[PDF] Application Of Artificial Intelligence To Wastewater Treatment Plant Operation eBook

Author : Praewa Wongburi
Publisher :
Page : 0 pages
File Size : 41,28 MB
Release : 2021
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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 :
Page : 46 pages
File Size : 15,25 MB
Release : 1988
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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 : Shikuku, Victor
Publisher : IGI Global
Page : 289 pages
File Size : 12,82 MB
Release : 2023-08-25
Category : Computers
ISBN : 1668467933

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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 : Byung Joo Kim
Publisher :
Page : 43 pages
File Size : 48,94 MB
Release : 1988
Category : Artificial intelligence
ISBN :

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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 : Jingzheng Ren
Publisher : Elsevier
Page : 542 pages
File Size : 21,10 MB
Release : 2021-06-05
Category : Technology & Engineering
ISBN : 012821743X

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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 : Rakesh Kumar
Publisher : Independently Published
Page : 0 pages
File Size : 48,51 MB
Release : 2024-04-08
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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 : Riyaz Shariff
Publisher : American Water Works Association
Page : 184 pages
File Size : 40,39 MB
Release : 2006
Category : Computers
ISBN : 1583215123

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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