A bifacial photovoltaic thermal system design with parameter optimization and performance beneficial validation

Dong

Yang

2020

Self Cite

This study synthesized the water-permeable and hydrophobic property of water-based polyurethane (WPU) and applied it to nylon fiber-based functional textiles. Specifically, poly-stearyl acrylate (PSA) homopolymers with different molecular weights were used as end-capping agents in the WPU to form water-based polyurethane with acrylate terminal (WPUA). In the WPUA process, dimethylacetamide was adopted as a neutralizing agent to form a stable WPUA emulsion. The effects of the design parameters, such as the hydrophilic ionic group dimethylol propionic acid (DMPA), the PSA molecular weight and content, were set as the control factors and are analyzed by the Taguchi method and gray relational analysis (GRA). The water-repellent characteristic, water vapor permeability (WVP) and fabric flexural rigidity were considered as performance parameters as multi-qualities. The Taguchi method was based on the analysis of variance and implemented orthogonal arrays for experimental design. Each performance parameter was optimized independently. Then, the performance parameters were optimized together with GRA. According to the experimental results, the most important factor for the water-repellent characteristic, WVP and fabric flexural rigidity is the DMPA content, followed by the PSA molecular weight and content. The corresponding results showed that in the optimal parameter combination, the content of DMPA was 30.8 phm (parts per hundred monomer by weight), the PSA molecular weight was 3000 g/mol and the PSA content was 4 phm. After WPUA padding, the water contact angle of the water-repellent nylon was 135.3°, the WVP was 2271.7 g/m2/day and the flexural rigidity was 2.7 cm.

Section: Methodsmentioning

confidence: 99%

Optimization of the water-based polyurethane with acrylate terminal process in nylon fabrics application using the Taguchi-based gray relational analysis method

Dong

Yang

2020

Self Cite

“…Thus, the factor number and level value in this study use the last six sets of factors of L 18 (2 1 Â 3 7 ) as the orthogonal array for this experiment. [38][39][40] The control factor levels are adjusted, as shown in Table 4.…”

Section: Taguchi Methodsmentioning

confidence: 99%

A study of optimum processing parameters and abnormal parameter identification of the twin-screw co-rotating extruder mixing process based on the distribution and dispersion properties for SiO₂/low-density polyethylene nano-composites

Huang

Lin

et al. 2019

Self Cite

This study took nano-silica particles mixed in low-density polyethylene to implement the optimum processing parameters and make abnormal parameter identification for the twin-screw co-rotating extruder used in the manufacturing process. The mixing quality was divided into distribution and dispersion, where distribution was tested by an energy dispersive spectrometer and evaluated using the coefficient of variation. Dispersion was assessed by the surface effect and specific surface equations, as based on the spectrum of scanning electron microscopy. By using the Taguchi method in planning the experiment coupled with an analysis of variance, we conducted the single-quality characteristic analysis of the experimental results of the two quality characteristics, namely the distribution and dispersion. Then, by using the hierarchical architecture of analytic level process, we can obtain the optimized parameter factors and levels and the calculation of the total weighting of various parameter levels, as well as the ranking of the parameter levels. According to the confirmation experimental results, the signal-to-noise ratios of distribution and dispersion fell within 95% confidence intervals, indicating that the experiment can be represented and reliable. The optimum parameters combination is SiO2 addition level 1%, screw speed 60 rpm, mixing time 5 min, temperature (upper) 150℃, temperature (middle), 175℃ and temperature (lower) 190℃. After that, by using the optimal parameters and operation processing parameters for support vector machine classification, the abnormality of the processing parameters can be identified for 100%. The good quality of the production can be guaranteed during the extrusion.

“…The number of proposals is reduced until the criteria with multiple advantageous proposals are left for multiple attribute decision-making analysis. 19,20 The advantages of ELECTRE include straightforward decision-making logic and systematic processes, as well as clarification of the performance of various proposals. The proposals are classified as Reject and Non-reject and the concordance and discordance indexes of the proposals are established.…”

Section: Electrementioning

confidence: 99%

Preparation of boron nitride nanosheets using a chemical exfoliation method as a thermal conductive filler for the development of silicone thermal pads: Part II: Optimization of process parameter design on the development of a silicone thermal pad

Chen

2020

Self Cite

This study developed a silicone thermal pad with high thermal conductivity and high mechanical properties. Boron nitride nanosheets (BNNSs) with different lamella thicknesses were prepared in Part I and Al2O3 was filled with silicone rubber to prepare silicone thermal pads. The silicone thermal pads were prepared using the Taguchi method and the elimination and choice translating reality method. BNNSs with different lamella thicknesses, BNNS loading level, and Al2O3 loading level were set as control factors for the experiment. The effect of the three sets of control factors on the thermal conductivity, tensile property, and hardness of the silicone thermal pads was discussed so as to determine the optimum product optimization process parameters. According to the experimental results, the optimum experimental combination was BNNSs with lamella thickness (B24), a BNNS loading level of 20 wt%, and an Al2O3 loading level of 60 wt%. The thermal conductivity of the silicone thermal pads prepared by thermosetting was 5.25 W/mK, the maximum tensile strength was 7.55 kg/cm2, and the Shore hardness was 65.2 (Shore A). Under identical conditions, the thermal conductivity and tensile strength were higher than that of commercially available silicone thermal pads by 34.48% and 20.26%, respectively. The control factor influencing the thermal conductivity and tensile strength of silicone thermal pads was the BNNS loading level, with contribution degrees of 34.66% and 50.13%, respectively. Accurate parameter settings could be obtained for silicone thermal pad processes.