Learning goals
Post-process datasets for enhanced information extraction
Take informed decisions by applying exploratory data analysis tecnhiques to dataset
Create a statistically significant supervised machine learning model Optimize an industrial operation based on a (surrogate) supervised machine learningProject statement - updated
Dataset Part 1
Dataset Part 2
GlossaryTemp = temperature in ºC
Total_flow = total inlet flowrate in m3/h
Ar_flow = argon inlet flowrate in m3/h
CH4_flow = methane inlet flowrate in m3/h
O2_flow = oxygen inlet flowrate in m3/h
CH4/O2 = inlet molar ratio of methane to oxygen
CT = contact time in h
CH4_conv = methane conversion
C2s = selectivity towards C2 compounds
C2H6s = selectivity towards ethane
C2H4s = selectivity towards ethylene
Teams
Same as before.
Guidelines for presentation
| Dos | Dont's |
| 1 question = 1 slide; make exceptions (2 slides) for most complex answers | Show code or discuss Python details |
| Goal, strategy, key data & conclusion for every question | Intro longer than 15 s - go straight to the questions (we all know each other, we all know why are you presenting) |
| A conclusion requires a justification based on either evidence (data/statistics) or hypothesis/theory | Index slide |
| Theoretical arguments require at the least a biblio reference | Various potential hypothesis to explain data - make that clear by what is said (when you're are relatively confident, but more explanations are possible: "the most likely explanation is"; when various explanations are equally likely: "Among various possible explanations, one is") |
Evaluation
The final individual grade (iFG) will be computed from the team average presentation grade (tPG) and individual discussion grade (iDG)
iFG= 2/3 x tPG + 1/3 x iDG
Delivery