Synergizing Artificial Intelligence, 5G, and Cloud Computing for Efficient Energy Conversion Using Agricultural Waste

Synergizing Artificial Intelligence, 5G, and Cloud Computing for Efficient Energy Conversion Using Agricultural Waste

DOI: 10.4018/979-8-3693-1186-8.ch026
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The combination of artificial intelligence, 5G technology, and cloud computing has altered energy conversion processes, most notably the use of agricultural waste for sustainable energy generation. This book chapter digs into AI, 5G, and cloud computing research and development for efficient energy conversion, environmental concerns, and the viability of agricultural waste as a renewable energy resource. AI technologies provide real-time monitoring and control, while cloud computing enables data analytics and optimization. The synergistic method increases the efficiency of energy conversion, predictability, flexibility, optimization, grid integration, energy storage, and cost reduction. Compatibility, data security, and financial sustainability, on the other hand, must be addressed. The chapter emphasises the importance of this integrated strategy in addressing global energy and environmental challenges.
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The integration of AI, 5G technology, and cloud computing has revolutionized energy conversion processes, particularly in utilizing agricultural waste for sustainable energy generation. This book chapter explores research and development in synergizing AI, 5G, and cloud computing for efficient energy conversion, addressing environmental concerns and exploring the potential of agricultural waste as a renewable resource (Saravanan et al., 2022). The synergistic approach offers enhanced efficiency, predictability, flexibility, optimization, grid integration, energy storage, and cost savings. However, challenges like compatibility, data security, and economic viability must be addressed. Renewable energy sources like solar, wind, hydroelectric, and geothermal power are increasingly being used for long-term energy sustainability.

Organic waste materials, particularly agricultural waste, are also being explored for energy production. These materials have significant energy potential but are often underutilized, posing environmental challenges like pollution, odors, and health hazards(Esenogho et al., 2022). Converting agricultural waste into usable energy promotes a circular economy approach, maximizing resource efficiency and minimizing environmental impact. However, efficient conversion faces challenges due to waste composition, moisture content, and energy content. AI, 5G technology, and cloud computing can help optimize conversion processes(Boopathi, 2021; Boopathi, Pandey, et al., 2023; Boopathi & Kanike, 2023). AI algorithms analyze data on waste characteristics, energy conversion technologies, and operational parameters to optimize energy production. Machine learning and predictive modeling improve efficiency, reduce waste, and enable real-time monitoring. 5G technology facilitates seamless communication, real-time data transmission, remote monitoring, control, and edge computing integration(S. Babu et al., 2022).

Cloud computing offers scalability, computational resources, and collaborative decision-making for data storage, processing, and analytics. By integrating AI, 5G, and cloud computing, agricultural waste can be harnessed as a sustainable energy source, resulting in more efficient energy conversion processes, reduced waste generation, improved operational control, and enhanced system performance. This approach contributes to a circular economy in the agricultural sector, promoting a more sustainable and greener future. Agricultural waste, derived from farming and food production, has significant potential as a renewable energy resource. Properly managed, this organic waste can contribute to sustainable energy production, waste management, and a circular economy(Hasan et al., 2017; Markovic et al., 2013).

Agricultural waste, including crop residues, animal manure, food processing waste, and livestock by-products, is abundant and widely available globally. This makes it a valuable resource for energy production. Agricultural waste has significant energy content due to its organic composition, allowing its constituents like carbohydrates, fats, and proteins to be converted into heat, electricity, and biofuels. Biomass and biogas production are common methods for utilizing agricultural waste. Biomass is organic matter from plants and animals, which can be burned or converted into biofuels(Hanumanthakari et al., 2023). Biogas production involves anaerobic digestion, where microorganisms break down waste, producing methane-rich biogas for heat and power generation. Agricultural waste can be converted into biofuels, offering an alternative to fossil fuels. Advanced biofuels like bioethanol and biodiesel can be produced from waste, blending with gasoline for transportation and replacing diesel fuel. Combined Heat and Power (CHP) systems utilize agricultural waste to generate heat and electricity simultaneously(Selvakumar et al., 2023; Sengeni et al., 2023a). The heat can be used for space heating, crop drying, or industrial processes, while the electricity can meet on-site or community power needs.

Key Terms in this Chapter

EBITDA: Earnings Before Interest, Taxes, Depreciation, and Amortization

CO2: Carbon Dioxide

ROI: Return on Investment

NOx: Nitrogen Oxides

IoE: Internet of Energy

IoT: Internet of Things

GIS: Geographic Information System

DMS: Distribution Management System

CCHP: Combined Cooling, Heating, and Power

ICT: Information and Communication Technology

SCADA: Supervisory Control and Data Acquisition

MW: Megawatt

PM2.5: Particulate Matter 2.5

DG: Distributed Generation

5G: Fifth Generation

ML: Machine Learning

DL: Deep Learning

SO2: Sulfur Dioxide

DER: Distributed Energy Resources

PaaS: Platform as a Service

GHG: Greenhouse Gas

API: Application Programming Interface

CSP: Concentrated Solar Power

GW: Gigawatt

TWh: Terawatt-hour

EaaS: Energy as a Service

IaaS: Infrastructure as a Service

EM: Energy Management

HVAC: Heating, Ventilation, and Air Conditioning

RES: Renewable Energy Sources

DaaS: Data as a Service

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