach is modified to enhance maximum settling velocity prediction.
• The MZ method exhibits superior prediction than DE and NM methods.
• The aggregate rarefaction is identified in the simulation of batch settling tests.
Determining the settling properties of flocculated suspensions is crucial in the design and optimization of thickeners. A novel method was proposed to predict the settling parameters of the Vesilind function. Such a method determines the maximum settling velocity by minimizing the difference between the predicted and measured batch settling curves. The performance of the method was compared with those of the differential evolution (DE) and Nelder-Mead (NM) algorithms. The results indicate that the method predicts the settling parameters with higher accuracy than the DE and NM algorithms do. Finally, the thickening process of flocculated tailings suspensions in batch settling tests was simulated. An increase in flocculant dosage causes an increase in compressive yield stress, leading to aggregate rarefaction. Consequently, the underflow concentration decreases at high solid fractions of suspensions as flocculant dosage increases.
