Bacterial Biofuel Production: An Assessment of Machine Learning Technologies in Valorisation of Waste/Wastewater and Circular Economy
摘要
The world is facing a dual challenge of addressing waste management and finding sustainable alternatives to conventional energy sources. In this context, bacterial biofuels produced from waste and wastewater have emerged as a promising solution. These biofuels not only provide an environmentally friendly energy source but also contribute to waste reduction and resource optimization, aligning with the principles of a circular economy. Machine learning tools have the capacity to aid in the identification of the best process parameters, estimation of feedstock characteristics, and assessment of environmental effects like greenhouse gas emissions. The circular economy emphasizes the continuous use, regeneration, and optimization of resources to minimize waste and environmental impact. Machine learning algorithms play a pivotal role in optimizing the circular economy of bacterial biofuels from waste and wastewater, offering innovative ways to enhance efficiency, predict outcomes, and reduce environmental footprints. Through quick and precise understanding, machine learning is helping to foster the growth of eco-friendly and well-coordinated biofuel sectors. This, in turn, reduces the environmental impact and enhances social welfare by promoting job opportunities.