Artificial Intelligence Systems for Water Treatment Plant Optimization
Author: Christopher W. Baxter
Publisher: American Water Works Association
Published: 2001
Total Pages: 170
ISBN-13: 1583211403
DOWNLOAD EBOOK →Author: Christopher W. Baxter
Publisher: American Water Works Association
Published: 2001
Total Pages: 170
ISBN-13: 1583211403
DOWNLOAD EBOOK →Author: Riyaz Shariff
Publisher: American Water Works Association
Published: 2006
Total Pages: 184
ISBN-13: 1583215123
DOWNLOAD EBOOK →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: Shikuku, Victor
Publisher: IGI Global
Published: 2023-08-25
Total Pages: 289
ISBN-13: 1668467933
DOWNLOAD EBOOK →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: Shikha Gulati
Publisher: Springer
Published: 2024-10-26
Total Pages: 0
ISBN-13: 9783031694325
DOWNLOAD EBOOK →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.
Author: Jagdish Chand Bansal
Publisher: Springer
Published: 2018-06-06
Total Pages: 190
ISBN-13: 3319913417
DOWNLOAD EBOOK →This book is a delight for academics, researchers and professionals working in evolutionary and swarm computing, computational intelligence, machine learning and engineering design, as well as search and optimization in general. It provides an introduction to the design and development of a number of popular and recent swarm and evolutionary algorithms with a focus on their applications in engineering problems in diverse domains. The topics discussed include particle swarm optimization, the artificial bee colony algorithm, Spider Monkey optimization algorithm, genetic algorithms, constrained multi-objective evolutionary algorithms, genetic programming, and evolutionary fuzzy systems. A friendly and informative treatment of the topics makes this book an ideal reference for beginners and those with experience alike.
Author: Dr. Vidya Patil
Publisher: JEC PUBLICATION
Published:
Total Pages: 127
ISBN-13:
DOWNLOAD EBOOK →The textbook explorers the intersection of artificial intelligence (AI) and machine learning (ML) within water supply distribution systems offer comprehensive insights into cutting-edge applications. Covering fundamental concepts, these texts delve into the intricacies of data collection, preprocessing, and modeling specific to water networks. By utilizing AI and ML algorithms, this book elucidate how to optimize system performance, addressing challenges such as pressure management and leak detection. Decision support systems powered by AI play a pivotal role in forecasting demands and efficiently managing distribution networks. Through engaging case studies, readers gain valuable perspectives on real-world implementations, fostering a deeper understanding of the transformative potential of AI and ML in enhancing water supply infrastructure.
Author: Jingzheng Ren
Publisher: Elsevier
Published: 2021-06-05
Total Pages: 542
ISBN-13: 012821743X
DOWNLOAD EBOOK →Applications of Artificial Intelligence in Process Systems Engineering offers a broad perspective on the issues related to artificial intelligence technologies and their applications in chemical and process engineering. The book comprehensively introduces the methodology and applications of AI technologies in process systems engineering, making it an indispensable reference for researchers and students. As chemical processes and systems are usually non-linear and complex, thus making it challenging to apply AI methods and technologies, this book is an ideal resource on emerging areas such as cloud computing, big data, the industrial Internet of Things and deep learning. With process systems engineering's potential to become one of the driving forces for the development of AI technologies, this book covers all the right bases. Explains the concept of machine learning, deep learning and state-of-the-art intelligent algorithms Discusses AI-based applications in process modeling and simulation, process integration and optimization, process control, and fault detection and diagnosis Gives direction to future development trends of AI technologies in chemical and process engineering
Author: B. J. Kim
Publisher:
Published: 1988
Total Pages: 46
ISBN-13:
DOWNLOAD EBOOK →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: Praewa Wongburi
Publisher:
Published: 2021
Total Pages: 0
ISBN-13:
DOWNLOAD EBOOK →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: Alaa El Din Mahmoud
Publisher: CRC Press
Published: 2023-04-25
Total Pages: 345
ISBN-13: 1000829782
DOWNLOAD EBOOK →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.