Application of Artificial Intelligence in the Management of Coagulation Treatment Engineering System
Abstract
:1. Introduction
2. Concepts and Principles
2.1. Machine Learning
2.2. Deep Learning
2.3. Transfer Learning
3. Research Statistics and Analysis
4. Application of Neural Networks in Coagulation
4.1. Coagulant Dosing Control System
4.1.1. Automatic Coagulant Dosing Control Modes
4.1.2. Coagulant Dosing Optimization
4.1.3. Multiple Model Comparison
4.1.4. Parameter Control
4.2. Prediction and Analysis of Contaminants
4.2.1. Monitoring of Inlet and Outlet Water
4.2.2. Image Processing of Inlet and Outlet Water
5. Challenges and Prospects
5.1. Optimization of Coagulation Neural Network Models
5.1.1. Improvement in Data Quality and Quantity
5.1.2. Feature Engineering
5.1.3. Model Selection
5.1.4. Cross-Validation
5.1.5. Continuous Monitoring and Optimization
5.2. Various Challenges in Neural Networks for Coagulation
5.2.1. Information Structure Issues
5.2.2. Feedback Control Signal Acquisition and Feedback Mechanism
5.2.3. Transfer Learning Challenges
5.2.4. Real Production Challenges
5.2.5. Analysis of Cost Control Issues
5.2.6. Application of Neural Network in Drinking Water Treatment
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
Abbreviations
AI | Artificial Intelligence |
ANN | Artificial Neural Network |
FNN | Feed Forward Neural Network |
RNN | Recurrent Neural Network |
CNN | Convolutional Neural Network |
MLP | Multi-layer Perceptron |
BP | Back Propagation |
ANFIS | Artificial Fuzzy Neural Network |
RBF | Radial Basis Function |
RBNN | Radial Basis Neural Network |
MMPC | Multi-model Predictive Control |
LSTM | Long Short-Term Memory |
PSO | Particle Swarm Optimization |
DCNN | Deep Convolution Neural Network |
RSM | Response Surface Methodology |
ENN | Edited Nearest Neighbors |
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Category | Network Structure | Representative Models | Pros and Cons | Application Areas | References |
---|---|---|---|---|---|
Feedforward Neural Network (FNN) | Each layer is only connected to the immediately subsequent layer. | Multi-layer Perceptron (MLP), Improved Multi-layer Perceptron (BP, RBF, DBN), Fully Connected Neural Networks | It is parallelizable, making it suitable for processing various data types efficiently. However, a potential drawback is its proneness to overfitting. | Classification, Regression, Clustering, Image, Speech, Natural Language Processing | [38,41,42] |
Recurrent Neural Network (RNN) | It enables information to propagate cyclically through the network, making it suitable for processing sequential data. | Elman Network, Jordan Network, Long Short-Term Memory Network (LSTM), Gated Recurrent Unit (GRU) | It is particularly suitable for sequential data, capable of modeling long sequences effectively. It can be vulnerable to the issue of gradient vanishing, which may hinder the training process. | Speech, Text, Natural Language Processing | [43,44,45] |
Convolutional Neural Network (CNN) | It reduces the number of parameters by leveraging local connections and weight sharing, rendering it ideally suited for spatial feature processing. | LeNet, AlexNet, VGG, GoogLeNet, ResNet | It excels in image recognition tasks, and the use of parameter sharing significantly reduces the total number of parameters required. However, it is not naturally suited for processing sequential data. | Image, Video, Natural Language Processing | [46,47,48] |
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Liu, J.; Long, Y.; Zhu, G.; Hursthouse, A.S. Application of Artificial Intelligence in the Management of Coagulation Treatment Engineering System. Processes 2024, 12, 1824. https://doi.org/10.3390/pr12091824
Liu J, Long Y, Zhu G, Hursthouse AS. Application of Artificial Intelligence in the Management of Coagulation Treatment Engineering System. Processes. 2024; 12(9):1824. https://doi.org/10.3390/pr12091824
Chicago/Turabian StyleLiu, Jingfeng, Yizhou Long, Guocheng Zhu, and Andrew S. Hursthouse. 2024. "Application of Artificial Intelligence in the Management of Coagulation Treatment Engineering System" Processes 12, no. 9: 1824. https://doi.org/10.3390/pr12091824