Intelligent approaches for sustainable management and valorisation of food waste

Said, Zafar; Sharma, Prabhakar; Nhuong, Quach Thi Bich; Bora, Bhaskor Jyoti; Lichtfouse, Éric; Khalid, Haris M.; Luque, Rafael; Nguyen, Xuan Phuong; Hoang, Anh Tuan

doi:10.1016/j.biortech.2023.128952

Cited by 50 publications

(16 citation statements)

References 137 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, SVM is regarded as among the finest machine learning techniques for both regression and classification, according to some statistical learning theories (Gao et al, 2003;Yuan et al, 2010). When the results of SVM were compared to those of other strong data-driven empirical techniques like ARIMA, RBF, MLP, and IIR-LRNN, the SVR results were observed to exceed or be equivalent to those of other learning machines (Erfianto and Rahmatsyah, 2022;Moura et al, 2011;Said et al, 2023). Additionally, SVR is thought to function well for time series analysis because of better generalizability and the capability of ensuring a global minimum for certain training data (Fuadi et al, 2021;Wu et al, 2004).…”

Section: Support Vector Machine (Svm) In Energy Regulationmentioning

confidence: 99%

Harnessing artificial intelligence for data-driven energy predictive analytics: A systematic survey towards enhancing sustainability

Le,

Priya,

et al. 2024

Int. J. Renew. Energy Dev.

View full text Add to dashboard Cite

The escalating trends in energy consumption and the associated emissions of pollutants in the past century have led to energy depletion and environmental pollution. Achieving comprehensive sustainability requires the optimization of energy efficiency and the implementation of efficient energy management strategies. Artificial intelligence (AI), a prominent machine learning paradigm, has gained significant traction in control applications and found extensive utility in various energy-related domains. The utilization of AI techniques for addressing energy-related challenges is favored due to their aptitude for handling complex and nonlinear data structures. Based on the preliminary inquiries, it has been observed that predictive analytics, prominently driven by artificial neural network (ANN) algorithms, assumes a crucial position in energy management across various sectors. This paper presents a comprehensive bibliometric analysis to gain deeper insights into the progression of AI in energy research from 2003 to 2023. AI models can be used to accurately predict energy consumption, load profiles, and resource planning, ensuring consistent performance and efficient resource utilization. This review article summarizes the existing literature on the implementation of AI in the development of energy management systems. Additionally, it explores the challenges and potential areas of research in applying ANN to energy system management. The study demonstrates that ANN can effectively address integration issues between energy and power systems, such as solar and wind forecasting, power system frequency analysis and control, and transient stability assessment. Based on the comprehensive state-of-the-art study, it can be inferred that the implementation of AI has consistently led to energy reductions exceeding 25%. Furthermore, this article discusses future research directions in this field.

show abstract

Section: Support Vector Machine (Svm) In Energy Regulationmentioning

confidence: 99%

Harnessing artificial intelligence for data-driven energy predictive analytics: A systematic survey towards enhancing sustainability

Le,

Priya,

et al. 2024

Int. J. Renew. Energy Dev.

View full text Add to dashboard Cite

show abstract

“…Such monitoring can be made more efficient with the innovative application of AI. Applying intelligent approaches can enable real-time decision-making and process optimisation, and if supported by the government, food waste can be minimised and maximised for a sustainable future [91].…”

Section: Potential Benefits Of Ai Optimisation Including Reduced Food...mentioning

confidence: 99%

Using Artificial Intelligence to Tackle Food Waste and Enhance the Circular Economy: Maximising Resource Efficiency and Minimising Environmental Impact: A Review

Onyeaka,

Tamasiga,

Nwauzoma

et al. 2023

Sustainability

View full text Add to dashboard Cite

Food waste is a global issue with significant economic, social, and environmental impacts. Addressing this problem requires a multifaceted approach; one promising avenue is using artificial intelligence (AI) technologies. This article explores the potential for AI to tackle food waste and enhance the circular economy and discusses the current state of food waste and the circular economy, highlighting specific ways that AI can be used to monitor and optimise food production and supply chains, redistribute excess food to those in need, and support circular economy initiatives. As a result, we can maximise resource efficiency and minimise environmental impact with these applications, ultimately creating a more sustainable and equitable food system.

show abstract

“…However, there are still many shortcomings, including the rationality of the model parameters, that require further investigation to confirm our findings' applicability in practical situations owing to the complexity of the influencing factors. Existing literature has applied intelligent algorithms and novel machine learning methods in the field of circular economy research [ 40 , 41 ], and this will also be the direction of our future efforts. Additionally, the government's regulatory efforts toward Internet recycling companies aiming to achieve optimal policy effects require further research.…”

Section: Conclusion and Policy Recommendationsmentioning

confidence: 99%

Achieving a win-win situation by promoting internet recycling of waste electronics and preventing information leakage in a multi-party game

Liu,

Wang,

et al. 2023

Heliyon

View full text Add to dashboard Cite

Intelligent approaches for sustainable management and valorisation of food waste

Cited by 50 publications

References 137 publications

Harnessing artificial intelligence for data-driven energy predictive analytics: A systematic survey towards enhancing sustainability

Harnessing artificial intelligence for data-driven energy predictive analytics: A systematic survey towards enhancing sustainability

Using Artificial Intelligence to Tackle Food Waste and Enhance the Circular Economy: Maximising Resource Efficiency and Minimising Environmental Impact: A Review

Achieving a win-win situation by promoting internet recycling of waste electronics and preventing information leakage in a multi-party game

Contact Info

Product

Resources

About