Difference between revisions of "Worksheets/Week6"
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* B: | * B: | ||
* C: mixer type: -1 = R mixer; +1 = W mixer | * C: mixer type: -1 = R mixer; +1 = W mixer | ||
Line 20: | Line 20: | ||
| Numeric factor | | Numeric factor | ||
|- | |- | ||
| | | '''B''' | ||
| | | Feed concentration | ||
| | | 75% | ||
| | | 85% | ||
| | | Numeric | ||
|- | |- | ||
| | | '''C''' | ||
| | | Mixer type | ||
| | | R | ||
| | | W | ||
| | | Categorical | ||
|} | |} | ||
We will show what we loose out if we pretend we only did half the experiments. In other words, we actually have 8 experiments, but we will see what happens if we only use 4 of them. | |||
Revision as of 09:02, 17 October 2019
Part 1
Case study: Achieve a stability value of 50 days or more, for a new product. We had a full factorial set of experiments in 3 factors:
- B:
- C: mixer type: -1 = R mixer; +1 = W mixer
Factor name | Description | Low value | High value | Type of factor |
---|---|---|---|---|
A | Enzyme strength | 20% | 30% | Numeric factor |
B | Feed concentration | 75% | 85% | Numeric |
C | Mixer type | R | W | Categorical |
We will show what we loose out if we pretend we only did half the experiments. In other words, we actually have 8 experiments, but we will see what happens if we only use 4 of them.
# This is the half-fraction, when C = A*B
A = c(-1, +1, -1, +1)
B = c(-1, -1, +1, +1)
C = A * B
# The response: stability [units=days]
y = ...
model_stability_poshalf
model_stability_poshalf = lm(y ~ A*B*C)
summary(model_stability_poshalf)
# Uncomment this line if you run the code in RStudio
#library(pid)
# Comment these 2 lines if you run this code in RStudio
source('https://yint.org/paretoPlot.R')
source('https://yint.org/contourPlot.R')
paretoPlot(model_stability_poshalf)
# This is the other half-fraction, when C = -A*B
A = c(-1, +1, -1, +1)
B = c(-1, -1, +1, +1)
C = -1 * A * B
# The response: stability [units=days]
y = ...
model_stability_neghalf = lm(y ~ A*B*C)
summary(model_stability_neghalf)
Part 2
Data from a bioreactor experiment is available, were we were investigating four factors:
- A = feed rate: 5 g/min or 8 g/min
- B = initial inoculate amount: 300 g or 400 g
- C = feed substrate concentration: 40 g/L or 60 g/L
- D = dissolved oxygen set-point: 4 mg/L or 5 mg/L
The 16 experiments from a full factorial, 24, were randomly run, and the yields, y, the outcome variable were given in standard order: [60, 59, 63, 61, 69, 61, 94, 93, 56, 63, 70, 65, 44, 45, 78, 77]
base <- c(-1, +1)
design <- expand.grid(A=base, B=base, C=base)
A <- design$A
B <- design$B
C <- design$C
D <- A * B * C
# Confirm that you can find these 8 runs yourself:
y <- c(60, 63, 70, 61, 44, 61, 94, 77)
model.bio <- lm(y ~ A*B*C*D)
summary(model.bio)
# Uncomment this line if you run the code in RStudio
#library(pid)
# Comment these 2 lines if you run this code in RStudio
source('https://yint.org/paretoPlot.R')
source('https://yint.org/contourPlot.R')
paretoPlot(model.bio)