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 1 st Project Develop Mass and Energy Balance for Alumina from clay Acid Leach process using a computer before ASPEN exists 2 nd Project Al 2 O 3 Nodules Dryer 200C Shaft Kiln 1800C Hot Hot Gas H2O Al2O3

3. 2 nd Project Rotating Pan Nodulizer for Al 2 O 3 – 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 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. F m (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 – 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. Analysis of variance

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

11. Comparison I Design of Experiments 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 Traditional Experimentation Tests Theory Develop a new Theory End up with a mathematical understanding of experimental results

12. How is this approach different? Design of ExperimentsTraditional 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 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 – 2 5+1 = 64 experiments – 2 experiments/day ~ 1 month work 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

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 = (X h + X L )/2 and b = (X h −X L )/2 Effect E i = [Σ Ri (+) – Σ Ri (-) ]/N Responses, R’s – Diameter of Nodules – Water Content of Nodules – Pore Volume – Dust in Dryer – Sintering Temperature Variance (StDEV 2 ) Software – Stat-ease, MiniTab Response Surface R i = E1 X 1 + E2 X 2 + E3 X 3 + … +E11 X 1 2 + E22 X 2 2 + E33 X 3 2 + … +E12 X 1 X 2 + E13 X 1 X 3 + E23 X 2 X 3 + … +E123 X 1 X 2 X 3

15. Response Surface Map Bleaching Cotton Effects (PVs) – % NaOH – %H 2 O 2 – 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 R i = E1 X 1 + E2 X 2 + E3 X 3 + … +E11 X 1 2 + E22 X 2 2 + E33 X 3 2 + … +E12 X 1 X 2 + E13 X 1 X 3 + E23 X 2 X 3 + … +E123 X 1 X 2 X 3 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 – Important Effects (in order of importance) Water to alumina ratio RPM Pan angle Dust Production – Important Effects (in order of importance) Water to alumina ratio Additive concentration RPM

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