Harnessing artificial intelligence for sustainable Bioenergy: Revolutionizing Optimization, waste Reduction, and environmental sustainability.

Assessing the mutual benefits of artificial intelligence (AI) and bioenergy systems, to promote efficient and sustainable energy production. By addressing issues with conventional bioenergy techniques, it highlights how AI is revolutionising optimisation, waste reduction, and environmental sustainability. With its capacity for intelligent decision-making, predictive modelling, and adaptive controls to maximise bioenergy processes, artificial intelligence (AI) emerges as a crucial catalyst for overcoming these obstacles. The focus on particular uses of AI to enhance bioenergy systems. Algorithms for machine learning are essential for forecasting biomass properties, selecting feedstock optimally, and enhancing energy conversion procedures in general. Enhancing real-time adaptability and guaranteeing optimal performance under a range of operational conditions is made possible by the integration of AI-driven monitoring and control systems. Additionally, it looks at how AI supports precision farming methods in bioenergy settings, enhancing crop management strategies and increasing the output of biofuels. AI-guided autonomous systems help with precision planting, harvesting, and processing, which reduces resource use and maximises yield. AI's contribution to advanced biofuel technology by using data analytics and computational models, it can hasten the creation of new, more effective bioenergy sources. AI-driven grid management advancements could guarantee the smooth integration of bioenergy into current energy infrastructures. The revolutionary role that artificial intelligence (AI) has played in bioenergy systems, making a strong case for the incorporation of AI technologies to drive the global energy transition towards a more ecologically conscious and sustainable future.

• Publication year

  2024

• Venue

  Bioresource Technology

• Publication date

  2024-11-01

• Fields of study

  Medicine, Computer Science, Engineering, Environmental Science

• Identifiers
  DOI 10.1016/j.biortech.2024.131893PMID 39608419
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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