The vapor-liquid equilibrium data of four binary systems (acetic acid +p-xylene, methyl acetate +n-propyl acetate, n-propyl acetate +p-xylene and methyl acetate +p-xylene) are measured at 101.33 kPa with Ellis equilib...
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The vapor-liquid equilibrium data of four binary systems (acetic acid +p-xylene, methyl acetate +n-propyl acetate, n-propyl acetate +p-xylene and methyl acetate +p-xylene) are measured at 101.33 kPa with Ellis equilibrium still, and then both the NRTL and UNIQUAC models are used in combination with the HOC model for correlating and estimating the vapor-liquid equilibrium of these four binary systems. The estimated binary VLE results using correlated parameters agree well with the measured data except the methyl acetate +p-xylene system which easily causes bumping and liquid rushing out of the sampling tap due to their dramatically different boiling points. The correlation results by NRTL and UNIQUAC models have little difference on the average absolute deviations of temperature and composition of vapor phase, and the results by NRTL model are slightly better than those by UNIQUAC model except for the methyl acetate +n-propyl acetate system, for which the latter gives more accurate correlations.
This paper investigates the uniformly ultimate boundedness (UUB) of an identifier-based adaptive dynamic programming (ADP) algorithm proposed in [7]. It is demonstrated that the estimation errors of weights in both cr...
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This paper investigates the uniformly ultimate boundedness (UUB) of an identifier-based adaptive dynamic programming (ADP) algorithm proposed in [7]. It is demonstrated that the estimation errors of weights in both critic and action networks are UUB during iteration learning. Moreover, a selection method on learning rates is also given.
Metal-free heteroatom doped nanocarbons are promising alternatives to the metal-based materials in catalytic ozonation for destruction of aqueous organic contaminants. In this study, N, S co-doped hollow carbon micros...
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Metal-free heteroatom doped nanocarbons are promising alternatives to the metal-based materials in catalytic ozonation for destruction of aqueous organic contaminants. In this study, N, S co-doped hollow carbon microspheres (NSCs) were synthesized from the polymerization products during persulfate wet air oxidation of benzothiazole. The contents of doped N and S as well as the structural stability were maneuvered by adjusting the subsequent N_(2)-annealing temperature. Compared with the prevailing single-walled carbon nanotubes, the N_(2)-annealed NSCs demonstrated a higher catalytic ozonation activity for benzimidazole degradation. According to the quantitative structure-activity relationship (QSAR) analysis, the synergistic effect between the graphitic N and the thiophene-S which redistributed the charge distribution of the carbon basal plane contributed to the activity enhancement of the N_(2)-annealed NSCs. Additionally, the hollow structure within the microspheres served as the microreactor to boost the mass transfer and reaction kinetics via the nanoconfinement effects. Quenching and electron paramagnetic resonance (EPR) tests revealed that benzimidazole degradation was dominated by the produced singlet oxygen (^(1)O_(2)) species, while hydroxyl radicals (^(·)OH) were also generated and participated. This study puts forward a novel strategy for synthesis of heteroatom-doped nanocarbons and sheds a light on the relationship between the active sites on the doped nanocarbons and the catalytic performance.
As a new neural network model,extreme learning machine(ELM)has a good learning rate and generalization ***,ELM with a single hidden layer structure often fails to achieve good results when faced with large-scale multi...
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As a new neural network model,extreme learning machine(ELM)has a good learning rate and generalization ***,ELM with a single hidden layer structure often fails to achieve good results when faced with large-scale multi-featured *** resolve this problem,we propose a multi-layer framework for the ELM learning algorithm to improve the model’s generalization ***,noises or abnormal points often exist in practical applications,and they result in the inability to obtain clean training *** generalization ability of the original ELM decreases under such *** address this issue,we add model bias and variance to the loss function so that the model gains the ability to minimize model bias and model variance,thus reducing the influence of noise signals.A new robust multi-layer algorithm called ML-RELM is proposed to enhance outlier robustness in complex *** results show that the method has high generalization ability and strong robustness to noise.
Considering that the performance of a genetic algorithm （GA） is affected by many factors and their rela-tionships are complex and hard to be described,a novel fuzzy-based adaptive genetic algorithm （FAGA） combined...
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Considering that the performance of a genetic algorithm （GA） is affected by many factors and their rela-tionships are complex and hard to be described,a novel fuzzy-based adaptive genetic algorithm （FAGA） combined a new artificial immune system with fuzzy system theory is proposed due to the fact fuzzy theory can describe high complex *** FAGA,immune theory is used to improve the performance of selection ***,crossover probability and mutation probability are adjusted dynamically by fuzzy inferences,which are developed according to the heuristic fuzzy relationship between algorithm performances and control *** experi-ments show that FAGA can efficiently overcome shortcomings of GA,i.e.,premature and slow,and obtain better results than two typical fuzzy ***,FAGA was used for the parameters estimation of reaction kinetics model and the satisfactory result was obtained.
The solutions of dynamic optimization problems are usually very difficult due to their highly nonlinear and multidimensional nature. 13enetic algorithm （GA） has been proved to be a teasibte method when the gradient ...
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The solutions of dynamic optimization problems are usually very difficult due to their highly nonlinear and multidimensional nature. 13enetic algorithm （GA） has been proved to be a teasibte method when the gradient is difficult to calculate. Its advantage is that the control profiles at all time stages are optimized simultaneously, but its convergence is very slow in the later period of evolution and it is easily trapped in the local optimum. In this study, a hybrid improved genetic algorithm （HIGA） for solving dynamic optimization problems is proposed to overcome these defects. Simplex method （SM） is used to perform the local search in the neighborhood of the optimal solution. By using SM, the ideal searching direction of global optimal solution could be found as soon as possible and the convergence speed of the algorithm is improved. The hybrid algorithm presents some improvements, such as protecting the best individual, accepting immigrations, as well as employing adaptive crossover and Ganssian mutation operators. The efficiency of the proposed algorithm is demonstrated by solving several dynamic optimization problems. At last, HIGA is applied to the optimal production of secreted protein in a fed batch reactor and the optimal feed-rate found by HIGA is effective and relatively stable.
A Neural Network Model is developed for a Kraft digester which is a fundamental stage in the pulp production. A deterministic model is used to describe the main features of the process and will provide the data to ver...
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In this paper, an automatic arithmetic generation system which can be remotely controlled by a particular speaker is designed and implemented to improve children's interest in learning math and facilitate parents ...
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Anthropogenic carbon dioxide(CO_(2))emission from the combustion of fossil fuels aggravates the global greenhouse *** implementation of CO_(2)capture and transformation technologies have recently received great attent...
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Anthropogenic carbon dioxide(CO_(2))emission from the combustion of fossil fuels aggravates the global greenhouse *** implementation of CO_(2)capture and transformation technologies have recently received great attention for providing a pathway in dealing with global climate *** these technologies,electrochemical CO_(2)capture technology has attracted wide attention because of its environmental friendliness and flexible operating *** membranes(BPMs)are considered as one of the key components in electrochemical devices,especially for electrochemical CO_(2)reduction and electrodialysis *** create an alkaline environment for CO_(2)capture and a stable pH environment for electrocatalysis on a single *** key to CO_(2)capture in these devices is to understand the water dissociation mechanism occurring in BPMs,which could be used for optimizing the operating conditions for CO_(2)capture and *** this paper,the references and technologies of electrochemical CO_(2)capture based on BPMs are reviewed in detail,thus the challenges and opportunities are also discussed for the development of more efficient,sustainable and practical CO_(2)capture and transformation based on BPMs.
In the era of exponential growth of data availability,the architecture of systems has a trend toward high dimensionality,and directly exploiting holistic information for state inference is not always computationally *...
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In the era of exponential growth of data availability,the architecture of systems has a trend toward high dimensionality,and directly exploiting holistic information for state inference is not always computationally *** paper proposes a novel Bayesian filtering algorithm that considers algorithmic computational cost and estimation accuracy for high-dimensional linear *** high-dimensional state vector is divided into several blocks to save computation resources by avoiding the calculation of error covariance with immense *** that,two sequential states are estimated simultaneously by introducing an auxiliary variable in the new probability space,mitigating the performance degradation caused by state ***,the computational cost and error covariance of the proposed algorithm are analyzed analytically to show its distinct features compared with several existing *** results illustrate that the proposed Bayesian filtering can maintain a higher estimation accuracy with reasonable computational cost when applied to high-dimensional linear systems.
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