Journal of the Taiwan Institute of Chemical Engineers最新文献

Background

The heterogeneous catalytic system of metal-organic frameworks (MOFs)-activated peroxymonosulfate (PMS) can effectively remove organic pollutants, and has become a research hotspot in the field of wastewater treatment.

Methods

In this work, novel bimetallic MOFs (CLB-2:1) were prepared from a one-step solvothermal method with the simultaneous introduction of transition metal cobalt and rare earth metal lanthanum.

Significant findings

With methylene blue (MB) as the target pollutant, the degradation rate of the CLB-2:1/PMS system can reach 97.54 % within 10 min, and the corresponding reaction rate constant (k_obs) is 0.7519 min⁻¹. This system maintains high stability and catalytic activity in a wide pH range and different water quality conditions. Notably, the CLB-2:1/PMS system can effectively treat various organic dyes, and is widely applicable to alleviating the pollution of printing and dyeing effluents. Mechanism studies have shown that non-radical pathways such as singlet oxygen (¹O₂) and high-valent metal species play a leading role in the reaction process. Based on LC-MS test analysis, combined with density functional theory (DFT) calculations, the MB possible degradation pathway was proposed. In general, the bimetallic MOFs/PMS system constructed here has great application prospects in the field of organic wastewater pollution treatment.

Background

The improvement of conventional mixing methods on the problems of uneven solid phase distribution and bottom mineral deposition in multi-stage vanadium-bearing shale agitated leach tanks is limited, and more novel mixing methods need to be proposed and developed.

Method

In this study, vapor-liquid-solid flow, dead-zone buildup and bubble residence time in a multistage vanadium-bearing shale leaching tank with inlet and outlet were numerically investigated and compared. Three different stirring methods, namely mechanical stirring, vapor blowing stirring, and combined mechanical vapor blowing stirring, were used to stir the samples, and different inlet speeds were compared, and the numerical models were validated by water modeling tests.

Significant Findings

The results showed that the combination of mechanical mixing and vapor blowing could reduce the ratio of the "dead zone", in which the top low-concentration zone was reduced from 0.84% to 0.178%, and the bottom deposition zone was reduced from 0.32% to 0.026% compared with only mechanical mixing. Increasing the inlet vapor flow rate would enhance the stirring effect of vanadium shale leaching tank and reduce the residence time of bubble particles in the tank. The minimum residence time of bubble particles was shortened from 10.05 s to 5.95 s, and the peak residence time of bubble particles and the distribution of vapor residence time were improved significantly. The combination of mechanical stirring and vapor blowing was favorable for solid-liquid two-phase leaching reaction. Increasing the flow rate could effectively reduce the effect of bubbles on the mixing of solid-liquid phase.

Background

CO₂ is one of the dominant greenhouse gases that causes global warming and a series of serious environmental problems. The catalytic chemical conversion of CO₂ into value-added products is one of the attractive approaches.

Methods

A novel zeolitic imidazolate framework (ZIF-67) has been successfully synthesized by incorporating choline chloride and thiosemicarbazide-based deep eutectic solvent onto the surface of ZIF-67, denoted as ChTSC@ZIF-67. The material's textural and physical characteristics were analyzed using powder XRD, TGA, zeta potential, SEM, and BET surface area measurements.

Significant Findings

The utilization of ChTSC@ZIF-67 as a catalyst for the conversion of epoxides and carbon dioxide into cyclic carbonates, in the absence of a co-catalyst or solvent, was investigated under various experimental conditions. Optimum conditions (3 mg catalyst, 4.0 bar CO₂ pressure, 80 °C, and 3 h reaction time) led to the production of diverse cyclic carbonates with excellent yield and selectivity. The synergistic effect between the active site in ZIF-67 and the deep eutectic solvent may be the main reason for the high catalytic activity. Furthermore, the catalyst retains its heterogeneous nature for more than six cycles, exhibiting no substantial decline in yield.

Background

The co-gasification of solid waste and biomass to produce syngas is an environmentally friendly technology. Unfortunately, the tar formation in the solid waste/biomass co-gasification process would degrade the product gas quality and the overall process efficiency.

Methods

In this study, the kinetics of the solid waste/biomass co-gasification is shown by the Aspen Plus simulation. Through the model validation and sensitivity analysis, it is validated that tar yield, syngas composition, and syngas yield are sensitive to gasifier temperature, steam-to-feed ratio (S/F), and blending weight ratio (B/W). It shows that the increase of the product gas yield (GY) increases CO₂ concentration in the product gas, but the tar yield is reduced. To address the sustainable solid waste/biomass co-gasifier, the multi-objective optimization (MOO) algorithm is implemented to maximize GY and minimize CO₂ concentration. For solving the MOO problem, the standard genetic algorithm (GA) coupled with response surface methodology (RSM) is performed to find the Pareto frontier plot, and the technique for order of preference by similarity to the ideal solution (TOPSIS) is used to determine optimal operating conditions.

Significant Findings

Under the Pareto frontier plot and TOPSIS, a GY of 2.672 Nm³/kg, CO₂ concentration of 8.045 vol.%, and tar yield of 17.0617 g/Nm³ can be achieved under the optimal conditions of T = 1099.95 °C, S/F ratio = 0.79, and B/W ratio = 10.02. In addition, the CO₂ absorption using CaO is added to purify CO₂ up to 99.999 % of purity.

Background

While the potential benefits of hybrid nanofluids for heat transfer applications have been recognized, a comprehensive understanding of their thermal behavior remains elusive due to limited modeling data.

Methods

This study addresses this knowledge gap by leveraging Ansys Fluent and the Lagrangian-Eulerian technique to simulate a micro pin fin heat sink containing a novel hybrid nanofluid, composed of MoS₂-Cu₃O₄ nanoparticles suspended in water. The validity of the simulations is established through meticulous comparison with established experimental data documented in the literature. The hybrid nanofluid employed in the simulations is formulated with a concentration ranging from 0.1 % to 0.5 %. Thermo-fluidic characteristics of the studied cases such as thermal performance, friction factor, and Nusselt number are presented and discussed.

Significant Findings

The findings indicate that the use of a hybrid nanofluid with a 5 % concentration increases the average Nusselt number by 7.6 %, 9.1 %, and 12.8 % in different sections of the heat sink compared to a 1 % concentration. The maximum thermal performance in this study is associated with case D, where using MoS₂-Cu₃O₄ at a 5 % concentration and Reynolds number of 2000 results in an 8 % increase compared to the simple case.

Background

Chitosan is an organic polymer derived from chitin, showcasing commendable biocompatibility and biodegradability, while tricalcium phosphate emerges as an active ceramic with proven biocompatibility and superior compatibility with bone tissue. This composite material, endowed with a unique amalgamation of attributes including biocompatibility, porosity, and mechanical strength, proves highly applicable in diverse fields such as tissue engineering, drug delivery systems, wound repair, and as scaffolds for cell proliferation in regenerative medicine.

Methods

The focal point of this study is an exploration of the nuanced interplay between the mechanical properties of silica aerogel/chitosan tricalcium phosphate nanocomposites with increasing initial temperature. Employing molecular dynamics (MD) simulation, the research aims to unveil the temperature-induced variations in the critical properties.

Significant Findings

The results reveal that the ultimate strength and Young's modulus values are determined to converge to 772.28 MPa and 62.291 GPa, at 297 K. As the initial temperature escalates from 300 to 350 K, the US decreases from 72.28 to 714.47 MPa. The decrease in US could be due to higher temperatures, the increased thermal energy can lead to greater atomic vibrations within the material, which can promote easier dislocation movement and result in reduced resistance to deformation. The results reveal that as temperature increases to 320 K, YM increases to 67.134, and with further increase in temperature, YM decreases to 62.865 GPa

Background: Recovery of Al and V from red mud, a hazardous residue of the Bayer process, using bioleaching is a nature-based waste management solution that simultaneously improves environment protection and metallurgical recoveries.

Methods: For the first time, a novel hybrid multilayer Perceptron (MLP) network enhanced by an Imperialist Competitive Algorithm (ICA), and a response surface developed based on a factorial experiment design methodology (RSM) were employed and compared for, prediction optimization and optimization prediction of Al and V bioleaching by strains of A. Niger microorganisms isolated from pistachio shells and grape skins. The controlling variables were fungi source (A), adoption strategy (B), solid activation (C), solid percent (D), and bioleaching time. Considering the stochastic nature of ICA, a multi-criteria-ranking system based on accuracy and error indices was developed to select the best MLP. Probability value at 95% confidence interval, lack-of-fit, analysis of variance, R², Adj. R², and predicted R² were the judges for the determination of the developed model's statistical significance.

Significant Findings: Based on ANOVA, between A, B, C, and D, the effectiveness orders of A > D > C>AC>AD on Al, and A>AD>C>AC>D on V bioleaching were obtained. The superiority of the developed hybrid ICA-MLP model over the developed RSM model in predicting Al and V dissolution was determined by NSE, RSME, MAE, and MEDAE. Parameters optimization and consequently evaluating optimum condition repeatability through three repeating experiments resulted in maximum dissolution recoveries of RSM: 96.5% for Al and 91.2% for V, and ICA-MLP: 97.1% for Al and 90.3% for V. Considering the lower relative errors of repeating validation tests, it can be concluded that, although both models provide reasonable results, but the ICA-MLP methodology is more reliable (relative error <1.8%).

Background

Furfural residue is a by-product in the furfural production from the biomass, and enriched in cellulose and lignin. Lignin is considered efficient candidates for the preparation of bio-based adsorbents because of a variety of functional groups and reactive sites involved.

Methods

In this study, we prepared the tunable covalent binding of the aldehyde-based furfural residue lignin onto amine-functionalized Fe₃O₄ magnetic nanoparticles using cyanuric chloride as a chemoselective cross-linker. The novel lignin-based magnetic adsorbent in core-shell structure was reported for the first time to remove Congo red from aqueous solution.

Significant Findings

A distinct core-shell structure is formed, according to the SEM, TEM, FTIR, BET, XPS, and VSM techniques. Adsorption studies suggested that endothermic processes occured for CR adsorption onto AFRL@AMNP, and both pseudo-first and pseudo-second order models could provide a good description of the adsorption kinetics. The Langmuir model estimated the q_max of CR by AFRL@AMNP were 218.2 mg/g at 290 K. According to FTIR, XPS, and DFT calculations, electrostatic interactions, hydrogen bonds, and π-π interactions were identified as main mechanisms for CR onto AFRL@AMNP. More importantly, due to the incorporation of Fe₃O₄ magnetic nanoparticle, AFRL@AMNP can be easier to separate from the aqueous solutions and be reused.

Background

The critical role of thermal conductivity (TC) as a significant thermo-physical property in MXene/graphene-based nanofluids for photovoltaic/thermal systems has motivated recent research into developing precision predictive models. The multilayer perceptron neural network (MLPNN) has emerged as an eminent AI algorithm for this task.

Methods

This study employs Bayesian optimization, random search (RS), and grid search (GS) to fine-tune MLPNN hyperparameters—hidden layers, neurons, activation functions, standardization, and regularization—to elevate TC modeling efficiency. The proposed methodology unfolds in sequential phases: data analysis, data pre-processing, and introduction of MLPNN, GS, RS, Bayesian approach, and their integration algorithm. The next phase entails developing predictive models and presenting optimal cases. Lastly, the final models undergo statistical evaluation and graphical comparison for a thorough analysis.

Findings

Results manifest that the GS-MLPNN model excels, achieving the lowest testing data error (MAPE = 0.5261%) and high conformity with empirical data (R = 0.99941). Meanwhile, the RS method adjusts hyperparameters with negligible precision loss (MAPE = 0.6046%, R = 0.99887). Contrarily, Bayesian optimization lags, increasing errors (MAPE = 3.1981%) and lower correlation (R = 0.98099), suggesting its relative inefficacy for this specific application. The optimized models provide efficient predictions, significantly reducing the financial/computing costs associated with experimental/numerical analysis.

Background

The ever–growing concern for environmental pollution and the need for clean energy sources have driven research toward sustainable technologies. Semiconductor photocatalysis has emerged as a promising approach for both environmental remediation and clean energy generation due to its efficiency and environment–friendly nature. This work focuses on developing a novel photocatalyst capable of addressing two crucial environmental challenges: Cr(VI) removal and water splitting for clean hydrogen production.

Methods

This study presents the development of a dual–Z–scheme heterojunction photo(electro)catalyst based on a combination of metal vanadates (FeVO₄ and InVO₄) and ultrasound–exfoliated carbon–rich graphitic carbon nitride (Ex–C–g–CN), denoted as IVO/FVO/Ex–C–g–CN. The synthesized nanocomposite was thoroughly characterized using various spectroscopic and microscopic techniques (such as XRD, XPS, UV–DRS, FESEM, EDX, and PL) to understand its material properties and structure. These techniques are crucial for elucidating the relationship between the composition of the material and its photocatalytic performance.

Significant findings

The key innovation of this work lies in the design of the dual–Z–scheme heterojunction within the IVO/FVO/Ex–C–g–CN photo(electro)catalyst. This design fosters efficient separation of photogenerated charges, a critical factor for enhancing photocatalytic activity. The effectiveness of this approach is evident in the achieved removal efficiency of 97.17 % for 100 ppm Cr(VI) within just 60 min of visible light irradiation. This demonstrates the superior ability of the developed photocatalyst to address chromium contamination. Furthermore, the photocatalyst exhibits a remarkable photocurrent of 3.16 mA and a low onset potential of 112 mV for the photoelectrochemical oxygen evolution (OER) reaction. These findings highlight the potential of this material for solar–driven water splitting, a clean and sustainable method for hydrogen production. Additionally, the IVO/FVO/Ex–C–g–CN composite demonstrates excellent recyclability, maintaining high Cr(VI) removal efficiency over multiple cycles, indicating its reusability and cost-effectiveness. Overall, the exceptional photo(electro)catalytic performance of the IVO/FVO/Ex–C–g–CN dual–Z–scheme heterojunction positions it as a promising candidate for tackling environmental pollution and generating clean energy.