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The thermoplastic high-ortho phenolic resins (HPRs) were synthesized by a one-step synthesis using phenol, formaldehyde, zinc acetate, sulfuric acid, and small amounts of ethanol. It was then blended with epoxy resins, and a small amount of ethylenediamine (EDA) was added as a curing agent for dry spinning to produce the as-spun fibers. Heat curing and microwave curing treatments were performed to obtain phenolic epoxy fibers. Using Fourier transform infrared spectroscopy to study the structural changes during the fiber curing process revealed that the amino groups in EDA opened the epoxy groups to generate the hydroxyl groups. The hydroxyl groups can further react with the epoxy groups, which accelerate the curing reaction. Thermogravimetric analysis (TGA) results showed a higher residual carbon degree in the fibers after curing. Mechanical property testing demonstrated a significant improvement in the mechanical properties of the fibers. By modifying the blending and optimizing the curing process, the heat resistance of the fibers was successfully improved, resolving the issue of poor mechanical properties in the high-ortho phenolic epoxy fibers (HPEFs). [ABSTRACT FROM AUTHOR]

Reliable interval forecasts can quantify the potential risk of wind speeds, which can help people make better use of wind energy. In this study, a new probabilistic prediction model, called Quantile Regression Closed-Form Continuous-time Neural Network (QRCfC), is proposed by combining quantile regression and closed-form continuous-time neural network. A new combined model combining QRCfC, secondary decomposition, multi-objective optimization, and dynamic weight combination strategy is proposed, which makes full use of the advantages of each single model to obtain accurate deterministic prediction and reliable probabilistic interval prediction of wind speed. First, the original wind speed series is divided into several subseries using a secondary decomposition technique built on variational mode decomposition and singular spectrum analysis. Then, four base models are used to predict these subseries separately. After that, the predicted values of the four base models are input into QRCfC for training, where the hyperparameters of QRCfC are dynamically adjusted by a multi-objective ant lion optimization algorithm. Finally, to verify the effectiveness of the proposed models, experiments are conducted using data sets from three wind farms in Gansu, China. Simulation results show that the proposed model achieves excellent prediction performance in both deterministic and probabilistic prediction. For example, compared with the unoptimized quantile regression time convolution network, quantile regression long-short time memory, quantile regression gated recurrent unit, and quantile regression neural network, the proposed model reduces the MAPE by 21.89% and the CRPS by 11.14% for 1-step prediction at site 1. [ABSTRACT FROM AUTHOR]

AbstractIn order to improve the toughness of melamine formaldehyde (MF) fibers, the modified fibers with varying synthesis mole ratios were fabricated through wet spinning utilizing melamine and paraformaldehyde as the primary constituents and phenol as the modifying agent. The structural characteristics and properties of the fibers were assessed and examined through Fourier transform infrared spectroscopy (FTIR), nuclear magnetic resonance (NMR), thermogravimetric analysis (TGA), scanning electron microscopy (SEM), and mechanical property evaluations. The results showed that the incorporation of phenol led to a significant enhancement in both the thermal stability and mechanical properties of the fibers. Specifically, when the mole ratio of melamine, phenol, and paraformaldehyde was 1:1:4 and the heat curing temperature was 180 °C, the fiber exhibited a breaking strength of 151 MPa and an elongation at break of 15.1%. This represented an increase in elongation at break of approximately 84% compared to the unmodified fiber without phenol. Furthermore, the thermal properties of the modified fibers were notably improved, as evidenced by an increase in char yield at 700 °C from 27.7% to 62.5%. [ABSTRACT FROM AUTHOR]