Gamma spectrum analysis for in situ automatic monitoring of radioactivity in seawater

The NaI(Tl) detector has been an important research topic and application in the field of in situ marine radioactive automatic monitoring because of its advantages of low power consumption, low cost, and good efficiency. However, its energy resolution is not high enough. This paper investigated an analytical method: spectrum de-noising, background correction based on the SNIP operator, peak search based on the top-hat transform and peak fitting using Gaussian distribution. Simulation and gamma spectra measured from seawater showed that the established energy spectrum analysis method presents satisfactory automatic analytical ability for identification and quantitatively analysis.

     

Application of SiO2–water nanofluid to enhance oil recovery

The present study aims to investigate effects of nanofluid flooding on EOR and also compares its performance with water flooding in field scale using the published experimental data provided from core-scale studies. The nanofluid is based on water including silica nanoparticles. The relative permeability curves of water, nanofluid and oil for a light crude oil core sample obtained in an experimental study are used in this numerical investigation. A 2D heterogeneous reservoir model is constructed using the permeability and porosity of the last layer of SPE-10 model. It has been shown that nanofluid flooding can substantially improve the oil recovery in comparison with the water flooding case. Afterward, the operational parameters of the 13 injection and production wells have been optimized in order to meet the maximum cumulative oil production. First, pattern search (PS) algorithm was implemented which has a good convergence speed, but with a high probability of trapping in local optimum points. Particle swarm optimization (PSO) approach has also been employed, which requires a large number of population (to approach the global optimum) with so many simulations. Accordingly, a hybrid PSO–PS algorithm with confined domain is proposed. The hybrid algorithm starts with PSO and depending on the distribution density of the values of each parameter, confines the searching domain and provides a proper initial guess to be used by PS. It is concluded that the hybrid PSO–PS method could obtain the optimal solution with a high convergence speed and reduced possibility of trapping in local optimums.

     

Strategies for Optimal Chemical Mechanical Polishing (CMP) Slurry Design

Abstract

Chemical mechanical polishing (CMP) has become the preferred route for achieving wafer‐level global planarization in microelectronics device manufacturing. However, the micro‐ to molecular‐level mechanisms that control its performance and optimization are not well understood. In CMP, complex slurry chemistries react with the first few atomic layers on the wafer surfaces forming a chemically modified film. This film is subsequently mechanically abraded by nanosized slurry particles to achieve local and global planarity for multi‐level metalization. For optimal CMP performance, high material removal rates with minimal surface defectivity are required. This can be achieved by controlling the extent of interparticle and particle–substrate interactions, which are facilitated through the manipulation of the slurry composition, solution chemistry, as well as operational parameters. Interparticle interactions must be engineered to maintain slurry stability to minimize the number and extent of surface defects during polishing while maintaining adequate removal rates. The fundamental considerations, which are necessary for the development of high performance CMP slurries, are discussed in this article through model silica CMP systems.     

A wavelength selection method based on random decision particle swarm optimization with attractor for near‐infrared spectral quantitative analysis

In this paper, we proposed a wavelength selection method based on random decision particle swarm optimization with attractor for near‐infrared (NIR) spectra quantitative analysis. The proposed method was incorporated with partial least square (PLS) to construct a prediction model. The proposed method chooses the current own optimal or the current global optimal to calculate the attractor. Then the particle updates its flight velocity by the attractor, and the particle state is updated by the random decision with the new velocity. Moreover, the root‐mean‐square error of cross‐validation is adopted as the fitness function for the proposed method. In order to demonstrate the usefulness of the proposed method, PLS with all wavelengths, uninformative variable elimination by PLS, elastic net, genetic algorithm combined with PLS, the discrete particle swarm optimization combined with PLS, the modified particle swarm optimization combined with PLS, the neighboring particle swarm optimization combined with PLS, and the proposed method are used for building the components quantitative analysis models of NIR spectral datasets, and the effectiveness of these models is compared. Two application studies are presented, which involve NIR data obtained from an experiment of meat content determination using NIR and a combustion procedure. Results verify that the proposed method has higher predictive ability for NIR spectral data and the number of selected wavelengths is less. The proposed method has faster convergence speed and could overcome the premature convergence problem. Furthermore, although improving the prediction precision may sacrifice the model complexity under a certain extent, the proposed method is overfitted slightly. Copyright © 2015 John Wiley & Sons, Ltd.     

Protein structure optimization using improved simulated annealing algorithm on a three-dimensional AB off-lattice model

This paper proposed an improved simulated annealing (ISA) algorithm for protein structure optimization based on a three-dimensional AB off-lattice model. In the algorithm, we provided a general formula used for producing initial solution, and designed a multivariable disturbance term, relating to the parameters of simulated annealing and a tuned constant, to generate neighborhood solution. To avoid missing optimal solution, storage operation was performed in searching process. We applied the algorithm to test artificial protein sequences from literature and constructed a benchmark dataset consisting of 10 real protein sequences from the Protein Data Bank (PDB). Otherwise, we generated C_α space-filling model to represent protein folding conformation. The results indicate our algorithm outperforms the five methods before in searching lower energies of artificial protein sequences. In the testing on real proteins, our method can achieve the energy conformations with C_α-RMSD less than 3.0 Å from the PDB structures. Moreover, C_α space-filling model may simulate dynamic change of protein folding conformation at atomic level.     

Life Cycle Assessment in Polymer Industry Toward the 21st Century

Abstract

Life cycle assessments to protect global environment and to control waste in the polymer industry are reviewed. The focus is on 1) environmentally sound technology, 2) energy conservation, and 3) recycling of industrial products. The competitiveness of a nation depends on how it balances economic development of sustainable growth and responsible care of the global environment in a borderless economy. It fully depends on the development of both environmentally sound manufacturing and effective recycling technologies for the products. Current environmental issues of the Japanese chemical industries as well as Toray's performance and new products development for environment business are reviewed: 1) Energy conservation and technology of the Japanese manufacturing industry, 2) Development of environmentally sound technology, 3) Effective products recycling technologies, and 4) Sustainable growth in the new chemical age. The future issues of life cycle assessment and environmental protection in manufacturing industries are discussed.     

Variable selection for QSAR by artificial ant colony systems

Derivation of quantitative structure-activity relationships (QSAR) usually involves computational models that relate a set of input variables describing the structural properties of the molecules for which the activity has been measured to the output variable representing activity. Many of the input variables may be correlated, and it is therefore often desirable to select an optimal subset of the input variables that results in the most predictive model. In this paper we describe an optimization technique for variable selection based on artificial ant colony systems. The algorithm is inspired by the behavior of real ants, which are able to find the shortest path between a food source and their nest using deposits of pheromone as a communication agent. The underlying basic self-organizing principle is exploited for the construction of parsimonious QSAR models based on neural networks for several classical QSAR data sets.     

Optimizing control of simulated moving bed separations of mixtures subject to the generalized Langmuir isotherm

Simulated moving bed (SMB) is a cost-efficient separation technique that offers high productivity and low solvent consumption. SMB has gained importance in the pharmaceutical and fine chemical industry to perform complex separation tasks. However, an open and challenging problem is the optimal, robust operation of the SMB process. We have developed a control scheme that integrates the optimization and control of the SMB unit. A significant feature of the controller is that only minimal information of the system has to be provided, i.e. the linear adsorption behavior of the mixture to be separated and the average void fraction of the columns. Therefore, a full characterization of the adsorption behavior of the mixture and the columns is no longer required. In this ‘cycle to cycle’ control scheme, the measurements, optimization and control actions are performed once in every cycle. This paper presents simulation results of the control scheme applied to the separation of binary mixtures characterized by generalized Langmuir isotherms. The results are presented and analyzed in the frame of the triangle theory that has been recently extended to encompass these types of isotherms. Besides, online optimum performance of the SMB unit is compared with off-line optimization carried out using genetic algorithm. The results show that the controller fulfills the product and process specifications while operating the SMB unit optimally, regardless of the different types of Langmuir isotherms that the systems exhibit.     

Fate of CS2 in viscose process: a chemistry perspective

Cellulose dissolution in the viscose process has been facilitated through derivatization by carbon disulphide (CS₂) at xanthation stage by converting alkali cellulose (AC) to cellulose xanthate (CX). CX formation has been always accompanied with sulphur based byproducts formation as dictated by the mechanism published in earlier study (Gondhalekar et al. (Cellulose 26 3 1595–1604, 2019)). The sulphur byproducts formed during viscose synthesis are sodium sulphide (Na₂S), sodium trithiocarbonate (Na₂CS₃: TTC) and other minor sulphur compounds. These byproducts continue to form during ripening process as dictated by time and temperature coupled with concentration of free caustic and CS₂ present in the system. These byproducts get converted into sodium sulphate (Na₂SO₄), hydrogen sulphide (H₂S), CS₂ and other sulphurous compounds during spinning. Overall, uncontrolled ripening without parametric optimization adversely impacts raw material (RM) consumption and creates sustainability challenges. Overall optimization based on viscose process fundamental insights presented in this study will effectively help in achieving operational excellence by reducing rate of undesired reactions to improve RM specific consumption and will compliment overall sustainability efforts in viscose industry.

     

基于人工蜂群优化的MR图像分割算法研究

针对传统图像阈值分割算法在MR图像分割时存在的易受采集图像灰度不均、医学图像易受噪声干扰,因而难以得到准确分割阈值的问题,本文将人工蜂群算法与二维OSTU阈值分割算法相结合,提出一种基于人工蜂群优化的MR图像分割算法。使用医学图像的离散度矩阵的迹作为人工蜂群优化的目标函数,得到二维OSTU的最佳分割阈值;根据得到的最佳阈值,对图像采用二维OSTU分割的方法进行分割。实验结果证明,对于医学MR图像,本文所提出的算法具有精度高和鲁棒性强的特点,能够得到精确的分割后图像。     

Mathematical modeling of cellulose conversion to ethanol by the simultaneous saccharification and fermentation process

Ethanol, a promising alternative fuel, can be produced by the simultaneous saccharification and fermentation (SSF) of lignocellulosic biomass, which combines the enzymatic hydrolysis of cellulose to glucose and the fermentation of glucose to ethanol by yeast in a single step.

A mathematical model that depicts the kinetics of SSF has been developed based on considerations of the quality of the substrate and enzyme, and the substrate-enzyme-microorganism interactions. Critical experimentation has been performed in conjunction with multiresponse nonlinear regression analysis to determine key model parameters regarding cell growth and ethanol production. The model will be used for rational SSF optimization and scale-up.

     

遗传算法用于液相分离条件的优化

 在参考传统的优化方法的基础上 ,将遗传算法用于等度反相多元流动相分离条件的优化。详细介绍了基于线杂交和面变异的遗传算法的原理及其用于液相分离条件优化的过程。将此法用于 9种小肽的反相离子对分离条件的优化 ,经过 3次寻优操作 ,确定了最佳分离条件 ,实验保留值与预测值的平均相对偏差为0 75 % ,优化结果比较理想。     

Vacancy-Rich Ni(OH)2 Drives the Electrooxidation of Amino C−N Bonds to Nitrile C≡N Bonds

Electrochemical synthesis based on electrons as reagents provides a broad prospect for commodity chemical manufacturing. A direct one-step route for the electrooxidation of amino C−N bonds to nitrile C≡N bonds offers an alternative pathway for nitrile production. However, this route has not been fully explored with respect to either the chemical bond reforming process or the performance optimization. Proposed here is a model of vacancy-rich Ni(OH)₂ atomic layers for studying the performance relationship with respect to structure. Theoretical calculations show the vacancy-induced local electropositive sites chemisorb the N atom with a lone pair of electrons and then attack the corresponding N(sp³)−H, thus accelerating amino C−N bond activation for dehydrogenation directly into the C≡N bond. Vacancy-rich nanosheets exhibit up to 96.5 % propionitrile selectivity at a moderate potential of 1.38 V. These findings can lead to a new pathway for facilitating catalytic reactions in the chemicals industry.     

Optimization strategy based on genetic algorithms and neural networks applied to a polymerization process

The article presents a simple and general methodology, especially destined to the optimization of complex, strongly nonlinear systems, for which no extensive knowledge or precise models are available. The optimization problem is solved by means of a simple genetic algorithm, and the results are interpreted both from the mathematical point of view (the minimization of the objective function) and technological (the estimation of the achievement of individual objectives in multiobjective optimization). The use of a scalar objective function is supported by the fact that the genetic algorithm also computes the weights attached to the individual objectives along with the optimal values of the decision variables. The optimization strategy is accomplished in three stages: (1) the design and training of the neural model by a new method based on a genetic algorithm where information about the network is coded into the chromosomes; (2) the actual optimization based on genetic algorithms, which implies testing different values for parameters and different variants of the algorithm, computing the weights of the individual objectives and determining the optimal values for the decision variables; (3) the user's decision, who chooses a solution based on technological criteria. © 2007 Wiley Periodicals, Inc. Int J Quantum Chem, 2008     

Vacancy‐Rich Ni(OH)2 Drives the Electrooxidation of Amino C−N Bonds to Nitrile C≡N Bonds

Electrochemical synthesis based on electrons as reagents provides a broad prospect for commodity chemical manufacturing. A direct one‐step route for the electrooxidation of amino C?N bonds to nitrile C≡N bonds offers an alternative pathway for nitrile production. However, this route has not been fully explored with respect to either the chemical bond reforming process or the performance optimization. Proposed here is a model of vacancy‐rich Ni(OH)₂ atomic layers for studying the performance relationship with respect to structure. Theoretical calculations show the vacancy‐induced local electropositive sites chemisorb the N atom with a lone pair of electrons and then attack the corresponding N(sp³)?H, thus accelerating amino C?N bond activation for dehydrogenation directly into the C≡N bond. Vacancy‐rich nanosheets exhibit up to 96.5 % propionitrile selectivity at a moderate potential of 1.38 V. These findings can lead to a new pathway for facilitating catalytic reactions in the chemicals industry.     

Chemometric Approach for Simultaneous Optimization of Resolution and Analysis Time in CCC

The chemometric approach combining Box–Behnken response surface model and Derringer’s desirability function was applied for simultaneous optimization of resolution and analysis time in countercurrent chromatography (CCC). The mergence of the two parameters was accomplished using the Derringer’s desirability function with subsequent optimization by a Box–Behnken response surface design. The developed model was checked by statistical analysis. By implementing the optimal flow rate, rotation speed and temperature predicted by the validated model, enhanced resolution between two similar analytes (phenol and resorcinol) was achieved in a reasonable time. The analyses and results obtained in this paper will benefit to improve the efficiency of CCC separation.

     

Theoretical limit of energy consumption for removal of organic contaminants in U.S. EPA Priority Pollutant List by NRTL,UNIQUAC and Wilson models

This paper quantifies the theoretical limit of energy consumption for the removal of 20 representative organic contaminants (9 chlorinated alkyl hydrocarbons, 3 chlorinated alkenes, 3 brominated methanes, 5 aromatic hydrocarbons and their derivatives) in the United States Environmental Protection Agency (U.S. EPA) Priority Pollutant List by physical procedures. The general rules of the theoretical limit of energy consumption with different initial concentrations at 298.15 K and 1.01325 × 10⁵ Pa by NRTL, UNIQUAC and Wilson models are obtained from the thermodynamic analysis with our previously established method based on the thermodynamic first and second law. The results show that the waste treatment process needs a high energy consumption and the theoretical limit of energy consumption for organic contaminant removal increases with decreasing initial concentrations in aqueous solutions. The theoretical limit of energy consumption decreases with the more C–H bonds being replaced by C–Cl or C–Br bonds in chlorinated methanes, ethanes, ethenes or brominated methanes except for 1,1,2,2-tetrachloroethane, and the energy consumption for the removal of chlorinated methanes is higher than that of chlorinated ethanes with the same C–H bonds being replaced by C–Cl bonds. For the removal of chlorinated ethenes, brominated methanes and benzene and its derivatives studied, the energy consumption has corresponding relationship with solubility and the energy consumption is higher for the removal of organics with higher solubility.     

Multiobjective optimization of a free radical bulk polymerization reactor using genetic algorithm

A multiobjective optimization technique has been developed for free radical bulk polymerization reactors using genetic algorithm. The polymerization of methyl methacrylate in a batch reactor has been studied as an example. The two objective functions which are minimized are the total reaction time and the polydispersity index of the polymer product. Simultaneously, end‐point constraints are incorporated to attain desired values of the monomer conversion (x_m) and the number average chain length (μ_n). A nondominated sorting genetic algorithm (NSGA) has been adapted to obtain the optimal control variable (temperature) history. It has been shown that the optimal solution converges to a unique point and no Pareto set is obtained. It has been observed that the optimal solution obtained using the NSGA for multiobjective function optimization compares very well with the solution obtained using the simple genetic algorithm (SGA) for a single objective function optimization problem, in which only the total reaction time is minimized and the two end‐point constraints on x_m and μ_n are satisfied.     

First Step Towards Planning of Syntheses in Solid-State Chemistry: Determination of Promising Structure Candidates by Global Optimization

A method is presented here that allows, in principle, the prediction of the existence and structure of (meta)stable solid compounds. It is based on a set of adjustable modules that are applied to the study of the energy function of the chemical system of interest. The main elements are a set of routines for global optimization and local minimization, as well as algorithms for the investigation of the phase space structure near local minima of the potential energy, and the analysis and characterization of the structure candidates. The current implementation focuses on ionic compounds, for which empirical potentials are used for the evaluation of the energy function in the first stage, and a Hartree–Fock algorithm for refinements. The global optimization is performed with a stochastic simulated annealing algorithm, and the local minimization employs stochastic quenches and gradient methods. The neighborhoods of the local minima are studied with the threshold algorithm. The results of this approach are illustrated with a number of examples: compounds of binary noble gases, and binary and ternary ionic compounds. These include several substances that have not been synthesized yet, but should stand a fair chance of being kinetically stable, for example further alkali metal nitrides besides Li₃N, as well as Ca₃SiBr₂ or SrTi₂O₅.     

A comprehensive three-dimensional modeling of an internal reforming planar solid oxide fuel cell with different interconnect designs

Development of low-emission or zero-emission power generation systems is one of the most important subjects humanity is dealing with. Among different under development technologies and energy systems, a solid oxide fuel cell (SOFC) is an efficient and low-emission energy conversion device that is passing its research and development career. The current study aims to investigate a hydrocarbon fueled anode-supported planar-type SOFC due to simpler geometry, higher power density, and low overpotentials. In this study, electric performance of a SOFC with different interconnect designs under different operating conditions, such as operating voltage, channel inlet temperature, pre-reforming rate of methane, and inlet fuel and air velocity, has been investigated by use of a three-dimensional model considering complicated systems of equations: species mass conservation, first law of thermodynamics, conservation of momentum, and non-linear electrochemical models including multi-specious diffusion. It has been concluded that at a given voltage, inlet temperature, inlet air and fuel velocity, and pre-reforming rate, wider gas channels help to more uniform distribution and better penetration of reactant gases. Therefore, considering low-concentration polarization as an object, narrow ribs are preferred over wide ribs. By increasing the rate of the electrochemical reaction, the current and power density and subsequently the temperature difference increase but the fuel consumption in all cases has almost a decreasing trend. Also, it has been found that increasing inlet air velocity has little effect on current and power density but because of more cooling effect, it reduces the temperature difference and fuel consumption coefficient. On the other hand, increasing the inlet temperature has no meaningful effect on the temperature difference along the channels.