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In this study, co-pyrolysis experiments were carried out by blending coal slime (CS) and traditional Chinese medicine residue (CMR) in different proportions under nitrogen atmosphere using thermogravimetry-Fourier infrared spectroscopy-mass spectrometry (TG-FTIR-MS). Kissinger-Akahira-Sunose (KAS) and Flynn-Wall-Ozawa (FWO) were used to calculate the activation energy (E) of samples with different conversion rates (α). The PCA method was used to analyze and compare the contribution of samples with different blending ratios to the principal components, and draw its score and load graph. A variety of artificial neural network models composed of different network topologies are selected to simulate and predict the remaining mass of the CS and CMR mixture pyrolysis, and the optimum model is obtained. [ABSTRACT FROM AUTHOR]

This study investigated the co-pyrolysis characteristics and kinetics of sewage sludge (SS) and wet waste (WW) using thermogravimetric-Fourier transform infrared spectrometry-gas chromatography/mass spectrometry (TG-FTIR-GC/MS) and artificial neural network (ANN). The proportion of WW is 0, 10, 30, 50, 70, and 100%, respectively. These mixtures were heated from 30 to 900 °C at three heating rates (10, 20, and 40 °C/min). The change of gas functional groups with different blends (-OH, –CH, CO 2 , C C, phenol, CO, and NH 3) was detected by FTIR. S3W7 has a synergistic effect on the pyrolysis in all temperature ranges and can also greatly suppress CO 2 emission (−35.25%), which is of practical significance to carbon neutrality. S3W7 was recommended as the best ratio. The gas products of S3W7 were obtained by GC/MS, which were mainly nitrides (C 5 H 5 N, C 4 H 11 N, etc.), hydrocarbons containing C O (C 3 H 6 O2, C 7 H 8 O 2 , etc.), and furans (C 5 H 6 O 2 , C 6 H 8 O, etc.). The apparent activation energy (E) was measured using Flynne-Walle-Ozawa (FWO) and Kissinger-Akahira-Sunose (KAS) methods. Machine learning methods were used to analyze the pyrolysis. ANN19 was found the best prediction model of 21 models. The equation to predict TG data was established. [Display omitted] [ABSTRACT FROM AUTHOR]

This work focused on the evaluation of co-combustion of sewage sludge (SS) and rice husk (RH) using thermogravimetric–Fourier transform infrared spectrometry method (TG–FTIR). The thermal behavior was assessed through the combustion characteristic, interaction, kinetics, and gaseous product characteristics. The (co-)combustion process was divided into three stages. Index D exponentially increased as RH blending ratio increased and index S also increased from 10 to 70% RH blending ratio, indicating that the combustion behavior was improved by the RH blending. The activation energy was evaluated by two model-free iso-conversional methods: Flynn–Wall–Ozawa (FWO) and Kissinger–Akahira–Sunose (KAS). The minimum value of average activation energy among blends was obtained at sample with 30% RH blending, which was 103.31 kJ/mol by FWO and 97.81 kJ/mol by KAS. Several typical gaseous products and functional groups were detected by FTIR spectrum. The results showed that CO2 was the main volatile and its yield increased with the RH blending ratio. [ABSTRACT FROM AUTHOR]

Scrap tire is a typical waste fuel, for which the combustion in the form of pellets is a feasible way. In this work, the ignition behavior, combustion process and pollutant emission of the scrap tire in pellet form are explored. The experiments were performed in the vertical heating tube furnace under different coflow conditions in CO2/O2 atmosphere. The temperature was set to 873, 973, 1073 K and the gas flow rate was set to 5, 10 and 20 L/min. The combustion images of scrap tire were recorded by the high-performance camera. The scrap tire pellet presented the characteristics of homogeneous ignition and the fly ash was captured around the scrap tire pellets. Increasing oxygen concentration and temperature shortened the ignition delay time and reduced the internal ignition temperature. The splash flame occurred during the volatile combustion under the conditions of high temperature, high oxygen concentration, and high gas flow rate. The distinct pores and cracks were found on the surface of char during the char combustion. The yields of pollutant gas increased as the gas flow rate increased from 5 to 20 L/min. With the increase of temperature, the emission of NO and SO2 was increased but NO2 emission was decreased. [ABSTRACT FROM AUTHOR]

Summary: To solve the problems of energy shortage and waste accumulation, the method of co‐combustion of sewage sludge (SS) and peanut shells (PS) was proposed. SS‐PS co‐combustion characteristics in air were investigated using artificial neural networks (ANN) and thermogravimetric analyses (TGA). The proportion of PS in the mixture was 10%‐50%. The temperature range of PS combustion (160‐560°C) is lower than that of PS combustion (170‐600°C). Activation energy was estimated from two non‐isothermal kinetic analysis methods: Kissinger‐Akahira‐Sunose (KAS) and Flynn‐Wall‐Ozawa (FWO). The kinetic mechanism of the combustion process was determined by using the master‐plots method. Multiple ANN models were established to predict TG data of SS‐PS co‐combustion. The best prediction model (ANN21) was obtained. The results showed a good overlap between the predicted and experimental TG data. The ANN model and the master‐plots method are the main innovations of this study. This study can promote the utilization and reduction of solid waste, and give guidance for the large‐scale application of SS‐PS co‐combustion. [ABSTRACT FROM AUTHOR]

Based on mass loss data of sewage sludge (SS) and peanut shell (PS) during combustion and pyrolysis obtained by thermogravimetric analysis (TGA), artificial neural networks (ANN) models were established to predict the experimental results in this paper. The effects of mixing ratio and temperature on the combustion and pyrolysis of SS-PS were studied. Mixing ratio and experimental temperature were taken as input parameters of ANN model, and sample mass was taken as output parameter. The optimal ANN models (ANN12 for combustion and ANN11 for pyrolysis) were determined, which can well predict the thermogravimetric curves of combustion and pyrolysis with the correlation coefficient of 0.99999. The predicted data are in good agreement with the experimental data of the ANN model, which proves the reliability and accuracy of the application of ANN for the thermogravimetric experiments of SS and PS. The established optimal ANN model can be used to predict the weight loss of SS-PS heat treatment without experiment. [ABSTRACT FROM AUTHOR]

Summary: This study applied the artificial neural networks (ANNs) model to the thermal data obtained by suspension ignition and combustion experiment of single peanut shells (PS, millimeter scale) pellet under O2/CO2 atmosphere. ANN11 was the best ANN model for predicting the relevant parameters of PS combustion. The coincidence between ANN prediction data and experimental data was over 99%. Two modes of biomass pellet ignition were observed: homogeneous ignition of volatiles and hetero‐homogeneous ignition of volatiles and char simultaneously. The ignition mode was transformed from homogeneous ignition to hetero‐homogeneous ignition when oxygen concentration was 50%. In addition, it was observed that ignition at the bottom emerged first, and then the upper end was ignited, finally generating an envelope flame. This phenomenon occurred when gas flow temperature exceeded 873 K or the oxygen concentration was greater than 50%. The reduction of ignition delay time and internal ignition temperature from 21% to 50% oxygen concentration was more intense than that of 50% to 100% oxygen concentration. Increasing oxygen concentration or temperature resulted in a shorter, brighter, and more stable volatile flame of biomass pellets, which reduced volatile burnout time. Nevertheless, the impact of gas flow rate on biomass combustion was intricate and irregular. [ABSTRACT FROM AUTHOR]

• Artificial neural network predicted experiment data of samples combustion. • Principal component analysis determined the main reaction of samples combustion. • Adding coffee industry residue to samples could improve combustion performance. • Co-combustion of samples could inhibit the emission of toxic and harmful gases. Faced with the problems of the environment and resources, the co-combustion of sewage sludge has gradually become a trend. In this paper, the combination of thermogravimetric and Fourier transform infrared spectroscopy (TG-FTIR) was used to study the co-combustion of sewage sludge (SS) and coffee industry residues (CIR). SS and CIR were mixed according to the mass ratios of 1:0, 9:1, 7:3, 5:5, 3:7, 1:9, and 0:1, and the temperature was programmed to be heated to 800 ℃ in the atmosphere with an air flow rate of 50 mL/min. The percentages of mass loss during combustion of SS and CIR were 55.8% and 96.8%, respectively. As the mass percentage of CIR increased, the comprehensive combustion index (CCI) of the sample improved. The Kissinger-Akahira-Sunose (KAS), Flynn-Wall-Ozawa (FWO), and Starink methods were used to evaluate the activation energy (E α) of the reaction. Principal component analysis (PCA) and artificial neural network (ANN) were used to determine the main reaction and predict experimental data of co-combustion of SS and CIR, respectively. [ABSTRACT FROM AUTHOR]

The harm done to the environment by coal gangue was very serious, and it is urgent to adopt effective methods to dispose of coal gangue in order to prevent further environmental damage. Co-pyrolysis experiments of coal gangue (CG) and peanut shell (PS) were carried out using thermogravimetry-Fourier transform infrared spectroscopy (TG-FTIR) under nitrogen atmosphere. The heavy metal was detected using the inductively coupled plasma-optical emission spectroscopy (ICP-OES). CG and PS were mixed according to the mass ratio of 1:0, 3:1, 1:1, 1:3 and 0:1. The samples were heated to 1000 °C at the heating rate of 10 °C/min, 20 °C/min and 30 °C/min. The comprehensive pyrolysis index (CPI) of CG, C3P1, C1P1, C1P3 and PS is 0.17 × 10−8, 9.75 × 10−8, 35.47 × 10−8, 100.94 × 10−8 and 192.72 × 10−8%2 ·min−2·°C−3. The kinetic parameters were calculated by model-free methods (Flynn–Wall–Ozawa and Kissinger–Akahira–Sunose). The gas products generated at different temperatures during the pyrolysis experiment were detected by Fourier transform infrared spectrometer. The heating rate, temperature and mixing ratio are the input parameters of artificial neural network (ANN), and the remaining mass percentage of sample during the pyrolysis is the output parameter. The ANN model was established and used to predict thermogravimetric experimental data. The ANN18 model is the best model for predicting the co-pyrolysis of CG and PS. Unlabelled Image • First study on application of ANN model to co-pyrolysis of CG and PS • Synergistic effect between CG and PS was found during C3P1 and C1P3 co-pyrolysis. • The TG-FTIR was used to detect the functional groups of gas during pyrolysis. • Activation energy was estimated from the KAS and FWO methods. • The ultimate goal of this study is to reduce environmental pollution caused by CG. [ABSTRACT FROM AUTHOR]