Biofuels for a sustainable future: Examining the role of nano-additives, economics, policy, internet of things, artificial intelligence and machine learning technology in biodiesel production

Shelare, Sagar D.; Belkhode, Pramod N.; Nikam, Keval Chandrakant; Jathar, Laxmikant D.; Shahapurkar, Kiran; Soudagar, Manzoore Elahi M.; Veza, Ibham; Khan, T.M. Yunus; Kalam, M.A.; Nizami, Abdul-Sattar; Rehan, Mohammad

doi:10.1016/j.energy.2023.128874

Cited by 31 publications

(10 citation statements)

References 280 publications

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“…In the evolving panorama of material science, the influence of ML stands out, predominantly steering the enhancement and development of sustainable materials and biobased polymers. − The prospect of designing advanced materials with precise properties for specific applications is heightened by employing ML algorithms for property prediction and optimization. ,, A comprehensive analysis of large data sets performed by ML efficiently discerns patterns and correlations that might remain unnoticed by conventional methods . Through this, expedited and more informed decisions are made in the development of biobased polymers for diverse applications.…”

Section: Ml: Revolutionizing Materials Sciencementioning

confidence: 99%

“…In the ever-evolving landscape of fuel development, the substantial influence of ML is being acknowledged. , A significant surge in efficiency, sustainability, and innovation in fuel development is being observed, owing to the integration of advanced ML algorithms . An exploration into the multifaceted impact of ML on fuel development is explored in this section, underlining its pivotal role in reshaping and enhancing the industry.…”

Section: Energy and Fuelsmentioning

confidence: 99%

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

Bin Abu Sofian,

Sun,

Gupta

et al. 2024

Energy Fuels

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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.

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Section: Ml: Revolutionizing Materials Sciencementioning

confidence: 99%

Section: Energy and Fuelsmentioning

confidence: 99%

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

Bin Abu Sofian,

Sun,

Gupta

et al. 2024

Energy Fuels

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“…The generated plan is executed by solving a constrained task and motion planning problem using PDDLStream solvers, aiming to prevent spillages of liquids and collisions in chemistry laboratories. 55 The effectiveness of their approach is demonstrated through planning chemistry experiments, with plans successfully executed on a real robot utilizing a repertoire of robot skills and laboratory tools. Specifically, they showcase the utility of their framework in pouring skills for various materials and executing two fundamental chemical experiments for materials synthesis: solubility and recrystallization.…”

Section: Role Of ML and Ai: Optimizing Synthesismentioning

confidence: 99%

Toward Making Polymer Chemistry Autonomous

Basak,

Bandyopadhyay

2024

ACS Appl. Eng. Mater.

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The rise of fully automated chemical synthesis represents a groundbreaking shift in laboratory research driven by rapid technological advancements. Instead of relying on traditional methods, this change brings about a new era where cutting-edge technologies like robotics, advanced programming, machine learning, and artificial intelligence work together seamlessly to conduct precise chemical reactions. This transformation not only alters how laboratories operate but also promises improved precision, reproducibility, and a faster pace of research. Looking ahead, there is the potential for a universal synthesis platform that could make chemical synthesis tools more accessible to everyone. This platform's adherence to regulatory standards is crucial for efficient and standardized manufacturing practices, going beyond just making things easier. It also signifies collaboration across different fields, enhancing human potential by combining human intellect with machine precision. Our narrative is to explore the fascinating phenomenon of fully autonomous chemical synthesis. We delve into its components, advantages, and challenges, offering insights into how it affects laboratory-based research and signals a new frontier in scientific exploration. Additionally, we examine the trajectory of polymer chemistry within this context, envisioning a future landscape shaped by autonomy. Ultimately, we hope this narrative serves as a guide for readers, providing valuable insights into the evolving path of this exciting field of chemistry.

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“…Artificial Intelligence (AI) , is a new technological science based on computer science that is used to simulate, extend, and expand human intelligence . In recent years, Li et al have conducted research on the application of artificial intelligence in oil and gas reservoir development.…”

Section: Introductionmentioning

confidence: 99%

Current Status and Prospects of Artificial Intelligence Technology Application in Oil and Gas Field Development

Wang,

Wei,

Xiong

et al. 2024

ACS Omega

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Artificial intelligence technology will be increasingly applied in the oil and gas industry. The rapid development of artificial intelligence technology can solve problems such as high environmental sensitivity and complex production processes in the oil and gas industry. In recent years, emerging technologies represented by artificial intelligence have developed rapidly, assisting petroleum enterprises in digital transformation and intelligent upgrading. This article elaborates on the development trend of artificial intelligence technology. Based on the business scenarios and characteristics of the oil and gas industry, the application status of artificial intelligence technology in domestic and foreign petroleum technology service enterprises was summarized and analyzed. The application scenarios of artificial intelligence technology in the fields of dynamic analysis of oil and gas reservoirs, intelligent historical fitting, numerical simulation proxy models, and production plan optimization were analyzed with emphasis. Based on the problems and challenges faced in the development process of oil and gas reservoirs, it is proposed that petroleum enterprises should attach importance to the "three modernizations" innovation of data standardization, oil and gas field intelligence, and platform collaboration, in order to achieve more refined intelligent analysis and management of oil and gas reservoirs and quickly develop more targeted oil and gas reservoir development plans to assist in the intelligent transformation of oil and gas reservoir development. On this basis, prospects for future artificial intelligence technology are proposed, pointing out that the development of artificial intelligence technology will be faster and faster, and there will be higher demand for artificial intelligence technology in the construction of digital oil and gas fields in China in the future. The research results have important reference value for the development of the oil and gas industry.

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Biofuels for a sustainable future: Examining the role of nano-additives, economics, policy, internet of things, artificial intelligence and machine learning technology in biodiesel production

Cited by 31 publications

References 280 publications

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

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

Toward Making Polymer Chemistry Autonomous

Current Status and Prospects of Artificial Intelligence Technology Application in Oil and Gas Field Development

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