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Author: Shikuku, Victor
Publisher: IGI Global
ISBN: 1668467933
Category : Computers
Languages : en
Pages : 289
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Book Description
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: Shikuku, Victor
Publisher: IGI Global
ISBN: 1668467933
Category : Computers
Languages : en
Pages : 289
Get Book Here
Book Description
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
ISBN: 1583211403
Category : Artificial intelligence
Languages : en
Pages : 170
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Book Description
Author: Manoj Chandra Garg
Publisher: Springer Nature
ISBN: 3031672372
Category :
Languages : en
Pages : 383
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Book Description
Author: Praewa Wongburi
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
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: Shikha Gulati
Publisher: Springer Nature
ISBN: 3031694333
Category :
Languages : en
Pages : 327
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Book Description
Author: Jingzheng Ren
Publisher: Elsevier
ISBN: 012821743X
Category : Technology & Engineering
Languages : en
Pages : 542
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Book Description
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: Jagdish Chand Bansal
Publisher: Springer
ISBN: 3319913417
Category : Technology & Engineering
Languages : en
Pages : 194
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Book Description
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: Ali Reza Khataee
Publisher: Nova Novinka
ISBN: 9781611227819
Category : Neural networks (Computer science)
Languages : en
Pages : 0
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Book Description
Artificial neural networks (ANNs) are computer based systems that are designed to simulate the learning process of neurons in the human brain. ANNs have been attracting great interest during the last decade as predictive models and pattern recognition. Artificial neural networks possess the ability to "learn" from a set of experimental data without actual knowledge of the physical and chemical laws that govern the system. Therefore, ANNs application in data treatment is high, especially where systems present non-linearities and complex behaviour. This book describes the application of artificial neural networks for modelling of water and wastewater treatment processes.
Author: Anirban Roy
Publisher: John Wiley & Sons
ISBN: 1119536065
Category : Science
Languages : en
Pages : 412
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Book Description
The book Modeling in Membranes and Membrane-Based Processes is based on the idea of developing a reference which will cover most relevant and “state-of-the-art” approaches in membrane modeling. This book explores almost every major aspect of modeling and the techniques applied in membrane separation studies and applications. This includes first principle-based models, thermodynamics models, computational fluid dynamics simulations, molecular dynamics simulations, and artificial intelligence-based modeling for membrane separation processes. These models have been discussed in light of various applications ranging from desalination to gas separation. In addition, this breakthrough new volume covers the fundamentals of polymer membrane pore formation mechanisms, covering not only a wide range of modeling techniques, but also has various facets of membrane-based applications. Thus, this book can be an excellent source for a holistic perspective on membranes in general, as well as a comprehensive and valuable reference work. Whether a veteran engineer in the field or lab or a student in chemical or process engineering, this latest volume in the “Advances in Membrane Processes” is a must-have, along with the first book in the series, Membrane Processes, also available from Wiley-Scrivener.
Author: Shikha Gulati
Publisher: Springer
ISBN: 9783031694325
Category : Science
Languages : en
Pages : 0
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Book Description
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.
