Research and application of superstructure can effectively solve the problem of energy efficiency in the fields of chemical industry, water treatment, steel, metallurgy and so on, and were commonly found in heat exchanger network, mass exchanger networks, water networks and other networks. This article reviews the heat exchanger network based on the state-space superstructure model, stage-wise superstructure model and mass exchanger network, water network based on two classical ideological superstructure model. After the construction of the superstructure, according to the actual situation of industrial production, the total annual cost is chosen as the general objective function, appropriate algorithm is selected to optimize network superstructure, finally the required new or renovation program are obtained. This paper also summarizes common optimization algorithm, it divided into two categories:deterministic algorithm and random algorithm. Finally, the article describes the problems of superstructure modeling in scientific research and practical application, and its future research directions.

Hybrid organic-inorganic perovskite materials have received much attention in recent years because of their super light absorption coefficient, high charge carrier mobility, ambipolar property, and simple preparation procedure. Photon conversion efficiency of perovskite solar cells was raised from around 3.8% to over 20.4% in past 7 years and had potentials to be continuously enhanced. With a brief introduction of structures and optoelectronic properties of perovskite materials, development and device structures of perovskite solar cells were summarized. Further, challenging issues with current devices were explored and future development directions of perovskite solar cells were proposed.

Lignin derived from lignocellulose is a renewable resource for the production of chemicals and fuels. However, due to its highly irregular polymeric structure and the carbon based inactive property, lignin valorization is very difficult. Lignin is usually viewed as a waste by-product in the current biorefinery processes and most of the lignin is burned to produce heat and power for the biorefinery processes. There were a series of studies on the lignin conversion such as depolymerization over acid or base, pyrolysis, hydroprocessing and oxygenation. The degradation of lignin over hydroprocessing was the most efficient method to produce alkane fuels and high value-added chemicals such as phenols. However, there were some problems remained to be solved such as catalyst deactivation and low yield. This review focuses on the catalytic systems for lignin hydroprocessing and current challenges in order to provide a reference for efficient and large-scale application of lignin.

This review summarizes the research progress of separation technologies based on hydrate formation for both solution concentration and gas separation areas. It is found that, although single hydrate separation method shows to be relevant for seawater desalination, bioengineering, oil-gas separation, etc., it still has some drawbacks such as concentrate liquid entrainment phenomenon, low separation efficiency, and/or high operation pressure among others which need to be solved to realize industrialization. For the separation of gas mixtures, the absorption-hydration and adsorption-hydrate hybrid separation technologies show some advantages compared to the single hydrate separation process. These two technologies have higher gas treatment capacity, higher separation efficiency, and/or could perform a continuous gas separation process, among others. However, some fundamental issues such as the flow characteristics of hydrate/oil slurry, or the interaction relationship between hydrate crystals and adsorbents, are still need to be measured or addressed for these two hybrid gas separation technologies. Furthermore, suggestions for further research of these separations are proposed. For solution concentration, methods like developing more efficient hydrate formation promoters, or coupling with some other separation technologies can be considered. And for gas mixtures separation, some directions such as founding more efficient hydrate formation promoters, obtaining the actual flow characteristics of hydrate/oil slurry when using water/oil separation media, determining the interaction mechanism between hydrate phase and adsorbents when using water containing porous materials, etc., are proposed.

Molecular dynamics (MD) simulation was performed to investigate the super cell of ANPyO and its cutting model along (4,0,-2) crystalline surface at different temperature (195, 245, 295, 345, 395 K) by COMPASS force field in NPT ensemble. The results show that, with the temperature increasing, the maximum bond length of the trigger bond (L_max) increases, the interaction energy between C and N of the trigger bond (E_C-N) decreases and so does the cohesive energy density (CED). These results agree well with the fact that the sensitivity of the explosive increase with the temperature increasing. L_max, E_C-N and CED can be used as theoretical criteria to predict the sensitivity of the explosive. The mechanical property of ANPyO at different temperature were obtained, which provides the change regularity of mechanical property with temperature increasing.

In the extraction process of catechol removal from waste water, mesityl oxide was used as solvent,in order to provide fundamental data for this design and development. Experimental LLE data for mesityl oxide-water-catechol ternary system were measured at 30, 40 and 50℃ under atmospheric pressure. Meanwhile, the reliability of the experimental LLE data was ascertained via Hand and Bachman equation where all of the square of the linear correlation coefficients were greater than 0.99. On the other hand, the experimental data were correlated with the NRTL activity coefficient model by data regression system of Aspen Plus. Accordingly, binary interaction parameters of the ternary systems were obtained. As a result, the compositions of LLE were calculated and compared with the corresponding experimental data by using the regressed parameters.And the relative root mean square deviations (RMSD) and absolute mean deviations (AMD) were both less than 1%, which show that the NRTL model and the regressed parameters agree well with the phase equilibrium behavior of this system.

Submerged combustion vaporizer is indispensable heat exchanger equipment for liquefied natural gas (LNG) receiving terminals, it mainly utilizes water bath system as intermediate media to achieve heat transfer between flue gas and LNG. In this work, an unabridged experimental apparatus is built to study the complex heat transfer characteristics of it. Experimental results reveal some unique fluid dynamic phenomena inside SCV system (local water bath freezes). Meanwhile, the simulated model is established for coupled flow and heat transfer process between two-phase mixture and trans-critical LNG. The influences of inlet LNG pressure, inlet LNG velocity, static water height, flue gas flux on the NG outlet temperature and water bath temperature are analyzed. The outcomes can provide some important guidance to localization design of SCV.

Acoustic agglomeration is a potential pretreatment technology for efficient reduction of particulate matter emission in flue gas. However, high energy consumption restricts its commercial application. To improve acoustic agglomeration efficiency, droplet spray was added to aerosol. The influences of operating parameters on agglomeration efficiency were studied to explore compounding effect of acoustic field and droplet spray. The results showed that acoustic agglomeration efficiency was increased by 25% to 40% with addition of droplet spray. Whether the droplet spray existed or not, similar influence of sound frequency on agglomeration was observed with optimum frequency at 1400 Hz. The agglomeration efficiency rose with the increase of sound power, liquid-gas ratio or aerosol residence time, which reached to maximum when liquid-gas ratio was over 0.10 or aerosol residence time exceeded 4.2 s. Two main mechanisms were proposed for the improvement of agglomeration efficiency with presence of droplet spray. One was that droplet spray created liquid bridge forces between particles, which were much stronger than van der Waals forces, and increased collision efficiency greatly. The other was that liquid droplets acted as particle seeds for agglomeration, which had significantly different entrainment factors compared to surrounding small particles. Consequently, relative movement among aerosol particles was enhanced so agglomeration efficiency was promoted. The results suggest that energy consumption of acoustic agglomeration process can be reduced dramatically with addition of droplet spray.

Ice slurries of nanosilica fluid at different concentrations were prepared by dispersing silica nanoparticles in aqueous solutions of either ethylene glycol or sodium chloride. Images of ice crystal particles in ice slurries were obtained by a microscopic observation system. The resulting size distributions of ice crystals were compared to normal, log-normal, Gamma, and Weibull distributions so as to investigate nanosilica effect on average diameter and size distribution of ice particles and to study dimensional change of ice particles during storage of ice slurries. The experimental results indicated that ice particle sizes were fitted well with Gamma distribution before and after addition of nanosilica. Nanosilica played an important role in grain refinement such that sizes of ice particles decreased linearly with increasing concentrations of nanosilica. When ethylene glycol aqueous solution was base fluid, nanosilica addition effectively inhibited growth of ice particles during slurry storage. When sodium chloride aqueous solution was base fluid, concentration of nanosilica had to be increased from 0.25% to 0.75% for similar inhibition effect. Therefore, nanosilica could be used to reduce particle size and to control growth of ice crystals, which has an important application for flow and heat transfer improvement of ice slurry.

In the Euler-Lagrange coordinate system, mixing characteristics of particles in a single jet fluidized bed were studied with discrete element method. A mixing index was introduced to quantitatively analyze axial and radial mixing quality in the bed, as well as to investigate influnce of superficial gas velocity and spring constant on mixing charcteristics. The simulation provided sequence diagrams of axial and radial mixing, distributions of gas and particle velocities, and mixing index distributions of particles across the bed at various parameter settings. The results showed that particle mixing process in the bed was determined by capacity of particle circulation and diffusion. Axial mixing rate was mainly controlled by internal circulation speed of particles, whereas radial mixing rate was mainly controlled by particle diffusion capacity. With the increase of superficial gas velocity, the internal circulation speed of particles was enhanced and thus axial mixing process was accelerated but radial mixing was weakly influenced. When spring constant was increased, both mixing speed and quality of particles were decreased as well as radial mixing process was less affected than that axial mixing by spring constant.

Relatively low productivity is a common problem for conventional tubular solar stills when running at atmospheric pressure. An improved tubular solar still is proposed to enhance the productivity, which can operate under conditions of vacuum pressure and feeding water continuously. An experimental prototype is tested under both constant heating temperature and constant heating power conditions. Its productivity and systematic temperature under vacuum condition is obtained, proving that it has better performance than a common distillation method running at atmospheric pressure. At operating pressure 40 kPa and fixed heat power of 200 W, the temperature difference between distilling water and condensation shell decreases 40%, and the daily accumulated freshwater yield increases 22.5%, with a value of 1.9 kg. An modified correlation to calculate the mass transfer inside the cavity under vacuum condition is obtained against the experimental data in steady state, based on that a model is built to predict the freshwater yield of the tubular solar still under vacuum condition. The model has deviations for present prototype of about 2.1% for daily accumulated productivity, and 4% for maximum freshwater yield rate.

Catalytic combustion of methane and n-butane with Pt/ZSM-5 in a microtube was studied on self-sustaining combustion limits, product concentrations, wall temperature, and surface heat loss. The catalytic combustion of methane and n-butane could be sustained at very high molar ratio (Φ) in rich burn condition. At the same flow rate, n-butane had broader self-sustaining combustion limit than methane with lean burn limit around 0.4. Conversion of the two fuels decreased steeply with flow rate increased from 200 to 1000 ml·min^-1, which were largely due to decrease of residence time and increase of weight hourly space velocity (WHSV). The combustion conversion of methane and n-butane showed minimal difference within the range of experiments. As a result of activation of gas-phase reactions posterior to catalyst and its feedback effect to catalytic reaction, n-butane had higher conversion than methane at the same chemical normality upon flow rate reaching to 600 ml·min^-1. Both methane and n-butane could have self-sustaining combustion with above 95% conversion even when ratio of through-wall heat loss to input power was reached to 70%. Compared to that of methane, n-butane combustion produced higher wall temperature, through-wall heat loss rate, and ratio of through-wall heat loss rate to input power, which was directly related to the activation of gas-phase reactions in n-butane combustion. Overall, considered broader combustion limit, similar conversion curve, minimal difference in heat generate rate and through-wall heat loss, n-butane could be an alternative fuel to methane when necessary.

Three novel hyperbranch-macromolecule-bridged salicylaldimine cobalt catalysts were synthesized by Schiff and complex reactions of 1.0G hyperbranched macromolecules, salicylaldehyde, and cobalt chloride hexahydrate, and were characterized by FT-IR, UV, MS and TG. The influences of solvent, co-catalyst, reaction temperature, reaction pressure, and Al/Co molar ratio on their catalytic performances in ethylene oligomerization were studied. The results showed that the hyperbranch-macromolecule-bridged salicylaldimine cobalt catalysts had excellent catalytic activities and selectivity for ethylene oligomerization of high carbon number olefins (C₁₀₊). The catalytic activity first increased and then decreased with the increase of reaction temperature and Al/Co molar ratio, however, it continuously increased with the increase of reaction pressure. The highest activity of 1.87×10⁵ g·(mol Co)^-1·h^-1 and 42.90% selectivity for high carbon number olefins were achieved under the condition of toluene as solvent, DEAC as co-catalyst, reaction temperature at 25℃, reaction pressure at 0.5 MPa, and Al/Co molar ratio at 500.

In order to effectively analyze ethylene plant's energy efficiency status, evaluate ethylene industry's energy level, and improve the energy efficiency, this paper proposes an energy efficiency evaluation method of index decomposition analysis based data envelopment analysis including slack variable in ethylene industry. In the proposed method, firstly, the IDA method is used to acquire the three energy performance indexes of the influencing energy consumption, i.e., activity effect, structure effect, and intensity effect. Then by analyzing the three energy performance indexes, the DEA method based on slack variable is used to obtain a strategy for improving ethylene energy efficiency and production. Finally, the operation guidance can be applied to enhance the production efficiency of ethylene plants. The results verified the feasibility and effectiveness of the IDA-DEA evaluation method and the proposed IDA-DEA method provides a new idea for saving energy in the petrochemical industry.

To meet the requirements on accuracy and computational time of value approximation algorithms, a recursive least-squares temporal difference reinforcement learning algorithm with eligibility traces based on improved extreme learning machine (RLSTD(λ)-IELM) was proposed. First, a recursive least-squares temporal difference reinforcement learning (RLSTD) was created by introducing recursive method into least-squares temporal difference reinforcement learning algorithm (LSTD), in order to eliminate matrix inversion process in least-squares algorithm and to reduce complexity and computation of the proposed algorithm. Then, eligibility trace was introduced into RLSTD algorithm to form the recursive least-squares temporal difference reinforcement learning algorithm with eligibility trace (RLSTD(λ)), in order to solve issues of slow convergence speed of LSTD(0) and low efficiency of experience exploitation. Furthermore, since value function in most reinforcement learning problem was monotonic, a single suppressed approximation Softplus function was used to replace sigmoid activation function in the extreme learning machine network in order to reduce computation load and improve computing speed. The experiment result on generalized Hop-world problem demonstrated that the proposed algorithm RLSTD(λ)-IELM had faster computing speed than the least-squares temporal difference learning algorithm based on extreme learning machine (LSTD-ELM), and better accuracy than the least-squares temporal difference learning algorithm based on radial basis functions (LSTD-RBF).

In recent years, higher requirements have been put forward to process monitoring and key variable prediction with increasing complexity of chemical processes. Traditional point predictions do not meet these actual needs nor describe uncertainty concern, so that they could not predict variable trending well. An interval prediction method was proposed from principal component independent analysis and mixed kernel RVM. First, kernel principal component analysis (KPCA) and independent element analysis (ICA) were combined to extract principal components from original variables in complex process and to form independent principal components by independent analysis. Second, mixed kernel from Gauss and polynomial kernel functions and RVM were combined to generate a regression prediction model for the independent principal components, and T distribution was used to make interval estimation on predicted values of the model. Third, comprehensive interval evaluation function was constructed to analyze quality of the interval estimation results. Based on prediction interval coverage probability (PICP) and normal mean prediction interval width (NMPIW), accumulative deviation (AD) was introduced to improve rationality of the interval evaluation. The interval prediction analysis on TE simulation process showed that the proposed interval prediction method had better prediction accuracy and interval estimation quality, which could effectively predict trending of key variables in actual production process.

Conventional water system optimization often only considered flowrate of fresh water, but ignored flowrates of desalted water, deaerated water, all-level steam and condensate water. The relationship among those types of water is lack of analysis. The minimum freshwater flowrate for the whole water system cannot be determined in one step via the conventional water system optimization model. In order to overcome such a limitation, this paper proposed a general model of water-using process including multiple water types, and presented a general superstructure of industrial water network optimization and a related mathematical model. The material balance equations that relate all types of water are integrated in the model. The commercial software, GAMS, was used for modeling and solving a water system for a certain refinery plant. The results of the case study indicate that the flowrate of desalted water is declined and flowrate of reuse water is increased. Thus the total flowrate of fresh water of overall water system is decreased from 489.44 t·h^-1 to 283.94 t·h^-1 with rain water flowrate keeping unchanged as 469.36 t·h^-1 and the water conservation ratio reaches 21.4% on the basis of the summation flowrate of freshwater and rain water. The quantity of water intake per ton crude oil is reduced from 0.649 t to 0.510 t, which approaches the advanced level in China. Case study shows that the freshwater flowrate for the whole refinery water system can be calculated effectively via the proposed optimization model and it demonstrates the applicability of the model.

Based on process characteristics and operation economic demands of heavy oil catalytic pyrolysis in two-stage riser, a multi-objective optimization model, which attempted to maximize yields of propylene and gasoline as well as to minimize yield of dry gas simultaneously, was developed with consideration of process principle model and various constraints. The model was solved by a standardized normal constraint method to formulate a complete set of uniformly distributed Pareto-optimal solutions. Numerical simulations show that the multi-objective optimization could comprehensively and quantitatively describe optimal trade-off among yields of propylene, gasoline and dry gas, as well as influence of operation variables and constraints on product distribution, which could offer guidelines for optimal operation of TMP process.

In case of complex and changeable working conditions, traditional multi-model soft-sensor techniques lacked an online update mechanism and decreased accuracy upon updating. A new soft-sensor method based on just-in-time algorithm (JIT) and multi-model online support regression (MOSVR) was proposed. In offline phase, fuzzy C-mean clustering (FCM) was employed to classify training data and SVR was used to build initial model set. In online phase, main output was multi-model SVR works, which would be switched to JIT model by online strategy of model updating (OSMU) and the current model set was updated online simultaneously when new working condition was encountered. The new method not only possessed rapidity and accuracy of multi-model outputs, but also guaranteed continuity, stability and accuracy of JIT outputs at model updating. Method effectiveness was demonstrated by numerical simulation and application in soft-sensor measurement of ethane concentration in ethylene production.

To achieve the desired crystal size distribution (CSD) for the cooling crystallization process of β form L-glutamic acid (LGA), the population balance equation (PBE) of size-dependent growth rate was established by using the method of characteristics (MOCH), and then the optimal supersaturation and temperature curves during the crystallization process were determined by parameter estimation for the established PBM. Due to the nonlinearity and non-convexity of the objective function for model parameter identification, a small number of economic batch crystallization experiments were conducted in combination with image analysis on the crystal seeds and product shapes, in order to fit the model parameters. According to the practically required cycling time for crystallization, to realize the target CSD, the optimal curve of decreasing temperature was determined by optimizing the supersaturation curve of the crystallization process, so as to implement the control of crystal growth process based on the maintained supersaturation level. Experiment results showed that the desired CSD could be achieved by implementing the optimal supersaturation control scheme via the optimized temperature control curve.

Oscillation phenomenon is an inherent characteristic in biological systems and plays an important role in many dynamic bioprocesses. In order to explore the certain conditons that could possibly boost a oscillation, the metabolic pathway of the Saccharomyces cerevisiae, glycolysis were researched, and the parameters of mathmatical model was analyzed. Firstly, the simulation results associated the phase diagrams indicated that the model exists sustained oscillations with constant amplitude (limit cycle oscillations). Next, bifurcation analysis approach was used to investigate the infulence of parameters for the system producing oscillations. The results showed that several parameters of the model could lead to oscillations and the range of parameters' value was obtained, which could be applied to direct the manipulation of metabolic oscillations.

When soft sensor models were constructed for complicated chemical processes by traditional stochastic gradient Boosting (SGB), improper selection of shrinkage parameters would reduce convergence rate of the algorithm, engender overfitting, and sometimes make it difficult to obtain a satisfactory generalization performance. In order to solve this problem, a modified SGB ensemble learning soft sensor was proposed, in which Gaussian process regression (GPR) was adopted as base learner and shrinkage parameters were automatically adjusted according to feedback of a weak learner in each iteration such that both estimation accuracy and learning efficiency were improved. Simulation results in an industrial process of bisphenol A production showed that the modified integration algorithm had higher learning efficiency and generalization performance than traditional SGB models.

Considered that key variables in glutamate fermentation process could not be measured inline, which would make it difficult to control and optimize the fermentation process, a model for glutamate concentration prediction in a 5 L fermentation tank was established on the basis of partial least squares (PLS) and least square support vector machine (LSSVM). PLS was applied first to extract features of input variables, to reduce number of variable dimensions, and to eliminate correlations such that model complexity was simplified and performance was improved. Coupled simulated annealing (CSA) arithmetic was later combined with grid search to determine model parameter values of LSSVM for improved prediction accuracy. Further model simplification was completed by deleting parameters with weak correlation to glutamate concentration. The simplified model was compared to kinetic model in order to select the best model of glutamate fermentation. Experimental results showed that the simplified LSSVM model equipped with CSA parameter optimization outperformed both PLS and kinetic models,which root mean square errors (RMSE) were 1.597, 8.49 and 2.934 respectively. The LSSVM prediction model had excellent performance with high accuracy, so it would be more suitable for online prediction of glutamate concentration and offer an effective guidance for control and optimization of the glutamate fermentation process.

Traditional time difference (TD) model may deteriorate as time increases. In order to enhance reliability and prediction accuracy of TD model, an adaptive soft sensor was proposed on the basis of a selective ensemble of local time difference Gaussian process regression (LTDGPR) models and consideration of process time-delay characteristics. First, data for modelling was reconstructed by time delay and dynamic information extracted from database. Then, an adaptive localization step was used to statistically classify the reconstructed time-difference dataset and to establish an LTDGPR model set. For new input samples, prediction of dynamic drift for primary variables at certain time lapse was achieved through selective ensemble of LTDGPR models which had strong generalization capability. Finally, spontaneous online prediction of primary variable was achieved on the basis of TD model theory. Simulation results of a real debutanizer process indicated the effectiveness and accuracy of the proposed soft sensor.

The multi-delays of the whole equipment unit in the process industry are indicated in the form of a sequence of integers. A restructured data matrix is generated based on the sequence and the original data. The time-correlation analysis is defined to describe the correlation between the columns in the data matrix and H_∞ norm is used to quantify the correlation of the matrix so that the multi-delays identification problem can be converted to calculating the biggest H_∞ norm. At last, the discrete state transition algorithm is adopted to quickly search the biggest H_∞ norm and the multi-delays are identified. The proposed method is used for multi-delays identification of alumina evaporation process and the result of multi-delays identification is applied to the data preprocessing of a prediction model. The accuracy of the prediction model is improved by 34.4% and the proposed identification method is effective.

A polymetallic analytical method based on an improved Monte Carlo uninformative variable elimination (MC-UVE) was proposed to determine ion concentration by ultraviolet visible (UV-Vis) spectra of Cu²⁺, Co²⁺ and Zn²⁺ mixture solution. The improved MC-UVE, developed by incorporating exponential attenuation function (EDF), was applied to selecting wavelength of UV-Vis spectra. And the partial least squares calibration model was built on concentrations of polymetallic components at the selected wavelength. Experimental results showed that the improved MC-UVE could select higher contributing model variables and develop more precise PLS model than MC-UVE.

In the multi-effect evaporation process in Bayer process for alumina production, liquid level of each effect is an important parameter that influences other parameters and important for optimization of evaporator operation. But in the actual production, the liquid level is usually set empirically in a large rang, so that the process cannot run in optimal situation. This paper proposed a liquid level optimization method based on exergy analysis. By deeply analyzing the liquid level's effect on other evaporation parameters and using the actual running data, the relationships between liquid level and other parameters are obtained. Combining the material balance of the evaporator and the exergy analysis method, optimization model for energy consumption based on the maximum exergy efficiency is established. The optimization model is then solved by STA using two different constraint handling technology under a certain condition to get better solutions. Finally the optimal level under three different pickling cycle is calculated and evaporation liquid level curve for each effect is obtained.

The double-stream alumina digestion process is a key production chain in Bayer process of alumina production. It is the unique production technology in China. However, the double-stream alumina digestion process has problems that the evaporate water capacity of flash vessel is not fully exploited, scaling causes increased new steam consumption, meanwhile, working points are set manually, which causes the high energy consumption. Thus, the exergy optimization model of the double-stream alumina digestion is established, and the digestion process is divided into three stages based on the degree of scab in a pickling cycle. An adaptive state transition algorithm is proposed to optimize the operating parameter of the three stages of the digestion process, respectively. Then, optimal process control parameters are obtained, which is meaningful to provide suggestions for practical production.

Methods for monitoring flotation processes based on machine vision has been widely used, which surface texture feature of froths is one of the key visual parameters in process monitoring. Static texture features can only describe images in space dimensions but do not well describe inherent variation characteristics of the image sequence in time dimension, so they can not accurately reflect dynamic characteristics of froths in flotation process. An extraction and analysis method for dynamic texture features of flotation froth images was proposed on a basis of space-time characteristics of complex networks. After pixels of each image were mapped into nodes of complex networks, a complex network model was established by adjacent matrix. The image characteristics at different time were described by network-weighted dynamic evolution and dynamic texture characteristics of image sequences were obtained by the space-time characteristics of complex networks. Simulation results with actual production data showed that the method could accurately identify flotation dynamic conditions and provide guidance for instant regulation of flotation process.

A soft-sensor method for online detection of effluent ammonia nitrogen (NH₄-N) in waste water treatment process was proposed on the basis of interval type-2 fuzzy neural networks (IT2FNN). First, actual operation data related to pre-treatment process variables was collected and process variables having strong correlation to effluent NH₄-N were selected by principal component analysis (PCA) technique. Second, a self-sensor model between principal component variables and effluent NH₄-N was established via IT2FNN and model parameters were adjusted by gradient algorithm. Finally, the proposed soft-sensor method was used in a real waste water treatment process (WWTP). The experimental results show that the new method can predict effluent NH₄-N online with better accuracy than traditional methods.

A fault diagnosis method of kernel Fisher projection was proposed to solve issues of nonlinear data, complex classes, and difficult fault-diagnosing in chemical processes. The proposed method provided a uniform solution for partial sample mix-up induced by a large difference in category distances and nebulous classification of different category's boundary data after projection of the original data sample. First, an improved category distance was used to change sample distribution in the projection space so that the sample had a good projection. Then, boundary data was screened out by a defined threshold parameter and classified by improved K-Nearest Neighbor (K-NN) algorithm, which none-boundary data was classified by Mahalanobis distance. Simulation in a TE process showed that training accuracy was increased by 2.25% and testing accuracy was increased by 2.41% in the first experiment, whereas training accuracy was increased by 4.75% and testing accuracy was increased by 6.75% in the second experiment. Therefore, the method improved both fault diagnosis time and accuracy.

In order to establish an accurate prediction model for heat consumption rate of steam turbines, an integrated modeling method was proposed by combination of oppositely adaptive whale optimization algorithm (AWOA) and fast learning network (FLN). Compared to basic whale algorithm, improved particle swarm optimization algorithm, and differential evolution algorithm, the improved whale algorithm had higher convergence accuracy and faster convergence speed. A prediction model for heat consumption rate of a 600 MW supercritical steam turbine generator set in a thermal power plant was established from the collected operation data, which was also compared to FLN model, improved particle swarm optimization, differential evolution algorithm, and whale optimization algorithm. The results show that the AWOA-FLN prediction model had higher prediction accuracy and stronger generalization ability, which therefore could predict heat consumption rate of steam turbine more accurately.

To meet the requirement that the export product of component content has zone fluctuation in rare earth extraction, the component content control algorithm with zone control based on generalized predictive control of rare earth extraction process is proposed in this paper. Based on the data of the rare earth extraction process, the model of echo state network (ESN) is built up. According to the different running states of the rare earth extraction process, component content predictive controller is designed by using an improved generalized predictive control algorithm, and the algorithm brings the constraint of output variable into the optimization problem for obtaining the control law. Such a design can take various control to different regions of output and realize the stability as possible as zone control of production purity. Simulation results for the CePr/Nd countercurrent extraction process are presented to show the effectiveness of the proposed control approach.

In order to solve the problem that the actual description of the water bloom behavior is not entirely conform to reality and water bloom prediction is not accurate enough by existing algae growth dynamics model due to the neglect of model parameters change with time, this paper builds cyanobacteria feeding and nutrient cycling model, and proposes algae growth dynamics model with time-varying parameters based on time variation influences of water temperature and illumination on model parameters. The calibration method for constant parameters of the model is optimized based on genetic and numerical algorithm. And the model time-varying parameters is modeled and predicted by multivariate time series method. Nonlinear dynamic mechanism of cyanobacteria bloom behavior is analyzed by bifurcation theory and time varying system theory, and then a new method of cyanobacteria bloom prediction is put forward. The monitoring data analysis of the Taihu River Basin in Jiangsu shows that cyanobacterial growth dynamics model with time-varying parameters can reflect nonlinear dynamic characteristics of cyanobacteria bloom behavior in cyanobacteria growth time-varying system. The model is more consistent with the actual situation, and cyanobacteria bloom prediction result is more accurate.

In order to improve the diversity of initial population and consider the operation sequence constraints of the same artifact at the same time, the stack was used to storage all operations in the view of the FJSP machine selection problem, in which the makespan was the optimization objective. Global random initialization method based on the key operation was proposed to solve the machine selection problem of FJSP, in which the key operation containing the only optional machine can directly affect the total load machine and processing time. To avoid the basic genetic algorithm trapped in local optimum when solving FJSP, re-activation mechanism was added to the GA algorithm, by which the diversity of population can be increased. Finally, in the view of the FJSP benchmark examples, the effectiveness of the GRS initialization mechanism and the reliability of the proposed improved algorithm were verified respectively by analyzing the performance comparison of the initial machine selection parts and the experimental results of solving FJSP by the genetic algorithm with different initializations.

Batch processes are not with highly nonlinearity, but also suffer from the actuator failures. Study of the stability of nonlinear batch processes under failure conditions is of great significance. With considering on the actuator gain faults and the highly nonlinearity, a new T-S fuzzy model-based iterative learning fault-tolerant control method is proposed for nonlinear batch process. Firstly, the T-S fuzzy model is employed to represent the nonlinear batch process. Then a 2D compound iterative learning fault-tolerant controller is proposed by exploiting the 2D and repetitive nature of batch processes, and the equivalent 2D Rosser model of the fuzzy model is constructed. Lastly, the sufficient condition guaranteeing the system stable is given through a Lyapunov function method, and the controller gains are designed in terms of linear matrix inequalities (LMIs). Simulation to a highly nonlinear continuous stirred tank reactor (CSTR) demonstrates the feasibility and efficiency of the proposed method.

The control of substrate concentration for microbial fuel cell (MFC) is an important part of the entire MFC system, which have a great effect on the output voltage of MFC. The effects of input variables and control variables on the output voltage of MFC are studied, and a neural network predictive control strategy for anode feed flow of MFC is proposed, in which the load current is regarded as disturbance, aiming to solve the problem of overshoot and slow response of output voltage under conventional control strategy. The simulation results show that, compared with the PID control method, the system output voltage response of the neural network predictive control strategy is fast, the overshoot is small, and the dynamic performance of system is greatly improved.

False alarm rate (FAR) is a key indicator to evaluate performance of chiller fault detection methods, since customers cannot accept high FAR. In order to reduce FAR of support vector data description (SVDD)-based chiller fault detection, a density weighted support vector data description (DW-SVDD)-based chiller fault detection method was proposed by integration of density weight into SVDD with a consideration of density distribution of sample data in real space. The proposed method was validated with experimental data of RP-1043 ASHRAE and detection results were compared to those of traditional SVDD chiller fault detection methods. The results showed that the new method could reduce FAR from 10.5% to 7%, which was lowered about 30%, and had excellent detection performance for 7 typical chiller faults at 4 severity levels.

In most complex chemical processes, measurements are often collected with noises and some outliers. These contaminated data would have negative effect on the accuracy of data-based process modelling and fault detection. A new robust semi-supervised PLVR model (RSSPLVR) was proposed by consideration of the real measuring environment in chemical processes and extended to a nonlinear model K-RSSPLVR with a kernel methodology. In both RSSPLVR and K-RSSPLVR, a weighted coefficient based on sample similarity among all observations was used as prior checking parameter of probability model to effectively eliminate influence of outliers on modelling. Model parameter training was accomplished by analysis of the weighted dataset with EM algorithm and a fault detection scheme was developed. Finally, TE process simulation demonstrated effectiveness of the proposed modelling methods.

Na_0.44MnO₂ nanorods were prepared from KMnO₄, MnSO₄ and NaOH as initial raw materials by hydrothermal soft chemical method. XRD confirmed that the products have S-shaped channel structure, and TEM characterization indicates that Na_0.44MnO₂ nanorods is single-crystal structure, which can be suited for preparation of high performance cathode material. Graphene prepared by hummer method were used as conductive agent. Electrochemical test research shows that the capacity, cycle and rate performance of Na_0.44MnO₂ nanorods/graphene are improved with the increase of the amount of graphene. When the content of graphene reached to 45%, the capacity of electrode is up to 192.5 mAh·g^-1 at the current density of 0.1 A·g^-1. In the rate test, the capacity of electrode is still located at 123.4 mAh·g^-1 at the current density of 2.0 A·g^-1, and it indicates that these electrode materials have potential application in the next generation of advanced batteries.

By variation of liquid Wood metal's environment in NaOH solution and application of electric field with graphite electrode, surface tension of the liquid metal, interfacial electrochemical reaction, generation of oxidation film, and other dynamic processes were studied. The results showed that thin oxidation film on the surface of Wood alloy droplets which was formed by anodic oxidation reaction quickly reduced surface tension of Wood alloy and the oxidation film on surface of liquid alloy droplets would return to neutral state by cathodic reduction reaction within 3 seconds. The electrical capillary force broke the oxide film so that metal oxides reacted with NaOH to generate ions of Sn(OH)₆^2-、SnO₃^2-、SnO₂^2-、PbO₃^2-、Cd(OH)₄^2- which altered solution colors with a white precipitate of Bi₂O₃-Bi(OH)₃ copolymer. The dielectric wetting and solution electrowetting on thin film essentially had the same mechanism. With the increase of voltage, the surface tension was decreased and electrowetting spreading process was occurred obviously, but the wetting angle exhibited saturation as a result of limited number of OH^- ions adsorbed on alloy droplets by electrochemical reactions.

In-mold micro assembly molding technology should be expected to be high efficiency low cost industrialized manufacturing technology of polymer micro-mechanical systems, but how to predict accurately and control precisely thermal-fluid-structure coupling deformation still is its industrialized technical bottlenecks of in-mold micro assembly molding technology. The theoretical prediction model of viscoelastic thermal-fluid-structure coupling deformation in molding process was established based on the boundary constraints of secondary viscoelastic melt filling flow. Research shows that viscoelastic thermal-fluid-structure coupling deformation is controlled by the coupling pressure, viscoelastic supporting normal stress, viscous friction drag shear stress on the micro assembly interface and anti-deformation stiffness, and will reduce with increasing of melt injection speed. The PMMA anti-deformation stiffness in near-surface local region exceeded 393 K will be drastically reduced, which is the key control factor of thermal-fluid-structure coupling deformation reducing with melt injection speed increasing.

The pyrolysis characteristics of petroleum sludge samples collected from crude oil tank and cleaning water reservoir under two slow heating rates (5℃·min^-1 and 2℃·min^-1) were experimentally studied in a fixed bed reactor. Thermogravimetric analysis was carried out for deducing reaction kinetics based on Doyle equation. Results show that around 65% of oil can be recovered through pyrolysis and the fraction of cyclic compounds increased with heating rate caused by enhanced C-H bonds breaking and cyclization reactions. Moreover, the apparent activation energy grows 20%-37% with higher heating rate.

The influences of operating parameters (condenser inlet water temperature, high-temperature evaporator inlet water temperature and low-temperature evaporator inlet water temperature) on the performance of bi-evaporator compression/ejection refrigeration cycle (BCERC) and two-phase ejector are experimentally investigated. Results show that the ejector entrainment ratio decreases with the rising of condenser inlet water temperature and high-temperature evaporator inlet water temperature, but rises with the increasing of low-temperature evaporator inlet water temperature. The pressure lift ratio of ejector increases with the rising of condenser inlet water temperature and high-temperature evaporator inlet water temperature, but decreases with the rising of low-temperature evaporator inlet water temperature. The condenser inlet water temperature and the high-temperature evaporator inlet water temperature have significant influences on the performance of the BCERC system. However, the low-temperature evaporator inlet water temperature has little effects on the performance of the BCERC system. As the condenser inlet water temperature decreases 5℃, the COP increases 0.44. As the high-temperature evaporator inlet water temperature increases 2℃, the increase of system COP is 0.16. The results can be references for the design and operation of the BCERC system.

Based on the process of pressurized carbonation with phosphogypsum, the effect of raw material types on carbonation conversion was experimentally studied. In addition, the influence of some operation parameters on the conversion had been analyzed by Aspen plus process simulation software. The results showed that the carbonation reaction can reach equilibrium within 5 minutes under pressured conditions, and it was much more easily for pure anhydrous calcium sulfate to convert completely. However, because of containing crystal water or other impurities, it was difficult for both pure dihydrate calcium sulfate and phosphogypsum to convert completely. Besides, it was found that increasing the initial ammonia concentration, the mole ratio of added ammonia to SO₃ in the raw materials, and properly increasing the reaction temperature and system pressure can effectively improve the equilibrium carbonation conversion. Most importantly, the formed ammonium carbonate from the reaction between ammonia and CO₂ under high temperature and pressure condition is unstable when pressure decreased. Thus combing the flash operation after pressured carbonation, the formed ammonium carbonate can decompose to release ammonia and CO₂, which can further be absorbed and returned to the pressurized carbonation process. This could effectively improve the ammonia utilization rate and reduce the production cost of ammonium sulfate as well.

Chemical Looping Combustion (CLC) is a novel technology for its attractive advantage in the inherent separation of CO₂. Due to the existence of sulfur contaminants in the fossil fuels, the interaction of the gaseous products of sulfur species with oxygen carrier is a great concern in the CLC operational aspect. Density Functional Theory (DFT) calculations were performed to investigate interaction mechanism between H₂S and NiFe₂O₄ oxygen carrier at molecule level. The results show that H₂S is easier to be adsorbed on NiFe₂O₄(001) defect surface than that on NiFe₂O₄(001) perfect surface. On both NiFe₂O₄(001) perfect surface and defect surface, the adsorption energies of H₂S adsorbed on Ni site are greater than those of H₂S adsorbed on Fe site, which infers that H₂S is preferred to adsorb on Ni site of the oxygen carrier surface. Furthermore, the reduction reactions between NiFe₂O₄ oxygen carrier and H₂S-containing synthesis gas were investigated by means of thermodynamic simulation. The result shows that generation of various sulfur-containing species are related to the reduction process of oxygen carrier. Mole percentage of Ni₃S₂ is higher than those of FeS due to thermodynamic limitation of reduction of iron oxides, indicating that nickel sulfides are easier to be generated than iron sulfides under equilibrium conditions. Both DFT calculations and thermodynamic simulations indicate that H₂S is preferred to interact with Ni atom of the oxygen carrier, which may have adverse effects on the reactivity of NiFe₂O₄ oxygen carrier.

Taking the Kalina cycle driven by the low temperature flue gas waste heat as the research object, net work (W_net) and levelized energy cost (LEC) versus outlet temperature of flue gas (T_go) were analyzed from the perspective of thermo-economics under variations of the pinch point temperature difference of evaporator (ΔT_e), evaporation pressure (p_e) and basic ammonia mass fraction (x). Considering the corrosion of low temperature flue gas, the necessity and reasonability of limiting T_go at its minimum allowed discharge temperature were studied. Results showed that the system existed an optimal outlet temperature of flue gas (T_go,opt) and evaporation pressure (p_e,opt) for LEC, while T_go,opt was associated with the pinch point temperature difference of evaporator (ΔT_e), p_e and x. For W_net, it only existed p_e,opt and W_net decreased approximately linearly with the increase of T_go. The economic factors affected directly the optimal operation parameters of the system. The economic factors and low temperature flue gas corrosion problem should be considered comprehensively to choose the appropriate operation parameters in view of different heat source conditions.

The effects of Na/K additives (NaOH, Na₂CO₃, NaCl, KOH, K₂CO₃ and KCl) on NO reduction in the selective non-catalytic reduction (SNCR) process were simulated by using Chemkin software based on the established chemical mechanisms of Na-K-C-H-O-N-Cl. The mechanism and routes of Na/K additives on NO reduction in the SNCR process were discussed based on the sensitivity analysis and rate of production (ROP) analysis. The simulated results indicated that NO removal efficiency was almost zero at temperature of 700-800℃ in the SNCR process without Na/K additives. Na/K additives had a significant promotion on NO reduction at temperature lower than 800℃, and had less promotion on NO reduction at temperature higher than 900℃. At temperature of 700℃, NO removal efficiency was 43.86%-60.76% in the SNCR process with Na/K additives. The promotion order of Na/K additives on NO reduction in the SNCR process was NaOH≈Na₂CO₃ > KOH≈K₂CO₃ > KCl > NaCl. However, the concentration of Na/K additives (6.25-25.0 μmol·mol^-1) showed little influence on NO reduction. NO reduction in the SNCR process was performed through NH₂ radicals, which were promoted by OH radicals via respective reaction routes promoted by Na/K additives. The promotion of Na/K additives on NO reduction in the SNCR process was enhanced by alkali metal hydroxides (MOH) via the route of NaOH→NaO₂→Na→NaO→NaOH, but weakened by alkali metal chlorides (MCl) via the route of MCl→M→MCl.

The gas evolution is one of the key factors for alumina distribution in bath and current efficiency in aluminum electrolysis cell using vertical inert anodes. A see-through cell is presented to investigate the gas evolution and releasing behavior with big inert anode. The test results show that with small anode, the bubble behavior of nucleation, growth and detaching periodically occurs on surface of big anode. However, the sliding and coalescence behavior on big surface is different to that on small anode. At working face of the anode, however, multi layers of bubbles are formed. The bubbles adjacent to the anode are moving fast than those at the outer layer. Most of bubbles go directly into the atmosphere at the liquid surface, while the remaining bubbles are carried by the electrolyte to move horizontally and they are released gradually. There is a bubble foam layer on the electrolyte surface. The moving velocity of bubble generated at inert anode is measured by image processing, which is in the range of 0.006-0.445 m·s^-1. The bubbles at the bottom have a wide velocity range, while the bubbles at the middle have a narrow velocity range under the influence of the electrolyte.

The effects of different gasification temperature and gas-solid ratio (S/B) on steam gasification reaction of waste pine sawdust were investigated in the self-made steam gasifier by using high-temperature steam gasification technology. Fourier transform infrared spectroscopy (FT-IR) and XRD ray diffraction were employed to analyze the reaction residue and the gasification tar, respectively. The specific surface area and pore characteristics of the reaction residue were separately measured by BET multi-point method and BJH method. The results showed that the increase of steam flow promoted the reactions of steam reforming, carbon reduction, and carbon monoxide conversion. When the ratio of S/B increased from 0.5 to 1.5, the volume fraction of H₂ increased from 52.32% to 67.3% at 900℃,the volume fraction of CO decreased with increasing temperature, and then slightly increased, while the change rule of CO₂ was opposite to CO. The volume fraction of hydrocarbon gases (CH₄ and C_nH_m (n≥2)) of small molecule decreased gradually. In the temperature range of 750-950℃, the gasification reaction increased with increasing temperature. When S/B was 1, the volume fraction of H₂ increased from 31.91% at 750℃ to 62.89% at 900℃, increasing by nearly one time. With the increase of temperature, the weight loss rate of pine briquette fuel increased from 82.91% to 91.27%, the pore structure of the residual solid phase was fully developed, and the average pore diameter decreased from 20.96 nm to 3.76 nm. The content of aliphatic hydrocarbons in gasification tar also increased with increasing temperature, while the content of aromatic hydrocarbon decreased due to the ring opening reaction, which was beneficial to reduce the tar content in the gasification gas.

The MoO₃/graphene/carbon nano-tube composites were synthesized via a facile hydrothermal method. The morphology of the materials were observed using scanning electron microscope (SEM) and the structures were characterized, with X-ray diffraction (XRD). The electro catalytic activity of oxygen reduction of the materials were measured by cyclic voltammetry (CV) and linear sweep voltammetry (LSV). The results revealed that the composites exhibited better electro catalytic activity towards oxygen reduction with a higher oxygen reduction current and more positive oneset potential than pure MoO₃. The microbial fuel cell assembled with 3 mg·cm^-2 MoO₃/GNS/CNT composites as cathode catalyst delivered a higher power density of 510 mW·m^-2, which was 83% as much as the MFCs using Pt/C-catalyst cathode. Therefore, the inexpensive MoO₃/GNS/CNT composites as MFCs cathode oxygen reduction catalyst had great potential for application.

The start-up process and power generation of microbial fuel cells (MFCs) influence the practical application of MFCs for wastewater treatment. In this paper, we reported a novel way to accelerate the electrochemical active biofilm formation by conducting the cyclic voltammetry (CV) scans on the cell. The MFCs with CV scan of 24 h reduced the start-up time by 71.4% from 420 to 120 h, and increased power density by 21.5% from 31.1 to 37.8 W·m^-3, comparing to the MFCs started-up with an external resistance of 1000 Ω. The fast growth rate of biomass having dominant electrogenic bacteria under CV scans contributed to the enhancement of MFCs performance. This technology provides a novel approach to short the start-up time and increase the power density of MFCs.

The treatment of radioactive wastewater by chemical precipitation method has the advantages of being simple, wide application and large amount of water treatment. Membrane separation technology can improve the effectiveness of solid liquid separation. In this paper, precipitation and membrane separation technology were combined to treat the simulated wastewater containing radioactive iodide on a laboratory scale. The initial iodide concentration was approximately 5 mg·L^-1. The concentration of added Na₂SO₃ used for removing oxygen from the influent was 40 mg·L^-1 and the dosage of CuCl used as a precipitant was 260 mg·L^-1. The entire system was under the protection of nitrogen gas that was recycled after drying. The whole process was operated continuously, which was controlled by programmable logic controller (PLC). The I^- removal mechanism was the formation of sparingly soluble CuI. Besides that, Cu₂O and Cu(II) ion were produced in this reaction system, which were demonstrated by solid phase analysis. This study investigated I^- removal efficiency and other water quality parameters under different temperatures and the effects of two membrane flux 4.17×10^-6 m·s^-1 and 8.33×10^-6 m·s^-1 on membrane fouling. The operation time was 168 h and the volumes of treated wastewater were 1540 L. In two tests, the average I^- removal efficiency was 94.8% with stable effluent water qualities. However, the Cu(II) ion concentrations in the effluent were higher and it was required for the subsequent removal. Under the conditions that the membrane fluxes were 4.17×10^-6 m·s^-1 and 8.33×10^-6 m·s^-1, the final membrane specific flux decreased to 65.0% and 55.0% of the initial one, respectively, and the membrane specific flux values of the membrane modules were recovered to 90.0% and 79.0% of the initial one, respectively after physical and chemical cleaning. Furthermore, the average concentration factor value was 2.02×10³, and the volume of the sludge produced in the experiment was small.

To investigate the synergetic effects in co-pyrolysis of macroalgae and terrestrial biomass, enteromorpha clathrate (EN), rice husk (HU) and their mixtures with different proportions were used for pyrolysis in a fixed-bed reactor. The co-pyrolysis effects were studied via analyzing the yields of gas, liquid and solid, GC-MS analysis, FT-IR analysis, heating value of bio-oil and GC analysis of gas product. It was found that the co-pyrolysis resulted in an increase in gas yield but a decrease in liquid yield, which indicated that a significantly synergetic effect happened during the process, promoting the generation of more gas. In addition, the low molecules in bio-oil were strongly affected by the co-pyrolysis, and the increase in CH₄, C₂H₆, C₂H₄, C₃H₈ yields in gas production was observed. Besides, the alkali metals in EN ash could perform a catalytic role in promoting the cracking of large molecules, which proved the synergetic effects in co-pyrolysis of EN and HU.

Porous PZT ceramic membranes were fabricated by dry pressing PZT powder. Study of sintering temperature on mechanical strength, porosity and pure water permeability showed that the membrane obtained at 950℃ sintering temperature had pure water permeability of 850 L·m^-2·h^-1·MPa^-1, average pore size about 300 nm, mechanical strength of 47.8 MPa, and porosity of 34%. Further study of poling temperature and electric voltage on piezoelectric property of porous PZT ceramic membranes, which were extracted and plasma etched after poling, showed that after poling at temperature of 120℃ and electric field of 4 kV·mm^-1, hot alcohol extraction, and 4 min plasma etching, the porous PZT ceramic membranes could create a resonance signal with an amplitude of 34.8 mV when applied to 20 V of an alternating current (AC). Filtration study of the membrane in wastewater oil emulsion with particles of size about 600nm showed that flux decreased to 4% within 2 h without electric field whereas the flux was stabilized at 20% with AC, which indicated the porous PZT membrane had an excellent anti-fouling performance.

In this work, monolithic SSZ-13 zeolite was synthesized using dry-gel conversion method. The influences of the gel composition and crystallization conditions on the crystallization of SSZ-13 zeolite were investigated. The as-synthesized product was characterized by X-ray diffraction (XRD), X-ray fluorescence (XRF),thermo gravimetric-differential thermal analysis (TG-DTA, scanning electron microscope(SEM), NH₃ temperature-programmed desorption(NH₃-TPD, Nuclear Magnetic Resonance (NMR). The roles of N,N,N-trimethyl-1-adamantanammonium (TMADa⁺) and choline chloridein crystallization reaction were discussed. According to the result, the monolithic SSZ-13 zeolite has typical accumulated mesopores, larger surface area and better thermal stability in air. The results in FT-IR and NMR spectra show that choline chloride could act as a secondary structure-directing agent and fill in the pores of zeolite, which significantly reduced the amount of TMADa⁺. It was confirmed that Choline Chloride favors accelerating zeolite nucleation, shortening the crystallization time, and improving crystallinity of the as-synthesized product.

A stepwise co-precipitation route (precipitation with different ratios of Ni, Mn elements in three steps)has been adopted to synthesize the precursor of LiNi_0.5Co_0.2Mn_0.3(OH)₂, which was mixed with Li₂CO₃ afterwards in a certain proportion. After post-heat treatment, the obtained LiNi_0.5Co_0.2Mn_0.3O₂ spherical cathode materials exhibit a gradient element distribution (nickel-rich in centre and manganese-rich around surface). The structure, morphology and chemical composition of the samples were characterized by X-Ray Diffraction (XRD), Field Emission Scanning Electron Microscopy (FESEM), Energy Dispersive Spectrometer (EDS) and Inductively Coupled Plasma Mass Spectrometry(ICP-MS), respectively. The electrochemical performances of the as-prepared LiNi_0.5Co_0.2Mn_0.3O₂ cathode materials were studied by galvanostatic charge/discharge measurements, Cyclic Voltammetry (CV) and Electrochemical Impedance Spectroscopy (EIS). The testing results show that the materials derived from stepwise co-precipitation exhibit higher rate capability (104.1 mAh·g^-1 at 20 C), better cycle performance (with capacity retention of 95.8% after 200 cycles at 0.5 C) and more efficient fast charge and discharge performance (85.4 mAh·g^-1 at 20 C/20 C), compared with the counterparts obtained from the conventional co-precipitation. In summary, this stepwise co-precipitation strategy is extremely promising for the preparation of LiNi_0.5Co_0.2Mn_0.3O₂ electrode materials with enhanced electrochemical performances.

A series of polyurethane aqueous dispersions, which were prepared from raw materials of toluene diisocyanate (TDI), polypropylene glycol (PPG), and dimethylol propionic acid (DMPA), were used to study post-chain extension process with ethylene diamine (EDA) as chain extender, including factors of H₂O, temperature of post-chain extension, molar ratio of -NCO/-OH in raw materials, and chain extension ratio. FT-IR results showed that the post-chain extension process of polyurethanes with EDA was affected by H₂O in aqueous dispersion through competitive reactions of H₂O with residual -NCO groups in polyurethane chains. Measurement of molecular weight, particle diameter, and zeta potential of polyurethane dispersions before and after chain extension indicated that chain extension by H₂O resulted in instability of polyurethane dispersions. The influence of H₂O on EDA post-chain extension process was significant at low chain extension ratio, whereas molecular weight of chain-extended polyurethanes was lowered at high chain extension ratio. When chain extension temperature was increased, molecular weight of EDA chain-extended polyurethanes was decreased,but diameters of polyurethane dispersions were increased. With molar ratio of -NCO/-OH in raw materials at 1.20, chain extension temperature at 30℃, and chain extension ratio at 60%, the H₂O impact on post-chain extension of polyurethanes with EDA was reduced effectively.

By propylene acylation crosslinked chitosan microspheres (AGCS) reacting with ethylenediamine, the ethylenediaminepropionyl crosslinked chitosan microspheres (EAGCS) with an abundant of amine groups were prepared. The structure of EAGCS was characterized by Fourier transform infrared spectroscopy (FT-IR), X-ray photoelectron spectroscopy (XPS) and X-ray diffraction analysis (XRD) respectively. The particle size was identified by laser particle size analyzer. The effects of the pH, temperature, concentration and EAGCS dosage of methyl orange(MO) solution on adsorption performance were also investigated systematically. The results show that the structure of EAGCS with spherical morphology consistents with expected design structure, the volume average particle size of EAGCS is 57.4 μm and the particle size distribution coefficient is 1.53. It's also found that the highest adsorption capacity and highest removal rate are up to 545.40 mg·g^-1 and 99.6% respectively under the optimal conditions. Furthermore, the adsorption thermodynamics studies indicate that the adsorption is a spontaneous as well as exothermic process, also the kinetic and isothermal adsorption model studies suggest that this adsorption kinetics is pseudo-second-order, which is fit to Langmuir and Freundlich isothermal adsorption model.

The effect of magnetic nanoparticles(MNPs) on the devitrification isothermal crystallization of typical vitrification solution Vs55 in the process of glass crystallization was systematically explored by Differential Scanning Calorimetry (DSC) and cryomicroscope system. The results show that:(1)The MNPs coated by both Carboxylic Acid (CA) and Polyethylene Glycol (PEG) have little effect on the glass transition temperature(T_g) of Vs55, but significant effect on the devitrification transition temperature(T_d) and devitrification enthalpy(H_Td);(2)At the range of the devitrification area (-85~-60℃), the MNPs coated by CA can significantly promote the devitrification of Vs55 as increasing of the isothermal temperatures and the cooling rates, and the ice growth rate was 0.37 μm·s^-1 at the isothermal temperature of -85℃, but it is about 2.19 μm·s^-1 for -75℃. Also, the ice growth rates raised from 1.72 μm·s^-1 to 3.54 μm·s^-1 when the cooling rates were increased from 2℃·min^-1 to 100℃·min^-1(at the isothermal temperature of -75℃); (3) Compared with Vs55, magnetic nanoparticles coated by both PEG and CA could promote the devitrification of Vs55. The crystal growth rates at the isothermal temperature of -80℃ were 0 for Vs55, but 1.04 μm·s^-1 and 2.31 μm·s^-1 for CA and PEG respectively. Compared with CA coating, the MNPs coated by PEG promoted the much more devitrification of Vs55, and the ice growth rates were 0.62 μm·s^-1 and 6.25 μm·s^-1 for the isothermal temperature of -85℃ and -75℃, respectively, which means that the surface coating of MNPs can significantly affect the crystallization of Vs55.