Bagging based ensemble learning approaches for modeling the emission of PCDD/Fs from municipal solid waste incinerators

Intelligent technologies powering clean incineration of municipal solid waste: A system review

Cleanliness has been paramount for municipal solid waste incineration (MSWI) systems. In recent years, the rapid advancement of intelligent technologies has fostered unprecedented opportunities for enhancing the cleanliness of MSWI systems. This paper offers a review and analysis of cutting-edge intelligent technologies in MSWI, which include process monitoring, intelligent algorithms, combustion control, flue gas treatment, and particulate control. The objective is to summarize current applications of these techniques and to forecast future directions. Regarding process monitoring, intelligent image analysis has facilitated real-time tracking of combustion conditions. For intelligent algorithms, machine learning models have shown advantages in accurately forecasting key process parameters and pollutant concentrations. In terms of combustion control, intelligent systems have achieved consistent prediction and regulation of temperature, oxygen content, and other parameters. Intelligent monitoring and forecasting of carbon monoxide and dioxins for flue gas treatment have exhibited satisfactory performance. Concerning particulate control, multi-objective optimization facilitates the sustainable utilization of fly ash. Despite remarkable progress, challenges remain in improving process stability and monitoring instrumentation of intelligent MSWI technologies. By systematically summarizing current applications, this timely review offers valuable insights into the future upgrade of intelligent MSWI systems.

Complementary vitrification of municipal solid waste incineration fly ash from grate furnaces and fluidised bed incinerators via a co-reduction process

The increasing application of municipal solid waste incineration (MSWI) emphasises the need for MSWI fly ash (FA) safe treatment. Based on the compositional complementarity of FA from grate furnaces (G-FA) and fluidised bed incinerators (F-FA), we proposed a co-reduction process to treat G-FA and F-FA together for producing vitrified slag and ferroalloys. The clean vitrified slag and Fe-Cr-Ni-Cu alloy were obtained with the mass ratios of 1:9 ∼ 6:4 (G-FA:F-FA) at 1300℃, which is about 300℃ lower than the conventional G-FA vitrification. The metals Zn, Cd, and Pb were mostly volatilised into the flue gas for potential recovery from the secondary FA. The thermodynamic SiO2-Al2O3-CaO ternary system demonstrated that an optimal mass ratio of the two complementary FA types contributes to the system shifting to the low-temperature melting zone. The co-reduction process of G-FA and F-FA could be a promising option for FA beneficial reutilization with environmental advantages.

Identification and verification of PCDD/Fs indicators from four typical large-scale municipal solid waste incinerations with large sample size in China

Monitoring PCDD/Fs emissions from municipal solid waste incinerations (MSWIs) is of paramount importance, yet it can be time-consuming and labor-intensive. Predictive models offer an alternative approach for estimating their levels. However, robust models specific to PCDD/Fs were lacking. In this study, we collected 190 PCDD/Fs samples from 4 large-scale MSWIs in China, with the average PCDD/Fs levels and TEQ levels of 0.987 ng/m3 and 0.030 ng TEQ/m3, respectively. We developed and evaluated predictive models, including traditional statistical methods, e.g., linear regression (LR) as well as machine learning models such as back propagation-artificial neural networks (BP ANN) and random forest (RF). Correlation analysis identified 2,3,4,7,8-PeCDF, 1,2,3,6,7,8-HxCDF, 2,3,4,6,7,8-HxCDF were better indicator congeners for PCDD/Fs estimation (R2 > 0.9, p < 0.001). The predictive results favored the RF model, exhibiting a high R2 value and low root mean square error (RMSE) and mean absolute error (MAE). Additionally, the RF model showed excellent prediction ability during external validation, with low absolute relative error (ARE) of 10.9 %-12.6 % for the three indicator congeners in the normal PCDD/F TEQ levels group (<0.1 ng TEQ/m3) and slightly higher ARE values (13.8 %-17.9 %) for the high PCDD/F TEQ levels group (>0.1 ng TEQ/m3). In conclusion, our findings strongly support the RF model's effectiveness in predicting PCDD/Fs TEQ emission from MSWIs.

Advances and applications of machine learning and deep learning in environmental ecology and health

Machine learning (ML) and deep learning (DL) possess excellent advantages in data analysis (e.g., feature extraction, clustering, classification, regression, image recognition and prediction) and risk assessment and management in environmental ecology and health (EEH). Considering the rapid growth and increasing complexity of data in EEH, it is of significance to summarize recent advances and applications of ML and DL in EEH. This review summarized the basic processes and fundamental algorithms of the ML and DL modeling, and indicated the urgent needs of ML and DL in EEH. Recent research hotspots such as environmental ecology and restoration, environmental fate of new pollutants, chemical exposures and risks, chemical hazard identification and control were highlighted. Various applications of ML and DL in EEH demonstrate their versatility and technological revolution, and present some challenges. The perspective of ML and DL in EEH were further outlined to promote the innovative analysis and cultivation of the ML-driven research paradigm.

Dioxin emission prediction from a full-scale municipal solid waste incinerator: Deep learning model in time-series input

The immeasurability of real-time dioxin emissions is the principal limitation to controlling and reducing dioxin emissions in municipal solid waste incineration (MSWI). Existing methods for dioxin emissions prediction are based on machine learning with inadequate dioxin datasets. In this study, the deep learning models are trained through larger online dioxin emissions data from a waste incinerator to predict real-time dioxin emissions. First, data are collected and the operating data are preprocessed. Then, the dioxin emission prediction performance of the machine learning and deep learning models, including long short-term memory (LSTM) and convolutional neural networks (CNN), with normal input and time-series input are compared. We evaluate the applicability of each model and find that the performance of the deep learning models (LSTM and CNN) has improved by 36.5% and 30.4%, respectively, in terms of the mean square error (MSE) with the time-series input. Moreover, through feature analysis, we find that temperature, airflow, and time dimension are considerable for dioxin prediction. The results are meaningful for optimizing the control of dioxins from MSWI.

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.

Prediction of successful aging using ensemble machine learning algorithms

BACKGROUND

Aging is a chief risk factor for most chronic illnesses and infirmities. The growth in the aged population increases medical costs, thus imposing a heavy financial burden on families and communities. Successful aging (SA) is a positive and qualitative view of aging. From a biomedical perspective, SA is defined as the absence of diseases or disability disorders. This is distinct from normal aging, which is associated with age-related deterioration in physical and cognitive functions. From a social perspective, SA highlights life satisfaction and individual well-being, usually attained through socialization. It is an abstract and multidimensional concept surrounded by imprecision about its definition and measurement. Our study attempted to find the most effective features of SA as defined by Rowe and Kahn's theory. The determined features were used as input parameters of six machine learning (ML) algorithms to create and validate predictive models for SA.

METHODS

In this retrospective study, the raw data set was first pre-processed; then, based on the data of a sample of 983, five basic ML techniques including artificial neural network, decision tree, support vector machine, Naïve Bayes, and k-nearest neighbors (K-NN) with one ensemble method (that gathers 30 K-NN algorithms as weak learners) were trained. Finally, the prediction result was yielded using the majority vote method based on the output of the generated base models.

RESULTS

The experimental results revealed that the predictive system has been more successful in predicting SA with a 93% precision, 92.40% specificity, 87.80% sensitivity, 90.31% F-measure, 89.62% accuracy, and a ROC of 96.10%, using a five-fold cross-validation procedure.

CONCLUSIONS

Our results showed that ML techniques potentially have satisfactory performance in supporting the SA-related decisions of social and health policymakers. The KNN-based ensemble algorithm is superior to the other ML models in classifying people into SA and non-SA classes.

Predicting wetland area and water depth in Barind plain of India

The present study attempts to delineate wetlands in the lower Tangon river basin in the Barind flood plain region using spectral water body extraction indices. The main objectives of this present study are simulating and predicting wetland areas using the advanced artificial neural network-based cellular automata (ANN-CA) model and water depth using statistical (adaptive exponential smoothing) as well as advanced machine learning algorithms such as Bagging, Random Subspace, Random Forest, Support vector machine, etc. The result shows that RmNDWI and NDWI are the representative wetland delineating indices. NDWI map was used for water depth prediction. Regarding the prediction of wetland areas, a remarkable decline is likely to be identified in the upcoming two decades. The small wetland patches away from the master stream are expected to dry out during the predicted period, where the major wetland patches nearer to the master stream with greater water depth are rather sustainable, but their depth of water is predicted to be reduced in the next decades. All models show satisfactory performance for wetland depth mapping, but the random subspace model was identified as the best-suited water depth predicting method with an acceptable prediction accuracy (root mean square error <0.34 in all the years) and the machine learning models explored better result than adaptive exponential smoothing. This recent study will be very helpful for the policymakers for managing wetland landscape as well as the natural environment.

An ensemble learning approach to map the genetic connectivity of the parasitoid Stethynium empoasca (Hymenoptera: Mymaridae) and identify the key influencing environmental and landscape factors

The effect of landscape patterns and environmental factors on the population structure and genetic diversity of organisms is well-documented. However, this effect is still unclear in the case of Mymaridae parasitoids. Despite recent advances in machine learning methods for landscape genetics, ensemble learning still needs further investigation. Here, we evaluated the performance of different boosting algorithms and analyzed the effects of landscape and environmental factors on the genetic variations in the tea green leafhopper parasitoid Stethynium empoasca (Hymenoptera: Mymaridae). The S. empoasca populations showed a distinct pattern of isolation by distance. The minimum temperature of the coldest month, annual precipitation, the coverage of evergreen/deciduous needleleaf trees per 1 km2, and the minimum precipitation of the warmest quarter were identified as the dominant factors affecting the genetic divergence of S. empoasca populations. Notably, compared to previous machine learning studies, our model showed an unprecedented accuracy (r = 0.87) for the prediction of genetic differentiation. These findings not only demonstrated how the landscape shaped S. empoasca genetics but also provided an essential basis for developing conservation strategies for this biocontrol agent. In a broader sense, this study demonstrated the importance and efficiency of ensemble learning in landscape genetics.

A comparative study on characteristics and leaching toxicity of fluidized bed and grate furnace MSWI fly ash

Grate furnace and fluidized bed are the most widely used technologies for municipal solid waste incineration (MSWI), which play significant roles in characteristics of MSWI fly ash. A comparative study of the physicochemical characteristics, microstructure morphology and leaching toxicity of fluidized bed and grate furnace MSWI fly ash was conducted in this work, and some resource utilization and disposal treatments were proposed. Results showed that calcium salt and chlorine salt formed the dominant components of MSWI fly ash. CaO-SiO₂-Al₂O₃ ternary system indicated that MSWI fly ash had potential pozzolanic activity, similar to coal fly ash and blast furnace slag. The total Pb, Cd and Zn contents in fluidized bed MSWI fly ash was only 1/2, 1/3 and 2/3 of grate furnace MSWI fly ash, respectively. Leachability of Pb in MSWI fly ash collected from Dalian, Shanghai, Zhuji and Hangzhou was 4.25 mg/L, 3.83 mg/L, 3.84 mg/L and 3.68 mg/L, 28.3, 25.5, 25.6 and 24.5 times as much as the national standard limitation (GB16889-2008), respectively. However, grate furnace MSWI fly ash with high chloride and unstable chemical speciation distribution of heavy metals would pose more environmental risk toits immobilization and disposal. Fluidized bed fly ash is a promising candidate for preparing the Portland cement clinker, microcrystalline glass and ceramics due to its high Si and Al content. Grate furnace MSWI fly ash is more appropriate for Alinite cement preparation because of high chloride content.

Ground-level ozone simulation using ensemble WRF/Chem predictions over the Southeast United States

Being detrimental to human health and vegetation growth, ground-level ozone (O₃) is becoming a huge concern as an air pollutant. The processes of formation, diffusion, transformation, and transport of O₃ in the atmosphere are highly affected by meteorological conditions such as solar radiation, temperature, precipitation, and wind. Chemical transport models (CTMs) are widely used in simulating O₃ pollution with two main inputs of the meteorological condition and emission inventory. Meteorological inputs play a crucial role in the model simulation accuracy especially in areas where emission has been well constrained such as the United States (U.S.). However, most O₃ simulations today still use only one set of meteorological input, which leaves room for model performance improvement by using ensemble meteorological conditions. In this study, O₃ over the Southeast U.S. was simulated for one week in the summer of each year from 2016 to 2018 by using ensemble meteorological inputs offered by Short Range Ensemble Forecast products. The predictions were conducted through the Weather Research and Forecasting model coupled with Chemistry. The calculated ensemble prediction results got at least 66.7% improvement in agreement with O₃ observations compared with single runs in the three selected cities (Miami, Atlanta, and Baton Rouge) from 2016 to 2018. This study emphasized the accuracy and provided a new idea of using ensemble meteorological inputs to improve O₃ prediction than using traditional single meteorology by CTMs.