This paper discusses the forward osmosis (FO) module model based on a previously demonstrated hemodiafilter (HDF) model, which was modified by a three-concentration-polarization (3CP) membrane model. It was verified using derivative membrane models and the optimization technique to maximize the permeate flow rate of the FO module, where the membrane properties, hollow fiber geometries and membrane orientations were the objective factors. It was clarified that the proposed FO module model comprising of the 3CP model is the most preferable one from a good agreement with the measured values. The solute permeabilities in the active layer and the support layer as the membrane properties were optimally analyzed, and the inner diameter and the length of hollow fiber were quantitatively examined.
Fluidization quality is severely affected by a decrease in volume of the fluidizing gas in the emulsion phase. Several causes underlie this phenomenon, one of which is gas adsorption on particles. In this study, the fluidization behavior was observed by switching the fluidizing gas from Ar to CO2. This gas combination was chosen to eliminate the influence of nonequimolar diffusion due to a difference in molecular weight. Because the adsorption capacity of CO2 on porous particles was large, temporary defluidization occurred when the fluidizing gas was switched from Ar to CO2. The lower limit of the adsorption capacity was obtained using particles with different adsorptivities. Two types of defluidization were observed: channeling for smaller particles and plugging for larger particles. The type of defluidization was represented by a map constructed using the minimum fluidizing velocity and the superficial gas velocity as parameters. In addition, gradual gas replacement was explored as a method to prevent defluidization.
In this study, the hydrothermal conversion of FAU-type zeolites using an aqueous ammonia solution was investigated to understand the solid-phase transformation mechanism of the selective synthesis of target zeolites. In addition, the CO2/N2 separation ability of prepared materials was estimated from single-component adsorption isotherms by the ideal adsorbed solution theory. ABW and ANA-type zeolites were successfully synthesized from the FAU-type zeolites such as Li–LSX and Na–X, respectively, by a hydrothermal treatment using an aqueous ammonia solution. Li–LSX was completely converted into ABW by treating at a temperature above 150°C for 24 h, and ANA was mainly produced from Na–X at 250°C after treating for over 72 h. The pellet shape of the raw zeolites was maintained even after the hydrothermal treatment. Considering the transformation mechanisms, the results suggest that the transformation of the zeolite topology was caused by the ion exchange between the counter cation of the zeolite and ammonium ion in the solvent and the cation structure-directing effect under hydrothermal conditions. It can be concluded that the counter cations of the raw zeolites played an important role in the structural transformation. The simple synthesis method presented in this study enables the conversion of FAU-type zeolites, particularly X-type zeolite, into other types of zeolites without using strong bases, such as alkali hydroxides. In terms of the CO2/N2 separation ability, CO2/N2 selectivity for post-combustion CO2 capture conditions drastically increased upon the hydrothermal treatment. In particular, the ABW-type zeolite exhibited extremely high CO2/N2 selectivity over 10000.
To investigate the effect of uncertainty in biodiesel production from microalgae, a supply chain network (SCN) of the process from cultivation of microalgae to distribution of biodiesel is developed using mixed-integer linear programming. Biodiesel demandis the crucial factor in the design of SCN, but is quite uncertain, so a stochastic approach that considers uncertain scenarios is developed, and its recommendations are compared to those of a deterministic model. Also, three different scenario-generation methodologies are employed to illustrate the applicability of the approach. The proposed model determines: (1) the numbers, locations, and sizes of a carbon capture and storage system; (2) the numbers, locations, sizes, and types of bio-refineries; (3) the transportation paths of CO2 and water from feedstock fields to bio-refineries; and (4) the transportation paths of biodiesel from bio-refinery to demand cities, while minimizing the expected total cost considering several constraints such as locations of power plants as carbon sources, potential locations of bio-refineries, and demands for biodiesel at each site. The proposed model is validated by applying it to a case study based on the predicted biodiesel demand of Korea in the year 2030. A numerical example illustrates that the unit production cost of alga-derived biodiesel by the stochastic model (US$ 2.84 per liter) is at least 5% more economical than that of the deterministic model (US$ 2.99 per liter). The proposed approach is able to respond to uncertain demand situations in SCN design.
Nitrogen oxide (NOx) emissions are major pollutants of coal-fired boilers. An adaptive nonlinear model-predictive control approach is presented to reduce NOx emissions of power plant boilers. Firstly, the boiler load and the NOx emissions are dynamically predicted by a differential evolution-based least-square support vector machine. Subsequently, based on data-driven prediction modeling, a nonlinear optimization model, with load and capacity constraints, is proposed for NOx emission minimization. Finally, a differential evolution algorithm is used to solve this optimization problem and obtain the optimal control variable settings. Experimental results based on practical data indicate that the proposed approach exhibits a promising performance in the prediction of the boiler load and NOx emissions. Compared with that obtained using the normal control strategy, the proposed approach can reduce NOx emissions by 3.2% and 4.3% under increasing and decreasing loads, respectively.
As nanostructured materials with wide applicability, hollow TiO2 nanotubes (HTNTs) were fabricated for the first time using an “in-flight” coating method. TiO2 coating layers were deposited on the surface of carbon nanotubes (CNTs) suspended in gas, and the resulting products were then heat-treated. Depending on the annealing process, products with various morphologies, including HTNTs, were produced. Raman spectroscopy, scanning electron microscopy, and energy-dispersive X-ray spectroscopy analyses confirmed the absence of CNTs in the HTNTs. The obtained results will provide valuable information for controlling hollow nanostructured materials for a wide range of applications in fields including photocatalysis, gas sensors, and solar cells.