Produktbild: Application of Machine Learning in Chemical and Process Industries
Vorbesteller Neu

Application of Machine Learning in Chemical and Process Industries

169,99 €

inkl. gesetzl. MwSt., Versandkostenfrei


Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

20.11.2026

Abbildungen

XIV, 480 p. 120 illus., 115 illus. in color.

Herausgeber

Feroz Shaik + weitere

Verlag

Springer Singapore

Seitenzahl

480

Maße (B/H)

15,5/23,5 cm

Sprache

Englisch

ISBN

978-981-9211-74-6

Beschreibung

Portrait

Prof. Dr. Shaik Feroz is currently working at Prince Mohammed Bin Fahd University, Kingdom of Saudi Arabia. Dr. Feroz obtained his doctorate in the field of chemical engineering from Andhra University, India, in 2004 and Post Doc Research Fellow from Leibniz University, Germany, in 2015; M.Tech. in chemical engineering from Osmania University, India, in 1998; B.Tech. in chemical engineering from S. V. University, India, in 1992; and post graduate diploma in environmental studies from Andhra University in 2003. Dr. Feroz Shaik is Visiting Professor for School of Renewable Energy, Maejo University, Thailand. Dr. Feroz has expertise in process engineering, plant design and troubleshooting, quality control using advance analytical equipment, wastewater treatment, solar energy systems (PV & CSP) for energy and desalination, hot water systems and water treatment, synthesis of nano photo catalysts, simultaneous treatment of wastewater and production of hydrogen, and environmental impact assessment.

Dr. Sani I. Abba is Assistant Researcher Professor in the Department of Civil Engineering at Prince Mohammad Bin Fahd University (PMU), KSA. He holds a B.Sc. degree from Bayero University Kano (BUK), an M.Tech. degree from Sharda University, India, and a Ph.D. from Near East University (NEU), Cyprus. Dr. Abba has over a decade of experience working with Yusuf Maitama Sule University, Baze University, Nigeria, and King Fahd University of Petroleum and Minerals (KFUPM), Saudi Arabia. He has extensive experience in teaching, research, and curriculum development at graduate and undergraduate levels. His research interests span artificial intelligence, water footprint, water security, groundwater, membrane desalination, wastewater, water quality, water resources, public health, pollution control, climate change, sustainable development, hydroclimatology, hydroenvironmental modeling, computational engineering, soft computing, and optimization algorithms.

Dr. Jamal F. Nayfeh is Dean of the College of Engineering and Professor of mechanical engineering at Prince Mohammad Bin Fahd University (PMU) since September 2009. Previously, he was Associate Dean for academics, marketing, and outreach in the College of Engineering and Computer Science and Professor of mechanical engineering in the Department of Mechanical, Materials, and Aerospace Engineering (MMAE) at the University of Central Florida (UCF). He received his Ph.D. in engineering mechanics from Virginia Tech in 1990. Dr. Nayfeh is Member of Tau Beta Pi Engineering Honor Society, American Society of Mechanical Engineers, American Institute of Aeronautics and Astronautics, Society of Automotive Engineers, and American Society for Engineering Education.

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

20.11.2026

Abbildungen

XIV, 480 p. 120 illus., 115 illus. in color.

Herausgeber

Verlag

Springer Singapore

Seitenzahl

480

Maße (B/H)

15,5/23,5 cm

Sprache

Englisch

ISBN

978-981-9211-74-6

Herstelleradresse

Springer Singapore
No. 12-2F 101 Business Park
47100 Puchong, Selangor D.E.
MY
Email: sdc-bookservice@springer.com
Telephone: +49 6221 3454301
Fax: +49 6221 3454229

Noch keine Bewertungen vorhanden

Verfassen Sie die erste Bewertung zu diesem Artikel

Helfen Sie anderen Kundinnen und Kunden durch Ihre Meinung.

Kundinnen und Kunden meinen

Bewertungen (0)

  • Produktbild: Application of Machine Learning in Chemical and Process Industries
  • 1. Introduction to Machine Learning in Chemical and Process Engineering.- 2. Data Acquisition and Preprocessing in Industrial Systems.- 3. Supervised Learning in Process Optimization and Quality Control.- 4. Unsupervised Learning for Process Understanding.- 5. Deep Learning for Complex Process Systems.- 6. Reinforcement Learning for Process Control.- 7. ML Applications in Environmental Chemical Engineering.- 8. Wastewater Treatment and Water Quality Prediction using Machine Learning.- 9. Machine Learning for Air Pollution and Emissions Control.- 10. Circular Economy and Sustainable Process Design.- 11. Digital Twins and Predictive Maintenance in Process Plants.- 12. Safety, Risk Assessment, and Ethical Considerations in ML Deployment.