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

Cover
Half Title
Title Page
Copyright Page
Table of Contents
About the Authors
Chapter 1 Impacts of Sustainable Development Goals (SDGs) in Wastewater Management
1.1 Introduction
1.2 Sources of Wastewater in Our Environment
1.2.1 Domestic Wastewater
1.2.2 Industrial Wastewater
1.2.3 Municipal Wastewater
1.3 Strategies for Effective Management of Wastewater
1.4 Contributions of Sustainable Development Goals (SDGs) in Promoting Wastewater Management
1.5 Conclusion
References
Chapter 2 Solving the Shortage of Clean Water Through Wastewater Treatment
2.1 Introduction
2.2 Conventional Sewage Treatment Technology
2.3 Preliminary Treatment of Wastewater
2.4 Primary Treatment of Wastewater
2.5 Secondary Treatment of Wastewater
2.6 Tertiary Treatment of Wastewater
2.7 Biological Method of Wastewater Treatment
2.8 Conclusion
References
Chapter 3 Microalgae Cultivation for Wastewater Treatment and Bioenergy Generation
3.1 Introduction
3.2 Microalgae
3.3 High-Rate Algal Ponds (HRAPs)
3.4 Photobioreactors (PBRs) and Membrane Bioreactors (MBRs)
3.5 Microbial Fuel Cell (MFC)
3.6 Conclusion
References
Chapter 4 Cultivation of Aquatic Plants for Wastewater Treatment
4.1 Introduction
4.2 Phytoremediation of Wastewater
4.3 Mechanisms of Phytoremediation
4.4 The Roles of Aquatic Plants in Phytoremediation of Wastewater
4.5 Applications of P. Stratiotes, S. Molesta and E. Crassipes in Phytoremediation of Wastewater
4.5.1 Pistia Stratiotes Plants
4.5.1.1 Distribution of P. Stratiotes
4.5.1.2 Taxonomy of P. Stratiotes
4.5.1.3 Description of P. Stratiotes
4.5.1.4 Growth of P. Stratiotes
4.5.1.5 Efficiency of P. Stratiotes in Phytoremediation of Wastewater
4.5.2 Salvinia Molesta Plants
4.5.2.1 Distribution of S. Molesta
4.5.2.2 Taxonomy of S. Molesta
4.5.2.3 Description of S. Molesta
4.5.2.4 Growth of S. Molesta
4.5.2.5 Nutrient Uptake by S. Molesta Plants in Phytoremediation of Wastewater
4.5.3 Eichhornia Crassipes Plants
4.5.3.1 Distribution of E. Crassipes
4.5.3.2 Taxonomy of E. Crassipes
4.5.3.3 Description of E. Crassipes
4.5.3.4 Potentials of E. Crassipes Plants in Phytoremediation of Wastewater
4.6 Conclusion
References
Chapter 5 Phytoremediation of Wastewater in Hydroponic Systems
5.1 Introduction
5.2 Overview of Hydroponic Systems in Wastewater Treatment
5.3 Nutrient Recovery by Aquatic Plants
5.4 Case Study: Demonstration of Aquatic Plants Cultivation in Wastewater
5.4.1 Cultivation Area
5.4.2 Research Setup
5.4.3 Source of the Plant Samples
5.4.4 Method of Plant Cultivation
5.4.5 Method of Water Sample Collection
5.5 Case Study: Relative Growth Rate (RGR) of Aquatic Plants in Phytoremediation Systems
5.6 Management of the Harvested Aquatic Plants
5.7 Conclusion
References
Chapter 6 Water Quality Monitoring in Wastewater Phytoremediation
6.1 Introduction
6.2 Case Study: Water Quality Monitoring in Phytoremediation of Domestic Wastewater
6.2.1 Determination of pH
6.2.2 Determination of Colour
6.2.3 Determination of Turbidity
6.2.4 Determination of BOD[sub(5)]
6.2.5 Determination of COD
6.2.6 Determination of Phosphate
6.2.7 Determination of Ammonia Nitrogen
6.2.8 Determination of Nitrate
6.2.9 Statistical Analysis
6.3 Outcome of the Water Assessment of the Influent and Effluent Water Samples
6.3.1 Analysis of Colour
6.3.2 Analysis of Turbidity
6.3.3 Analysis of pH
6.3.4 Analysis of COD
6.3.5 Analysis of BOD[sub(5)]
6.3.6 Analysis of Phosphate
6.3.7 Analysis of Ammonia Nitrogen
6.3.8 Analysis of Nitrate
6.4 Conclusion
References
Chapter 7 Water Quality Monitoring Using Internet of Things (IoT)
7.1 Introduction
7.2 Internet of Things (IoT) in Wastewater Monitoring
7.3 Hardware Design of the Arduino IoT System
7.4 Sensor Nodes of the Arduino IoT System
7.4.1 Temperature Sensor
7.4.2 Turbidity Sensor
7.4.3 Oxidation Reduction Potential (ORP) Sensor
7.4.4 Total Dissolved Solids (TDS) Sensor
7.5 Liquid Crystal Display (LCD)
7.6 Wi-Fi Module
7.7 Global System Mobile (GSM Shield)
7.8 Coding Development of the IoT System
7.9 Conclusion
References
Chapter 8 Machine Learning Techniques in Water Quality Monitoring
8.1 Introduction
8.2 Concept of Machine Learning (ML) Techniques in Phytoremediation of Wastewater
8.3 Artificial Neural Network (ANN)
8.4 Support Vector Machine (SVM)
8.5 Adaptive Neuro-Fuzzy Inference System (ANFIS)
8.6 Multilinear Regression (MLR)
8.7 Error Ensemble Learning Approach
8.8 Development of ANN, SVM, ANFIS and MLR for Phytoremediation of Wastewater
8.9 Conclusion
References
Chapter 9 Case Study: Monitoring and Evaluation of Phytoremediation System Using Internet of Things (IoT) and Machine Learning Techniques
9.1 Introduction
9.2 Development of Internet of Things (IoT)-based Salvinia Molesta Plants in Wastewater Treatment
9.2.1 Collection of Data
9.2.2 Modelling and Prediction of the Turbidity (TURBt)
9.2.3 Water Quality Parameters Used
9.2.4 Influent and Effluent Concentration of Turbidity
9.3 Results and Discussion
9.3.1 Results of TURBt (ANN, SVM, ANFIS, and MLR)
9.4 Conclusion
References
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
10.1 Introduction
10.2 Materials and Methodology
10.2.1 Water Quality Parameters Used
10.2.2 Data Processing and Statistical Analysis
10.2.3 Artificial Neural Network (ANN)
10.2.4 Concept of Support Vector Machine (SVM)
10.2.5 Concept of Adaptive Neuro-Fuzzy Inference System (ANFIS)
10.2.6 Multilinear Regression (MLR)
10.3 Model Validation and Performance Evaluation
10.4 Results and Discussion
10.4.1 Descriptive Statistical Analysis
10.4.2 Results of ORPt (ANN, SVM, ANFIS and MLR)
10.5 Conclusion
References
Index