Yield stress distribution in injection‐moulded glassy polymers

A methodology for structural analysis simulations is presented that incorporates the distribution of mechanical properties along the geometrical dimensions of injection‐moulded amorphous polymer products. It is based on a previously developed modelling approach, where the thermomechanical history experienced during processing was used to determine the yield stress at the end of an injection‐moulding cycle. Comparison between experimental data and simulation results showed an excellent quantitative agreement, both for short‐term tensile tests as well as long‐term creep experiments over a range of strain rates, applied stresses, and testing temperatures. Changes in mould temperature and component wall thickness, which directly affect the cooling profiles and, hence, the mechanical properties, were well captured by the methodology presented. Furthermore, it turns out that the distribution of the yield stress along a tensile bar is one of the triggers for the onset of the (strong) localization generally observed in experiments. © 2015 Society of Chemical Industry     

Development and experimental validation of explicit dynamics simulation of composite structures using a stacked thick-shell methodology

The stacked thick-shell modelling approach is investigated in the frame of explicit dynamics FE method for the simulation of composite structures. The methodology is developed for static and dynamic loading conditions and demonstrated in the case of three-point bending of laminated strips. For the validation of the stacked thick-shell modelling approach, experimental testing using laminated short beam shear coupons of the AS4/8552 composite material system is performed and the interlaminar shear strength under impact loading is determined. The specimen dynamic tests were performed using a drop tower apparatus and a specially designed three-point loading fixture. In parallel, conventional three-dimensional solid models are also analysed for comparison purposes. Test results correlate well to the respective numerical predictions, demonstrating the accuracy of the stacked thick-shell approach and the efficiency it provides in interlaminar stresses prediction, which makes the proposed approach suitable for large-scale composite structures simulation, with emphasis in delamination damage propagation.     

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.     

Local dynamic partial least squares approaches for the modelling of batch processes

The application of multivariate statistical projection based techniques has been recognized as one approach to contributing to an increased understanding of process behaviour. The key methodologies have included multi‐way principal component analysis (PCA), multi‐way partial least squares (PLS) and batch observation level analysis. Batch processes typically exhibit nonlinear, time variant behaviour and these characteristics challenge the aforementioned techniques. To address these challenges, dynamic PLS has been proposed to capture the process dynamics. Likewise approaches to removing the process nonlinearities have included the removal of the mean trajectory and the application of nonlinear PLS. An alternative approach is described whereby the batch trajectories are sub‐divided into operating regions with a linear/linear dynamic model being fitted to each region. These individual models are spliced together to provide an overall nonlinear global model. Such a structure provides the potential for an alternative approach to batch process performance monitoring. In the paper a number of techniques are considered for developing the local model, including multi‐way PLS and dynamic multi‐way PLS. Utilising the most promising set of results from a simulation study of a batch process, the local model comprising individual linear dynamic PLS models was benchmarked against global nonlinear dynamic PLS using data from an industrial batch fermentation process. In conclusion the results for the local operating region techniques were comparable to the global model in terms of the residual sum of squares but for the global model structure was evident in the residuals. Consequently, the local modelling approach is statistically more robust.     

Multiscale modelling of mass transfer in gas jets and bubble plumes

The injection of high‐speed gas streams into liquids is common in many industrial applications, such as sparging in multiphase reactors and contacting in mass transfer devices. Modelling the fluid dynamics and associated heat and mass transfer processes in such a system is complex because it involves many governing scales and drastic changes in physical properties. In this study, one formulation of a multiscale computational fluid dynamics model is proposed to simulate the fluid dynamics and mass transfer in such systems. The model uses volume‐of‐fluid interface capturing in regions where high mesh resolution can be attained and the drift‐flux or mixture model approximation in regions where mesh resolution is too low to directly resolve interface dynamics. The model was developed to provide a tunable, automatic transition between the two modelling approaches for both fluid dynamics and mass transfer predictions. The approach was validated through a comparison with results from two published studies. In the first case, the implementation of the drift‐flux model was validated through the simulation of a dispersed gas bubble plume injected into a cylindrical tank. In the second case, the fluid dynamics and mass transfer predictions were compared to results from an experimental study involving the horizontal injection of air into a rectangular tank filled with water for the application of aeration. The results show that the modelling approach can provide a good prediction of the experimental data using only limited fitting of empirical parameters, making it applicable to a broad range of other applications.     

Subspace based model identification for missing data

This article addresses the problem of missing process data in data-driven dynamic modeling approaches. The key motivation is to avoid using imputation methods or deletion of key process information when identifying the model, and utilizing the rest of the information appropriately at the model building stage. To this end, a novel approach is developed that adapts nonlinear iterative partial least squares (NIPALS) algorithms from both partial least squares (PLS) and principle component analysis (PCA) for use in subspace identification. Note that the existing subspace identification approaches often utilize singular value decomposition (SVD) as part of the identification algorithm which is generally not robust to missing data. In contrast, the NIPALS algorithms used in this work leverage the inherent correlation structure of the identification matrices to minimize the impact of missing data values while generating an accurate system model. Furthermore, in computing the system matrices, the calculated scores from the latent variable methods are utilized as the states of the system. The efficacy of the proposed approach is shown via simulation of a nonlinear batch process example.     

Dynamic modelling of CO2 absorption for post combustion capture in coal-fired power plants

Power generation from fossil fuel-fired power plants is the largest single source of CO₂ emissions. Post combustion capture via chemical absorption is viewed as the most mature CO₂ capture technique. This paper presents a study of the post combustion CO₂ capture with monoethanolamine (MEA) based on dynamic modelling of the process. The aims of the project were to compare two different approaches (the equilibrium-based approach versus the rate-based approach) in modelling the absorber dynamically and to understand the dynamic behaviour of the absorber during part load operation and with disturbances from the stripper. A powerful modelling and simulation tool gPROMS was chosen to implement the proposed work. The study indicates that the rate-based model gives a better prediction of the chemical absorption process than the equilibrium-based model. The dynamic simulation of the absorber indicates normal absorber column operation could be maintained during part load operation by maintaining the ratio of the flow rates of the lean solvent and flue gas to the absorber. Disturbances in the CO₂ loading of the lean solvent to the absorber significantly affect absorber performance. Further work will extend the dynamic modelling to the stripper for whole plant analysis.     

Online prediction of subcutaneous glucose concentration for type 1 diabetes using empirical models and frequency‐band separation

Online glucose prediction which can be used to provide important information of future glucose status is a key step to facilitate proactive management before glucose reaches undesirable concentrations. Based on frequency‐band separation (FS) and empirical modeling approaches, this article considers several important aspects of on‐line glucose prediction for subjects with type 1 diabetes mellitus. Three issues are of particular interest: (1) Can a global (or universal) model be developed from glucose data for a single subject and then used to make suitably accurate on‐line glucose predictions for other subjects? (2) Does a new FS approach based on data filtering provide more accurate models than standard modeling methods? (3) Does a new latent variable modeling method result in more accurate models than standard modeling methods? These and related issues are investigated by developing autoregressive models and autoregressive models with exogenous inputs based on clinical data for two groups of subjects. The alternative modeling approaches are evaluated with respect to on‐line short‐term prediction accuracy for prediction horizons of 30 and 60 min, using independent test data. © 2013 American Institute of Chemical Engineers AIChE J 60: 574–584, 2014     

Modelling of a clinker rotary kiln using operating functions concept

Modelling and parameter identification of complex dynamic systems/processes is one of the main challenging problems in control engineering. An example of such a process is clinker rotary kiln (CRK) in cement industry. In the prevailing models independently of which structure is used to describe the kiln's dynamics and the identification algorithm, parameters are assumed to be centralised and constant while the CRK is well known as a distributed parameter system with a strongly varying dynamic through time. In this work, the kiln's dynamic is described in the form of a state‐space representation with three state variables using a system of partial differential equations (PDE). The structure is chosen so that it can easily be embedded in classical state‐space control algorithms. The parameters of the PDE system are called operating functions since their numerical values vary with respect to different operating conditions of the kiln, to their position in the kiln, and through time. A phenomenological approach is also proposed in this paper to identify the operating functions for a given steady‐state operation of the kiln. The model is then used to perform a semi‐dynamic simulation of the process through manipulating main process variables.     

Identification and control of a riser‐type FCC unit

This paper addresses the use of feedforward neural networks for the steady‐state and dynamic identification and control of a riser type fluid catalytic cracking unit (FCCU). The results are compared with a conventional PI controller and a model predictive control (MPC) using a state space subspace identification algorithm. A back propagation algorithm with momentum term and adaptive learning rate is used for training the identification networks. The back propagation algorithm is also used for the neuro‐control of the process. It is shown that for a noise‐free system the adaptive neuro‐controller and the MPC are capable of maintaining the riser temperature, the pressure difference between the reactor vessel and the regenerator, and the catalyst bed level in the reactor vessel, in the presence of set‐point and disturbance changes. The MPC performs better than the neuro controller that in turn is superior to the conventional multi‐loop diagonal PI controller.     

Long‐term ceramic reliability analysis including the crack‐velocity threshold and the “bathtub” curve

A thermally activated crack‐velocity formulation that includes a threshold at thermodynamic equilibrium is used in the prediction of long‐term time‐to‐failure for brittle materials. A new closed‐form time‐to‐failure solution is derived for straight cracks propagating under the influence of constant stress. Explicit connections are made between the macroscopic crack‐velocity parameters and the underlying bond‐rupture parameters. A feature of the solution is the divergence of time‐to‐failure for applied loading approaching the thermodynamic threshold. A new reliability framework is developed and long‐term reliability and hazard predictions made using the time‐to‐failure solution. A bathtub hazard curve is shown to be generated by a single crack‐velocity failure mechanism.     

Mathematical modelling of fluidized‐bed reactors for non‐catalytic gas–solid reactions

Mathematical modelling of a continuous fluidized‐bed reactor has been carried out for non‐catalytic gas–solid reactions. The two‐phase bubbling bed model has been used and the elutriation phenomenon for the fine particles has been investigated. The feed stream consisting particles with size distribution and reversible or irreversible first‐order kinetics can be treated by the model. The reduction behaviour of solid reactants was described by the grain model. A program was developed in MATLAB software for solving the governing equations at conditions of different temperatures and pressures. The model was validated using experimental data and simulation results available in the literature for the iron ore reduction with a gas mixture containing hydrogen [Srinivasan and Staffansson, Chem. Eng. Sci. 45(5), 1253–1265 (1990)]. The mathematical modelling was also used for predicting the extent of reaction for reduction of cobalt oxide by methane.     

Model Optimization for the Dynamic Simulation of Reactive Absorption Processes

The optimal design of reactive separations is impossible without reliable process models. Especially for the dynamic simulation and the model‐based control of complex reactive absorption processes the model development leads to a contradiction between the required model accuracy to reflect the process complexity and the feasibility of process simulations regarding the computation time. In this respect, we have developed a new rigorous dynamic two‐phase model based on the two‐film theory as a first step, which takes into account the influence of chemical reactions and additional driving forces in electrolyte systems on mass transfer considering thermodynamic nonidealities as well as the impact of column internals on the process hydrodynamics. For a model optimization, we have performed an analysis of different model approaches for complicated industrial absorption processes and determined an appropriate model complexity. Based on results of sensitivity studies, we have accomplished different model modifications leading to a stabilization of the numerical solution without affecting the good agreement between simulation results and the experimental data. This time‐optimized model can be considered superior as compared to previous approaches and facilitates for the first time a rigorous dynamic simulation of entire reactive absorption columns and the application within an on‐line process control system.     

A New Thermo‐, pH‐ and CO2‐Responsive Fluorescent Four‐Arm Star Polymer with Aggregation‐Induced Emission for Long‐Term Cellular Tracing

Development of fluorescent bioprobes for long‐term cell tracking is of great importance to monitor the processes of genesis, development, invasion, and metastasis of cancerous cells. Herein, a new multistimuli‐responsive star polymer of tetraphenylethene‐graft‐tetra‐poly[N‐[2‐(diethylamino)‐ethyl]acrylamide] (TPE‐tetraPDEAEAM) with aggregation‐induced emission (AIE) effect for tracing live cells over a long period of time is synthesized by atom transfer radical polymerization using TPE derivative as initiator. TPE‐tetraPDEAEAM gives both lower critical solution temperature and fluorescence responses to the stimulus of the temperature, pH, and CO₂ by combining the thermoresponsive and pH/CO₂‐responsive moieties of the diethylamino and acrylamide groups. The AIE‐active TPE‐tetraPDEAEAM has the advantages of very low cytotoxicity, efficient cellular uptake, and strong fluorescence of polymer‐treated cells, which ensure its good performance in long‐term cell tracing. This facile tracking of HeLa cells for as long as nine passages exhibits superior performance in long‐term cell tracing as compared with some commercial cell tracing probes.     

Environmental effects on fiber-reinforced composites

The use of fiber-reinforced composites in structures is discussed with emphasis on the need for a method to obtain long time properties from short time tests. Results of vibration, constant strain-rate and creep tests are presented for boron epoxy laminates. The data is statistically conditioned and a time-temperature shifting procedure is used to obtain master curves for response at an individual temperature. Three analytical linear viscoelastic characterization techniques are used to obtain long term creep response from dynamic data. Comparisons between analytical predictions are given for vibration and constant strain-rate data. Creep predictions are also compared to experimental observations.     

Towards Controlling the Glycoform: A Model Framework Linking Extracellular Metabolites to Antibody Glycosylation

Glycoproteins represent the largest group of the growing number of biologically-derived medicines. The associated glycan structures and their distribution are known to have a large impact on pharmacokinetics. A modelling framework was developed to provide a link from the extracellular environment and its effect on intracellular metabolites to the distribution of glycans on the constant region of an antibody product. The main focus of this work is the mechanistic in silico reconstruction of the nucleotide sugar donor (NSD) metabolic network by means of 34 species mass balances and the saturation kinetics rates of the 60 metabolic reactions involved. NSDs are the co-substrates of the glycosylation process in the Golgi apparatus and their simulated dynamic intracellular concentration profiles were linked to an existing model describing the distribution of N-linked glycan structures of the antibody constant region. The modelling framework also describes the growth dynamics of the cell population by means of modified Monod kinetics. Simulation results match well to experimental data from a murine hybridoma cell line. The result is a modelling platform which is able to describe the product glycoform based on extracellular conditions. It represents a first step towards the in silico prediction of the glycoform of a biotherapeutic and provides a platform for the optimisation of bioprocess conditions with respect to product quality.     

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.     

A backoff strategy for model‐based experiment design under parametric uncertainty

Model‐based experiment design techniques are an effective tool for the rapid development and assessment of dynamic deterministic models, yielding the most informative process data to be used for the estimation of the process model parameters. A particular advantage of the model‐based approach is that it permits the definition of a set of constraints on the experiment design variables and on the predicted responses. However, uncertainty in the model parameters can lead the constrained design procedure to predict experiments that turn out to be, in practice, suboptimal, thus decreasing the effectiveness of the experiment design session. Additionally, in the presence of parametric mismatch, the feasibility constraints may well turn out to be violated when that optimally designed experiment is performed, leading in the best case to less informative data sets or, in the worst case, to an infeasible or unsafe experiment. In this article, a general methodology is proposed to formulate and solve the experiment design problem by explicitly taking into account the presence of parametric uncertainty, so as to ensure both feasibility and optimality of the planned experiment. A prediction of the system responses for the given parameter distribution is used to evaluate and update suitable backoffs from the nominal constraints, which are used in the design session to keep the system within a feasible region with specified probability. This approach is particularly useful when designing optimal experiments starting from limited preliminary knowledge of the parameter set, with great improvement in terms of design efficiency and flexibility of the overall iterative model development scheme. The effectiveness of the proposed methodology is demonstrated and discussed by simulation through two illustrative case studies concerning the parameter identification of physiological models related to diabetes and cancer care. © 2009 American Institute of Chemical Engineers AIChE J, 2010