A CoFe-based magnetic biomass-derived porous carbon was synthesized via a simple impregnation/calcination method by using chestnut shells as the raw material. The materials were characterized via powder X-ray diffraction (XRD), scanning electron microscopy (SEM), and specific surface area and zeta potential analyses. Further, the effect of calcination temperature on the properties of the synthesized materials was investigated. The results indicated that the calcination temperature had a significant impact on the crystal form and morphology of the materials. The maximum adsorption capacity of the material calcinated at 600°C (CSCF-6) for malachite green (MG) was 69.93 mg/g (0.1916 mmol/g). Furthermore, the MG-adsorption efficiency of CSCF-6 was greater than 90% after repeated use for 7 times and the used CSCF-6 could be recycled easily by applying an external magnetic field. The process of the adsorption of MG onto CSCF-6 could be described by the Langmuir adsorption isotherm model. Further, the results of the adsorption kinetics study showed that the adsorption process fitted well with the pseudo-second-order kinetics model, indicating that the adsorption process was mainly controlled by chemical adsorption. Moreover, the results of the thermodynamic analysis indicated that the adsorption process was spontaneous and endothermic, and higher temperatures were conducive to the adsorption.
In controlling the quality of crystalline particles, the comprehensive uniformity of the distribution of characteristics, such as particle size and crystal shape, is essential. Improvement of comprehensive uniformity is also required for multicomponent crystals, such as cocrystals. Because cocrystals are composed of two or more pure components, undesirable crystals may precipitate. Therefore, it is necessary to consider comprehensive uniformity, including component characteristics. We proposed the “single component excess (s.c.e.)” index as the absolute difference between the concentrations of each pure component. In this paper, an operation method under high-productivity conditions for improving the quality of cocrystals has been proposed by evaluating the changes in s.c.e. and uniformity in distribution of particle size and crystal shape using homogeneity. CBZ–SAC (1 : 1) cocrystal Form I was obtained by integrating the two driving forces generated by the reaction crystallization and anti-solvent crystallization methods. The homogeneity was initially high when using the driving force of the reaction crystallization method and further increased under the driving force of the anti-solvent crystallization method. Considering these results, both homogeneity and productivity were improved by the sequential generation of the two driving forces. Thus, it was possible to design a method that achieves quality and productivity in co-crystallization using homogeneity, including the s.c.e.
Cost-efficiency removal of hydrogen sulfide from effluent gases is a research topic of immense importance, for which rotating packed beds (RPBs) with small volumes and high efficiencies have attracted much attention. To reveal the complex interactions between hydrogen sulfide and sodium carbonate under the role of rotating packing in RPB, a mathematical model was developed. It contained correlations of mass-transfer rates (gas to liquid phase and in the liquid phase interior), chemical reactions between hydrogen sulfide and sodium carbonate, gas–liquid effective interfacial area, and liquid holdup. Experimental data and model predications were compared. The operating conditions, such as high gravity factor, liquid flow rate, gas flow rate, sodium carbonate concentration and hydrogen sulfide inlet concentration, were optimized by the model. Contribution ratios of gas and liquid mass transfer rates, gas–liquid effective interfacial area, and liquid holdup on the removal efficiency at different high gravity factors, liquid flow rates, and gas flow rates were analyzed in an RPB, and compared with a packed bed. The model enabled better understanding of RPBs and determined optimum operating conditions.
Induction thermal plasma is applied to prepare carbon coated silicon nanoparticles as the anode materials of a battery and the effect of methane injection methods is investigated. Silicon nanoparticles are fabricated as main products and show spherical morphologies with an average diameter of around 50 nm. The unfavorable formation of SiC, which is a byproduct and limits the practical capacity of batteries, can be identified when the methane injection position is near to plasma torch. An amorphous hydrogenated carbon coating is synthesized successfully instead of pure carbon materials. The CH4 injection position can determine the decomposition temperature of methane as well as the concentration of released H atoms. Consequently, the properties of prepared carbon coatings, including the sp2 ratio and H content, are tunable with injection positions through the etching effect of hydrogen atoms. These results are significant for the synthesis of silicon nanoparticles with carbon coating and the design of lithium ion batteries with higher energy density.
Conventional multivariate statistical process monitoring methods constrained by the assumptions of linear and normal distributions for the measurements, such as principal component analysis and canonical variate analysis, demonstrate significantly higher fault alarm rates and lower fault detection rates in nonlinear dynamic industrial process monitoring. Kernel principal component analysis (KPCA) based on the radial basis function, has already been applied in numerous nonlinear industrial processes. However, an infinite-dimensional nonlinear mapping might be inefficient and redundant. To improve the efficiency of traditional methods, this study proposes the implementation of canonical variate nonlinear principal component analysis for monitoring nonlinear dynamic processes. The training data are first preprocessed by performing canonical variate analysis to reduce the effects of the dynamic characteristics of the data. Then, the state vectors are projected into a high dimension feature space by an explicit second-order polynomial mapping. The first k principal components and the remaining residual vectors are obtained in the feature space via conventional principal component analysis for fault detection. The combined statistic Qc is proposed for monitoring the variations in the linear and nonlinear residual spaces; its upper control limits can be estimated by the kernel density function. In comparison to the results of KPCA and nonlinear dynamic principal component analysis, the proposed method yielded significantly higher fault detection rates and relatively lower fault alarm rates in a simulated nonlinear dynamic process and the benchmark Tennessee Eastman process.
This paper proposes a hybrid online batch fault monitoring method that combines the signed digraph (SDG) with the k-nearest neighbors classifier (kNN) using dynamic time warping (DTW) distance. The sum of the k smallest DTW distances between the ongoing batch and normal-operational batch references is calculated for online detection. If the increase in the sum is greater than the predefined detection control limits, the sample is labeled as “abnormal” and an SDG diagnosis is made. The k corresponding normal samples from the k nearest reference batches are retrieved to calculate the upper and lower variable control limits for each variable online. Quantitative values are transformed into qualitative ones using these control limits, and the variable nodes with non-zero signs are diagnosed through SDG. The signs of a specified portion of causality arcs in the SDG are updated with calculations using online measurements. Each diagnostic route is given a weight determined by both the normal historical behavior and ongoing behavior of its root variable, and the diagnostic routes with the highest weights are considered to be the root causes of the occurring fault. The proposed DTW-kNN-SDG method was validated using data from a simulated batch production of penicillin with a variety of fault types, magnitudes, and fault duration times, and novel diagnosis results were subsequently achieved.
This study used process simulation to assess the overall economic feasibility of a hydrogen energy system involving a power-to-ammonia process as a transportation energy source. While the relevant individual processes for the overall energy system have already been used in the chemical industry, it is financially important to approach them in a holistic simulation framework. According to the proposed simulation, the ammonia and hydrogen production costs were 288.3 $/t and 5.43–6.13 $/kg, respectively. The results could motivate further studies on accelerating the implementation of renewable energy systems to mitigate climate change.
Gel microbeads composed of chitosan biopolymers were synthesized via membrane emulsification, as novel adsorbents for the adsorption of anionic azo dye (Acid Orange 7 (AO7)) and protein (bovine serum albumin (BSA)). The synthesis route involved the production of size-controlled water-in-oil emulsion droplets containing the chitosan solution using a porous, hydrophobic polytetrafluoroethylene membrane. The resulting size-controlled emulsion droplets were successfully converted into stable gel microbeads via external gelation, followed by chemical crosslinking using ethylene glycol diglycidyl ether (EGDE). Scanning electron microscopy results revealed the size of the gel microbeads having a diameter ranging from 15 to 20 µm, which was comparable to the size of the emulsion droplets. The effect of the crosslinking reaction between the prepared gel microbeads with EGDE was confirmed via Fourier transform infrared spectroscopy. The batch adsorption studies employed the Langmuir adsorption model with maximum adsorption capacities at pH values of 5 and 6 for AO7 dye (qmax=1.84 g/g) and BSA (qmax=0.87 g/g), respectively, with electrostatic interaction being the dominant mechanism of adsorption for both the azo dye and protein. The prepared crosslinked chitosan gel microbeads in this study exhibited excellent recyclability up to 30 times without a decline in their adsorptive capacities, a shelf life of >100 d, and a fair susceptibility toward enzymatic biodegradation, making them some of the most effective and versatile green adsorbents for wastewater treatment.
As a first step towards understanding the effects of the local micro-structure of gas diffusion layers (GDLs) on water accumulation in the GDL pores, ex situ X-ray computed tomography (XRCT) measurements were performed with a water injection device. When polymer electrolyte fuel cells are operated, the liquid water resulting from water vapor condensation adversely affects their performance because the water clusters obstruct oxygen diffusion. The water network constructed from XRCT images indicated that the water clusters could be categorized into three types: fully connected clusters, which started from the microporous layer (MPL) surface and reached the end side of the GDL substrate; MPL-connected clusters, which started from the MPL surface and were dead-ended; and isolated clusters, which did not connect either the MPL surface or the end side of the substrate. The results also suggested that many water paths were created in the substrate by branching, but that only a small portion of the water paths permeated the GDL. The methodology developed in this research is expected to be effective in comparing GDLs with different manufacturing processes.