Phytoremediation of Domestic Wastewater with the Internet of Things and Machine Learning Techniques

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. This book highlights advances in phytoremediation of wastewater using the latest technologies. It discusses applications and experiences utilizing phytoremediation methods for environmental sustainability and the remediation of wastewater. Cover 1 Half Title 2 Title Page 4 Copyright Page 5 Table of Contents 6 About the Authors 12 Chapter 1 Impacts of Sustainable Development Goals (SDGs) in Wastewater Management 14 1.1 Introduction 14 1.2 Sources of Wastewater in Our Environment 15 1.2.1 Domestic Wastewater 16 1.2.2 Industrial Wastewater 16 1.2.3 Municipal Wastewater 16 1.3 Strategies for Effective Management of Wastewater 17 1.4 Contributions of Sustainable Development Goals (SDGs) in Promoting Wastewater Management 17 1.5 Conclusion 18 References 19 Chapter 2 Solving the Shortage of Clean Water Through Wastewater Treatment 22 2.1 Introduction 22 2.2 Conventional Sewage Treatment Technology 22 2.3 Preliminary Treatment of Wastewater 23 2.4 Primary Treatment of Wastewater 24 2.5 Secondary Treatment of Wastewater 24 2.6 Tertiary Treatment of Wastewater 24 2.7 Biological Method of Wastewater Treatment 24 2.8 Conclusion 25 References 25 Chapter 3 Microalgae Cultivation for Wastewater Treatment and Bioenergy Generation 28 3.1 Introduction 28 3.2 Microalgae 30 3.3 High-Rate Algal Ponds (HRAPs) 31 3.4 Photobioreactors (PBRs) and Membrane Bioreactors (MBRs) 32 3.5 Microbial Fuel Cell (MFC) 33 3.6 Conclusion 34 References 34 Chapter 4 Cultivation of Aquatic Plants for Wastewater Treatment 40 4.1 Introduction 40 4.2 Phytoremediation of Wastewater 40 4.3 Mechanisms of Phytoremediation 41 4.4 The Roles of Aquatic Plants in Phytoremediation of Wastewater 41 4.5 Applications of P. Stratiotes, S. Molesta and E. Crassipes in Phytoremediation of Wastewater 43 4.5.1 Pistia Stratiotes Plants 43 4.5.1.1 Distribution of P. Stratiotes 43 4.5.1.2 Taxonomy of P. Stratiotes 43 4.5.1.3 Description of P. Stratiotes 44 4.5.1.4 Growth of P. Stratiotes 44 4.5.1.5 Efficiency of P. Stratiotes in Phytoremediation of Wastewater 44 4.5.2 Salvinia Molesta Plants 50 4.5.2.1 Distribution of S. Molesta 50 4.5.2.2 Taxonomy of S. Molesta 51 4.5.2.3 Description of S. Molesta 51 4.5.2.4 Growth of S. Molesta 52 4.5.2.5 Nutrient Uptake by S. Molesta Plants in Phytoremediation of Wastewater 53 4.5.3 Eichhornia Crassipes Plants 54 4.5.3.1 Distribution of E. Crassipes 54 4.5.3.2 Taxonomy of E. Crassipes 54 4.5.3.3 Description of E. Crassipes 55 4.5.3.4 Potentials of E. Crassipes Plants in Phytoremediation of Wastewater 55 4.6 Conclusion 57 References 57 Chapter 5 Phytoremediation of Wastewater in Hydroponic Systems 64 5.1 Introduction 64 5.2 Overview of Hydroponic Systems in Wastewater Treatment 64 5.3 Nutrient Recovery by Aquatic Plants 65 5.4 Case Study: Demonstration of Aquatic Plants Cultivation in Wastewater 66 5.4.1 Cultivation Area 66 5.4.2 Research Setup 66 5.4.3 Source of the Plant Samples 67 5.4.4 Method of Plant Cultivation 67 5.4.5 Method of Water Sample Collection 68 5.5 Case Study: Relative Growth Rate (RGR) of Aquatic Plants in Phytoremediation Systems 69 5.6 Management of the Harvested Aquatic Plants 70 5.7 Conclusion 70 References 71 Chapter 6 Water Quality Monitoring in Wastewater Phytoremediation 74 6.1 Introduction 74 6.2 Case Study: Water Quality Monitoring in Phytoremediation of Domestic Wastewater 74 6.2.1 Determination of pH 75 6.2.2 Determination of Colour 75 6.2.3 Determination of Turbidity 75 6.2.4 Determination of BOD[sub(5)] 75 6.2.5 Determination of COD 76 6.2.6 Determination of Phosphate 76 6.2.7 Determination of Ammonia Nitrogen 76 6.2.8 Determination of Nitrate 76 6.2.9 Statistical Analysis 77 6.3 Outcome of the Water Assessment of the Influent and Effluent Water Samples 77 6.3.1 Analysis of Colour 77 6.3.2 Analysis of Turbidity 80 6.3.3 Analysis of pH 82 6.3.4 Analysis of COD 84 6.3.5 Analysis of BOD[sub(5)] 87 6.3.6 Analysis of Phosphate 90 6.3.7 Analysis of Ammonia Nitrogen 93 6.3.8 Analysis of Nitrate 95 6.4 Conclusion 98 References 98 Chapter 7 Water Quality Monitoring Using Internet of Things (IoT) 102 7.1 Introduction 102 7.2 Internet of Things (IoT) in Wastewater Monitoring 102 7.3 Hardware Design of the Arduino IoT System 103 7.4 Sensor Nodes of the Arduino IoT System 103 7.4.1 Temperature Sensor 103 7.4.2 Turbidity Sensor 105 7.4.3 Oxidation Reduction Potential (ORP) Sensor 105 7.4.4 Total Dissolved Solids (TDS) Sensor 106 7.5 Liquid Crystal Display (LCD) 106 7.6 Wi-Fi Module 107 7.7 Global System Mobile (GSM Shield) 107 7.8 Coding Development of the IoT System 108 7.9 Conclusion 108 References 109 Chapter 8 Machine Learning Techniques in Water Quality Monitoring 110 8.1 Introduction 110 8.2 Concept of Machine Learning (ML) Techniques in Phytoremediation of Wastewater 110 8.3 Artificial Neural Network (ANN) 111 8.4 Support Vector Machine (SVM) 112 8.5 Adaptive Neuro-Fuzzy Inference System (ANFIS) 114 8.6 Multilinear Regression (MLR) 116 8.7 Error Ensemble Learning Approach 117 8.8 Development of ANN, SVM, ANFIS and MLR for Phytoremediation of Wastewater 118 8.9 Conclusion 120 References 120 Chapter 9 Case Study: Monitoring and Evaluation of Phytoremediation System Using Internet of Things (IoT) and Machine Learning Techniques 126 9.1 Introduction 126 9.2 Development of Internet of Things (IoT)-based Salvinia Molesta Plants in Wastewater Treatment 127 9.2.1 Collection of Data 127 9.2.2 Modelling and Prediction of the Turbidity (TURBt) 127 9.2.3 Water Quality Parameters Used 129 9.2.4 Influent and Effluent Concentration of Turbidity 129 9.3 Results and Discussion 129 9.3.1 Results of TURBt (ANN, SVM, ANFIS, and MLR) 129 9.4 Conclusion 137 References 137 Chapter 10 Case Study: Emerging Black Box System Identification Model with Neuro-Boasting Machine Learning Techniques for Experimental Validation of Phytoremediation of Wastewater: A Data Intelligent Approach 142 10.1 Introduction 142 10.2 Materials and Methodology 143 10.2.1 Water Quality Parameters Used 143 10.2.2 Data Processing and Statistical Analysis 143 10.2.3 Artificial Neural Network (ANN) 144 10.2.4 Concept of Support Vector Machine (SVM) 145 10.2.5 Concept of Adaptive Neuro-Fuzzy Inference System (ANFIS) 145 10.2.6 Multilinear Regression (MLR) 147 10.3 Model Validation and Performance Evaluation 147 10.4 Results and Discussion 147 10.4.1 Descriptive Statistical Analysis 148 10.4.2 Results of ORPt (ANN, SVM, ANFIS and MLR) 150 10.5 Conclusion 155 References 155 Index 158 Water,Treatment;,Sewage,Treatment;,Biological,Treatment;,Aquatic,Plants;,Hydroponic,Systems;,Nutrient,Recovery;,Phosphorus;,Nitrogen;,Pistia,stratiotes;,Salvinia,molesta;,Eichhornia,crassipes;,Water,Quality;,Water,Modeling Water Treatment,Sewage Treatment,Biological Treatment,Aquatic Plants,Hydroponic Systems,Nutrient Recovery,Phosphorus,Nitrogen,Pistia stratiotes,Salvinia molesta,Eichhornia crassipes,Water Quality,Water Modeling 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 practicing professionals in the field. "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"-- Provided by publisher