Recent advancements in biomass to bioenergy management and carbon capture through artificial intelligence integrated technologies to achieve carbon neutrality

Abstract

Biomass, a renewable resource crucial for carbon neutrality, serves as a sustainable alternative to fossil fuels by closing the carbon loop. The biotransformation of biomass into carbon–neutral fuels for bioenergy and bioelectricity plays a key role in addressing climate change. Recent advancements in biomass bioenergy management, carbon capture, and carbon-negative emission technologies have been pivotal in reducing atmospheric CO₂. However, the integration of artificial intelligence (AI) has markedly enhanced these traditional models by optimizing the biomass supply chain, selecting optimal feedstocks, and refining the operation of bioenergy plants. This review delves into the recent applications of AI in biomass bioenergy, highlighting AI-driven decision-making systems that improve computing and reasoning techniques toward carbon neutrality. Our analysis reveals a wide array of AI techniques, including genetic algorithms, swarm intelligence, artificial neural networks, fuzzy logic, and supervised machine learning, which have been deployed across the biomass bioenergy value chain. Notable outcomes suggested that AI can reduce CO₂ emissions by 5% to 10%, equivalent to 2.6 to 5.3 gigatons of CO₂. This review emphasizes AI’s transformative role in enhancing biomass bioenergy production, positioning it as a critical tool for sustainable energy solutions and future environmental policies to achieve carbon neutrality.