Municipal Solid Waste Incineration Process Research Articles

Adaptive multi-objective competitive swarm optimization algorithm based on kinematic analysis for municipal solid waste incineration

Multi-objective optimization for the municipal solid waste incineration process is considered as a valuable technique to improve energy recovery and reduce pollutant emission. However, the complex mechanism analysis and multimodal problem of the municipal solid waste incineration process set challenges for both the modeling and optimization studies. To overcome this problem, an adaptive multi-objective optimization for the municipal solid waste incineration process is proposed in this paper. First, a bi-objective model of the municipal solid waste incineration, the basis for optimization, is established based on mass balance and energy balance, which takes furnace temperature and flue gas oxygen content as decision variables to mathematically deduce the generated heat and exhaust gases. Second, an adaptive multi-objective competitive swarm optimization algorithm is proposed for the optimization of the municipal solid waste incineration process. Two-step competition and multi-strategy learning are designed to provide a clear division of labor for particles and a novel idea for detecting evolutionary environment is proposed based on kinematic analysis of particles. Finally, relevant experiments are conducted on benchmark instances and the municipal solid waste incineration optimization model. The proposed algorithm shows promising convergence, diversity, fastness by comparing with several representative and state-of-the-art algorithms. The proposed algorithm achieves the optimization effects with 4.36% improvement of the available heat for power generation and 4.13% reduction of the exhaust gas assessment in the optimization of the municipal solid waste incineration process.

NOx emissions prediction for MSWI process based on dynamic modular neural network

Accurate prediction of nitrogen oxides (NOx) is crucial for improving the efficiency of denitrification systems in municipal solid waste incineration (MSWI) process. Due to the change of feed composition and operation mode, it is difficult to predict NOx emissions with complex dynamics. For this reason, a dynamic modular neural network (DMNN) is proposed for NOx emissions prediction in MSWI process. First, a principal component analysis (PCA)-based dynamic task decomposition method is proposed, and then the original task with time varying characteristic is divided into several sub-tasks for effectual handing. Next, an adaptive long short-term memory (ALSTM) network is designed driven by the corresponding sub-task. Then, the nonlinearity between dominant variables and NOx value is learned to guarantee the prediction accuracy. Finally, the merits of proposed DMNN are confirmed on a benchmark and real industrial data of a MSWI process. The experimental results further demonstrate the superiority and potential of DMNN for industrial applications.

Multi-condition operational optimization with adaptive knowledge transfer for municipal solid waste incineration process

To satisfy incineration efficiency and nitrogen oxides ultra-low emission requirements, it is of great importance to improve the operational performance of the municipal solid waste incineration (MSWI) process. However, due to the complex and fluctuating composition of municipal solid waste, it is challenging to guarantee optimal operation under changing conditions. To address this issue, a multi-condition operational optimization method based on adaptive knowledge transfer (MCOO-TR) is proposed for the MSWI process. First, an integrated optimization scheme, where different operation conditions are identified based on cluster analysis and expert knowledge, is developed. Second, a surrogate-assisted multi-objective particle swarm optimization algorithm is designed to solve the data-driven optimization problems for optimal operation, where the surrogate model selection mechanism is proposed to cope with multi-condition characteristics. Third, an adaptive knowledge transfer strategy, corresponding to condition identification, is designed aiming to improve optimization efficiency. Finally, the proposed MCOO-TR method is evaluated based on actual industrial data. The experimental results demonstrate that the MCOO-TR method can achieve satisfactory operational performance under changing operation conditions.

A Non-Manipulated Variable Analysis of Solid-Phase Combustion in the Furnace of Municipal Solid-Waste Incineration Process Based on the Biorthogonal Numerical-Simulation Experiment

The operating conditions of municipal solid waste incineration (MSWI) are influenced by manipulated variables, such as the feed rate, primary air, and grate speed, as well as non-manipulated variables, such as municipal solid waste (MSW) particle size, mixing coefficient, emissivity, moisture content, and the ratio of C to O. Based on the actual data of an MSWI plant in Beijing, a non-manipulated variable single-factor analysis of solid-phase combustion in the furnace was carried out based on the biorthogonal numerical simulation experiment. First, a solid-phase combustion analysis of the MSWI process was performed for non-manipulated variables, with the main non-manipulated variables determined. Then, based on FLIC 2.3c software, the numerical model was established under benchmark operating conditions. Based on the biorthogonal experiment, several groups of numerical model inputs were designed to generate mechanism data in multi-operating conditions. Finally, a multi-condition numerical simulation experiment was used to study solid-phase combustion under different conditions and analyze non-manipulated variables. The simulation results showed that the maximum solid temperature was 1360 K under the benchmark operating condition and ranged from 1120 to 1470 K under five conditions. Large-size particles and large emissivity were beneficial to solid-phase combustion, while high moisture content and a large mixing coefficient weakened combustion. The results provide support for the subsequent optimal control of the whole MSWI process.

Onsite treatment of wastes in municipal waste incinerator: Co-sintering of fly ash and leachate sludge into value-added ceramic granule

The leachate sludge (LS) and fly ash (FA) are the foci of hazardous wastes which generated from the municipal solid waste incineration (MSWI). The current work developed a new way to use energy from MSWI process for the on-site sintering of LS and FA at a relatively low temperature. With the assistance of CaF2, granule of LS and MSWI FA were co-sintered. The influence of temperature, the mass of CaF2, and the mass ratio of LS/MSWI FA were investigated. As a result, heavy metals volatilization and leaching in the form of chlorinated salts were controlled. In addition, CaF2 improved the compressive strength of the granule under low-temperature sintering. Moreover, the scale-up co-sintering test was achieved in an MSWI chamber. The results showed that the optimum condition was sintering at 973K for 1 h. The compressive strength of sintered product reached 4.25 MPa, which met the standard of ceramic granule. Moreover, with the addition of CaF2, the volatilization rate of Pb, Zn, and Cd decreased by 6%, 7%, and 6%, respectively. This method can be a promising technique for the utilization of solid wastes.

Three-Dimensional Numerical Modeling and Analysis for the Municipal Solid-Waste Incineration of the Grate Furnace for Particulate-Matter Generation

A 3D numerical model of the municipal solid waste incineration (MSWI) process was constructed based on a grate furnace with a daily processing capacity of 800 tons. Fluent was used for analyzing key factors affecting the concentration and diffusion level of particulate matter (PM). According to the actual MSWI plant working condition, a 3D model of the incinerator and the waste heat boiler has been constructed under benchmarks. Key factors affecting PM generation were determined by combining mechanistic knowledge and experts’ experience. They were the combustion temperature of solid phase municipal solid waste (MSW), the wall’s PM collision mode, and the second baffle length. Subsequently, the process of resolving the 3D numerical model was delineated. Then, a univariate analysis of the aforementioned 3D model was conducted for the three pivotal factors mentioned above. Conclusively, the effect of the important factors on the number of particles at the outflow of the incinerator was analyzed via orthogonal experiments to obtain the optimal combination. PM concentration initially diminished and then rose with the increased combustion temperature of the solid-phase MSW. Furthermore, a noteworthy reduction in PM concentration was observed when the second baffle length was 12.45–12.95 m. The greatest influence on the PM concentration of the outlet was posed by the wall’s PM collision mode, followed by the second baffle length. The appropriate adjustment of the combustion temperature of the solid-phase MSW, selection of wall materials, and design of the second baffle length were beneficial for diminishing PM concentration and ensuring long-term stable operation of the MSWI process. The combinative optimality of the three key factors was acquired via orthogonal experiments, which proved the subsequent optimal control of PM concentration at the outlet.

Multiblock dynamic enhanced canonical correlation analysis for industrial MSW combustion state monitoring

Combustion state monitoring in industrial municipal solid waste incineration (MSWI) power generation process is very difficult. There are two main reasons for this dilemma: (1) there is a large time lag between the combustion chamber and the steam generation stage; (2) reactions continue to occur and raw waste is fed into the incinerator continuously, so there are strong correlations and remarkable dynamic characteristics among process variables. The complex correlations in time and in space cause difficulties in combustion state monitoring for incinerators. Those correlations are common in industrial processes because the large scale equipment is usually needed and a whole process is realized by equipment in sequence. To overcome those difficulties, a multi-block dynamic enhanced canonical correlation analysis (MBDCCA) method is proposed to help monitor the MSWI process. Firstly, canonical correlation analysis (CCA) is extended to MBDCCA to model the MSWI process. Secondly, a combustion state online monitoring approach is designed based on the proposed MBDCCA algorithm. Finally, the proposed method is applied in an industrial MSWI plant to demonstrate the effectiveness.

Temporal and spatial analysis of anthropogenic mercury and CO2 emissions from municipal solid waste incineration in China: Implications for mercury and climate change mitigation

The contribution of municipal solid waste incineration (MSWI) to anthropogenic mercury and CO2 emissions have become increasingly important over the past decade. This study developed an inventory of anthropogenic mercury emissions and CO2 emissions during the period of 2014–2020, of MSWI process in China using a bottom-up inventory at the plant level. Overall, national MSWI anthropogenic mercury emissions increased from 2014 to 2020 by province. It was estimated that total 8321.09 kg of anthropogenic mercury emissions from 548 MSWI plants were scattered in 31 provinces of mainland China in 2020. The average intensity of mercury emission in China was 0.06 g·t−1 in 2020, which was much lower than the pre-2010 level. Furthermore, the increased CO2 emission generated by MSWI from 2014 to 2020 is 1.97 times. Anthropogenic mercury emissions and CO2 emissions were concentrated mainly in developed coastal provinces and cities. The general uncertainty of national mercury emissions and CO2 emissions was estimated to be −123% to 323% and −130% to 335%, respectively. Furthermore, future emissions were predicted from 2030 to 2060 based on different scenarios of the independent and collaborative effects of control proposals, the results indicate that the enhancement of advanced air pollution control technologies and effective management of MSWI represent pivotal factors in realizing future reductions in CO2 and mercury emissions. The findings will supplement those for mercury and CO2 emissions, and be useful for relevant policy-making and to improve urban air quality, as well as human health.

Time-series prediction using a regularized self-organizing long short-term memory neural network

Industrial process data are naturally in the form of complex time-series with high nonlinearities and dynamics. Long short-term memory (LSTM) networks are suitable for developing prediction models to handle nonlinear and dynamic process. However, LSTM neural networks have typically large and predefined structures, which may result in overfitting, and an optimal hidden neurons for a given problem cannot be automatically obtained. For this reason, a regularized self-organizing LSTM (RSO-LSTM) is proposed to optimize both the structure and the parameters of the network. First, an adaptive learning algorithm based on l2-norm regularization is introduced for parameter adjustment. Thereafter, both the prediction accuracy and weight dispersion are considered to avoid overfitting. Second, a growing strategy is designed based on hidden neuronal sensitivity. The structure of the LSTM can then be determined automatically with improved compactness. Finally, a convergence analysis is performed to ensure the feasibility of RSO-LSTM. To demonstrate the merits of the proposed RSO-LSTM for time-series​ prediction, its results for three benchmark experiments and real industrial data of a municipal solid waste incineration process were examined and compared with those of other methods. The results indicated the superiority and potential of RSO-LSTM for industrial applications.

Dioxin emission modeling using feature selection and simplified DFR with residual error fitting for the grate-based MSWI process

Municipal solid waste incineration (MSWI) with grate technology is a widely applied waste-to-energy process in various cities in China. Meanwhile, dioxins (DXN) are emitted at the stack and are the critical environmental indicator for operation optimization control in the MSWI process. However, constructing a high-precision and fast emission model for DXN emission operation optimization control becomes an immediate difficulty. To address the above problem, this research utilizes a novel DXN emission measurement method using simplified deep forest regression (DFR) with residual error fitting (SDFR-ref). First, the high-dimensional process variables are optimally reduced following the mutual information and significance test. Then, a simplified DFR algorithm is established to infer or predict the nonlinearity between the selected process variables and the DXN emission concentration. Moreover, a gradient enhancement strategy in terms of residual error fitting with a step factor is designed to improve the measurement performance in the layer-by-layer learning process. Finally, an actual DXN dataset from 2009 to 2020 of the MSWI plant in Beijing is utilized to verify the SDFR-ref method. Comparison experiments demonstrate the superiority of the proposed method over other methods in terms of measurement accuracy and time consumption.

Numerical simulation modelling on whole municipal solid waste incineration process by coupling multiple software for the analysis of grate speed and air volume ratio

Uncertainties in the calorific value and composition of municipal solid waste (MSW), the variability in operations, equipment performance decline, maintenance needs, and other dynamic factors make it challenging for operational experts to accurately gauge the overall combustion impact under fluctuating conditions. There is a need for a visual numerical simulation of the full MSW incineration (MSWI) process, with a focus on the effectiveness of the grate speed and air volume ratio. Unfortunately, software like Aspen Plus struggles to visually depict the effect of typically manipulated variables such as grate speed, and it is not well-suited for simulating solid-phase combustion and flue gas treatment. In response to these challenges, our study introduces a numerical simulation method for the MSWI process using a multi-software coupling approach. This simulation method incorporates (a) the use of custom software for simulating solid-phase combustion on the grate, (b) the application of Computational Fluid Dynamics (CFD) software for gas-phase combustion in the furnace, and (c) the utilization of chemical process simulation software for non-grate solid-phase combustion. The benchmark condition was determined using real-time running data from the Beijing MSWI Power Plant. The results show that for the incinerator’s third flue, flue gas G1, and flue gas G3, the relative errors between the simulated and actual values for flue gas temperature and oxygen concentration were 0.3% and 1.1%, 3.5% and 20%, and 0.2% and 20% respectively. In addition, we used eight non-benchmark conditions to study the effects of grate speed and air volume ratio. When the grate speed was gradually increased from 6 m/h to 8 m/h, O2, CO2, sulfur dioxide (SO2), sulfur trioxide(SO3), nitrogen monoxide (NO), and nitrogen oxide (NO2) at G1 had error fluctuation ranges of 5.9%− 48.5%, 2.5%− 11.5%, 10.42–28.57%, 7–23%, 29.10–45.52%, and 24.39–48.78%, respectively. When the primary air ratio was gradually increased from 0.84 to 0.96, the O2 concentration at G1 exhibited a steady upward trend. The simulation results provide insights into the adjustment range of the manipulated variables and offer additional support for the visualized and optimized control operation of the MSWI plant.

Assessment of PCDD/Fs formation and emission characteristics at a municipal solid waste incinerator for one year

Municipal solid waste incineration (MSWI) has become a predominant emission source of polychlorinated dibenzo-p-dioxins and furans (PCDD/Fs). Research focusing on the impact of operating conditions, environmental changes, and operating time on the generation and emissions of PCDD/Fs has not been resolved. To this end, this study tracked and investigated the PCDD/Fs and 17 congener emissions of a typical grate incinerator (800 t/d) continuously for one year. Results showed that the PCDD/Fs concentration at the boiler outlet, stack inlet, and bag filter, including normal and abnormal operation conditions, ranges from 2.11E-02–41.86 ng I-TEQ/Nm3, 7.00E-04–6.76 ng I-TEQ/Nm3, and 1.12–2.90E+03 ng I-TEQ/Nm3, respectively. The 2,3,4,7,8-P5CDF has the highest contribution in all samples, in which a proportion of TEQ ranged from 30 % to 77.73 %. Moreover, by applying the correlation analysis between PCDD/Fs and operating parameters, the emission characteristic is mainly affected by incinerators and boilers during the normal period, and it is affected by the whole MSWI process under abnormal conditions. In addition, the PCDD/Fs emission from the MSWI plant gradually increases from spring to winter. This study is beneficial for supporting the control of PCDD/Fs emission reduction and assisting the operators to optimize the relevant operating parameters of the MSWI plant to achieve a stable and up-to-substandard emissions during the operation period.

Prediction of NO concentration using modular long short-term memory neural network for municipal solid waste incineration

Air pollution control poses a major problem in the implementation of municipal solid waste incineration (MSWI). Accurate prediction of nitrogen oxides (NOx) concentration plays an important role in efficient NOx emission controlling. In this study, a modular long short-term memory (M-LSTM) network is developed to design an efficient prediction model for NOx concentration. First, the fuzzy C means (FCM) algorithm is utilized to divide the task into several sub-tasks, aiming to realize the divide-and-conquer ability for complex task. Second, long short-term memory (LSTM) neural networks are applied to tackle corresponding sub-tasks, which can improve the prediction accuracy of the sub-networks. Third, a cooperative decision strategy is designed to guarantee the generalization performance during the testing or application stage. Finally, after being evaluated by a benchmark simulation, the proposed method is applied to a real MSWI process. And the experimental results demonstrate the considerable prediction ability of the M-LSTM network.

Dynamic modeling of multi-input and multi-output controlled object for municipal solid waste incineration process

Municipal solid waste incineration (MSWI) is an important part of energy recovery and sustainable development, constructing the model of controlled object is the basis of studying its optimal control. MSWI is a typical multi-input and multi-output industrial process, which has many uncertain characteristics such as strong nonlinearity and multivariable coupling. To solve this problem, a model of controlled object in MSWI process is proposed in this paper. Firstly, the MSWI process based on grate incinerator is analyzed to summarize its relevant variables and control requirements. Secondly, the solid phase and gas phase of MSWI process are divided to construct material balance equations of subsystems. Next, the energy transfer process in incinerator is deduced and calculated through thermodynamic laws. Finally, the validity of the model is verified by the process data of a MSWI plant in Beijing, China, which lays a model foundation for the study of the optimal control of MSWI process.

Event-triggered-based self-organizing fuzzy neural network control for the municipal solid waste incineration process

Event-triggered-based self-organizing fuzzy neural network control for the municipal solid waste incineration process

FOPDT Model Based on Experimental Data from Municipal Solid Waste Incineration Process Temperature Control in Fixed Bed

Incineration is the waste combustion at controlled high temperatures which converts waste into flue gas with the main components being CO2 and water. Temperature control aims to ensure safe combustion operation. In this study, the temperature controller uses a two-position temperature controller whose main components include two thermocouples, MAX6675, two burners, blower, Arduino, and one laptop. Temperature controllers were used to maintain the temperature in the combustion chamber and afterburner at the specified setpoint, namely 630oC and 850oC, respectively. To test the performance of the controller, two models were made. The modeling was made using the experimental data obtained. The results show that the model and experimental results are in good agreement.

Heterogeneous feature ensemble modeling with stochastic configuration networks for predicting furnace temperature of a municipal solid waste incineration process

Heterogeneous feature ensemble modeling with stochastic configuration networks for predicting furnace temperature of a municipal solid waste incineration process

Dioxin emission prediction based on improved deep forest regression for municipal solid waste incineration process

Dioxin (DXN) emission concentration is an important environmental indicator in the municipal solid waste incineration (MSWI) process. The prediction model of DXN emission can be used for pollution control to realize actual requirements of operation optimization. Therefore, a DXN emission concentration prediction model based on improved deep forest regression (ImDFR) is proposed in this study. A feature reduction layer based on out-of-bagging error is first introduced into the ImDFR to eliminate redundant variables and feed all confidence information on DXN emission into the feature enhancement layer of the MSWI process. A deep ensemble stacking model is subsequently built to depict deep features and increase diversity and accuracy using random forests, completely random forests, GBDT, and XGBoost as subforests. Finally, the predicted value of the DXN prediction model is determined in the decision layer. The DXN emission prediction model is verified using actual historical data of two incinerators operated with a daily processing capacity of 800 tons. The experimental results showed that the proposed prediction model presents higher accuracy and better generalization ability than state-of-the-art models.

Exergy analysis of municipal solid waste incineration processes: The use of O2-enriched air and the oxy-combustion process

This work provides a complete exergy analysis of municipal solid waste incineration process. The work was based on a previous study, and was enriched considering the possibility to increase the O2 %mol in the combustion air, up to oxy-combustion conditions. Two configurations have been considered, with and without flue gas recirculation, and the environmental aspects of oxy-combustion were taken into account, as well as its exergetic cost. The flue gas was used in a boiler for high pressure steam production, expanded in three turbines for power generation. The exergy analysis allowed to identify the process units characterized by major irreversibility and exergy loss as waste. The results showed that the flue gas recirculation led to an exergy efficiency increase of the whole process of about 3% (from 31.1% up to 34% at adiabatic flame temperature equal to 1200 °C). The O2 %mol increase in the combustion air allowed to reduce the flue gas flowrate, leading to environmental benefits. Oxygen-enriched air adoption led to limited exergetic improvements, due to the exergy cost of the air separation unit. The specific energy generation of the plant increased at fixed combustion chamber temperature adopting flue gas recirculation.

Three-Dimensional Modeling of a Chinese Circulating Fluidized Bed Incinerator Firing Municipal Solid Waste

Currently waste incineration is a widely used method of waste management in China. Effective incineration requires understanding and tools to analyze the incineration process leading to good incinerator performance and efficiency as well as lower emissions. In this work, a Chinese circulating fluidized bed (CFB) incinerator firing municipal solid waste (MSW) and coal is modeled with a three-dimensional CFB furnace model to evaluate the incineration process. First, the modeling results are verified with measurement data from the incinerator. Then, the furnace model is applied in the simulation of a case with more dry MSW without coal. The objective of the research is to provide insight and increase understanding of the MSW incineration process. The simulation case of MSW with the lower moisture content highlights the possible reductions of fossil carbon emissions associated with the utilization of coal. To achieve this, a moderate reduction in the moisture content of MSW is required. A comparison between the MSW and coal versus only the MSW shows minor differences in the performance of the incinerator. Utilization of modeling in incinerator studies can aid in development of more efficient CFB incinerators, improving the waste management and reducing the utilization of fossil coal.