Noble metal water gas shift catalysis: Kinetics study and reactor design

Based on the water gas shift (WGS) catalytic mechanism on precious metal catalyst, a Langmuir–Hinshelwood (LH) kinetics model was derived for the operating conditions of syngas from natural gas reforming at near-ambient pressure. A power law kinetics model was also presented for comparative purpose. These two kinetics models were integrated in a dynamic distributed reactor model for design of full-scale WGS reactors for a natural gas fuel processing system. Modeling results indicated that the LH kinetics model gives predictions of reactor performance closer to the experimental data. Using the LH kinetics model, optimization of operating conditions for the high-temperature shift (HTS) and low-temperature shift (LTS) reactors was also attempted.     

Numerical study of fluid flow and heat transfer in microchannel cooling passages

Numerical investigation was conducted for fluid flow and heat transfer in microchannel cooling passages. Effects of viscosity and thermal conductivity variations on characteristics of fluid flow and heat transfer were taken into account in theoretical modeling. Two-dimensional simulation was performed for low Reynolds number flow of liquid water in a 100 μm single channel subjected to localized heat flux boundary conditions. The velocity field was highly coupled with temperature distribution and distorted through the variations of viscosity and thermal conductivity. The induced cross-flow velocity had a marked contribution to the convection. The heat transfer enhancement due to viscosity-variation was pronounced, though the axial conduction introduced by thermal-conductivity-variation was insignificant unless for the cases with very low Reynolds numbers.     

Towards sustainable industry 4.0: A green real-time IIoT multitask scheduling architecture for distributed 3D printing services

As the keystones of the personalized manufacturing, the Industrial Internet of Things (IIoT) consolidated with 3D printing pave the path for the era of Industry 4.0 and smart manufacturing. By resembling the age of craft manufacturing, Industry 4.0 expedites the alteration from mass production to mass customization. When distributed 3D printers (3DPs) are shared and collaborated in the IIoT, a promising dynamic, globalized, economical, and time-effective manufacturing environment for customized products will appear. However, the optimum allocation and scheduling of the personalized 3D printing tasks (3DPTs) in the IIoT in a manner that respects the customized attributes submitted for each model while satisfying not only the real-time requirements but also the workload balancing between the distributed 3DPs is an inevitable research challenge that needs further in-depth investigations. Therefore, to address this issue, this paper proposes a real-time green-aware multi-task scheduling architecture for personalized 3DPTs in the IIoT. The proposed architecture is divided into two interconnected folds, namely, allocation and scheduling. A robust online allocation algorithm is proposed to generate the optimal allocation for the 3DPTs. This allocation algorithm takes into consideration meeting precisely the customized user-defined attributes for each submitted 3DPT in the IIoT as well as balancing the workload between the distributed 3DPs simultaneously with improving their energy efficiency. Moreover, meeting the predefined deadline for each submitted 3DPT is among the main objectives of the proposed architecture. Consequently, an adaptive real-time multi-task priority-based scheduling (ARMPS) algorithm has been developed. The built ARMPS algorithm respects both the dynamicity and the real-time requirements of the submitted 3DPTs. A set of performance evaluation tests has been performed to thoroughly investigate the robustness of the proposed algorithm. Simulation results proved the robustness and scalability of the proposed architecture that surpasses its counterpart state-of-the-art architectures, especially in high-load environments.     

Making costly manufacturing smart with transfer learning under limited data: A case study on composites autoclave processing

The integration of advanced manufacturing processes with ground-breaking Artificial Intelligence methods continue to provide unprecedented opportunities towards modern cyber-physical manufacturing processes, known as smart manufacturing or Industry 4.0. However, the “smartness” level of such approaches closely depends on the degree to which the implemented predictive models can handle uncertainties and production data shifts in the factory over time. In the case of change in a manufacturing process configuration with no sufficient new data, conventional Machine Learning (ML) models often tend to perform poorly. In this article, a transfer learning (TL) framework is proposed to tackle the aforementioned issue in modeling smart manufacturing. Namely, the proposed TL framework is able to adapt to probable shifts in the production process design and deliver accurate predictions without the need to re-train the model. Armed with sequential unfreezing and early stopping methods, the model demonstrated the ability to avoid catastrophic forgetting in the presence of severely limited data. Through the exemplified industry-focused case study on autoclave composite processing, the model yielded a drastic (88%) improvement in the generalization accuracy compared to the conventional learning, while reducing the computational and temporal cost by 56%.     

Building a right digital twin with model engineering

In recent years, the concept of digital twin (DT) is attracting more and more attention from researchers and engineers. But there is still no consensus on what a right DT is. On one hand, some common models are renamed as DTs. On the other hand, some DTs extremely pursue ‘the same’ as physical objects, which bring unnecessary complexities to them. In this paper, we try to answer two questions from the point of view of model engineering: how to define a right digital twin, and how to build a right digital twin. The concept and related technologies of model engineering are introduced. Some basic principles and a set of metrics for a right DT are given. An evolutionary concurrent modeling method for DT (ECoM4DT) is proposed not only inheriting the theory from classic M&S methods but also highlighting the characteristics of DT compared with traditional models to systemically guide the DT modeling process.     

Posterior representation with a multi‐modal likelihood using the gaussian sum filter for localization in a known map

A Gaussian sum filter (GSF) with component extended Kalman filters (EKF) is proposed as an approach to localizing an autonomous vehicle in an urban environment with limited GPS availability. The GSF uses vehicle‐relative vision‐based measurements of known map features coupled with inertial navigation solutions to accomplish localization in the absence of GPS. The vision‐based measurements have multimodal measurement likelihood functions that are well represented as weighted sums of Gaussian densities. The GSF is used because of its ability to represent the posterior distribution of the vehicle pose with better efficiency (fewer terms, less computational complexity) than a corresponding bootstrap particle filter with various numbers of particles because of the interaction with measurement hypothesis tests. The expectation‐maximization algorithm is used off line to determine the representational efficiency of the particle filter in terms of an effective number of Gaussian densities. In comparison, the GSF, which uses an iterative condensation procedure after each iteration of the filter to maintain real‐time capabilities, is shown through a series of in‐depth empirical studies to more accurately maintain a representation of the posterior distribution than the particle filter using 37 min of recorded data from Cornell University's autonomous vehicle driven in an urban environment, including a 32 min GPS blackout. © 2012 Wiley Periodicals, Inc.     

Evolutionary Generalized Radial Basis Function neural networks for improving prediction accuracy in gene classification using feature selection

Radial Basis Function Neural Networks (RBFNNs) have been successfully employed in several function approximation and pattern recognition problems. The use of different RBFs in RBFNN has been reported in the literature and here the study centres on the use of the Generalized Radial Basis Function Neural Networks (GRBFNNs). An interesting property of the GRBF is that it can continuously and smoothly reproduce different RBFs by changing a real parameter τ. In addition, the mixed use of different RBF shapes in only one RBFNN is allowed. Generalized Radial Basis Function (GRBF) is based on Generalized Gaussian Distribution (GGD), which adds a shape parameter, τ, to standard Gaussian Distribution. Moreover, this paper describes a hybrid approach, Hybrid Algorithm (HA), which combines evolutionary and gradient-based learning methods to estimate the architecture, weights and node topology of GRBFNN classifiers. The feasibility and benefits of the approach are demonstrated by means of six gene microarray classification problems taken from bioinformatic and biomedical domains. Three filters were applied: Fast Correlation-Based Filter (FCBF), Best Incremental Ranked Subset (BIRS), and Best Agglomerative Ranked Subset (BARS); this was done in order to identify salient expression genes from among the thousands of genes in microarray data that can directly contribute to determining the class membership of each pattern. After different gene subsets were obtained, the proposed methodology was performed using the selected gene subsets as new input variables. The results confirm that the GRBFNN classifier leads to a promising improvement in accuracy.     

Electrokinetically driven concentration of particles and cells by dielectrophoresis with DC-offset AC electric field

Insulator-based dielectrophoresis (iDEP) has been successfully used for on-chip manipulations of biological samples. Despite its effectiveness, iDEP typically requires high DC voltages to achieve sufficient electric field; this is mainly due to the coupled phenomena among linear electrokinetics: electroosmosis (EO) and electrophoresis (EP) and nonlinear electrokinetics: dielectrophoresis (DEP). This paper presents a microfluidic technique using DC-offset AC electric field for electrokinetic concentration of particles and cells by repulsive iDEP. This technique introduces AC electric field for producing iDEP which is decoupled from electroosmosis (EO) and electrophoresis (EP). The repulsive iDEP is generated in a PDMS tapered contraction channel that induces non-uniform electric field. The benefits of introducing AC electric field component are threefold: (i) it contributes to DEP force acting on particles, (ii) it suppresses EO flow and (iii) it does not cause any EP motion. As a result, the required DC field component that is mainly used to transport particles on the basis of EO and EP can be significantly reduced. Experimental results supported by numerical simulations showed that the total DC-offset AC electric field strength required to concentrate 15-μm particles is significantly reduced up to 85.9% as compared to using sole DC electric field. Parametric experimental studies showed that the higher buffer concentration, larger particle size and higher ratio of AC-to-DC electric field are favorable for particle concentration. In addition, the proposed technique was demonstrated for concentration of yeast cells.     

An investigation into vehicle rollover prevention by coordinated control of active anti-roll bar and electronic stability program

This paper presents a method for designing a controller that uses an active anti-roll bar (AARB) and an electronic stability program (ESP) for rollover prevention. ESP with longitudinal speed control (LSC) can carry out active braking to reduce vehicle speed and lateral acceleration to prevent a rollover. To enhance the rollover prevention capability of the ESP, an AARB is adopted. The controller for the AARB was designed based on linear quadratic (LQ) static output feedback (SOF) control methodology, which attenuates the effect of lateral acceleration on the roll angle and roll rate by control of the suspension stroke and the tire deflection of the vehicle. Although this AARB significantly increases ride comfort and rollover prevention, it has a drawback — the vehicle loses its maneuverability. Therefore, the ESP with LSC is used to overcome this drawback. Simulations showed that the proposed method was effective in preventing a rollover.     

Modelling of the mist formation in a segmented grinding wheel system

It has been found that using a segmented grinding wheel with a fluid chamber can significantly minimise the quantity of coolant while improving the ground surface integrity. The present investigation aims to explore the fluid flow mechanism in such a wheel system. To this end, the Weber theory for Newtonian jet instability was applied to quantitatively determine the contribution of coolant flow rate to mist and ligament modes. A semi-analytical model was then developed to predict the mist flow rate by taking into account both the grinding parameters and fluid properties. It was shown that the model prediction was in good agreement with experimental measurements. Because of the comprehensive integration of variables in the formulation, the model provides a good fundamental understanding of the mist formation and offers a practical guideline for the selection and use coolant in minimising the mist flow rate.