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| USBI-NABC24-10_Tobias_Schweitzer-AI-Driven_Biochar_Innovation.pdf | 34.22 MB |
Biochar Production
Oral Presentation
Revolutionizing Biochar Production: AI-Driven Innovation
Tobias Schweitzer
Marius Köppel
AIRA Holding GmbH
This talk focuses on the potential of using Artificial Intelligence (AI) to enhance biochar production. By analyzing vast datasets, AI can identify correlations and make predictions to optimize various aspects of the production process. The talk presents three possible areas of optimization and shares initial empirical results from the implementation of AI at PYREG's laboratory plant.
The areas of optimization include:
Efficiency: AI can identify patterns to optimize resource utilization.
Quality: AI can optimize the properties of the biochar to meet the final product requirements.
Predictive Maintenance: AI can facilitate proactive maintenance scheduling, reducing downtime and enhancing operational efficiency.
Initial tests focusing on reduction of NOx Emissions already showed promising results. The real-word implementation of our machine learning approach in PYREG’s laboratory facilities, called project Emissiontech, demonstrated proof of concept, particularly in temperature prediction.
The forthcoming North American Biochar Conference (12–14 Feb. 2024) will provide an opportunity to present additional empirical results from academia and industry, further solidifying the effectiveness of the AI-based approach.
This talk furthermore highlights the vision of emissiontech, which aims to provide an open AI infrastructure for the entire biochar industry. The success of this vision relies on crucial factors such as digitization, sensor technology, sufficient IT resources, building trust in data sharing, and effectively managing costs. Collaboration among partners plays a pivotal role as it enables the accumulation of valuable data, resulting in improved output and cost efficiency for all stakeholders involved. By working together and sharing data, the biochar industry can unlock its full potential and pave the way for sustainable and cost-effective practices.
Keywords: artificial intelligence, machine learning, biochar production, emission tech
References:
https://figshare.com/articles/poster/Reducing_NOx_Emissions_in_Pyrolysis_Machines_for_Biochar_Production_A_machine_learning_approach/23772729/1