Study on thermal decomposition kinetics of azobenzene-4,4′-dicarboxylic acid by using compensation parameter method and nonlinear fitting evaluation

Recently,azobenzene-4,4'-dicarboxylic acid(ADCA)has been produced gradually for use as an organic synthesis or pharmaceutical intermediate due to its eminent performance.With large quantities put into application in the future,the thermal stability of this substance during storage,transportation,and use will become quite important.Thus,in this work,the thermal decomposition behavior,thermal decomposition kinetics,and thermal hazard of ADCA were investigated.Experiments were conducted by using a SENSYS evo DSC device.A combination of differential iso-conversion method,compensation parameter method,and nonlinear fitting evaluation were also used to analyze thermal kinetics and mechanism of ADCA decomposition.The results show that when conversion rate α increases,the activation energies of ADCA's first and main decomposition peaks fall.The amount of heat released during decomposition varies between 182.46 and 231.16 J·g^(-1).The proposed kinetic equation is based on the Avrami-Erofeev model,which is consistent with the decomposition progress.Applying the Frank-Kamenetskii model,a calculated self-accelerating decomposition temperature of 287.0℃is obtained.

Machine tool selection based on fuzzy evaluation and optimization of cutting parameters

The paper analyzes the factors influencing machine tool selection. By using fuzzy mathematics theory, we establish a theorietical model for optimal machine tool selection considering geometric features, clamping size, machining range, machining precision and surface roughness. By means of fuzzy comprehensive evaluation method, the membership degree of machine tool selection and the largest comprehensive evaluation index are determined. Then the reasonably automatic selection of machine tool is realized in the generative computer aided process planning （CAPP） system. Finally, the finite element model based on ABAQUS is established and the cutting process of machine tool is simulated. According to the theoretical and empirical cutting parameters and the curve of surface residual stress, the optimal cutting parameters can be determined.

Fuzzy Synthetic Evaluation of Water Quality of Naoli River Using Parameter Correlation Analysis

In order to improve the effectiveness of Fuzzy Synthetic Evaluation (FSE) models, a Parameter Correlation Analysis (PCA) was introduced into the FSE and a case study was carried out in the Naoli River in the Sanjiang Plain, Northeast China. The basic principle of the PCA is that the pairs of parameters which are highly correlated and linear with each other would contribute the same information to an assessment and one of them should be eliminated. The method of the PCA is that a correlation relationship among candidate parameters is examined before the FSE. If there is an apparent nonlinear or curvilinear relationship between two parameters, then both will be retained; if the correlation is significant (p<0.01), and the scatter plot suggests a linear relationship, then one of them will be deleted. However, which one will be deleted? For solving this problem, a sensitivity test was conducted and the higher sensitivity parameters remained. The results indicate that the original data should be preprocessed through the PCA for redundancy and variability. The study shows that introducing the PCA into the FSE can simplify the FSE calculation process greatly, while the results have not been changed much.

Synthetically quantitative evaluation function of characteristic parameters on CO_2 arc welding process

The statistical probability and their variation regularity of the measurable characteristic parameters in the CO 2 arc welding droplet short circuiting transfer process have been studied. The statistical analysis shows that the sensitivity of each characteristic parameter with regard to the variation of the short circuiting transfer process is different. The sensitivity of 4 kinds among these characteristic parameters is more intense than that of the short circuiting transfer frequency. In order to take account of the synthetic influence of these characteristic parameters, by means of the characteristic parameters synthetic value, a quantitative evaluation function is built up to describe and evaluate the short circuiting transfer process of CO 2 arc welding in real time. The testing shows that the evaluation function can give a suitable synthetic valuation for the short circuiting transfer process with a variety of welding variables.

Selection Method of Evaluation Indicators with Three-Parameter Interval Grey Number

The evaluation problem with three-parameter interval grey number (T-PIGN) widely exists in real world. To select effective evaluation indicators of the problem, this paper puts forward evaluation index system selection principle of T-PIGN based on distance entropy model, and gives out evaluation index system selection judgment criterion of T-PIGN. Furthermore, for the redundancy of evaluation index system with T-PIGN, a selection method of evaluation index system with T-PIGN is proposed. Finally, the applicability of the proposed method is verified by concrete examples.

Evaluation of Uncertainty of Earthquake Parameters for the Purpose of Seismic Zoning of Iran

An efficient procedure is used for explicit description and evaluation of uncertainty of earthquake parameters in the uniform catalog of earthquakes in Iran and neighboring regions.An inadequate number of local and regional seismographic stations,poor station distribution,and Inadequacy of velocity models have resulted in conspicuous uncertainty in different parameters of recorded events.In a comprehensive seismic hazard analysis such uncertainties should be considered.Uncertainty of magnitude and location of events are evaluated for three different time periods,namely,historical,early instrumental,and modern instrumental time periods,for which existing seismological information differ widely in quantity,quality,and type.It is concluded that an uncertainty of 0.2-0.3 units of magnitude and 10-15 km in epicenter determinations should be considered in the most favorable conditions.None of the hypocenters of earthquakes in Iran can be considered as reliable,unless supported by other information such as

Z-parameter Method for Damage Evaluation in HK40 Steel

A Z-parameter method is used to evaluate the damage process of HK40 austenitic steel. By using Z-parameter based on the Larson-Miller method, the nonlinear master curve of the log stress vs Larson-Miller parameter P can be expressed as： P=27.74-3.41gσ-0.032σ, and a family of curves parallel to the master curve can be written as： P=（27.74-Z）-3.41gσ-0.032σ, where Z represents the magnitude of the deviation from the master curve. According to the creep rupture data both from different segments of a serviced tube and from the same segment locations with different service time, the value of parameter Z has close relationship with the deterioration of creep rupture properties. The damage state of the samples is evaluated by monitoring the changes in natural frequency f and Young＇s modulus E, and the relationships between Z and the damage parameters are discussed.

Assessment on Evaluating Parameters of Rice Core Collections Constructed by Genotypic Values and Molecular Marker Information

Eleven evaluating parameters for rice core collection were assessed based on genotypic values and molecular marke＇ information. Monte Carlo simulation combined with mixed linear model was used to eliminate the interference from environment in order to draw more reliable results. The coincidence rate of range （CR） was the optimal parameter. Mean Simpson index （MD）, mean Shannon-Weaver index of genetic diversity （M1） and mean polymorphism information content （MPIC） were important evaluating parameters. The variable rate of coefficient of variation （VR） could act as an important reference parameter for evaluating the variation degree of core collection. Percentage of polymorphic loci （p） could be used as a determination parameter for the size of core collection. Mean difference percentage （MD） was a determination parameter for the reliability judgment of core collection. The effective evaluating parameters for core collection selected in the research could be used as criteria for sampling percentage in different plant germplasm populations.

A NOVEL PARAMETER FOR EVALUATING THE FATIGUE CRACK GROWTH RATE IN CARBON STEELS

A novel parameter is suggested for evaluating the fatigue crack growth rate in carbon steels. Fatigue crack propagation tests of an annealed 0.42% carbon steel were carried out under different conditions to investigate the relationship between this dominating parameter and the crack opening displacement (COD). A new equation of fatigue crack growth rate is formulated in terms of the suggested parameter. The physical meanings of the material parameters in this equation are explored experimentally. Considering the relation of crack growth and deformation properties, a simple and applicable method is proposed to evaluate the fatigue crack growth rate. It is also observed that the material parameters in the fatigue crack growth rate equation of carbon steels are related linearly to the material strength. The results are in a good agreement with experimental results.

A deep reinforcement learning(DRL)based approach for well-testing interpretation to evaluate reservoir parameters

Parameter inversions in oil/gas reservoirs based on well test interpretations are of great significance in oil/gas industry.Automatic well test interpretations based on artificial intelligence are the most promising to solve the problem of non-unique solution.In this work,a new deep reinforcement learning(DRL)based approach is proposed for automatic curve matching for well test interpretation,by using the double deep Q-network(DDQN).The DDQN algorithms are applied to train agents for automatic parameter tuning in three conventional well-testing models.In addition,to alleviate the dimensional disaster problem of parameter space,an asynchronous parameter adjustment strategy is used to train the agent.Finally,field applications are carried out by using the new DRL approaches.Results show that step number required for the DDQN to complete the curve matching is the least among,when comparing the naive deep Q-network(naive DQN)and deep Q-network(DQN).We also show that DDQN can improve the robustness of curve matching in comparison with supervised machine learning algorithms.Using DDQN algorithm to perform 100 curve matching tests on three traditional well test models,the results show that the mean relative error of the parameters is 7.58%for the homogeneous model,10.66%for the radial composite model,and 12.79%for the dual porosity model.In the actual field application,it is found that a good curve fitting can be obtained with only 30 steps of parameter adjustment.

Application of Bayesian Econometrics in Policy Evaluation

This study explores the application of Bayesian econometrics in policy evaluation through theoretical analysis. The research first reviews the theoretical foundations of Bayesian methods, including the concepts of Bayesian inference, prior distributions, and posterior distributions. Through systematic analysis, the study constructs a theoretical framework for applying Bayesian methods in policy evaluation. The research finds that Bayesian methods have multiple theoretical advantages in policy evaluation: Based on parameter uncertainty theory, Bayesian methods can better handle uncertainty in model parameters and provide more comprehensive estimates of policy effects;from the perspective of model selection theory, Bayesian model averaging can reduce model selection bias and enhance the robustness of evaluation results;according to causal inference theory, Bayesian causal inference methods provide new approaches for evaluating policy causal effects. The study also points out the complexities of applying Bayesian methods in policy evaluation, such as the selection of prior information and computational complexity. To address these complexities, the study proposes hybrid methods combining frequentist approaches and suggestions for developing computationally efficient algorithms. The research also discusses theoretical comparisons between Bayesian methods and other policy evaluation techniques, providing directions for future research.

Reservoir evaluation using petrophysics informed machine learning:A case study

relationships between logging data and reservoir parameters.We compare our method’s performances using two datasets and evaluate the influences of multi-task learning,model structure,transfer learning,and petrophysics informed machine learning(PIML).Our experiments demonstrate that PIML significantly enhances the performance of formation evaluation,and the structure of residual neural network is optimal for incorporating petrophysical constraints.Moreover,PIML is less sensitive to noise.These findings indicate that it is crucial to integrate data-driven machine learning with petrophysical mechanism for the application of artificial intelligence in oil and gas exploration.

Relationship among Parameters Evaluating Stress Corrosion Cracking

The threshold stress, σc, for sulfide stress corrosion cracking (SCC) of seven pipeline steels and five other steels, the critical stress, Sc, for seven pipeline steels and two drill rod steels with various strengths and the susceptibility to SCC, IRA or σf(SCC)/σf, for four pipeline steels, two drill rod steels and five other steels were measured. The results showed that there are no definite relationships among σc, Sc and IRA or σf(SCC)/σf.The threshold stress for hydrogen induced cracking (HlC) during charging with loading in the H2S04 solution, σc(H), decreased linearly with logarithm of the concentration of diffusible hydrogen c0, i.e., σc(H)=A-B Inco for four pipeline steels. σc(H) obtained with a special cathodic current ic, which was corresponding to the diffusible hydrogen concentration during immersing in the H2S solution, were consistent with /c for sulfide SCC for four pipeline steels. Therefore, σc for sulfide SCC can be measured using dynamically charging in the H2SO4 solution with the special cathodic current ic.

Lake Eutrophic Evaluation Based on Bee Immune Evolutionary Algorithm

In order to establish the lake eutrophic evaluation model for multiple indices,based on the gauge transformation,an index formula in the form of a logarithmic power function was proposed to design an eutrophic evaluation model for the ＂ normalized values＂ of multi-indexes.The parameters in the formula were also optimized by bee immune evolutionary algorithm（BEIEA）.The universal index formula was suitable to multiindices items for eutrophic evaluation.At the same time,the formula was applied to practical eutrophic evaluations in 10 regions of Dong Lake.The evaluation results were coincident with those obtained from the power function of weighted sums and also with actual conditions.It was shown that the bee immune evolutionary algorithm was suitable to the parameter optimization in the eutrophic evaluation model.

Quantitative evaluation methods for waterflooded layers of conglomerate reservoir based on well logging data

The rapid changing near source, multi-stream depositional environment of conglomerate reservoirs leads to severe heterogeneity, complex lithology and physical properties, and large changes of oil layer resistivity. Quantitative evaluation of water-flooded layers has become an important but difficult focus for secondary development of oilfields. In this paper, based on the analysis of current problems in quantitative evaluation of water-flooded layers, the Kexia Group conglomerate reservoir of the Sixth District in the Karamay Oilfield was studied. Eight types of conglomerate reservoir lithology were identified effectively by a data mining method combined with the data from sealed coring wells, and then a multi-parameter model for quantitative evaluation of the water-flooded layers of the main oil-bearing lithology was developed. Water production rate, oil saturation and oil productivity index were selected as the characteristic parameters for quantitative evaluation of water-flooded layers of conglomerate reservoirs. Finally, quantitative evaluation criteria and identification rules for water-flooded layers of main oil-bearing lithology formed by integration of the three characteristic parameters of water-flooded layer and undisturbed formation resistivity. This method has been used in evaluation of the water-flooded layers of a conglomerate reservoir in the Karamay Oilfield and achieved good results, improving the interpretation accuracy and compliance rate. It will provide technical support for avoiding perforation of high water-bearing layers and for adjustment of developmental programs.

Parameters Optimization of a Novel 5-DOF Gasbag Polishing Machine Tool

The research on the parameters optimization for gasbag polishing machine tools, mainly aims at a better kinematics performance and a design scheme. Serial structural arm is mostly employed in gasbag polishing machine tools at present, but it is disadvantaged by its complexity, big inertia, and so on. In the multi-objective parameters optimization, it is very difficult to select good parameters to achieve excellent performance of the mechanism. In this paper, a statistics parameters optimization method based on index atlases is presented for a novel 5-DOF gasbag polishing machine tool. In the position analyses, the structure and workspace for a novel 5-DOF gasbag polishing machine tool is developed, where the gasbag polishing machine tool is advantaged by its simple structure, lower inertia and bigger workspace. In the kinematics analyses, several kinematics performance evaluation indices of the machine tool are proposed and discussed, and the global kinematics performance evaluation atlases are given. In the parameters optimization process, considering the assembly technique, a design scheme of the 5-DOF gasbag polishing machine tool is given to own better kinematics performance based on the proposed statistics parameters optimization method, and the global linear isotropic performance index is 0.5, the global rotational isotropic performance index is 0.5, the global linear velocity transmission performance index is 1.012 3 m/s in the case of unit input matrix, the global rotational velocity transmission performance index is 0.102 7 rad/s in the case of unit input matrix, and the workspace volume is 1. The proposed research provides the basis for applications of the novel 5-DOF gasbag polishing machine tool, which can be applied to the modern industrial fields requiring machines with lower inertia, better kinematics transmission performance and better technological efficiency.

Evaluation of the mold-filling ability of alloy melt in squeeze casting

The mold-filling ability of alloy mclt in squceze casting process was evaluated by means of the maximum length of Archimedes spiral line. A theoretical evaluating model to predict the maximum filling length was built based on the flowing theory of the incompressible viscous fluid. It was proved by experiments and calculations that the mold-tilling pressure and velocity are prominent influencing factors on the mold-filling ability of alloy melt. The mold-filling ability increases with the increase of the mold-filling pressure and the decrease of the proper mold-filling velocity. Moreover, the pouring temperature relatively has less effect on the mold-filling ability under the experimental conditions. The maximum deviation of theoretical calculating values with experimental results is less than 15%. The model can quantitatively estimate the effect of every factor on the mold-filling ability.

Dynamical analysis and performance evaluation of a biped robot under multi-source random disturbances

During bipedal walking,it is critical to detect and adjust the robot postures by feedback control to maintain its normal state amidst multi-source random disturbances arising from some unavoidable uncertain factors.The radical basis function（RBF）neural network model of a five-link biped robot is established,and two certain disturbances and a randomly uncertain disturbance are then mixed with the optimal torques in the network model to study the performance of the biped robot by several evaluation indices and a specific Poincar′e map.In contrast with the simulations,the response varies as desired under optimal inputting while the output is fluctuating in the situation of disturbance driving.Simulation results from noise inputting also show that the dynamics of the robot is less sensitive to the disturbance of knee joint input of the swing leg than those of the other three joints,the response errors of the biped will be increasing with higher disturbance levels,and especially there are larger output fluctuations in the knee and hip joints of the swing leg.

Multi-parameter quantitative assessment of 3D geological models for complex fault-block oil reservoirs

In the field of 3 D geologic modeling, researchers often pay more attention to modeling methods and workflows, but neglect the quantitative evaluation of models. If the evaluation is narrowed to the same reservoir type, the comparability and practicality of quantitative assessment will be emerging. The evaluation system should include three parts: data verification, geological understanding and process check. Data verification mainly involves testing the accuracy of local prediction with actual data, and geological understanding is to examine whether the global estimation honors geological principles and prior insights. Process check is also indispensable to avoid occasionality. To this end, we produced a set of assessment criteria, taking complex fault-block sandstone oil reservoir as an example. To be specific, thirteen characteristic parameters were totally selected, setting weights according to their rated importance, formulating three-level evaluation standards in a centesimal system for each characteristic parameter, and obtaining the final assessment based on the cumulative score. The results indicate that such evaluation can not only access the quality of the model objectively and comprehensively, but also identify the aspects in need of improvement through the deduction items.

Mathematical basis,correlativity and selection of seismicity parameters

Using the analytic expression of seismicity parameters, the dependency and correlativity between the statisticalparameters and seismic frequency or seismic intensity are discussed. The statistical parameters are divided into twokinds. One kind is the regional seismicity parameter, 17 parameters are analyzed in this paper. The other kind isthe seismicity distribution parameter. They are the distribution parameters of time, space and magnitude. The existent base and rationality of distribution parameters depend on the application of distribution model. We analyzeand draw an analogy between the natural probability, Poisson, Weibull distributions and multi-f racial analyticformula in time, space and magnitude. And some examples are given in this paper. The P value and H value of aftershock sequence attenuation, the U value and F value of eanhquake swarm sequence and the entropy of information are discussed preliminarily. Another method about analyzing relationships among time-series curves aregiven. The resemblance relativity degree R,.,, and the relativity degree G,.,, of relative change slope can be usedas the determining values. At last, some preliminary ideas about sifting and using for seismicity parameters are advanced in this paper.