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

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

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.

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?

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

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

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

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

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.

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

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

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

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

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

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

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

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

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