Back

PyADM1: a Python implementation of Anaerobic Digestion Model No. 1

Sadrimajd, P.; Mannion, P.; Howley, E.; Lens, P. N. L.

2021-03-04 systems biology
10.1101/2021.03.03.433746 bioRxiv
Show abstract

Anaerobic Digestion (AD) is a waste treatment technology widely used for wastewater and solid waste treatment, with the advantage of being a source of renewable energy in the form of biogas. Anaerobic digestion model number 1 (ADM1) is the most common mathematical model available for AD modelling. Commercial software implementations of ADM1 are available but have limited flexibility and availability due to the closed sources and licensing fees. Python is the fastest growing programming language and is open source freely available. Python implementation of ADM1 makes this AD model available to the mass user base of the Python ecosystem and its libraries. The open easy to use implementation in PyADM1 makes it more accessible and provides possibilities for flexible direct use of the model linked to other software, e.g. machine learning libraries or Linux operating system on embedded hardware.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

1
PLOS ONE
● 5266 papers in training set
Top 8%
19.7%
Similar papers in this journal
2
Bioinformatics
◐ 1204 papers in training set
Top 2%
12.6%
Similar papers in this journal
3
Gigabyte
● 62 papers in training set
Top 0.1%
8.4%
Similar papers in this journal
4
BMC Bioinformatics
○ 457 papers in training set
Top 1%
7.7%
Similar papers in this journal
5
Computational and Structural Biotechnology Journal
● 242 papers in training set
Top 1%
3.4%
50% of probability mass above
6
Scientific Reports
○ 3612 papers in training set
Top 29%
3.4%
Similar papers in this journal
7
PLOS Computational Biology
● 1863 papers in training set
Top 11%
2.5%
8
Computer Methods and Programs in Biomedicine
○ 28 papers in training set
Top 0.3%
2.3%
9
Metabolites
○ 53 papers in training set
Top 0.5%
1.8%
10
BMC Research Notes
○ 33 papers in training set
Top 0.5%
1.4%
11
Frontiers in Bioengineering and Biotechnology
○ 98 papers in training set
Top 1%
1.2%
12
Limnology and Oceanography: Methods
○ 11 papers in training set
Top 0.2%
1.1%
13
npj Systems Biology and Applications
○ 125 papers in training set
Top 1%
1.1%
14
HardwareX
○ 18 papers in training set
Top 0.2%
1.1%
15
JMIRx Med
◐ 32 papers in training set
Top 2%
0.9%
16
International Journal of Molecular Sciences
○ 494 papers in training set
Top 14%
0.9%
17
Environmental Pollution
○ 37 papers in training set
Top 1%
0.9%
18
IFAC-PapersOnLine
○ 13 papers in training set
Top 0.2%
0.9%
19
Scientific Data
○ 209 papers in training set
Top 2%
0.9%
20
Chemical Engineering Journal
○ 11 papers in training set
Top 0.2%
0.9%
21
Frontiers in Molecular Biosciences
○ 102 papers in training set
Top 2%
0.9%
22
BMC Public Health
○ 158 papers in training set
Top 5%
0.9%
23
Applied Sciences
○ 25 papers in training set
Top 0.7%
0.9%
24
Water Research
○ 79 papers in training set
Top 0.9%
0.9%
25
Epidemics
○ 116 papers in training set
Top 2%
0.9%
26
Computers in Biology and Medicine
○ 128 papers in training set
Top 4%
0.9%
27
PeerJ
◐ 308 papers in training set
Top 11%
0.6%
28
Frontiers in Microbiology
○ 427 papers in training set
Top 9%
0.5%
29
Biology
○ 45 papers in training set
Top 1%
0.5%
30
Bioinformatics Advances
◐ 203 papers in training set
Top 5%
0.5%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.