Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download The Ai Cleanse Transforming Wastewater Treatment Through Artificial Intelligence PDF full book. Access full book title The Ai Cleanse Transforming Wastewater Treatment Through Artificial Intelligence.
| Author | : Manoj Chandra Garg |
| Publisher | : Springer Nature |
| Total Pages | : 383 |
| Release | : |
| Genre | : |
| ISBN | : 3031672372 |
| Author | : Manoj Chandra Garg |
| Publisher | : Springer |
| Total Pages | : 0 |
| Release | : 2024-10-11 |
| Genre | : Computers |
| ISBN | : 9783031672361 |
This groundbreaking book goes beyond conventional approaches and explores how AI is revolutionizing the field of wastewater treatment, offering innovative solutions to pressing challenges. "The AI Cleanse" takes you on a captivating journey through the convergence of AI and wastewater treatment, revealing the potential for enhanced efficiency, effectiveness, and sustainability. From optimizing treatment processes to intelligent monitoring and fault detection, this book showcases how AI-driven technologies can reshape the way we approach wastewater treatment. Gain a comprehensive understanding of the basics of wastewater treatment and the limitations of traditional methods. Explore the practical applications of AI, such as data acquisition and analysis, process optimization, and resource recovery. Learn about cutting-edge technologies, emerging trends, and future directions in the field. Written in a reader-friendly style, "The AI Cleanse" bridges the gap between theoretical knowledge and practical implementation. Packed with real-world examples, case studies, and insights from experts in the field, this book equips researchers, professionals, and students with the knowledge needed to harness the full potential of AI in wastewater treatment. If you are passionate about environmental preservation, sustainable practices, and the power of technology, "The AI Cleanse" is your guide to unlocking the transformative potential of artificial intelligence in wastewater treatment. Embrace a cleaner future and be at the forefront of this revolution in the field.
| Author | : Shikuku, Victor |
| Publisher | : IGI Global |
| Total Pages | : 289 |
| Release | : 2023-08-25 |
| Genre | : Computers |
| ISBN | : 1668467933 |
The emergence of a plethora of water contaminants as a result of industrialization has introduced complexity to water treatment processes. Such complexity may not be easily resolved using deterministic approaches. Artificial intelligence (AI) has found relevance and applications in almost all sectors and academic disciplines, including water treatment and management. AI provides dependable solutions in the areas of optimization, suspect screening or forensics, classification, regression, and forecasting, all of which are relevant for water research and management. Artificial Intelligence Applications in Water Treatment and Water Resource Management explores the different AI techniques and their applications in wastewater treatment and water management. The book also considers the benefits, challenges, and opportunities for future research. Covering key topics such as water wastage, irrigation, and energy consumption, this premier reference source is ideal for computer scientists, industry professionals, researchers, academicians, scholars, practitioners, instructors, and students.
| Author | : Christopher W. Baxter |
| Publisher | : American Water Works Association |
| Total Pages | : 170 |
| Release | : 2001 |
| Genre | : Artificial intelligence |
| ISBN | : 1583211403 |
| Author | : Rakesh Kumar |
| Publisher | : Independently Published |
| Total Pages | : 0 |
| Release | : 2024-04-08 |
| Genre | : |
| ISBN | : |
In the realm of wastewater management, Sewage Treatment Plants (STPs) stand as critical infrastructure responsible for safeguarding public health and the environment by treating wastewater from diverse sources. The efficient operation of STPs is paramount, not only for ensuring water quality but also for optimizing resource usage and minimizing environmental impact. However, the complexity of STP operations, coupled with the ever-growing challenges posed by population growth, urbanization, and industrialization, calls for innovative solutions to enhance management practices. Artificial Intelligence (AI) emerges as a transformative force in the domain of STP management, offering novel approaches to address the multifaceted challenges encountered in wastewater treatment processes. By harnessing the power of AI algorithms, machine learning techniques, and data-driven insights, STPs can revolutionize their operations, from predictive maintenance and process optimization to energy efficiency and regulatory compliance. The book "AI for STP Management" delves into the intersection of AI technologies and sewage treatment, providing a comprehensive exploration of how AI-driven solutions can revolutionize STP operations, improve efficiency, and mitigate environmental impacts. Through a combination of theoretical foundations, practical applications, case studies, and hands-on Python implementations, this book aims to equip readers with the knowledge and tools necessary to leverage AI for optimizing STP performance. Drawing upon real-world examples, cutting-edge research, and industry best practices, each chapter of this book navigates through key aspects of AI-enabled STP management, including: - Foundations of Sewage Treatment: Providing an overview of wastewater treatment processes, components of STPs, and challenges in management. - Introduction to Artificial Intelligence in Water Management: Exploring the fundamentals of AI and its applications in water resource management and STP operations. - Applications of AI in STP Operations: Delving into specific use cases of AI in STP operations, such as predictive maintenance, process optimization, and energy efficiency. - Benefits and Challenges of AI Adoption in STP Management: Analyzing the advantages and limitations of implementing AI solutions in STP management. - Real-world Examples and Success Stories: Showcasing real-world implementations and success stories of AI-driven STP management initiatives. - Implications for the Future: Discussing emerging trends, advances in AI technologies, and potential future developments in STP management. By the end of this book, readers will gain a deep understanding of how AI technologies can revolutionize sewage treatment practices, drive operational efficiencies, and pave the way for sustainable wastewater management in the modern era. Whether you are a water resource engineer, environmental scientist, researcher, policymaker, or industry professional, "AI for STP Management" offers invaluable insights and practical guidance to navigate the transformative potential of AI in sewage treatment operations.
| Author | : Alaa El Din Mahmoud |
| Publisher | : CRC Press |
| Total Pages | : 345 |
| Release | : 2023-04-25 |
| Genre | : Technology & Engineering |
| ISBN | : 1000829782 |
Artificial intelligence and the use of computational methods to extract information from data are providing adequate tools to monitor and predict water pollutants and water quality issues faster and more accurately. Smart sensors and machine learning models help detect and monitor dispersion and leakage of pollutants before they reach groundwater. With contributions from experts in academia and industries, who give a unified treatment of AI methods and their applications in water science, this book help governments, industries, and homeowners not only address water pollution problems more quickly and efficiently, but also gain better insight into the implementation of more effective remedial measures. FEATURES Provides cutting-edge AI applications in water sector. Highlights the environmental models used by experts in different countries. Discusses various types of models using AI and its tools for achieving sustainable development in water and groundwater. Includes case studies and recent research directions for environmental issues in water sector. Addresses future aspects and innovation in AI field related to watersustainability. This book will appeal to scientists, researchers, and undergraduate and graduate students majoring in environmental or computer science and industry professionals in water science and engineering, environmental management, and governmental sectors. It showcases artificial intelligence applications in detecting environmental issues, with an emphasis on the mitigation and conservation of water and underground resources.
| Author | : Praewa Wongburi |
| Publisher | : |
| Total Pages | : 193 |
| Release | : 2021 |
| Genre | : |
| ISBN | : |
In a wastewater treatment plant (WWTP), big data is collected from sensors installed in various unit processes, but limited data is used for operation and regulatory permit requirements. With the advancement in information technology, the data size in wastewater treatment systems has increased significantly. However, WWTPs have not used big data systematically to aid the operation and detect potential operational issues due to the lack of specialized analytical tools.The objectives of the study were to: (1) develop analytics methods suitable for the management of big data generated in WWTPs, (2) interpret analytics results for extracting meaningful information, (3) implement a recurrent neural network (RNN) and Long Short-Term Memory (LSTM) to predict effluent water quality parameters and Sludge Volume Index (SVI), (4) apply an Explainable Artificial Intelligence (AI) algorithm to determine causes of predicted values, and (5) propose a real-time control using a predictive model to monitor and optimize the operation of WWTPs. The predictive AI models in WWTPs were developed by applying big data analytics, statistical analysis, and RNN algorithms with an Explainable AI algorithm. The models successfully and accurately predicted the effluent water quality data and a key operational parameter, SVI. Furthermore, the Explainable AI algorithm provided insight into which influent parameters affected higher predicted effluent concentrations and SVI on a specific day, allowing operators to take corrective actions. From a WWTP's operational data analysis, the RNN model successfully predicted the effluent concentrations of BOD℗Ơ5, total nitrogen (TN) and total phosphorus (TP), and SVI. Furthermore, the Explainable AI analysis found that higher influent NH3N values lead to higher effluent BOD5, and higher influent total suspended solids (TSS) and TP values resulted in lower effluent BOD5, implying the importance of controlling dissolved oxygen (DO) in aeration basins. Since aeration is one of the major energy consumption sources in WWTPs, real-time prediction of the effluent water quality using the self-learning AI system developed in this study can be adopted to lower the energy cost significantly while improving effluent water quality. WWTPs must develop control methods based on the RNN prediction and Explainable AI analysis due to different operational conditions.
| Author | : B. J. Kim |
| Publisher | : |
| Total Pages | : 46 |
| Release | : 1988 |
| Genre | : |
| ISBN | : |
As the Army faces increasing reductions in budget and personnel for supporting functions such as operation and maintenance (O & M) of wastewater treatment plants (WWTPs), it is clear that reliance on automation will continue to grow. While computer systems will not replace operators, they will provide valuable assistance in optimizing the operator's time and effort. Findings suggest that Al/expert systems technology is not yet at an economically practical level for use in O & M of the Army WWTPs. However, as the technology becomes refined and produced at lower cost, it should be reconsidered; this study has shown through a proof-of-concept exercise that Al/expert systems have potential value to the O & M process. Keywords: Waste water treatment; Water pollution. (KT).
| Author | : Riyaz Shariff |
| Publisher | : American Water Works Association |
| Total Pages | : 184 |
| Release | : 2006 |
| Genre | : Computers |
| ISBN | : 1583215123 |
This study shows that advanced artificial neural network (ANN) model-based control systems can be used for drinking water treatment process control. ANN technology, an artificial intelligence technology that has the ability to learn patterns and relationships contained in sets of data, is the most powerful modeling tool currently available to the drinking water treatment industry. ANN predicts the output of a process given the values of process inputs and process control variables. The results of this project have the potential to revolutionize the way in which drinking water utilities optimize and control their unit processes to efficiently and consistently supply high quality drinking water
| Author | : Shikha Gulati |
| Publisher | : Springer |
| Total Pages | : 0 |
| Release | : 2024-10-26 |
| Genre | : Science |
| ISBN | : 9783031694325 |
This book offers a comprehensive exploration of the integration of artificial intelligence (AI) techniques in addressing challenges and optimizing processes within wastewater treatment. The coverage of the book spans a spectrum of applications, including AI-driven monitoring and control systems, predictive modeling for pollutant removal, and the development of smart sensor networks for real-time data analysis in wastewater treatment plants. By amalgamating AI methodologies with wastewater treatment processes, the book provides insights into enhancing efficiency, reducing costs, and mitigating environmental impacts. In the current research scenario, the theme of the book is highly pertinent as it responds to the pressing need for sustainable and efficient wastewater treatment solutions. The book defines the theme by elucidating how AI technologies, such as machine learning algorithms and data analytics, can revolutionize wastewater treatment processes by enabling proactive decision-making, optimizing resource allocation, and predicting potential system failures. This intersection of AI and wastewater treatment not only addresses operational challenges but also contributes to the broader goal of achieving environmentally conscious and economically viable solutions.
