Figure 2 - uploaded by Young Min Wie
Content may be subject to copyright.
Source publication
The purpose of this study is to experimentally design the drying, calcination, and sintering processes of artificial lightweight aggregates through the orthogonal array, to expand the data using the results, and to model the manufacturing process of lightweight aggregates through machine-learning techniques. The experimental design of the process c...
Citations
Water electrolysis, powered by renewable energy, is a leading method for producing high-purity hydrogen and oxygen. Among electrolyzer systems, alkaline and polymer exchange membrane (PEM) electrolyzers are prevalent, with PEM being preferred for large-scale energy storage due to its superior energy efficiency. Therefore, the current development of PEM technology is mainly focused on costs reduction, improvement of performance and durability. However, the degradation at high current densities has not received thorough exploration thus far.
In this research, machine learning algorithms were applied to experimental data from commercial PEM water electrolyzers (PEMWE) to examine the impact of various factors including operating temperature, current density, the transport/catalyst layer interface, and catalyst loading. The study focused on how temperature affects three anode catalysts (Ir-black, IrO2 and
), the role of pore diameter in the transport layer, and different anode catalyst loadings on cell potential performance. Additionally, the influence of temperature and current density on the degradation of PEMWEs, particularly on the anode side, was analyzed using machine learning techniques. In this study, the machine learning techniques employed were support vector regression (SVR) and artificial neural network (ANN). The findings indicated that SVR, when its hyper-parameters are fine-tuned using a genetic algorithm (GA), can achieve prediction accuracy comparable to an ANN with a fully connected architecture consisting of two hidden layers, each with 30 nodes. The algorithms’ predictions indicated that PEMWE can be operated at conditions that enhance the performance and life time, subsequently leading to cost reduction in hydrogen ensuring widespread adoption of PEM electrolyzers for hydrogen production.
The effect of the addition of Fe0 and Fe3+ on the formation of expanded clay aggregates was studied using iron-free kaolin as an aluminosilicates source. Likewise, the incorporation of cork powder as a source of organic carbon and Na2CO3 as a flux in the mixtures was investigated in order to assess its effect in combination with the iron phases. An experimental protocol, statistically supported by a mixture experiments/design of experiments approach, was applied to model and optimize the bloating index, density, absorption capacity, and mechanical strength. The process of expansion and pore generation and the associated decrease in density required the addition of iron, such that the optimum mixtures of these properties presented between 25 and 40 wt.% of Fe0 or Fe3+, as well as the incorporation of 3.5–5 wt.% of organic carbon. The addition of Fe3+ produced a greater volumetric expansion (max. 53%) than Fe0 (max. 8%), suggesting that the formation of the FeO leading to this phenomenon would require reducing and oxidizing conditions in the former and the latter, respectively. The experimental and model-estimated results are in good agreement, especially in the aggregates containing Fe0. This reinforces the application of statistical methods for future investigations.
In this study, in an effort to manufacture lightweight aggregates recycled from anaerobic digested sewage sludge (ADSS), the firing process characteristics of artificial lightweight aggregates containing a large amount of ADSS were identified and the manufacturing process was optimized and modeled using machine learning techniques. Samples were prepared by rapid firing and according to the orthogonal arrangement experimental design, the particle density and water absorption rate were measured, and a pore analysis was conducted by 3D-CT and the mercury intrusion method. Random forest, SVR, and XGBoost techniques were used as machine learning techniques to analyze the data. As a result of the rapid firing test, samples with an ADSS content of 40 wt% or more were found to have a low density and were found not to show bloating. For aggregates containing more than 40% ADSS, a loss of organic matter played a greater role in the decreased density than bloating due to viscous behavior. Data expansion using an orthogonal arrangement table and modeling by the SVR method for the ADSS 50 mixture found an RMSE value of 0.013 with significant predicted values of the physical properties also obtained. Aggregates with a content of 50 wt% of anaerobically digested sludge were successfully prepared on a pilot scale, and this aggregate mix met EN-13055 standards.
China’s shallow coal resources are gradually diminishing, and deep coal resources have slowly become the main energy source. However, the destruction mechanism and evolution of deep rock formation structure are not clear, which seriously restricts the exploitation and utilization of deep energy. Here, the optimization of the physical parameters and the deformation law of the overlying rock in a deep mine in Shandong Province were studied with an integrated approach including similar simulation, mechanical analysis, numerical simulation, and measurement verification, etc. First, the paper simplified the rock formation and developed a numerical model using the field exploration data; second, we analyzed the mechanical properties of each rock formation, obtaining the key rock formation that affects the surface deformation of the mining area. Furthermore, we tested the physical parameters of rock formation by using the orthogonal test, optimizing the physical parameters of rock formation with the extreme difference, and variance analysis of the orthogonal test results. Then, using FLAC3D, we conducted numerical calculations for strip mining of deep wells with numerous working faces, analyzing the maximum surface subsidence value, the maximum horizontal movement value of ground surface at different mining depths, and the change in the subsidence coefficient. By analyzing the linkage relationship between the surface phenomenon and deep mining, we obtained the optimal mathematical model of the three and the coal seam mining depth, which revealed the linkage law of “deep formation–earth surface”. Finally, the model relationships of the influence boundary value, maximum subsidence value, maximum horizontal movement value, and mining depth for each rock layer were separately established.
Osthol (osthole), known as a neuroprotective drug, has shown potent anticancer activity. However, the potential clinical application of osthol is limited due to its low water solubility and low bioavailability. Polybutyl cyanoacrylate (PBCA) has been widely used to improve the solubility of drugs with poor water solubility. In this study, an orthogonal experimental design (OED) was applied to design the preparation process of PBCA nanoparticles (NPs). Then, nanoparticles were prepared and evaluated in terms of physicochemical properties, in vitro release, and cellular uptake, etc. Further, the anti-cancer activity of osthol-PBCA NPs was demonstrated in SH-SY5Y cells. The pharmacokinetics and area under the curve (AUC) were investigated. The obtained osthol-NPs presented a spherical shape with a particle size of 110 ± 6.7 nm, a polydispersity index (PDI) of 0.126, and a zeta potential of −13 ± 0.32 mV. Compared with the free osthol, the drugs in osthol-NPs presented better stability and sustained release pattern activity. In vitro analysis using SH-SY5Y neuroblastoma cells showed that osthol-loaded nanoparticles displayed a significantly enhanced intracellular absorption process (three times) and cytotoxicity compared with free osthol (p < 0.05, increased 10–20%). The in vivo pharmacokinetic study revealed that the AUC of osthol-NPs was 3.3-fold higher than that of free osthol. In conclusion, osthol-PBCA NPs can enhance the bioactivity of osthol, being proposed as a novel, promising vehicle for drug delivery.
The relationship between the proportions of multicomponent mixtures with the technological properties of ceramic granular materials (synthetic aggregates) has been studied using statistical methods. The four phases involved in the formulations have been: kaolin (K) as aluminosilicate source; cork powder (C) as organic carbon source; sodium carbonate (N) as flux and pyrite (P) as source of iron and sulfur. The Mixture Experiments - Design of Experiments (ME-DOE) has been the statistical methodology applied from the initial configuration of the 36 starting formulations to the final validation of the models and optimums. After granulation, artificial aggregates have been obtained by sintering in a rotary kiln, and their main technological properties have been determined. Bloating index (BI), particle density (ρrd), water absorption (WA24) and crushing strength (S) were selected as the four key characteristics to be modeled and optimized, using response surface and effect plots to assess the effect of K, C, N and P on such properties. 32 out of 36 starting varieties met the density criteria for lightweight aggregates. In the optimum formulations obtained, the minimum percentage of K was 83 wt%, so that the variations in the percentages of P, C and N were the critical variables for determining the final properties of the aggregate. The contrast between experimental and estimated data has shown that the models fit adequately, indicating that this type of approach may have enormous potential for future research on artificial aggregates and other ceramic materials.
We believe that the chosen topic is nowadays extremely current and of great interest [...]
