Industrial & Engineering Chemistry Research最新文献

This paper explores the application of causal discovery frameworks to infer the topology of industrial chemical processes, which is crucial for operational decision-making and system understanding. While traditional data-driven methods entail process interventions, causal discovery offers a noninvasive approach. Challenges such as temporal aggregation, subsampling, and unobserved confounders, which can lead to false predictions, are emphasized in the paper. Through simulation case studies, the performance of various causal discovery methods under different observation scenarios is evaluated. Our findings underscore the importance of simultaneously considering instantaneous and lagged causal relations, highlight the suitability of structural equation modeling for temporally aggregated processes, and caution against misinterpretation of subsampled data. Additionally, we demonstrate the utility of the Wiener separation in identifying unobserved confounders, which is essential for navigating the complexity of industrial processes.

Heavy oil reserves are recognized as being a significant yet challenging energy resource due to high viscosities. It is common knowledge that thermal recovery methods like in situ combustion rely on fuel deposition and oxidation to enhance oil mobility. This study explored micro and nanostructured manganese oxide catalysts to improve the efficiency of heavy oil oxidation. MnO composites were synthesized and characterized by X-ray powder diffraction (XRD), Scanning Electron Microscopy (SEM), Energy-dispersive X-ray (EDX), Thermogravimetric analysis (TGA), and N₂ physisorption. It has been found that the smaller nanoparticles showed higher surface area (38 m²/g), oleic acid content, and mesoporosity compared to the larger microparticles which exhibited a surface area of 12.85 m²/g. Moreover, differential scanning calorimetry (DSC) analysis confirmed the catalytic activity of both particle types by intensifying oxidation peaks and lowering activation energies. However, the isoconversional calculations revealed minimal difference in oxidation times between MnO micro and nanoparticles at various conversion rates. To overcome aggregation issues, MnO nanoparticles were incorporated onto SiO₂ nanospherical particles. As a result, MnO/SiO₂ composites exhibited increased surface area and pore volume. Most importantly, they demonstrated significantly enhanced heavy oil oxidation rates, especially at a high temperature oxidation region which is considered the main key of a successful application of in situ combustion. This work highlights the promise of nanostructured MnO catalysts to improve the efficiency and economics of thermal heavy oil recovery. Further optimization of parameters like size, morphology, and dispersion extent within the reservoir could enable these materials to stabilize the combustion front and maximize heavy oil production.

Maleic anhydride (MA) is a significant chemical raw material to produce industrial products. Vanadium phosphorus oxide (VPO) catalyst is considered an excellent catalyst for the selective oxidation of n-butane to prepare MA. However, MA selectivity is still limited by the complex crystalline phases and morphological characteristics of VPO catalysts. Herein, orthorhombic Sb₂O₃ was proposed as a structural directing agent and electronic promoting agent to modify the VPO catalyst. The relationship between the structure and the catalytic performance was systematically investigated to reveal the effect of orthorhombic Sb₂O₃. Physical characterizations reveal the introduced orthorhombic Sb₂O₃ greatly contributes to improving the active phases and inhibiting the disadvantageous δ-VOPO₄ phase. Besides, the active phase and surface chemical properties of the VPO catalyst are effectively regulated, which is beneficial to the selective oxidation of n-butane. As expected, the optimal 1Sb₂O₃–O@VPO catalyst demonstrates considerable n-butane conversion, MA selectivity, and MA yield. This work should open a feasible way to prepare efficient VPO catalysts for industrial catalysis.

Electrohydrodimerization of acrylonitrile (AN) to adiponitrile (ADN) is considered to be one of the largest and most successful electrochemical processes. The reutilization of electrolytes and the ease of removal of ADN from the electrolyte have emerged as critical challenges in this process. However, the current methods to recover the electrolyte and remove ADN are very costly in terms of both energy consumption and equipment cost. In the current work, a novel heterogeneous extraction method was proposed to separate ADN from the electrolyte using benzene or carbon tetrachloride (CTC) as the solvent. In addition, the nondominated sorting genetic algorithm (NSGA-III) was employed to optimize process parameters with respect to economy, environment, and safety. Results indicate that compared to traditional distillation separation, heterogeneous extraction using benzene and carbon tetrachloride decreased by 9.59 and 27.19% in terms of the total annual cost (TAC), respectively, while CO₂ emission was reduced by 82.82 and 83.55%, and exergy efficiency was enhanced by 13.94 and 26.94%, while the safety index closely correlates with the selected solvents. This new heterogeneous extraction method opens possibilities for an efficient and sustainable design for recycling diverse electrolytes.

All-dry solid-phase synthesis (ADSPS) is considered an eco-friendly and cost-effective method for preparing Ni-rich Co-poor cathodes, yet slow ion diffusion during the solid-phase sintering process results in agglomerate particles with severe Li/Ni mixing. Herein, a Mg/Sr-codoped and ZrO₂-coated single-crystalline LiNi_0.73Co_0.05Mn_0.22O₂ cathode with a well-layered structure is fabricated through the ADSPS method. The Sr ions effectively accelerate the ion migration at the grain boundary to facilitate particle coarsening, while the Mg ions act as “pillar ions” to decrease the Li/Ni mixing and improve the structural stability. Moreover, the ZrO₂ coating layer can further alleviate the interfacial side reactions to hinder the structure degradation and enhance the particle integrity. Therefore, the resultant single-crystalline cathodes deliver a high reversible capacity of 192.4 mAh g^–1 and display an impressive retention of 87.5% after 300 cycles at 0.5 C in a pouch-type full cell. The ADSPS strategy in this work shows great potential for the synthesis of single-crystalline Ni-rich ternary cathodes for Li-ion batteries.

Due to the complex interaction among the process operating parameters originated by the two refluxes in dual reflux pressure swing adsorption, to foresee the influence of changes in the main operating parameters is not trivial. On one hand, the reliable rules of thumb effective to design such a process are not available. On the other hand, multi-objective optimization (MOO) algorithms coupled with detailed modeling of the process are also not viable to be used as a daily design tool since the aforementioned complexity can result in huge CPU time demand to achieve cyclic steady-state conditions. In this work, a design tool based on MOO using a properly simplified model of the unit was used to identify suitable relationships between selected target parameters (typically, product purity and energy demand) and the main operating parameters, as suggested by the equilibrium theory. These relationships allowed building a Master plot summarizing the effect of changing such operating parameters on separation performance as well as quickly identifying a small enough operating window where both the target parameters achieve the desired values. The whole procedure has been investigated and validated with reference to the separation of the binary mixture CO₂–N₂ as a case study.

Ultralow-loss thermosetting resins cured via free-radical polymerization have been extensively applied as the polymeric matrix in high-frequency and high-speed printed circuit boards and electronic packaging substrates. In recent years, silicon doping has been commonly acknowledged for its potential to reduce the dielectric loss of materials. Three silicon-containing cross-linkers are investigated in this work for use in ultralow-loss thermosetting poly(phenylene oxide) (PPO) materials. The study compares the influence of these silicon-containing cross-linkers with two commercially available cross-linkers, focusing on their effects on curing temperature, dielectric properties, thermal characteristics, moisture resistance, and aging resistance. Silicon-containing cross-linkers showed appropriate reaction temperatures that were comparable to that of conventional epoxy materials. More importantly, they decreased the dielectric loss of cured samples with the lowest dissipation factor (D_f) value of 0.00159 at 10 GHz. The addition of silicon atoms also slowed the deterioration of dielectric properties during high-temperature aging experiments and reduced moisture absorption in PPO samples. However, there are also concerns regarding the reduction in the glass-transition temperature and the increase in the coefficient of thermal expansion. These results demonstrate the promising potential of silicon-containing cross-linkers in enhancing the performance of ultralow-loss PPO materials for advanced electronic packaging applications.

Kinetic studies of chemical compounds in crude oil are essential to provide a better understanding of the reaction mechanisms occurring under heavy crude oil upgrading conditions. This study analyzes the reaction kinetics of asphaltenes, the most complex fraction of crude oil, under supercritical water (SCW) conditions for the upgrading of heavy crude oil. Experimental data reported in the literature and a proper reaction scheme were analyzed to develop kinetic models capable of predicting the reactive behavior of asphaltenes and their reaction products. The results showed that asphaltenes are mainly converted to coke, while the selectivity toward maltene and gas compounds varies considerably concerning the reaction temperature. Additionally, produced maltenes undergo cracking reactions to produce gases or condensation/polyaddition, generating asphaltenes. Furthermore, the effect of NaOH on the diverse reaction rates during asphaltene conversion was analyzed. The predictions of the developed models show adequate agreement with the experimental data, corroborating that the proposed reaction scheme correctly represents the asphaltene transformation and its reaction products under SCW conditions.

The scaling phenomenon poses a great challenge for industrial production because of its widespread presence and the complex scaling process. Hence, we report a novel terpolymer (PIA-SAS-AM) scale inhibitor with high efficiency to obstruct the formation of calcium sulfate. Furthermore, the scale inhibition performance and mechanism were further evaluated and systematically investigated. Herein, the various influence factors (monomer ratio, polymerization temperature, initiator content, etc.) on scale inhibition were discussed in detail. By various characterization methods, the terpolymer scale inhibitor was identified, consistent with the expected structure. Based on the static scale inhibition method, the performance of the copolymer on the control of the CSD (calcium sulfate dihydrate) scale was also explored under different external environments (copolymer concentration, retention temperature, calcium concentration, etc.). Especially, the scale inhibition efficiency of CSD reached nearly 100% when the concentration of the copolymer was 16 mg/L. Additionally, the prepared terpolymer scale inhibitor has great aqueous solubility, salt, and heat resistance. This work provides novel insights into inhibiting crystal scaling and offers further guidance on polymer scale inhibitor design.

The jet impact-negative pressure reactor (JI-NPR) is a novel wastewater treatment technology developed for the efficient removal of high-concentration ammonia nitrogen. However, the complex and transient nature of the flow behavior within the JI-NPR poses significant challenges for understanding the underlying fluid dynamics. In this work, a comprehensive signal-processing framework was developed to elucidate the flow characteristics inside the JI-NPR. First, a flow signal acquisition platform was established to capture the negative pressure signals during the treatment process. The empirical mode decomposition (EMD) technique was then employed to decompose the turbulent flow signals into a series of intrinsic mode functions (IMFs), representing multiscale turbulent eddy characteristics. To mitigate the effects of local noise and abrupt changes, various curve fitting methods, including cubic spline interpolation, piecewise cubic Hermite interpolating polynomial, and Makima interpolation, were utilized to smooth the IMF signals. The Hilbert transform was subsequently applied to extract the instantaneous frequency features of the smoothed IMFs, enabling more accurate quantification of the nonstationary and nonlinear flow behavior. The results revealed that the low-frequency IMFs were associated with the interactions between the wastewater jet and negative pressure, while the high-frequency IMFs reflected the internal dynamic evolution of the fluid. Furthermore, the multiple regression analysis approach was adopted to quantify the relationship between the IMF feature parameters and the critical performance metric of the denitrification efficiency. The decision tree regression model was identified as a particularly suitable technique, as it can flexibly capture both linear and nonlinear dependencies and effectively identify the most influential variables. This integrated approach of EMD, curve fitting, Hilbert transform, and regression analysis methods provides valuable insights into the quantitative impact of multiscale turbulent eddies on the overall performance of the JI-NPR system. These findings are expected to guide targeted optimization of the reactor design to enhance the denitrification efficiency, a crucial goal for the practical application of this wastewater treatment technology.