• Energy Research

Abstract The present study experimentally quantified the pyrolysis behaviors of waste tea (WT) as a function of four heating rates using thermogravimetric-Fourier transform infrared spectrometry and pyrolysis-gas chromatography-mass spectrometry analyses. The maximum weight loss of WT (66.79%) occurred at the main stage of devolatilization between 187.0 and 536.5 °C. The average activation energy estimates of three sub-stages of devolatilization were slightly higher (161.81, 193.19 and 224.99 kJ/mol, respectively) by the Flynn-Wall-Ozawa than Kissinger-Akahira-Sunose method. Kinetic reaction mechanisms predicted using the master-plots were f (α) = (3/2)(1 − α)2/3[1 − (1 − α)1/3]−1, f (α) = (1 − α)2, and f (α) = (1 − α)2.5 for the three sub-stages, respectively. The prominent volatiles of the WT pyrolysis were CO2 > C O > phenol > CH4 > C O > NH3 > H2O > CO. A total of 33 organic compounds were identified including alkene, acid, benzene, furan, ketone, phenol, nitride, alcohol, aldehyde, alkyl, and ester. This study provides a theoretical and practical guideline to meeting the engineering challenges of introducing WT residues in the bioenergy sector.

Given their non-biodegradable, space-consuming, and environmentally more benign nature, waste bicycle tires may be pyrolyzed for cleaner energies relative to the waste truck, car, and motorcycle tires. This study combined thermogravimetry (TG), TG-Fourier transform infrared spectroscopy (TG-FTIR), and pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS) analyses to dynamically characterize the pyrolysis behavior, gaseous products, and reaction mechanisms of both waste rubber (RT) and polyurethane tires (PUT) of bicycles. The main devolatilization process included the decompositions of the natural, styrene-butadiene, and butadiene rubbers for RT and of urethane groups in the hard segments, polyols in the soft segments, and regenerated isocyanates for PUT. The main TG-FTIR-detected functional groups included C-H, C=C, C=O, and C-O for both waste tires, and also, N-H and C-O-C for the PUT pyrolysis. The main Py-GC/MS-detected pyrolysis products in the decreasing order were isoprene and D-limonene for RT and 4, 4'-diaminodiphenylmethane and 2-hexene for PUT. The kinetic, thermodynamic, and comprehensive pyrolysis index data verified the easier decomposition of PUT than RT. The pyrolysis mechanism models for three sub-stages of the main devolatilization process were best described by two-dimensional diffusion and two second-order models for RT, and the three consecutive reaction-order (three-halves order, first-order, and second-order) models for PUT.

This study aimed to quantify the catalytic effects of CaO, Fe2O3, and their blend on the Lentinus edodes stipe (LES) and pileus (LEP) combustion performances, kinetics and emissions in bioenergy generation. Apparent activation energy (Ea) of LES and LEP increased with CaO, decreased with Fe2O3 and differed with their blend. The catalysts mainly affected the maximum intensity of volatiles combustion and partly the fixed carbon combustion. CaO, Fe2O3, and their blend decreased the release intensity of NOx from the LES combustion. Fe2O3 increased SO2 emission, while CaO, and the blend narrowed the emission temperature to the range of 200 to 450 °C. Kinetic triplets were estimated via the integral master-plots methods, and the best-fit reaction for the three sub-stages were obtained coupled with the model-free models. Our study provides a reference for the catalyzed biomass combustion in terms of pollution control, bioenergy generation, optimal design of incinerator, and industrial-scale application.

Abstract Bioenergy generation from biomass waste through co-combustion/pyrolysis fulfills simultaneously multiple objectives of reductions in fossil fuel use, greenhouse gas emission, and solid waste stream. This experimental study aimed to quantify the multiple co-combustion/pyrolysis responses of textile dyeing sludge (TDS) and incense sticks (IS) as a function of blend ratio (BR), heating rate (HR), atmosphere type (Atm), and temperature (Temp). Joint optimizations, and predictor importance, sensitivity, uncertainty and interaction analyses were conducted using data-driven models for the responses of remaining mass (RM), derivative thermogravimetry (DTG), and differential scanning calorimetry (DSC). The data-driven models compared in this study were Box Behnken design (BBD)-based regression models, general linear models (GLM), and the six full models with all the predictors included of multivariate adaptive regression splines, multiple linear regressions, random forests (RF), regression decision tree (RDT), RDT with ensemble and bagger, and gradient boosting machine. BBD, GLM, and Sobol’s total and first-order indices indicated HR as the most important and sensitive predictor in the joint optimizations. GLM pointed to a three-way interaction among HR, BR, and Atm, while BBD, and Sobol’s second-order index showed a two-way interaction between HR and BR as the most important ones. RF outperformed the other full models for all the responses in terms of validation metrics. RF showed the two most important predictors as Temp and BR for RM; HR and Temp for DSC; and Temp and HR for DTG, respectively, which also constituted the most important two-way interactions.

Abstract The present study aims at quantifying mass loss percentage (MLP, %) predictions and their stochastic uncertainty when co-combustion of sewage sludge (SS) and water hyacinth (WH) are applied as alternative biomass materials under different blend ratios (BR), heating rates (HR, °C/min) and temperatures (T, °C). Optimization and validation of experimental data through Box–Behnken design pointed to 630.9 °C for T, 60.1% SS for BR, and 29.9 °C/min for HR as the optimal co-combustion parameters to achieve the maximum MLP of 92.4%. Monte Carlo (MC) simulations were used to quantify uncertainty in MLP predictions of the best-fit multiple non-linear regression (MNLR) model derived from the entire experimental data as a function of MC-generated T as the only continuous predictor of the MNLR. Mean MLP value of the MNLR predictions was higher by 19% than that of the MC-simulated T whose mean was higher by only 1% than mean measured T. Incorporating the uncertainty estimation based on Monte Carlo simulations with response surface approach for co-combustion of SS and WH was one of the main novel contributors of the present study to related literature.

Abstract Co-combustion characteristics of textile dyeing sludge (TDS) and pomelo peel (PP) under O2/N2 and O2/CO2 atmospheres were investigated using a thermogravimetric analysis (TGA) and artificial neural networks. 30% O2/70% CO2 and air atmospheres led to a similar co-combustion performance. Increases in O2 concentration and PP significantly improved the oxy-fuel co-combustion performance of TDS. Principal component analysis was applied to reduce the dimensionality of differential TGA curves and to identify the principal reactions. The interaction between TDS and PP occurred mainly at 490–600 °C, thus improving the process of residue co-combustion. Radial basis function was found to have more reliable and robust predictions of TGA under different O2/CO2 atmospheres than did Bayesian regularized network. Regardless of Flynn-Wall-Ozawa (FWO) and Kissinger-Akahira-Sunose (KAS) methods used, the lowest mean value of apparent activation energy (155.4 kJ·mol−1 by FWO and 153.2 kJ·mol−1 by KAS) was obtained under the 30% O2/70% CO2 atmosphere.

Abstract This study aimed at the quantification of thermochemical behaviors of textile dye sludge (TDS), paper mill sludge (PMS), and their various blend ratios during (co-)combustion. Changes in mass loss percentage (MLP) and rate (MLR) were estimated as a function of temperature, heating rate, and blend ratio. Our results pointed to a direct influence of blend ratios on mass loss percentage. Stochastic uncertainties and sensitivities associated with best-fit predictions of MLP and MLR responses were detected using all-at-once versus one-at-a-time Monte Carlo simulations. Co-combustion of TDS and PMS provided more flame stability owing to their volatile matter contents than did the combustion of pure TDS or PMS. The high C and H contents of TDS relative to those of PMS led to high lower heating value (higher energy). Effect of increased temperature on the co-combustion manifested itself in the breakdown of (hemi-)cellulose initially and lignin progressively in TDS and PMS. The increasing TDS of the blend ratio increased both MLP and MLR. Stochastic uncertainty analysis of the deterministic empirical models pinpointed overestimation by 15.6% in mean MLP and by 50% in mean MLR. Sensitivity analysis pointed to blend ratio as the most influential predictor on both MLP and MLR.

(Co-)combustion of pine sawdust (PS) and lignite coal (LC) were investigated using artificial neural networks (ANN), particle swarm optimization (PSO), and Monte Carlo simulation (MC) as a function of blend ratio, heating rate, and temperature via thermal conversion characteristics. The order of degraded compounds in terms of hemi-cellulosic and lignin-based compounds demonstrated the main oxidation and degradation mechanism of co-combustion of PS and LC. The best prediction (R2 of 99.99%) was obtained by ANN28 model. Operating conditions of 90LC10PS, 425 °C, and 19 °C min-1 were determined by PSO as optimum levels with TG value of 67.5%. Once three-replicated validation experiments were performed under PSO-optimized conditions, mean TG values ware observed as 67.5% with a standard deviation of ±0.4%. Consequently, MC was used to identify the stochastic variability and uncertainty associated with ANN models that were derived to predict TG values.

Abstract The catalytic combustions of bamboo leaves (BL) and branches (BB) with textile dyeing sludge ash (SA), Fe2O3, and CaO were qualitatively analyzed using thermogravimetric and Fourier transform infrared spectroscopy analyses, and thermodynamic equilibrium simulations. The catalysts (Fe2O3 > SA > CaO) exerted a more pronounced effect in the char combustion (third) stage and enhanced the volatiles and comprehensive combustion indices with 40 °C/min. The catalysts (CaO > SA > Fe2O3) reduced C- and N-containing gas emissions in the devolatilization (second) stage. CaO elevated the N-containing gas emission in the third stage, whereas Fe2O3, and SA inhibited the formation of NO precursors. BB presented a higher risk of slagging than did BL, while the improved empirical indices of the ash deposition pointed to CaO as the optimal catalyst. Our simulations showed the final ash components of BL and BB were mainly as SiO2 and K2Si4O9. The addition of CaO alone helped to form a high-melting point Ca-silicate. Although the addition of Fe2O3 had no effect on the ash conversion, SA reduced the formation of K-silicate in the ash. The catalysts (CaO > SA > Fe2O3) reduced the activation energy. Overall, the catalytic combustions improved the bioenergy and the N-containing gas emissions. SA as a Fe and Ca-rich industrial waste enhanced the combustion performance in terms of reductions in waste streams, gas emissions, and ash deposition. Our results supplied new insights into the efficient and clean bioenergy production of bamboo residues, and the waste utilization of SA.