Big data can bring big wins for ethanol producers
Numbers and analytics are a big part of how ethanol plants operate. Here’s how technical service can help ethanol producers understand and use the vast amount of information pouring out of their plants.
The term “big data” is used a lot these days in everything from industrial production, finance and IT, to agriculture, advertising and online retail. It refers to the huge amounts of information that is gathered, analyzed and used to improve processes or consumer experience.
Ethanol plants are no exception. By understanding the data these plants generate, technical service can help ethanol producers get more yield out of the corn, find ways to improve plant performance and make more money. Laurie Duval, senior manager for Biofuel Technical Services at Novozymes, explains to Think Bioenergy how it works.
Why is big data important for ethanol plants?
Ethanol plants produce very large amounts of data across their processes every day. The data is critical for understanding and guiding their operation, and for troubleshooting and optimizing processes. In fermentation, for example, data is used to model how enzymes and yeast perform to make sugars and ethanol. So it’s about improving yields; that ethanol producers get as much as they can from every kernel of corn.
Where does the data come from?
Data is gathered from process instruments: pumps, valves, the flow rates across the instruments, from control systems. There’s also lab data, as every plant has a lab that runs key tests on samples collected across the process. Then there is data around maintenance schedules, which ensure you optimize exactly when to replace something. There’s also accounting data where you track how much corn is ground in the plant and how much ethanol is produced.
How does Novozymes help ethanol producers use big data?
If a change is made to a process, it can be difficult to see that in the day-to-day collection of data within a plant. But by doing statistical analysis, our scientists can determine whether the change improved the process or not.
A good example is a customer we recently worked with that produces up to 55 million gallons of ethanol every year. Data from the plant showed significant variation in ethanol production for each fermented batch. We analyzed the data and realized that, when things went wrong in the cook process, the plant operators would respond by increasing the dosage of enzymes, but not reduce the dosage later. By close monitoring, we were able to recommend a specific dosage and help the facility tighten its process and save on costs.
Does big data help Novozymes improve its own ethanol products?
When we launch a new solution, we monitor how benefits of that product are proven in the data collected. That could be data about improved ethanol or corn oil yield, how a plant can load more corn solids into the process so they get more throughput, or chemical reduction through use of enzymes. We run trials across many plant sites. We analyze that trial data and feed our conclusions back to R&D, which helps Novozymes make even better products going forward.
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