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Mendeley readers
Attention Score in Context
| Title |
Role of regression analysis and variation of rheological data in calculation of pressure drop for sludge pipelines
|
|---|---|
| Published in |
Water Research, March 2018
|
| DOI | 10.1016/j.watres.2018.02.059 |
| Pubmed ID | |
| Authors |
E Farno, K Coventry, P Slatter, N Eshtiaghi |
| Abstract |
Sludge pumps in wastewater treatment plants are often oversized due to uncertainty in calculation of pressure drop. This issue costs millions of dollars for industry to purchase and operate the oversized pumps. Besides costs, higher electricity consumption is associated with extra CO2 emission which creates huge environmental impacts. Calculation of pressure drop via current pipe flow theory requires model estimation of flow curve data which depends on regression analysis and also varies with natural variation of rheological data. This study investigates impact of variation of rheological data and regression analysis on variation of pressure drop calculated via current pipe flow theories. Results compare the variation of calculated pressure drop between different models and regression methods and suggest on the suitability of each method. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Australia | 2 | 67% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 67% |
| Science communicators (journalists, bloggers, editors) | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 29 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 6 | 21% |
| Student > Ph. D. Student | 5 | 17% |
| Student > Bachelor | 3 | 10% |
| Student > Master | 3 | 10% |
| Lecturer | 2 | 7% |
| Other | 5 | 17% |
| Unknown | 5 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemical Engineering | 8 | 28% |
| Engineering | 8 | 28% |
| Environmental Science | 5 | 17% |
| Agricultural and Biological Sciences | 1 | 3% |
| Neuroscience | 1 | 3% |
| Other | 1 | 3% |
| Unknown | 5 | 17% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 10 March 2018.
All research outputs
#15,745,807
of 25,382,440 outputs
Outputs from Water Research
#6,172
of 11,877 outputs
Outputs of similar age
#194,221
of 346,135 outputs
Outputs of similar age from Water Research
#118
of 222 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 11,877 research outputs from this source. They receive a mean Attention Score of 5.0. This one is in the 46th percentile – i.e., 46% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 346,135 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 222 others from the same source and published within six weeks on either side of this one. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.