List of Figures

Figure 1.1 Process flow of a grate-type MSWI plant in Beijing Figure 1.2 MSW components ratios of different countries/regions Figure 1.3 Schematic diagram of manual control mode in the MSWI process in China Figure 1.4 Schematic diagram of operation optimization process in complex process industries Figure 1.5 Requirements and relationships in academic research and industrial applications Figure 1.6 Relationship between the incineration mechanism, the actual MSWI process, human brain cognitive theory, numerical simulation challenges, and DT model construction Figure 1.7 Structure of "Real-Real" simulation platform Figure 1.8 Structure of "Real-Virtual" simulation platform Figure 1.9 Structure of "Virtual-Real" simulation platform Figure 1.10 Structure of "Virtual-Virtual" simulation platform Figure 1.11 The book's structure Figure 2.1 Process flows of MSWI plants with a daily processing capacity of 800 tons Figure 2.2 Internal structure and zoning diagram of mechanical grate furnace Figure 2.3 Diagram of flue gas cleaning process Figure 2.4 Solid-phase combustion zone and gas-phase combustion zone for nitrogen element products Figure 2.5 Schematic diagram of NxOy generation in high-temperature combustion area Figure 2.6 Numerical simulation and modeling analysis framework Figure 2.7 Simulation modeling strategy for MSWI whole process Figure 2.8 Multi-software-coupled whole-process numerical simulation strategy under benchmark conditions Figure 2.9 Incinerator simplified structure (left side), its 2D model (middle), and mesh division (right side) Figure 2.10 Non-grate solid-phase combustion simulated by Aspen Plus Figure 2.11 Combustion results of solid MSW on the grate. (a) Rate; (b) Mass fraction Figure 2.12 Combustion results of gas-phase combustion under benchmark conditions: (a) temperature, (b) O2 mass fraction, and (c) CO2 mass fraction Figure 2.13 Temperature distribution in the incinerator under typical. (a-h) Case 1-Case 8 Figure 2.14 O2 distribution in the incinerator under typical. (a-h) Case 1-Case 8 Figure 2.15 CO2 distribution in the incinerator under typical. (a-h) Case 1-Case 8 Figure 2.16 Probability density of temperature in the incinerator under typical. (a-h) Case 1-Case 8 Figure 2.17 Exhaust emission results obtained from the simulation: (a) CO, (b) CO2, (c) O2, (d) SO2, and (e) NOx Figure 2.18 Single factor analysis curve in terms of feed rate based on MIMO-LRDT mechanism model. (a) CO concentration; (b) CO2 concentration; (c) O2 concentration; (d) SO2 concentration; (e) NOx concentration Figure 2.19 Single factor analysis curve in terms of primary air temperature based on MIMO-LRDT mechanism model. (a) CO concentration; (b) CO2 concentration; (c) O2 concentration; (d) SO2 concentration; (e) NOx concentration Figure 2.20 Dual-factor analysis curve based on MIMO-LRDT mechanism model. (a) Grate speed vs Feed rate; (b) Grate speed vs Primary air temperature Figure 3.1 Strategy diagram of the proposed virtual data and real data hybrid-driven modeling approach Figure 3.2 Aspen Plus model diagram Figure 3.3 Structure diagram of MISO LRDT model Figure 3.4 LSTM structure diagram Figure 3.5 Impact of three inputs on CO under multi-operating conditions Figure 3.6 Prediction curves of different models based on virtual mechanism data Figure 3.7 Prediction curves of different models based on real data Figure 3.8 Prediction curves of different models for offline training verification phase Figure 3.9 Prediction curves of the offline training verification phase Figure 3.10 Prediction curves of different models for the online testing verification phase Figure 3.11 Prediction curves of online testing verification phase Figure 3.12 Relationship between the hyperparameter and R2 indicator Figure 4.1 DXN generation mode during MSW combustion process Figure 4.2 DXN generation mode after MSW combustion process Figure 4.3 Schematic diagram of the generation mechanism and temperature range of DXN Figure 4.4 SEN modeling strategy based on Bayesian inference and binary tree Figure 4.5 Schematic diagram of BT candidate submodels construction Figure 4.6 Prediction curves of the candidate submodels for the benchmark datasets Figure 4.7 Posterior information of the candidate submodels for the benchmark datasets Figure 4.8 Posterior information of the selected ensemble submodels for the benchmark datasets Figure 4.9 Fitting curves of the SEN model for the benchmark datasets Figure 4.10 Posterior information of the SEN model for the DXN dataset Figure 4.11 Posterior information of the selected ensemble submodels for the DXN dataset Figure 4.12 Fitting curves of the DXN dataset Figure 4.13 Hyperparameter sensitivity analysis curves of the BBTSEN model Figure 5.1 Semi-supervised RF optimization strategy for DXN emission soft sensing Figure 5.2 Schematic of the parameter coding design for the semi-supervised RF optimization Figure 5.3 Particle decoding schematic for the semi-supervised RF optimization strategy Figure 5.4 Modeling results after CCS dataset optimization for the semi-supervised RF optimization strategy Figure 5.5 Prediction curve of the testing set on the CCS data for the semi-supervised RF optimization strategy Figure 5.6 Modeling results of the DXN dataset for the semi-supervised RF optimization strategy Figure 5.7 Prediction curve of the testing set on the DXN data for the semi-supervised RF optimization strategy Figure 5.8 Relationship between the hyperparameters and RMSE in the CCS original dataset for the semi-supervised RF optimization strategy Figure 5.9 Relationship between the hyperparameters and RMSE in the CCS mixed dataset for the semi-supervised RF optimization strategy Figure 5.10 Relationship between the hyperparameters and RMSE in the DXN original dataset for the...