Chemical Engineering Science最新文献

CO₂ nonequilibrium dissolution in heavy oil was thoroughly studied, and the relevant mass-transfer parameters were determined. Experiments were conducted at different scales, including a single ultrathin microfluidic channel and ultrathin bulk-phase cell. A continuum-scale simulator coupled with an external real-time programing command was developed to determine the mass-transfer parameters, including the intraphase effective diffusion coefficients and interphase nonequilibrium mass-transfer rates. The former were correlated with the solvent concentration and chemical potential of the system, while the latter were calculated using nonequilibrium chemical potential decay, which was correlated with the system pressure and free energy. The newly developed simulator was verified using literature data from various experimental scales, including PVT cells and microfluidics. The simulator incorporates the effect of nonequilibrium free energy on interphase mass transfer in quiescent molecular diffusion-dominated processes, providing a viable technique for accurately simulating continuum-scale mass transfer, particularly in solvent-based injection.

The distribution and deposition of hydrates in deep-water gas transportation pipelines are crucial for safety. Current simulations of hydrate flow based on the population balance model have predominantly focused on water-dominant systems and neglect the nucleation and deposition of hydrates. By establishing a model for a hydrate flow in a gas-dominant system that considers the uneven distribution of the liquid film on the wall, hydrate nucleation, gas–liquid mass transfer, particle adhesion and aggregation, and dynamic deposition, we achieved simulation of the entire process of hydrate formation, aggregation, breakage, and deposition. Simulations in undulating pipelines were carried out to investigate the effects of gas velocity, liquid injection, pressure, and pipeline structure on distribution patterns. The results showed increasing the gas velocity enhanced the dispersion of particles and increasing the pressure increased the rate of aggregation. The formation of blocky aggregates posed significant risk in the rear section of the lower-bend pipe.

Revealing the gas separation capabilities of amorphous porous materials remains a critical challenge in the materials community for their development as novel adsorbents. This work aims to unlock the potential of amorphous materials for adsorption-based CH₄/H₂ separation at pressure swing adsorption (PSA) condition using grand canonical Monte Carlo (GCMC) simulations. Several adsorbent performance evaluation metrics, including adsorption selectivity, working capacity, adsorbent performance score (APS) and regenerability (R%) were computed at 298 K for polymers of intrinsic microporosity (PIMs), amorphous carbons, kerogens, and amorphous zeolitic imidazole frameworks (ZIFs). The CH₄/H₂ selectivities and CH₄ working capacities of the amorphous materials were estimated to be 9–62 and 0.1–5 mol/kg under PSA condition. Kerogens exhibited the highest APS, and most of the structures provided high R%>80 %. However, none of the materials could reach the maximum APS (802 mol/kg) of crystalline MOFs. Diffraction pattern analysis of crystalline and amorphous ZIF-4 was also performed, and the structural changes were monitored to independently confirm the amorphization. Although crystalline ZIFs exhibited higher adsorption selectivities for CH₄/H₂ separation than amorphous ZIFs, their R% were significantly lower. Gas mixture adsorption isotherms of promising amorphous materials were also computed to reveal gas adsorption mechanism. The developed computational approach will be useful in predicting the performance of amorphous materials for CH₄/H₂ separation under industrial conditions and monitoring amorphization by diffraction analysis during mass production.

The heavy-metal contamination of aquatic environments presents imminent threat. Herein, we report a class of dual-heteroatomic conjugated microporous poly(aniline)s showing high-affinity separation performance toward heavy metals. The prepared keto-CMPA shows monolayer adsorption capacity for Hg (II) as high as 980 mg g⁻¹ according to the Langmuir model, and ultra-rapid kinetic with h reaching 30.41 mg g⁻¹ that could be described by the pseudo-second-order model. It maintains excellent stability across six reuses under harsh conditions, and furthermore demonstrates ultradeep separation efficiency that could adsorb almost all of heavy metals to ppb level with low usage. For further industrialization, a competent adsorption device was developed to remove heavy metals down to 1 ppb with a remarkable breakthrough over 20,000 BV. Characterizations and DFT calculation showed that the triangular synergistic region formed by the N-O-sites in the singular CMPA structure provided a feasible binding energy to enable the above impressive performance.

Data-driven modeling plays a vital role in petrochemical industry, especially fluid catalytic cracking (FCC). However, the long operational cycles, large-scale measurements, multivariate data, and intricate temporal correlations in FCC units may lead to the problem of low prediction accuracy when only use a single time series data-driven modeling neural network such as long short-term memory (LSTM) network. To address these challenges, an effective prediction framework is proposed that integrates LSTM network with extreme gradient boosting (XGBOOST)-based feature selection and temporal-attention (TA) mechanisms. XGBOOST is applied to filter features related to the predictive variables in order to eliminate redundant variables. TA mechanisms within an LSTM network is used to capture the relevant historical time steps of the current moment. The results of our efficient prediction framework, applied to FCC process and three additional petrochemical processes, have proven to be superior to other methods.

Coupling of green hydrogen with the coal chemical industry is pivotal for clean coal utilization and low-carbon transition. This study aims to predict syngas composition efficiently using artificial intelligence-assisted machine learning models, particularly the BP-MLPNN model, addressing raw material diversity and process uncertainties. BP-MLPNN model demonstrates superior reliability and robustness in syngas component prediction, as indicated by significantly lower MSE and RMSE values ranging from 0.002 to 11.61 and 0.05 to 3.41, respectively, along with R² values ranging from 0.84 to 1.00. This performance surpasses other models without overfitting. Subsequently, the BP-MLPNN model underwent SHAP analysis to elucidate the internal mechanism of “black box” model. A simple interface input APP was developed to achieve human–machine interaction. This model can mitigate uncertainties in analyzing the integrated coal chemical industry and green hydrogen production system, providing technical guidance and references to quantify its advantages and potential in producing various chemical products.

Utilizing physical adsorption for ethylene purification from complex mixtures can significantly reduce the energy cost for production. The purification of multi-component systems through a single adsorption process presents a considerable challenge, largely stemming from the absence of customized strategies and thorough investigations into adsorption behaviours. We aim to understand adsorbents through a unique cage recognition approach and leverage these insights to develop an effective purification adsorbent, with the successful synthesis of SSZ-16 zeolite meeting requirements for multi-component purification. This zeolite, featuring a long aft supercage and a narrow gme cage construct two adsorption cages. The dense π-electron cloud of C₂H₂ enables a stronger interaction in aft cage, while CO₂ is selectively captured within the environmentally compatible gme cage, demonstrating significantly higher C₂H₂ and CO₂ adsorption capacity. The separation experiments reveal that SSZ-16 exhibits exceptional performance, with an unprecedented polymer-grade C₂H₄ productivity of 2099.16 L/kg from the CO₂/C₂H₂/C₂H₄ mixture.

This work focuses on heat exchanger networks (HENs) synthesis (HENS) considering the optimal locations of multiple utilities. Based on an extended stage-wise superstructure where available heaters and coolers are placed at all stages, HENS is modeled as a computationally-hard mixed integer nonlinear programming (MINLP) problem. To obtain high-quality solutions, we propose a new hybrid algorithm framework that combines deterministic algorithm (commercial solver) and genetic algorithm (GA) without the use of penalty functions. In the outer level of the framework, GA is employed to optimize the integer variables which represent the existences of matches between process streams as well as the available heaters and coolers at intermediate stages. In the inner level, a reduced-size MINLP model is built to minimize the total annualized costs (TACs) of HENs generated in the outer level. We also propose three new sets to exclude infeasible stream matches, thereby the HENs generated in the outer level are all feasible and our GA does not need any penalty terms. Four literature examples are tested and optimal solutions with lower TACs are obtained within acceptable computing time compared to solutions reported in literature.

The design of catalysts for the mile production of cyclic carbonates by CO₂ cycloaddition is of value. Magnetic polymer nanoparticles-supported imidazole tribromide zinc ionic liquid catalysts, incorporating N-functional hydrogen bond donors (HBD), was successfully accomplished. The correlation between the N-functional HBD and catalytic activity was thoroughly investigated through experimental studies and DFT calculations. The catalyst featuring a secondary amine group (NHM) demonstrated remarkable catalytic performance in CO₂ cycloaddition reactions. Under optimized conditions, including 0.12 mol% catalyst dose, 100 °C, and $n_{\mathrm{PO}}:n_{{\mathrm{CO}}_2}$ = 1:1.2 for 3 h, a complete conversion was achieved. The exceptional performance of the catalyst was attributed to the multicenter synergistic effect, arising from the appropriate electronegativity, minimal spatial site resistance, a balanced distance between the NHM and imidazolium ion, and the presence of multiple active centers (NHM, Zn²⁺, and Br⁻). The integration of a magnetic component enabled swift separation and exceptional recovery stability over 8 consecutive cycles.