Optimal Yield: A Moving Target

A relentless focus on yield requires vigilance and nimble decision making.
By Holly Jessen | August 06, 2012

Leading ethanol producers can wring out 2.8 gallons of ethanol from a bushel of corn. Others settle for reduced revenues due to lagging yield. What are the characteristics of the most efficient ethanol producers, and what can producers on the other end of the spectrum learn from them?

Neal Jakel, general manager of Illinois River Energy LLC in Rochelle, Ill., is a firm believer that to make money, an ethanol producer must be willing to spend money. The company always has its eye on new technologies and has made multiple improvements to the plant since it was built. “You have to invest if you want to improve the process,” he says. “The base ethanol plant was a great entry level design. It was not well optimized and … to continue to push these plants you have to invest the capital.”

With minimum capital, the company has increased from 102 MMgy to 120 MMgy while also increasing yield from 2.77 gallons denatured ethanol per bushel to 2.82 gallons, Jakel says.  That includes additional hammer mill capacities for a better grind profile and beefed-up distributive control systems. “We can track every single flow meter and pump and all those details so we know exactly what is going on in the plant,” he says. “The key thing here is you’ve got to have the data to really make the correlation.” The list continues with the addition of a custody transfer scale to accurately measure corn before it is ground up and mass flow meters to measure exactly what concentration of thin stillage is backset. Efforts to maximize energy and heat recovery have paid off too. “Even though we had 100 degree weather [in early August] we still were able to run 120 million gallon rate at our plant—we didn’t have to slow down at all,” he says. “And that’s another key to yield … making sure you don’t put your yeast to stress because you definitely inhibit the yield performance.”

Evaluating new technologies isn’t easy, adds Hans Foerster, director of marketing for grain processing for DuPont’s biorefineries business. He’s noticed that the best producers typically exchange ideas with their peers and ask questions. “By having a network of partners with whom you can assess ideas together, plants come to a better set of answers than they do merely relying on the folks that are trying to sell them something,” he says. “That’s one of the things you will see among the folks that have been in the industry the longest.”

Constant Obsession
The quest to find an ethanol plant’s yield sweet spot isn’t about nailing down a magical number and then staying there. Forester tells EPM the best run ethanol plants have a constant obsession with improving their facilities. “Yield improvement—in a fermentation process, in particular—it’s an ongoing process, frankly that never ends,” he says. Ethanol producers can’t afford to ignore this vital piece of the puzzle—especially in a tight margin environment. “It’s the difference between being able to continue to operate and shutting down,” he adds.

Another reason the sweet spot is a moving target is that conditions in the financial marketplace are constantly changing. Jakel, educated as a chemical engineer, makes a distinction between running for gallons and running for yield maximization. Contrasting the conditions at the end of 2011, when commodity margins spiked more than $1 per gallon, and the extremely tight margins of this year perfectly illustrates why producers need to have the ability to assess and adapt quickly. “We had record, record production fourth quarter—every plant was running as hard as they could,” he says. “Yield did not matter at that point, just pure gallons out the back end, regardless of your variable costs, regardless of your yield losses. The actual physical gallons out the door were the huge financial driver.”

When that situation shifted, the industry took about four months too long to adjust the plan of attack and just kept pushing out the gallons, Jakel tells EPM. That led to a major hangover with an oversupply of ethanol in the marketplace and collapsed commodity margins, he said Illinois River Energy, on the other hand, used a financial model it had spent months developing to determine that it was time to cut production by about 7 percent and focus on maximizing the amount of ethanol it could get from a bushel of corn. “We knew right away when we started running the numbers that running max gallons in January made no sense from a financial point of view,” he says. “We’ve been running for yield optimization since the first of the year, and still are today.”

The Data Difference
Illinois River Energy recently ramped up efforts in data collection and analysis. In the past year, the company hired an intern with a background in statistics to analyze the data from every fermentation since the plant began operation—more than 5,000 in all. The information was entered into a newly purchased data historian program, less about 25 percent, which was thrown out due to incomplete data. The goal, Jakel says, was to determine which of the dependent variables had the greatest correlation to yield. The answer wasn’t surprising but it has helped the ethanol plant make better decisions and bring in more money. “We now have a graphical equation, a line equation that basically tells us that the No. 1 dependent variable is solids loading,” he says. “We know that as our solids loadings increase, our yield efficiencies go down.”

In mid-August, for example, the ethanol plant was running at 33 percent solids, with a yield of 2.82 denatured ethanol—about a 120 MMgy run rate. “If we start going too high, yeah, we’ll get more gallons out of the plant,” he says, “but they are going to be less efficient gallons so it will be more costly and the commodity margin isn’t there today.” If the plant drops to 32 percent solids, yield jumps slightly and production goes down. Having this information hasn’t meant constant changes. In fact, in eight months, the company adjusted solids levels only three times. “We don’t jerk it around, so to speak,” he says. “It takes probably two weeks to make a significant change, [so if] we want to change by 1 percent solids it’s going to take us two solid weeks to kind of get the water and everything balanced back together.” Notably, the variable with the second highest correlation to yield is total fermentation time.

The next step was integrating the yield data with a financial model that takes into account all the variables. That includes factors such as corn pricing, corn basis, natural gas prices and variable costs in chemicals and enzymes, depending on the yield strategy. “We wanted to generate a robust financial model based on this operational model that tells us—given a certain financial situation—where should we be running the plant,” Jakel says. “This helped us hone in and really get down to within a half a penny on a commodity margin numbers to help us make better-informed financial decisions.”

Since it was developed, management has used the model to run various scenarios and make decisions a couple times a month, Jakel says. For example, the information helps Illinois River Energy decide what to do during any operational challenges, such temporary loss of its distillers grains dryer due to a broken conveyor. In the past, the ethanol plant would have continued production and simply turned out wet cake, despite the fact that the product is discounted compared to dried distillers grains. “Right now, given the tight market conditions and the premiums we see on distillers grains, if we have any operational challenges in our plant we will not make wet cake,” he says. “We’d rather shut down and make less gallons currently.”

Novozymes agrees that data gathering has an important role in increasing yield. “In addition to our new product development efforts, where we see enzymes still having a lot of potential to further increase yield,” says Jack Rogers, Novozymes North America Inc.’s bioenergy marketing manager for the Americas, “we have put a lot of resources into the analytical side of measuring yield and then adjusting either enzyme usage or plant conditions in order to maximize the conditions you are getting.”

In the past year, Novozymes has been working on several new tools and services that have been used to increase yield by 1.5 percent or more. Specifically, Rogers outlined progress with three service-marked offerings— Insight into Dextriantion, known as InDex, Neural Network Model and Novo Golden Batch.

The first two help increase yield by optimizing enzyme dosing. InDex goes beyond looking at dextrose equivalent to measure liquefaction by quantifying the amount of starch going into the solution to optimize alpha-amylase dosing. The company recently launched this product by performing analysis for its customers and, in the future, hopes to provide producers with software so they can do it on their own, Rogers says.

The Neural Network Model, on the other hand, aims for glucoamylase optimization. This predictive model aids in selecting the best type and amounts of glucoamylase, depending on unique plant parameters. “What we have seen is that just by adjusting the dosing scheme, we have been able to—for some of our customers—achieve yield increases greater than one percent,” he says. “So it’s not necessarily adding more or less glucoamylase, it could just be adjusting the timing of it going in.”

Finally, there’s the Novo Golden Batch, a statistical process control tool that uses a data-driven approach to increase yield. This offering may use the Neural Network Model as well as other statistical and mathematical modeling to identify and increase the number of high-yield fermentation batches. It helps producers by developing control charts to monitor the process to single out events, such as stuck fermentation due to a temperature spike. “By using these control charts and knowing where the variation is, you can make changes in the process to eliminate the source of the variation,” he says. “The idea is that you are reducing or eliminating the source of poor batches, so you are getting more well performing batches, or golden batches, by this measurement process.”

Author: Holly Jessen
Features Editor of Ethanol Producer Magazine
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