查看更多>>摘要：Rice husk powder was used as a carbon source in a high-temperature carbonization reaction for the production of rice husk ash(RHA).Under the catalysis of ferric nitrate,onion-like carbon(OLC)nanomaterial with a particle size of approximately 200 nm was successfully prepared and incorporated into waterborne polyurethane(WPU).The tribological properties of the coatings were determined using a controlled-atmosphere tribometer(WMT-2E)under dry-friction conditions.Following the friction test,the friction mechanism was investigated by characterizing the abrasive spot surfaces of the test samples using 3D laser microscopy and scanning electron microscopy/energy dispersive spectrometer.The final results demonstrated that the thermal stability of WPU composite coatings containing various concentrations of OLC nanoparticles was significantly enhanced,binding forces between coatings and steel sheets increased,and hardness improved compared to pure WPU coatings.Tribological tests revealed a notable enhancement in the anti-wear properties of WPU coatings due to the presence of OLC particles.Specifically,the wear rate of the 1.5%OLC/WPU coating was reduced by 45.3%.The coating's anti-wear mechanism was attributed to the improvement in the mechanical properties of WPU due to OLC,as well as OLC's participation in the formation of a transfer film under induced friction,which protected the matrix.
查看更多>>摘要：Acquiring accurate molecular-level information about petroleum is crucial for refining and chemical enterprises to implement the"selection of the optimal processing route"strategy.With the development of data prediction systems represented by machine learning,it has become possible for real-time prediction systems of petroleum fraction molecular information to replace analyses such as gas chromatography and mass spectrometry.However,the biggest difficulty lies in acquiring the data required for training the neural network.To address these issues,this work proposes an innovative method that utilizes the Aspen HYSYS and full two-dimensional gas chromatography-time-of-flight mass spectrometry to establish a comprehensive training database.Subsequently,a deep neural network prediction model is developed for heavy distillate oil to predict its composition in terms of molecular structure.After training,the model accurately predicts the molecular composition of catalytically cracked raw oil in a refinery.The validation and test sets exhibit R2 values of 0.99769 and 0.99807,respectively,and the average relative error of molecular composition prediction for raw materials of the catalytic cracking unit is less than 7%.Finally,the SHAP(SHapley Additive ExPlanation)interpretation method is used to disclose the relationship among different variables by performing global and local weight comparisons and correlation analyses.
查看更多>>摘要：To overcome the limitations of traditional experimental"trial and error"methods in lubricant additive design,a new molecular design method based on molecular structure parameters is established here.The molecular mechanism of the antioxidant reaction of hindered phenol,diphenylamine,and alkyl sulfide are studied via molecular simulations.Calculation results show that the strong electron-donating ability and high hydrogen-donating activity of the antioxidant molecule and the low hydrogen-abstracting activity of free radicals formed after dehydrogenation are the internal molecular causes of the shielding of phenol and diphenylamine from scavenging peroxy free radicals,and the strong electron-donating ability is the internal molecular cause of the high activity of thioether in decomposing alkyl hydrogen peroxide.Based on this antioxidant molecular mechanism,a molecular design rule of antioxidant is proposed,namely"high EHOMO,large Q(S),low bond dissociation energy BDE(O—H)and BDE(N—H)".Two new antioxidants,PAS-I and PAS-II,are designed and prepared by chemical bonding of hindered phenol,diphenylamine,and sulfur atoms.Experimental results show that these antioxidants both have excellent antioxidant effects in lubricating oil,and that PAS-II is the superior antioxidant,consistent with theoretical predictions.
查看更多>>摘要：Based on data from a petrochemical company's MIP unit over the past three years,19 input variables and 2 output variables were selected for modeling using the maximum information coefficient and Pearson correlation coefficient among 155 variables,which included properties of feedstock oil and spent catalyst,operational variables,and material flows.The distillation range variables were reduced using factor analysis,and the feedstock oils were clustered into three types using the K-means++algorithm.Each feedstock oil type was then used as an input variable for modeling.An XGBoost model and a back propagation(BP)neural network model with a structure of 20-15-15-2 were developed to predict the combined yield of gasoline and propylene,as well as the coke yield.In the test set,the BP neural network model demonstrated better fitting and generalization abilities with a mean absolute percentage error and determination coefficient of 1.48%and 0.738,respectively,compared to the XGBoost model.It was therefore chosen for further optimization work.The genetic algorithm was utilized to optimize operational variables in order to increase the combined yield of gasoline and propylene while controlling the growth of coke yield.Seven commercial test results in the MIP unit showed an average increase of 1.39 percentage points for the combined yield of gasoline and propylene and an average decrease of 0.11 percentage points for coke yield.These results indicate that the model effectively improves the combined yield of gasoline and propylene while controlling the increase in coke yield.
查看更多>>摘要：To address the problem of identifying multiple types of additives in lubricating oil,a method based on mid-infrared spectral band selection using the eXtreme Gradient Boosting(XGBoost)algorithm combined with the ant colony optimization(ACO)algorithm is proposed.The XGBoost algorithm was used to train and test three additives,T534(alkyl diphenylamine),T308(isooctyl acid thiophospholipid octadecylamine),and T306(trimethylphenol phosphate),separately,in order to screen for the optimal combination of spectral bands for each additive.The ACO algorithm was used to optimize the parameters of the XGBoost algorithm to improve the identification accuracy.During this process,the support vector machine(SVM)and hybrid bat algorithms(HBA)were included as a comparison,generating four models:ACO-XGBoost,ACO-SVM,HBA-XGboost,and HBA-SVM.The results showed that all four models could identify the three additives efficiently,with the ACO-XGBoost model achieving 100%recognition of all three additives.In addition,the generalizability of the ACO-XGBoost model was further demonstrated by predicting a lubricating oil containing the three additives prepared in our laboratory and a collected sample of commercial oil currently in use.
查看更多>>摘要：Hydrate-based CO2 sequestration is an effective method for reducing the greenhouse effect,and the presence of porous media and NaCl can impact the formation characteristics of hydrates.This study uses the constant volume temperature search method to investigate the effects of quartz sand particle size(0.006‒0.03 mm),water saturation(30%-90%),and NaCl concentration(1%‒9%)on the phase equilibrium and kinetics of CO2 hydrates within a temperature range of 273‒285 K and pressure range of 1.0‒3.5 MPa.The results indicate that a decrease in quartz sand particle size or an increase in NaCl concentration shifts the hydrate phase equilibrium curve towards lower temperatures and higher pressures,making hydrate generation conditions more demanding.In different particle size systems,there are no significant changes in the rate of CO2 hydrate formation or conversion rate.The highest hydrate conversion rate of 71.1%is observed in a 0.015 mm particle size system.With increasing water saturation,both the generation rate and conversion rate of CO2 hydrates show a trend of first increasing and then decreasing.Meanwhile,low concentrations of NaCl(1%-3%)are found to enhance the formation and conversion rates of CO2 hydrates.However,as NaCl concentration increases,the rate of CO2 hydrate formation and conversion rate decrease.
Li XiupingZhang JiayinHou LiangpeiZhao Rongxiang...
178-186页
查看更多>>摘要：A series of Lewis-acid deep eutectic solvents(DESs)were synthesized by stirring phosphoric acid and zinc chloride as raw materials at 80 ℃ to form H3PO4/n ZnCl2(n=0.1,0.25,0.5,0.75,1).The DESs were characterized by Fourier transform infrared spectrophotometry(FT-IR),thermogravimetry/differential thermogravimetry(TG/DTG),and electron spray ionization mass spectrometry(ESI-MS).The DESs were used as both extractants and catalysts to remove dibenzothiophene from fuels via oxidative desulfurization(ODS).Experiments were performed to investigated the influence of factors such as composition of DES,temperature,oxidant dosage(molar ratio of O:S),DES dosage(volume ratio of DES:oil),and number of cycles on desulfurization rate.The results indicated that the removal rate of dibenzothiophene(DBT)was affected by the Lewis acidic DESs,with that of H3PO4/0.25∙ZnCl2 reaching 96.4%under optimal conditions(Voil=5 mL,VDES=1 mL,an oxidant dosage of 6,T=50℃).After six cycles,the desulfurization rate of H3PO4/0.25∙ZnCl2 remained above 94.1%.The apparent activation energy of dibenzothiophene(DBT)removal reaction was determined by a pseudo-first order kinetic equation according to the Arrhenius equation to be 32.34 kJ/mol,as estimated.A reaction mechanism is proposed based on the experimental data and characterization results.
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