Green Technology and Industry 4.0 for Sustainable Future

Green Technology and Industry 4.0 for Sustainable Future portes grátis

Green Technology and Industry 4.0 for Sustainable Future

Babu, Rohit; Verma, Sumit; Raj, Saurav; Mahapatra, Sheila

Taylor & Francis Ltd

03/2026

400

Dura

Inglês

9781032746623

Pré-lançamento - envio 15 a 20 dias após a sua edição

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1. Assessment of Sub-synchronous Oscillations in DFIG-Based Wind Farms. 2. Integration of Electric Transport with Smart Grids. 3. Biomining: A Sustainable Approach for Managing Dumpsite Waste - An In-depth Review. 4. Enhancing Solar Irradiance Forecasting: A Comprehensive Exploration of Various Clustering Methods Combined with MLPNN. 5. GREEN ICT: Management of E-Waste for Environmental Sustainability. 6. Impact of Tetrabutylammonium Bromide-Based Deep Eutectic Solvent on the Kinetic Hydrate Inhibition of Methane Hydrate. 7. K-Means Clustering Boosted Hybrid CNN-LSTM Models for Indoor Occupancy, Temperature, and Energy Consumption Prediction. 8. Modelling and Numerical Simulation of Domestic Electrical Induction Cooker Using Chebfun for Uniform Temperature Distribution on Working Plate. 9. Smart Grid and Energy Management. 10. Importance of Green Technology and Sustainable Development in Higher Educational Institutes: A Critical Study. 11. Enhanced Energy Planning in Jammu and Kashmir Through AI-Driven Solar Power Forecasting. 12. Sustainable Solutions: Exploring the Latest Innovations in Green Technology. 13. Estimating Levels of Surface Contamination of Silicone Rubber Insulators by Employing Wavelet Decomposition and Machine Learning-Based Techniques. 14. Exploiting Smart City Technology: Using Convolution Neural Network Computer Vision tools for COVID-19 Monitoring. 15. Solubility of Curcuminoids in Natural Deep Eutectic Solvents: A COSMO-SAC led approach. 16. Wastewater management: from ancient times to advanced technologies.
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environmental engineering;bioplastics applications;smart grid integration;machine learning forecasting;waste to energy systems;energy-efficient technologies;advanced industrial process optimization