This review delves into the critical role of automation and sensor technologies in optimizing parameters for thermal treatments within electrical power generation. The demand for efficient and sustainable power generation has led to a significant reliance on thermal treatments in power plants. However, ensuring precise control over these treatments remains challenging, necessitating the integration of advanced automation and sensor systems. This paper evaluates the pivotal aspects of automation, emphasizing its capacity to streamline operations, enhance safety, and optimize energy efficiency in thermal treatment processes. Additionally, it highlights the indispensable role of sensors in monitoring and regulating crucial parameters, such as temperature, pressure, and flow rates. These sensors enable real-time data acquisition, facilitating immediate adjustments to maintain optimal operating conditions and prevent system failures. It explores the recent technological advancements, including machine learning algorithms and IoT integration, which have revolutionized automation and sensor capabilities in thermal treatment control. Incorporating these innovations has significantly improved the precision and adaptability of control systems, resulting in heightened performance and reduced environmental impact. This review underscores the imperative nature of automation and sensor technologies in thermal treatments for electrical power generation, emphasizing their pivotal role in enhancing operational efficiency, ensuring reliability, and advancing sustainability in power generation processes.
9 October 2025 in International Journal of Electrical Power & Energy Systems
This review delves into the critical role of automation and sensor technologies in optimizing parameters for thermal treatments within electricity power generation. The demand for efficient and sustainable power generation has led to a significant reliance on thermal treatments in power plants. However, ensuring precise control over these treatments remains challenging, necessitating the integration of advanced automation and sensor systems. This paper evaluates the pivotal aspects of automation, emphasizing its capacity to streamline operations, enhance safety, and optimize energy efficiency in thermal treatment processes. Additionally, it highlights the indispensable role of sensors in monitoring and regulating crucial parameters such as temperature, pressure, and flow rates. These sensors enable real-time data acquisition, facilitating immediate adjustments to maintain optimal operating conditions and prevent system failures. It explores the recent technological advancements, including machine learning algorithms and IoT integration, which have revolutionized automation and sensor capabilities in thermal treatment control. Incorporating these innovations has significantly improved the precision and adaptability of control systems, resulting in heightened performance and reduced environmental impact. This review underscores the imperative nature of automation and sensor technologies in thermal treatments for electricity power generation, emphasizing their pivotal role in enhancing operational efficiency, ensuring reliability, and advancing sustainability in power generation processes.
This study explores innovative strategies for decarbonizing sludge thermal treatments used in electrical power generation, with a focus on integrating sensor technologies and artificial intelligence. Sludge, a carbon-intensive byproduct of wastewater treatment, presents both environmental challenges and opportunities for energy recovery. The paper provides a comprehensive analysis of thermal processes such as pyrolysis, gasification, co-combustion, and emerging methods, including hydrothermal carbonization and supercritical water gasification. It evaluates their carbon mitigation potential, energy efficiency, and economic feasibility, emphasizing the importance of catalyst selection, carbon dioxide capture techniques, and reactor optimization. The role of real-time monitoring via sensors and predictive modeling through artificial intelligence (AI) is highlighted as critical for enhancing process control and sustainability. Case studies and recent advances are discussed to outline future pathways for integrating thermal treatment with circular economy principles. This work contributes to sustainable waste-to-energy practices, supporting global decarbonization efforts and advancing the energy transition.
This paper presents a comprehensive overview of decarbonization strategies in sludge thermal treatments for electrical power generation, addressing the critical need for sustainable waste management in global efforts to mitigate climate change. Sludge, a byproduct of wastewater treatment, poses environmental challenges due to its high carbon content, contributing to greenhouse gas emissions. The study explores various decarbonization techniques applied to sludge thermal treatments, emphasizing their potential to enhance energy recovery while minimizing carbon footprint. The overview covers innovative technologies such as pyrolysis, gasification, and co-combustion, assessing their efficacy in reducing carbon emissions during sludge-to-energy conversion. Additionally, the paper evaluates the impact of process parameters, feedstock characteristics, and operational conditions on the success of decarbonization methods. Insights are drawn from recent advancements and case studies, providing a holistic understanding of the state-of-the-art practices in decarbonizing sludge-based power generation. The findings underscore the significance of integrating decarbonization strategies into sludge treatment processes for environmental sustainability and optimizing energy production. By outlining research gaps and proposing future directions for advancing decarbonization technologies in sludge thermal treatments, this work contributes to a more sustainable approach to wastewater management and energy generation.