Chinese Journal of Chemical Engineering最新文献

At present, many countries are becoming more and more stringent in terms of sulfur content in fuel oil. S Zorb is a kind of desulfurization technology with advantages of exceptional desulfurization efficiency and small impact on octane number. To meet the needs of environmental requirements and the trend of digitalization in the petrochemical industry, a first-principle model of S Zorb was established based on industry data. In order to describe the desulfurization and the other side reactions, a reaction network was designed and the kinetic parameters were estimated by the particle swarm optimization algorithm. Two hybrid models based on the first-principle model and support vector regression method were established to correct the mass fraction of sulfur and predict the research octane number of the refined gasoline respectively. The results indicate that the hybrid models can predict the mass fraction of PIONA, sulfur content and research octane number of the refined gasoline accurately, of which the mean absolute percentage errors are less than 6%. Hybrid models were then applied to optimize the decision variables to minimize the research octane number loss. Optimization results show that the average reduction of the loss of research octane number is 21.8%, which suggests that the models developed hold promise for guiding practical production.

High-efficient production of 5-hydroxymethylfurfural (HMF), a “sleeping giant” in sustainable chemistry, from cellulose depends significantly on the effective separation of cellulose from lignocellulosic biomass. Herein, we report the fractional separation of wheat straw cellulose (WSC) from wheat straw under solvothermal conditions using a mixed solvent of γ-valerolactone (GVL) and H₂O as the separating solvent, wherein the impacts of fractional separation parameters (solvent composition, temperature, and time) on removals of lignin and hemicellulose as well as purity and recovery of cellulose were studied by a Box-Behnken Design of response surface method. The optimization of the solvothermal parameters enabled an optimal fractional separation condition (V_GVL: ∼60.0%, T: 205 °C, t: ∼1.7 h) that led to a higher purity (89.4%) and recovery (86.7%) of cellulose in WSC. A further correlation of the removals of lignin and hemicellulose as well as purity and recovery of cellulose with the yield of HMF excluded an independent influence of the above factors. Instead, a comprehensive contribution of high fractional separation efficiency (defined as the product of cellulose purity and recovery) and low crystallinity of WSC was found to improve the HMF yield. However, the heat- and freeze-dryings of WSC after the solvothermal separation were found to lower the HMF molar yield because it re-improved the crystallinity of WSC. A high HMF yield of 58.6% was achieved after reacting wet-WSC in a mixed solvent of 1,4-dioxane and H₂O at 180 °C for 20 min, which was 1.5 fold higher than that from microcrystalline cellulose. This work highlights the importance of enhancing the fractional separation efficiency of cellulose from lignocellulosic biomass while avoiding the drying process for future HMF biorefinery.

Rapid and sensitive detection of dissolved gases in seawater is quite essential for the investigation of the global carbon cycle. Large quantities of in situ optical detection techniques showed restricted measurement efficiency, owing to the single gas sensor without the identification ability of multiple gases. In this work, a novel gas−liquid Raman detection method of monitoring the multi-component dissolved gases was proposed based on a continuous gas−liquid separator under a large difference of partial pressure. The limit of detection (LOD) of the gas Raman spectrometer could arrive at about 14 μl·L⁻¹ for N₂ gas. Moreover, based on the continuous gas−liquid separation process, the detection time of the dissolved gases could be largely decreased to about 200 s compared with that of the traditional detection method (30 min). Effect of equilibrium time on gas−liquid separation process indicated that the extracted efficiency and decay time of these dissolved gases was CO₂ >O₂ >N₂. In addition, the analysis of the relationship between equilibrium time and flow speed indicated that the decay time decreased with the increase of the flow speed. The validation and application of the developed system presented its great potential for studying the components and spatiotemporal distribution of dissolved gases in seawater.

Neural networks are often viewed as pure ‘black box’ models, lacking interpretability and extrapolation capabilities of pure mechanistic models. This work proposes a new approach that, with the help of neural networks, improves the conformity of the first-principal model to the actual plant. The final result is still a first-principal model rather than a hybrid model, which maintains the advantage of the high interpretability of first-principal model. This work better simulates industrial batch distillation which separates four components: water, ethylene glycol, diethylene glycol, and triethylene glycol. GRU (gated recurrent neural network) and LSTM (long short-term memory) were used to obtain empirical parameters of mechanistic model that are difficult to measure directly. These were used to improve the empirical processes in mechanistic model, thus correcting unreasonable model assumptions and achieving better predictability for batch distillation. The proposed method was verified using a case study from one industrial plant case, and the results show its advancement in improving model predictions and the potential to extend to other similar systems.

The development of efficient systems for the catalytic oxidation of 2-nitro-4-methylsulfonyltoluene (NMST) to 2-nitro-4-methylsulfonyl benzoic acid (NMSBA) with atmospheric air or molecular oxygen in alkaline medium presents a significant challenge for the chemical industry. Here, we report the synthesis of FeOOH/Fe₃O₄/metal–organic framework (MOF) polygonal mesopores microflower templated from a MIL-88B(Fe) at room temperature, which exposes polygonal mesopores with atomistic edge steps and lattice defects. The obtained FeOOH/Fe₃O₄/MOF catalyst was adsorbed onto glass beads and then introduced into the microchannel reactor. In the alkaline environment, oxygen was used as oxidant to catalyze the oxidation of NMST to NMSBA, showing impressive performance. This sustainable system utilizes oxygen as a clean oxidant in an inexpensive and environmentally friendly NaOH/methanol mixture. The position and type of substituent critically affect the products. Additionally, this sustainable protocol enabled gram-scale preparation of carboxylic acid and benzyl alcohol derivatives with high chemoselectivities. Finally, the reactions can be conducted in a pressure reactor, which can conserve oxygen and prevent solvent loss. Moreover, compared with the traditional batch reactor, the self-built microchannel reactor can accelerate the reaction rate, shorten the reaction time, and enhance the selectivity of catalytic oxidation reactions. This approach contributes to environmental protection and holds potential for industrial applications.

Circulating fluidized bed flue gas desulfurization (CFB-FGD) process has been widely applied in recent years. However, high cost caused by the use of high-quality slaked lime and difficult operation due to the complex flow field are two issues which have received great attention. Accordingly, a laboratory-scale fluidized bed reactor was constructed to investigate the effects of physical properties and external conditions on desulfurization performance of slaked lime, and the conclusions were tried out in an industrial-scale CFB-FGD tower. After that, a numerical model of the tower was established based on computational particle fluid dynamics (CPFD) and two-film theory. After comparison and validation with actual operation data, the effects of operating parameters on gas–solid distribution and desulfurization characteristics were investigated. The results of experiments and industrial trials showed that the use of slaked lime with a calcium hydroxide content of approximately 80% and particle size greater than 40 μm could significantly reduce the cost of desulfurizer. Simulation results showed that the flow field in the desulfurization tower was skewed under the influence of circulating ash. We obtained optimal operating conditions of 7.5 kg·s⁻¹ for the atomized water flow, 70 kg·s⁻¹ for circulating ash flow, and 0.56 kg·s⁻¹ for slaked lime flow, with desulfurization efficiency reaching 98.19% and the exit flue gas meeting the ultraclean emission and safety requirements. All parameters selected in the simulation were based on engineering examples and had certain application reference significance.

Ionic liquids (ILs), because of the advantages of low volatility, good thermal stability, high gas solubility and easy recovery, can be regarded as the green substitute for traditional solvent. However, the high viscosity and synthesis cost limits their application, the hybrid solvent which combining ILs together with others especially water can solve this problem. Compared with the pure IL systems, the study of the ILs–H₂O binary system is rare, and the experimental data of corresponding thermodynamic properties (such as density, heat capacity, etc.) are less. Moreover, it is also difficult to obtain all the data through experiments. Therefore, this work establishes a predicted model on ILs-water binary systems based on the group contribution (GC) method. Three different machine learning algorithms (ANN, XGBoost, LightBGM) are applied to fit the density and heat capacity of ILs–water binary systems. And then the three models are compared by two index of MAE and R². The results show that the ANN-GC model has the best prediction effect on the density and heat capacity of ionic liquid-water mixed system. Furthermore, the Shapley additive explanations (SHAP) method is harnessed to scrutinize the significance of each structure and parameter within the ANN-GC model in relation to prediction outcomes. The results reveal that system components (X_IL) within the ILs–H₂O binary system exert the most substantial influence on density, while for the heat capacity, the substituents on the cation exhibit the greatest impact. This study not only introduces a robust prediction model for the density and heat capacity properties of IL-H₂O binary mixtures but also provides insight into the influence of mixture features on its density and heat capacity.

Pd-based catalysts are extensively employed to catalyze CO oxidative coupling to generate DMO, while the expensive price and high usage of Pd hinder its massive application in industrial production. Designing Pd-based catalysts with high efficiency and low Pd usage as well as expounding the catalytic mechanisms are significant for the reaction. In this study, we theoretically predict that Pd stripe doping Co(1 1 1) surface exhibits excellent performance than pure Pd(1 1 1), Pd monolayer supporting on Co(1 1 1) and Pd single atom doping Co(1 1 1) surface, and clearly expound the catalytic mechanisms through the density functional theory (DFT) calculation and micro-reaction kinetic model analysis. It is obtained that the favorable reaction pathway is COOCH₃–COOCH₃ coupling pathway over these four catalysts, while the rate-controlling step is COOCH₃+CO+OCH₃→2COOCH₃ on Pd stripe doping Co(1 1 1) surface, which is different from the case (2COOCH₃→DMO) on pure Pd(1 1 1), Pd monolayer supporting on Co(1 1 1) and Pd single atom doping Co(1 1 1) surface. This study can contribute a certain reference value for developing Pd-based catalysts with high efficiency and low Pd usage for CO oxidative coupling to DMO.

In recent years, scientists have become increasingly concerned in recycling electronic trash, particularly waste printed circuit boards (WPCBs). Previous research has indicated that the presence of Cu impacts the pyrolysis of WPCBs. However, there may be errors in the experimental results, as printed circuit boards (PCBs) with copper and those without copper are produced differently. For this experiment, we blended copper powder with PCB nonmetallic resin powder in various ratios to create the samples. The apparent kinetics and pyrolysis properties of four resin powders with varying copper concentrations were compared using nonisothermal thermogravimetric analysis (TG) and thermal pyrolysis-gas chromatography mass spectrometry (Py-GC/MS). From the perspective of kinetics, the apparent activation energy of the resin powder in the pyrolysis reaction shows a rise (0.1＜α＜0.2)-stable (0.2＜α＜0.4)-accelerated increase (0.4＜α＜0.8)- decrease (0.8＜α＜0.9) process. After adding copper powder, the apparent activation energy changes more obviously when (0.2＜α＜0.4). In the early stage of the pyrolysis reaction (0.1＜α＜0.6), the apparent activation energy is reduced, but when α = 0.8, it is much higher than that of the resin sample without copper. Additionally, it is discovered using thermogravimetric analysis and Py-GC/MS that copper shortens the temperature range of the primary pyrolysis reaction and prevents the creation of compounds containing bromine. This inhibition will raise the temperature at which compounds containing bromine first form, and it will keep rising as the copper level rises. The majority of the circuit board molecules have lower bond energies when copper is present, according to calculations performed using the Gaussian09 software, which promotes the pyrolysis reaction.