Fouling Propensity of Modified Heat Transfer Surfaces

Abstract

As much as attention that has been paid to surface treatment as an efficient, and environmentally friendly approach toward fouling mitigation, the characterization of many innovative modified surfaces has become a matter of much debate. The latter is closely associated with the intermolecular interaction energies which would profoundly influence the adhesion of precursors onto the modified surfaces. In this study, based on the extended Derjaguin, Landau, Verwey and Overbeek (DLVO) theory, a new criterion is proposed to predict the propensity of a surface when prone to crystallization fouling or biofouling. Thereafter, the proposed criterion is examined against the present experimental results as well as those from previous studies where the required information for the determination of new criterion is available. The comparison shows that deposit formation onto heat transfer surfaces decreases with increasing the new proposed fouling propensity indicator criterion. Moreover, nearly 75% of the collated crystallization and biological fouling data points are predictable with this criterion and reasons for those that are not in compliance with the proposed criterion are discussed.     

Study of phase behavior and Congo red dye partitioning in aqueous two-phase systems composed of hydrophilic alcohols (1-propanol/ 1-butanol) and sodium salts

ABSTRACT

The partitioning of the Congo red dye in ATPSs formed by alcohols (1-butanol, 1-propanol)/sodium salts was considered. Binodal and the LLE data were experimentally determined at 298.15 K. The salting-out abilities of the salts follow the order Na₃C₆H₅O₇ > NaH₂PO₄ > C₂H₃ O₂Na. The phase-forming abilities of the alcohols follow the order: 1-butanol > 1-propanol. The four-parameter equation was applied to correlate the binodal curves data. Therefore, The Bancroft and Othmer-Tobias equations were used to prove the reliability of the corresponding LLE data. ATPS composed of 6.5% of 1-butanol and 20% of Na₃C₆H₅O₇ had the highest values of extraction by the yield of 98.54%.     

Physicochemical Properties of Foam‐Templated Oleogel Based on Gelatin and Xanthan Gum

In this paper, gelatin and xanthan are applied to produce a foam‐templated oleogel. For this reason, the oleogel is prepared at different concentrations of biopolymers and the properties of solution, cryogel, and related oleogel are determined. The results show that xanthan addition increases viscosity and foam stability of solution. Also, an increment in biopolymer concentration increases cryogel network density (ND) and firmness but has no significant effect on moisture sorption. The oil binding capacity of all oleogels is >92%. In terms of high foam stability (96.87 ± 4.42), low ND (0.016 ± 0.00), and consequently suitable oil sorption (46.10 ± 4.40), the oleogel containing 3% gelatin and 0.2% xanthan is selected as the best sample. Complementary tests exhibit that the oleogel, with thixotropic behavior and 60% structural recovery, can bind the oil at temperature <100 °C. The oleogel network can protect the edible oil from oxidative reaction during 2 month storage. Nonetheless, more studies are needed to attest the application of this oleogel type in food products. Practical Application: Biopolymers of gelatin and xanthan are GRAS and available so that they are applied in many food products. This research shows that the cryogel of these biopolymers, as a hydrophilic oleogelator, can be utilized to structure oil and produce oleogel in an indirect method. This procedure that forms strong gel and keeps oil even at high temperatures can be of interest to scientists who are searching for solid fat substitutes in food products such as cakes, biscuits, and muffins.     

Numerical simulation of a falling droplet surrounding by air under electric field using VOF method: A CFD study

This is a numerical study of a falling droplet surrounding by air under the electric field modeled with finite volume method by means of CFD. The VOF method has been employed to model the two-phase flow of the present study. Various capillary numbers are investigated to analyze the effects of electric field intensity on the falling droplet deformation. Also, the effects of electric potential on the heat transfer coefficient have been examined. The obtained results showed that by applying the electric field at a capillary number of 0.2 the droplet tends to retain its primitive shape as time goes by, with a subtle deformation to an oblate form. Intensifying the electric field to a capillary number of 0.8 droplet deformation is almost insignificant with time progressing; however, further enhancement in capillary number to 2 causes the droplet to deform as a prolate shape and higher values of this number intensify the prolate form deformation of the droplet and result in pinch-off phenomenon. Ultimately, it is showed that as the electric potential augments the heat transfer coefficient increases in which for electric potential values higher than 2400 V the heat transfer coefficient enhances significantly.     

Green bio-synthesis of Ni/montmorillonite nanocomposite using extract of Allium jesdianum as the nano-catalyst for electrocatalytic oxidation of methanol

In this work, synthesis of Ni nanoparticles was carried out successfully by water extract of Allium jesdianum as a biochemical reducing agent in the presence of montmorillonite clay (MMT) as a natural solid support for the first time. Then the electrochemical activity of the synthesized nanocomposite was investigated in methanol electrocatalytic oxidation. MMT with high cation exchange capacity and nano layer structure was exposed to ion exchange conditions in nickel solution. Then Ni²⁺ ion exchanged form was used in this process as a source of ions and also capping agent. Water extract of Allium jesdianum used as a reducing agent due to abundant availability of phenolic and flavonoid contents. The synthesized Ni/MMT nanocomposite was characterized using UV–Vis spectroscopy (UV–Vis), Fourier Transform Infrared Spectroscopy (FT-IR), X-ray diffraction (XRD), Scanning Electron Microscopy (SEM), Transmission electron microscopy (TEM) and Energy-dispersive X-ray spectroscopy (EDX). The surface of prepared modified electrode has been characterized using SEM to evaluate the morphology, showing uniform dispersion of Ni nanoparticles with mean diameter of 12 to 20 nm. The modified carbon paste electrode was then used in methanol electrocatalytic oxidation reaction. Methanol oxidation on the proposed modified electrode surface occurs at 0.6 V and 0.3 V in alkaline and acidic medium respectively. Also, the results showed the better performance of modified electrode toward methanol electrocatalytic oxidation in comparison with carbon paste electrode that is modified by ion exchanged MMT. Charge transfer coefficients and apparent charge transfer rate constant for the modified electrode in the absence of methanol in alkaline medium were respectively found as: α_a = 0.53, α_c = 0.37 and k_s = 1.6 × 10⁻¹ s⁻¹. Also, the average value of catalytic rate constant for the electrocatalytic oxidation of methanol by the prepared nano-catalyst was estimated to be about 0.9 L·mol^-1·s^-1 by chronoamperometry technique. The prepared electrode was also effective for electrocatalytic oxidation of ethanol and formaldehyde in alkaline medium.     

Prediction of pressure drop and minimum spouting velocity in draft tube conical spouted beds using genetic programming approach

The smart method of genetic programming (GP) is used to predict the operating pressure drop (ΔP_s) and the minimum spouting velocity u_ms for conical spouted beds (CSBs) equipped with nonporous draft tubes. Accordingly, six dimensionless variables have been taken as model inputs, including crucial parameters associated with the bed and tube geometric and operating conditions. Two general correlations comprising almost all constitutive and operating variables have been derived for the first time by the GP approach. Both ΔP_s and u_ms values predicted by the GP technique are in a fair agreement with the values corresponding to the experiments, with average absolute relative errors (AARE) of 18.9 and 19.9 %, respectively. The results of the proposed correlations show that the GP method is a powerful tool to make reasonable estimates.     

β-Polymorph enhancement in poly(vinylidene fluoride) by blending with polyamide 6 and barium titanate nanoparticles

Polyamide 6 (PA6) as a cost-effective polar polymer and barium titanate (BT) as piezoelectric ceramic nanofiller were melt compounded with poly(vinylidene fluoride) (PVDF) matrix to enhance the β electroactive phase. A series of samples with two blending ratios of PVDF/PA6 (90/10 and 70/30 [wt%/wt%]) each containing 1, 3, and 5 wt% of BT were prepared. The SEM results revealed that the addition of BT to the neat blends refined the biphasic morphology which is mainly due to selective localization of BTs in PA6 dispersed phase as confirmed by TEM observation and wetting parameter predictions. The EDX analysis demonstrated a uniform distribution of BT nanoparticles in the filled systems. FTIR and XRD results showed that β content increased as a result of blending while the α phase was suppressed. The BT nanoparticles inclusion to the blends showed a synergistic effect on the β-polymorph content. These results in combination with the data derived from DSC (indicating reduction of the total crystallinity) complement the idea of β enhancement by the addition of BT nanoparticles and PA6 into PVDF.     

Quick integrative optimizers for minimizing the error of neural computing in pan evaporation modeling

To achieve an efficient methodology for approximating pan evaporation (E_P), this study offers two metaheuristic-integrated predictors. Shuffled complex evolution (SCE) and electromagnetic field optimization (EFO) are two of the fastest metaheuristic algorithms that are synthesized with artificial neural network (ANN). By doing this, the ANN is optimized in a noticeably shorter time compared to its integration with other metaheuristic techniques. Five-year climatic data of the Bakersfield station (California, USA) with an 80:20 ratio are used for developing and testing the methods. The proposed hybrids are implemented with appropriate population sizes (20 and 35 for the SCE and EFO, respectively) and their results are compared to a single ANN. Accuracy evaluation (correlation coefficients > 0.99) professed that the neural network with both conventional and sophisticated trainers is a competent approach for the E_P simulation. Besides, it was observed that the error of prediction by the ANN-SCE and ANN-EFO is 6.02 and 9.27% lower than the single ANN, respectively. Therefore, the used strategies can enhance the applicability of the ANN. The time elapsed in the optimization using SCE and EFO was 479.0 and 281.9 s, respectively. A comparison between these algorithms revealed that the EFO is both a faster and more accurate optimizer. The ANN-EFO is accordingly recommended as a new efficient model for predicting the E_P.

     

Application of surrogate artificial intelligent models for real‐time flood routing

Developing a robust flood forecasting and warning system (FFWS) is essential in flood‐prone areas. Hydrodynamic models, which are a major part of such systems, usually suffer from computational instabilities and long runtime problems, which are particularly important in real‐time applications. In this study, two artificial intelligence models, namely artificial neural network (ANN) and adaptive neuro‐fuzzy inference system (ANFIS), were used for flood routing in an FFWS in Madarsoo river basin, Iran. For this purpose, different rainfall patterns were transformed to run‐off hydrographs using the Hydrologic Engineering Center (HEC)‐1 hydrological model and routed along the river using HEC river analysis system RAS hydrodynamic model. Then, the simulated hydrographs with different lag times were used as inputs for training of ANN and ANFIS models to simulate flood hydrograph at the basin outlet. Results showed that the simulations obtained from ANN and ANFIS coincided with the results simulated by the HEC‐RAS, and application of such models is strongly suggested as a backup tool for flood routing in FFWSs.     

Rare earth elements leaching from Chadormalu apatite concentrate: Laboratory studies and regression predictions

The extraction of rare earth elements from apatite concentrate of Chadormalu plant of Iran was studied with the dissolution of ore in nitric acid. The parameters of acidity: 60%, solid to liquid ratio: 30%, leaching time: 30 minute, agitation rate: 200 rpm, temperature: 60 °C and particle size (d₈₀): 50 microns were determined as the optimum operational conditions. The recoveries of lanthanum, cerium, neodymium and yttrium were achieved at 74, 59, 72 and 73%, respectively, in the optimized conditions. Multivariable regression was used to predict La, Ce, Nd, Y and total REEs (Y+Nd+Ce+La) leaching recoveries, using experimental data from laboratory studies. It was achieved quite satisfactory correlations of 0.93, 0.98, 0.99, 0.97 and 0.99 for the prediction of Y, Nd, Ce, La and total REEs recoveries, respectively. It was shown that the proposed equations accurately reproduce the effects of operational variables on the different REEs recoveries, and can be used to optimize the REEs leaching plant.