Discrete element reduced‐order modeling of dynamic particulate systems

One of the key technical challenges associated with modeling particulate processes is the ongoing need to develop efficient and accurate predictive models. Often the models that best represent solids handling processes, like discrete element method (DEM) models, are computationally expensive to evaluate. In this work, a reduced‐order modeling (ROM) methodology is proposed that can represent distributed parameter information, like particle velocity profiles, obtained from high‐fidelity (DEM) simulations in a more computationally efficient fashion. The proposed methodology uses principal component analysis (PCA) to reduce the dimensionality of the distributed parameter information, and response surface modeling to map the distributed parameter data to process operating parameters. This PCA‐based ROM approach has been used to model velocity trajectories in a continuous convective mixer, to demonstrate its applicability for pharmaceutical process modeling. © 2014 American Institute of Chemical Engineers AIChE J, 60: 3184–3194, 2014     

Using hybrid neural networks in scaling up an FCC model from a pilot plant to an industrial unit

The scaling up of a pilot plant fluid catalytic cracking (FCC) model to an industrial unit with use of artificial neural networks is presented in this paper. FCC is one of the most important oil refinery processes. Due to its complexity the modeling of the FCC poses great challenge. The pilot plant model is capable of predicting the weight percent of conversion and coke yield of an FCC unit. This work is focused in determining the optimum hybrid approach, in order to improve the accuracy of the pilot plant model. Industrial data from a Greek petroleum refinery were used to develop and validate the models. The hybrid models developed are compared with the pilot plant model and a pure neural network model. The results show that the hybrid approach is able to increase the accuracy of prediction especially with data that is out of the model range. Furthermore, the hybrid models are easier to interpret and analyze.     

Application of stepwise regression for dynamic parameter estimation

Dynamic parameter estimation in cases where it may be impossible to identify all the model parameters is considered. The objective is to obtain reliable estimates to the maximal number of physical parameters in a stable regression model where the modeling of the noise in the data is avoided. The modifications required in the stepwise regression algorithm to accommodate various nonlinear terms in the regression model are investigated and a new algorithm is presented. The algorithm considers the hierarchy among the parameters, the initial trends of the experimental data curves and the initial values of the state variables in order to establish a minimal initial set of parameters to be included in the model. Additional parameters are then added in a stepwise manner, while considering the hierarchy of the parameters and the associated reduction of the objective function value. The process continues as long as significant and physically feasible values for the parameters are obtained. The new method is demonstrated with several examples from the literature. Additional issues investigated include the proper combination of the simultaneous and sequential solution methods in the stepwise regression algorithm, the preferred method for the estimation of the derivatives and the effect of variable scaling.     

4-Lump kinetic model for vacuum gas oil hydrocracker involving hydrogen consumption

A 4-lump kinetic model including hydrogen consumption for hydrocracking of vacuum gas oil in a pilot scale reactor is proposed. The advantage of this work over the previous ones is consideration of hydrogen consumption, imposed by converting vacuum gas oil to light products, which is implemented in the kinetic model by a quadratic expression as similar as response surface modeling. This approach considers vacuum gas oil (VGO) and unconverted oil as one lump whilst others are distillate, naphtha and gas. The pilot reactor bed is divided into hydrotreating and hydrocracking sections which are loaded with different types of catalysts. The aim of this paper is modeling the hydrocracking section, but the effect of hydrotreating is considered on the boundary condition of the hydrocracking part. The hydrocracking bed is considered as a plug flow reactor and it is modeled by the cellular network approach. Initially, a kinetic network with twelve coefficients and six paths is considered. But following evaluation using measured data and order of magnitude analysis, the three route passes and one activation energy coefficient are omitted; thus the number of coefficients is reduced to five. This approach improves the average absolute deviation of prediction from 7.2% to 5.92%. Furthermore, the model can predict the hydrogen consumption for hydrocracking with average absolute deviation about 8.59% in comparison to those calculated from experimental data.     

Flow properties of PLZTN aqueous suspensions for tape casting

Several aqueous suspensions of lead zirconate titanate powder doped with lanthanum and niobium (PLZTN) were prepared, tape cast and characterized by rheological measurements in steady shear flow. Experimental data were fitted by some viscous models available on literature and the model parameters compared in order to obtain information about the effects of the composition on suspension viscosity. Such approach represents a necessary step in the desirable optimization of tape casting process, which includes also the analysis by flow simulations. Five viscous models have been considered: from the classic plastic models of Bingham and Herschel–Bulkley, to the modified plastic model of Papanastasiou, up to the pseudoplastic models of Cross and Roberts–Barnes–Carew. The parameter-based comparison was carried on with the latter, resulting, with its eight parameters, the best-fit model, while for flow simulation purposes the ones with few parameters, as the Cross and Herschel–Bulkley models, resulted the more appropriate from some simple considerations.     

Immunotherapy in Advanced Prostate Cancer—Light at the End of the Tunnel?

Immunotherapeutic treatment approaches are now an integral part of the treatment of many solid tumors. However, attempts to integrate immunotherapy into the treatment of prostate cancer have been disappointing so far. This is due to a highly immunosuppressive, “cold” tumor microenvironment, which is characterized, for example, by the absence of cytotoxic T cells, an increased number of myeloid-derived suppressor cells or regulatory T cells, a decreased number of tumor antigens, or a defect in antigen presentation. The consequence is a reduced efficacy of many established immunotherapeutic treatments such as checkpoint inhibitors. However, a growing understanding of the underlying mechanisms of tumor–immune system interactions raises hopes that immunotherapeutic strategies can be optimized in the future. The aim of this review is to provide an overview of the current status and future directions of immunotherapy development in prostate cancer. Background information on immune response and tumor microenvironment will help to better understand current therapeutic strategies under preclinical and clinical development.     

Extracellular Vesicles and Their Role in the Spatial and Temporal Expansion of Tumor–Immune Interactions

Extracellular vesicles (EVs) serve as trafficking vehicles and intercellular communication tools. Their cargo molecules directly reflect characteristics of their parental cell. This includes information on cell identity and specific cellular conditions, ranging from normal to pathological states. In cancer, the content of EVs derived from tumor cells is altered and can induce oncogenic reprogramming of target cells. As a result, tumor-derived EVs compromise antitumor immunity and promote cancer progression and spreading. However, this pro-oncogenic phenotype is constantly being challenged by EVs derived from the local tumor microenvironment and from remote sources. Here, we summarize the role of EVs in the tumor–immune cross-talk that includes, but is not limited to, immune cells in the tumor microenvironment. We discuss the potential of remotely released EVs from the microbiome and during physical activity to shape the tumor–immune cross-talk, directly or indirectly, and confer antitumor activity. We further discuss the role of proinflammatory EVs in the temporal development of the tumor–immune interactions and their potential use for cancer diagnostics.     

Immunotherapy with 4-1BBL-Expressing iPS Cell‐Derived Myeloid Lines Amplifies Antigen-Specific T Cell Infiltration in Advanced Melanoma

We have established an immune cell therapy with immortalized induced pluripotent stem-cell–derived myeloid lines (iPS-ML). The benefits of using iPS-ML are the infinite proliferative capacity and ease of genetic modification. In this study, we introduced 4-1BBL gene to iPS-ML (iPS-ML-41BBL). The analysis of the cell-surface molecules showed that the expression of CD86 was upregulated in iPS-ML-41BBL more than that in control iPS-ML. Cytokine array analysis was performed using supernatants of the spleen cells that were cocultured with iPS-ML or iPS-ML-41BBL. Multiple cytokines that are beneficial to cancer immunotherapy were upregulated. Peritoneal injections of iPS-ML-41BBL inhibited tumor growth of peritoneally disseminated mouse melanoma and prolonged survival of mice compared to that of iPS-ML. Furthermore, the numbers of antigen-specific CD8⁺ T cells were significantly increased in the spleen and tumor tissues treated with epitope peptide-pulsed iPS-ML-41BBL compared to those treated with control iPS-ML. The number of CXCR6-positive T cells were increased in the tumor tissues after treatment with iPS-ML-41BBL compared to that with control iPS-ML. These results suggest that iPS-ML-41BBL could activate antigen-specific T cells and promote their infiltration into the tumor tissues. Thus, iPS-ML-41BBL may be a candidate for future immune cell therapy aiming to change immunological “cold tumor” to “hot tumor”.     

Assessment of plate-wire electrostatic precipitators based on dimensional and similarity analyses

This paper is focused towards dimensional analysis in ESP model building, showing both the reduction in effort and more effective modeling that can result. Electrostatic precipitators (ESP) are widely used in industry today and much research has been carried out during the last decades. However, dimensional analysis is still an unsettled matter, in spite of it allows to reduce the number of parameters necessary for defining the ESP performance, provides a reliable scaling-up of the desired operating conditions from the pilot-scale to full-scale plant (based on the invariance of the pi-space) as well as a consistent extrapolation within the range covered by dimensionless numbers, and gives a greater flexibility in choice of parameters. This analysis together with the similarity analysis is presented in this work, in order to obtain a functional dependence between a target number and a set of few dimensionless numbers. The target selected has been the ratio of particle dust concentration at the outlet of the ESP and that at the inlet of the ESP. Thus, after doing these analyses, several quite reduced models have been formulated theoretically and later tested and validated with experimental data obtained in a pilot ESP, in order to show an application of the study. By this way, a non-linear regression model matches well with experimental data from a pilot plant.     

Dimensional analysis of a novel low‐pressure device for the production of size‐tunable nanoemulsions

A novel low pressure device was used to generate nanoemulsions of methyl methacrylate. This device is based on a strong elongational flow known to be more efficient than the shear flow for dispersive mixing. The influence of process parameters (pressure drop number of cycles, number and size of holes) and composition parameters (monomer fraction, surfactant concentration, etc) on droplet size has shown that the average droplet size can be tailored in the range 30–200 nm by adjusting these parameters. The objective of the present paper is to find correlations that relate the obtained droplet size to the studied process and composition parameters. This model is based on a dimensional analysis using the Buckingham theorem in order to determine appropriate dimensionless numbers. This approach represents a first step for scaling up the device besides giving a set of parameters allowing to achieve a given droplet size. © 2014 American Institute of Chemical Engineers AIChE J, 61: 23–30, 2015     

Reduced Hessian based parameter selection and estimation with simultaneous collocation approach

Parameter estimation is a critical step in the building of process models. Given the nonlinear structure and limited measurements, it is often difficult to correctly estimate all the parameters involved in the model. Linear dependence and low correlation among the parameters are the main problems to be handled in parameter estimation. The common approach is to estimate a subset of the parameters by fixing the others at reasonable values. However, it is a challenge to determine which parameters can be properly estimated. In this work, the ratio between standard deviation and estimated parameter value is introduced for evaluating the estimability. A Gauss-Jordan elimination based approach is proposed for parameter estimability ranking. Combined with the proposed ranking approach and approximate ratio criterion, a reduced Hessian based approach is proposed for parameter selection and estimation under a simultaneous collocation framework. The proposed approach is at least as effective and more efficient than competing approaches based on multiple eigenvalue decompositions or orthogonalizations for larger problems. Three case studies with increasing complexity are presented to demonstrate the performance of the proposed approach.     

Analysis of composite single lap joints using numerical and experimental approach

Adhesives are widely used to execute the assembly of aerospace and automotive structures due to their ability to join dissimilar materials, reduced stress concentration, and improved fatigue resistance. The mechanical behavior of adhesive joints can be studied either using analytical models or by conducting mechanical tests. However, the complexity owing to multiple interfaces, layers with different properties, material and geometric nonlinearity and its three-dimensional nature combine to increase the difficulty in obtaining an overall system of governing equations to predict the joint behavior. On the other hand, experiments are often time consuming and expensive due to a number of parameters involved. Finite element analysis (FEA) is profoundly used in recent years to overcome these limitations. The work presented in this paper involves the finite element modeling and analysis of a composite single lap joint where the adhesive–adherend interface region was modeled using connector elements. The computed stresses were compared with the experimental stresses obtained using digital image correlation technique. The results showed an agreement. Further, the failure load predicted using FEA was found to be closer to the actual failure load obtained by mechanical tests.     

Bayesian analysis for determination and uncertainty assessment of strength and crack growth parameters of brittle materials

To determine the strength and the crack growth parameters of brittle materials, the common approach is to first evaluate the strength and then the crack growth parameters. When the parameters are needed for different temperatures, the procedure is repeated for each temperature of interest. Since the number of test specimens is generally small, the separated analysis of strength and crack growth parameters for each tested temperature leads to large parameters uncertainty. In order to improve the accuracy of the parameters, we propose a Bayesian method that allows to combine all strength and lifetime data obtained at different temperatures and determine the distribution of all material parameters in a single analysis. The results obtained from the analysis of measured skutterudite data show that in comparison to the standard approach the presented method significantly reduces the material parameters uncertainty and therefore is well adapted for a reduced number of samples.     

Operation simulation of a recycled electrochemical ozone generator using artificial neural network

The present work has focused on the modeling and simulation of a recycled ozone generator system via electrochemical oxidation of water. To produce ozone, a Pyrex glass electrochemical reactor, comprised of two separate half-cell by Nafion 117 membrane was applied. The applied anode and cathode electrodes were Ti/Sn-Sb-Ni and platinized titanium, respectively. The modeling and simulation of the reactor operation were done via artificial neural network (ANN) technique. In this regard, four important operational parameters (i.e. electrolyte concentration, applied voltage, flow rate and electrolysis time) and the generated ozone concentration were considered as the independent inputs and the network output, respectively. To find out the best model, six numbers of three-layered ANNs with different functions were constructed and optimized. Best simulation was related to a model, consist of Levenberg–Marquardt Back propagation learning algorithm (trainlm) and tangent sigmoid (tansig) as transfer function in the both hidden and output layers. Also, application of 10 hidden neurons and 80 iterations for the network calibration caused to satisfy the network training while overfitting was prevented. The K-fold cross-validation method, employed for the model evaluation, showed high correlation coefficient (0.9936) and low mean square error (3.58 × 10⁻⁴) for the testing data. Sensitivity analysis indicated order of relative importance the operational parameters on the ozone production as: time > [electrolyte] > voltage > flow rate.     

Perspectives on Vascular Regulation of Mechanisms Controlling Selective Immune Cell Function in the Tumor Immune Response

The vasculature plays a major role in regulating the tumor immune cell response although the underlying mechanisms explaining such effects remain poorly understood. This review discusses current knowledge on known vascular functions with a viewpoint on how they may yield distinct immune responses. The vasculature might directly influence selective immune cell infiltration into tumors by its cell surface expression of cell adhesion molecules, expression of cytokines, cell junction properties, focal adhesions, cytoskeleton and functional capacity. This will alter the tumor microenvironment and unleash a plethora of responses that will influence the tumor’s immune status. Despite our current knowledge of numerous mechanisms operating, the field is underexplored in that few functions providing a high degree of specificity have yet been provided in relation to the enormous divergence of responses apparent in human cancers. Further exploration of this field is much warranted.     

Dimensional analysis for assessing the performance of electrostatic precipitators

Electrostatic precipitators (ESP) are widely used in industry today and much research has been carried out during the last decades. Dimensional analysis (DA) allows to reduce the number of parameters necessary for defining the ESP performance and provides a reliable scaling-up of the desired operating conditions from the pilot-scale to full-scale plant (based on the invariance of the pi-space). Likewise, DA gives a consistent extrapolation within the range covered by dimensionless number and a greater flexibility in choice of parameters. DA together with similarity analysis is presented in this work in order to obtain a functional dependence between a target number and a set of few dimensionless numbers. The target selected has been the penetration, i.e., the ratio of particle dust concentration at the outlet of the ESP (C_o) and that at the inlet of the ESP (C_i). The results can be valuable to assess the data from ESP of different operating characteristics, under geometric, electrical, fluid dynamics and electro-hydrodynamics similarity. Thus, as a consequence of these analyses, several quite reduced models have been formulated theoretically and later tested and validated with experimental data obtained in a pilot ESP in order to show an application of the study. Four models have been fitted by linear regression, resulting unsatisfactory. However, two additional models fitted by non-linear regression predict values of particle removal efficiency that agrees well with the experimental data. Thus, this paper is focused towards dimensional analysis in ESP model building, showing both the reduction in effort and more effective modeling that can result.     

3D modeling of filtration process via polyurethane nanofiber based nonwoven filters prepared by electrospinning process

Full 3D particle filtration modeling at low pressures considering slip/transition/free molecular flow regime, particle–fiber interactions, air/particle slip, sieve and homogenous flow field has been performed for the polyurethane nanofiber filter prepared by electrospinning process and the obtained theoretical predictions for the filtration efficiency have been compared with the corresponding experimental data. Moreover, the effect of air velocity, viscosity, temperature, pressure and particle–fiber friction coefficient on the produced polyurethane nanofiber filter efficiency has been investigated in more details. In order to take all real structure features of the nanofiber filter into account (such as varying fiber diameter, curvature along its length, inhomogeneity and mat defects), a new approach for 3D nanofiber mat model construction from corresponding SEM images has been proposed and utilized.     

Dynamic model-based batch process monitoring

An integrated framework consisting of a multivariate autoregressive (AR) model and multi-way principal component analysis (MPCA) is described for the monitoring of the performance of a batch process. After pre-processing the data, i.e., batch data unfolding, mean-centring and scaling, the data are then filtered using an AR model to remove the auto- and cross-correlation inherent within the pre-processed batch data. Model order is determined using Akaike information criterion and the model parameters are estimated through the application of partial least squares to attain a stable solution. MPCA is then applied to the residuals from the AR model. Three monitoring statistics are considered for the detection of the onset of process abnormalities in the batch process. The main advantage of the proposed approach is that it can monitor batch dynamics along the mean trajectory without the requirement to estimate future observed values. The proposed AR model-based approach is illustrated through its application to two polymerization processes. The case studies indicate that it gives better monitoring results in terms of sensitivity and time to fault detection than the approaches proposed by Nomikos and MacGregor [1994. Monitoring batch processes using multi-way principal components. A.I.Ch.E. Journal 40(8), 1361-1375] and Wold et al. [1998. Modelling and diagnostics of batch processes and analogous kinetic experiments. Chemometrics and Intelligent Laboratory Systems 44, 331-340].     

Asymptotic analysis of a complex reaction scheme in solid-liquid system

Asymptotic analysis of a complex reaction, Claisen condensation, is discussed. Under certain assumptions related to experimental observations the kinetics scheme of the reaction can be considerably reduced. Additional complications in the problem arise due to the fact that one of the substances is present in a sparingly soluble solid phase. We show how reductions for increasingly complex systems with kinetics, mass transfer and feed can be systematically handled by employing the methodology of asymptotic analysis. Generalizations of the reduction approach for the case of an arbitrary number of fast intermediate reactions are also considered.The identification of model parameters is considered with aid of the reduced systems. Questions of parameter estimation and optimal design of experiments are discussed using the MCMC methods.     

Investigations on hydrodynamics and mass transfer in gas–liquid stirred reactor using computational fluid dynamics

In this work, the hydrodynamics and mass transfer in a gas–liquid dual turbine stirred tank reactor are investigated using multiphase computational fluid dynamics coupled with population balance method (CFD–PBM). A steady state method of multiple frame of reference (MFR) approach is used to model the impeller and tank regions. The population balance for bubbles is considered using both homogeneous and inhomogeneous polydispersed flow (MUSIG) equations to account for bubble size distribution due to breakup and coalescence of bubbles. The gas–liquid mass transfer is implemented simultaneously along with the hydrodynamic simulation and the mass transfer coefficient is obtained theoretically using the equation based on the various approaches like penetration theory, slip velocity, eddy cell model and rigid based model. The CFD model predictions of local hydrodynamic parameters such as gas holdup, Sauter mean bubble diameter and interfacial area as well as averaged quantities of hydrodynamic and mass transfer parameters for different mass transfer theoretical models are compared with the reported experimental data of [Alves et al., 2002a] and [Alves et al., 2002b] . The predicted hydrodynamic and mass transfer parameters are in reasonable agreement with the experimental data.