Phytoremediation of Domestic Wastewater with the Internet of Things and Machine Learning Techniques
Auteurs : Mustafa Hauwa Mohammed, Hayder Gasim
Phytoremediation of Domestic Wastewater with the Internet of Things and Machine Learning Techniques highlights the most recent advances in phytoremediation of wastewater using the latest technologies. It discusses practical applications and experiences utilizing phytoremediation methods for environmental sustainability and the remediation of wastewater. It also examines the various interrelated disciplines relating to phytoremediation technologies and plots industry?s best practices to share this technology widely, as well as the latest findings and strategies. It serves as a nexus between artificial intelligence, environmental sustainability and bioremediation for advanced students and practising professionals in the field.
Hauwa Mohammed Mustafa is an Academic Lecturer in the Department of Pure and Applied Chemistry, Kaduna State University (KASU), Nigeria. She obtained her Doctorate Degree from Universiti Tenaga Nasional (UNITEN), Malaysia. Her research areas focus on Wastewater Treatment, Bioenergy, IoT, Artificial Intelligence, and Material Science.
Gasim Hayder is a Senior Lecturer and the Head of the Water & Environmental Engineering Unit at the Civil Engineering Department, College of Engineering, Universiti Tenaga Nasional (UNITEN), Malaysia. He is also a Chartered Engineer-MIET (CEng) from the Engineering Council (UK). He received his Ph.D. in Civil Engineering from Universiti Teknologi PETRONAS (UTP), Malaysia. His research focus is in the field of Environmental Engineering and Water and Wastewater Assessment, Treatment, Modelling, and Monitoring.
Date de parution : 03-2023
15.6x23.4 cm
Thème de Phytoremediation of Domestic Wastewater with the... :
Mots-clés :
water treatment; sewage treatment; biological treatment; aquatic plants; Hydroponic Systems; Nutrient Recovery; Phosphorus; Nitrogen; Pistia stratiotes; Salvinia molesta; Eichhornia crassipes; water quality; water modeling; ANFIS; Effluent Water Samples; ANFIS M1; SVM; MFCs; Stratiotes; IoT System; Ml Model; MLR Model; Taylor Diagram; Water Quality Parameters; GSM Module; Ann Model; UNO; Validation Phase; Water Hyacinth; MLR; Wastewater Treatment; HRAP; Influent Samples; Pic Microcontroller; Hydroponic Tank