Evolutionary Operation

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Introduction3/4/2017Ronald Morgan Shewchuk1The experimental designs we have explored thus far may be considered revolutionary in nature. We purposely chose high and low levels for the input factors that were expected to make a large change in the output response variable. This allowed us to observe changes in the output variable that were not due to random noise and which permitted mapping of the total response surface. Incurring a bad result in the output response variable is not considered Faustian but rather, a natural part of the discovery process. There are situations, however, where a reduction in the response variable, throughput or quality level cannot be tolerated. Process improvements in these situations are best deployed through the technique of Evolutionary Operation.Evolutionary Operation (EVOP) was developed by George Box and first published in his 1957 article Evolutionary Operation: A Method for Increasing Industrial Productivity in the Journal of Applied Statistics.

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Evolutionary OperationIntroduction3/4/2017Ronald Morgan Shewchuk2As with many experimental designs, EVOP pre-dates the advent of personal computers. The underlying statistic fundamentals of EVOP have been established for many years. Personal computers, with their associated software programs, simply facilitate the analysis of the underlying statistics. Dr. Box developed a simple worksheet method by which two-level full factorial experiments with center points are conducted in phases as part of routine production operations. The objective is to nudge the operating parameters along the pathway of steepest ascent of the response variable without any interruption to the process or any increase in defective product. This nudging process may be visualized as in Figure 9.23 where the boxes represent individual 22 full factorial experiments.



Evolutionary OperationFigure 9.23 Successive 22 Full Factorial Experiments Conducted in EVOP3/4/2017Ronald Morgan Shewchuk3

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Evolutionary OperationIntroduction3/4/2017Ronald Morgan Shewchuk4Of course, we do not know the surface topography of the response variable, if we did it would be elementary to dial-in the optimum factor settings. Thus, we set the center point of our first experiment at the current operating conditions and measure the response variable at the high and low tolerances of the two input factors. We must conduct a sufficient number of replicates to ensure that our conclusions about the direction of steepest ascent are statistically significant and not the result of random process noise. This is not a problem since replicates result in good production lots which we need to meet market demand. Replication is continued within successive cycles until a new optimum corner point has been validated. This milestone represents the conclusion of the first phase of the EVOP. The second phase of the EVOP thus begins with its center point at the optimum corner point identified in phase one.



Evolutionary OperationIntroduction3/4/2017Ronald Morgan Shewchuk5The process is continued until the response variable is maximized. It is important to recognize that the topography of a response variable is not etched in stone. The response variable surface is influenced by noise factors such as variations in supplied raw materials, ambient environment, uncontrolled process conditions, etc. Consequently, practitioners of EVOP have found it useful to perpetually continue Evolutionary Operation experiments to account for these noise variables which move the response variable maxima.It is typical for EVOPs to be limited to two factors although the technique can easily be extrapolated to three or more factors. In the latter case, it is best to use a statistical software package to analyze the EVOP phase results to reduce the chance of transcription errors. We will demonstrate the use of Evolutionary Operation to optimize the process of producing biodiesel in Case Study XVI. The analysis of this case study will be facilitated by the 22 EVOP Worksheet as shown in Figure 9.24.



Evolutionary OperationFigure 9.24 22 EVOP Worksheet3/4/2017Ronald Morgan Shewchuk6



Evolutionary Operation3/4/2017Ronald Morgan Shewchuk7Case Study XVI: Optimization of Biodiesel Production Yield by EVOPKuwat Pramana is beaming with pride. His small Indonesian start-up company is successfully producing biodiesel from the transesterification of palm oil. It has been a long, hard road building the reaction, purification and filling plant followed by the process equipment commissioning and yield ramp. Over several months, Kuwat and his operators have been able to sustain production yields of 89%. Kuwat can now begin paying back the loan which he received from the Economic Development Administration in Jakarta to build his facility.

Indonesia has an abundant supply of crude palm kernel oil, the primary raw material used in the transesterification reaction. The price of palm oil is driven by demand from the food and cosmetics industries. Kuwat has calculated that he must operate at yields above 88% to ensure that his operation remains profitable. The higher the yield, the quicker he can repay his loan and reduce his interest expenses.

The transesterification process consists of reacting the crude palm kernel oil with methanol in the presence of a base catalyst, sodium hydroxide. Glycerine is produced as a by-product of the reaction. Reactant purity, mixing time, reaction temperature, catalyst type, catalyst concentration, and mass ratio of methanol to oil have been identified as key factors affecting biodiesel yield. Kuwat has decided to hold all factors constant and use the technique of Evolutionary Operation to optimize production yield through incremental changes in methanol to oil mass ratio (factor A) and sodium hydroxide catalyst concentration (factor B) within the reactor. Phase 1 through phase 3 experiments are captured in the EVOP 22 Worksheets of Figures 9.25 through 9.35.



Evolutionary Operation3/4/2017Ronald Morgan Shewchuk8Case Study XVI: Optimization of Biodiesel Production Yield by EVOPWe begin at phase 1, cycle 1 using a new 22 EVOP worksheet. The names of the factors to be optimized are entered in cells F14 and B7 for factor A and factor B respectively. The biodiesel process is currently operating at a methanol to oil ratio of 0.32 and a catalyst concentration of 0.92 wt % NaOH. These values are entered as the zero point of phase one in cells F11 and D6 respectively. Kuwat does not wish to upset the apple cart so he decides with his team that the maximum change in MeOH : Oil Mass Ratio will be 0.02 units and the maximum change in wt% NaOH will be 0.03%. The high (+1) and low (-1) values of the factors are entered in the appropriate cells in the worksheet. We now have a game plan to conduct phase one experiments. The first experiment is conducted at the current factor conditions, point z. The date and time that the experiment is begun is entered in cells C24 and C25 respectively. The production yield resulting from these factor settings is entered in the new observation line of the table. Rows one and two are left blank for the first cycle of any phase. Rows 4, 5 and 6 are calculated fields based upon rows 1, 2 and 3. Factor settings are next changed to those of point a in the design box and another batch of biodiesel is produced at these settings. The measured production yield is entered in the new observation field for treatment combination a. This process is continued for points b, c and d within the design box concluding phase one cycle one. The average yield for these first five batches of biodiesel is 88.8% which is above Kuwats breakeven point. The maximum yield was obtained at point b and the minimum at point a.



Evolutionary OperationFigure 9.25 Case Study XVI Phase 1, Cycle 1 - 22 EVOP Worksheet3/4/2017Ronald Morgan Shewchuk9



Evolutionary Operation3/4/2017Ronald Morgan Shewchuk10Case Study XVI: Optimization of Biodiesel Production Yield by EVOPCycle two is a replicate of cycle one. The objective is to generate enough data points to detect a statistically significant signal that a preferred operating condition exists within the design box. Previous sums and average yields are linked to the new sums and average yields of the previous cycle. The previous sum of the standard deviation and the previous average standard deviation are left blank for cycle two. New observations at the five treatment combinations are entered in line three. This permits the calculation of a new average standard deviation. Since this standard deviation is only based upon ten data points it is not recommended to make any changes in operating conditions based upon cycle two.






Evolutionary Operation3/4/2017Ronald Morgan Shewchuk12Case Study XVI: Optimization of Biodiesel Production Yield by EVOPCycle three is the third replicate in phase one. Previous sums and averages of the yields are linked to the new sums and new average yields of the prior cycle. The previous sum of the standard deviation and the previous average standard deviation are linked to the new sum of the standard deviation and the new average standard deviation of cycle two. The maximum new average yield is observed at point b while the minimum is observed at point z. The difference between the new average yield at point b versus point z is 4.03. This value is above the standard error limit of 2.35 for changes in center effect indicating that we have received a signal that point b represents a better operating condition than point z. But lets be sure about this conclusion. We are producing good product without affecting throughput rate or increasing impurities. There is no harm to seek confirmation.






Evolutionary Operation3/4/2017Ronald Morgan Shewchuk14Case Study XVI: Optimization of Biodiesel Production Yield by EVOPCycle four represents the fourth replicate. New observations are entered in line three. Point b is observed to represent the maximum new average yield while point a represents the minimum. The difference between the new average yield at point b versus point z is 4.01. This value is above the standard error limit of 2.00 calculated for the change in center effect confirming the signal that point b represents a better operating condition than point z. Kuwat and his team decide to move the standard operating conditions of the reactor to a MeOH : Oil Mass Ratio of 0.34 and a Catalyst Concentration of 0.95% NaOH. These conditions will serve as the zero point of phase two. The twenty batches of phase one have been produced at an average biodiesel yield of 88.9%.






Evolutionary Operation3/4/2017Ronald Morgan Shewchuk16Case Study XVI: Optimization of Biodiesel Production Yield by EVOPPhase two starts with a clean worksheet and a new starting point on the DOE mountain. Remember that we have no idea of the topography of the yield with respect to factor A and factor B. We are defining a small experimental boxand illuminating the corner points with a penlight to see which corner we should jump to. Phase two, cycle one begins with the factor settings as shown in Figure 9.25E. The first set of yield observations from the five experiments are entered in line three. The new sums and averages for these yields are linked to the second cycle, line one and two respectively. The second cycle yield observations allow the calculation of the standard error limits but as in phase one, we make no decisions based upon cycle two on account of the uncertainty in the standard deviation. We proceed to cycle three and enter the yield observations for the five treatment combinations. The maximum new average yield is observed at point b while the minimum is observed at point a. The difference between the new average yield at point b versus point z is 3.00. This value is above the standard error limit of 2.55 for changes in center effect indicating that we have received a signal that point b represents a better operating condition than point z. We could proceed to confirmatory cycles but Kuwat and the operators are excited about the improvements and would like to change the standard operating conditions of the reactor to point b and proceed to phase three. The fifteen batches of phase two have been produced at an average biodiesel yield of 90.2%, a slight improvement over the average yield of 88.9% obtained in phase one.












Evolutionary Operation3/4/2017Ronald Morgan Shewchuk20Case Study XVI: Optimization of Biodiesel Production Yield by EVOPPhase three, cycle one begins at a MeOH : Oil Mass Ratio of 0.36 and a Catalyst Concentration of 0.98% NaOH. The high (+1) and low (-1) values of the factors are as shown in Figure 9.32. Yield observations at the five treatment combinations are entered into line three. Similarly, yield observations are entered in cycle two for line three. Cycle three yield observations result in point b being identified as the new average maximum. The difference between the new average yield at point b versus point z is 3.89. This value is above the standard error limit of 1.40 for changes in center effect indicating that we have received a signal that point b represents a better operating condition than point z. But notice that the yield for point a is the same as point z. This causes Kuwat and the team some concern so they decide to continue at the current operating conditions to cycle four. Point b is validated in cycle four as the new average yield maximum at 95.8%. The difference between the new average yield at point b versus point z is 4.53. Since this value is above the standard error limit of 1.34 for changes in center effect, point b represents a better operating condition than point z. The reactor operating conditions should be moved to a MeOH : Oil Mass Ratio of 0.38 and a Catalyst Concentration of 1.01% NaOH in phase four. The twenty batches of phase three have been produced at an average biodiesel yield of 92.5%, a significant improvement over the average yield of 88.9% obtained in phase one.















Evolutionary Operation3/4/2017Ronald Morgan Shewchuk25It has taken nine days and fifty-five batches to cause a yield increase of 3.6%. The key point of this improvement process was that no defective batches were produced and no reduction in throughput was incurred. Consequently, the improvement came without cost. If there were other response variables which Kuwat was interested in, such as the concentration of a specific impurity, separate EVOP worksheets would be carried through for these response variables. Decisions about changes in operating conditions would have to be justified against all response variables.Evolutionary Operation is well suited to processes with constraints on their output response due to market demands or constraints on their input factors due to design limitations. In the latter case it is better to avoid input limitations which constrain output responses through the judicious use of Design for Lean Six Sigma. Summary



Evolutionary Operation3/4/2017Ronald Morgan Shewchuk26References

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Evolutionary Operation