• Energy Research

Abstract In this study, the external surface of a finned tube was coated by 13X zeolite powder, and the CO2 adsorption equilibrium and dynamics were investigated experimentally. A slurry consisting of dionized water, 13X zeolite powder, and Acrylic latex emulsion (ALE) was used to coat the finned tube. The finned tube was coated by deep coating method. The equilibrium isotherms were measured at the range of 20–90 °C and fitted well by the dual-site Langmuir model. The average difference between the model and the results obtained from the experiments is about 2.5%. The nitrogen adsorption/desorption at 77 K was used for characterization of adsorbents. A 11% reduction was observed in pore volume and surface area. The dynamic test showed that the desorption of adsorbed CO2 takes place in about 14 s which is an order of magnitude faster than the fastest developed method. A conservative criterion was developed for estimating adsorbent working capacity. This criterion showed that the working capacities of the adsorbent are about 80% of its ideal values.

Abstract In this study, the external surface of a finned tube was coated by 13X zeolite powder, and the CO2 adsorption equilibrium and dynamics were investigated experimentally. A slurry consisting of dionized water, 13X zeolite powder, and Acrylic latex emulsion (ALE) was used to coat the finned tube. The finned tube was coated by deep coating method. The equilibrium isotherms were measured at the range of 20–90 °C and fitted well by the dual-site Langmuir model. The average difference between the model and the results obtained from the experiments is about 2.5%. The nitrogen adsorption/desorption at 77 K was used for characterization of adsorbents. A 11% reduction was observed in pore volume and surface area. The dynamic test showed that the desorption of adsorbed CO2 takes place in about 14 s which is an order of magnitude faster than the fastest developed method. A conservative criterion was developed for estimating adsorbent working capacity. This criterion showed that the working capacities of the adsorbent are about 80% of its ideal values.

Abstract Heat transfer characteristics of γ-Al 2 O 3 /water and TiO 2 /water nanofluids were measured in a shell and tube heat exchanger under turbulent flow condition. The effects of Peclet number, volume concentration of suspended nanoparticles, and particle type on the heat characteristics were investigated. Based on the results, adding of naoparticles to the base fluid causes the significant enhancement of heat transfer characteristics. For both nanofluids, two different optimum nanoparticle concentrations exist. Comparison of the heat transfer behavior of two nanofluids indicates that at a certain Peclet number, heat transfer characteristics of TiO 2 /water nanofluid at its optimum nanoparticle concentration are greater than those of γ-Al 2 O 3 /water nanofluid while γ-Al 2 O 3 /water nanofluid possesses better heat transfer behavior at higher nanoparticle concentrations.

Abstract Heat transfer characteristics of γ-Al 2 O 3 /water and TiO 2 /water nanofluids were measured in a shell and tube heat exchanger under turbulent flow condition. The effects of Peclet number, volume concentration of suspended nanoparticles, and particle type on the heat characteristics were investigated. Based on the results, adding of naoparticles to the base fluid causes the significant enhancement of heat transfer characteristics. For both nanofluids, two different optimum nanoparticle concentrations exist. Comparison of the heat transfer behavior of two nanofluids indicates that at a certain Peclet number, heat transfer characteristics of TiO 2 /water nanofluid at its optimum nanoparticle concentration are greater than those of γ-Al 2 O 3 /water nanofluid while γ-Al 2 O 3 /water nanofluid possesses better heat transfer behavior at higher nanoparticle concentrations.

CaO–Al2O3/ZrO2 mixed oxide catalyst was prepared using free-solvent method. The catalyst was characterized using X-ray diffraction, BET surface area, acidity index (obtained by titration method), and scanning electron microscopy (SEM). With calcium aluminate and calcium zirconate been successfully formed, the mix exhibited small crystal size, high acidity, and large surface area, pore size, and pore volume, making it a catalyst of choice for biodiesel production. The activity of catalyst was evaluated in the course of esterification of oleic acid as well as transesterification of waste cooking oil (WCO) into biodiesel. Based on a four-variable central composite design (CCD), response surface methodology (RSM) was used to optimize effective variables on oleic acid conversion. The optimum yield of 94.68% was obtained at the following set of optimum conditions: reaction temperature of 120 °C, methanol/oleic acid molar ratio of 15.64, catalyst concentration of 2.94 wt%, and reaction time of 4 h; the result was in excellent agreement with the predicted values. Furthermore, under the optimum conditions, the catalyst succeeded to convert 93.48% of WCO into biodiesel.

CaO–Al2O3/ZrO2 mixed oxide catalyst was prepared using free-solvent method. The catalyst was characterized using X-ray diffraction, BET surface area, acidity index (obtained by titration method), and scanning electron microscopy (SEM). With calcium aluminate and calcium zirconate been successfully formed, the mix exhibited small crystal size, high acidity, and large surface area, pore size, and pore volume, making it a catalyst of choice for biodiesel production. The activity of catalyst was evaluated in the course of esterification of oleic acid as well as transesterification of waste cooking oil (WCO) into biodiesel. Based on a four-variable central composite design (CCD), response surface methodology (RSM) was used to optimize effective variables on oleic acid conversion. The optimum yield of 94.68% was obtained at the following set of optimum conditions: reaction temperature of 120 °C, methanol/oleic acid molar ratio of 15.64, catalyst concentration of 2.94 wt%, and reaction time of 4 h; the result was in excellent agreement with the predicted values. Furthermore, under the optimum conditions, the catalyst succeeded to convert 93.48% of WCO into biodiesel.

Abstract Three different types of nanofluids were prepared by dispersing γ-Al2O3, TiO2 and CuO nanoparticles in a 0.5 wt% of carboxymethyl cellulose (CMC) aqueous solution. Thermal conductivity of the base fluid and nanofluids with various nanoparticle loadings at different temperatures were measured experimentally. Results show that the thermal conductivity of nanofluids is higher than the one of the base fluid and the increase in the thermal conductivity varies exponentially with the nanoparticle concentration. In addition to increase with the nanoparticle concentration, the thermal conductivity of nanofluids increases with the temperature. Neural network models were proposed to represent the thermal conductivity as a function of the temperature, nanoparticle concentration and the thermal conductivity of the nanoparticles. These models were in good agreement with the experimental data. On the other hand, the Hamilton Crosser model was only satisfactory for low nanoparticle concentrations.

Abstract Three different types of nanofluids were prepared by dispersing γ-Al2O3, TiO2 and CuO nanoparticles in a 0.5 wt% of carboxymethyl cellulose (CMC) aqueous solution. Thermal conductivity of the base fluid and nanofluids with various nanoparticle loadings at different temperatures were measured experimentally. Results show that the thermal conductivity of nanofluids is higher than the one of the base fluid and the increase in the thermal conductivity varies exponentially with the nanoparticle concentration. In addition to increase with the nanoparticle concentration, the thermal conductivity of nanofluids increases with the temperature. Neural network models were proposed to represent the thermal conductivity as a function of the temperature, nanoparticle concentration and the thermal conductivity of the nanoparticles. These models were in good agreement with the experimental data. On the other hand, the Hamilton Crosser model was only satisfactory for low nanoparticle concentrations.