Chao-xia Wang, Mao Li, Guang-wei Jiang, Kuan-jun Fang and An-li An-li
The cotton fabrics were surface modified for water repellent finishing with silicon sol, which was prepared with the tetraethoxysilane(TEOS) and solvent ethanol, catalyzer HCl…
Abstract
The cotton fabrics were surface modified for water repellent finishing with silicon sol, which was prepared with the tetraethoxysilane(TEOS) and solvent ethanol, catalyzer HCl, water and modified with additives, such as Methyltriethoxysilane(MTEOS), Octyltriethoxysilane(OTEOS) and Hexadec-ltrimethoxysilane(HTEOS). As a result, acceptable water repellence could only be achieved via the addition of longer chain length additives such as OTEOS, HTEOS, while the use of additives containing a shorter alkyl chain length such as MTES led to insufficient water repellence. The factors which influence contact angles were examined. Excellent water repellent properties could be achieved on the cotton fabrics treated with the silica sols by twice dip and pad and cured at 160°. The silica sol preparation preference conditions were with TEOS: H2O: EtOH=1: 5: 8 (mol) by stirring for 6 hours at 65°C which was added with HTEOS. The water repellence contact angle was able to be reached around 140° and the hydrostatic pressure was 46cm.
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Chao Xia, Bo Zeng and Yingjie Yang
Traditional multivariable grey prediction models define the background-value coefficients of the dependent and independent variables uniformly, ignoring the differences between…
Abstract
Purpose
Traditional multivariable grey prediction models define the background-value coefficients of the dependent and independent variables uniformly, ignoring the differences between their physical properties, which in turn affects the stability and reliability of the model performance.
Design/methodology/approach
A novel multivariable grey prediction model is constructed with different background-value coefficients of the dependent and independent variables, and a one-to-one correspondence between the variables and the background-value coefficients to improve the smoothing effect of the background-value coefficients on the sequences. Furthermore, the fractional order accumulating operator is introduced to the new model weaken the randomness of the raw sequence. The particle swarm optimization (PSO) algorithm is used to optimize the background-value coefficients and the order of the model to improve model performance.
Findings
The new model structure has good variability and compatibility, which can achieve compatibility with current mainstream grey prediction models. The performance of the new model is compared and analyzed with three typical cases, and the results show that the new model outperforms the other two similar grey prediction models.
Originality/value
This study has positive implications for enriching the method system of multivariable grey prediction model.