This study utilized a thermogravimetric analyzer to assess the thermal decomposition behaviors and kinetics properties of vacuum residue (VR) and low-density polyethylene (LDPE) polymers. The kinetic parameters were calculated using the Friedman technique. To demonstrate the interactive effects between LDPE and VR during the co-pyrolysis process, the disparity in mass loss and mass loss rate between the experimental and calculated values was computed. The co-pyrolysis curves obtained through estimation and experimentation exhibited significant deviations, which were influenced by temperature and mixing ratio. A negative synergistic interaction was observed between LDPE and VR, although this inhibitory effect could be mitigated or eliminated by reducing the LDPE ratio in the mixture and increasing the co-pyrolysis temperature. The co-pyrolysis process resulted in a reduction in carbon residue, which could be attributed to the interaction between LDPE and the heavy fractions, particularly resin and asphaltene, present in VR. These findings align with the pyrolysis behaviors exhibited by the four VR fractions. Furthermore, it was observed that the co-pyrolysis process exhibited lower activation energy as the VR ratio increased, indicating a continuous enhancement in the reactivity of the mixed samples during co-pyrolysis.

Removal of boric acid from seawater and wastewater using reverse osmosis membrane technologies is imperative and yet remains inadequately addressed by current commercial membranes. Existing research efforts performed post-modification of reverse osmosis membranes to enhance boron rejection, which is usually accompanied by substantial sacrifice in water permeability. This study delves into the surface engineering of low-pressure reverse osmosis membranes, aiming to elevate boron removal efficiency while maintaining optimal salt rejection and water permeability. Membranes were modified by the self-polymerization and co-deposition of dopamine and polystyrene sulfonate at varying ratios and concentrations. The surfaces became smoother and more hydrophilic after modification. The optimum membrane exhibited a water permeability of 9.2 ± 0.1 L·m⁻²·h⁻¹·bar⁻¹, NaCl rejection of 95.8% ± 0.3%, and boron rejection of 49.7% ± 0.1% and 99.6% ± 0.3% at neutral and alkaline pH, respectively. The water permeability is reduced by less than 15%, while the boron rejection is 3.7 times higher compared to the blank membrane. This research provides a promising avenue for enhancing boron removal in reverse osmosis membranes and addressing water quality concerns in the desalination process.

In this study, perovskite-type La_0.7Ca_0.3Co_0.3Fe_0.6M_0.1O_3–δ (M = Cu, Zn) powders were synthesized using a scalable reverse co-precipitation method, presenting them as novel materials for oxygen transport membranes. The comprehensive study covered various aspects including oxygen permeability, crystal structure, conductivity, morphology, CO₂ tolerance, and long-term regenerative durability with a focus on phase structure and composition. The membrane La_0.7Ca_0.3Co_0.3Fe_0.6Zn_0.1O_3–δ exhibited high oxygen permeation fluxes, reaching up to 0.88 and 0.64 mL·min⁻¹·cm⁻² under air/He and air/CO₂ gradients at 1173 K, respectively. After 1600 h of CO₂ exposure, the perovskite structure remained intact, showcasing superior CO₂ resistance. A combination of first principles simulations and experimental measurements was employed to deepen the understanding of Cu/Zn substitution effects on the structure, oxygen vacancy formation, and transport behavior of the membranes. These findings underscore the potential of this highly CO₂-tolerant membrane for applications in high-temperature oxygen separation. The enhanced insights into the oxygen transport mechanism contribute to the advancement of next-generation membrane materials.

Methanol-to-olefins, as a promising non-oil pathway for the synthesis of light olefins, has been successfully industrialized. The accurate prediction of process variables can yield significant benefits for advanced process control and optimization. The challenge of this task is underscored by the failure of traditional methods in capturing the complex characteristics of industrial processes, such as high nonlinearities, dynamics, and data distribution shift caused by diverse operating conditions. In this paper, we propose a novel hybrid spatial-temporal deep learning prediction model to address these issues. Firstly, a unique data normalization technique called reversible instance normalization is employed to solve the problem of different data distributions. Subsequently, convolutional neural network integrated with the self-attention mechanism are utilized to extract the temporal patterns. Meanwhile, a multi-graph convolutional network is leveraged to model the spatial interactions. Afterward, the extracted temporal and spatial features are fused as input into a fully connected neural network to complete the prediction. Finally, the outputs are denormalized to obtain the ultimate results. The monitoring results of the dynamic trends of process variables in an actual industrial methanol-to-olefins process demonstrate that our model not only achieves superior prediction performance but also can reveal complex spatial-temporal relationships using the learned attention matrices and adjacency matrices, making the model more interpretable. Lastly, this model is deployed onto an end-to-end Industrial Internet Platform, which achieves effective practical results.

The nitridation reaction of calcium carbide and N₂ at high temperatures is the key step in the production of lime-nitrogen. However, the challenges faced by this process, such as high energy consumption and poor product quality, are mainly attributed to the lack of profound understanding of the reaction. This study aimed to improve this process by investigating the non-isothermal kinetics and reaction characteristics of calcium carbide nitridation reaction at different heating rates (10, 15, 20, and 30 °C·min⁻¹) using thermogravimetric analysis. The kinetic equation for the nitridation reaction of additive-free calcium carbide sample was obtained by combining model-free methods and model-fitting method. The effect of different calcium-based additives (CaCl₂ and CaF₂) on the reaction was also investigated. The results showed that the calcium-based additives significantly reduced reaction temperature and activation energy E_a by about 40% with CaF₂ and by 55%–60% with CaCl₂. The reaction model f(α) was also changed from contracting volume (R3) to 3-D diffusion models with D3 for CaCl₂ and D4 for CaF₂. This study provides valuable information on the mechanism and kinetics of calcium carbide nitridation reaction and new insights into the improvement of the lime-nitrogen process using calcium-based additives.

Lignin, an abundant aromatic polymer in nature, has received significant attention for its potential in the production of bio-oils and chemicals owing to increased resource availability and environmental issues. The hydrodeoxygenation of guaiacol, a lignin-derived monomer, can produce cyclohexanol, a nylon precursor, in a carbon-negative and environmentally friendly manner. This study explored the porous properties and the effects of activation methods on the Ru-based catalyst supported by environmentally friendly and cost-effective hydrochar. Highly selective cleavage of C_aryl–O bonds was achieved under mild conditions (160 °C, 0.2 MPa H₂, and 4 h), and alkali activation further improved the catalytic activity. Various characterization methods revealed that hydrothermal treatment and alkali activation relatively contributed to the excellent performance of the catalysts and influenced their porous structure and Ru dispersion. X-ray photoelectron spectroscopy results revealed an increased formation of metallic ruthenium, indicating the effective regulation of interaction between active sites and supports. This synergistic approach used in this study, involving the valorization of cellulose-derived hydrochar and the selective production of nylon precursors from lignin-derived guaiacol, indicated the comprehensive and sustainable utilization of biomass resources.