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Polypropylene is a useful and widely consumed thermoplastic with very good properties that can be mixed with a combination of rubber and nanoparticles in order to eliminate its shortcomings. In this paper, the simultaneous combination of both the toughening effect of styrene–butadiene rubber (SBR) and the reinforcing effect of silicon carbide (SiC) nanoparticles were investigated in polypropylene matrix. Mechanical properties (tensile and impact strength [TS and IS]) of compounds were analyzed using the central composite design approach in Design‐Expert software. With two factors for input variables of SBR and SiC weight percentages, and their five levels (5, 10, 15, 20, and 25) and (0, 1.5, 3, 4.5, and 6), respectively, the central composite design approach employs 11 experiments for creation of models and the analysis of variance investigations with two output responses of TS and IS. Statistical results revealed that SBR and SiC contents had a significant effect on TS, IS, and the microstructure of compounds, as confirmed by scanning electron microscopy and energy‐dispersive spectroscopy images. To maximize all mechanical properties simultaneously with desirability functions, TS and IS of the optimum sample were computed to be 21.98 MPa and 8.66 kJ/m2 in amounts of 13.45 and 2.80 weight percentage (wt%) of SBR and SiC, respectively. Highlights: By increasing silicon carbide (SiC) in polypropylene/styrene–butadiene rubber (SBR), tensile strength decreases after a peak due to agglomeration effect.By increasing SiC in polypropylene/SBR, impact strength decreases after a peak due to agglomeration effect.By increasing SiC, the rubber particles' dimensions decrease according to scanning electron microscopy analysis.Simultaneous maximum of tensile strength and impact strength are 21.98 MPa and 8.66 KJ/m2, respectively.Optimum value of SBR and SiC in above maximums are 13.45 and 2.8 wt%, respectively. [ABSTRACT FROM AUTHOR]

Groundwater quality modelling plays an important role in water resources management decision making processes. Accordingly, models must be developed to account for the uncertainty inherent in the modelling process, from the sample measurement stage through to the data interpretation stages. Artificial intelligence models, particularly fuzzy inference systems (FIS), have been shown to be effective in groundwater quality evaluation for complex aquifers. In the current study, fuzzy set theory is applied to groundwater-quality related decision-making in an agricultural production context; the Mamdani, Sugeno, and Larsen fuzzy logic-based models (MFL, SFL, and LFL, respectively) are used to develop a series of new, generalized, rule-based fuzzy models for water quality evaluation using widely accepted irrigation indices and hydrological data from the Sarab Plain, Iran. Rather than drawing upon physiochemical groundwater quality parameters, the present research employs widely accepted agricultural indices (e.g., irrigation criteria) when developing the MFL, SFL and LFL groundwater quality models. These newly-developed models, generated significantly more consistent results than the United States Soil Laboratory (USSL) diagram, addressed the inherent uncertainty in threshold data, and were effective in assessing groundwater quality for agricultural uses. The SFL model is recommended as it outperforms both MFL and LFL in terms of accuracy when assessing groundwater quality using irrigation indices.

Knowledge of soil hydraulic and thermal properties is essential for studies involving the combined effects of soil temperature and water input on water flow and redistribution processes under field conditions. The objective of this study was to estimate the parameters characterizing these properties from a transient water flow and heat transport field experiment. Real-time sensors built by the authors were used to monitor soil temperatures at depths of 40, 80, 120, and 160 cm during a 10-hour long ring infiltration experiment. Water temperatures and cumulative infiltration from a single infiltration ring were monitored simultaneously. The soil hydraulic parameters (the saturated water content θ s, empirical shape parameters α and n, and the saturated hydraulic conductivity Ks) and soil thermal conductivity parameters (coefficients b1, b2, and b3 in the thermal conductivity function) were estimated from cumulative infiltration and temperature measurements by inversely solving a two-dimensional water flow and heat transport using HYDRUS-2D. Three scenarios with a different, sequentially decreasing number of optimized parameters were considered. In scenario 1, seven parameters (θ s, Ks, α, n, b1, b2, and b3) were included in the inverse problem. The results indicated that this scenario does not provide a unique solution. In scenario 2, six parameters (Ks, α, n, b1, b2, and b3) were included in the inverse problem. The results showed that this scenario also results in a non-unique solution. Only scenario 3, in which five parameters (α, n, b1, b2, and b3) were included in the inverse problem, provided a unique solution. The simulated soil temperatures and cumulative infiltration during the ring infiltration experiment compared reasonably well with their corresponding observed values.

In this research, the tensile properties of polypropylene/natural rubber composite reinforced with perlite nanoparticles produced using a biaxial internal mixer were studied. The mathematical modeling of analysis of variance using the surface response method was used to confirm the experimental results. The role of each materials variable and the weight percentage of natural rubber and pearlite nanoparticles on the tensile properties of the new nanocomposite samples was investigated. It was observed that the values of tensile strength and elongation at break by increasing the amount of natural rubber from 20 wt.% to 40 wt.% and the constant amount of perlite nanoparticles at 3 wt.%, decreased by 14.68% (from 8.92 to 7.61 MPa) and increased by 72% (from 51.05% to 87.81%), respectively. The results obtained from the parameters optimization showed that the maximum values of tensile strength and elongation at break are 8.332 MPa and 74.38% was obtained in the sample with 3.268 wt.% of natural rubber and 4.046 wt.% of perlite nanoparticles, respectively. The scanning electron microscope images showed the positive effect of adding nanoparticles to the polypropylene/natural rubber composite due to the reduction in the size of the elastomeric phase and as a result, the increase in the tensile strength of the composite samples. Also, the proper dispersion of nanoparticles in the polymer matrix phase occurred in the sample with 5 wt.% of perlite nanoparticles and 30 wt.% of natural rubber. However, with increasing the weight percentages of nanoparticles up to 9 wt.%, some agglomerations of pearlite nanoparticles were observed in the thermoplastic phase. [ABSTRACT FROM AUTHOR]