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The increasing amount of sludge resources has brought great challenges to the ecological environment and sustainable development. Co-combustion of low calorific value sewage sludge and biosolid waste has become a new and effective way to treat sludge. The co-combustion characteristics of sewage sludge and bamboo scraps in air were studied by thermogravimetric mass spectrometry and artificial neural network. The thermogravimetric analysis of combustion at three different heating rates was performed with five different mixing ratios. Through comprehensive combustion index and cooperative analysis, it is concluded that the co-burning effect and cooperative effect of sludge and bamboo are better. Flynn-Wall-Ozawa and Kissinger-Akahira-Sunose were used to calculate the apparent activation energy. The average activation energy of the sludge mixed with 75% bamboo scraps was the lowest (126.3 kJ mol−1) under the co-combustion condition. The ion current of combustion gas products during combustion was studied by mass spectrometry. Co-combustion can increase NOx and reduce SO 2 , and 75% bamboo slag mixed sludge has the least gas production. Finally, the prediction model of sludge co-combustion is established by artificial neural network, which provides a prospective guidance for the resource utilization of solid waste. [Display omitted] [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]

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]

Confronted with the shortage of fossil energy, the large inventory and the serious pollution of industrial solid waste, the development of clean and efficient industrial solid waste disposal methods have become a trend. In this study, Thermogravimetric-Fourier transform infrared spectrometry was utilized to carry out the co-pyrolysis experiment of coal slime and lignin. Pyrolysis experiments were carried out following 7 different mass mixing ratios. The initial pyrolysis temperatures of CS, S9G1, S7G3, S5G5, S3G7, S1G9, and LIG were 414.5, 373.3, 287.6, 233.3, 225.8, 218.6, and 209.6 °C, respectively. By observing the evolution of the gaseous products of the sample pyrolysis, the results showed that the gaseous products mainly include hydrocarbons, aldehydes, ethers, and alcohols. The ratio of lignin in the mixture was changed, and the interaction between the sample particles was different. The principal component analysis method provided helps to understand the mechanism of co-pyrolysis of coal slime and lignin. The relative error of the established artificial neural network prediction was less than 2.5%. This paper comprehensively analyzed the interaction and gas evolution law during the co-pyrolysis of coal slime and lignin. [Display omitted] • The relative error of ANN 46 model was less than 2.5%. • The main reaction of sample pyrolysis was studied by principal component analysis. • The blends had a strong interaction between 700 and 900 °C. • Blending 10% lignin in coal slime could inhibit the release of CO 2 by 4.2003%. [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]

• First study on application of PCA to co-pyrolysis of CS and CIR. • Using ANN model to predicted TG data of co-pyrolysis of CS and CIR. • The pyrolysis properties of the blends were improved by adding CIR. • The co-pyrolysis of CS and CIR had obvious synergistic effect. • The emissions of greenhouse gases could be reduced by adding 30% CIR to the blends. In this study, thermogravimetry and Fourier transform infrared spectroscopy (TG-FTIR) were used to analyze the co-pyrolysis characteristics of coal slime (CS) and coffee industry residue (CIR) at different heating rates. The CS and CIR were mixed according to five mass ratios of 1:0, 7:3, 5:5, 3:7 and 0:1. Through the detection of gas emission and mass loss rate with temperature changing, the results showed that the co-pyrolysis of CS - CIR revealed a synergistic effect, and blending of 30 % CIR in CS could reduce greenhouse gas emissions. Principal component analysis (PCA) was utilized to reduce the dimensionality of experiment and identify the main reactions of CIR-CS co-pyrolysis. Two non-isothermal methods (Kissinger – Akahira - Sunose and Flynn – Wall - Ozawa) determined the law of kinetic parameter (E α) and thermodynamic parameters changing with the degree of conversion (α). Three input parameters (temperature, heating rate, and blending ratio) and one output parameter (mass loss percentage) were used in artificial neural network (ANN) model to predict CS and CIR co-pyrolysis TG data. ANN 11 was the best predictive model for the co-pyrolysis of CS and CIR. [ABSTRACT FROM AUTHOR]

• First study on application of ANN model to co-pyrolysis of SS and PS. • The predicted data of ANN were in good agreement with the experimental results. • The gas products of co-pyrolysis were detected. • Activation energy was estimated from the KAS and FWO methods. • ANN21 is the best model for predicting SS and PS pyrolysis. The co-pyrolysis characteristics of sewage sludge (SS) and peanut shell (PS) under nitrogen atmosphere were studied by thermogravimetric – Fourier transform infrared spectrometry (TG-FTIR) and artificial neural network (ANN). The proportion of PS in the experimental samples is 0, 10, 20, 30, 40, 50, 60, 70, 80, 90 and 100%. The experiments were performed at three heating rates of 5 °C/min, 7 °C/min and 10 °C/min. The variation of sample mass with temperature and gas emission were detected in this paper. SS and PS in the co-pyrolysis experiment have a synergistic effect in terms of mass loss at 335 °C ∼ 500 °C. Gases generated by pyrolysis of the mixture of SS and PS include H 2 O, CH 4 , CO 2 , CO, phenol and NH 3. The functional groups detected in the experiment include C O and C O. Apparent activation energy E was obtained by two non-isothermal kinetic analysis methods (Flynn–Wall–Ozawa and Kissinger–Akahira–Sunose). The artificial neural network method was used to predict the relationship between the mass loss of pyrolysis experiments and the change of temperature. The best ANN model (ANN21) for predicting SS and PS pyrolysis was identified. [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]

The combustion behavior and gas product characteristics of coal gangue (CG) and peanut shell (PS) in air atmosphere were studied by thermogravimetry-Fourier transform infrared spectroscopy (TG-FTIR). Artificial neural network (ANN) method was used to establish the optimal prediction model of CG and PS co-combustion. The heating rate of TG-FTIR experiment was set to 10 °C/min, 20 °C/min and 30 °C/min. The mass fractions of PS in the experimental samples were 0%, 25%, 50%, 75% and 100%. Some functional groups in the gas products were detected by Fourier transform infrared spectrometer. Moreover, the apparent activation energy (E) was calculated by Flynn-Wall-Ozawa (FWO) and Kissinger-Akahira-Sunose (KAS). The activation energies of CG and PS mixture combustion are significantly lower than that of pure substance. ANN models have been established to predict the relationship between mass loss and experimental conditions. By comparing errors and correlation coefficients, it is found that the ANN20 model is optimal. Image 1 • First study on application of ANN model to co-combustion of CG and PS. • The predicted data of ANN were in good agreement with the experimental results. • The gas products of co-combustion were detected. • Activation energy was estimated from the KAS and FWO methods. • ANN20 is the best model for predicting CG and PS combustion. [ABSTRACT FROM AUTHOR]

In this study, Thermogravimetric-Mass spectrometry-Fourier transform infrared spectrometry (TG-MS-FTIR) were used to study the co-pyrolysis behavior and gaseous products of coal slime (CS) and cattle manure (CM). By establishing different artificial neural network (ANN) prediction models, it was found that MLP13 model is the best prediction model. It was determined that the pyrolysis process of CM and CS can be divided into three stages, of which the second stage has the largest mass loss. Due to the different mixing ratio, there will be synergistic interaction or inhibitory effect during co-pyrolysis of CM and CS. Adding CM to CS will improve the pyrolysis performance of CS. The Kissinger-Akahira-Sunose (KAS) and Flynn-Wall-Ozawa (FWO) methods were used to calculate the activation energy. The activation energy was the lowest when the mixing ratio is CM:CS = 7:3, which was 195.377 kJ/mol (FWO) and 195.008 kJ/mol (KAS), respectively. [Display omitted] • Activation energy of co-pyrolysis were calculated by two different methods. • Adding cattle manure to coal slime can improve its pyrolysis performance. • Use the ANN model to study the co-pyrolysis of cattle manure to coal slime. • The prediction of MLP13 model were good agreement with the thermogravimetric data. [ABSTRACT FROM AUTHOR]