Modeling a photocatalytic reactor for water purification

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0.1 Background 0.2 Problem statement 0.3 Delimitation 0.4 Approach 1 Modeling and simulation 1.1 Introduction 1.2 Complex systems 1.3 Classification of systems Behavior-based classification 1.3.1 1.3.1.1 Linear systems 1.3.1.2 Nonlinear systems 1.3.2 Zeigler’s classification 1.3.2.1 Continuous-time systems 1.3.2.2 Discrete-time systems 1.3.2.3 Discrete event systems 1.4 Modeling 1.4.1 Introduction to modeling 1.4.2 Principles and practices of modeling 1.4.3 Computational models 1.5 Theory of modeling and simulation 1.5.1 Levels of system specification 1.5.1.1 Klir’s knowledge levels 1.5.1.2 Klir’s systems problems 1.5.1.3 Hierarchy of system specifications 1.5.2 Framework of M&S 1.5.2.1 Source system 1.5.2.2 Experimental frame 1.5.2.3 Model 1.5.2.4 Simulator 1.5.2.5 Model and simulator separation 1.6 Conclusion 2 Fundamentals and techniques in machine learning and deep learning 2.1 Introduction 2.2 Foundations of Deep Learning 2.2.1 Core Concepts of Machine Learning 2.2.1.1 Artificial Intelligence 2.2.1.2 Applications in chemistry 2.2.1.3 Machine learning 2.2.1.4 Categories of machine learning 2.2.1.5 Machine learning tasks 2.2.1.6 Limitations of machine learning 2.2.2 Introduction to artificial neural networks 2.2.2.1 Context and history of artificial neural networks 2.2.2.2 Functioning of an artificial neural network 2.2.2.3 Learning and optimization of a neural network 2.2.3 Key concepts of deep learning 2.2.3.1 Incorporating hidden layers 2.2.3.2 Mathematical formalization of a deep neural network 2.2.3.3 Activation functions 2.2.3.4 Learning and optimization 2.2.4 Attention mechanisms 2.2.4.1 Self-attention 2.2.5 Ensemble learning 2.2.5.1 Types of ensemble methods 2.3 Conclusion 3 Photocatalytic degradation model 3.1 Introduction 3.2 Photocatalytic degradation 3.2.1 Mechanism 3.2.2 Photocatalyst properties 3.2.3 Pollutant characteristics 3.2.4 Environmental factors 3.2.5 Experimental methods 3.2.5.1 Flow reactor systems 3.2.5.2 Batch reactor experiments 3.3 Related work 3.4 Dataset 3.5 Data preprocessing 3.6 Data partitioning 3.7 Base model 3.8 Meta model 3.9 Training 3.10 Results and discussion 3.10.1 Point-wise prediction and parameter-wise prediction 3.10.2 Dependence on experimental methodology 3.10.3 Degree of extrapolation 3.10.4 Hyperparameter tuning 3.10.5 Validation 3.10.6 Predictions 3.11 Conclusion Bibliography

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