Data-driven models applying in household hazardous waste: Amount prediction and classification in Shanghai.

Precisely predicting the amount of household hazardous waste (HHW) and classifying it intelligently is crucial for effective city management. Although data-driven models have the potential to address these problems, there have been few studies utilizing this approach for HHW prediction and classification due to the scarcity of available data. To address this, the current study employed the prophet model to forecast HHW quantities based on the Integration of Two Networks systems in Shanghai. HHW classification was performed using HVGGNet structures, which were based on VGG and transfer learning. To expedite the process of finding the optimal global learning rate, the method of cyclical learning rate was adopted, thus avoiding the need for repeated testing. Results showed that the average rate of HHW generation was 0.1 g/person/day, with the most significant waste categories being fluorescent lamps (30.6 %), paint barrels (26.1 %), medicine (26.2 %), battery (15.8 %), thermometer (0.03 %), and others (1.22 %). Recovering rare earth element (18.85 kg), Cd (3064.10 kg), Hg (15643.43 kg), Zn (14239.07 kg), Ag (11805.81 kg), Ni (4956.64 kg) and Li (1081.45 kg) from HHW can help avoid groundwater pollution, soil contamination and air pollution. HVGGNet-11 demonstrated 90.5 % precision and was deemed most suitable for HHW sorting. Furthermore, the prophet model predicted that HHW in Shanghai would increase from 794.43 t in 2020 to 2049.67 t in 2025.

Agglomeration-influenced transformation of heavy metals in gas-solid phases during simulated sewage sludge co-incineration: Effects of phosphorus and operating temperature.

Phosphorus and operating temperature not only affect the agglomeration behavior but also the transformation and migration of heavy metals. Accordingly, this study examined the effect of temperature and phosphorus in a fluidized bed combustion process to understand the emission and distribution of heavy metals by both experimental and thermodynamic calculations. The experimental results indicated that the sodium-phosphate reactions occur before the sodium-silicate reaction in the solid phase when the ratio of P/Na was 1/2. A low-melting-point sodium phosphate component, such as NaPO3, leads to easier particle agglomeration than Na2O-SiO2. In terms of the emissions of heavy metals, Pb and Cd show a similar trend: both the amount of emission smaller than that without adding phosphorus and the amount of emission share an upward trend with the operating time increased during MSS fluidized bed combustion. However, with the presence of phosphorus, the emission of Cr shows slightly decreased, and then sharply dropped, after that, increasing with operating time increased. Generally speaking, the maximum amount of Pb and Cd emitted was at 900 °C, followed by 800 °C and 700 °C. The higher temperature would promote the volatilization of Pb and Cd to emit. On the other hand, Cr emitted at the beginning tended to increase but later decreases when the temperatures were 700 and 900 °C, which may be due to the emission of Cr being influenced by the different affinities of both Al and Cr, reacting with Na in a fluidized bed incinerator. As for the distribution of heavy metals in the solid phase, a higher concentration of heavy metals was found in both the coarsest and finest particles during the process of agglomeration/defluidization.

Deep learning hybrid predictions for the amount of municipal solid waste: A case study in Shanghai.

It is crucial to precisely estimate the municipal solid waste (MSW) amount for its sustainable management. Owing to learning complicated and abstract features between the factors and target, deep learning has recently emerged as one of the useful tools with potential to predict the MSW amount. Therefore, this study aimed to design an MSW amount predicted system in Shanghai, consisting of Attention (A), one-dimensional convolutional neural network (C), and long short-term memory (L), to investigate the relationship between exogenous series (24 socioeconomics factors and past MSW amount) and target (MSW amount). The role of Attention, 1D-CNN, LSTM played on the MSW predicted amount also have investigated. The results show that attention is crucial for decoding the encoding information, which would improve performance between predicted and known MSW amount (R2 in A-L-C, L-A-C, L-C-A was 89.45%, 90.77%, and 95.31%, respectively.). CNN modules appear to be positioned similarly across the MSW predicted system. Finally, R2 in L-A-C, A-L-C, and A-C-L was 85.44%, 91.61%, and 89.45%, which suggested that LSTM as an intermediary between CNN and Attention modules seems a wise measure to predict the MSW amount based on the correlation efficiency. In addition, some socioeconomic factors including the average number of people in households and budget revenue may be chosen for the decision-making of MSW management in Shanghai city in the future, according to the weight of neurons in fully connected layers by the visual technology.

Ash-to-emission pollution controls on co-combustion of textile dyeing sludge and waste tea.

Given the globally increased waste stream of textile dyeing sludge (TDS), its co-combustion with agricultural residues appears as an environmentally and economically viable solution in a circular economy. This study aimed to quantify the migrations and chemical speciations of heavy metals in the bottom ashes and gas emissions of the co-combustion of TDS and waste tea (WT). The addition of WT increased the fixation rate of As from 66.70 to 83.33% and promoted the chemical speciation of As and Cd from the acid extractable state to the residue one. With the temperature rise to 1000 °C, the fixation rates of As, Cd, and Pb in the bottom ashes fell to 27.73, 8.38, and 15.40%, respectively. The chemical speciation perniciousness of Zn, Cu, Ni, Mn, Cr, Cd, and Pb declined with the increased temperature. The ash composition changed with the new appearances of NaAlSi3O8, CaFe2O4, NaFe(SO4)2, and MgCrO4 at 1000 °C. The addition of WT increased CO2 and NOx but decreased SO2 emissions in the range of 680-1000 °C. ANN-based joint optimization indicated that the co-combustion emitted SO2 slightly less than did the TDS combustion. These results contribute to a better understanding of ash-to-emission pollution control for the co-combustion of TDS and WT.

Influence of different fluidization and gasification parameters on syngas composition and heavy metal retention in a two-stage fluidized bed gasification process.

Herein, we investigated the influence of gasification and fluidization parameters on the H2 content of syngas and the retention of heavy metals (Cu and Pb) in a bed material during a two-stage fluidized bed gasification process. The results indicated that a temperature of 900 °C in both stages resulted in the highest H2 content (32.4 mol%) in syngas. When different equivalence ratios (ERs) were investigated, it was found that the highest H2 content in syngas (25.4 mol%) was achieved at an ER of 0.3. A particle size of 0.46 mm in the fluidized bed led to an increase in the H2 content of syngas. Moreover, increasing the operating gas velocity led to an increase in the H2 content of syngas. The heavy metal concentration in the bed material was the highest at 500 °C. When the influences of different particle sizes and operating gas velocities were compared, it was observed that a particle size of 0.46 mm and gas velocity of 1.5 U/Umf resulted in increased heavy metal concentrations in the bed material, which indicates that the reduction in the particle size and the increase in the operating gas velocity enhanced gasification and improved the retention of heavy metals.

Sequential extraction for heavy metal distribution of bottom ash from fluidized bed co-combusted phosphorus-rich sludge under the agglomeration/defluidization process.

Agglomeration that occurs during municipal sewage sludge (MSS) fluidized bed co-combustion might affect heavy metal distribution and the transformation of bottom ash. A study on the mobility and speciation of heavy metals that accompanies agglomeration behavior and phosphorus addition should be examined during MSS co-combustion. Meanwhile, the aim of this study was to evaluate the total content and speciation of heavy metals during the MSS fluidized bed co-combustion by the chemical sequential extraction procedure (SEP). The risk assessment code (RAC) and individual contamination factor (ICF) are calculated to evaluate the mobility of heavy metals and their environmental risks in agglomerates. Moreover, identification of agglomerates is established by both characterization (scanning electron microscopy/energy-dispersive spectroscopy, X-ray photoelectron spectroscopy) and thermodynamic simulation (HSC chemistry software). The experimental results indicated that P and Na would form the lower melting-point compounds such as NaPO3 and Na2O in the bottom ash, which promoted agglomeration during MSS fluidized bed co-combustion. According to the simulation, Na and P have a stronger affinity than Si and Cr, and this reaction is not only influenced by particle agglomeration, but also by heavy metal distribution during modified MSS co-combustion. Nevertheless, the results of ICFs and RACs obtained from the SEP indicated that for heavy metals trapped in agglomerates, a weaker binding such as physical covering by eutectics might be considered as the dominant reaction compared with chemical binding to form a metal complex.

Parametric assessment of stochastic variability in co-combustion of textile dyeing sludge and shaddock peel.

This study aimed at quantification of co-combustion behaviors and kinetic parameters of textile dyeing sludge (TDS) and shaddock peel (SP) in response to blend ratio, heating rate, and temperature. The experimental responses of mass loss (ML) and mass loss rate (MLR) measured using a thermogravimetric analyzer were also estimated using the best-fit multiple non-linear regression (MNLR) models. The independent validations of the models led to high coefficients of determination of 99.8% for ML and 83.8% for MLR. Stochastic uncertainty associated with the model predictors was assessed using Monte Carlo simulations. Our results indicated that the overall cumulative uncertainty was greater in the model predictions of MLR than ML.

Thermogravimetric and mass-spectrometric analyses of combustion of spent potlining under N2/O2 and CO2/O2 atmospheres.

Thermal decomposition and gaseous evolution of the spent potlining (SPL) combustion were quantified using thermogravimetric and mass-spectrometric analyses in CO2/O2 and N2/O2 atmospheres using three heating rates (15, 20 and 25 °C/min). The thermal decomposition of SPL occurred mainly between 450 and 800 °C. Based on the four kinetic methods of Friedman, Starink, Kissinger-Akahira-Sunose and Flynn-Wall-Ozawa under the various conversion degrees (α) from 0.1 to 0.7, the lowest apparent activation energy was estimated at 149.81 kJ/mol in the 70% CO2/30% O2 atmosphere. The pre-exponential factor, and changes in entropy, enthalpy and free Gibbs energy were also estimated. The reaction model did not suggest a single reaction of the SPL combustion. With the α value of 0.25-0.7, the following function best described the reaction based on the Malek method: f(α) = 1/2α and G(α) = lnα2. The gases released during the combustion process included CO2, CO, NOx, HCN, and HF.

Kinetics, thermodynamics, gas evolution and empirical optimization of (co-)combustion performances of spent mushroom substrate and textile dyeing sludge.

Spent mushroom substrate (SMS) and textile dyeing sludge (TDS) were (co-)combusted in changing heating rates, blend ratios and temperature. The increased blend ratio improved the ignition, burnout and comprehensive combustion indices. A comparison of theoretical and experimental thermogravimetric curves pointed to significant interactions between 350 and 600 °C. High content of Fe2O3 in TDS ash may act as catalysis at a high temperature. Ignition activation energy was lower for TDS than SMS due to its low thermal stability. 40% SMS appeared to be the optimal blend ratio that significantly decreased the activation energy, as was verified by the response surface methodology. D3 model best described the (co-)combustions. SMS led to more NO and NO2 emissions at about 300 °C and less HCN emission than did TDS. The addition of 40% SMS to TDS lowered SO2 emission. The co-combustion of TDS and SMS appeared to enhance energy generation and emission reduction.

Characterizing and optimizing (co-)pyrolysis as a function of different feedstocks, atmospheres, blend ratios, and heating rates.

(Co-)pyrolysis behaviors were quantified using TG and Py-GC/MS analyses as a function of the two fuels of sewage sludge (SS) and water hyacinth (WH), five atmospheres, six blend ratios, and three heating rates. Co-pyrolysis performance, gaseous characterizations and optimization analyses were conducted. Relative to N2 atmosphere, co-pyrolysis was inhibited at low temperatures in CO2 atmosphere, while the CO2 atmosphere at high temperatures promoted the vaporization of coke. The main (co-)pyrolysis products of SS and WH were benzene and its derivatives, as well as alkenes and heterocyclic compounds. Average apparent activation energy decreased gradually with the increased atmospheric CO2 concentration and was highest (377.5 kJ/mol) in N2 atmosphere and lowest (184.7 kJ/mol) in CO2 atmosphere. Significant interaction effects on the mean responses of mass loss, derivative TG, and differential scanning calorimetry were found for fuel type by heating rate and atmosphere type by heating rate.

(Co-)combustion of additives, water hyacinth and sewage sludge: Thermogravimetric, kinetic, gas and thermodynamic modeling analyses.

Additives and biomass were co-combusted with sewage sludge (SS) to promote SS incineration treatment and energy generation. (Co-)combustion characteristics of sewage sludge (SS), water hyacinth (WH), and 5% five additives (K2CO3, Na2CO3, Mg2CO3, MgO and Al2O3) were quantified and compared using thermogravimetric-mass spectrometric (TG-MS) and numerical analyses. The combustion performance of SS declined slightly with the additives which was demonstrated by the 0.03-to-0.25-fold decreases in comprehensive combustibility index (CCI). The co-combustion performed well given the 0.31-fold increase in CCI. Kinetic parameters were estimated using the Ozawa-Flynn-Wall (OFW) and Kissinger-Akahira-Sunose (KAS) methods. Apparent activation energy estimates by OFW and KAS were consistent. The addition of K2CO3 and MgCO3 decreased the weighted average activation energy of SS. Adding K2CO3 to the blend reduced CO2, NO2, SO2, HCN and NH3 emissions. CO2, NO2 and SO2 emissions were higher from WH than SS. Adding WH or K2CO3 to SS increased CO2, NO2 and SO2 but HCN and NH3 emissions. Based on both catalytic effects and evolved gases, K2CO3 was potentially an optimal option for the catalytic combustion among the tested additives.

Thermal degradations and processes of waste tea and tea leaves via TG-FTIR: Combustion performances, kinetics, thermodynamics, products and optimization.

The present study characterized the kinetic, thermodynamic and performance parameters, products, factorial interactions, and optimal conditions of combustions of waste tea (WT) and tea leaves (TL) in N2/O2 and CO2/O2 atmospheres through a thermogravimetric/Fourier transform infrared spectrometry (TG-FTIR). The main combustion occurred in the range of 200-600 °C. The increased heating rate increased all the combustion parameters regardless of the fuel and atmosphere type. Activation energy was shown different change tendency with the increasing conversion (α). CO2, H2O, CH4, CO, CO, NH3, and HCN were the main gas products of WT and TL combustions. A three-way interaction among fuel type, atmosphere type and heating rate was found to be significant. The joint optimization of mass loss, derivative TG, and differential scanning calorimetry was achieved using 1049.3 °C, TL, 40 °C/min, and CO2/O2 atmosphere for the operational settings of temperature, fuel type, heating rate, and atmosphere type, respectively.

Combustion behaviors of spent mushroom substrate using TG-MS and TG-FTIR: Thermal conversion, kinetic, thermodynamic and emission analyses.

The present study systematically investigated the combustion characteristics of spent mushroom substrate (SMS) using TG-MS (thermogravimetric/mass spectrometry) and TG-FTIR (thermogravimetric/Fourier transform infrared spectrometry) under five heating rates. The physicochemical characteristics and combustion index pointed to SMS as a promising biofuel for power generation. The high correlation coefficient of the fitting plots and similar activation energy calculated by various methods indicated that four suitable iso-conversional methods were used. The activation energy varied from 130.06 to 192.95 kJ/mol with a mean value of 171.49 kJ/mol using Flynn-Wall-Ozawa and decreased with the increased conversion degree. The most common emissions peaked at the range of 200-400 °C corresponding to volatile combustion stage, except for CO2, NO2 and NO. The peak CO2 emission occurred at 439.11 °C mainly due to the combustion of fixed carbon.

Comparative thermogravimetric analyses of co-combustion of textile dyeing sludge and sugarcane bagasse in carbon dioxide/oxygen and nitrogen/oxygen atmospheres: Thermal conversion characteristics, kinetics, and thermodynamics.

Thermodynamic and kinetic parameters of co-combustion of textile dyeing sludge (TDS) and sugarcane bagasse (SB) were studied using thermogravimetric analysis in CO2/O2 and N2/O2 atmospheres. Our results showed that the comprehensive combustion characteristic index (CCI) of the blends was improved by 1.71-4.32 times. With the increased O2 concentration, co-combustion peak temperature decreased from 329.7 to 318.2 °C, with an increase in its maximum weight loss rate from 10.04 to 14.99%/min and its CCI by 1.31 times (ß = 20 °C·min-1). To evaluate the co-combustion characteristics, thermodynamic and kinetic parameters (entropy, Gibbs free energy and enthalpy changes, and apparent activation energy) were obtained in the five atmospheres. The lowest apparent activation energy of the TB64 blend was obtained in oxy-fuel atmosphere (CO2/O2 = 7/3).

Thermogravimetric Analysis of Textile Dyeing Sludge (TDS) in N2/CO2/O2 Atmospheres and its Combustion Model with Coal.

The combustion characteristics of textile dyeing sludge (TDS) in N2/O2, CO2/O2, and N2/CO2 atmospheres, and blends of TDS with coal were analyzed using TGA (thermogravimetric analysis). Results showed that the replacement of N2 by CO2 resulted in negative effects on the combustion and pyrolysis of TDS. Comparing N2/O2 and CO2/O2 atmospheres, combustion of TDS was easier in a N2/O2 atmosphere, but the residual mass after TDS pyrolysis in pure CO2 was less than that in N2 by approximately 4.51%. When the proportion of TDS was 30-50% in the blends of coal with TDS, a synergistic interaction clearly occurred, and it significantly promoted combustion. In considering different combustion parameters, the optimal proportion of TDS may be between 20-30%. The activation energy Ea value decreased from 155.6 kJ/mol to 53.35 kJ/mol with an increasing TDS proportion from 0% to 50%, and it rapidly decreased when the TDS proportion was below 20%.

Co-combustion of sewage sludge and coffee grounds under increased O2/CO2 atmospheres: Thermodynamic characteristics, kinetics and artificial neural network modeling.

(Co-)combustion characteristics of sewage sludge (SS), coffee grounds (CG) and their blends were quantified under increased O2/CO2 atmosphere (21, 30, 40 and 60%) using a thermogravimetric analysis. Observed percentages of CG mass loss and its maximum were higher than those of SS. Under the same atmospheric O2 concentration, both higher ignition and lower burnout temperatures occurred with the increased CG content. Results showed that ignition temperature and comprehensive combustion index for the blend of 60%SS-40%CG increased, whereas burnout temperature and co-combustion time decreased with the increased O2 concentration. Artificial neural network was applied to predict mass loss percent as a function of gas mixing ratio, heating rate, and temperature, with a good agreement between the experimental and ANN-predicted values. Activation energy in response to the increased O2 concentration was found to increase from 218.91 to 347.32 kJ·mol-1 and from 218.34 to 340.08 kJ·mol-1 according to the Kissinger-Akahira-Sunose and Flynn-Wall-Ozawa methods, respectively.

Influence of catalysts on co-combustion of sewage sludge and water hyacinth blends as determined by TG-MS analysis.

Effects of the three metal carbonates (K2CO3, Na2CO3, and MgCO3) were quantified on catalytic co-combustion of the sewage sludge and water hyacinth (SW) blend using a thermogravimetric-mass spectrometric (TG-MS) analysis and kinetics modeling. The main dominating steps of the catalysts were the organic volatile matter release and combustion stage. Weighted mean values of activation energy (Em) were estimated at 181.18KJ·mol-1, 199.76KJ·mol-1, 138.76KJ·mol-1, and 177.88KJ·mol-1 for SW, SW+5% K2CO3, SW+5% Na2CO3, and SW+5% MgCO3, respectively. The lowest Em occurred with SW+5% Na2CO3. Overall, catalyst effect on co-combustion appeared to be negligible as indicated by Gibbs free energy (ΔG). The normalized intensities of SW+MgCO3 were strongest. The addition of Na2CO3 and MgCO3 to SW increased flue gases emissions (CO2, NO2, SO2, HCN, and NH3) of SW, whereas the addition of K2CO3 to SW reduced flue gases emissions from the entire combustion process.

Investigation of co-combustion characteristics of sewage sludge and coffee grounds mixtures using thermogravimetric analysis coupled to artificial neural networks modeling.

Artificial neural network (ANN) modeling was applied to thermal data obtained by non-isothermal thermogravimetric analysis (TGA) from room temperature to 1000°C at three different heating rates in air to predict the TG curves of sewage sludge (SS) and coffee grounds (CG) mixtures. A good agreement between experimental and predicted data verified the accuracy of the ANN approach. The results of co-combustion showed that there were interactions between SS and CG, and the impacts were mostly positive. With the addition of CG, the mass loss rate and the reactivity of SS were increased while charring was reduced. Measured activation energies (Ea) determined by the Kissinger-Akahira-Sunose (KAS) and Ozawa-Flynn-Wall (OFW) methods deviated by <5%. The average value of Ea (166.8kJ/mol by KAS and 168.8kJ/mol by OFW, respectively) was the lowest when the fraction of CG in the mixture was 40%.

Enhanced bioelectricity generation and azo dye treatment in a reversible photo-bioelectrochemical cell by using novel anthraquinone-2,6-disulfonate (AQDS)/MnOx-doped polypyrrole film electrodes.

A novel anthraquinone-2,6-disulfonate/MnOx-doped polypyrrole film (AQDS/Mn/PPy) electrode was prepared by one-step electropolymerization method and was used to improve performance of a reversible photo-bioelectrochemical cell (RPBEC). The RPBEC was operated in polarity reversion depended on dark/light reaction of alga Chlorella vulgaris by which sequential decolorization of azo dye and mineralization of decolorization products coupled with bioelectricity generation can be achieved. The results showed that formation of uniform AQDS/Mn/PPy film significantly enhanced electroactive surface area and electrocatalytic activity of carbon electrode. The RPBEC with AQDS/Mn/PPy electrodes demonstrated 77% increases in maximum power and 73% increases in Congo red decolorization rate before polarity reversion, and 198% increases in maximum power and 138% increases in decolorization products mineralization rate after polarity reversion, respectively, compared to the RPBEC with bare electrode. This was resulted from simultaneous dynamics improvement in half-reaction rate of anode and photo-biocathode due to enhanced electron transfer and algal-bacterial biofilm formation.

Experimental investigation of synthetic gas composition in a two-stage fluidized bed gasification process: effect of activated carbon as bed material.

In this study, a two-stage fluidized bed gasifier was used to investigate the effect of the equivalence ratio (ER) and steam/biomass ratio (S/B) on the synthetic gas distribution while activated carbon (AC) was added as the bed material in secondary gasifier (Stage II). The experimental results showed that when the empty bed (without the bed material) was used for the Stage II reaction, the hydrogen (H2) content in the synthetic gas emitted from the Stage II reactor was 2-3 mol% higher than that from the first-stage gasifier (Stage I). It was supposed that using the Stage II reactor prolongs the reaction time and thereby increases the H2 production. Besides, when the AC was added in the Stage II gasifier, the H2 concentration, the total gas yield, and gas heating value reached their maximum (30 mol%) when ER and S/B were 0.3 and 1.5, respectively.