The use of neural networks for fitting complex kinetic data

In this paper the use of neural networks for fitting complex kinetic data is discussed. To assess the validity of the approach two different neural network architectures are compared with the traditional kinetic identification methods for two cases: the homogeneous esterification reaction between propionic anhydride and 2-butanol, catalysed by sulphuric acid, and the heterogeneous liquid-liquid toluene mononitration by mixed acid. A large set of experimental data obtained by adiabatic and heat flux calorimetry and by gas chromatography is used for the training of the neural networks. The results indicate that the neural network approach can be used to deal with the fitting of complex kinetic data to obtain an approximate reaction rate function in a limited amount of time, which can be used for design improvement or optimisation when, owing to small production levels or time constraints, it is not possible to develop a detailed kinetic analysis.     

Effect of electrode geometry and charge on the production of polymer microbeads by electrostatics

Experimental parameters which are critical for producing small diameter (i.e. 100-300 μm) polymer microbeads, using electrostatic droplet generation, were investigated with three types of electrodes; a parallel plate, a positively charged needle and a grounded needle with alginate as the polymer. Electrode spacing was a critical factor controlling microbead size, but only for the parallel plate set-up. While the applied potential affected droplet size in all three set-ups, the smallest droplet size was produced with the positively charged needle. In some experiments needle oscillation was observed resulting in even smaller microbeads (i.e. < 100 μm). Calculated microbead diameters agreed well with experimental values.     

Dynamic Modelling and Prediction of Cytotoxicity on Microelectronic cell Sensor Array

A real‐time cell electronic sensing (RT‐CES) system has been used for label‐free dynamic measurements of cell responses to toxicant. Cells are grown onto the surfaces of the microelectronic sensors. Changes in cell number expressed as cell index (CI) have been recorded on‐line as time series. The CI data are used for dynamic modelling or parameter estimation for cell cytotoxicity process. We consider two dynamic modelling approaches, namely data‐based system identification and first principle modelling. It is shown that data‐based system identification can provide a quick solution for the cytotoxicity dynamic models and is effective for short‐term predictions. It, however, can be poor for long‐term predictions, particularly if there is no output correction, i.e., when the model is used for simulation. In view of this, the first principle modelling approach by considering fundamental physical principles such as toxicant transport is explored. For long‐term prediction or simulation, the prediction performance for some of cytotoxicity process is dramatically improved using the models obtained from the latter approach. This happens only if the underlying mechanism is truly understood. Through several cytotoxicity modelling and validation studies, it is shown that the black box modelling and first principle modelling both should be considered in challenging modelling problems such as the cytotoxicity. Pros and cons of the two modelling approaches are discussed.     

A discrete model for non-wetting liquid flow from a point source in a packed bed under the influence of gas flow

A study of non-wetting flow in a packed bed under the influence of gas flow has been carried out. Departing from the usual continuum models, a discrete and deterministic model for liquid flow has been presented to model the liquid flow from single and multiple point sources. Liquid flow is modelled based on force balance approach considering gas drag, bed resistance and gravity forces. Gas flow is modelled using k-ε model for turbulent flow. An X-ray flow visualization technique, developed by our group, is used to study the liquid flow paths in the packed bed. Liquid flow path and velocity has been obtained for various liquid and gas flow rate. Flow paths obtained from the simulation results are in good agreement to those obtained from flow visualization procedure under various conditions. Also, liquid distribution at the bed bottom is reported and compared with the experimental results.     

Surface turbulence in a physical model of a steel thin slab continuous caster

In the thin slab continuous casting (TSCC) of steel, the issue of optimum fluid flow is very important due to higher casting speeds and has direct influence on the formation of solidified shells and the quality of final products. In the current work, a full-scale physical mod-eling of a thin slab caster on the basis of dimensionless Reynolds and Froude similarity criteria was constructed. The flow pattern in the fun-nel shaped mold with a new tetra-furcated submerged entry nozzle (SEN) was investigated. To ...     

长北气田山2段小层砂体三维建模

鄂尔多斯盆地长北气田山西组2段为一套海相辫状河三角洲沉积体系,为揭示不同时期小层砂体发育规模及空间分布,以高分辨率层序地层分析为基础,相控建模为指导思想,短期基准面旋回为地层单元,等时地层格架为约束条件,采用序贯指示数学方法对长北气田山2段4个短期层序的水上分流河道、水下分流河道及河口坝微相小层砂体的发育规模及空间分布进行了随机模拟.砂体展布的三维模拟结果表明,河道自北向南沿物源方向延展,其中SSC1～SSC4短期基准面上升时期,河道加宽,小层砂体沉积厚度加大,物性较好,有利储层发育;下降时期河道变窄,小层砂体沉积厚度减小,物性变差.小层砂体的三维模拟结果在该区勘探实践中得到了很好的验证.     

汽车随机振动的建模与仿真

以整车九自由度模型为对象,用有限元思想将汽车振动结构离散。通过单元位移变换得到单元刚度阵和阻尼阵。按照单元位移在结构位移列阵中的位置组装汽车振动数学模型的系数矩阵。经过强迫位移约束处理,得到汽车振动数学模型。用有限元的思想求取振动响应量,应用虚拟激励理论实现汽车振动响应仿真。结果表明:建模与仿真相结合为复杂汽车随机振动分析提供了可行的方法。     

刚度矩阵的数值模拟方法

刚度矩阵是将一个受力物体划分为n个单元,各单元刚度矩阵集成为结构总刚度矩阵,实现了从单元刚度矩阵形成总体刚度矩阵的过程。本文应用单元刚度矩阵的方法,从工程实例出发,解决实际工程中的平面应力问题。     

变速恒频风力发电机组的建模与仿真

通过机理建模法对变速恒频风力发电机组进行建模,建立了其风速、风轮气动、传动系统以及发电机的数学模型,在Matlab/Simulink环境中搭建了各个环节的模型,并针对低于额定风速时的转速控制和高于额定风速时的功率控制,采用PID控制方法对所构建的模型进行仿真控制,实现低风速和高风速时的控制目标,仿真结果验证了所构建模型的正确性和可行性.     

300t精炼钢包透气砖应用的数学物理模拟

针对300t精炼钢包,在LF设备电极位置与钢包透气砖所在位置的对应关系确定的基础上,通过水模、数模分别采用单吹、复吹时钢包内钢水流动特性和夹杂物去除的对比研究,探讨了透气砖结构性能的影响作用。结果表明,单吹时应使用抗渗透较好且能形成有利于夹杂物去除的气泡形态狭缝式透气砖（PBI）,吹气卷渣临界气量为1．45m^3／h,气量需稳定,混匀时间为82．44s,钢液渗透小,夹杂物去除率为62．86％,能够满足搅拌钢液起到成分和温度的快速均匀以及净化钢液的作用。