Summary
This experiment investigates laundry stain removal. Plackett-Burman screening of water temperature, detergent dose, soak time, agitation level, bleach type, and spin speed for stain removal and fabric care.
The design varies 6 factors: water temp c (C), ranging from 20 to 60, detergent ml (mL), ranging from 15 to 60, soak min (min), ranging from 0 to 30, agitation (level), ranging from 1 to 5, bleach ml (mL), ranging from 0 to 30, and spin rpm (rpm), ranging from 600 to 1400. The goal is to optimize 2 responses: stain removal pct (%) (maximize) and fabric wear (pts) (minimize). Fixed conditions held constant across all runs include load size = medium, fabric = cotton.
A Plackett-Burman screening design was used to efficiently test 6 factors in only 8 runs. This design assumes interactions are negligible and focuses on identifying the most influential main effects.
Key Findings
For stain removal pct, the most influential factors were water temp c (38.7%), agitation (23.7%), bleach ml (16.1%). The best observed value was 99.0 (at water temp c = 20, detergent ml = 15, soak min = 30).
For fabric wear, the most influential factors were water temp c (30.0%), soak min (22.4%), agitation (19.5%). The best observed value was 1.1 (at water temp c = 20, detergent ml = 60, soak min = 0).
Recommended Next Steps
- Follow up with a response surface design (CCD or Box-Behnken) on the top 3–4 factors to model curvature and find the true optimum.
- Consider whether any fixed factors should be varied in a future study.
- The screening results can guide factor reduction — drop factors contributing less than 5% and re-run with a smaller, more focused design.
Experimental Setup
Factors
| Factor | Low | High | Unit |
|---|---|---|---|
water_temp_c | 20 | 60 | C |
detergent_ml | 15 | 60 | mL |
soak_min | 0 | 30 | min |
agitation | 1 | 5 | level |
bleach_ml | 0 | 30 | mL |
spin_rpm | 600 | 1400 | rpm |
Fixed: load_size = medium, fabric = cotton
Responses
| Response | Direction | Unit |
|---|---|---|
stain_removal_pct | ↑ maximize | % |
fabric_wear | ↓ minimize | pts |
Configuration
use_cases/139_laundry_stain_removal/config.json
{
"metadata": {
"name": "Laundry Stain Removal",
"description": "Plackett-Burman screening of water temperature, detergent dose, soak time, agitation level, bleach type, and spin speed for stain removal and fabric care"
},
"factors": [
{
"name": "water_temp_c",
"levels": [
"20",
"60"
],
"type": "continuous",
"unit": "C"
},
{
"name": "detergent_ml",
"levels": [
"15",
"60"
],
"type": "continuous",
"unit": "mL"
},
{
"name": "soak_min",
"levels": [
"0",
"30"
],
"type": "continuous",
"unit": "min"
},
{
"name": "agitation",
"levels": [
"1",
"5"
],
"type": "continuous",
"unit": "level"
},
{
"name": "bleach_ml",
"levels": [
"0",
"30"
],
"type": "continuous",
"unit": "mL"
},
{
"name": "spin_rpm",
"levels": [
"600",
"1400"
],
"type": "continuous",
"unit": "rpm"
}
],
"fixed_factors": {
"load_size": "medium",
"fabric": "cotton"
},
"responses": [
{
"name": "stain_removal_pct",
"optimize": "maximize",
"unit": "%"
},
{
"name": "fabric_wear",
"optimize": "minimize",
"unit": "pts"
}
],
"settings": {
"operation": "plackett_burman",
"test_script": "use_cases/139_laundry_stain_removal/sim.sh"
}
}
Experimental Matrix
The Plackett-Burman Design produces 8 runs. Each row is one experiment with specific factor settings.
| Run | water_temp_c | detergent_ml | soak_min | agitation | bleach_ml | spin_rpm |
|---|---|---|---|---|---|---|
| 1 | 60 | 60 | 30 | 1 | 0 | 600 |
| 2 | 20 | 15 | 30 | 5 | 0 | 600 |
| 3 | 20 | 60 | 0 | 5 | 0 | 1400 |
| 4 | 60 | 60 | 30 | 5 | 30 | 1400 |
| 5 | 20 | 60 | 0 | 1 | 30 | 600 |
| 6 | 60 | 15 | 0 | 5 | 30 | 600 |
| 7 | 20 | 15 | 30 | 1 | 30 | 1400 |
| 8 | 60 | 15 | 0 | 1 | 0 | 1400 |
Step-by-Step Workflow
1
Preview the design
Terminal
$ doe info --config use_cases/139_laundry_stain_removal/config.json
2
Generate the runner script
Terminal
$ doe generate --config use_cases/139_laundry_stain_removal/config.json \
--output use_cases/139_laundry_stain_removal/results/run.sh --seed 42
3
Execute the experiments
Terminal
$ bash use_cases/139_laundry_stain_removal/results/run.sh
4
Analyze results
Terminal
$ doe analyze --config use_cases/139_laundry_stain_removal/config.json
5
Get optimization recommendations
Terminal
$ doe optimize --config use_cases/139_laundry_stain_removal/config.json
6
Multi-objective optimization
With 2 competing responses, use --multi to find the best compromise via Derringer–Suich desirability.
Terminal
$ doe optimize --config use_cases/139_laundry_stain_removal/config.json --multi
7
Generate the HTML report
Terminal
$ doe report --config use_cases/139_laundry_stain_removal/config.json \
--output use_cases/139_laundry_stain_removal/results/report.html
Features Exercised
| Feature | Value |
|---|---|
| Design type | plackett_burman |
| Factor types | continuous (all 6) |
| Arg style | double-dash |
| Responses | 2 (stain_removal_pct ↑, fabric_wear ↓) |
| Total runs | 8 |
Analysis Results
Generated from actual experiment runs using the DOE Helper Tool.
Response: stain_removal_pct
Top factors: water_temp_c (38.7%), agitation (23.7%), bleach_ml (16.1%).
ANOVA
| Source | DF | SS | MS | F | p-value |
|---|---|---|---|---|---|
| Source | DF | SS | MS | F | p-value |
| water_temp_c | 1 | 648.0000 | 648.0000 | 4.994 | 0.0605 |
| detergent_ml | 1 | 32.0000 | 32.0000 | 0.247 | 0.6347 |
| soak_min | 1 | 0.5000 | 0.5000 | 0.004 | 0.9522 |
| agitation | 1 | 242.0000 | 242.0000 | 1.865 | 0.2143 |
| bleach_ml | 1 | 112.5000 | 112.5000 | 0.867 | 0.3828 |
| spin_rpm | 1 | 60.5000 | 60.5000 | 0.466 | 0.5167 |
| water_temp_c*detergent_ml | 1 | 0.5000 | 0.5000 | 0.004 | 0.9522 |
| water_temp_c*soak_min | 1 | 32.0000 | 32.0000 | 0.247 | 0.6347 |
| water_temp_c*agitation | 1 | 112.5000 | 112.5000 | 0.867 | 0.3828 |
| water_temp_c*bleach_ml | 1 | 242.0000 | 242.0000 | 1.865 | 0.2143 |
| water_temp_c*spin_rpm | 1 | 338.0000 | 338.0000 | 2.605 | 0.1506 |
| detergent_ml*soak_min | 1 | 648.0000 | 648.0000 | 4.994 | 0.0605 |
| detergent_ml*agitation | 1 | 60.5000 | 60.5000 | 0.466 | 0.5167 |
| detergent_ml*bleach_ml | 1 | 338.0000 | 338.0000 | 2.605 | 0.1506 |
| detergent_ml*spin_rpm | 1 | 242.0000 | 242.0000 | 1.865 | 0.2143 |
| soak_min*agitation | 1 | 338.0000 | 338.0000 | 2.605 | 0.1506 |
| soak_min*bleach_ml | 1 | 60.5000 | 60.5000 | 0.466 | 0.5167 |
| soak_min*spin_rpm | 1 | 112.5000 | 112.5000 | 0.867 | 0.3828 |
| agitation*bleach_ml | 1 | 648.0000 | 648.0000 | 4.994 | 0.0605 |
| agitation*spin_rpm | 1 | 32.0000 | 32.0000 | 0.247 | 0.6347 |
| bleach_ml*spin_rpm | 1 | 0.5000 | 0.5000 | 0.004 | 0.9522 |
| Error | (Lenth | PSE) | 7 | 908.2500 | 129.7500 |
| Total | 7 | 1433.5000 | 204.7857 |
Pareto Chart
Main Effects Plot
Normal Probability Plot of Effects
Half-Normal Plot of Effects
Model Diagnostics
Response: fabric_wear
Top factors: water_temp_c (30.0%), soak_min (22.4%), agitation (19.5%).
ANOVA
| Source | DF | SS | MS | F | p-value |
|---|---|---|---|---|---|
| Source | DF | SS | MS | F | p-value |
| water_temp_c | 1 | 4.9613 | 4.9613 | 1.735 | 0.2292 |
| detergent_ml | 1 | 0.0113 | 0.0113 | 0.004 | 0.9517 |
| soak_min | 1 | 2.7612 | 2.7612 | 0.966 | 0.3585 |
| agitation | 1 | 2.1013 | 2.1013 | 0.735 | 0.4197 |
| bleach_ml | 1 | 0.4513 | 0.4513 | 0.158 | 0.7030 |
| spin_rpm | 1 | 1.7112 | 1.7112 | 0.598 | 0.4645 |
| water_temp_c*detergent_ml | 1 | 2.7612 | 2.7612 | 0.966 | 0.3585 |
| water_temp_c*soak_min | 1 | 0.0112 | 0.0112 | 0.004 | 0.9517 |
| water_temp_c*agitation | 1 | 0.4512 | 0.4512 | 0.158 | 0.7030 |
| water_temp_c*bleach_ml | 1 | 2.1012 | 2.1012 | 0.735 | 0.4197 |
| water_temp_c*spin_rpm | 1 | 11.2813 | 11.2813 | 3.945 | 0.0874 |
| detergent_ml*soak_min | 1 | 4.9612 | 4.9612 | 1.735 | 0.2292 |
| detergent_ml*agitation | 1 | 1.7113 | 1.7113 | 0.598 | 0.4645 |
| detergent_ml*bleach_ml | 1 | 11.2812 | 11.2812 | 3.945 | 0.0874 |
| detergent_ml*spin_rpm | 1 | 2.1013 | 2.1013 | 0.735 | 0.4197 |
| soak_min*agitation | 1 | 11.2812 | 11.2812 | 3.945 | 0.0874 |
| soak_min*bleach_ml | 1 | 1.7113 | 1.7113 | 0.598 | 0.4645 |
| soak_min*spin_rpm | 1 | 0.4513 | 0.4513 | 0.158 | 0.7030 |
| agitation*bleach_ml | 1 | 4.9612 | 4.9612 | 1.735 | 0.2292 |
| agitation*spin_rpm | 1 | 0.0113 | 0.0113 | 0.004 | 0.9517 |
| bleach_ml*spin_rpm | 1 | 2.7613 | 2.7613 | 0.966 | 0.3585 |
| Error | (Lenth | PSE) | 7 | 20.0156 | 2.8594 |
| Total | 7 | 23.2787 | 3.3255 |
Pareto Chart
Main Effects Plot
Normal Probability Plot of Effects
Half-Normal Plot of Effects
Model Diagnostics
Response Surface Plots
3D surfaces fitted with quadratic RSM. Red dots are observed data points.
fabric wear agitation vs bleach ml
fabric wear agitation vs spin rpm
fabric wear bleach ml vs spin rpm
fabric wear detergent ml vs agitation
fabric wear detergent ml vs bleach ml
fabric wear detergent ml vs soak min
fabric wear detergent ml vs spin rpm
fabric wear soak min vs agitation
fabric wear soak min vs bleach ml
fabric wear soak min vs spin rpm
fabric wear water temp c vs agitation
fabric wear water temp c vs bleach ml
fabric wear water temp c vs detergent ml
fabric wear water temp c vs soak min
fabric wear water temp c vs spin rpm
stain removal pct agitation vs bleach ml
stain removal pct agitation vs spin rpm
stain removal pct bleach ml vs spin rpm
stain removal pct detergent ml vs agitation
stain removal pct detergent ml vs bleach ml
stain removal pct detergent ml vs soak min
stain removal pct detergent ml vs spin rpm
stain removal pct soak min vs agitation
stain removal pct soak min vs bleach ml
stain removal pct soak min vs spin rpm
stain removal pct water temp c vs agitation
stain removal pct water temp c vs bleach ml
stain removal pct water temp c vs detergent ml
stain removal pct water temp c vs soak min
stain removal pct water temp c vs spin rpm
Multi-Objective Optimization
When responses compete, Derringer–Suich desirability finds the best compromise. Each response is scaled to a 0–1 desirability, then combined via a weighted geometric mean.
Overall Desirability
D = 0.6191
Per-Response Desirability
| Response | Weight | Desirability | Predicted | Dir |
|---|---|---|---|---|
stain_removal_pct |
1.5 |
0.7241
|
87.34 0.7241 87.34 % | ↑ |
fabric_wear |
1.0 |
0.4895
|
3.96 0.4895 3.96 pts | ↓ |
Recommended Settings
| Factor | Value |
|---|---|
water_temp_c | 46.38 C |
detergent_ml | 37.56 mL |
soak_min | 2.996 min |
agitation | 1.113 level |
bleach_ml | 2.144 mL |
spin_rpm | 634.5 rpm |
Source: from RSM model prediction
Trade-off Summary
Sacrifice = how much worse than single-objective best.
| Response | Predicted | Best Observed | Sacrifice |
|---|---|---|---|
fabric_wear | 3.96 | 1.10 | +2.86 |
Top 3 Runs by Desirability
| Run | D | Factor Settings |
|---|---|---|
| #6 | 0.4982 | water_temp_c=60, detergent_ml=15, soak_min=0, agitation=5, bleach_ml=30, spin_rpm=600 |
| #7 | 0.4303 | water_temp_c=20, detergent_ml=60, soak_min=0, agitation=1, bleach_ml=30, spin_rpm=600 |
Model Quality
| Response | R² | Type |
|---|---|---|
fabric_wear | 0.9987 | linear |
Full Multi-Objective Output
doe optimize --multi
============================================================
MULTI-OBJECTIVE OPTIMIZATION
Method: Derringer-Suich Desirability Function
============================================================
Overall desirability: D = 0.6191
Response Weight Desirability Predicted Direction
---------------------------------------------------------------------
stain_removal_pct 1.5 0.7241 87.34 % ↑
fabric_wear 1.0 0.4895 3.96 pts ↓
Recommended settings:
water_temp_c = 46.38 C
detergent_ml = 37.56 mL
soak_min = 2.996 min
agitation = 1.113 level
bleach_ml = 2.144 mL
spin_rpm = 634.5 rpm
(from RSM model prediction)
Trade-off summary:
stain_removal_pct: 87.34 (best observed: 99.00, sacrifice: +11.66)
fabric_wear: 3.96 (best observed: 1.10, sacrifice: +2.86)
Model quality:
stain_removal_pct: R² = 0.9997 (linear)
fabric_wear: R² = 0.9987 (linear)
Top 3 observed runs by overall desirability:
1. Run #1 (D=0.6050): water_temp_c=60, detergent_ml=60, soak_min=30, agitation=1, bleach_ml=0, spin_rpm=600
2. Run #6 (D=0.4982): water_temp_c=60, detergent_ml=15, soak_min=0, agitation=5, bleach_ml=30, spin_rpm=600
3. Run #7 (D=0.4303): water_temp_c=20, detergent_ml=60, soak_min=0, agitation=1, bleach_ml=30, spin_rpm=600
Full Analysis Output
doe analyze
=== Main Effects: stain_removal_pct ===
Factor Effect Std Error % Contribution
--------------------------------------------------------------
water_temp_c -18.0000 5.0595 38.7%
agitation 11.0000 5.0595 23.7%
bleach_ml -7.5000 5.0595 16.1%
spin_rpm 5.5000 5.0595 11.8%
detergent_ml -4.0000 5.0595 8.6%
soak_min -0.5000 5.0595 1.1%
=== ANOVA Table: stain_removal_pct ===
Source DF SS MS F p-value
-----------------------------------------------------------------------------
water_temp_c 1 648.0000 648.0000 4.994 0.0605
detergent_ml 1 32.0000 32.0000 0.247 0.6347
soak_min 1 0.5000 0.5000 0.004 0.9522
agitation 1 242.0000 242.0000 1.865 0.2143
bleach_ml 1 112.5000 112.5000 0.867 0.3828
spin_rpm 1 60.5000 60.5000 0.466 0.5167
water_temp_c*detergent_ml 1 0.5000 0.5000 0.004 0.9522
water_temp_c*soak_min 1 32.0000 32.0000 0.247 0.6347
water_temp_c*agitation 1 112.5000 112.5000 0.867 0.3828
water_temp_c*bleach_ml 1 242.0000 242.0000 1.865 0.2143
water_temp_c*spin_rpm 1 338.0000 338.0000 2.605 0.1506
detergent_ml*soak_min 1 648.0000 648.0000 4.994 0.0605
detergent_ml*agitation 1 60.5000 60.5000 0.466 0.5167
detergent_ml*bleach_ml 1 338.0000 338.0000 2.605 0.1506
detergent_ml*spin_rpm 1 242.0000 242.0000 1.865 0.2143
soak_min*agitation 1 338.0000 338.0000 2.605 0.1506
soak_min*bleach_ml 1 60.5000 60.5000 0.466 0.5167
soak_min*spin_rpm 1 112.5000 112.5000 0.867 0.3828
agitation*bleach_ml 1 648.0000 648.0000 4.994 0.0605
agitation*spin_rpm 1 32.0000 32.0000 0.247 0.6347
bleach_ml*spin_rpm 1 0.5000 0.5000 0.004 0.9522
Error (Lenth PSE) 7 908.2500 129.7500
Total 7 1433.5000 204.7857
Note: Error estimated using Lenth's pseudo-standard-error (unreplicated design)
=== Interaction Effects: stain_removal_pct ===
Factor A Factor B Interaction % Contribution
------------------------------------------------------------------------
detergent_ml soak_min -18.0000 13.6%
agitation bleach_ml -18.0000 13.6%
water_temp_c spin_rpm -13.0000 9.8%
detergent_ml bleach_ml 13.0000 9.8%
soak_min agitation 13.0000 9.8%
water_temp_c bleach_ml 11.0000 8.3%
detergent_ml spin_rpm -11.0000 8.3%
water_temp_c agitation -7.5000 5.7%
soak_min spin_rpm 7.5000 5.7%
detergent_ml agitation -5.5000 4.2%
soak_min bleach_ml -5.5000 4.2%
water_temp_c soak_min -4.0000 3.0%
agitation spin_rpm 4.0000 3.0%
water_temp_c detergent_ml -0.5000 0.4%
bleach_ml spin_rpm 0.5000 0.4%
=== Summary Statistics: stain_removal_pct ===
water_temp_c:
Level N Mean Std Min Max
------------------------------------------------------------
20 4 78.7500 15.2398 62.0000 99.0000
60 4 60.7500 5.4391 53.0000 65.0000
detergent_ml:
Level N Mean Std Min Max
------------------------------------------------------------
15 4 71.7500 18.2460 61.0000 99.0000
60 4 67.7500 11.5866 53.0000 77.0000
soak_min:
Level N Mean Std Min Max
------------------------------------------------------------
0 4 70.0000 8.2462 61.0000 77.0000
30 4 69.5000 20.2402 53.0000 99.0000
agitation:
Level N Mean Std Min Max
------------------------------------------------------------
1 4 64.2500 9.9121 53.0000 77.0000
5 4 75.2500 17.2892 61.0000 99.0000
bleach_ml:
Level N Mean Std Min Max
------------------------------------------------------------
0 4 73.5000 19.6214 53.0000 99.0000
30 4 66.0000 7.4386 61.0000 77.0000
spin_rpm:
Level N Mean Std Min Max
------------------------------------------------------------
1400 4 67.0000 6.7823 62.0000 77.0000
600 4 72.5000 20.2896 53.0000 99.0000
=== Main Effects: fabric_wear ===
Factor Effect Std Error % Contribution
--------------------------------------------------------------
water_temp_c -1.5750 0.6447 30.0%
soak_min 1.1750 0.6447 22.4%
agitation 1.0250 0.6447 19.5%
spin_rpm 0.9250 0.6447 17.6%
bleach_ml -0.4750 0.6447 9.0%
detergent_ml 0.0750 0.6447 1.4%
=== ANOVA Table: fabric_wear ===
Source DF SS MS F p-value
-----------------------------------------------------------------------------
water_temp_c 1 4.9613 4.9613 1.735 0.2292
detergent_ml 1 0.0113 0.0113 0.004 0.9517
soak_min 1 2.7612 2.7612 0.966 0.3585
agitation 1 2.1013 2.1013 0.735 0.4197
bleach_ml 1 0.4513 0.4513 0.158 0.7030
spin_rpm 1 1.7112 1.7112 0.598 0.4645
water_temp_c*detergent_ml 1 2.7612 2.7612 0.966 0.3585
water_temp_c*soak_min 1 0.0112 0.0112 0.004 0.9517
water_temp_c*agitation 1 0.4512 0.4512 0.158 0.7030
water_temp_c*bleach_ml 1 2.1012 2.1012 0.735 0.4197
water_temp_c*spin_rpm 1 11.2813 11.2813 3.945 0.0874
detergent_ml*soak_min 1 4.9612 4.9612 1.735 0.2292
detergent_ml*agitation 1 1.7113 1.7113 0.598 0.4645
detergent_ml*bleach_ml 1 11.2812 11.2812 3.945 0.0874
detergent_ml*spin_rpm 1 2.1013 2.1013 0.735 0.4197
soak_min*agitation 1 11.2812 11.2812 3.945 0.0874
soak_min*bleach_ml 1 1.7113 1.7113 0.598 0.4645
soak_min*spin_rpm 1 0.4513 0.4513 0.158 0.7030
agitation*bleach_ml 1 4.9612 4.9612 1.735 0.2292
agitation*spin_rpm 1 0.0113 0.0113 0.004 0.9517
bleach_ml*spin_rpm 1 2.7613 2.7613 0.966 0.3585
Error (Lenth PSE) 7 20.0156 2.8594
Total 7 23.2787 3.3255
Note: Error estimated using Lenth's pseudo-standard-error (unreplicated design)
=== Interaction Effects: fabric_wear ===
Factor A Factor B Interaction % Contribution
------------------------------------------------------------------------
water_temp_c spin_rpm -2.3750 13.5%
detergent_ml bleach_ml 2.3750 13.5%
soak_min agitation 2.3750 13.5%
detergent_ml soak_min -1.5750 8.9%
agitation bleach_ml -1.5750 8.9%
water_temp_c detergent_ml 1.1750 6.7%
bleach_ml spin_rpm -1.1750 6.7%
water_temp_c bleach_ml 1.0250 5.8%
detergent_ml spin_rpm -1.0250 5.8%
detergent_ml agitation -0.9250 5.2%
soak_min bleach_ml -0.9250 5.2%
water_temp_c agitation -0.4750 2.7%
soak_min spin_rpm 0.4750 2.7%
water_temp_c soak_min 0.0750 0.4%
agitation spin_rpm -0.0750 0.4%
=== Summary Statistics: fabric_wear ===
water_temp_c:
Level N Mean Std Min Max
------------------------------------------------------------
20 4 3.7500 2.1871 1.9000 6.7000
60 4 2.1750 1.1500 1.1000 3.8000
detergent_ml:
Level N Mean Std Min Max
------------------------------------------------------------
15 4 2.9250 2.5487 1.1000 6.7000
60 4 3.0000 1.1225 1.8000 4.1000
soak_min:
Level N Mean Std Min Max
------------------------------------------------------------
0 4 2.3750 1.2580 1.1000 4.1000
30 4 3.5500 2.2927 1.8000 6.7000
agitation:
Level N Mean Std Min Max
------------------------------------------------------------
1 4 2.4500 1.1030 1.8000 4.1000
5 4 3.4750 2.4171 1.1000 6.7000
bleach_ml:
Level N Mean Std Min Max
------------------------------------------------------------
0 4 3.2000 2.3424 1.8000 6.7000
30 4 2.7250 1.4569 1.1000 4.1000
spin_rpm:
Level N Mean Std Min Max
------------------------------------------------------------
1400 4 2.5000 0.8832 1.9000 3.8000
600 4 3.4250 2.5316 1.1000 6.7000
Optimization Recommendations
doe optimize
=== Optimization: stain_removal_pct ===
Direction: maximize
Best observed run: #4
water_temp_c = 20
detergent_ml = 15
soak_min = 30
agitation = 1
bleach_ml = 30
spin_rpm = 1400
Value: 99.0
RSM Model (linear, R² = 0.7991, Adj R² = -0.4063):
Coefficients:
intercept +69.7500
water_temp_c -5.0000
detergent_ml -6.5000
soak_min +6.7500
agitation -5.5000
bleach_ml -0.2500
spin_rpm -0.2500
Predicted optimum (from linear model, at observed points):
water_temp_c = 20
detergent_ml = 15
soak_min = 30
agitation = 1
bleach_ml = 30
spin_rpm = 1400
Predicted value: 93.0000
Surface optimum (via L-BFGS-B, linear model):
water_temp_c = 20
detergent_ml = 15
soak_min = 30
agitation = 1
bleach_ml = 0
spin_rpm = 600
Predicted value: 94.0000
Model quality: Good fit — general trends are captured, some noise remains.
Factor importance:
1. soak_min (effect: 13.5, contribution: 27.8%)
2. detergent_ml (effect: -13.0, contribution: 26.8%)
3. agitation (effect: -11.0, contribution: 22.7%)
4. water_temp_c (effect: -10.0, contribution: 20.6%)
5. bleach_ml (effect: -0.5, contribution: 1.0%)
6. spin_rpm (effect: 0.5, contribution: 1.0%)
=== Optimization: fabric_wear ===
Direction: minimize
Best observed run: #5
water_temp_c = 20
detergent_ml = 60
soak_min = 0
agitation = 1
bleach_ml = 30
spin_rpm = 600
Value: 1.1
RSM Model (linear, R² = 0.9763, Adj R² = 0.8342):
Coefficients:
intercept +2.9625
water_temp_c -0.4875
detergent_ml -1.1875
soak_min +0.7625
agitation -0.5125
bleach_ml -0.0625
spin_rpm +0.5875
Predicted optimum (from linear model, at observed points):
water_temp_c = 20
detergent_ml = 15
soak_min = 30
agitation = 1
bleach_ml = 30
spin_rpm = 1400
Predicted value: 6.4375
Surface optimum (via L-BFGS-B, linear model):
water_temp_c = 60
detergent_ml = 60
soak_min = 0
agitation = 5
bleach_ml = 30
spin_rpm = 600
Predicted value: -0.6375
Model quality: Excellent fit — surface predictions are reliable.
Factor importance:
1. detergent_ml (effect: -2.4, contribution: 33.0%)
2. soak_min (effect: 1.5, contribution: 21.2%)
3. spin_rpm (effect: -1.2, contribution: 16.3%)
4. agitation (effect: -1.0, contribution: 14.2%)
5. water_temp_c (effect: -1.0, contribution: 13.5%)
6. bleach_ml (effect: -0.1, contribution: 1.7%)