Energy

This chapter explores the integration of artificial intelligence (AI) in biohydrogen production, a promising renewable energy technology. Biohydrogen is regarded as a potential renewable bioenergy resource. There are many processes through which it can be produced, for example, thermochemical and biological processes like pyrolysis, electrolysis, dark fermentation, and photo-fermentation. It is more economically viable when it is produced from waste materials such as waste biomass via microbial fermentation or light-driven chemical reactions. In the last decade, AI or intelligent systems have revolutionized scientific research. Prospectively, classical AI, machine learning (ML), and deep learning algorithms can be applied to optimize biohydrogen production processes. These techniques including reinforcement learning, artificial neural networks, and genetic algorithms can help optimize crucial influential parameters affecting biohydrogen production efficiency and yield. Random forest and support vector machine are two specific ML algorithms that can improve process monitoring, yield prediction, and address challenges for biohydrogen production by managing complex data, accurately predicting outcomes with improved scalability for industrial production processes. The chapter also highlights AI applications in biohydrogen production employing various AI tools like jellyfish optimizer and adaptive neuro-fuzzy inference system that optimize operational conditions in microbial electrolysis cells, enhancing hydrogen yield from wastewater. However, there are many challenges to implement AI-based systems in practice at large that include data limitations, real-world variability, scalability, and supportive technology to AI. Moreover, intelligent systems’ limited adaptability, to date proven credibility and human oversight importance were also discussed with associated ethical concerns. It also needs continuous monitoring and improvement for economically viable and sustainable production processes. Emerging technological trends in biohydrogen production focus on autonomous AI-based production systems, predictive modeling, appropriate management of supply chain, and sustainability valuation. Future AI developments aim to make biohydrogen production more cost-effective, efficient, and scalable.

Daytime radiative cooling and photovoltaic energy generation are poorly compatible, as they have competing physical demands. Here, a transmission-based radiative cooling system is integrated with solar cells, allowing simultaneous cooling and photovoltaic power generation in sunny weather.

This chapter supports SDG 7 (Affordable and Clean Energy) by exploring innovative approaches to enhance solar energy harvesting using plasmonic nanoparticles, thereby improving the efficiency and cost-effectiveness of solar energy systems. It also contributes to SDG 13 (Climate Action) by promoting sustainable energy technologies that help reduce reliance on fossil fuels and mitigate climate change.

This chapter supports UN SDGs 7 (Affordable and Clean Energy), 9 (Industry, Innovation, and Infrastructure), 11 (Sustainable Cities and Communities), 13 (Climate Action), and 17 (Partnerships for the Goals) by promoting the transition to renewable energy sources, reducing greenhouse gas emissions, enhancing energy efficiency, fostering technological innovation, and emphasizing collaboration and innovation to drive the development of cleaner and more efficient energy solutions for a sustainable future.