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

Study on microstructure evolution and oxidation kinetics in Coal-Oil Symbiosis

Differences in the spontaneous combustion mechanism characteristics of Coal-Oil Symbiosis (COS) significantly affect coal mines' safety management and ecological environment maintenance. Accordingly, this study aims to investigate COS's macroscopic and microstructural characteristics with different oil mass percentage using simultaneous thermal analysis, low-temperature N2 adsorption, scanning electron microscopy (SEM), and in-situ Fourier transform infrared spectroscopy (FTIR). The results showed that with the increase of oil mass percentage, the COS displayed the weakening of oxygen absorption and the advance of some characteristic temperatures, and 11.5 °C advanced the maximum weight loss temperature on average. For the 25 % oil sample, the ignition temperature was 9.5 °C lower than that of the raw coal. Additionally, the apparent activation energy of the high oil mass percentage sample was significantly reduced in the pyrolysis and combustion stages, and when the oil mass percentage was 25 %, the activation energies of the two stages decreased by 89 % and 60.65 %, respectively. Compared to raw coal, COS exhibits fewer macropores and surface pores covered by oil, which limits oxygen adsorption. Moreover, COS with higher oil mass percentage had an increase in hydroxyl and aliphatic hydrocarbon groups, and the CH3 + CH2 content of COS increased by 69.2 % on average, providing more active groups, thereby promoting spontaneous combustion. This study provides an important reference and theoretical support for further understanding the structural evolution and oxidation kinetic behavior of COS, contributing to disaster prevention and ecological environmental protection in coal-oil coexistence mining areas.

Uptake of zinc from the soil to the wheat grain: Nonlinear process prediction based on artificial neural network and geochemical data

Trace elements in plants primarily derive from soils, subsequently influencing human health through the food chain. Therefore, it is essential to understand the relationship of trace elements between plants and soils. Since trace elements from soils absorbed by plants is a nonlinear process, traditional multiple linear regression (MLR) models failed to provide accurate predictions. Zinc (Zn) was chosen as the objective element in this case. Using soil geochemical data, artificial neural networks (ANN) were utilized to develop predictive models that accurately estimated Zn content within wheat grains. A total of 4036 topsoil samples and 73 paired rhizosphere soil-wheat samples were collected for the simulation study. Through Pearson correlation analysis, the total content of elements (TCEs) of Fe, Mn, Zn, and P, as well as the available content of elements (ACEs) of B, Mo, N, and Fe, were significantly correlated with the Zn bioaccumulation factor (BAF). Upon comparison, ANN models outperformed MLR models in terms of prediction accuracy. Notably, the predictive performance using ACEs as input factors was better than that using TCEs. To improve the accuracy, a two-step model was established through multiple testing. Firstly, ACEs in the soil were predicted using TCEs and properties of the rhizosphere soil as input factors. Secondly, the Zn BAF in grains was predicted using ACE as input factors. Consequently, the content of Zn in wheat grains corresponding to 4036 topsoil samples was predicted. Results showed that 85.69 % of the land was suitable for cultivating Zn-rich wheat. This finding offers a more accurate method to predict the uptake of trace elements from soils to grains, which helps to warn about abnormal levels in grains and prevent potential health risks.

Comprehensive kinetic modeling and product distribution for pyrolysis of pulp and paper mill sludge

Pyrolysis holds immense potential for clean treatment of pulp and paper mill sludge (PPMS), enabling efficient energy and chemical recovery. However, current understanding of PPMS pyrolysis kinetics and product characteristics remains incomplete. This study conducted detailed modeling of pyrolysis kinetics for two typical PPMSs from a wastepaper pulp and paper mill, namely, deinking sludge (PPMS-DS) and sewage sludge (PPMS-SS), and analyzed comprehensively pyrolysis products. The results show that apparent activation energy of PPMS-DS (169.25-226.82 kJ/mol) and PPMS-SS (189.29-411.21 kJ/mol) pyrolysis undergoes significant change, with numerous parallel reactions present. A distributed activation energy model with dual logistic distributions proves to be suitable for modeling thermal decomposition kinetics of both PPMS-DS and PPMS-SS, with coefficient of determination >0.999 and relative root mean square error <1.99 %. High temperature promotes decomposition of solid organic materials in PPMS, and maximum tar yield for both PPMS-DS (53.90 wt%, daf) and PPMS-SS (56.48 wt%, daf) is achieved at around 500 °C. Higher levels of styrene (24.45 % for PPMS-DS and 14.71 % for PPMS-SS) and ethylbenzene (8.61 % for PPMS-DS and 8.33 % for PPMS-SS) are detected in tar and could be used as chemicals. This work shows great potential to propel development of PPMS pyrolysis technology, enabling green and sustainable production in pulp and paper industry.

Estimation of the main air pollutants from different biomasses under combustion atmospheres by artificial neural networks

The production of biofuels to be used as bioenergy under combustion processes generates some gaseous emissions (CO, CO2, NOx, SOx, and other pollutants), affecting living organisms and requiring careful assessments. However, obtaining such information experimentally for data evaluation is costly and time-consuming and its in situ obtaining for regional biomasses (e.g., those from Northeast Brazil (NEB) is still a major challenge. This paper reports on the application of artificial neural networks (ANNs) for the prediction of the main air pollutants (CO, CO2, NO, and SO2) produced during the direct biomass combustion (N2/O2:80/20%) with the use of ultimate analysis (carbon, hydrogen, nitrogen, sulfur, and oxygen). 116 worldwide biomasses were used as input data, which is a relevant alternative to overcome the lack of experimental resources in NEB and obtain such information. Cross-validation was conducted with k-fold to optimize the ANNs and performance was analyzed with the use of statistical errors for accuracy assessments. The results showed an acceptable statistical performance for all architectures of ANNs, with 0.001-12.41% MAPE, 0.001-5.82 mg Nm-3 MAE, and 0.03-52.30 mg Nm-3 RMSE, highlighting the high precision of the emissions studied. On average, the differences between predicted and real values for CO, CO2, NO, and SO2 emissions from NEB biomasses were approximately 0.01%, 10-6%, 0.14%, and 0.05%, respectively. Pearson coefficient provided consistent results of concentration of the ultimate analysis in relation to the emissions studied and effectiveness of the test set in the developed models.

Research on the Combustion Performance of Municipal Solid Waste in Different Sorting Scenarios: Thermokinetics Investigation via TG-DSC-FTIR-MS

Waste sorting is regarded as one of the most important strategies for municipal solid waste (MSW) management. The changes in the combustion parameters after MSW sorting had a significant impact on the actual operation of the boiler. In the present study, the effects of heating rate on combustion characteristics and dynamics of MSW in different sorting scenarios were studied using the thermogravimetry (TG)-differential scanning calorimetry (DSC)-Fourier transform infrared (FTIR)-mass spectrometry (MS) technique. TG-DSC analysis showed that the heat released from MSW combustion at different heating rates ranged from 1394.1 to 4130.1 J/g. According to the TG-DTG curves, the combustibility of 30% sorted MSW was increased by 1.2 times compared to that of the unsorted scenario. In the 30% sorted scenario, the average activation energies were estimated to be 161.24 and 159.93 kJ/mol based on the Flynn-Wall-Ozawa (FWO) and Kissinger-Akahira-Sunose (KAS) methods, respectively. Based on the Coats-Redfern (CR) method, the minimum activation energies for unsorted and 20% sorted scenarios were 148.74 and 135.53 kJ/mol at 523 to 606 K, respectively, while they were 29.42 and 33.22 kJ/mol at 606 to 780 K. XRF analysis showed that the alkali and alkaline earth metal oxides in the ash contributed to a high risk of slagging and scaling. This work can provide a scientific basis for the real situation of MSW incineration.

Comparative Study of Different Pretreatment and Combustion Methods on the Grindability of Rice-Husk-Based SiO2

The rice husk (RH) combustion pretreatment method plays a crucial role in the extraction of nanoscale SiO2 from RH as a silicon source. This study examined the effects of diverse pretreatment methods and combustion temperatures on the particle size distribution of nanoscale high-purity amorphous SiO2 extracted from rice husk ash (RHA) post RH combustion. The experiment was structured using the Taguchi method, employing an L9 (21 × 33) orthogonal mixing table. The median diameter (D50) served as the output response parameter, with the drying method (A), combustion temperature (B), torrefaction temperature (C), and pretreatment method (D) as the input parameters. The results showed the torrefaction temperature (C) as being the predominant factor affecting the D50, which decreased with an increasing torrefaction temperature (C). The optimal parameter combination was identified as A2B2C3D2. The verification test revealed that roasting could improve the abrasiveness of Rh-based silica and reduce the average particle size. Torrefaction at medium temperatures might narrow the size distribution range of RHA-SiO2. We discovered that the purity of silica increased with an increasing roasting temperature by evaluating the concentration of silica in the sample. The production of RHA with silica concentrations up to 92.3% was investigated. X-ray diffraction analysis affirmed that SiO2's crystal structure remained unaltered across different treatment methods, consistently presenting as amorphous. These results provide a reference for extracting high-value products through RH combustion.

Study on Pyrolysis of Shale Gas Oil-Based Drilling Cuttings: Kinetics, Process Parameters, and Product Yield

The main reaction range (350-550 °C) of oil-based drilling cutting (OBDC) pyrolysis was studied by a thermogravimetric analyzer and a vacuum tube furnace. The average activation energies calculated by four model-free methods were 185.5 kJ/mol (FM), 184.16 kJ/mol (FWO), 166.17 kJ/mol (KAS), and 176.03 kJ/mol (Starink). The reaction mechanism was predicted by the Criado (Z-master plot) method. It is found that a high heating rate is helpful to predict the reaction mechanism, but it cannot be described by a single reaction model. Under the conditions of target temperature higher than 350 °C, residence time higher than 50 min, laying thickness less than 20 mm, and heating rate lower than 15 °C, the residual oil content is lower than 0.3% and the recovery rate of mineral oil is higher than 98.43%. Solid phase products accounted for more than 70%, reached the maximum 17.04% at 450 °C, and then decreased to 15.87% at 500 °C. Aromatic hydrocarbons, as coking precursors, are transformed from a low ring to a high ring. Recycled mineral oil can reconfigure oil-based mud (OBM). The research results can provide a theoretical basis for process optimization.

Design of biorefineries towards carbon neutrality: A critical review

The increase in worldwide demand for energy is driven by the rapid increase in population and exponential economic development. This resulted in the fast depletion of fossil fuel supplies and unprecedented levels of greenhouse gas in the atmosphere. To valorize biomass into different bioproducts, one of the popular and carbon-neutral alternatives is biorefineries. This system is an appropriate technology in the circular economy model. Various research highlighted the role of biorefineries as a centerpiece in the carbon-neutral ecosystem of technologies of the circular economy model. To fully realize this, various improvements and challenges need to be addressed. This paper presents a critical and timely review of the challenges and future direction of biorefineries as an alternative carbon-neutral energy source.