Uranium resources are an important strategic resource for the development of the country's nuclear industry, and safe and sustainable uranium resource supply is the key to the healthy development of nuclear power. Except uranium ore resource, uranium extraction from seawater becomes the most potential way to provide uranium resource. Adsorption is a common method for uranium extraction from seawater and uranium-containing wastewater treatment. Facing the complex marine environment, the design and preparation of adsorbent materials with high efficiency and high selectivity is essential. It is a new sustainable development strategy to prepare high value-added biomass-based materials for uranium adsorption. The classification, preparation methods and adsorption properties of biomass-based adsorption materials were reviewed, the research status and hotspots of biomass-based adsorption materials are summarized. At the same time, the development direction and research trend of high-efficiency biomass-based adsorption materials in the future are forecasted.

As a“star of tomorrow”in the field of new energy, hydrogen energy has been gradually developed and promoted globally. However, safety is still a key bottleneck problem in the entire life cycle of hydrogen energy, and high pressure is the most prominent risk factor among them, which is likely to cause major safety accidents such as hydrogen leakage, diffusion, and even combustion and explosion. Based on this, the research status of the evolution process and internal mechanism of typical accidents such as high-pressure hydrogen leakage and diffusion, leakage spontaneous combustion, jet fire and gas cloud explosion are summarized and the current shortcomings are summarized, and the future development direction is proposed, which may have guiding significance on the research of hydrogen safety, and prevention and control of accidents.

The problem of membrane fouling in the membrane separation process has severely restricted the large-scale application of membrane separation technology. The mechanisms of membrane fouling can be described by the extended Derjaguin-Landau-Verwey-Overbeek (XDLVO) theory, which can be used to analyze and predict membrane fouling, and provide guidance for membrane fouling control. Firstly, the descriptions of XDLVO theory were elaborated. Secondly, the applications of XDLVO theory in the analysis of membrane fouling behavior with different foulants were reviewed. Thirdly, the influence of membrane surface properties and operating conditions on membrane fouling and the guiding function for membrane fouling control were discussed. Finally, the problems of the application of XDLVO theory in the studies of membrane fouling and prospects of future research direction were put forward.

In this paper, the MgO/Mg(OH)₂ heat storage system is taken as the research topic. Based on microscopic heat storage mechanism, the molecular crystal structure model of corresponding heat storage materials is established. The first principle density functional theory was used to analyze the effects of adsorption distance and configuration on adsorption stability, and the influence of doping on heat storage was further studied. It was found that for different adsorption distances, physical adsorption and chemical adsorption work together resulting in different adsorption energy, and the optimal adsorption distance was 0.15 nm. Mg atom has a strong ability to lose electrons at the highly symmetrical Mg-top position, i.e., the magnesium atom in the center of the first layer of the supercell. Thus, it loses the most electrons, and the chemical bonds and molecular force on the surface are also stronger here. Therefore, this is the most stable adsorption site. Apart from this, doping Li, Na and K atoms is beneficial to heat storage of magnesium oxide. This study partly reveals the microscopic mechanism of the thermochemical heat storage system, which has very good reference value for the subsequent research, especially in the improvement of material performance and multi-component adsorption, such as the influence of doped atomic crystal itself and two-component adsorption.

To study the influence mechanism of methane dissolution on the intermolecular interaction between crude oil molecules. The lowest energy configurations of n-heptane and n-heptane, wax, colloid, asphaltene were constructed by molecular dynamics method, and the effect of CH₄ atmosphere on the interaction between crude oil molecules was analyzed. Based on the molecular dynamics simulation of CH₄ / crude oil molecular system model, the effect of CH₄ dissolution on the viscosity of crude oil molecular system was investigated. Five parameters, including intermolecular interaction, radial distribution function, volume strain, self diffusion coefficient, and cohesive energy density, were used to reveal the influence mechanism of intermolecular interaction of crude oil by CH₄ dissolution. The results showed that the dissolved CH₄ in crude oil molecular system increased the distance between the crude oil molecules, weakened the van der Waals effects between crude oil molecules. Under this condition, volume expansion provided more space for the thermal movement of crude oil molecules, intensified the thermal movement of crude oil molecules and then enhanced the flow capacity of crude oil. Moreover, there were similar rules of the effect of dissolved CH₄ on viscosity, radial distribution function, volume strain, self-diffusion coefficient and cohesion energy density in different crude oil molecular system. However, the existence of wax, colloid and asphaltene in crude oil molecular system does not change the influence mechanism of CH₄ on the intermolecular interaction of crude oil.

Ammonia (NH₃) is a harmful gas, and there are so many defects for traditional NH₃ absorption technologies. It is urgent to find ammonia absorbent with superior performance to develop new NH₃ separation technology. Ionic liquids and deep eutectic solvents are potential absorbents in gas separation process. They have attracted more and more attentions due to their low volatility, good thermal stability and flexible controllability. However, there are so large amount of ionic liquids and deep eutectic solvents that it is difficult to screen the one with superior performance. The thermodynamic analysis is used to analyze NH₃ separation process using ionic liquids and deep eutectic solvents as NH₃ absorbents. On the basis of the Gibbs free energy change, the optimal operational conditions of the NH₃ separation process were fitted. The energy use and the amount of absorbents considered as the criteria and [Omim][BF₄] was screened with good performance. The performance of [Omim][BF₄] is compared with that of water, which is a traditional NH₃ absorbents, and it can be concluded that [Omim][BF₄] shows lower energy use. Finally, the law between the screening criteria and the critical properties of ionic liquids/deep eutectic solvents is fitted, which can be used as the basis to develop new NH₃ absorbents and new NH₃ separation technology.

Based on the bench-scale opposed multi-burner (OMB) coal-water slurry entrained-flow gasifier and its visualization device, the combustion process of particle volatiles in the plane non-jet zone of the gasifier nozzle is studied. Image processing technology was applied to analyze the change of particles' volatile flame trail shape under the gasification condition with the particles smaller than 300 μm. The results show that the particle volatile flames are not typical envelope flames, but a volatile flame trail is formed. The trail shape of particle volatile matter is affected by the phase of the particle devolatilization and the movement condition of the particle relative to the air flow, which changes continuously with time. The maximum flame size of particle volatiles increases as the particle size increases. The combustion time of the volatile matter of the particles under the condition of reducing atmosphere of entrained-flow gasification is significantly longer than that of the particles in the oxygen-rich atmosphere.

A high-speed camera was used to study the effect of surface tension changes on the bursting process of a bubble-containing liquid jet. By changing the concentration of surfactants, liquid jets with different surface tensions are obtained. Six different concentrations of surfactant solution were adapted as the working fluid and the dynamic surface tension was measured. The results show that decreasing surface tension leads to the increase of jet breakup length when the jet is in the Rayleigh regime. The surfactant in the jet has two effects on the jet breakup. On the one hand, surfactants in the jet decreases the dynamic surface tension and the growth rate of the most unstable wave, which leads to the larger jet breakup length. On the other hand, the stretch of the jet results in the inhomogeneous distribution of surfactants on the jet surface, which induces Marangoni effect. The liquid moves towards to the stretch zone of the jet according to the Marangoni stress and jet breakup is delayed, the jet breakup length increases. The expression predicting the breakup length of liquid jet with various surface tension was derived. It was also found that inner bubbles decreased the breakup length of surfactant-laden jet significantly. It is attributed to the combined effect of jet velocity fluctuations due to the bubble and the absorption of surfactants on the bubble.

The conduction form factor (m), which is used to calculate the equivalent thermal conductivity of foam metal, is introduced through theoretical analysis, and m is calculated and analyzed based on a large number of experimental data reported in the literature. It is shown that m fluctuates with materials, porosities as well as pore densities of metal foams randomly and erratically. In other words, the directional deformation effect of porous foam structure should be taken into account sufficiently to accurately predict the effective thermal conductivity. Accordingly, by direct numerical simulation, a dimensionless criterion correlation characterizing the variation of m with directional deformation parameters which are defined as the ratio of cell diameter between the macro heat transfer direction of metal foam and its orthogonal direction was obtained. A new method for predicting directional effective thermal conductivity was further proposed by employing the conduction form factor, which was verified through literature reported experimental data. It is found that the effective thermal conductivity can be accurately calculated by the prediction method proposed in present study, with a small prediction deviation of 0.77%. Compared with previous theoretical prediction models derived on a basis of isotropic porous structure assumption whose prediction deviations from experimental data are more than 14%, our method can significantly improve the prediction accuracy of effective thermal conductivity.

The use of ultra-long gravity heat pipes to extract thermal energy from hot dry rocks can avoid the problems of working fluid loss, corrosion and scaling, and difficulties in underground communication in enhanced geothermal systems. Moreover, compared with downhole heat exchanger (DHE) geothermal system, the evaporation and condensation process in heat pipe can increase the temperature difference between heat pipe and heat reservoir, probably leading to a much higher heat extraction rate. The present work develops a numerical model that couples the phase transition and flow process in heat pipe with the seepage flow and heat transfer process in fractured reservoir. The operation of a 4500 m heat pipe geothermal system is thus simulated and compared with single-well DHE geothermal system. The influence of nature fluid flow in fractured reservoir is particularly analyzed. Furthermore, the electricity-production cost of heat pipe geothermal system is calculated and compared with that of the DHE geothermal system and the traditional enhanced geothermal system (EGS). The results show that the capacity of heat pipe geothermal power plant is about 240 kW and the cost of electricity-production is 1.124 CNY/（kW·h）, which is almost the same as that for the EGS and much lower than that for the DHE. If abandoned oil and gas wells are used to construct a gravity heat pipe geothermal system, the cost of power generation is only 0.644 CNY/（kW·h）.

Through the measurement of high-speed cameras and pressure sensors, the generation mechanism of pulse flow, the influence of the number of sieve plates, the propagation velocity and frequency of liquid phase pulses have been systematically studied. It is found that the pulsed flow is a dynamic process in which the liquid phase fluctuation is superimposed and amplified in the downward propagation due to the action of gravity and airflow drag force, and is related to the flow rate of gas and liquid and the number of sieve plates. A minimum liquid flow rate is required for the pulse flow under a certain range of gas flow, and the increase of liquid flow can promote the generation of local pulses and increase the liquid pulse velocity and frequency. Above the minimum liquid flow rate, the disturbance of gas phase is enhanced with the increase of gas volume, and the local pulse is more likely to be generated, which leads to the increase of pulse propagation velocity and frequency. With the further increase of gas flow, the liquid pulse will be gradually dispersed, resulting in the decrease of pulse velocity and pulse frequency. Increasing the number of sieve plates is beneficial to increase the intensity of the pulse flow, which leads to a wider range of pulse flow. Pulse flow will not occur when the number of sieve plates is less than three. Finally, based on the analysis of the experimental results, the predictive correlations of pulse velocity and frequency were proposed.

The flow and heat transfer characteristics of the liquid-liquid two-phase flow in circular microchannels are experimentally studied. Deionized water was selected as the dispersed phase and high viscosity silicone oil as the continuous phase. The flow pattern and droplet length/shape characteristics were obtained by processing the visualized images taken by a high-speed camera. On this basis, the heat transfer characteristics of the slug flow on the microchannels were investigated. The results show that the average Nusselt number increases with the increase of Reynolds number, and the larger the oil-to-water rate ratio, the more significant the increase in heat transfer coefficient. The Nusselt number decreases with the increase of water content within the range of 0.17—0.83. Although the two-phase average heat capacity increases with the increasing of water content, this increase is offset by the weaker circulating strength in the long liquid droplet under the low Re. Three different types of junctions were selected to generate different lengths of the droplet with controlled mixture velocity and water content. The shorter droplet/slug is more conducive to heat transfer, and the optimization of the droplet length can increase the overall heat transfer coefficient by nearly 26%.

With the rapid development of the electronics industry, traditional heat exchange working fluids can no longer meet the increasingly high heat exchange requirements due to their low thermal conductivity. On the other hand, the relatively narrow liquid range of traditionally used working fluids makes the infeasibility towards the complicated temperature conditions along with specific working situation. Deep eutectic solvents (DESs) possess the similar properties with ionic liquids, such as low saturated vapor pressure, high boiling point and promising stability, which make them to be the promising candidates for energy transfer. In this paper, nanofluids filled with graphene, Al₂O₃ and TiO₂ nanoparticles respectively were prepared using urea /choline chloride DESs as the base fluid, and their viscosity, thermal conductivity and stability were experimentally studied. The results show that the viscosity of graphene nanofluids is greater than that of Al₂O₃ and TiO₂ nanofluids. Compared with Al₂O₃ and TiO₂, graphene displays a clearly superior thermal conductivity enhancement and 6%(mass) graphene nanofluids are able to afford a 29.0% thermal conductivity enhancement over the pristine base fluid.

Aiming at the problem of heat protection for scramjet regenerative cooling, a simulation study on the heat transfer of supercritical pressure n-decane in a horizontal tube with top heating and bottom heating was carried out. The heat transfer characteristics and differences under these two heating conditions have been analyzed. The influence mechanisms of outer-wall heat flux, mass flux, operating pressure and tube thermal conductivity on heat transfer were investigated. The prediction of average heat transfer in the circumferential direction was obtained. The results show that the different inner-wall temperature and heat flux distributions under the two heating conditions have been observed because of the different buoyancy effects. The heat transfer deterioration is gradually weakened with the increase of circumferential angle when the top is heated. The secondary flow is more significant under the bottom heating condition, the heat transfer deterioration always occurs in the circumferential direction. The pseudo-film thermal resistance of high-temperature fluid is the cause of this heat transfer deterioration. Using the high thermal conductivity channel, increasing the operating pressure, and decreasing the heat-to-mass ratio can reduce the differences of inner-wall temperature and heat flux in the circumferential direction. The proposed correlation can reasonably predict the average heat transfer in the circumferential direction, and meet the engineering application of thermal protection design.

Using the method of combining cold state research and high temperature reaction, the influence of backmixing on the reaction characteristics and kinetics in the gas-solid reaction process was explored. Firstly, the gas residence time distribution (RTD) was measured by the pulse tracer method to investigate the gas back-mixing characteristics in micro fluidized beds. Parametric influences on the gas back-mixing were analyzed with respect to bed diameter (D), superficial gas velocity (U_g) and medium particle size (d_p). The deviation degree of the gas flow in micro fluidized beds from the ideal plug flow of gas was quantitatively compared to obtain the appropriate operating parameters guaranteeing the minimal gas back-maxing or maximal approach to the ideal plug flow. On this basis, the combustion reaction of active coke was tested using the micro fluidized bed reaction analyzer to understand the variation of reaction kinetics with gas back-mixing at different operating conditions. Obvious effects of D, U_g and d_p were identified for the estimated activation energy of the combustion reaction. There was suppressed gas back-mixing by decreasing inner diameter of bed and increasing the particle size and superficial gas velocity. These make the gaseous reaction products reach the online detection instrument quickly via a nearly plug flow throughout the reactor so that the obtained activation energy is generally higher at lower gas-back mixing.

Microwave-assisted chemistry has become a hot research topic, but the lack of systematic research on microwave reaction kinetics model has seriously hindered the application of microwave in chemical industrialization. And the amplification design and application of microwave chemical reactions in chemical engineering is lack of foundation. In this paper, the decomposition reaction of AIBA is taken as an example. A series of mixed solvents with different boiling points were obtained by adjusting the mixture ratio of solvents. And the reaction was carried out under the boiling point of mixed solvents, so as to ensure the consistency of temperature and the continuous action of microwave in the reaction process. The influence factors of momentum transfer, heat transfer and mass transfer under microwave irradiation were considered comprehensively. The necessary and sufficient factors for microwave chemical reaction were selected, including microwave power density p, viscosity μ,density ρ, reactant concentration C_A, temperature T, thermal conductivity λ, loss angle tangent tan δ, and microwave radiation frequency f. A kinetic model of microwave decomposition reaction of AIBA is established by dimensionless dimensional analysis. The model parameters of the specific reaction are regressed by fitting a large number of experimental data. The error between the estimated value and the experimental value is small and the correlation is high, which indicates that the model has certain predictive ability. So the model can solve the basic problem of microwave reaction amplification, and is expected to be used to guide the industrial production of microwave reaction.

A series of hydrogenation catalysts with γ-Al₂O₃ and phosphorus-modified γ- Al₂O₃ as the carrier and Ni and W as the active metal components were prepared by the equal volume impregnation method. The supports and catalysts were characterized with N₂ adsorption-desorption, XRD, NH₃-TPD and Py- IR. The phosphorus modification effects on the properties of hydrogenation catalysts were examined, and the relationship between adsorption behavior of quinoline, indole, dibenzothiophene(DBT) and the catalysts properties as well as the properties of the adsorbate itself were investigated. The results indicated that quinoline was the most easily adsorbed on Al₂O₃ catalysts and phosphorus-modified Al₂O₃ catalysts, while the adsorption capacity of indole and DBT was almost the same. The catalysts modified by phosphorus showed smaller specific surface area and pore volume, but on which the adsorption capacity of quinoline, indole and DBT was promoted. The adsorption capacity of sulfur and nitrogen compounds on the catalysts increased with the increase of the catalyst surface acidity or the acid site quantities and the increase of the active metal dispersion, as well as the increase of the heteroatom electron cloud density or molecular polarity of the sulfur and nitrogen compounds.

The mechanism of NO reduction with H₂ on Pd atom anchored on single-vacancy graphene (Pd/SVG) was studied by the density functional theory (DFT) method. The formation pathways of N₂ and NH₃ by NO reduction on Pd/SVG were explored. The activation of NO to HNO on Pd/SVG is more likely to occur, and the energy barrier is 67.0 kJ·mol^-1, showing a very high catalytic activity. The mechanisms of NO reduction with H₂ to N₂ and NH₃ on Pd/SVG were clarified. The favorable formation route of N₂ is that the hydrogenation of NO leads to HNO, HNO continues to hydrogenate through intermediates NH₂O and NH₂OH, and then NH₂OH dissociates to generate NH₂ and OH. The generated NH₂ intermediate combines with NO to form NH₂NO, and then NHNOH was formed by the isomerization of NH₂NO, finally NHNOH was dissociated to N₂ and H₂O. From NO activation to the formation of intermediate NH₂, the favorable route of NH₃ is the same as that of N₂, and then NH₂ hydrogenation leads to NH₃. The energy barriers of rate-determining steps for the formation of N₂ and NH₃ are 144.3 and 86.4 kJ·mol^-1, respectively. In addition, Pd/SVG catalyst shows higher selectivity to NH₃ than N₂, indicating that NH₃ is the main product. This study will provide a theoretical reference for the experiment and industrial application of H₂ reduction of NO on graphene-supported Pd-based catalysts.

A three-dimensional spiral plate-type microchannel (3D-SPM) with high separation flux was designed and processed. The coupling effect between the unique oleophilic and hydrophobic surface in the microchannel and the 3D-SPM microstructure was utilized to strengthen the demulsification process of the O/W emulsion. Combined with computer simulation methods, the effects of the number of microchannels, emulsion volume flow rate, and residence time on the demulsification rate were studied. The results showed that the demulsification efficiency increased with the increased plate number of the microchannel, and the size of liquid droplets decreased gradually after the demulsification. With the increase of the volume flow rate of emulsion, the demulsification efficiency firstly increases to a maximum and then decreases, which reflects the coupling effect of the surface effect and the Dean vortex. The maximum single demulsification efficiency was 25%. With the increase of residence time, the demulsification efficiency increased. After 8 cycles of demulsification, the maximum demulsification efficiency reached 85.9%. The numerical simulation by computer simulation revealed that as the flow rate increases, both of the number and intensity of Dean vortex inside the rectangular cross-section increase, and the vortex region expands to the outer walls continuously. When the volume flow rate was 8 ml/min, the number of Dean vortex reached a maximum of 2 pairs, the maximum shear rate inside the microchannel was 5527 s^-1 and the demulsification efficiency reached the maximum. Further increase the flow rate, the demulsification rate decreases due to the excessive disturbance of the shear rate at the wall and the Dean vortex.

Based on Donnan steric pore model with dielectric exclusion (DSPM-DE), this work develops a surrogate model for the multi-stage nanofiltration separation of industrial saline wastewater. Firstly, a high-fidelity surrogate model with a high fitting degree is established to describe the DSPM-DE-based nanofiltration process via high dimensional model representation. On this basis, we construct the optimization model of multi-stage nanofiltration separation for minimizing the specific energy consumption (SEC) of unit NaCl separated. The impacts of the changing feed ratio [Cl^-]/[\mathrm{S}{\mathrm{O}}_{\mathrm{4}}^{\mathrm{2}-}] on the separation performance and optimal operating conditions of the nanofiltration system are investigated under the given design parameters. The results indicate that an increased feed ratio [Cl^-]/[\mathrm{S}{\mathrm{O}}_{\mathrm{4}}^{\mathrm{2}-}] is beneficial to improve the total recovery of NaCl and to reduce the SEC. Under the same investment cost of nanofiltration membranes, the two-stage system is superior in reducing energy consumption, and can save up to 5.84% compared with the single-stage system.

The reduction of the number of water molecules in a nano-system actively promotes the hydrolysis of CO₃^2- to HCO₃^- and OH^-, leading to a moisture-swing nano-structured CO₂ sorbent that spontaneously binds CO₂ in ambient air when dry, while releasing it when wet. As it trades the input of heat in a thermal swing or the mechanical energy in a pressure swing of the traditional sorbents, against the consumption of water, the energy input for CO₂ capture is very low. This brings about an inexpensive and efficient direct air capture technology for mitigating the greenhouse problem. The sorbents are usually heterogeneous sheets composed of amine-based anion exchange resins and a matrix polymer. In order to obtain a hierarchical microporous structure to allow sufficient access of the air to the ion-exchange resins buried inside the matrix, previously, hydrothermal pretreatment of the resin sheets was employed before put into operation. In addition, the desorption ratio (desorption quantity/adsorption quantity) of the material is only ~30% when desorption is activated by exposing to bulk water. The present work aims to reduce the energy consumption for pretreatment and improve the desorption performance. The hydrothermal pretreatment time and temperature are studied systematically. It was found that the room-temperature soaking pretreatment instead of hydrothermal pretreatment of the resin sheets, can also lead to excellent microporous structures and carbon capture performances, concerning both the capture capacity and kinetics. More importantly, based on the infiltration mechanism of gas and liquid into nanopores, we found that the micron-sized water particles produced by ultrasonic atomization could greatly promote the desorption ratio, from ~30% to ~60%, compared to that by exposing the sorbent to bulk water. The mechanism is analyzed from the perspectives of the water quantity, water particle size and its diffusion/infiltration ability. The optimization of these pretreatment energy consumption and desorption performance provides favorable conditions for the engineering implementation of large-scale air capture.

Crude oil distillation units separate crude oil into different intermediate products. As a leading device in the refining process, it is essential for the production planning and efficiency improvement of the refining process. The purpose of this article is to establish a crude oil distillation units model with high-precision and good solution efficiency. In order to solve the key indicators of true boiling point (TBP) curve, We comprehensively consider the influence of the product's TBP and crude oil TBP, flow rate, temperature and other variables, and construct a nonlinear equation model to characterize the relationship between input and output. We also use the feature selection method to select the relevant variables (including feed properties, adjacent TBP and its quadratic terms, etc.), and choose the whale optimization algorithm to optimize the coefficients of the equations. The simulation results show that the model has higher accuracy in predicting the TBP curve of each distillation and cutting product of crude oil distillation units compared with the existing literature work, and this model is applied to refinery planning optimization. Compared with the traditional pendulum cutting model, the optimization result is better than the traditional swing-cut model.

Aiming at the optimized synthesis of the structure of the three-component distillation system, a data-driven classification regression decision tree (CART) model based on information entropy minimization is proposed. Rigorous simulation based optimization results in the literature for ternary distillation configuration synthesis are used to construct data set, which include four ternary mixtures, 34 compositions and seven candidates for distillation configuration. A decision tree trained by using the data and a set of rules for the decision on the optimal ternary distillation configuration is identified. The model test result shows that the classification accuracy of the decision tree model is 88.2%, and the influential factors and the order of their importance are identified.

Sintering is an important production unit in the blast furnace ironmaking system, and its production level is directly related to the production efficiency of ironmaking enterprises. As the modernization process of iron-making enterprises continues to accelerate, higher demands are placed on the stability of the sintering production process. At present, the existing manual adjustment method in sintering production is not enough to achieve stable production. Constructing an advance prediction model for the state of the sintering system can enhance the immediateness and accuracy of the operation and improve the sintering production level. Sintering process has two prominent characteristics: time-delay and non-linear. To precisely predict the sintering production state, this paper builds a causal-based sintering state prediction model by integrating autocorrelation analysis, convergent cross-mapping and error back-propagation neural network. At a given threshold level, the autocorrelation length of the six state variables (the negative pressure and exhaust gas temperature of No. 14 and No. 22 bellows and exhaust gas temperature, sintering end position and temperature) is 27 min, and the influence window of the operating variable on the state variable is 40 min. The selected 9 sets of operating variables can produce a significant causal relationship with 6 state variables, and have a good explanation for the changes of 6 state variables. An error back-propagation neural network model was constructed, and the combined window input of the operating variable and the state variable was used to predict the average relative error of the six state variables. The sintering state prediction model proposed in this paper combines causal mechanism and black-box model, and achieves an accurate prediction of six key state variables in the sintering unit. The average relative error is within 3.1% according to industrial data test, which shows its capability to aid decision making in iron-making production.

Due to the increasingly serious environmental problems caused by the continuous increase in global carbon emissions, the development of low-carbon technologies is imminent. Adding carbon capture device at the end of fossil fuel power plants can effectively reduce the carbon footprint of coal-fired power plants and achieve emission reduction. However, the high equipment cost, efficiency decrease, and economic penalty will pose huge challenges to the operation of carbon capture device, which hinders the wide deployment of integrated system of power plant and carbon capture device. In order to improve the economic benefit of the integrated system, this work integrates the power plant with a carbon capture device together, and optimizes the system in the manner of flexible scheduling by means of setting flue gas bypass and solvent storage tanks. Furthermore, a staged steam turbine superstructure of stream power cycle is introduced into the model so as to explore the deployment of turbines. The target of this work is to optimize the scheduling patterns under the consideration of electricity price fluctuation. Finally, the reliability and effectiveness of the model are verified through calculation examples, and the main characteristics and laws of the coordinated dispatch of power plants and carbon capture devices are analyzed and summarized.

The working fluid pair is the energy conversion medium when the absorption heat pump is working, which directly affects the performance of the entire system. However, existing research on working fluid pairs focuses on the experiment and the measurement of the physical properties of a specific system or design and prediction of pure working fluid. The property prediction and design of unknown working fluid pairs is insufficient. Through the method of computer-aided molecular design and synchronous modeling and optimization of absorption heat pump system, a MINLP model is established with the maximum of COP as the objective function which is able to predict the performance of new working fluids. Based on the 7 basic elements, specific heat capacity group contribution model of 12 common groups are established, which are applied to the model, and 5 available working fluid pairs are obtained. Finally, the designed working fluid pairs are compared with the original data, which verifies the efficiency of the method.

There are plenty of liquid-liquid homogeneous organic reactions in the drug research and development. A suitable reaction solvent can significantly increase the rate and selectivity for these reactions, thereby improve the synthesis efficiency and enhance the quality of drug synthesis. Taking the aromatic nucleophilic reaction (S_NAr) of 2,4-dichloro-5-nitropyrimidine and p-aminobenzonitrile as the research object, the computer-aided molecular design (CAMD) method was used to design the reaction solvent. First, the reaction rate constants for a small number of solvents are obtained using quantum mechanics (QM) method. Then, the obtained reaction rate constants are used to build a surrogate reaction kinetic model which correlates the solvent properties. Afterward, a mixed integer nonlinear programming (MINLP) multi-objective optimization model considering both selectivity and reaction rate is established. Finally, a decomposed algorithm is used to solve the established model, which achieves the objective of reaction solvent design in pharmaceutical reactions.

With the trend of processing sulfur-containing crude oil, the circulation of desulfurization solvent is increasing in refineries, which raises the energy consumption for solvent regeneration dramatically. In this paper, Aspen HYSYS is used to simulate a group of desulfurization units, and the minimum desulfurization solvent circulation of each unit is obtained. On this basis, two retrofitting methods, cascaded operation and cascaded-parallel operation, are put forward and used to retrofit the desulfurization system. The results show that the desulfurization solvent circulation can be reduced by 8.82% and 10.01% through the two methods, respectively, and have achieved good economic benefits. Finally, the advantages and disadvantages of the two methods are analyzed and discussed, which provides directions and suggestions for the optimization of refinery desulfurization system.

In chemical production, soft-sensing methods can effectively solve the problem that some key variables cannot be obtained in real time due to instrument failure. When building a soft-sensing measurement model, the accuracy of the model will be directly affected by the selection of variables and regression methods, especially in modern chemical industry, there are a large number of process variables with complex nonlinear relationships among them. Therefore, it is important to select proper variables and regression methods. In this paper, a support vector regression (SVR) algorithm based on maximum information coefficient (MIC) is proposed for soft-sensing measurement. Benefiting from the advantages of MIC in nonlinear correlation measurement between the process variables and target variable, the data redundancy can be avoided by selecting the appropriate modeling variables. On this basis, the SVR method is further applied to extract the relationship between the modeling variables and the target variable, by which a soft sensing model is established to predict the target variable. This method is applied to the soft-sensing measurement of the hot end pressure drop of a heat exchanger in a catalytic reforming unit. The results show that this method can effectively realize the soft measurement of pressure drop and the data correction when the sensor failure occurs.

A language-like descriptor for organic compounds was used to describe 29000 organic solar cell donor molecules collected from the Harvard Clean Energy Project Database (CEPDB). Inspired by the similarity between organic chemistry and natural language, these molecules were decomposed into fragments (words) based on the nearest neighbor subgraph theory, and these fragments were arranged into a certain sequence (sentences) by the breadth first search algorithm. After the information of each fragment was embedded into a numerical vector, each molecule can be represented by an information matrix. This matrix is a descriptor called g-FSI, which can reflect the composition and structure information of molecules. The descriptor was then parsed by a deep neural network to extract the embedded information and correlate to the corresponding PCE. The prediction model has obtained the prediction result in which the determination coefficient (R²) is 0.97 and the mean square error (MSE) is 0.16. Compared with the existing research, this model is competitive in accuracy of prediction. The attention mechanism is introduced in the modeling process, and several molecular fragments that are decisive for the PCE value are identified, which can provide guidance information for the reverse design of organic photovoltaic materials.

By constructing structured deep neural network architecture, physics-informed neural networks (PINN) can be trained to solve supervised learning tasks with limited amount of boundary data while effectively integrating any given laws of physics described by general nonlinear partial differential equations (i.e., Navier-Stokes equation). However, the effect of PINN training is closely related to how the boundary conditions are set. In this work, two 2-D steady-state heat transfer problems, namely heat conduction model with internal heat source and convection heat transfer equation between plates are taken as examples. Two surrogate models are trained based on PINN by using two setting methods of soft boundary and hard boundary. The trained surrogate models are used to predict the output of temperature fields, which are verified and compared with the simulated data. The comparison results show that the prediction ability of PINN based on hard boundary is superior to the rival.

In order to understand the internal flow characteristics of the feedback fluidic oscillator, this paper established a three-dimensional numerical model of the feedback fluid oscillator. The internal flow characteristics of the jet oscillator are studied by numerical simulation. The internal flow process of the oscillator is quantified by monitoring the pressure change of the characteristic point and the mass flow rate change of the characteristic surface, and the internal flow process and principle are analyzed. The results show that the internal flow field of the positive feedback fluid oscillator oscillates periodically under the combined action of Coanda effect and eddy current. The fluid flow process in the oscillation cavity and feedback channel has good periodicity and stability, and the frequency is consistent with the oscillation frequency of the oscillator.

The proton exchange membrane fuel cell (PEMFC) model usually contains some unknown parameters that are difficult to determine. To extract the parameters of PEMFC model exactly and accurately, in this paper, an improved differential evolution (IDE) is proposed based on probability-selection model. During the evolution process, the probability-selection model assigns a selection probability value that related to the fitness values for each solution in the main population. Based on these probability values, some superior solutions are selected as parent solutions during the mutation and crossover stages. Experiments over some complex benchmark test functions indicate that the proposed IDE method performs better than original differential evolution in terms of convergence speed and accusation indictors. When the proposed IDE is applied to solve parameter optimal identification problem of PEMFC system, experimental results show that the obtained fitting accuracy is acceptable, which means IDE is an efficient method to identify the parameters of PEMFC model.

Organic amine absorption is an efficient and environmentally friendly flue gas desulfurization technology, and the analysis, optimization and energy consumption evaluation of the flue gas SO₂ capture process from the perspective of system engineering have not been reported in detail. In this paper, SO₂ capture process of flue gas was studied with N-methyldiethanolamine (MDEA) as the absorbent. The effects of MDEA concentration, temperature, and SO₂ desorption rate on the SO₂ capture were investigated. The results show that the optimized parameters for SO₂ absorption are as follows: MDEA concentration, feed temperature of flue gas and absorbent are 30%(mass), below 45℃ and 41℃, respectively. Increasing of the SO₂ desorption ratio is at the expense of the low SO₂ desorption purity and the high energy consumption of absorbent regenerative process, since water gasification is the main reason for the high energy consumption. In order to solve the problem of high energy consumption in the regenerative process, the modified process with heat pump technology was proposed. The energy consumption of absorbent regeneration can be reduced by 47%, TAC can be reduced by 9.93% in the heat pump assistant desorption process. It is expected from the study to strengthen the development of the industrial application of SO₂ capture of organic amine system.

Complex industry processes suffer from the problems on data scarcity of training samples which are collected for modeling, as a result of inaccessibility of difficult-to-measure variables or high cost in time or economy. To tackle the issues, we proposed a novel virtual sample generation embedding quantile regression into conditional generative adversarial networks (QRCGAN). First of all, a regression is embedded in the standard CGAN “generator-discriminator” two-element game structure, so that the model not only has the ability to generate label samples, but also has the ability to handle regression prediction problems. Secondly, the regressor is implemented by the quantile regression neural network (QRNN), together with the discriminator and generator for simultaneous adversarial training. Once the model reaches the Nash equilibrium, with the help of the QRNN regressor, the generator can generate new samples that fall within a certain confidence interval. Moreover, the Kullback-Leibler (KL) divergence was used to evaluate the quality of the generated samples. Finally, the effectiveness of the proposed method is verified by standard function data and actual chemical process data.

Large-scale blast furnaces are important equipment in the steel manufacturing process. Due to the complex operation of the blast furnaces and the many interference factors, abnormal furnace conditions often occur. In order to monitor the abnormal furnace conditions in time and ensure the blast furnace is running smoothly, this paper develops an algorithm based on the principal component analysis and gaussian mixture model to monitor the abnormal process of the blast furnace. Due to the non-Gaussian distribution and time-varying characteristics of blast furnace operating data, the Gaussian mixture model is used to improve the T² statistics of the traditional PCA monitoring model, so that the algorithm can adapt to the unique distribution characteristics of blast furnace data. And the sliding window mechanism is added to give the algorithm the ability to update in real time. Subsequently, the algorithm was applied to the real blast furnace data of a large iron and steel group in South China. The effectiveness of the algorithm was tested and compared with the basic algorithm to prove the improvement of the algorithm's ability to monitor blast furnace anomalies.

A performance assessment method based on multi-space nonlinear iterative partial least squares(Ms-NIPLS) and Gaussian process regression(GPG) is proposed to solve the insufficient accuracy caused by the non-linear relationship between input and output data of chemical process. First, this method divides the similar historical datasets into different sets of performance grades, extracting the feature subspaces of training datasets by Ms-NIPLS method, and then using GPR to obtain a non-linear mapping structure between the feature subspaces and the performance grades labels to achieve off-line modeling. With the model obtained, current performance can be assessed online, and the transition performance coefficient is constructed to distinguish the steady-state performance grades and the transition performance states between the steady-state performance grades. Finally, the method in this paper is applied to the online performance assessment of ethylene cracking process to illustrate the effectiveness and accuracy of the performance assessment method proposed.

The community detection algorithm is used to study a subsystem decomposition method of a complex nonlinear chemical system, and a distributed model predictive control design is carried out. The nodes of information graph were used to represent the state variables, input variables and output variables of nonlinear system. The weighted directed graph of nonlinear process system was then constructed. The nodes were connected by weighted edges and the weight reflects the strength of the connection between nodes. Thus, the weighted directed graph can better reflect the internal connectivity and connection strength of the system. The community structure detection algorithm was used to divide all variables into the combination of subsystems to get the subsystem decomposition of complex nonlinear system. The correlation within each group was much stronger than the interaction between different groups. For the process of continuous stirred tank reactor, the subsystem decomposition was implemented and the distributed model predictive control algorithm was designed. The results show that the proposed subsystem decomposition method can take the connection weight of subsystems into account, which is more conducive to improve the performance of chemical process system with distributed model predictive control.

In the process of anaerobic digestion wastewater treatment, various uncertain factors such as dynamically changing influent components, component concentrations, and coupling between components, make simple closed-loop control unable to obtain ideal wastewater treatment effects. In order to obtain satisfied effluent quality, a control technique, which is of fast response and less dependent on a process model, robust enough to disturbances, is able to satisfy the requirements of the control engineering. For the sake of achieving satisfied regulation, a disturbance rejection control, which estimates and cancels out the disturbance actively, is designed to regulate the output pollution level. The control technique utilized is capable of estimating and cancelling out uncertainties in the anaerobic digestion wastewater treatment process effectively. For the power of the control technique, the output pollution can be guaranteed in a desired level. Numerical results show that, the disturbance rejection control is robust enough to disturbances, and the closed-loop system performance can be guaranteed. It may be a feasible approach in the wastewater treatment process.

Combining the characteristics of the petrochemical industry and the existing research results of petrochemical cyber-physical systems, a method of building cyber-physical systems based on agents is proposed. Agents capable of sense and response can be applied on worknodes in the factory model with physical objects, measure objects and relations among them as central elements to enable the petrochemical cyber-physical system to calculate, predict and learn for intelligent analysis, prediction, early warning, production monitoring and visualization. The construction method for factory model, the work pattern of agents based on the factory model and the frame diagram of the cyber-physical system for the smart petrochemical factory with agents are depicted. And the physical effects are demonstrated by display of the applications in the smart factories of Sinopec.

Aiming at the problem of optimal operation of the process system, a global self-optimizing control (SOC) strategy based on Monte Carlo simulation was introduced. The nonlinear model was employed to calculate the average economic loss over the entire operating space. Reasonable assumptions were made for some conditions, and the analytical solution of the controlled variables (CVs) was obtained. Besides, mixed integer constraints were incorporated into the global SOC strategy with the aim of balancing the sensor investment and control system performance. The proposed method can handle additional structural constraints as well as determine the optimal subset of measurements with globally valid CVs. An evaporation process was investigated to show the effectiveness of dealing with the measurement subset selection, and a distillation column case was studied to illustrate the advantages of handling the structural constraints problem.

Renewable energy synthetic ammonia technology not only helps solve the problem of high energy consumption and high emissions in the traditional synthetic ammonia industry, but also provides a new way for the storage and consumption of renewable energy. In this work, an optimal design model of renewable energy synthetic ammonia system was established to address the seasonal demand of ammonia. The optimal configuration and operation scheme were determined, and the influences of seasonal ammonia demand on the design and operation characteristics of the system were analyzed. The results show that, compared with the case where ammonia is used as energy storage medium, the scale of the required synthetic ammonia production system powered by renewable energy increases significantly, and the cost of ammonia increases by 21% when ammonia is used as the raw material of nitrogen fertilizers. The loads of chemical production units and storage units present distinct performances of off-season and peak-season with the seasonal shift of the ammonia demands. Besides, in both of the above-mentioned two cases, the operation of the battery energy storage unit will be mainly determined by the downstream chemical production units, and the role of the energy storage battery unit is to smoothen the fluctuation of renewable energy so as to ensure the continuous and stable operation with low-load of chemical production units when the deficit of renewable energy is presented.

The penicillin fermentation process is a typical nonlinear, dynamic, multiphase, and uncertain process. A single model-based soft sensor is difficult to meet the requirements of system for estimation accuracy. A multi-model soft sensor method based on the improved density peak clustering algorithm has been proposed in this paper to estimate the product concentration in penicillin fermentation process. Firstly, a similarity function instead of the Euclidean distance is introduced to calculate the k-nearest neighbors of the sample points, and the shared neighbors between the sample points and their k-nearest neighbors are computed, then the k-nearest neighbors and the shared neighbors are used to redefine the local density for the sample points. Secondly, the k-nearest neighbors between sample points is used to redefine the allocation strategy of sample points. Finally, the improved clustering algorithm is used to obtain clustering subsets, and the soft sensors based on least squares support vector machine for each subset are established. The verification results of the Pensim simulation platform show that the improved clustering algorithm can more accurately cluster the sample data, thereby effectively improving the estimation accuracy of the soft sensor model of the penicillin fermentation process.

Aiming at the problem that the number of modes is difficult to determine in multi-modal industrial processes, a hierarchical variational Gaussian mixture model (HVGMM) is proposed. Based on this, principal polynomial analysis (PPA) was used for multimode nonlinear processes fault detection. Firstly, the variational Bayesian Gaussian mixture model (VBGMM) was used as the initial model to decompose the process data to obtain the initial number of condition modes, and the process was decomposed into sub-blocks according to the initial number of modes. Secondly, the VBGMM containing multiple local models was used to decompose each sub-block into subsidiary sub-blocks, and the information such as mean and precision matrix of subsidiary sub-blocks was used to reconstruct the VBGMM. After that, the reconstructed VBGMM was used again as the initial model to decompose the original process data, and the above steps are repeated until the reconstructed VBGMM cannot decompose each sub-block. Finally, multiple local PPA models were established in each sub-block, and T² and SPEstatistics were calculated in each local model for fault detection. Through a numerical example and the Tennessee Eastman (TE) process, the simulation result demonstrates that HVGMM-PPA outperformed conventional PCA and PPA techniques in the monitoring rate.

During the strong exothermic reaction in the fixed-bed reactor, the hot spot temperature of the reactor is sensitive to the change of operating parameters, which can easily cause flying temperature, resulting in a decrease in conversion rate and affecting catalyst life. In order to enhance the synergy of heat and mass transfer and chemical reaction in a fixed-bed reactor for carbonylation, a one-dimensional pseudo-homogeneous heat transfer model was established to investigate the effects of operating parameters on bed hot spot temperature, reaction conversion and bed temperature rise. The presented method not only reflects the synergistic effect of heat and mass transfer and reaction, but also has clear interrelation and convenient algorithm. Under the experimental application conditions, the catalyst particle diameter is less than or equal to 1.5 mm. The reactor inlet temperature / coolant oil temperature should not only meet the requirements of thermal stability of the bed, but also make the reaction conversion and bed temperature rise within a reasonable range. The simulation results show that with the increase of bed inlet temperature, good conversion rate and small bed temperature rise can be obtained by reducing the coolant oil temperature. On this basis, the effect of ethylene oxide inlet concentration on the conversion and bed temperature rise was investigated. This study can provide a basis for the selection of operating parameters such as catalyst particle diameter, bed inlet temperature, coolant oil temperature and bed inlet concentration for the fixed-bed reactor to meet the conversion requirements and reasonable bed temperature rise.

Given the current phenomenon that HSE management focuses on construction sites and operational plants without penetrating into the depth of early design stages and design process, as well as the intrinsic safety issues exposed in major accidents, this study explores the project HSE review methods developed by foreign advanced petrochemical enterprises, that is, PHSER, covering the entire project lifecycle. Advanced international companies pay special attention to HSE reviews of front end loading and the design stages, assuring that HSE issues are identified, plant intrinsic safety are ensured and design hazards are under control, which is essentially the weakness of project safety management of the majority of Chinese petrochemical enterprises. Hence, this exploration proposes the suggestion of “extending HSE management to the FEED stage and design stage by applying PHSER approach with stronger technical HSE emphasis”. PHSER review is an enterprise-based project HSE review. It is an enterprise autonomously pursuing project intrinsic safety or essential HSE design activities to ensure HSE management throughout the project, thereby achieving the goal of comprehensive project risk management.

The critical enzyme responsible for catalyzing the first step in the microcystin (MC) biodegradation pathway identified in bacterial strains is referred to as MlrA (also known as microcystinase). Its structural characteristics and substrate hydrolysis mechanism are not yet clear. In this paper, a molecular structure model of MlrA was established using a fold recognition method. Docking and site directed mutagenesis approach was employed to determine the binding modes and interaction network of enzyme-ligand complex. Factors affecting the catalytic activity of the enzyme were also elucidated by in vitro enzyme assays. The results show that MlrA is an integral membrane protein localized at the plasma membrane, its structure comprising mainly of eight transmembrane α-helices (TM1—8), in which the conserved ABI domain (TM4—7) forms a conical cavity with a large volume and opens to the periplasmic space. The catalytic residues (E172, H205, H260 and N264) are located into the membrane that projects their side chains into the cavity for catalysis. The MC-LR adopts a β-hairpin structure within a cavity to bind to MlrA, which then positions the scissile bond adjacent to a water molecule. Thus, a complete proteolytic mechanism of the enzyme was proposed. First, E172 and H205 general base-catalyzed deprotonation of a water molecule for nucleophilic attack on the Adda-Arg peptide bond. Then, H260 and N264, which form an oxyanion hole, stabilize the oxyanion transition state by hydrogen bonding. Finally, protonation of the amine leaving group of Adda-Arg peptide bond could be catalyzed by either H205 or E172 to allow collapse of the intermediate. Furthermore, this enzyme does not contain a metal-binding site (Ⅱ), its concentration-dependent inactivation by phenanthroline results from non-specific protein unfolding, EDTA competes for the binding site of the enzyme by developing an interaction network within the active site similar to that of the substrate. These findings suggest that MlrA is not a metalloproteinase. This study revealed the properties and hydrolysis mechanism of MlrA, and provided a reference for further exploration of the microbial degradation mechanism of MCs.

To explore the feasible method of using the air compression heat in the large-scale air separation unit, it was proposed to use the compression heat to drive the organic Rankine - vapor compression refrigeration coupling cycle. The cooling capacity was used to cool down compressors inlet air, to achieve the purpose of self-utilization of compression heat and self-enhancing of compression process. Taking the compression process in a 60000 m³/h scale air separation unit as an example, MATLAB was used to model the compression heat self-utilization system. Further, with the goal of maximizing the system economy, firefly algorithm was used for the optimization of heat exchange area of the main evaporators in typical humidity area. The result shows that when the relative humidity of the inlet air is 70% and 30%, the energy saving rate of the multi-stage air compression heat self-utilization system can reach 4.4% and 4.6%, respectively, and the payback time is 4.4 a and 5.5 a, respectively. The system shows better energy-saving effect and higher economic value.

Aiming at the problem that the copper electrode of the thermally regenerative ammonia-based battery (TRAB) is easily corroded to break, composite electrodes with relatively stable skeleton structure was constructed and applied to TRAB. The electroplating characteristics of composite electrodes with different base materials and the influence on the power generation characteristics and maximum power output of TRAB were studied. The research results show that, compared with other base material composite electrode batteries, although the TRAB with foam nickel base material composite electrode has a relatively small electrode surface area and copper plating volume, it has lower material transmission resistance and minimum ohmic internal resistance. Obtain the largest voltage output, the largest power generation and energy density, the highest coulombic efficiency and the highest power output (11.5 mW). It can be seen that nickel foam as a base material for the TRAB composite electrode is a relatively good choice. At the same time, follow-up research on the influence of the pore size of the composite electrode is required.

Using anaerobic/anoxic/aerobic sequencing batch reactors (An/A/O-SBRs), the long-term impact of the NO₃^- cultivation on denitrification phosphorus removal performance and N₂O emission characteristics was investigated in four SBRs of 10(R10), 20(R20), 30(R30) and 40 mg N/L(R40). The anaerobic and anoxic stoichiometric parameters related to various NO₃^- concentrations were monitored over time. The results showed that with the increasing of NO₃^- concentration, the total nitrogen (TN) removal efficiency decreased from more than 90% to 41.3%, and the N₂O emission ratio (N₂O emission/ NO_x^-_removal) were 1.68%, 4.17%, 8.92% and 14.28% respectively, while the total phosphorus removal (TP) efficiency increased at first and then decreased. NO₂^- accumulates in the process of high-concentration NO₃^- anoxic phosphorus absorption, which inhibits the activity of DPAOs, and the carbon source competition ability of GAOs is enhanced. The ratio of DPAOs/COD consumption in anaerobic process decreased from 33.5% to 25.1%, while that of GAOs increased from 59.3% to 74.1% as the NO₃^- increased from 10 to 40 mg N/L. The NO₂^- /HNO₂ accumulation coupled with the increase of GAOs, was the main reason for the reduction of nitrogen and phosphorus efficiencies and higher N₂O yields under higher NO₃^- concentration.

In this work, the interface modification and stability of ZnO /PbS heterojunction quantum dot solar cells were studied. Two interface modification methods, doping Mg in ZnO electron transport layer and introducing electron blocking layer into the device, were investigated. The results show that interface modification could adjust the interface energy level structure, reduce defect recombination, and enhance the charge transmission, thus improve the power conversion efficiency (PCE) of solar cells. The PCE of the device treated by interface modification is 9.46%, which improves approximately 75% and 491% comparing with the undoped one (PCE=5.41%) and the device without electron blocking layer (PCE=1.60%), respectively. Interface modification is demonstrated to be an effective strategy for optimizing the photovoltaic performance of ZnO/PbS heterojunction solar cells and maintaining great air storage stability. In addition, after 30 days of air exposure, the interface modified device can still maintain more than 95% of the original PCE.

For the biodegradation of p-nitrophenol at low temperature (10℃), p-nitrophenol was used as substrate to investigate the degradation characteristics by a single factor experiment, hydrophobicity, membrane permeability and kinetics experiment. The results showed that Pseudomonas sp. ZL was resistant and degradate p-nitrophenol of 303.71 mg·L^-1, at 10℃. And the optimal degradation pH was 8. At the same time, 0.5% NaCl, and 1.0 g·L^-1 NH₄NO₃ could promote the degradation of p-nitrophenol and significantly reduced lag time for degradation. Under optimal degradation conditions at 10℃, the strain on the nitrophenol inhibit the degradation kinetics fitting with the Aiba model, including the maximum specific growth rate(μ_max) was 0.205 h^-1,the half saturation (K_s) was 3.40 mg·L^-1, the self-inhibition constants (K_i)_{was 166.86 mg·L^-1. Therefore, 166.86 mg·L^-1 was the inhibitory concentration of the bacteria under the condition of low temperature degradation. Compared with other degrading strains, the strain Pseudomonas sp. ZL has larger Ki and μmax/Ks and smaller Ks/Ki value, indicating that the inhibitory effect of p-nitrophenol on the strain is smaller and the effective utilization rate of the strain is higher. That means the inhibition of p-nitrophenol for Pseudomonas sp. ZL was smaller. Utilization rate was higher than other bacteria. It has high application potential in the in-situ repair of low-temperature groundwater and soil pollution.}

Light aromatic hydrocarbons (BTEXN) such as benzene, toluene, ethylbenzene, xylene and naphthalene are used as high-value chemical raw materials and are widely used in industry. Refining of coal retorting tar is one of the important production methods of BTEXN. In this paper, we carry out fast pyrolysis research on Xinjiang high-alkali coal in order to expand the applications of Xinjiang high-alkali coal in chemical industry. Two typical high-alkali coals from Xinjiang which called Hongshaquan and Jiangjunmiao, which were prepeared through the two pretreatment methods of sequential extraction and external addition of sodium, were used to semi-quantitatively study the effect of sodium form and content in high alkali coal, as well as the types of mineral components on the fast pyrolysis tar and BTEXN precipitation characteristics by Py-GC/MS. The results show that the inherent minerals in both high-alkali coals could increase the precipitation of tar and BTEXN for which the water-soluble(Na, Ca) minerals played a key role. Endogenous water-soluble minerals and ammonium acetate-soluble minerals which mainly based on sodium and calcium both increased the precipitation of Hongshaquan coal tar by 22% and 55%. However, the addition of water-soluble NaCl and ammonium acetate-soluble CH₃COONa from external sources reduced the precipitation of Jiangjunmiao coal tar. Water-soluble minerals,1.0%(mass) NaCl and CH₃COONa respectively increased the precipitation of BTEXN in coal tar by 45%,42% and 62%. However, the ammonium acetate soluble substance and low content (0.4%(mass)) Na will inhibit the precipitation of BTEXN.

The polysulfone (PSF) ultrafiltration membranes were prepared with PSF raw material, N,N‐dimethylformamide (DMF), 4,4'-diaminodiphenyl sulfone (DDS) and pyromellitic dianhydride (PMDA) through the method of immersion precipitation phase inversion. Among them, DDS and PMDA were doped into the casting solution of PSF to synthesize polyamic acid (PAA) via “in-situ synthesis” method. The fourier transform infrared spectrometer (ATR-FTIR) and X-ray photoelectron spectroscopy (XPS) were used to analyze the chemical composition of the membrane surface, and the results showed that PAA was successfully introduced into the membrane surface. Performance tests of the membrane's chargeability, water contact angle, moisture retention performance and water flux show that the modified membrane has great moisture retention and water permeability. Under 0.1 MPa operating pressure, the pure water flux and bovine serum albumin (BSA) rejection rate of the modified membrane are higher than that of the pure PSF membrane. The pure water flux increased from 221.39 L/(m²·h) to 406.57 L/(m²·h), and the BSA rejection rate increased from 75.75% to 96.14%.With the operating pressure range of 0.01~0.1 MPa, the water flux of the modified membrane is higher than that of pure PSF membrane.

The phosphotungstic acid (PTA) was loaded on the zirconium-based metal organic framework materials (MOFs) by in-situ synthesis, and X-ray diffraction (XRD), infrared (FT-IR), nitrogen adsorption-desorption, scanning electron microscope (SEM) and thermogravimetric (TG) confirmed the successful preparation of PTA@MOF-808 composite material. The effects of PTA amounts, temperature, oil-adsorbent ratio, model oil sulfur content and type of sulfide on the adsorption desulfurization performance of PTA@MOF-808 adsorbents were investigated by the static adsorption desulfurization experiments. The results showed that the introduction of PTA increased the adsorption capacity of MOF-808 for benzothiophene (BT) by 2 times. PTA@MOF-808 has a fast adsorption rate and basically reaches adsorption equilibrium at 300 s. After 6 cycles of removal of BT, the adsorption capacity was still 86% of the first adsorption capacity. Finally, the kinetic and thermodynamic of BT adsorption onto PTA@MOF-808 were studied. The results showed that the adsorption process followed a quasi-second-order kinetics model and included physical adsorption and chemical adsorption.

Three kinds of anion-exchange membranes were prepared based on polyphenyl bromide (BPPO) modified by pyridine derivatives with different side chain lengths (4-ethylpyridine, 3-butyl pyridine and 3-hexylpyridine). With the increasing of the alkyl carbon chain length on the pyridine ring, the ion exchange capacity decreased from 1.92 mmol/g to 1.34 mmol/g and the membrane resistance was increased from 2.99 Ω·cm² to 10.59 Ω·cm². Compared with the desalination rate of commercial Fuji AEM, the three kinds of prepared AEMs all showed much higher desalination rate during electrodialysis experiment. The result of fouling experiment showed the transition-time of the membrane was more shortened with longer side-chains on the polymer backbone. The calculation and simulation results showed that the hydrophobic interaction played an important role in the affinity interaction between organic pollutants (sodium dodecylbenzene sulfonate) and the side chains of the polymer backbone of the ion exchange membrane.

Typical organic pollutants in organic chemical wastewater such as aniline and phenol are highly toxic, easily bio-enriched, and difficult to biodegrade, which not only adversely affect human health, but also cause serious environmental pollution. Therefore, it is urgent to efficiently remove organic pollutants from water. Hypercrosslinked resins have high adsorption capacity and excellent adsorption selectivity. Therefore, it is of great significance to develop functionalized hypercrosslinked resins with high specific surface area and plentiful functional groups. Herein, chloromethylated polystyrene was used as the precursor, 3,5-dimethylphenol was applied as the functional monomer, and formaldehyde dimethyl acetal was employed as the external crosslinking agent, the oxygen-rich functionalized hypercrosslinked resin was synthesized according to the nucleophilic substitution and Friedel-Crafts reaction. The resulting resin, namely, PS-KPM-HCLR has a relative high specific surface area (198 m²/g) and plentiful oxygen content (8.67%(mass)) with abundant micropores (micropores area in relation to the total surface area accounts for 36.3%), and it has an enhanced adsorption of anline with the equilibrium capacity of 156.4 mg/g at 300 K. The resin has an excellent adsorption efficiency at a low concentration of aniline (66.5 mg/L), which meets the first grade national discharges standard (<1.0 mg/L) with excellent recycling performance.

Plant protein derived from waste seed meal after oil extraction has been emerged as an important natural and renewable source for the preparation and production of environmentally friendly green materials. Compared to synthetic polymers, protein-based polymer materials, however, have lower mechanical properties and inferior thermal stability. To this end, natural plant fibers have been often incorporated into the protein matrix to reinforce them and to improve properties of the resulting composites. In this work, we used oriented natural sisal fiber (SF) as a reinforcement, and dialdehyde starch (DAS) as a green cross-linking agent to prepare cottonseed protein (CP)/SF green composites with desired mechanical properties and tight interfaces between the matrix and reinforcement. Specifically, after successive treatments including urea denaturation, glycerol plasticization, DAS cross-linking, cottonseed protein was mixed and combined with long and oriented SF, and then hot-pressed into CP/SF green composites. Tensile tests showed that with the incorporation of 5%(mass) SF, the fracture stress of CP matrix increased from 0.35 MPa to 1.28 MPa. The effects of DAS content on mechanical properties and thermal stability of the composites were investigated. Tensile tests, TGA and DSC showed that the composite with 20%(mass) DAS crosslinker exhibited the highest tensile strength (fracture stress 7.5 MPa), modulus (Young's modulus 93 MPa), thermal stability (maximum decomposing temperature 328℃) and glass transition temperature (102℃). Experimental evidences of the chemical structure, microscopic morphology and property analysis suggest that the improvements in the composite properties is mainly due to three factors: the formation of tight interfaces between CP matrix and SF reinforcement, the impregnating pretreatment on unidirectional arranged fiber, and the strong hydrogen bond forces between CP and SF biomacromolecules. The formation of imine bond detected by FTIR spectra at peak position of 1652 cm^-1 indicates that successful cross-linking reaction is taken place between DAS and CP/SF. From both surface and cross-section view of sample SEM images, tight CP/SF interfaces are clearly noticed where both components are closely attached with each other, creating strong bridges linking the protein and oriented fiber.

Expandable vermiculite powder was selected as the fire extinguishing powder in the study to improve the extinguishing effectiveness. Meanwhile, the expandable vermiculite powder was modified by the water solution of NaCl, NaHCO₃, MgCl₂ and KCl, respectively. Subsequently, the expansion rate, extinguishing fire effectiveness including extinguishing time and re-burning condition of the original and modified vermiculite powder were studied and analyzed. The results show that the expansion rate was increasing with the increase of vermiculite powder diameter. In addition, the expansion rate will increase obvious after the modification, in which the expansion rate of modified vermiculite powder by MgCl₂ is the largest, approximately 2.4. In the respect of extinguishing time, the order of extinguishing fire effectiveness from high to low is NaHCO₃ modified vermiculite powder, KCl modified vermiculite powder, NaCl modified vermiculite powder, MgCl₂ modified vermiculite powder, origin vermiculite powder and ABC dry powder fire extinguishing agent. From the perspective of the amount of fire extinguishing agent, it can be concluded that the amount of modified vermiculite powder is significantly less than that of unmodified vermiculite powder. Among them, the amount of NaHCO₃ modified vermiculite powder is relatively close to that of ABC dry powder fire extinguishing agent. In the re-ignition experiments, it was found that the density of vermiculite powder after expansion is lower and the expanded vermiculite powder can effectively cover the oil surface to prevent the oil fire re-burning.

In view of the lack of multi-source heterogeneous data fusion in process safety risk analysis of petrochemical enterprises, it is difficult to analyze the dynamic time-varying mechanism of risk. First, it is established a dynamic risk propagation model based on the improved fuzzy Petri net, consider parameters such as initial event failure, protection layer failure, and correction factors to obtain the real-time change probability of risk. Then the influence of failure of different protective layers on the probability of accidents is analyzed and the importance degree of different protective layers is obtained. Finally, taking n-hexane buffer tank overflow fire and explosion accident as an example, the dynamic risk calculation was carried out to analyze and compare the change of accident probability under the failure of different protective layers. The results showed that the safety interlock system played a key role in the accident, and the daily detection should be strengthened to prevent the accident.