Waste Incineration Process Research Articles

Mechanisms of Action of Dioxin-Like Compounds

Dioxins are one of the most potent environmental toxins known. They are commonly associated with industrial activities such as waste incineration, pulp bleaching and chemical synthesis processes. Their toxicity stems from the fact that dioxins can activate the aromatic hydrocarbon receptor (AhR), thereby triggering a series of harmful biological responses, including carcinogenicity, immunosuppression, endocrine disruption and reproductive toxicity. Dioxins and their analogues are a growing problem and a major challenge in modern ecology because of their highly chemically stable and heat-resistant structure, their stable chlorinated structure, which makes them difficult to metabolise, resulting in their bioaccumulation in living organisms and persistence in the environment. This research will describe the structure of AhR and the binding mechanism of dioxin and AhR, as well as the metabolic process of PCDD, in order to more accurately predict its potential harm to humans and the environment, and help develop targeted drugs or antidotes to reduce or prevent toxic effects. This research summarizes the new understanding of the mechanism of toxic action of dioxins and their metabolic pathways, which can provide a reference for environmental pollutant screening and risk assessment.

Research Progress on the Synergistic Treatment of Multi Pollutants at Low Temperatures Using Carbon-Based Materials

Hg0, VOCs and NO are the main pollutants in sintering flue gas from the steel industry. In the wake of the swift progression of China’s steel sector over recent years, the separate removal of gaseous pollutants can no longer meet the current ultra-low emission requirements. The efficient collaborative management of multiple pollutants has low operating costs and conforms to the mainstream of ultra-low emission transformation. The efficient multi-pollutant synergistic treatment has low operating costs in comparison to traditional individual removal of gaseous pollutants. Furthermore, it aligns with the mainstream of ultra-low emission retrofit in China. How to achieve collaborative control of Hg0, VOCs and NO has become the focus of current research. Carbon-based materials (CBMs) have long been favored worldwide as the mainstream adsorbents for removing multiple air pollutants from flue gas. Based on the summary of the mechanisms for simultaneous removal of pollutants, this paper analyzes the future development directions of CBMs for the concurrent detoxification of Hg0, VOCs and NO, and provides a prospect for future research, providing technical support and theoretical basis for low-temperature cooperative control of pollutants in sintering, waste incineration and other processes.

Influence of sulfur dioxide on the high temperature corrosion performance of TP347H alloy and titanium aluminide coatings in simulated waste incineration environments

The combustion of municipal waste produces a large amount of SO2 and SO3, which react with alkali metal chlorides to sulfate and produce Cl2 and HCl. Therefore, it is necessary to evaluate the effect of SO2 on corrosion process of waste incineration by systematically varying the corrosion conditions. The present study investigates the corrosion behavior of TP347H alloy and titanium aluminide coatings at 600 °C under different environmental conditions comprising NaCl + 20 vol% H2O + 300 ppm SO2 + 8 vol% O2, NaCl + 20 vol% H2O + 8 vol% O2, and 20 vol% H2O +300 ppm SO2 + 8 vol% O2. Titanium aluminide coatings were fabricated via a low-temperature aluminizing method at 700 °C. Our investigation revealed distinct corrosion trends, with NaCl exhibiting the highest corrosive effect, followed by H2O and SO2, at 600 °C. Remarkably, the coexistence of SO2 and NaCl resulted in a reduced corrosive impact compared to environments lacking SO2 exposure, observed across both TP347H alloy and titanium aluminide coatings. This reduction in corrosion can be attributed to the sulfate formation on the surface of NaCl upon exposure to SO2, which effectively mitigated the corrosive properties of deposited salts. Moreover, the involvement of water vapor in the sulfation reaction of NaCl led to decreased vapor content at the metal/deposit interface, consequently decelerating the corrosion rate. Additionally, the formation of a sulfate layer imparted resistance against chlorine transport towards the corrosion front. Overall, these findings emphasize the intricate interplay of environmental factors in the corrosion processes of waste incineration, thereby providing valuable insights into potential mitigation strategies that can be applied in industrial applications.

Simulation Study on the Stabilization Control Performance of Low-Calorific-Value Waste Combustion in Waste Incinerators.

To address the instability of low-calorific-value waste incineration processes and suppress the generation of toxic dioxins, this study examines a 600 t/day waste incineration furnace as a case study. Numerical simulations of the incineration process were conducted by using bed calculation software FLIC and Fluent. A waste incineration cleanliness index was defined, and the impact of the reduced calorific value of the incoming waste on the temperature distribution within the incineration furnace was explored. The effectiveness of biomass steady combustion was compared to that of natural gas steady combustion, leading to the development of a control model for biomass steady combustion of low-calorific-value waste. The results indicate that a decrease in the calorific value of incoming waste reduces the cleanliness index; when the calorific value is 4739 kJ/kg, the cleanliness index drops to 0.96. The residence time of flue gas in the high-temperature zone of the first flue duct is less than 2 s, which is insufficient to effectively suppress dioxin formation. Therefore, steady combustion control measures are necessary. Both biomass and natural gas steady combustion can effectively increase the temperature of the incineration furnace; however, biomass, as a carbon-free energy source, significantly reduces carbon emissions compared with natural gas. A biomass-mixed control model for steady combustion of low-calorific-value waste was constructed and validated, achieving precise residence times in the high-temperature zones of 2.18 and 2.11 s, both exceeding 2 s, thereby achieving the desired control effect.

Cross-Coupling of 1,2,3,4-Tetrachlororodibenzo-p-dioxin with Six Coexisting Polycyclic Aromatic Hydrocarbons during Photodegradation on a Fly Ash Surface.

The adverse conditions of the garbage incineration process can lead to the generation of dioxins and polycyclic aromatic hydrocarbons (PAHs). This study aimed to investigate the removal efficiency and possible cross-coupling effect of 1,2,3,4-tetrachlorodibenzo-p-dioxin (1,2,3,4-TCDD) and six coexisting low-molecular-weight PAHs during photodegradation on the fly ash surface. Due to their higher photoreactivity and light-shielding effect, the six PAHs exhibited inhibitory effects on the photodegradation of 1,2,3,4-TCDD, causing a reduction of 4.1%-21.2% in the removal efficiency. Common degradation products of 1,2,3,4-TCDD and PAHs were identified by LC-MS and GC-MS, and the formation of primary products was verified by theoretical calculations of bond dissociate energies, excitation energy, frontier electron densities, and transition states. In addition, high-molecular-weight coupling products of 1,2,3,4-TCDD and its interaction products with PAHs were observed in the mixed irradiation samples, and two coupling elimination mechanisms were proposed to illustrate their formation through C-O-C bonding and -COO- bonding, respectively. According to toxicity prediction analysis, the developmental toxicity and mutagenicity of most interaction products were higher than 1,2,3,4-TCDD. This study provided some new insights into the transformation, interaction, and related ecological risks of dioxins and PAHs coexisting on the surface of fly ash during the waste incineration process.

Improving the method for calculating carbon emissions from waste incineration: confirmed with carbon-14 testing of flue gas

An improved method was developed to calculate direct emissions from seven municipal solid waste incineration plants by adjusting the physical compositions of waste by invoking the proportion of co-incinerated waste and the bottom ash yield. The fossil carbon fractions of the waste components were determined by carbon-14 (14C) testing. Based on the improved method, direct emissions were in the range of 222–610 kg CO2-eq/t waste, corresponding to reductions of 3.4–221 kg CO2-eq/t waste compared with the method without waste composition adjustment. The 14C contents of the flue gas before and after gas cleaning were tested to validate the improved method, and indicated fossil CO2 emissions of 249–446 and 233–405 kg CO2-eq/t waste, respectively. The direct emissions obtained by the improved method were closer to the results of 14C testing, due to more accurate estimations of the actual waste composition. The method was further combined with a life cycle analysis of the waste incineration process, obtaining total carbon emissions in the range from –33.2 to 483 kg CO2-eq/t waste. The findings provide a new means of accurately calculating carbon emissions from waste incineration.Graphical

Robust multi-target regression with improved stochastic configuration networks and its applications

To improve the accuracy and robustness of stochastic configuration networks (SCNs) for resolving multi-target regression tasks, this paper proposes a robust modeling approach based on improved stochastic configuration networks. A parallel implementation of SCN models is designed to incrementally generate the hidden nodes, which enhances the diversity of hidden layer mapping through information superposition and spanning connection. We employ an elastic net regularization model to sparsely constrain the model parameters to characterize the correlation among multiple targets. Then, the mixture Laplace distributions are used as the prior distribution of each target modeling error, and the output weights of the SCN model are re-evaluated by maximizing a posteriori estimation to enhance model’s robustness with respect to some uncertainties presented in training samples. The modelling performance of the proposed solution is tested on six standard datasets and the historical data of a municipal solid waste incineration process. The experimental results show that the proposed modeling technique has advantages in terms of both the prediction accuracy and the robustness.

A Review of Model Predictive Control for the Municipal Solid Waste Incineration Process

Municipal solid waste incineration (MSWI) is essential for tackling urban environmental challenges and facilitating renewable energy recycling. The MSWI process has characteristics of multiple variables, strong coupling, and complex nonlinearity, requiring advanced process control (APC) technology. Although there have been several reviews on the modeling and control of the MSWI process, there is a lack of focus on model predictive control (MPC), a widely used APC technology. This article aims to comprehensively review MPC strategies in the MSWI process. First, it describes MSWI process technology in detail, examining control issues and objectives to highlight the complexity and challenges in controller design while providing an overview of MPC methods and their benefits. Second, it reviews incinerator modeling for control, including traditional modeling techniques and machine learning technologies such as fuzzy neural networks. Third, it reviews the controllers used for MSWI process, emphasizing the advantages of MPC over existing control methods. Fourth, it discusses the current status of MPC design and online updates, covering the need for an accurate dynamic predictive model and objective function and the online updates components such as predictive modeling, rolling optimization, and feedback correction. Finally, the study concludes with a summary of the findings.

Utilization of cow bone waste and calcium oxide for the solidification and stabilization of MSWI fly ash: Towards sustainable practices

The quest for sustainable waste management practices has catalysed the development of innovative approaches to transform waste into resourceful products. This study explores the effectiveness of cow bone waste and calcium oxide (CaO) as agents in the stabilization/solidification (S/S) process of Municipal Solid Waste Incineration (MSWI) fly ash derived from two distinct cities employing both grate-type and circulating fluidized bed incinerators in China. Employing a robust experimental framework, the research evaluated the stabilization efficacy for heavy metals, monitored the progression of pH, and quantified the compressive strength of the treated MSWI fly ash. Moreover, it conducted risk assessment and economic analyses. Incorporating CBW and CaO into the MSWI fly ash from grate-type incinerators significantly enhanced the Pb stabilization efficiency, achieving up to 99.7 % when adding 31 % CBW and 7 % CaO. This treatment resulted in the leaching of other heavy metals, which were also below the standard landfill limits. In the circulating fluidized bed incinerator fly ash, the leaching concentrations of heavy metals in the treated samples were reduced by up to 98.6 % for Cd and 64.4 % for Ni.Additionally, an increase in CaO content was found to substantially improve the compressive strength of the solidified samples across all tested curing periods (7, 14, and 28 days). The optimal compressive strength result, observed at 28 days, reached 1.2 MPa, compared to the raw fly ash, which exhibited only 0.77 MPa when adding just 9 % CaO. This highlights CaO's critical role in enhancing the mechanical integrity of the treated MSWI fly ash. The study concludes that strategically calibrated ratios of cow bone waste and CaO attenuate the environmental hazard presented by MSWI fly ash and bolster its mechanical attributes, thereby repurposing the waste into a viable material for construction.

Data-driven multi-objective intelligent optimal control of municipal solid waste incineration process

The municipal solid waste incineration (MSWI) process has become the primary technology for municipal solid waste (MSW) treatment worldwide due to its advantages of harmlessness, reduction, and resource recovery. However, the automatic combustion control (ACC) system developed in developed countries are often not effectively applicable to other countries, particularly China, due to regional differences in MSW composition. To achieve intelligent development of MSWI plants, it is crucial to develop intelligent optimal control technology tailored to national conditions to reduce comprehensive pollutant emission concentration (CPEC) and improve combustion efficiency (CE). This article proposes a data-driven multi-objective intelligent optimal control strategy. Firstly, based on the analysis of the whole process, the Tikhonov regularization-least regression decision tree (TR-LRDT) algorithm is used to establish a MSWI whole process model, incorporating serial controlled objects and parallel pollutant indicators, to support a multi-objective optimization model. Then, leveraging the ACC system and expert experience, a multi-input multi-output loop controller is established using the single neuron adaptive PID (SNA-PID) algorithm to achieve stable control of key controlled variables. Next, a mutation crossover strategy and termination condition are integrated with the multi-objective particle swarm optimization (MOPSO) algorithm to solve the multi-objective optimization model, and domain expert rules are applied to determine the optimal setpoints for key controlled variables in the Pareto front, aiming to reduce CPEC and improve CE. Finally, the effectiveness of the proposed intelligent optimal control strategy is validated using actual data. The experimental results demonstrate that this strategy not only maintains the stability of key controlled variables but also decreases CPEC by 1.82% and increases CE by 2.38%, laying a foundation for further research into intelligent optimization control of the MSWI process. Further, the software system based on the proposed strategy is developed and validated on a hardware-in-loop simulation platform.

Intelligent optimal control of furnace temperature for the municipal solid waste incineration process using multi-loop controller and particle swarm optimization

Furnace temperature (FT) is a key variable in municipal solid waste incineration (MSWI) processes, influenced by many manipulated variables and directly impacting pollutant concentrations in exhaust gas. Domain experts cannot achieve the optimal FT setpoint value under manual control, leading to abnormal pollutant emission concentrations. To address this, we propose an intelligent optimal control framework for FT aiming to minimize pollutant emission concentration. First, the FT controlled object model is established using the Tikhonov regularization-least regression decision tree (TR-LRDT) algorithm. Then, based on the experience of domain experts, a multi-loop controller is developed using an improved single neuron adaptive PID (ISNA-PID) algorithm to stabilize FT. Next, after establishing NOx and CO2 indicator models, the particle swarm optimization (PSO) algorithm is employed to determine the FT setpoint value in terms of minimum pollutant emission concentration. Finally, the FT intelligent optimal control framework is verified. Experimental results indicate that the optimal FT setpoint value can reduce NOx and CO2 emission concentrations by 19.93 % and 6.99 %, respectively.

Soft sensing of NOx emission from waste incineration process based on data de-noising and bidirectional long short-term memory neural networks.

Continuous emission monitoring system is commonly employed to monitor NOx emissions in municipal solid waste incineration (MSWI) processes. However, it still encounters the challenges of regular maintenance and measurement lag. These issues significantly impact the accurate and stable control of NOx emissions. Therefore, developing a soft NOx emission sensor to complement hardware monitoring becomes imperative. Considering data noise, dynamic nonlinearity, time series characteristics and volatility in the MSWI process, this article introduces a soft sensor model for NOx emission prediction utilizing the complete ensemble empirical mode decomposition adaptive noise (CEEMDAN)-wavelet threshold (WT) method and bidirectional long short-term memory (Bi-LSTM). Firstly, the original data signal is decomposed into a group of intrinsic mode functions (IMFs) using the CEEMDAN. Subsequently, the WT processes the high-frequency IMFs that are noise-dominant. Then, all IMFs are reconstructed to obtain the denoized signal. Finally, the Bi-LSTM model is employed to predict NOx emissions. Compared to conventional modelling approaches, the model proposed in this article demonstrates the best predictive performance. The mean absolute percentage error, root-mean-squared error and average absolute error on the test set of the proposed model are 3.75%, 5.34 mg m-3 and 4.34 mg m-3, respectively. The proposed model provides a new method to soft sensing NOx emissions. It holds significant practical value for precise and stable monitoring of NOx emissions in MSWI processes and provides a reference for research on modelling key process parameters.

A Recurrent Stochastic Configuration Network for Dynamic System Modeling and Its Application

In order to model nonlinear dynamic systems quickly and accurately, a recurrent stochastic configuration network (RSCN) with universal approximation ability is proposed in this paper. The method consists of network parameter learning and network structure determination. The network parameter learning strategy consists of input weights and the biases of hidden nodes generated randomly through a supervision mechanism, while the output weights of hidden nodes are determined by solving the least-squares problem. The network structure is determined incrementally by the training error of a single-layer recurrent neural network. The effectiveness of RSCN is tested and evaluated by using three nonlinear dynamic functions and historical data from a municipal solid waste incineration (MSWI) process. The experimental results show that the proposed method can improve modeling accuracy and reduce training time, which is valuable for applications in the field of industrial process modeling.

NOx Emission Trend Prediction for the Waste Incineration Process Based on Partial Least Squares with the Time Series Reconstruction and Exponential Weighting

AbstractAccurate prediction of nitrogen oxide (NOx) emission is crucial for effectively controlling pollution in municipal solid waste incineration processes. However, it is challenging to construct a NOx emission prediction model with high prediction accuracy and easy engineering application. To address this, this paper proposes a robust and easily applicable NOx emission trend prediction model oriented to engineering applications, utilizing the partial least squares (PLS) method with the time series reconstruction and exponential weighting (TS‐EW‐PLS). The model is verified using operational data from an actual waste incineration process, and comparative analysis with the PLS model showed that the TS‐EW‐PLS model achieved a remarkable improvement of 27–38 % in prediction performance.

Efficiency optimization methods for stochastic configuration networks

To solve the problems of the low efficiency of parameter allocation and time-consuming computation of output weights in the hidden layers of stochastic configuration networks (SCNs), an optimization method is proposed to improve the SCNs construction efficiency. Firstly, with the increase in the number of hidden layer nodes, the key parameters that determine the strictness of the supervision mechanism are reconstructed to speed up the configuration efficiency of hidden layer input weights and biases. Then, the incremental mechanism of the SCNs are combined with the QR decomposition method, and the output weights are calculated by iteratively updating the transformation matrix, thus reducing the computational complexity of training the SCNs. Finally, the proposed method is validated on four standard datasets and historical data of a municipal solid waste incineration process. The experimental results show that the proposed method improves the efficiency of SCN construction while guaranteeing the prediction accuracy of SCNs model.

Modular stochastic configuration network prediction interval for furnace temperature in municipal solid waste incineration

The high dimensionality of characteristic variables and the presence of numerous uncertain factors affecting furnace temperature during municipal solid waste incineration can lead to poor accuracy and generalization ability for furnace temperature prediction. This paper adopts the modular neural network modeling approach and incorporates Gaussian process regression analysis into stochastic configuration networks to propose a method for establishing a furnace temperature prediction interval model. First, a Gaussian mixture model is used to decompose the complex task into several subtasks. Then, considering the differences among the subtasks, Gaussian process regression with different kernel functions is combined with a stochastic configuration network to form corresponding base models, which are trained and learned. The prediction interval results are obtained through blending ensemble methods. Finally, the effectiveness of the proposed method is tested using historical data obtained from the municipal solid waste incineration process. The results indicate that the furnace temperature prediction model demonstrates advantages in terms of accuracy and generalization ability, making it applicable to the field of process parameter modeling.

Modification of the incinerator to prevent the emission of toxic air pollutant in biomedical waste incineration process at Base Hospital Udugama

Modification of the incinerator to prevent the emission of toxic air pollutant in biomedical waste incineration process at Base Hospital Udugama

Event-Triggered Online Learning Fuzzy-Neural Robust Control for Furnace Temperature in Municipal Solid Waste Incineration Process

Event-Triggered Online Learning Fuzzy-Neural Robust Control for Furnace Temperature in Municipal Solid Waste Incineration Process

The immobilization mechanisms of Pb in borates during low-temperature vitrification process of municipal solid waste incineration (MSWI) fly ash

The immobilization mechanisms of Pb in borates during low-temperature vitrification process of municipal solid waste incineration (MSWI) fly ash

Heterogeneous ensemble prediction model of CO emission concentration in municipal solid waste incineration process using virtual data and real data hybrid-driven

Carbon monoxide (CO) is a hazardous gas discharged during municipal solid waste incineration (MSWI). Its emission concentration serves as a vital indicator for assessing the stability of the MSWI process. Therefore, accurate prediction of CO emissions is crucial. While existing research predominantly relies on historical real data-driven models, it often overlooks the effective utilization of the combustion mechanism. This article introduced a novel approach: a heterogeneous ensemble prediction model that integrates virtual and real data. Firstly, virtual mechanism data was obtained through a multi-condition mechanism model constructed using coupled numerical simulation software of FLIC and Aspen Plus. Secondly, based on this virtual mechanism data, a linear regression decision tree (LRDT) algorithm was employed to establish the mechanism mapping model. Simultaneously, a real historical data-driven model based on a long short-term memory (LSTM) neural network algorithm was developed. In the offline training verification phase, the heterogeneous models were combined using an inequality-constrained random weighted neural network (CIRWNN) after aligning virtual and real samples representing operating conditions based on the k-nearest neighbor (KNN) approach. Subsequently, in the online testing verification stage, CO online prediction was achieved by ensemble the LRDT-based mechanism mapping model and.the LSTM-based historical data-driven model. The proposed method's effectiveness and rationality were validated through an industrial case study of MSWI process in Beijing.