Influence of biomass on coal slime combustion characteristics based on TG-FTIR, principal component analysis, and artificial neural network.

The development and utilization of solid waste is an effective way to solve the severe environmental and energy crisis. In this study, Thermogravimetry - Fourier transform infrared spectrometry (TG-FTIR) was used to carry out the co-combustion experiment of coal slime and rice husk under different mixing ratios. With the increase of the mass percentage of rice husk in the sample, the initial ignition temperature and burnout of the sample decreased, and the comprehensive combustion performance improved gradually. The dominant reaction in the co-combustion of coal slime and rice husk was determined by statistical method. When the mass percentage of rice husk in the mixture is between 30 and 90 %, it can inhibit the release of NOx and SO2. Taking Kissinger-Akahira-Sunose method as an example, the calculated average activation energies of coal slime and rice husk combustion are 105.66 and 148.93 kJ/mol respectively. With the increase of the mixing ratio of rice husk in the blend, the combustion mechanism of the sample changed. Finally, the mean absolute error, root mean square error and determination coefficient of the artificial neural network model are 0.52697, 0.67866 and 0.99941 respectively.

Research on the co-pyrolysis of coal gangue and coffee industry residue based on machine language: Interaction, kinetics, and thermodynamics.

Co-pyrolysis technology of urban solid waste and biomass has broad application prospects in alleviating energy crisis and environmental pollution. In this study, thermogravimetric-Fourier transform infrared spectroscopy (TG-FTIR) was used to study the co-pyrolysis characteristics of coal gangue (CG) and coffee industry residue (CIR). CG and CIR were uniformly mixed according to the mass ratios of 1: 0, 7:3, 5:5, 3:7, and 0:1. Then the samples were heated and pyrolyzed in an atmosphere with a nitrogen flow rate of 60 mL/min. As the proportion of CG increased, the comprehensive pyrolysis index (CPI) showed an exponential decrease. FTIR detected that the gas produced by pyrolysis of CG-CIR contained hydroxyl compounds, hydrocarbons, CO2, CO, Phenols, and NH3. CG-CIR co-pyrolysis had obvious interaction. By using Kissinger-Akahira-Sunose (KAS) and Flynn-Wall-Ozawa (FWO) methods, the relationship between activation energy (Eα) and kinetic parameters and conversion degree was studied. Principal component analysis (PCA) was used to determine the principal reaction of CG-CIR pyrolysis. ANN 21 was the best model for predicting the pyrolysis of CG-CIR.

Influence of lignin on coal gangue pyrolysis and gas emission based on multi-lump parallel reaction model and principal component analysis.

The effects of lignin (LIG) on coal gangue (CG) pyrolysis characteristics, gas emissions, and multi-lump parallel reaction modeling are studied in this paper. The combined thermogravimetry and Fourier infrared spectrometer were used to obtain the weight loss and gas product functional group data of the co-pyrolysis of coal gangue and lignin. Gaussian peaks were used to analyze the reaction degree of different proportions of lignin added to coal gangue at different temperatures. Moreover, principal component analysis (PCA) is applied to further analyze pyrolysis reaction process. During the pyrolysis of C1L3 samples, a synergistic interaction was discovered. The optimal mixing ratio of coal gangue and lignin during pyrolysis was obtained. The influence mechanism of lignin on coal gangue pyrolysis is studied in this article, which can provide technical support for the reduction and resource utilization of coal gangue. There are six-lump parallel reactions in pyrolysis after CG and LIG are mixed. The addition of LIG changed the CO2 release law of CG pyrolysis. The strengthening of coal gangue disposal research is of positive significance for improving the environment and increasing the utilization rate of coal.

Au Nanoparticles Supported on Iron-Based Oxides for Soot Oxidation: Physicochemical Properties Before and After the Reaction.

The catalytic performance of Au nanoparticles (NPs) supported on different transition-metal oxides for soot oxidation was studied in this paper. The changes in the morphology, phase structure, and physicochemical properties of Au-supported iron-based oxides before and after the reaction with soot particles were observed by high-resolution transmission electron microscopy, X-ray diffraction, X-ray photoelectron spectroscopy, and temperature-programed reduction. It was found that the catalytic activity of Fe3O4, Fe2O3, Co3O4, and NiO for soot oxidation was significantly improved after loading Au NPs. Especially, under the action of Au/Fe2O3 and Au/Fe3O4, the oxidation of soot was close to 20% below 420 °C, and their T 10 values were 73 and 50 °C, respectively. When Au/Fe2O3 and Au/Fe3O4 reacted with soot, the size of the catalysts increased, and the active oxygen and Fe 2p components decreased. Au promoted the reduction of iron ions to a lower temperature, which was beneficial to improving the oxidation performance of iron-based oxides.

Pyrolysis characteristics, artificial neural network modeling and environmental impact of coal gangue and biomass by TG-FTIR.

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.

The effect of biomass addition on pyrolysis characteristics and gas emission of coal gangue by multi-component reaction model and TG-FTIR-MS.

The pyrolysis experiment of biomass added to coal gangue was studied by thermogravimetric-Fourier transform infrared spectroscopy-mass spectrometry (TG-FTIR-MS) method. The multi-component reaction model was used to simulate the pyrolysis reaction of coal gangue and biomass. The most suitable model was obtained, and the pyrolysis mechanism was analyzed. According to the two-component reaction model of CG pyrolysis, the decomposition temperature range of components in CG is 340-800 °C and 400-620 °C. The five-component reaction model can well simulate the pyrolysis process of coal gangue and biomass. Meanwhile, the effects of different proportions of biomass in the mixture on the gas products of coal gangue pyrolysis were analyzed. It was found that the addition of biomass to coal gangue could promote the release of gaseous organic matter during pyrolysis. CG75PS25 only has a synergistic effect in the high temperature zone greater than 600 °C. CG25PS75 only has a synergistic effect in a small range of 230-300 °C, and there is an inhibitory effect in other temperature ranges. In general, there is an inhibitory effect between coal gangue and biomass on CO2 formation, which is of positive significance for greenhouse gas emission reduction.

A computer-aided method for identifying the presence of softwood growth ring boundaries.

The objective of this study was to develop a computer-aided method to quantify the obvious degree of growth ring boundaries of softwood species, based on data analysis with some image processing technologies. For this purpose, a 5× magnified cross-section color micro-image of softwood was cropped into 20 sub-images, and then every image was binarized as a gray image according to an automatic threshold value. After that, the number of black pixels in the gray image was counted row by row and the number of black pixels was binarized to 0 or 100. Finally, a transition band from earlywood to latewood on the sub-image was identified. If everything goes as planned, the growth ring boundaries of the sub-image would be distinct. Otherwise would be indistinct or absent. If more than 50% sub-images are distinct, with the majority voting method, the growth ring boundaries of softwood would be distinct, otherwise would be indistinct or absent. The proposed method has been visualized as a growth-ring-boundary detecting system based on the .NET Framework. A sample of 100 micro-images (see S1 Fig via https://github.com/senly2019/Lin-Qizhao/) of softwood cross-sections were selected for evaluation purposes. In short, this detecting system computes the obvious degree of growth ring boundaries of softwood species by image processing involving image importing, image cropping, image reading, image grayscale, image binarization, data analysis. The results showed that the method used avoided mistakes made by the manual comparison method of identifying the presence of growth ring boundaries, and it has a high accuracy of 98%.

Combined dilute hydrochloric acid and alkaline wet oxidation pretreatment to improve sugar recovery of corn stover.

Two-stage dilute hydrochloric acid (DA)/aqueous ammonia wet oxidation (AWO) pretreatment was used to recover the sugars of corn stover. The morphology characterizations of samples were detected by SEM, BET and SXT. The results showed that DA-AWO process demonstrated a positive effect on sugar recovery compared to AWO-DA. 82.8% of xylan was recovered in the first stage of DA-AWO process at 120 °C for 40 min with 1 wt% HCl. The second stage was performed under relative mild reaction conditions (130 °C, 12.6 wt% ammonium hydroxide, 3.0 MPa O2, 40 min), and 86.1% lignin could be removed. 71.5% of glucan was achieved with a low enzyme dosage (3 FPU·g-1) in the following enzymatic hydrolysis. DA-AWO pretreatment was effective due to its sufficient hydrolysis of hemicellulose in the first stage and remarkably removal of the lignin in the second stage, resulting in high sugar recovery with a low enzyme dosage.