Machine-learned digital phase switch for sustainable chemical production

Teng, Sin Yong; Galvis, Leonardo; Blanco, Carlos Mendez; Özkan, Leyla; Barendse, Ruud; Postma, G.J.; Jansen, Jeroen J.

doi:10.1016/j.jclepro.2022.135168

Cited by 6 publications

(4 citation statements)

References 49 publications

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“…LSTM helps computers understand and remember things over time like human brains do (Mele and Magazzino, 2020). NAS aims to systematically search and evaluate various architectures to identify the most effective network design that can achieve superior performance on a specific task (Teng et al , 2023). DL models excel at tasks such as image and speech recognition, natural language processing and generative modelling (Lan et al , 2021).…”

Section: Discussionmentioning

confidence: 99%

“…The adoption of ML techniques in various domains has shown promise in addressing sustainability challenges and promoting responsible consumption and production (Gunawan et al , 2020; D’Amato et al , 2017; Lu et al , 2022; McMeekin and Southerton, 2012). Teng et al (2023) use neural architecture search (NAS) to develop a system that dynamically optimizes energy consumption and minimizes environmental impact in chemical processes through phase switching. Nilashi et al (2019a) propose a model for recommending green hotels based on consumer feedback, using ML techniques such as self-organizing maps and adaptive neuro-fuzzy inference systems.…”

Section: Thematic Areasmentioning

confidence: 99%

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Machine learning for sustainable development: leveraging technology for a greener future

Kagzi,

Khanra,

Paul

2023

JSIT

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Purpose From a technological determinist perspective, machine learning (ML) may significantly contribute towards sustainable development. The purpose of this study is to synthesize prior literature on the role of ML in promoting sustainability and to encourage future inquiries. Design/methodology/approach This study conducts a systematic review of 110 papers that demonstrate the utilization of ML in the context of sustainable development. Findings ML techniques may play a vital role in enabling sustainable development by leveraging data to uncover patterns and facilitate the prediction of various variables, thereby aiding in decision-making processes. Through the synthesis of findings from prior research, it is evident that ML may help in achieving many of the United Nations’ sustainable development goals. Originality/value This study represents one of the initial investigations that conducted a comprehensive examination of the literature concerning ML’s contribution to sustainability. The analysis revealed that the research domain is still in its early stages, indicating a need for further exploration.

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Section: Discussionmentioning

confidence: 99%

Section: Thematic Areasmentioning

confidence: 99%

Machine learning for sustainable development: leveraging technology for a greener future

Kagzi,

Khanra,

Paul

2023

JSIT

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“…Other articles that use ML in the context of CE are [52], who apply a GBR to predict the remaining useful life of batteries, and [53], who apply a multi-criteria procedure with a NN to assess 63rd ERSA Congress Terceira Island 26-30 August 2024 the remaining useful life of end-of-life products. Another interesting approach in the area of sustainable chemical production using NN regression is provided by [54].…”

Section: Applied Machine Learning In the Fields Of Circular Economymentioning

confidence: 99%

Challenges and Opportunities of Artificial Intelligence and Machine Learning in Circular Economy

Despotovic,

Glatschke

2024

Preprint

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The inherent "take-make-waste" of the current linear economy is a major contributor to exceeding planetary boundaries. The transition to a circular economy (CE) and the associated challenges and opportunities require fast, innovative solutions. Artificial Intelligence (AI) and Machine Learning (ML) can play a key role in the transition to a CE paradigm by overcoming the challenges of increasing material extraction and use and creating a far more environmentally sustainable future. The objective of this article is to provide a status quo on the use of AI and ML in the transition to CE and to discuss the potential and challenges in this regard. The literature survey on Google Scholar using targeted queries with predefined keywords and search operators revealed that the number of experimental scientific contributions to AI and ML in the CE has increased significantly in recent years. As the number of research articles increased, so did the number of ML methods and algorithms covered in experimental CE publications. In addition, we found that there are 84% more AI and ML-affiliated research articles on CE in Google Scholar since 2020, compared to the total number of entries, and 55% more articles since 2023, compared to the respective articles up to 2023. The status quo of the scientific contributions shows that AI and ML are seen as extremely useful tools for the CE and their use is steadily increasing.

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“…Therefore, current studies also focus on the estimation of power consumption of 3D-printing in dependency of the geometry [182,183] or on the optimization of the printing process to become time, cost and energy efficient. [184] Thus, machine-learning can also assist to make additive manufacturing more sustainable in the future.…”

Section: Additive Manufacturingmentioning

confidence: 99%

Application of Digital Methods in Polymer Science and Engineering

Schuett,

Endres,

Standau

et al. 2023

Adv Funct Materials

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The development of new polymer materials is an emerging field for more than 100 years. However, it is currently facing major challenges and the application of digital methods can help to develop new processes, discover new materials and, thus, contribute to the challenges of today and the future. Though, the application of digital methods in the field of polymer science is currently very limited, when compared to other classes of materials such as small molecules or inorganic high‐performance materials. Nevertheless, there are already first, very promising approaches. The current review article focuses on the application of these methods in different aspects of polymer research including polymer design, synthesis, and characterization. Furthermore, the digital discovery of polymer engineering is highlighted in detail showing the broad range of potential applications of these methods in polymer science. Finally, future possibilities and opportunities are derived from the current state‐of‐the‐art and perspectives for a potential evolution of polymer science are provided.

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Machine-learned digital phase switch for sustainable chemical production

Cited by 6 publications

References 49 publications

Machine learning for sustainable development: leveraging technology for a greener future

Machine learning for sustainable development: leveraging technology for a greener future

Challenges and Opportunities of Artificial Intelligence and Machine Learning in Circular Economy

Application of Digital Methods in Polymer Science and Engineering

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