Preliminary Feedstock Recipes: Set Up For Success
Developing a biogas project from an exciting idea into a productive facility is a long path full of unique challenges. A pillar of the development of these projects is feedstock, or the organic material that will be fed into the digester. This cornerstone in anaerobic digestion plants can often be an unpredictable source of issues, and it is exactly in mitigating this that software and AI-powered technology can help. Creating ideal feedstock recipes before the project goes live can set you up for long-term success since it sets and maintains an optimized realistic baseline expectation of production, reduces the risk involved in operational experimentation down the line, and helps you with optimizing your project development and plant operation.
Have you ever gone to the grocery store with big ideas but no list? It probably led to a more chaotic experience than you expected (and in my case, it often means getting a surplus of chocolate). This can often be the case when beginning operations without an optimized recipe that accounts for feedstock availability or lacks the flexibility to simulate fluctuations in the available organic material. Creating feedstock recipes as soon as you have secured feedstock contracts can be essential when setting a baseline for production, an essential step to take before negotiating offtake agreements. Optimized recipes provide realistic projections to potential investors and off-takers, but when used in conjunction with simulation software they can allow you to peek into the future and explore what fluctuations in feedstock can mean for your project’s gas, electricity, heat or finances.
Preliminary feedstock recipes can also serve as a starting point with significantly less risk than experimenting with a recipe through trial-and-error. As the industry advances we must realize that the risks associated with trial-and-error feedstock recipe trials far outweigh the benefits. Simulation software allows us to experiment without the expense of a digester crash on the horizon.
AI-driven feedstock recipe generation can help you with optimizing your project across the board. The power of simulation software does not stop at creating an optimized feedstock recipe, but it can also account for feedstock availability, digestate composition, and distinct goals, like maximizing profit or gas production. Through predictive analytics, solutions that simulate operations through a digital twin have the power of evaluating millions of scenarios to reach the ideal recipe that aligns with your goals.
Overall, creating preliminary feedstock recipes before commencing the day-to-day operations of a biogas facility is not a common practice, but it emerges as a key step in biogas project development due to its multifaceted impact on operational safety, efficiency, and risk reduction associated with experimenting with feedstock. Proactive feedstock optimization yields reliable expectations for project stakeholders in addition to maximizing the use of available resources and storage. Simultaneously AI-powered proactive management has the potential of saving facilities both time and money by evaluating millions of scenarios until reaching the solution that helps you align with your goals.