1. What to do when you don’t have a clue.
Terry A. Ring
Chemical Engineering
University of Utah

2. First Job MS ChE at UC Berkeley, BS ChE Clarkson
Well Educated in traditional unit operations
1st Project Develop Mass and Energy Balance for Alumina from clay Acid Leach process using a computer before ASPEN exists
2nd Project Al2O3 Nodules
Al2O3
H2O
Shaft Kiln
1800C
Dryer
200C
Hot Gas
Hot

3. 2nd Project Rotating Pan Nodulizer for Al2O3 Process Variables
Control Pellet Size
Minimize Dust Generated
Minimize water Used
Minimize additives Used
Minimize Pore Volume
Process Variables
Pan (1 m pilot, 5 m plant)
RPM of Pan
Pan Angle
Spray Configuration
Alumina feed point
Ratio of Alumina to water fed
Conveyor Dryer
Drying Temperature
Airflow
Shaft Kiln
Sintering Temperature
Holding Time
Project finished in 6 mo.

4. Project 3 $1 million (1974 $s) in fuel savings ($4.83 million 2013 $s)
Found Synergism between additives
Decreased time/energy needed to sinter by ½
Lowered Operating costs to produce
US Patent 4,045,234 “Process For Producing High Density Sintered Alumina”
$1 million (1974 $s) in fuel savings ($4.83 million 2013 $s)
How much was I paid for this work?

5. Getting Started Call Plant and Talk to Engineer
Did not really know much
Relies on Operator to run Pan Nodulizer
Call Plant and Talk to Operator
Everything controls Everything
Call Technician who rate the Pilot Plant
Water and pan angle and RPM control nodule size
Literature Search
1 paper - P. Somasundaran and D. Feustenau
1 PhD thesis - P. Somasundaran and D. Feustenau

6. Fm (x,t) – Cumulative Mass Distribution
P. Somasundaran and D. Feustenau

7. What to do? Short Time for the Project – 6 months
No ChE Background that is useful!
No literature that is useful!
No people to help!
So complain at lunch to fellow employees

8. Design of Experiments Lunch Companion Corporate Librarian Saved Me
I think you might try statistically designed experiments or design of experiments
We had a consultant come to talk about this two years before you joined the company.
I do not know much about what the consultant said.
Corporate Librarian Saved Me

9. Other Names Statistically Designed Experiments Design of Experiments
Factorial Design of Experiments
ANOVA
Analysis of variance : A mathematical process for separating the variability of a group of observations into assignable causes and setting up various significance tests.

10. Comparison I Design of Experiments Traditional Experimentation Tests
Theory
Correlation
Develop a new
End up with a mathematical understanding of experimental results based on process variables

11. Comparison I Design of Experiments Traditional Experimentation
Determines if Process Variables are important (significant )
compared to experimental errors
Develops a mathematical relationship for experimental results based upon process variables
No Theory is developed or tested
Allows Predictions of Results for all process variables within ranges used in experimentation
Allows Process Optimizations
Understand the requirements on processing conditions needed to meet production specifications
Tests Theory
Develop a new Theory
End up with a mathematical understanding of experimental results

12. How is this approach different?
Design of Experiments
Traditional Experimentation
Do a series of experiments changing one variable at a time
5 Process Variables (PV)
RPM of Pan
Pan Angle
Spray Configuration
Alumina feed point
Ratio of Alumina to water fed
4 different values for PV
Number of Experiments
5^4= 625 experiments
2 experiments/day ~ 1 yr work

13. How is this approach different?
Design of Experiments
Traditional Experimentation
Do a series of experiments changing all variables at the same time
5 Process Variables (PV)
RPM of Pan
Pan Angle
Spray Configuration
Alumina feed point
Ratio of Alumina to water fed
2 levels for PV plus multiples of center point
Number of Experiments
25+1= 64 experiments
2 experiments/day ~ 1 month work
Do a series of experiments changing one variable at a time
5 Process Variables (PV)
RPM of Pan
Pan Angle
Spray Configuration
Alumina feed point
Ratio of Alumina to water fed
4 different values for PV
Number of Experiments
54= 625 experiments
2 experiments/day ~ 1 yr work

14. Different Nomenclature
Effects of PVs
Process Variables
RPM of Pan
Pan Angle
Spray Configuration
Alumina feed point
Ratio of Alumina to water fed
Scaled PVs ( -1 to +1)
original X value and converts to (X − a)/b, where a = (Xh + XL)/2 and b = (Xh−XL)/2
Effect Ei = [Σ Ri (+) – Σ Ri (-) ]/N
Responses, R’s
Diameter of Nodules
Water Content of Nodules
Pore Volume
Dust in Dryer
Sintering Temperature
Variance (StDEV2)
Software
Stat-ease, MiniTab
Response Surface
Ri = E1 X1 + E2 X2 + E3 X3+ …
+E11 X12 + E22 X22 + E33 X32 + …
+E12 X1 X2 + E13 X1 X3 + E23 X2 X3 + …
+E123 X1 X2 X3

15. Response Surface Map Bleaching Cotton
Effects (PVs)
% NaOH
%H2O2
Temp
Time
Responses
Reflectance
Fluidity
> 6 to be useful

16. Steps for DOE Identify process variables
Often more PVs than you initially think are important
Identify the range for each process variable
High
Low
Scale Process Variables
Set up experimental matrix
(+,-,-), (+,+,-),(+,-,+), (+,+,+)
Randomize Experiments
Identify Responses to be measured for each process variable
Run Experiments
Analyze Experimental results using ANOVA
Compare responses to experimental uncertainty (F-test)
Remove insignificant process variables
Calculate Response mathematics Ri = E1 X1 + E2 X2 + E3 X3+ … +E11 X12 + E22 X22 + E33 X32 + …
+E12 X1 X2 + E13 X1 X3 + E23 X2 X3 + … +E123 X1 X2 X3
Use for Process Optimization
Use for 6-sigma
Identify the range that a PV can vary and keep product within specification

17. Nodulizer Results Nodule Diameter Dust Production
Important Effects (in order of importance)
Water to alumina ratio
RPM
Pan angle
Dust Production
Additive concentration

18. Results Sintered Density Water Control is Critical
Important Effects
Sintering Time
Pan RPM
Water to alumina ratio
Additives
Water Control is Critical
IR water sensor and control system story