A Review of Model Predictive Control for the Municipal Solid Waste Incineration Process
Abstract
:1. Introduction
2. Description of Municipal Solid Waste Incineration (MSWI) Process and the Model Predictive Control (MPC) Method
2.1. Typical Process Flow Description
2.1.1. Solid Waste Fermentation Subsystem
2.1.2. Solid Waste Combustion Subsystem
2.1.3. Waste Heat Exchange Subsystem
2.1.4. Steam Power Generation Subsystem
2.1.5. Flue Gas Cleaning Subsystem
2.1.6. Flue Gas Emission Subsystem
2.2. Control Problem Description and Control Target Analysis
2.3. Control Difficulties and Complexities Analysis
- (1)
- Online real-time detection of key EIs
- (2)
- Multivariable coupling characteristics
- (3)
- Nonlinear and time-varying characteristics
- (4)
- Time-lag characteristics
- (5)
- Multi-source disturbed and uncertain dynamic characteristics
2.4. Model Predictive Control (MPC) Method and Its Advantages
- (1)
- Predictive model: It is used to predict the future behavior of the system. By establishing a mathematical model of the system, the predictive model can estimate future outputs based on current and past data for use in subsequent rolling optimizations. Its inputs are the historical and current state, and its output is the predicted future CV. For data-driven controlled variable predictive models, it is typically described as a nonlinear autoregressive exogenous (NARX) system:
- (2)
- Rolling optimization: It is used to minimize a predefined objective function by optimizing the future control input sequence. Rolling optimization is performed at each sampling instant, and only the first optimized control input is executed, with the remaining parts being re-optimized at the next instant. The implementation of the optimization algorithm is based on the accurate prediction of controlled variables by the predictive model and the design of the optimization algorithm. As a necessary condition for MPC, the objective function describes various error metrics and energy usage as a quadratic function. In classic MPC, the objective function is usually set as follows:
- (3)
- Feedback correction: It is used to adjusts the predictive model output at each control instant to address model mismatches and external disturbances. Through real-time measured feedback, MPC can correct the predictive model’s error, thereby maintaining system stability and performance. The general form of feedback correction can be expressed as
3. Current Status of Incinerator Modeling for Control
Method | MSWI Parameters | Comments and Summary | Author/Year | Reference |
---|---|---|---|---|
System identification and dynamic models based on first principles | · MVs: MSW inlet flow rate, grate speed, primary air flow rate, secondary air flow rate · CVs: Steam flow rate, flue gas oxygen content | · Model verification through system identification ensures the success of the model in practical applications; dynamic models contribute to optimizing process operational performance | Leskens et al., 2002 | [94] |
Auto-regressive with extra inputs (ARX) models | · MVs: MSW inlet flow rate, grate speed, primary air flow rate, secondary air flow rate · CVs: Steam flow rate, flue gas oxygen content | · Model reduction is applied to each transfer function of the MIMO ARX model separately, which simplifies the model while maintaining its essential dynamics | Leskens et al., 2002 | [95] |
System identification, state–space models | · MVs: Input mass flow rate of Ca (OH)2 · CVs/EIs: Outlet concentration of HCl, pressure drop across the baghouse filter | · Verified through system identification techniques, reliable and suitable for control, capable of reducing reagent consumption and optimizing control performance | Riccardo et al., 2022 | [96] |
Dynamic process and disturbance models obtained experimentally | · MVs: MSW inlet flow rate, grate speed, primary air flow rate, secondary air flow rate · CVs: Steam flow rate, flue gas oxygen content | · Capable of handling multivariable interactive processes, multiple conflicting objectives, and constraint conditions | Leskens et al., 2005 | [97] |
Neural networks models | · MVs: MSW inlet flow rate, grate speed, primary air flow rate, secondary air flow rate · CVs: Steam flow rate, flue gas oxygen content | · Data-driven neural network models can capture and describe the unique dynamic behavior of each controlled object | Qin et al., 2008 | [98] |
Multi-zone, zero-dimensional modeling approach | · MVs: Air flow rate, MSW supply rate, steam valve · CVs: Oxygen mole fraction in exhaust gas, steam generation rate, steam pressure | · Capable of simulating real plant operation data, accurately predicting system behavior, with relatively low computational resource requirements | Cho et al., 2017 | [99] |
Ensemble decision tree | · MVs: Air flow rate, MSW feed rate · CVs: Furnace temperature, flue gas oxygen concentration, steam flow | · Combines random forest and gradient boosting decision tree to construct a MIMO controlled object model with historical data | Wang et al., 2021 | [100] |
Cascaded transfer tunction, particle swarm optimization (PSO) | · MVs: Primary air flow rate · CVs: Furnace temperature, flue gas oxygen content, steam flow | · The predictive values of the established system model are consistent with the actual situation, capable of reflecting the dynamic characteristics of the system under actual working conditions | Chen et al., 2023 | [101] |
Random forest (RF) and gradient boosting decision tree (GBDT) | · MVs: Air flow rate of the left 1 drying grate, drying grate, right 1 air flow rate, total 37 variables · CVs: Furnace temperature, flue gas oxygen concentration, and steam flow | · Developed for actual industrial processes, provides a reliable engineering verification environment, and considers the MIMO characteristics of the MSWI process | Wang et al., 2023 | [102] |
Linear regression decision tree (LRDT) | · MVs: Primary air flow, secondary air flow, drying grate average speed, feeder average speed · CVs: Furnace temperature | · Furnace temperature model based on LRDT with leaf predictions based on a single path as the final output, and it has high interpretability | Xia et al., 2023 | [103] |
LRDT | · MVs: Primary air flow, secondary air flow, average drying grate speed, average feeder speed · CVs: Furnace temperature, flue gas oxygen content, and boiler steam flow rate | · Realize multivariable coupled control based on off-site data simulation | Wang et al., 2023 | [104] |
Mechanistic modeling based on material and energy balance | · MVs: MSW grab quantity, MSW push quantity, drying grate speed, combustion grate 1 speed, combustion grate 2 speed, ash grate speed, primary air flow and its branch flows, secondary air flow, induced draft fan flow · CVs: Furnace temperature, flue gas oxygen content, and steam flow | · Provides a mechanistic modeling based on material and energy balance, enabling independent analysis of the internal characteristics, and achieving dynamic simulation of the whole process by calculating the inputs and outputs of each subprocess | Ding et al., 2023 | [105] |
T-S FNN | · MVs: Primary air flow, secondary air pressure, primary air heating temperature · CVs: Furnace temperature | · Capable of reflecting the trend of furnace temperature changes with high fitting accuracy and realize furnace temperature control based on off-site data simulation | He et al., 2023 | [106] |
XGBoost-based serial-parallel ensemble model | · MVs: Feeder average speed, drying grate average speed, ammonia water injection amount, activated carbon injection amount, lime injection amount · CVs/EIs: Furnace temperature, boiler steam flow rate, flue gas oxygen content of G1 and G3, NOx, CO, CO2 | · Provides a whole-process MSWI model in terms of MVs as inputs | Wang et al., 2024 | [107] |
4. Current Status of Intelligent Controller Design for the MSWI Process
4.1. Expert Systems and Human-Simulated Intelligent Control
4.2. Fuzzy Logic Control
4.3. Machine Learning Control
4.3.1. Neural Network Control
4.3.2. Fuzzy Neural Network Control
4.4. Self-Tuning PID Control
Controller Type | Process Model | MSWI Parameter | Inference | Author/Year | Reference |
---|---|---|---|---|---|
Expert systems and human-simulated intelligent control | Transfer function | · MVs/AVs: Furnace temperature, mixing degree of MSW, flue gas residence time, excess air ratio · CVs/EIs: Dioxin-like organic chloride, particulate matter, NOx, and CO, etc. | · Integrated control strategy, based on HSIC and integrated with expert knowledge · With high control accuracy, good dynamic and steady-state quality, and strong robustness | Ni et al., 2014 | [118] |
Second-order lag transfer function | · MVs/AVs: MSW quantity, air flow volume · CVs: Furnace temperature | · Demonstrated strong anti-interference capability, good control quality, fast response, and small steady-state error | Xiao et al., 2015 | [119] | |
Transfer function | · MVs/AVs: Oxygen concentration, etc. · CVs: Furnace temperature, furnace pressure | · HSIC combined with PSO algorithm · The improved PSO algorithm enhances the ability to resist external pulse interference, showing strong robustness even when process parameters change significantly | Wu Qian et al., 2018 | [120] | |
Transfer function | · MVs: (Not explicitly mentioned) · CVs: Furnace temperature | · Strong robustness, small overshoot and oscillation, control quality superior to traditional PID control | Li et al., 2019 | [116] | |
(Not explicitly mentioned) | · MVs: (Not explicitly mentioned) · CVs: Furnace temperature | · HSIC combined with an improved PSO algorithm · Intelligent optimization control during the incineration process is achieved, improving control accuracy and the system’s dynamic stability | Ju et al., 2022 | [117] | |
Fuzzy logic control | Neural network | · MVs/AVs: Pile material speed, air flow volume · CVs: Steam flow, furnace temperature, flue gas oxygen content | · Improves control performance, solves the delay problem of moisture content estimation in traditional control methods | Qian et al., 1993 | [135] |
Transfer function | · MVs/AVs: MSW feeding quantity · CVs: Furnace temperature | · Fuzzy adaptive control for furnace temperature · Designed a basic fuzzy controller and an intelligent synchronous modification weight factor module | Chang et al., 2004 | [138] | |
Shenzhen 150 t/d MSWI power plant | · MVs/AVs: MSW feeding quantity (feeding time and stopping time) · CVs: Furnace temperature | · Fuzzy adaptive control strategy and parameter correction module to ensure the effectiveness of the adaptive factor | Shen et al., 2004 | [136] | |
Shenzhen 150 t/d MSWI power plant | · MVs/AVs: Waste feeding amount (feeding time and stopping time) · CVs: Furnace temperature | · Adaptive fuzzy control strategy and combining genetic algorithms and neural network technology to optimize control parameters | Shen et al., 2005 | [137] | |
Neural network control | Second-order inertia transfer function and lag delay transfer function | · MVs/AVs: (Not explicitly mentioned) · CVs: Furnace temperature | · Combining BPNN with PID for adjusting PID parameters · Optimizing the BPNN through PSO to improve the global optimality of control parameters | Luo et al., 2023 | [147] |
BPNN | · MVs: Lime slurry flow · CVs/EIs: Emission concentration of acidic gases (SO2 and HCl) | · Using a BPNN genetic algorithm strategy, replacing manual control by training an artificial neural network · The control output is smoother than the PID mode, can achieve the effect of manual control, and can reduce workload at the same time | You et al., 2024 | [146] | |
Fuzzy neural control | LRDT | · MVs: Primary air flow, secondary air flow, average feeder speed, average drying grate speed, and ammonia water injection amount · CVs: Furnace temperature | · Adaptive TS-FNN controller · Good decoupling performance, robustness, and anti-interference capabilities | Tian et al., 2023 | [155] |
TS-FNN | · MVs: Secondary air pressure, primary air heating temperature · CVs: Furnace temperature | · Event-triggered self-organizing FNN control | He et al., 2023 | [156] | |
TS-FNN | · MVs: Primary air flow, drying grate speed · CVs: Furnace temperature | · Event-triggering mechanism · Controller convergence based on Lyapunov’s second law | Ding et al., 2023 | [157] | |
MISO-LRDT | · MVs: Primary air flow, secondary air flow, feeder average speed, drying grate average speed, ammonia water injection amount · CVs: Furnace temperature | · Interval type-2 FNN controller · Stability analysis | Tang et al., 2023 | [158] | |
MIMO-TSFNN | · MVs: Primary air flow, drying grate speed, secondary air flow · CVs: Furnace temperature, flue gas oxygen content | · Data-driven modeling and self-organizing control method · Effectiveness of the model and controller was verified | Ding et al., 2023 | [159] | |
MIMO-TSFNN | · MVs: Primary air flow, drying grate speed · CVs: Furnace temperature, flue gas oxygen content | · Event-triggered FNN multivariable controller | Ding et al., 2023 | [160] | |
MIMO-TSFNN | · MVs: Primary air flow, drying grate speed · CVs: Furnace temperature, flue gas oxygen content | · Dynamic self-organizing mechanism | Ding et al., 2023 | [161] | |
Self-tuning PID control | Transfer function | · MVs: MSW feeding quantity (feeding time and stopping time), primary air flow · CVs: Furnace temperature | · Fuzzy control method, rule base established based on operator experience · Compare fuzzy control with PID control algorithms | Dai et al., 2019 | [168] |
TS-FNN | · MVs: Primary air flow · CVs: Furnace temperature | · Event-triggered RBF–PID controller · Online updating of network parameters through gradient descent algorithm and recursive least squares algorithm | He et al., 2022 | [167] | |
MIMO-TSFNN | · MVs: Primary air flow, secondary air flow, drying grate speed · CVs: Flue gas oxygen content, furnace temperature, steam flow | · Multi-loop PID controller based on quasi-diagonal recurrent neural network · Adaptively adjusting PID parameters | Ding et al., 2022 | [169] | |
Tikhonov-LRDT | · MVs: Primary air flow, secondary air flow, feeder average speed, drying grate average speed · CVs: Furnace temperature, steam flow, flue gas oxygen content | · Multi-input multi-output control method based on single-neuron adaptive PID controller · Online continuous adjustment of neuron weight coefficients through supervised Hebb learning algorithm to achieve set-point tracking control | Wang et al., 2023 | [104] |
- (1)
- Multi-dimensional and conflicting objectives: In the MSWI process, economic, environmental, and production indicators must be considered simultaneously, often leading to conflicting objectives. For instance, in furnace temperature control, increasing the combustion temperature can enhance energy recovery efficiency but may also damage the incineration equipment and increase emissions of harmful gases. This multi-objective optimization problem makes it challenging for a single control algorithm to find the optimal balance among all objectives.
- (2)
- Multivariable and interaction characteristics: The MSWI process involves multiple interacting process variables, and the complex coupling relationships between these variables make multi-loop control difficult to achieve. Existing intelligent controllers have limitations in handling multivariable interactions and must rely on advanced MIMO methods to effectively manage these complex relationships [117].
- (3)
- Constraints on process variables: Most process variables have strict operating ranges, and exceeding these ranges can lead to equipment damage or safety issues. Therefore, control algorithms must consider these constraints during execution to ensure that all MVs remain within safe limits. This requires control algorithms suitable for the MSWI process to have strong constraint-handling capabilities [44].
5. Current Status of Model Predictive Controller for the MSWI Process
5.1. MPC Controller Design
Prediction Model Type | Prediction Model Design | Objective Function Form | Special Term | Reference |
---|---|---|---|---|
Mechanism driven | Rolling time-domain state estimator, nonlinear state–space equations | Output terms | \ | [174] |
Linear state–space equations | Input and output terms | \ | [97,175,179] | |
Subspace identification, partial differential equations | Input and output terms | \ | [180] | |
Linear state–space equations | Input and output terms | Terminal constraint terms | [181] | |
Data driven | IT2FNN | Output terms | \ | [158] |
SOFNN | Input and output terms | \ | [182] | |
Improved long short-term memory (LSTM) | Input and output terms | \ | [183] | |
Improved LSTM | Input and output terms | \ | [184] | |
Improved random weight neural network (RWNN) | Input and output terms | \ | [185] | |
RWNN | Input and output terms | \ | [186] | |
LSTM | Input and output terms | \ | [187] | |
RBFNN | Input and output terms | \ | [188] | |
BPNN | Output terms | \ | [189] |
5.2. MPC Controller Online Update
5.2.1. Online Update of Prediction Model
5.2.2. Online Update of Rolling Optimization
5.2.3. Online Update of Feedback Correction
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
Abbreviations
Abbreviations | Detailed Meaning |
MSWI | Municipal solid waste incineration |
APC | Advanced process control |
MPC | Model predictive control |
MSW | Municipal solid waste |
WTE | Waste-to-energy |
NIMBY | Not in my backyard |
EI | Environmental indicator |
MV | Manipulated variables |
CV | Controlled variable |
AV | Auxiliary variable |
ACC | Automatic combustion control |
AI | Arifical intelligence |
WoS | Web of Science |
SNCR | Selective non-catalytic reduction |
CEMS | Continuous emission monitoring system |
FT | Furnace temperature |
FGOC | Flue gas oxygen content |
SF | Steam flow |
DXN | Dioxin |
HRGC/HRMS | High-resolution gas chromatography/high-resolution mass spectrometry |
MIMO | Multile input multiple output |
ARX | Auto-regressive with extra inputs |
NARX | Nonlinear auto-regressive with extra inputs |
T-S FNN | T-S fuzzny neural network |
PSO | Particle swarm optimization |
RF | Random forest |
GBDT | Gradient boosting decision tree |
LRDT | Linear regression decision tree |
HSIC | Human simulated intelligent control |
FLC | Fuzzy logic control |
TSK | Takagi–Sugeno–Kang |
LMPC | Linear MPC |
NMPC | Nonlinear MPC |
LSTM | Long short-term memory |
RWNN | Random weight neural network |
GD | Gradient descent |
LM | Levenberg–Marquardt |
QP | Quadratic programming |
SQP | Sequential quadratic programming |
References
- Gómez-Sanabria, A.; Kiesewetter, G.; Klimont, Z.; Schoepp, W.; Haberl, H. Potential for future reductions of global GHG and air pollutants from circular waste management systems. Nat. Commun. 2022, 13, 106. [Google Scholar] [CrossRef] [PubMed]
- Ali, K.; Kausar, N.; Amir, M. Impact of pollution prevention strategies on environment sustainability: Role of environmental management accounting and environmental proactivity. Environ. Sci. Pollut. Res. 2023, 30, 88891–88904. [Google Scholar] [CrossRef]
- Prasad, M.N.V. Recovery of resources from biowaste for pollution prevention. In Environmental Materials and Waste; Elsevier: Amsterdam, The Netherlands, 2024; pp. 1–34. [Google Scholar] [CrossRef]
- Lu, J.S.; Chang, Y.; Poon, C.S.; Lee, D.J. Slow pyrolysis of municipal solid waste (MSW): A review. Bioresour. Technol. 2020, 312, 123615. [Google Scholar] [CrossRef]
- Tang, J.; Xia, H.; Yu, W.; Qiao, J.F. Research status and prospects of intelligent optimization control for municipal solid waste incineration process. Acta Autom. Sin. 2023, 49, 2019–2059. [Google Scholar] [CrossRef]
- Song, Y.; Xian, X.; Zhang, C.; Zhu, F.; Yu, B.; Liu, J. Residual municipal solid waste to energy under carbon neutrality: Challenges and perspectives for China. Resour. Conserv. Recycl. 2023, 198, 107177. [Google Scholar] [CrossRef]
- Liu, J.; Zheng, L. Structure characteristics and development sustainability of municipal solid waste treatment in China. Ecol. Indic. 2023, 152, 110391. [Google Scholar] [CrossRef]
- Tang, L.; Guo, J.; Wan, R.; Jia, M.; Qu, J.; Li, L.; Bo, X. Air pollutant emissions and reduction potentials from municipal solid waste incineration in China. Environ. Pollut. 2023, 319, 121021. [Google Scholar] [CrossRef] [PubMed]
- Ding, Y.; Zhao, J.; Liu, J.W.; Zhou, J.; Cheng, L.; Zhao, J.; Shao, Z.; Iris, C.; Pan, B.; Li, X.; et al. A review of China’s municipal solid waste (MSW) and comparison with international regions: Management and technologies in treatment and resource utilization. J. Clean. Prod. 2021, 293, 126144. [Google Scholar] [CrossRef]
- Walser, T.; Limbach, L.K.; Brogioli, R.; Erismann, E.; Flamigni, L.; Hattendorf, B.; Juchli, M.; Krumeich, F.; Ludwig, C.; Prikopsky, K.; et al. Persistence of engineered nanoparticles in a municipal solid-waste incineration plant. Nat. Nanotechnol. 2012, 7, 520–524. [Google Scholar] [CrossRef]
- Istrate, I.R.; Galvez-Martos, J.L.; Vázquez, D.; Guillén-Gosálbez, G.; Dufour, J. Prospective analysis of the optimal capacity, economics and carbon footprint of energy recovery from municipal solid waste incineration. Resour. Conserv. Recycl. 2023, 193, 106943. [Google Scholar] [CrossRef]
- Sharma, B.; Goswami, Y.; Sharma, S.; Shekhar, S. Inherent roadmap of conversion of plastic waste into energy and its life cycle assessment: A frontrunner compendium. Renew. Sustain. Energy Rev. 2021, 146, 111070. [Google Scholar] [CrossRef]
- Fernando, Y.; Tseng, M.L.; Aziz, N.; Ikhsan, R.B.; Wahyuni-TD, I.S. Waste-to-energy supply chain management on circular economy capability: An empirical study. Sustain. Prod. Consum. 2022, 31, 26–38. [Google Scholar] [CrossRef]
- Bajić, B.Ž.; Dodić, S.N.; Vučurović, D.G.; Dodić, J.M.; Grahovac, J.A. Waste-to-energy status in Serbia. Renew. Sustain. Energy Rev. 2015, 50, 1437–1444. [Google Scholar] [CrossRef]
- Kalyani, K.A.; Pandey, K.K. Waste to energy status in India: A short review. Renew. Sustain. Energy Rev. 2014, 31, 113–120. [Google Scholar] [CrossRef]
- Gabrielli, P.; Rosa, L.; Gazzani, M.; Meys, R.; Bardow, A.; Mazzotti, M.; Sansavini, G. Net-zero emissions chemical industry in a world of limited resources. One Earth 2023, 6, 682–704. [Google Scholar] [CrossRef]
- Cho, B.H.; Nam, B.H.; An, J.; Youn, H. Municipal solid waste incineration (MSWI) ashes as construction materials—A review. Materials 2020, 13, 3143. [Google Scholar] [CrossRef]
- Liu, J.; Hu, L.; Tang, L.; Ren, J. Utilisation of municipal solid waste incinerator (MSWI) fly ash with metakaolin for preparation of alkali-activated cementitious material. J. Hazard. Mater. 2021, 402, 123451. [Google Scholar] [CrossRef]
- Fan, C.; Wang, B.; Qi, Y.; Liu, Z. Characteristics and leaching behavior of MSWI fly ash in novel solidification/stabilization binders. Waste Manag. 2021, 131, 277–285. [Google Scholar] [CrossRef]
- Song, L.; Sun, Y.; Song, J.; Feng, Z.; Gao, J.; Yao, Q. From “not in my backyard” to “please in my backyard”: Transforming the local responses toward a waste-to-energy incineration project in China. Sustain. Prod. Consum. 2024, 45, 104–114. [Google Scholar] [CrossRef]
- Liang, X.; Kurniawan, T.A.; Goh, H.H.; Zhang, D.; Dai, W.; Liu, H.; Goh, K.C.; Othman, M.H.D. Conversion of landfilled waste-to-electricity (WTE) for energy efficiency improvement in Shenzhen (China): A strategy to contribute to resource recovery of unused methane for generating renewable energy on-site. J. Clean. Prod. 2022, 369, 133078. [Google Scholar] [CrossRef]
- Liang, D.; Wang, F.; Lv, G. The resource utilization and environmental assessment of MSWI fly ash with solidification and stabilization: A review. Waste Biomass Valorization 2024, 15, 37–56. [Google Scholar] [CrossRef]
- Huang, B.; Gan, M.; Ji, Z.; Fan, X.; Wang, G.; Sun, Z.; Zhao, Q.; Wu, Y.; Lu, S. Co-treating MSWI fly ash in iron ore sintering process: Influence of water-washing and roll forming pretreatment on dioxins emission. Process Saf. Environ. Prot. 2023, 173, 143–153. [Google Scholar] [CrossRef]
- Jia, T.; Chen, S.; Teng, L.; Zhang, R.; Yuan, G.; Chen, Y.; Yin, L. Characteristics and mechanism of slagging in a 500 t/d MSW incinerator. J. Energy Inst. 2024, 114, 101585. [Google Scholar] [CrossRef]
- Tang, Y.; Chen, D.; Feng, Y.; Hu, Y.; Yin, L.; Qian, K.; Yuan, G.; Zhang, R. MSW pyrolysis volatiles’ reforming by incineration fly ash for both pyrolysis products upgrading and fly ash stabilization. Chemosphere 2023, 313, 137536. [Google Scholar] [CrossRef] [PubMed]
- Hunsinger, H.; Jay, K.; Vehlow, J. Formation and destruction of PCDD/F inside a grate furnace. Chemosphere 2002, 46, 1263–1272. [Google Scholar] [CrossRef] [PubMed]
- Dan, Z.; Che, Y.; Wang, X.; Zhou, P.; Han, Z.; Bu, D.; Lu, X.; Ma, W.; Chen, G. Environmental, economic, and energy analysis of municipal solid waste incineration under anoxic environment in Tibet Plateau. Environ. Res. 2023, 216, 114681. [Google Scholar] [CrossRef]
- Oischinger, J.; Kohl, M.; Meiller, M.; Walberer, J.; Daschner, R.; Hornung, A.; Grafmans, F.; Warnecke, R.; Breitenberger, R.; Dannerbeck, F.; et al. Characterization of different solid fuels from waste for an advanced online fuel control system designed for large-scale incineration plants. Waste Manag. Res. 2024, 42, 126–134. [Google Scholar] [CrossRef]
- Falconi, F.; Guillard, H.; Capitaneanu, S.; Raissi, T. Control strategy for the combustion optimization for waste-to-energy incineration plant. IFAC-PapersOnLine 2020, 53, 13167–13172. [Google Scholar] [CrossRef]
- Fan, Y.; Shao, J.; Sun, G.; Shao, X. Proportional–integral–derivative controller design using an advanced lévy-flight salp swarm algorithm for hydraulic systems. Energies 2020, 13, 459. [Google Scholar] [CrossRef]
- Tong, C.; Yang, X.; Chen, G.; Zhang, Y.; Chen, L.; Zhou, Y.; He, T.; Jin, B. Experimental investigation for the combustion characteristics of blends of three kinds of coal. Fuel 2021, 300, 120937. [Google Scholar] [CrossRef]
- Ping, X.; Wang, F.; Wang, H.; Pan, J.; Lu, L.; Qiu, J. High temperature melting of municipal solid waste incineration (MSWI) fly ash and co-disposal technology with blast furnaces. Waste Manag. 2024, 183, 191–198. [Google Scholar] [CrossRef]
- Yamada, T.; Asari, M.; Miura, T.; Niijima, T.; Yano, J.; Sakai, S.I. Municipal solid waste composition and food loss reduction in Kyoto City. J. Mater. Cycles Waste Manag. 2017, 19, 1351–1360. [Google Scholar] [CrossRef]
- Nemitallah, M.A.; Nabhan, M.A.; Alowaifeer, M.; Haeruman, A.; Alzahrani, F.; Habib, M.A.; Elshafei, M.; Abouheaf, M.I.; Aliyu, M.; Alfarraj, M. Artificial intelligence for control and optimization of boilers’ performance and emissions: A review. J. Clean. Prod. 2023, 417, 138109. [Google Scholar] [CrossRef]
- Piltz, G.; Annema, J.A.; Pesch, U. Experts’ perspectives on the sustainability and risks of freely applicable MSWI bottom ash: A Q-methodology study in the Netherlands. Environ. Dev. Sustain. 2023, 25, 1–25. [Google Scholar] [CrossRef]
- Gui, W.; Yue, W.; Xie, Y.; Zhang, H.; Yang, C. A review of intelligent optimal manufacturing for aluminum reduction production. Acta Autom. Sin. 2018, 44, 1957–1970. [Google Scholar] [CrossRef]
- Jan, Z.; Ahamed, F.; Mayer, W.; Patel, N.; Grossmann, G.; Stumptner, M.; Kuusk, A. Artificial intelligence for industry 4.0: Systematic review of applications, challenges, and opportunities. Expert Syst. Appl. 2023, 216, 119456. [Google Scholar] [CrossRef]
- Seraj, S.; Nikravan, M.; Ramezanianpour, A.A.; Zendehdel, P.; Student, P. Evaluation of the application of municipal solid waste incinerator (MSWI) ash in civil engineering using a sustainability approach. Detritus 2020, 9, 113–124. [Google Scholar] [CrossRef]
- Marques, F.H.; Alvarez, L.A. Advanced process control system with MPC as a new approach for layer of protection analysis. J. Loss Prev. Process Ind. 2023, 83, 104993. [Google Scholar] [CrossRef]
- Skogestad, S. Advanced control using decomposition and simple elements. Annu. Rev. Control 2023, 56, 100903. [Google Scholar] [CrossRef]
- Shengli, D.U.; Qingda, Z.; Boqi, C.A.; Junfei, Q. A review of model predictive control for urban wastewater treatment process. Inf. Control 2022, 51, 41–53. [Google Scholar] [CrossRef]
- Wang, Y.J.; Huang, J.Q.; Zhou, W.X.; Lu, F.; Xu, W.H. Neural network-based model predictive control with fuzzy-SQP optimization for direct thrust control of turbofan engine. Chin. J. Aeronaut. 2022, 35, 59–71. [Google Scholar] [CrossRef]
- Schwedersky, B.B.; Flesch, R.C.; Rovea, S.B. Echo state networks for online, multi-step MPC relevant identification. Eng. Appl. Artif. Intell. 2022, 108, 104596. [Google Scholar] [CrossRef]
- Ding, H.; Tang, J.; Qiao, J. Control methods of municipal solid wastes incineration process: A survey. In Proceedings of the 2021 40th Chinese Control Conference (CCC), Shanghai, China, 26–28 July 2021; IEEE: New York, NY, USA, 2021; pp. 662–667. [Google Scholar] [CrossRef]
- Tang, J.; Wang, T.; Xia, H.; Cui, C. An Overview of Artificial Intelligence Application for Optimal Control of Municipal Solid Waste Incineration Process. Sustainability 2024, 16, 2042. [Google Scholar] [CrossRef]
- Khan, S.; Anjum, R.; Raza, S.T.; Bazai, N.A.; Ihtisham, M. Technologies for municipal solid waste management: Current status, challenges, and future perspectives. Chemosphere 2022, 288, 132403. [Google Scholar] [CrossRef]
- Ilic, O.; Bermel, P.; Chen, G.; Joannopoulos, J.D.; Celanovic, I.; Soljačić, M. Tailoring high-temperature radiation and the resurrection of the incandescent source. Nat. Nanotechnol. 2016, 11, 320–324. [Google Scholar] [CrossRef]
- Kong, B.; Wang, E.; Li, Z. The effect of high temperature environment on rock properties—An example of electromagnetic radiation characterization. Environ. Sci. Pollut. Res. 2018, 25, 29104–29114. [Google Scholar] [CrossRef]
- Margallo, M.; Aldaco, R.; Irabien, A. A case study for environmental impact assessment in the process industry: Municipal Solid Waste Incineration (MSWI). Chem. Eng. 2014, 39, 613–618. [Google Scholar] [CrossRef]
- Xin, M.; Sun, Y.; Li, W.; Yin, J.; Wu, Y.; Long, Y.; Bian, R.; Wang, Y.-N.; Wang, H.; Yang, Y.; et al. Exposure levels of PCDD/Fs from co-landfill scenario with MSW and stabilized fly ash during hydrolysis-acidogenesis. J. Clean. Prod. 2023, 394, 136343. [Google Scholar] [CrossRef]
- Liu, J.; Xie, G.; Wang, Z.; Zeng, C.; Fan, X.; Li, Z.; Ren, J.; Xing, F.; Zhang, W. Manufacture of alkali-activated cementitious materials using municipal solid waste incineration (MSWI) ash: Immobilization of heavy metals in MSWI fly ash by MSWI bottom ash. Constr. Build. Mater. 2023, 392, 131848. [Google Scholar] [CrossRef]
- Fan, C.; Wang, B.; Ai, H.; Qi, Y.; Liu, Z. A comparative study on solidification/stabilization characteristics of coal fly ash-based geopolymer and Portland cement on heavy metals in MSWI fly ash. J. Clean. Prod. 2021, 319, 128790. [Google Scholar] [CrossRef]
- Lan, T.; Meng, Y.; Ju, T.; Chen, Z.; Du, Y.; Deng, Y.; Song, M.; Han, S.; Jiang, J. Synthesis and application of geopolymers from municipal waste incineration fly ash (MSWI FA) as raw ingredient-A review. Resour. Conserv. Recycl. 2022, 182, 106308. [Google Scholar] [CrossRef]
- Srivastava, V.K.; Syal, P. Implementation of RAM feeder cycles and their steps logic in waste to energy plant. In Intelligent Systems and Smart Infrastructure; CRC Press: Boca Raton, FL, USA, 2023; pp. 490–499. [Google Scholar]
- Ram, C.; Kumar, A.; Rani, P. Municipal solid waste management: A review of waste to energy (WtE) approaches. Bioresources 2021, 16, 4275. [Google Scholar] [CrossRef]
- Zhang, Q.; Cao, J.; Wu, Y.; Zhao, J.; Guo, W.; Huang, W.; Feng, Q.; Fang, F.; Aleem, M.; Luo, J. Shifts of microbial community and metabolic function during food wastes and waste activated sludge co-fermentation in semi-continuous-flow reactors: Effects of fermentation substrate and zero-valent iron. Bioresour. Technol. 2020, 313, 123686. [Google Scholar] [CrossRef]
- Magnanelli, E.; Tranås, O.L.; Carlsson, P.; Mosby, J.; Becidan, M. Dynamic modeling of municipal solid waste incineration. Energy 2020, 209, 118426. [Google Scholar] [CrossRef]
- Liu, J.; Goel, A.; Kua, H.W.; Wang, C.H.; Peng, Y.H. Evaluating the urban metabolism sustainability of municipal solid waste management system: An extended exergy accounting and indexing perspective. Appl. Energy 2021, 300, 117254. [Google Scholar] [CrossRef]
- Qi, W.; Geng, C.; Zhu, F.; Zhang, C.; Du, B.; Ji, Y.; Wang, F.; Zhang, S.; Liu, J. Complementary vitrification of municipal solid waste incineration fly ash from grate furnaces and fluidised bed incinerators via a co-reduction process. Waste Manag. 2024, 184, 92–100. [Google Scholar] [CrossRef] [PubMed]
- Hu, F.; Li, P.; Cheng, P.; Liu, Y.; Shi, G.; Gao, Y.; Liu, Z. A pilot-scale experimental study on MILD combustion of sawdust and residual char solid waste blend using low-temperature preheating air. Fuel 2023, 342, 127768. [Google Scholar] [CrossRef]
- Jiang, P.; Parvez, A.M.; Meng, Y.; Dong, X.; Xu, M.; Luo, X.; Shi, K.; Wu, T. Novel two-stage fluidized bed-plasma gasification integrated with SOFC and chemical looping combustion for the high efficiency power generation from MSW: A thermodynamic investigation. Energy Convers. Manag. 2021, 236, 114066. [Google Scholar] [CrossRef]
- Zhang, M.; Wei, J.; Li, H.; Chen, Y.; Liu, J. Comparing and optimizing municipal solid waste (MSW) management focused on air pollution reduction from MSW incineration in China. Sci. Total Environ. 2024, 907, 167952. [Google Scholar] [CrossRef]
- Zhao, Y.; Feng, J.; Chen, Y.; Fan, H.; Bai, W. Thermal process and NO emission reduction characteristics of a new-type coke oven regenerator coupled with SNCR process. Fuel 2021, 305, 121510. [Google Scholar] [CrossRef]
- Sorrentino, G.P.; Zanoletti, A.; Ducoli, S.; Zacco, A.; Iora, P.; Invernizzi, C.M.; Di Marcoberardino, G.; Depero, L.E.; Bontempi, E. Accelerated and natural carbonation of a municipal solid waste incineration (MSWI) fly ash mixture: Basic strategies for higher carbon dioxide sequestration and reliable mass quantification. Environ. Res. 2023, 217, 114805. [Google Scholar] [CrossRef] [PubMed]
- Hu, D.; Wang, Y.; Li, J.; Wang, J.; Yang, Q. Energy saving control of waste heat utilization subsystem for fuel cell vehicle. IEEE Trans. Transp. Electrif. 2023, 10, 3192–3205. [Google Scholar] [CrossRef]
- Zoghi, M.; Habibi, H.; Choubari, A.Y.; Ehyaei, M.A. Exergoeconomic and environmental analyses of a novel multi-generation system including five subsystems for efficient waste heat recovery of a regenerative gas turbine cycle with hybridization of solar power tower and biomass gasifier. Energy Convers. Manag. 2021, 228, 113702. [Google Scholar] [CrossRef]
- Patel, P.; Vaezi, M.; Billal, M.M.; Kumar, A. Development of data-intensive techno-economic models for the assessment of a biomass, waste heat, and MSW integrated waste-to-electricity facility. Resour. Conserv. Recycl. Adv. 2023, 20, 200188. [Google Scholar] [CrossRef]
- Malik, S.; Verma, S.; Gupta, A.; Sharma, G.; Singla, S. Performability evaluation, validation and optimization for the steam generation system of a coal-fired thermal power plant. MethodsX 2022, 9, 101852. [Google Scholar] [CrossRef]
- Wang, B.; Gupta, R.; Bei, L.; Wan, Q.; Sun, L. A review on gasification of municipal solid waste (MSW): Syngas production, tar formation, mineral transformation and industrial challenges. Int. J. Hydrogen Energy 2023, 48, 26676–26706. [Google Scholar] [CrossRef]
- Lu, M.; Ge, W.; Xia, Y.; Sun, C.; Lin, X.; Tsang, D.C.; Ling, T.-C.; Hu, Y.; Wang, L.; Yan, J. Upcycling MSWI fly ash into green binders via flue gas-enhanced wet carbonation. J. Clean. Prod. 2024, 440, 141013. [Google Scholar] [CrossRef]
- Cai, P.T.; Chen, T.; Chen, B.; Wang, Y.C.; Ma, Z.Y.; Yan, J.H. The impact of pollutant emissions from co-incineration of industrial waste in municipal solid waste incinerators. Fuel 2023, 352, 129027. [Google Scholar] [CrossRef]
- Sun, W.; Shao, Y.; Zhao, L.; Wang, Q. Co-removal of CO2 and particulate matter from industrial flue gas by connecting an ammonia scrubber and a granular bed filter. J. Clean. Prod. 2020, 257, 120511. [Google Scholar] [CrossRef]
- Chai, T.Y. Challenges of optimal control for plant-wide production processes in terms of control and optimization theories. Acta Autom. Sin. 2009, 35, 641–649. [Google Scholar] [CrossRef]
- Leskens, M.; Van’t Veen, P.P.; Van Kessel, L.B.M.; Bosgra, O.H.; Van den Hof, P.M.J. Improved economic operation of mswc plants with a new model based pid control strategy. IFAC Proc. Vol. 2010, 43, 655–660. [Google Scholar] [CrossRef]
- Hershkowitz, A.; Salerni, E. Municipal solid waste incineration in Japan. Environ. Impact Assess. Rev. 1989, 9, 257–278. [Google Scholar] [CrossRef]
- Tian-You, C. Development directions of automation science and technology. Acta Autom. Sin. 2018, 44, 1923–1930. [Google Scholar] [CrossRef]
- Falandysz, J.; Nose, K.; Ishikawa, Y.; Łukaszewicz, E.; Yamashita, N.; Noma, Y. HRGC/HRMS analysis of chloronaphthalenes in several batches of Halowax 1000, 1001, 1013, 1014 and 1099. J. Environ. Sci. Health Part A 2006, 41, 2237–2255. [Google Scholar] [CrossRef] [PubMed]
- El Asri, R.; Baxter, D. Process control in municipal solid waste incinerators: Survey and assessment. Waste Manag. Res. 2004, 22, 177–185. [Google Scholar] [CrossRef]
- Zhu, L.; Ji, J.; Liu, J.; Mine, S.; Matsuoka, M.; Zhang, J.; Xing, M. Designing 3D-MoS2 sponge as excellent cocatalysts in advanced oxidation processes for pollutant control. Angew. Chem. 2020, 132, 14072–14080. [Google Scholar] [CrossRef]
- Hassan, S.I.; Alam, M.M.; Illahi, U.; Al Ghamdi, M.A.; Almotiri, S.H.; Su’ud, M.M. A systematic review on monitoring and advanced control strategies in smart agriculture. IEEE Access 2021, 9, 32517–32548. [Google Scholar] [CrossRef]
- Nian, R.; Liu, J.; Huang, B. A review on reinforcement learning: Introduction and applications in industrial process control. Comput. Chem. Eng. 2020, 139, 106886. [Google Scholar] [CrossRef]
- Yao, Y.; Shekhar, D.K. State of the art review on model predictive control (MPC) in Heating Ventilation and Air-conditioning (HVAC) field. Build. Environ. 2021, 200, 107952. [Google Scholar] [CrossRef]
- Arroyo, J.; Manna, C.; Spiessens, F.; Helsen, L. Reinforced model predictive control (RL-MPC) for building energy management. Appl. Energy 2022, 309, 118346. [Google Scholar] [CrossRef]
- Ramasamy, V.; Kannan, R.; Muralidharan, G.; Sidharthan, R.K.; Veerasamy, G.; Venkatesh, S.; Amirtharajan, R. A comprehensive review on Advanced Process Control of cement kiln process with the focus on MPC tuning strategies. J. Process Control 2023, 121, 85–102. [Google Scholar] [CrossRef]
- Tamashiro, K.; Omine, E.; Krishnan, N.; Mikhaylov, A.; Hemeida, A.M.; Senjyu, T. Optimal components capacity based multi-objective optimization and optimal scheduling based MPC-optimization algorithm in smart apartment buildings. Energy Build. 2023, 278, 112616. [Google Scholar] [CrossRef]
- Zhang, J.; Li, Q.; Chen, D. Integrated adaptive cruise control with weight coefficient self-tuning strategy. Appl. Sci. 2018, 8, 978. [Google Scholar] [CrossRef]
- Moharana, P.K.; Guruprasath, M.; Ramkumar, K.; Gomathi, V.; Balasubramanian, G. A comparison study on residuals and estimated residuals in Spyrometer of a cement rotary kiln using Kalman Filter Estimation. In Proceedings of the 2015 International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM), Chennai, India, 6–8 May 2015; IEEE: New York, NY, USA, 2015; pp. 650–658. [Google Scholar]
- Schwenzer, M.; Ay, M.; Bergs, T.; Abel, D. Review on model predictive control: An engineering perspective. Int. J. Adv. Manuf. Technol. 2021, 117, 1327–1349. [Google Scholar] [CrossRef]
- Ryu, C.; Shin, D.; Choi, S. Effect of fuel layer mixing in waste bed combustion. Adv. Environ. Res. 2001, 5, 259–267. [Google Scholar] [CrossRef]
- Wurzenberger, J.C.; Wallner, S.; Raupenstrauch, H.; Khinast, J.G. Thermal conversion of biomass: Comprehensive reactor and particle modeling. AIChE J. 2002, 48, 2398–2411. [Google Scholar] [CrossRef]
- Di Blasi, C. Modeling and simulation of combustion processes of charring and non-charring solid fuels. Prog. Energy Combust. Sci. 1993, 19, 71–104. [Google Scholar] [CrossRef]
- Bardi, S.; Astolfi, A. Modeling and control of a waste-to-energy plant [applications of control]. IEEE Control Syst. Mag. 2010, 30, 27–37. [Google Scholar]
- Sun, B.; Zhang, B.; Yang, C.H.; Gui, W.H. Discussion on modeling and optimal control of nonferrous metallurgical purification process. Acta Autom. Sin 2017, 43, 880–892. [Google Scholar]
- Van Kessel, L.B.M.; Leskens, M.; Brem, G. On-line calorific value sensor and validation of dynamic models applied to municipal solid waste combustion. Process Saf. Environ. Prot. 2002, 80, 245–255. [Google Scholar] [CrossRef]
- Leskens, M.; Van Kessel, L.B.M.; Van den Hof, P.M.J. MIMO closed-loop identification of an MSW incinerator. Control Eng. Pract. 2002, 10, 315–326. [Google Scholar] [CrossRef]
- di Capaci, R.B.; Pannocchia, G.; Dal Pozzo, A.; Antonioni, G.; Cozzani, V. Data-driven models for advanced control of acid gas treatment in waste-to-energy plants. IFAC-PapersOnLine 2022, 55, 869–874. [Google Scholar] [CrossRef]
- Leskens, M.; van Kessel, L.; Bosgra, O.H. Model predictive control as a tool for improving the process operation of MSW combustion plants. Waste Manag. 2005, 25, 788–798. [Google Scholar] [CrossRef] [PubMed]
- Qin, Y.F.; Bai, Y.; Shen, Z.L.; Zhang, K.M. Design of combustion control system for urban waste-to-energy incineration plants. In Proceedings of the 2008 International Conference on Intelligent Computing Technology and Automation (ICICTA), Changsha, China, 20–22 October 2008; pp. 341–344. [Google Scholar] [CrossRef]
- Cho, J.; Kim, Y.; Song, J.; Lee, T.K.; Song, H.H. Design of dynamic plant model and model-based controller for a heat recovery system with a swirling flow incinerator. Energy 2018, 147, 1016–1029. [Google Scholar] [CrossRef]
- Wang, T.; Tang, J.; Xia, H. Key controlled variable model of MSWI process based on ensembled decision tree algorithm. In Proceedings of the 2021 China Automation Congress (CAC), Beijing, China, 22–24 October 2021; IEEE: New York, NY, USA, 2021; pp. 5038–5043. [Google Scholar]
- Chen, J.; Tang, J.; Xia, H.; Wang, D.; Wang, T.; Xu, W. Cascade transfer function models of MSWI process based on weight adaptive particle swarm optimization. In Proceedings of the 2021 China Automation Congress (CAC), Beijing, China, 22–24 October 2021; IEEE: New York, NY, USA, 2021; pp. 5553–5558. [Google Scholar]
- Wang, T.; Tang, J.; Xia, H.; Qiao, J. Hardware-in-the-loop simulation platform of loop control for municipal solid waste incineration process. J. Syst. Simul. 2023, 35, 241–253. [Google Scholar] [CrossRef]
- Xia, H.; Tang, J.; Wang, T.; Tian, H.; Cui, C.; Xu, W. Interpretable controlled object model of furnace temperature for MSWI process based on a novle linear regression decision tree. In Proceedings of the 2023 35th Chinese Control and Decision Conference (CCDC), Yichang, China, 20–22 May 2023; IEEE: New York, NY, USA, 2023; pp. 325–330. [Google Scholar] [CrossRef]
- Wang, T.; Tang, J.; Xia, H. Multiple Input Mulitple Output Control Method Based on Single Neuron Adaptive PID for Municipal Solid Waste Incineration Process. In Proceedings of the 2023 35th Chinese Control and Decision Conference (CCDC), Yichang, China, 20–22 May 2023; IEEE: New York, NY, USA, 2023; pp. 331–336. [Google Scholar] [CrossRef]
- Ding, H.; Tang, J.; Qiao, J. Dynamic modeling of multi-input and multi-output controlled object for municipal solid waste incineration process. Appl. Energy 2023, 339, 120982. [Google Scholar] [CrossRef]
- He, H.J.; Meng, X.; Tang, J.; Qiao, J. Research on Modeling and Control of Furnace Temperature for Municipal Solid Waste Incineration Process. Control Eng. 2023, 30, 1852–1862. (In Chinese) [Google Scholar] [CrossRef]
- Wang, T.Z.; Tang, J.; Xia, H.; Qiao, J.F. Data driven modeling of mswi whole process based on XGBoost serial and parallel ensemble. Comput. Integr. Manuf. Syst. 2024, 29, 1–20. (In Chinese) [Google Scholar]
- Wu, X.; Wang, M.; Liao, P.; Shen, J.; Li, Y. Solvent-based post-combustion CO2 capture for power plants: A critical review and perspective on dynamic modelling, system identification, process control and flexible operation. Appl. Energy 2020, 257, 113941. [Google Scholar] [CrossRef]
- Aliramezani, M.; Koch, C.R.; Shahbakhti, M. Modeling, diagnostics, optimization, and control of internal combustion engines via modern machine learning techniques: A review and future directions. Prog. Energy Combust. Sci. 2022, 88, 100967. [Google Scholar] [CrossRef]
- Fezzani, D.; Piquet, H.; Foch, H. Expert system for the CAD in power electronics-application to UPS. IEEE Trans. Power Electron. 1997, 12, 578–586. [Google Scholar] [CrossRef]
- Wu, B.P. The application of expert systems to process control. JOM 1991, 43, 13–16. [Google Scholar] [CrossRef]
- Yaghmai, N.S.; Maxin, J.A. Expert systems: A tutorial. J. Am. Soc. Inf. Sci. 1984, 35, 297–305. [Google Scholar] [CrossRef]
- Partridge, D. The scope and limitations of first generation expert systems. Future Gener. Comput. Syst. 1987, 3, 1–10. [Google Scholar] [CrossRef]
- Chen, J.; Sun, D.; Zhao, M.; Li, Y.; Liu, Z. A new lane keeping method based on human-simulated intelligent control. IEEE Trans. Intell. Transp. Syst. 2021, 23, 7058–7069. [Google Scholar] [CrossRef]
- Zhu, H.H.; Chen, D. Research of Humanoid Intelligent Control System Based on Knowledge Driving. In Proceedings of the 2006 1ST IEEE Conference on Industrial Electronics and Applications, Singapore, 24–26 May 2006; IEEE: New York, NY, USA, 2006; pp. 1–4. [Google Scholar]
- Li, H.J. Application research on intelligent control strategy for municipal solid waste incineration. J. Chongqing Univ. Technol. Nat. Sci. 2019, 33, 64–68. (In Chinese) [Google Scholar]
- Ju, Y. Intelligent Optimization Control Strategy for Secondary Pollution of Flue Gas in Municipal Solid Waste Incineration. Wirel. Commun. Mob. Comput. 2022, 2022, 6125760. [Google Scholar] [CrossRef]
- Ni, Y.M.; Li, L. Garbage incineration and intelligent fusion strategy of secondary pollution control. Adv. Mater. Res. 2014, 853, 323–328. [Google Scholar] [CrossRef]
- Xiao, Q.; Xu, H. Algorithm for human-simulated intelligent temperature control of incinerator combustion process of urban household garbage. CAAI Trans. Intell. Syst. 2015, 10, 881–885. (In Chinese) [Google Scholar]
- Wu, Q. Application study of PSO improving based intelligent algorithm in incineration pollution control. J. Chongqing Univ. Technol. Nat. Sci. 2018, 12, 133–138. (In Chinese) [Google Scholar]
- Kawamura, K.; Gordon, S.M.; Ratanaswasd, P.; Erdemir, E.; Hall, J.F. Implementation of cognitive control for a humanoid robot. Int. J. Humanoid Robot. 2008, 5, 547–586. [Google Scholar] [CrossRef]
- Kumar, P.; Nagapushpa, K.; Raj, V.; Kumar, V.; Kumaraswamy, R. Multi-functional intelligent humanoid using speech processing. In Proceedings of the 2017 International Conference on Communication and Signal Processing (ICCSP), Tamilnadu, India, 6–8 April 2017; IEEE: New York, NY, USA, 2017; pp. 0244–0248. [Google Scholar]
- Sharma, S.; Obaid, A.J. Mathematical modelling, analysis and design of fuzzy logic controller for the control of ventilation systems using MATLAB fuzzy logic toolbox. J. Interdiscip. Math. 2020, 23, 843–849. [Google Scholar] [CrossRef]
- Gopi, B.; Ramesh, G.; Logeshwaran, J. The fuzzy logical controller based energy storage and conservation model to achieve maximum energy efficiency in modern 5g communication. ICTACT J. Commun. Technol. 2022, 13, 2774–2779. [Google Scholar]
- Chen, C.T.; Peng, S.T. Intelligent process control using neural fuzzy techniques. J. Process Control 1999, 9, 493–503. [Google Scholar] [CrossRef]
- Davis, A.J.; Salameh, Z.M. Fuzzy logic modeling of a grid-connected wind/photovoltaic system with battery storage. In Proceedings of the 2004 Large Engineering Systems Conference on Power Engineering (IEEE Cat. No. 04EX819), Halifax, NS, Canada, 28–30 July 2004; IEEE: New York, NY, USA, 2004; pp. 129–135. [Google Scholar] [CrossRef]
- Sellitto, M.A.; Balugani, E.; Gamberini, R.; Rimini, B. A fuzzy logic control application to the cement industry. IFAC-PapersOnLine 2018, 51, 1542–1547. [Google Scholar] [CrossRef]
- Nguyen, A.T.; Taniguchi, T.; Eciolaza, L.; Campos, V.; Palhares, R.; Sugeno, M. Fuzzy control systems: Past, present and future. IEEE Comput. Intell. Mag. 2019, 14, 56–68. [Google Scholar] [CrossRef]
- Wu, D.R.; Zeng, Z.G.; Mo, H.; Wang, F.Y. Interval type-2 fuzzy sets and systems: Overview and outlook. ACTA Autom. Sin. 2020, 46, 1539–1556. [Google Scholar] [CrossRef]
- Tavoosi, J.; Mohammadzadeh, A.; Jermsittiparsert, K. A review on type-2 fuzzy neural networks for system identification. Soft Comput. 2021, 25, 7197–7212. [Google Scholar] [CrossRef]
- Takagi, T.; Sugeno, M. Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst. Man Cybern. 1985, 1, 116–132. [Google Scholar] [CrossRef]
- Tian, Y.; Chu, Z.; Ma, G. Fuzzy logic control theory in clinical anesthesia. Expert Syst. 2022, 39, e12761. [Google Scholar] [CrossRef]
- Wu, D.; Mendel, J.M. Recommendations on designing practical interval type-2 fuzzy systems. Eng. Appl. Artif. Intell. 2019, 85, 182–193. [Google Scholar] [CrossRef]
- Wu, D. Twelve considerations in choosing between Gaussian and trapezoidal membership functions in interval type-2 fuzzy logic controllers. In Proceedings of the 2012 IEEE International Conference on Fuzzy Systems, Brisbane, Australia, 10–15 June 2012; IEEE: New York, NY, USA, 2012; pp. 1–8. [Google Scholar]
- Qian, D.Q.; Sun, Z.F. An intelligent control system for waste incineration. Inf. Control 1993, 22, 374–377. (In Chinese) [Google Scholar]
- Shen, K.; Lu, J.; Chang, P.; Li, Z.; Liu, G. Application of adaptive fuzzy control method in combustion Temperature Process Control System of Incinerator. Power Eng. 2004, 2004, 366–369. (In Chinese) [Google Scholar]
- Shen, K.; Lu, J.; Li, Z.; Liu, G. An adaptive fuzzy approach for the incineration temperature control process. Fuel 2005, 84, 1144–1150. [Google Scholar] [CrossRef]
- Chang, P.; Lu, J.D.; Shen, K.; Li, Z.H. Research on the adaptive control method of furnace temperature weighted factor for waste incineration boilers. Boil. Technol. 2004, 35, 77–81. (In Chinese) [Google Scholar]
- Cervantes, J.; Yu, W.; Salazar, S.; Chairez, I. Time-varying output-based Takagi–Sugeno fuzzy controller of uncertain nonlinear systems. Int. J. Syst. Sci. 2020, 51, 1495–1510. [Google Scholar] [CrossRef]
- Han, H.; Sun, C.; Wu, X.; Yang, H.; Qiao, J. Training fuzzy neural network via multiobjective optimization for nonlinear systems identification. IEEE Trans. Fuzzy Syst. 2021, 30, 3574–3588. [Google Scholar] [CrossRef]
- Usuga Cadavid, J.P.; Lamouri, S.; Grabot, B.; Pellerin, R.; Fortin, A. Machine learning applied in production planning and control: A state-of-the-art in the era of industry 4.0. J. Intell. Manuf. 2020, 31, 1531–1558. [Google Scholar] [CrossRef]
- Norouzi, A.; Heidarifar, H.; Borhan, H.; Shahbakhti, M.; Koch, C.R. Integrating machine learning and model predictive control for automotive applications: A review and future directions. Eng. Appl. Artif. Intell. 2023, 120, 105878. [Google Scholar] [CrossRef]
- Yang, S.; Wan, M.P.; Chen, W.; Ng, B.F.; Dubey, S. Model predictive control with adaptive machine-learning-based model for building energy efficiency and comfort optimization. Appl. Energy 2020, 271, 115147. [Google Scholar] [CrossRef]
- Liu, Q.; Li, D.; Ge, S.S.; Ji, R.; Ouyang, Z.; Tee, K.P. Adaptive bias RBF neural network control for a robotic manipulator. Neurocomputing 2021, 447, 213–223. [Google Scholar] [CrossRef]
- Liu, Y.J.; Zhao, W.; Liu, L.; Li, D.; Tong, S.; Chen, C.P. Adaptive neural network control for a class of nonlinear systems with function constraints on states. IEEE Trans. Neural Netw. Learn. Syst. 2021, 34, 2732–2741. [Google Scholar] [CrossRef] [PubMed]
- You, M. Application of BP Neural Network in Acid Gas Emission Control of Waste Incineration Power Plant. Autom. Appl. 2024, 65, 35–37. (In Chinese) [Google Scholar] [CrossRef]
- Luo, D.B.; Yang, C.H.; Zhou, D.; Ma, W.Q. Research on improved BP neural network PID control for waste incineration based on particle swarm optimization. Electron. Mak. 2023, 31, 114–117. (In Chinese) [Google Scholar]
- Liang, Y.; Li, S.; Yan, C.; Li, M.; Jiang, C. Explaining the black-box model: A survey of local interpretation methods for deep neural networks. Neurocomputing 2021, 419, 168–182. [Google Scholar] [CrossRef]
- Buhrmester, V.; Münch, D.; Arens, M. Analysis of explainers of black box deep neural networks for computer vision: A survey. Mach. Learn. Knowl. Extr. 2021, 3, 966–989. [Google Scholar] [CrossRef]
- Lee, S.C.; Lee, E.T. Fuzzy neural networks. Math. Biosci. 1975, 23, 151–177. [Google Scholar] [CrossRef]
- Dote, Y. Fuzzy and neural network controller. In Proceedings of the IECON’90: 16th Annual Conference of IEEE Industrial Electronics Society, Pacific Grove, CA, USA, 27–30 November 1990; pp. 1314–1343. [Google Scholar]
- Yao, Q. Adaptive fuzzy neural network control for a space manipulator in the presence of output constraints and input nonlinearities. Adv. Space Res. 2021, 67, 1830–1843. [Google Scholar] [CrossRef]
- Fei, J.; Chen, Y.; Liu, L.; Fang, Y. Fuzzy multiple hidden layer recurrent neural control of nonlinear system using terminal sliding-mode controller. IEEE Trans. Cybern. 2021, 52, 9519–9534. [Google Scholar] [CrossRef]
- Berenji, H.R. A reinforcement learning—Based architecture for fuzzy logic control. Int. J. Approx. Reason. 1992, 6, 267–292. [Google Scholar] [CrossRef]
- Tian, H.; Tang, J.; Xia, H.; Wang, T.; Cui, C.; Pan, X. Furnace Temperature Control based on Adaptive TS-FNN for Municipal Solid Waste Incineration Process. In Proceedings of the 2023 35th Chinese Control and Decision Conference (CCDC), Yichang, China, 20–22 May 2023; IEEE: New York, NY, USA, 2023; pp. 360–365. [Google Scholar] [CrossRef]
- He, H.; Meng, X.; Tang, J.; Qiao, J. Event-triggered-based self-organizing fuzzy neural network control for the municipal solid waste incineration process. Sci. China Technol. Sci. 2023, 66, 1096–1109. [Google Scholar] [CrossRef]
- Ding, H.; Qiao, J.; Huang, W.; Yu, T. Event-triggered online learning fuzzy-neural robust control for furnace temperature in municipal solid waste incineration process. IEEE Trans. Autom. Sci. Eng. 2023, 21, 1201–1213. [Google Scholar] [CrossRef]
- Tang, J.; Tian, H.; Xia, H.; Qiao, J.F. Interval Type-II FNN-based furnace temperature control for municipal solid waste incineration (MSWI) process. J. Beijing Univ. Technol. 2023, 51, 1–11. (In Chinese) [Google Scholar]
- Ding, H.; Tang, J.; Qiao, J. Data-driven modeling and self-organizing control of municipal solid waste incineration process. Acta Autom. Sin. 2023, 49, 550–566. [Google Scholar] [CrossRef]
- Ding, H.; Qiao, J.; Huang, W.; Yu, T. Event-triggered fuzzy neural multivariable control for a municipal solid waste incineration process. Sci. China Technol. Sci. 2023, 66, 3115–3128. [Google Scholar] [CrossRef]
- Ding, H.; Qiao, J.; Huang, W.; Yu, T. Cooperative Event-Triggered Fuzzy-Neural Multivariable Control with Multi-Task Learning for Municipal Solid Waste Incineration Process. IEEE Trans. Ind. Inform. 2023, 20, 765–774. [Google Scholar] [CrossRef]
- Borase, R.P.; Maghade, D.K.; Sondkar, S.Y.; Pawar, S.N. A review of PID control, tuning methods and applications. Int. J. Dyn. Control 2021, 9, 818–827. [Google Scholar] [CrossRef]
- Liu, N.; Chai, T.Y. An optimal tuning method of PID controller parameters. Acta Autom. Sin-Ica 2023, 49, 2272–2285. [Google Scholar] [CrossRef]
- Chai, T.Y.; Zhou, Z.; Zheng, R.; Liu, N.; Jia, Y. PID tuning intelligent system based on end-edge-cloud collaboration. ACTA Autom. Sin. 2023, 49, 514–526. [Google Scholar] [CrossRef]
- El-Gendy, E.M.; Saafan, M.M.; Elksas, M.S.; Saraya, S.F.; Areed, F.F. Applying hybrid genetic–PSO technique for tuning an adaptive PID controller used in a chemical process. Soft Comput. 2020, 24, 3455–3474. [Google Scholar] [CrossRef]
- Somefun, O.A.; Akingbade, K.; Dahunsi, F. The dilemma of PID tuning. Annu. Rev. Control 2021, 52, 65–74. [Google Scholar] [CrossRef]
- He, H.J.; Meng, X.; Tang, J.; Qiao, J.F. ET–RBF–PID-based control method for furnace temperature of municipal waste incineration process. Control Theory Appl. 2022, 39, 2262–2273. [Google Scholar]
- Dai, S.; Zhang, W.; Zhang, X.H. Fuzzy control and simulation of waste incineration furnace combustion system. Sci. Technol. Innov. 2019, 22, 21–22. (In Chinese) [Google Scholar]
- Ding, H.; Tang, J.; Qiao, J. MIMO modeling and multi-loop control based on neural network for municipal solid waste incineration. Control Eng. Pract. 2022, 127, 105280. [Google Scholar] [CrossRef]
- Zeng, J.S.; Gao, C.H.; Su, H.Y. Data-driven predictive control for blast furnace ironmaking process. Comput. Chem. Eng. 2010, 34, 1854–1862. [Google Scholar] [CrossRef]
- Han, H.G.; Zhang, L.; Qiao, J.F. Data-based predictive control for wastewater treatment process. IEEE Access 2017, 6, 1498–1512. [Google Scholar] [CrossRef]
- Han, H.; Liu, Z.; Hou, Y.; Qiao, J. Data-driven multiobjective predictive control for wastewater treatment process. IEEE Trans. Ind. Inform. 2019, 16, 2767–2775. [Google Scholar] [CrossRef]
- Li, M.; Zhou, P.; Wang, L.; Yuan, Y. Data-driven multiobjective predictive optimal control of refining process with non-Gaussian stochastic distribution dynamics. IEEE Trans. Ind. Inform. 2021, 17, 7269–7278. [Google Scholar] [CrossRef]
- Haverbeke, N.; Van Herpe, T.; Diehl, M.; Van den Berghe, G.; De Moor, B. Nonlinear model predictive control with moving horizon state and disturbance estimation-application to the normalization of blood glucose in the critically ill. IFAC Proc. Vol. 2008, 41, 9069–9074. [Google Scholar] [CrossRef]
- Leskens, M.; van der Linden, R.J.P.; van Kessel, R.L.; Bosgra, O.H.; Van den Hof, P.M.J. Nonlinear model predictive control of municipal solid waste combustion plants. In Proceedings of the International Workshopon Assessment & Future Directions of NMPC, Pavia, Italy, 5–9 September 2008. [Google Scholar]
- Piga, D.; Forgione, M.; Formentin, S.; Bemporad, A. Performance-oriented model learning for data-driven MPC design. IEEE Control Syst. Lett. 2019, 3, 577–582. [Google Scholar] [CrossRef]
- Yu, K.; Zhuo, F.; Wang, F.; Jiang, X.; Gou, Y. MPC-based startup current shaping strategy with state-space model of DAB in DC distribution system. IEEE J. Emerg. Sel. Top. Power Electron. 2021, 10, 4073–4089. [Google Scholar] [CrossRef]
- Ławryńczuk, M. MPC Algorithms Using State-Space Wiener Models. In Nonlinear Predictive Control Using Wiener Models: Computationally Efficient Approaches for Polynomial and Neural Structures; Springer: Berlin/Heidelberg, Germany, 2022; pp. 285–308. [Google Scholar]
- Zhang, J.; Zhou, Y.; Li, Y.; Hou, G.; Fang, F. Generalized predictive control applied in waste heat recovery power plants. Appl. Energy 2013, 102, 320–326. [Google Scholar] [CrossRef]
- Zimmerman, N.; Kyprianidis, K.; Lindberg, C.F. Waste fuel combustion: Dynamic modeling and control. Processes 2018, 6, 222. [Google Scholar] [CrossRef]
- Meng, X.; Peng, Y.H.; Sun, Z.J.; Qiao, J.F. Model predictive control of furnace temperature in the incineration process of urban solid waste. In Proceedings of the 34th Chinese Conference on Process Control 2023, Hefei, China, 15–17 August 2022; pp. 1–11. (In Chinese). [Google Scholar]
- di Capaci, R.B.; Vaccari, M.; Pannocchia, G. Enhancing sustainability of acid gas treatment in a Waste-to-Energy plant via Model Predictive Control. J. Clean. Prod. 2023, 410, 137222. [Google Scholar] [CrossRef]
- Qiao, J.; Sun, J.; Meng, X. Event-triggered adaptive model predictive control of oxygen content for municipal solid waste incineration process. IEEE Trans. Autom. Sci. Eng. 2022, 21, 463–474. [Google Scholar] [CrossRef]
- Sun, J.; Meng, X.; Qiao, J. Data-driven optimal control for municipal solid waste incineration process. IEEE Trans. Ind. Inform. 2023, 19, 11444–11454. [Google Scholar] [CrossRef]
- Wang, R.; Yan, A. R-SCN based Model Predictive Control for NOx Emissions of MSWI Process. In Proceedings of the 2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), Yibin, China, 22–24 September 2023; IEEE: New York, NY, USA, 2023; pp. 1–5. [Google Scholar]
- Hu, K.C.; Yan, A.J.; Wang, D.H. Nonlinear model predictive control of furnace temperature in the incineration process of urban solid waste. Control. Theory Appl. 2024, 40, 1–9. (In Chinese) [Google Scholar]
- Sun, J.; Meng, X.; Qiao, J.F. Data-driven predictive control of oxygen content in flue gas for municipal solid waste incineration process. Control Theory Appl. 2024, 41, 484–495. (In Chinese) [Google Scholar]
- Sun, J.; Meng, X.; Qiao, J. Adaptive Predictive Control of Oxygen Content in Flue Gas for Municipal Solid Waste Incineration Process. Acta Autom. Sin 2023, 49, 2338–2349. [Google Scholar] [CrossRef]
- Wang, B.; Tang, J.; Xia, H.; Tian, H.; Wang, T.; Wu, Z. Furnace Temperature Control for MSWI Process Based on Neural Network Predictive Controller. In Proceedings of the 2024 36th Chinese Control and Decision Conference (CCDC), Xi’an, China, 25–27 May 2024; IEEE: New York, NY, USA, 2024; pp. 1731–1736. [Google Scholar]
- Zhou, P.; Guo, D.; Chai, T. Data-driven predictive control of molten iron quality in blast furnace ironmaking using multi-output LS-SVR based inverse system identification. Neurocomputing 2018, 308, 101–110. [Google Scholar] [CrossRef]
- Xie, S.; Xie, Y.; Huang, T.; Gui, W. Multiobjective-based optimization and control for iron removal process under dynamic environment. IEEE Trans. Ind. Inform. 2020, 17, 569–577. [Google Scholar] [CrossRef]
Prediction Model | Parameter Update | Structure Update | Performance Improvement | Author/Year | Reference |
---|---|---|---|---|---|
IT2FNN | GD | √ | · Dynamic optimization of model parameters and adjustment of network structure · Designed to improve prediction effects, enhance anti-interference capabilities, and ensure the closed-loop stability of the control system | Tang et al., 2023 | [158] |
SOFNN | LM algorithm | √ | · Obtained a prediction model with streamlined structure and higher prediction accuracy · Enhancing control precision and robustness | Meng et al., 2023 | [181] |
Improve LSTM | Back propagation algorithm | √ | · Self-organization of network structure based on neuron activity and importance | Sun et al., 2023 | [184] |
Improved RWNN | Recursive least squares | √ | · Online update of RWNN hidden layer output weights using recursive least squares to adapt to the dynamic characteristics of the MSWI process · Selection of the hidden layer neurons number through a supervisory mechanism to improve model generalization | Wang et al., 2023 | [185] |
RWNN | Recursive least squares | \ | · Improved the accuracy of the furnace temperature prediction model, adapting to the time-varying characteristics | Hu et al., 2023 | [186] |
LSTM | GD | √ | · Used adaptive fuzzy C-means algorithm to assist in determining RBF network parameters and online updating | Sun et al., 2023 | [187] |
RBFNN | GD | \ | · Improved the accuracy of the prediction model, enhancing the adaptability and stability of the control method | Sun et al., 2023 | [188] |
BPNN | GD | \ | · Real-time adjustment of BPNN weight parameters to improve tracking performance and interference capability | Wang et al., 2023 | [189] |
Improved LSTM | Backpropagation through time algorithm | √ | · Dynamic parameter adjustment based on prediction error and model parameter changes · Optimization of network structure through self-organizing methods and algorithms to enhance the adaptability of complex industrial dynamics | Qiao et al., 2024 | [183] |
Manipulated Variable | Rolling Optimization Method | Prediction Horizon | Control Horizon | Performance | Author/Year | Reference |
---|---|---|---|---|---|---|
MSW feed rate, grate speed, primary air flow, secondary air flow | Quadratic programming (QP) | \ | \ | Improved performance compared to conventional MSWI control systems, especially in handling large temporary disturbances | Leskens et al., 2005 | [97] |
MSW feed rate, grate speed, primary air flow, secondary air flow | QP, state estimation | 5 | 5 | Demonstrated good closed-loop performance | Leskens et al., 2005 | [174] |
MSW feed rate, grate speed, primary air flow, secondary air flow | Gradient method, sequential quadratic programming (SQP) | \ | \ | Demonstrated better disturbance rejection capabilities compared to multivariable PID and linear MPC | Leskens et al., 2008 | [175] |
Throttle valve position, pump speed, flu gas speed, air flow speed | System identification, QP | 10 | 10 | Achieved good transient and steady-state responses, as well as decoupling performance and satisfactory disturbance rejection | Zhang et al., 2013 | [179] |
MSW feed rate, feedwater, primary air flow, secondary air flow | QP, feedforward Control | \ | \ | MPC with feedforward provided superior performance, maintaining stable temperatures throughout the process | Nathan et al., 2018 | [180] |
Drying grate speed, combustion grate 1 speed, combustion grate 2 speed | Improved seagull optimization algorithm | 3 | 2 | Achieved smooth control of furnace temperature in the MSWI process | Hu et al., 2022 | [186] |
Secondary air flow | GD | 8 | 1 | Demonstrated effective control of furnace temperature, ensuring safe and stable operation and reducing pollutant emissions in flue gas | Tang et al., 2023 | [158] |
\ | GD | \ | \ | Validated by actual industrial data, the proposed method is effective and excellent in furnace temperature prediction modeling and control | Meng et al., 2023 | [181] |
Drying grate primary air flow, combustion grate 1 primary air flow, secondary air flow | Adaptive mutation PSO, GD | 1–5 | 1–5 | Achieved promising tracking control performance for flue gas oxygen content and improved operational performance | Sun et al., 2023 | [184] |
Drying grate 1 primary air flow, combustion grate 1 primary air flow, secondary air flow | PSO | 5 | 1 | Exhibited small control errors and good tracking performance | Wang et al., 2023 | [185] |
Primary air flow and combustion grate 1 section primary flow | GD | 5 | 1 | Demonstrated better control performance in flue gas oxygen content control, with fast response and stable performance | Sun et al., 2023 | [187] |
Drying grate primary air flow, combustion grate 1 primary air flow, secondary air flow | GD | 5 | 1 | Demonstrated better adaptability and disturbance rejection capabilities, achieving more precise control of flue gas oxygen content | Sun et al., 2023 | [188] |
Secondary air flow | GD | \ | \ | Demonstrated the effectiveness of the method, showing enhanced tracking and anti-interference capabilities | Wang et al., 2023 | [189] |
Drying grate, primary air flow, combustion grate 1 primary air flow | Event-triggered GD | 5 | 1 | Achieved satisfactory tracking control performance with fewer triggering events | Qiao et al., 2024 | [183] |
Whether Feedback Correction | Methods | Formula | Author/Year | Reference |
---|---|---|---|---|
Not explicitly mentioned | \ | \ | Leskens, et al., 2005 | [97] |
Not explicitly mentioned | \ | \ | Leskens, et al., 2005 | [174] |
Not explicitly mentioned | \ | \ | Leskens et al., 2008 | [175] |
Not explicitly mentioned | \ | \ | Zhang et al., 2013 | [179] |
Not explicitly mentioned | \ | \ | Nathan, et al., 2018 | [180] |
√ | Error Feedback | Hu et al., 2022 | [186] | |
√ | Error Feedback | Tang, et al., 2023 | [158] | |
Not explicitly mentioned | \ | \ | Meng et al., 2023 | [181] |
√ | Error Feedback | Sun et al., 2023 | [184] | |
√ | Error Feedback | Wang et al., 2023 | [185] | |
√ | Error Feedback | Sun, et al., 2023 | [187] | |
√ | Error Feedback | Sun, et al., 2023 | [188] | |
√ | Error Feedback | Wang et al., 2023 | [189] | |
√ | LSTM, PSO | Refer to Part IV of the reference for details | Qiao, et al., 2024 | [183] |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Tang, J.; Tian, H.; Wang, T. A Review of Model Predictive Control for the Municipal Solid Waste Incineration Process. Sustainability 2024, 16, 7650. https://doi.org/10.3390/su16177650
Tang J, Tian H, Wang T. A Review of Model Predictive Control for the Municipal Solid Waste Incineration Process. Sustainability. 2024; 16(17):7650. https://doi.org/10.3390/su16177650
Chicago/Turabian StyleTang, Jian, Hao Tian, and Tianzheng Wang. 2024. "A Review of Model Predictive Control for the Municipal Solid Waste Incineration Process" Sustainability 16, no. 17: 7650. https://doi.org/10.3390/su16177650