Advances, Synergy, and Perspectives of Machine Learning and Biobased Polymers for Energy, Fuels, and Biochemicals for a Sustainable Future

Abstract

This review illuminates the pivotal synergy between machine learning (ML) and biopolymers, spotlighting their combined potential to reshape sustainable energy, fuels, and biochemicals. Biobased polymers, derived from renewable sources, have garnered attention for their roles in sustainable energy and fuel sectors. These polymers, when integrated with ML techniques, exhibit enhanced functionalities, optimizing renewable energy systems, storage, and conversion. Detailed case studies reveal the potential of biobased polymers in energy applications and the fuel industry, further showcasing how ML bolsters fuel efficiency and innovation. The intersection of biobased polymers and ML also marks advancements in biochemical production, emphasizing innovations in drug delivery and medical device development. This review underscores the imperative of harnessing the convergence of ML and biobased polymers for future global sustainability endeavors in energy, fuels, and biochemicals. The collective evidence presented asserts the immense promise this union holds for steering a sustainable and innovative trajectory.

Effective Economic Model for Greenhouse Facilities Management and Digitalization

“…In terms of input composition, agricultural engineering assessments emphasize energy's outsized and rising share of production costs given the environmental control demands in controlled environment agriculture [21]. With multicultural expansion, heating, cooling, and lighting accruals are projected to continuously increase total greenhouse expenditure shares [22]. That is why the fifth hypothesis of this study is: H5: Energy inputs are positively associated with production costs.…”

“…This gap in the literature surrounding localized analytical tools tailored to nascent agricultural modernization priorities limits evidence-based policy guidance. Integrated methodologies accounting for on-theground realities across Uzbekistan's diverse cultivation zones are needed to optimize scaling strategies and align smart infrastructure investments with equitable rural livelihood impacts [21,22]. Therefore, this study examines how increased integration into digital supply chain platforms and direct coordination with value chain partners affect key financial and operational metrics across Uzbekistan's greenhouse industry.…”

Hydrothermal carbonization: Sustainable pathways for waste‐to‐energy conversion and biocoal production

“…Moreover, probing into the kinetic pathways of the HTC process will be indispensable in transitioning from batch to continuous processing, a leap necessary for industrial-scale applications. Furthermore, it is also important to explore technologies such as machine learning as they have been demonstrated to optimized various technology related to energy and sustainable materials development Bin Abu Sofian, Sun, et al, 2024).…”

Harnessing a Better Future: Exploring AI and ML Applications in Renewable Energy

“…By overcoming these challenges, the biomass energy industry has the potential to have a stronger influence in the fight against climate change, the enhancement of energy security, and the promotion of sustainable development [354]. Machine learning offers promising solutions for overcoming the many challenges that the biomass energy business faces, to enhance its efficiency, environmental friendliness, and viability from a financial standpoint [355], [356]. Machine learning may help handle these difficulties in a number of different ways, including the following:  Improving the Management of Biomass Resources:…”