Solubility of red palm oil in supercritical carbon dioxide: Measurement and modelling

The solubility of red palm oil(RPO) in supercritical carbon dioxide(scCO_2) was determined using a dynamic method at 8.5–25 MPa and, 313.15–333.15 K and at a fixed scCO_2 flow rate of 2.9 g·min~(-1) using a full factorial design. The solubility was determined under low pressures and temperatures as a preliminary study for RPO particle formation using scCO_2. The solubility of RPO was 0.5–11.3 mg·(g CO_2)~(-1) and was significantly affected by the pressure and temperature. RPO solubility increased with pressure and decreased with temperature. The Adachi–Lu model showed the best-fit for RPO solubility data with an average relative deviation of 14% with a high coefficient of determination, R~2 of 0.9667, whereas the Peng–Robinson equation of state thermodynamic model recorded deviations of 17%–30%.     

Pore network model of deactivation of immobilized glucose isomerase in packed-bed reactors. Part III: Multiscale modelling

A widely used method for converting glucose to fructose is by enzymatic isomerization. This process, which uses immobilized glucose isomerase, takes place in a packed-bed reactor that consists of microporous particles with a range of pore sizes, characterized by a pore size distribution. The micropores are also interconnected, giving rise to a three-dimensional (3D) network of pores with distributed sizes and connectivities. The particles themselves generate a 3D pore network at the reactor level with distributed pore sizes, but with a fixed connectivity. In this paper, Part III of a series, we develop a multiscale modelling approach to this problem, beginning with the relevant phenomena at the scale of the micropores, and integrating them into the particle and reactor length scales. As the efficiency of the process is significantly affected by deactivation of the microporous particles, we take this phenomenon into account at all the relevant length scales. We use a real random packing of particles, originally constructed by Finney (Proc. R. Soc. Lond. A, 319 (1970) 479), and map its pore space onto an equivalent 3D Voronoi network in which the pores are represented by the edges of the Voronoi polyhedra. The flow field in the Voronoi network is determined, and the convection-diffusion-reaction equation is then solved in the Voronoi network, taking into account the gradual deactivation of the microporous particles. Several plausible mechanisms of deactivation of the microporous particles are considered, and their effect on the performance of the reactor is investigated. Good agreement is found between the results of the computer simulations and the relevant experimental data.     

A network theory for the structured modelling of chemical processes

A structuring methodology for dynamic models of chemical engineering processes is presented. The main ideas of the methodology were outlined in a previous publication for the class of well-mixed systems. In this contribution, the methodology is extended to spatially distributed systems and to particulate processes. Furthermore, the structuring principle is used to make a conceptual link between the macroscopic world of process simulation and the microscopic world of molecular simulation. It is shown that a uniform structuring principle can be applied to the modularisation of most classes of chemical engineering models. The structuring principle can be used as a theoretical framework for the implementation of modular families of chemical engineering models in modern computer aided modelling tools.     

Artificial neural network modelling of supercritical fluid CO2 extraction of polyunsaturated fatty acids from common carp (Cyprinus carpio L.) viscera

Common carp viscera, obtained from Tikveš Lake in Macedonia, was investigated as a possible source of polyunsaturated (PUFA) fatty acids. Supercritical fluid CO₂ extraction (SFE-CO₂) was employed for extraction of investigated bioactive components. The GC-FID analysis on the total extract obtained by supercritical fluid CO₂ extraction confirmed the assumption of presence of these bioactive components. A three layer artificial neural network was created for prediction and modelling of the extraction yield of polyunsaturated fatty acids from lyophilized viscera matrixes. Operating values of pressure, temperature, mass flow of CO₂ and extraction time were defined as input vectors to the ANN where PUFA extraction yield was considered as an output vector. Created ANN model provided adequate fitting of experimental data, with a correlation coefficient of 0.9968 for the entire data set. RSM-3D method was employed for mathematical modelling of the ANN output values as a function of operating variables and their interactions.     

Water network design with stochastic optimization approach

Water network (called also water allocation) problem has been addressed in more than 200 papers to date – see recent reviews by Je?owski (2010) and Foo (2009). Though various solution methods have been developed they all have some limitations. This paper addresses water usage network with regeneration processes. Multiple contaminants and two types of water using processes are taken into regard. Simultaneous one stage optimization method was developed to synthesize the network. In order to solve complex MINLP formulation we propose to apply meta-heuristic optimization – adaptive random search method.The paper contains detailed solution algorithm. Several examples with specific features are solved to show efficiency and flexibility of the approach.     

基于随机配置网络的机载电子吊舱多工况热模型

机载电子吊舱是搭载多功能机载电子设备的主要平台,能够显著提升战机性能。然而,不断增加的电子设备功率和高度低气压力飞行环境会造成吊舱内恶劣的热环境,严重影响电子设备的可靠性。因此,十分有必要建立准确的电子吊舱热模型,用于预测不同飞行工况下的电子设备热响应。综合热网络分析思想和随机网络算法思想,提出一种基于随机配置网络的热模型建立方法,并通过采用冲压空气冷却系统的电子吊舱实验数据加以验证。为了建立准确的舱内温度响应模型,通过传热机制将高温贮存、高温工作、低温贮存、低温故障和低温工作工况下的实验数据分为3组并分别用于建立设备贮存热模型、设备工作热模型和综合热模型,利用热网络分析获取随机配置网络的有效输入,采用四折交叉验证和灰度图分析综合确定了3个热模型的超参数。建模结果表明：3个热模型的范围序列可统一为[1～40],最大隐含层节点数可分别设为6、9、11,设备温度拟合效果较好,仅在边界条件约束下进行多工况全过程的电子设备温度预测,预测误差在3.512℃内。总体看来,该热建模方法从数据挖掘的角度较为简单、准确、快速地描述了电子设备热关系,可用于开展预期飞行环境下的机载电子吊舱温度预测,用于评估热管理系统的性能。     

基于随机规划的炼厂氢网络改造设计

氢气是炼油企业重要的战略资源。越来越多的炼油企业开始改造其氢网络以应对日趋严格的环保标准和产品质量。炼厂的氢气产耗量受原油供应和产品需求的变化而随机波动,这使得传统的确定性模型无法有效描述氢网络设计问题。本文针对氢网络的改造设计问题,建立了同时考虑设计和调度问题的集成优化模型。采用基于场景的二阶段随机规划方法表达氢网络运行中的不确定因素,通过将决策变量分为一阶和二阶决策变量,优化计算使第一阶段决策成本与第二阶段决策的期望成本之和最小。算例表明了本文所提方法的有效性和与传统的确定性方法相比的优越性。     

Radiochemical ageing of an amine cured epoxy network. Part II: kinetic modelling

The radiochemical ageing (doses up to 70 MGy at a temperature of 120 °C in air) of an aromatic rich epoxide-amine stoichiometric network was studied by DMA (determination of the glass transition temperature), NMR (consumption of aliphatic CH groups) and ESR (radical decay). The number of chain scissions on elastically active chains is derived from T_g decreasing using the Di Marzio's theory. Then, a two steps mechanistic scheme is proposed, in which chain scissions occur in the initiation step but radicals partly recombine in the termination step. A kinetic model is derived from this scheme and its elementary rate constants are determined from NMR (initiation) and ESR (termination) measurements. It appears that the termination is diffusion controlled, which leaded us to introduce a time dependent termination rate constant using the Waite's theory. The model predictions are in acceptable agreement with experimental data in the range 0-70 MGy.     

Dynamic Modeling and Metabolic Flux Analysis for Optimized Production of Rhamnolipids

A comprehensive metabolic network based on the fundamental pathways representing the central metabolism of rhamnolipid by Pseudomonas aeruginosa is proposed and a dynamic model compatible with the underlying metabolic network is developed involving the macro-reactions derived from the elementary flux modes of the reaction network. The experimentally validated mathematical model is then coupled with a global optimization technique called differential evolution (DE) to optimize the medium composition as well as the extracellular and intracellular fluxes of the metabolic network. The analysis of the results shows the usefulness of the integrated approach involving the development of a dynamic model based on the metabolic network structure and model-based optimization of the medium composition and metabolic fluxes by an efficient evolutionary optimization technique to enhance the productivity of rhamnolipid.     

Design of flexible heat exchanger network for multi-period operation

Heat exchanger networks (HENs) increase heat recovery from industrial processes by matching hot and cold streams to exchange heat and reducing utility consumption. The design of HENs is a very complex task which generally involves mixed-integer non-linear programming (MINLP).This work evaluates and critically compares existing HEN design methods. It then presents a systematic methodology in the design of HENs under multiple periods of operation. The model presented in this work is a superstructure-based MINLP model which minimises the total annualised cost containing heat exchanger area cost and utility costs. The model is based on the superstructure by Yee and Grossmann [1990. Simultaneous optimisation models for heat integration—II, heat exchanger network synthesis. Computer & Chemical Engineering 14(10), 1165-1184], which was later formulated for multiple periods by Aaltola [2002. Simultaneous synthesis of flexible heat exchanger network. Applied Thermal Engineering 22, 907-918]. It includes a multi-period simultaneous MINLP model to design the HEN structure, and an NLP model to improve the solution and allow for non-isothermal mixing. Modifications to Aaltola's model include the use of maximum area per period in the area cost calculation of the MINLP objective function, and the removal of slack variables and weighed parameters from the existing NLP improvement model.The new model has been applied to one industrial case study, demonstrating that the new combined MINLP-NLP model can obtain better solutions by not relying on the average area assumption in the MINLP stage.     

Discrete modelling of the electrochemical performance of SOFC electrodes

The composite anode and cathode of solid oxide fuel cells (SOFC) are modelled as sintered mixtures of electrolyte and electrocatalyst particles. A particle packing is first created numerically by the discrete element method (DEM) from a loose packing of 40 000 spherical, monosized, homogeneously mixed, and randomly positioned particles. Once the microstructure is sintered numerically, the effective electrode conductivity is determined by discretization of the particle packing into a resistance network. Each particle contact is characteristic of a bond resistance that depends on contact geometry and particle properties. The network, which typically consists of 120 000 bond resistances in total, is solved using Kirchhoff's current law. Distributions of local current densities and particle potentials are then performed. We investigate how electrode performance depends on parameters such as electrode composition, thickness, density and intrinsic material conductivities that are temperature dependent. The simulations show that the best electrode performance is obtained for compositions close to the percolation threshold of the electronic conductor. Depending on particle conductivities, the electrode performance is a function of its thickness. Additionally, DEM simulations generate useful microstructural information such as: coordination numbers, triple phase boundary length and percolation thresholds.     

Towards multiscale modelling in product engineering

A concept of multiscale modelling of product manufacturing based on integration of three modelling methods currently applied at different scales of length and time: process system modelling, computational fluid dynamics and computational chemistry was presented. Major features of the three key types of modelling in the chemical and process industries were briefly described. The first applications and mutual benefits of joint use of two of the three approaches were presented along with the perspectives for the full integration of all three methods. The crucial role of a universal interface, such as the CAPE-OPEN standard, was emphasized.     

自增长混合神经网络及其在燃料电池建模中的应用

为了提高非线性辨识的精度, 提出了一种基于混合算子的自增长混合神经网络。该神经网络通过自增长的混合隐含层结构, 包括加算子和乘算子, 形成神经元个数少、结果精确、增长快速的网络。论文在级联神经网络的结构基础上, 提出GQPSOI算法来引导神经网络的结构自增长以及权值更新。通过对燃料电池的建模与比较分析, 证明了方法的有效性和良好的应用前景。     

Computer modelling of the combined effects of plant conditions and coal quality on burnout in utility furnaces

The aim of this paper is to describe the latest steps in the development of a computer model to predict the combined effects of plant conditions and coal quality on burnout. The work was conducted as part of RWE’s contribution to the recent ECSC project ‘Development of a carbon-in-ash notification system (CARNO)’. A burnout predictor code has been developed and validated; it includes both coal and plant effects and includes a burnout model based closely on CBK8. The agreement between predicted c-in-ash and plant data is encouraging, but further improvements are still desirable. The predictions obtained from the burnout predictor show that the calculated sensitivities to changes in plant condition can be very dependent on state of plant.     

Mathematical modelling of lignin pyrolysis

Seven lignins from different sources were pyrolysed (i) isothermally in vacuum over the temperature range 300–1300 °C and (ii) at a constant heating rate of 30 °C min^?1 and a pressure of 0.1 MPa over the temperature range 150–900 °C. The mass fraction of each product—char, tar and gas species—and the elemental composition of the char and the tar were determined for the flash pyrolysis experiments. The evolution rates of the gas species and the tar versus the dynamic temperature of pyrolysis were determined for the constant heating rate pyrolysis experiments. Although the amount of each product species varied from lignin to lignin, the evolution rates were insensitive to the lignin source and the extraction process. To model the data, modifications were made to a recently developed model of coal pyrolysis. The model proved to be successful in simulating both the data from vacuum flash pyrolysis and constant heating rate pyrolysis of Iotech lignin.     

Review of flash sintering: materials,mechanisms and modelling

Flash sintering (FS) is an energy efficient sintering technique involving electrical Joule heating, which allows very rapid densification (<60?s) of particulate materials. Since the first publication on flash-sintered zirconia (3YSZ) in 2010, it has been intensively researched and applied to a wide range of materials. Going back more than a century ago, we have found a close similarity between FS of oxides and Nernst glowers developed in 1897. This review provides a comprehensive overview of FS and is based on a literature survey consisting of 88 papers and seven patents. It correlates processing parameters (i.e. electric field magnitude, current density, waveforms (AC, DC) and frequency, furnace temperature, electrode materials/configuration, externally applied pressure and sintering atmosphere) with microstructures and densification mechanisms. Theorised mechanisms driving the rapid densification are substantiated by modelling work, advanced in situ analysis techniques and by established theories applied to electric current assisted/activated sintering techniques. The possibility of applying FS to a wider range of materials and its implementation in industrial scale processes are discussed.