Determination of thermal conductivity ratio of CuO/ethylene glycol nanofluid by connectionist approach

Enhancing Physical and Thermodynamic Properties of DNA Storage Sets with End-constraint

With the explosion of data, DNA is considered as an ideal carrier for storage due to its high storage density. However, low-quality DNA sets hamper the widespread use of DNA storage. This work proposes a new method to design high-quality DNA storage sets. Firstly, random switch and double-weight offspring strategies are introduced in Double-strategy Black Widow Optimization Algorithm (DBWO). Experimental results of 26 benchmark functions show that the exploration and exploitation abilities of DBWO are greatly improved from previous work. Secondly, DBWO is applied in designing DNA storage sets, and compared with previous work, the lower bounds of storage sets are boosted by 9%-37%. Finally, to improve the poor stabilities of sequences, the End-constraint is proposed in designing DNA storage sets. By measuring the number of hairpin structures, melting temperature, and minimum free energy, it is evaluated that with our innovative constraint, DBWO can construct not only a larger number of storage sets, but also enhance physical and thermodynamic properties of DNA storage sets.

Promising Nanoparticle-Based Heat Transfer Fluids-Environmental and Techno-Economic Analysis Compared to Conventional Fluids

Providing optimal operating conditions is one of the major challenges for effective heating or cooling systems. Moreover, proper adjustment of the heat transfer fluid is also important from the viewpoint of the correct operation, maintenance, and cost efficiency of these systems. Therefore, in this paper, a detailed review of recent work on the subject of conventional and novel heat transfer fluid applications is presented. Particular attention is paid to the novel nanoparticle-based materials used as heat transfer fluids. In-depth comparison of environmental, technical, and economic characteristics is discussed. Thermophysical properties including thermal conductivity, specific heat, density, viscosity, and Prandtl number are compared. Furthermore, the possible benefits and limitations of various transfer fluids in the fields of application are taken into account.

Co-immobilization of multi-enzyme on reversibly soluble polymers in cascade catalysis for the one-pot conversion of gluconic acid from corn straw

The difficulties in the process of cellulose cascade conversion based on immobilization technology lies in the recycling enzymes from rich solid-containing straw hydrolysate and the incompatibility of conventional immobilization with this process. In this study, three types of enzyme (cellulase, glucose oxidase and catalase) were successfully immobilized on a reversible soluble Eudragit L-100. Through the determination of the preparation conditions, enzymatic properties and catalytic conditions, the co-immobilized enzyme was applied to the catalytic reaction of one-pot conversion of corn straw to gluconic acid. The yield of gluconic acid achieved 0.28 mg/mg, conversion rate of cellulose in corn straw to gluconic acid reached 61.41%. The recovery of co-immobilized enzyme from solid substrate was achieved by using reversible and soluble characteristics of the carrier. After 6 times of recycling, the activity of co-immobilized enzyme was maintained at 52.38%, confirming the feasibility of multi-enzyme immobilization strategy using reversible soluble carrier in cascade reactions.

Prediction of Thermo-Physical Properties of TiO2-Al2O3/Water Nanoparticles by Using Artificial Neural Network

In this paper, an artificial neural network is implemented for the sake of predicting the thermal conductivity ratio of TiO₂-Al₂O₃/water nanofluid. TiO₂-Al₂O₃/water in the role of an innovative type of nanofluid was synthesized by the sol-gel method. The results indicated that 1.5 vol.% of nanofluids enhanced the thermal conductivity by up to 25%. It was shown that the heat transfer coefficient was linearly augmented with increasing nanoparticle concentration, but its variation with temperature was nonlinear. It should be noted that the increase in concentration may cause the particles to agglomerate, and then the thermal conductivity is reduced. The increase in temperature also increases the thermal conductivity, due to an increase in the Brownian motion and collision of particles. In this research, for the sake of predicting the thermal conductivity of TiO₂-Al₂O₃/water nanofluid based on volumetric concentration and temperature functions, an artificial neural network is implemented. In this way, for predicting thermal conductivity, SOM (self-organizing map) and BP-LM (Back Propagation-Levenberq-Marquardt) algorithms were used. Based on the results obtained, these algorithms can be considered as an exceptional tool for predicting thermal conductivity. Additionally, the correlation coefficient values were equal to 0.938 and 0.98 when implementing the SOM and BP-LM algorithms, respectively, which is highly acceptable.

Thermal Conductivity Modeling of Nanofluids Contain MgO Particles by Employing Different Approaches

The existence of solid-phase nanoparticles remarkably improves the thermal conductivity of the fluids. The enhancement in this property of the nanofluids is affected by different items such as the solid-phase volume fraction and dimensions, temperature, etc. In the current paper, three different mathematical models, including polynomial correlation, Multivariate Adaptive Regression Spline (MARS), and Group Method of Data Handling (GMDH), are applied to forecast the thermal conductivity of nanofluids containing MgO particles. The inputs of the model are the base fluid thermal conductivity, volume concentration, and average dimension of solid-phase, and nanofluids’ temperature. Comparing the proposed models revealed higher confidence of GMDH in estimating the thermal conductivity, which is attributed to its complicated structure and more appropriate consideration of the input’s interaction. The values of R-squared for the correlation, MARS, and GMDH are 0.9949, 0.9952, and 0.9991, respectively. In addition, based on the sensitivity analysis, the effect of thermal conductivity of the base fluid on the overall thermal conductivity of nanofluids is more remarkable compared with the other inputs such as volume fraction, temperature, and dimensions of the particles which are used as the inputs of the models.

Removal of methylene blue via bioinspired catecholamine/starch superadsorbent and the efficiency prediction by response surface methodology and artificial neural network-particle swarm optimization

This paper demonstrates coupling of the artificial neural network (ANN) technique with the particle swarm optimization (PSO) method and compares the performance of ANN-PSO with response surface methodology (RSM) in prediction of the adsorption of methylene blue (MB) by a novel bio-superadsorbent. To this, a starch-based superadsorbent was synthesized using acrylic acid and acryl amid polymers and then catecholamine functional groups were combined onto the surface with oxidative polymerization of dopamine. The adsorption of MB was considered as a function of pH, dye concentration, and contact time. The best topology of the ANN was found to be 3-7-1, and prediction model of the adsorption capacity was demonstrated as a matrix of explicit equations. ANN-PSO is more accurate than RSM. The results revealed that the root-mean-square error, correlation coefficient, and normalized standard deviation for the ANN-PSO are 22.46, 0.99, and 16.83, respectively, while for RSM are 82.89, 0.98, and 65.41, respectively.

Effect of Nanofluids on Boiling Heat Transfer Performance

At present, there are many applications of nanofluids whose research results are fruitful. Nanofluids can enhance the critical heat flux, but the effect on boiling heat transfer performance still has disagreement. Base liquids with higher viscosity improve the boiling heat transfer performance of nanofluids. When the base liquid is a multicomponent solution, the relative movement between the different solutions enhances the microscopic movement of the nanoparticles due to the different evaporation order during the boiling process, so that the boiling heat transfer performance is enhanced. Compared with the thermal conductivity of the heated surface, the deposition of the low thermal conductivity nanoparticles reduces the heat dissipation rate of the heated surface and improves the wall superheat. Then the enhancement of the boiling heat transfer coefficient should be attributed to the thermal conductivity improvement of base fluid and the bubble disturbance resulted from the nanoparticle’s microscopic motion.