Multimode Process Monitoring Based on Aligned Mixture Factor Analysis

To meet the demand for diversified products, industrial processes typically have multiple operating modes. In this article, a novel scheme based on aligned mixture factor analysis (AMFA) is proposed for multimode process monitoring. First, mixture factor analysis (MFA) is applied to produce a statistical fingerprint of the training data that gives a detailed description of the multiple operating modes. Then, unlike conventional multimodal algorithms in which multiple models are constructed and monitoring results are softly combined, the proposed method aims at aligning the separated local models together and performing the monitoring behaviors based on the global model. Through this approach of dividing and integrating, both the within-mode and cross-mode correlations can be greatly preserved in the global model. Finally, the utility and feasibility of the proposed method are demonstrated through a numerical example, a nonisothermal continuous stirred-tank reactor (CSTR) model, and the Tennessee Eastman benchmark process

Adsorption of CO on Low-Energy, Low-Symmetry Pt Nanoparticles: Energy Decomposition Analysis and Prediction via Machine-Learning Models

We present a systematic analysis of CO adsorption on Pt nanoclusters in the 0.2–1.5 nm size range with the aim of unraveling size-dependent trends and developing predictive models for site-specific adsorption behavior. Using an empirical-potential-based genetic algorithm and density functional theory (DFT) modeling, we show that there exists a size window (40–70 atoms) over which Pt nanoclusters bind CO weakly, the binding energies being comparable to those on (111) or (100) facets. The size-dependent adsorption energy trends are, however, distinctly nonmonotonic and are not readily captured using traditional descriptors such as <i>d</i>-band energies or (generalized) coordination numbers of the Pt binding sites. Instead, by applying machine-learning algorithms, we show that multiple descriptors, broadly categorized as structural and electronic descriptors, are essential for qualitatively capturing the CO adsorption trends. Nevertheless, attaining quantitative accuracy requires further refinement, and we propose the use of an additional descriptorsthe fully frozen adsorption energythat is a computationally inexpensive probe of CO–Pt bond formation. With these three categories of descriptors, we achieve an absolute mean error in CO adsorption energy prediction of 0.12 eV, which is similar to the underlying error of DFT adsorption calculations. Our approach allows for building quantitatively predictive models of site-specific adsorbate binding on realistic, low-symmetry nanostructures, which is an important step in modeling reaction networks as well as for rational catalyst design in general

Self-Consistent Charge Density-Functional Tight-Binding Parametrization for Pt–Ru Alloys

We present a self-consistent charge density-functional tight-binding (SCC-DFTB) parametrization for PtRu alloys, which is developed by employing a training set of alloy cluster energies and forces obtained from Kohn–Sham density-functional theory (DFT) calculations. Extensive simulations of a testing set of PtRu alloy nanoclusters show that this SCC-DFTB scheme is capable of capturing cluster formation energies with high accuracy relative to DFT calculations. The new SCC-DFTB parametrization is employed within a genetic algorithm to search for global minima of PtRu clusters in the range of 13–81 atoms and the emergence of Ru-core/Pt-shell structures at intermediate alloy compositions, consistent with known results, is systematically demonstrated. Our new SCC-DFTB parametrization enables computationally inexpensive and accurate modeling of Pt–Ru clusters that are among the best-performing catalysts in numerous energy applications

First-Principles Predictions of Structure–Function Relationships of Graphene-Supported Platinum Nanoclusters

Platinum-based materials play an important role as electrocatalysts in energy conversion technologies. Graphene-supported Pt nanoclusters were recently found to be promising electrocatalysts for fuel-cell applications due to their enhanced activity and tolerance to CO poisoning as well as their long-term stability toward sintering. However, structure–function relationships that underpin the improved performance of these catalysts are still not well understood. Here, we employ a combination of empirical potential simulations and density functional theory (DFT) calculations to investigate structure–function relationships of small Pt_<i>N</i> (<i>N</i> = 2–80) clusters on model carbon (graphene) supports. A bond-order empirical potential is employed within a genetic algorithm to go beyond local optimizations in obtaining minimum-energy structures of Pt_<i>N</i> clusters on pristine as well as defective graphene supports. Point defects in graphene strongly anchor Pt clusters and also appreciably affect the morphologies of small clusters, which are characterized via various structural metrics such as the radius of gyration, average bond length, and average coordination number. A key finding from the structural analysis is that the fraction of potentially active surface sites in supported clusters is maximized for stable Pt clusters in the size range of 20–30 atoms, which provides a useful design criterion for optimal utilization of the precious metal. Through selected <i>ab initio</i> studies, we find a consistent trend for charge transfer from small Pt clusters to defective graphene supports resulting in the lowering of the cluster d-band center, which has implications for the overall activity and poisoning of the catalyst. The combination of a robust empirical potential-based genetic algorithm for structural optimization with <i>ab initio</i> calculations opens up avenues for systematic studies of supported catalyst clusters at much larger system sizes than are accessible to purely <i>ab initio</i> approaches

Multisubspace Principal Component Analysis with Local Outlier Factor for Multimode Process Monitoring

According to different manufacturing strategies, modern chemical processes always have multiple modes. At the same time, variables within the same mode often follow a mixture of Gaussian and non-Gaussian distributions. In this study, an algorithm using multisubspace principal component analysis (MSPCA) with the local outlier factor (LOF) technique is proposed for process monitoring. Unlike conventional clustering methods, which require iterative processes, a new clustering strategy based on serial correlation and the LOF method is developed. To decrease the complexity of process analysis and simultaneously preserve information, a two-step principal-component selection scheme called full variable expression (FVE) is proposed in this article. Moreover, for the mixed distribution of a single mode, a monitoring statistic is established using LOF in the feature subspace. Then, the results in all feature subspaces are integrated through the Bayesian fusion strategy. Finally, to verify its superiority, the MSPCA–LOF scheme is applied to the Tennessee Eastman (TE) benchmark problem and a continuous stirred-tank reactor (CSTR) process

Le Peuple : organe quotidien du syndicalisme

13 janvier 19381938/01/13 (A18,N6201)-1938/01/13

The schematic representation of the computational analysis pipeline.

<p>The schematic representation of the computational analysis pipeline.</p

Comparison of nodes in miRNA–TF co-regulatory networks in MI progression (acute, subacute and chronic phases).

<p>Comparison of nodes in miRNA–TF co-regulatory networks in MI progression (acute, subacute and chronic phases).</p

Studying Dynamic Features in Myocardial Infarction Progression by Integrating miRNA-Transcription Factor Co-Regulatory Networks and Time-Series RNA Expression Data from Peripheral Blood Mononuclear Cells

<div><p>Myocardial infarction (MI) is a serious heart disease and a leading cause of mortality and morbidity worldwide. Although some molecules (genes, miRNAs and transcription factors (TFs)) associated with MI have been studied in a specific pathological context, their dynamic characteristics in gene expressions, biological functions and regulatory interactions in MI progression have not been fully elucidated to date. In the current study, we analyzed time-series RNA expression data from peripheral blood mononuclear cells. We observed that significantly differentially expressed genes were sharply up- or down-regulated in the acute phase of MI, and then changed slowly until the chronic phase. Biological functions involved at each stage of MI were identified. Additionally, dynamic miRNA–TF co-regulatory networks were constructed based on the significantly differentially expressed genes and miRNA–TF co-regulatory motifs, and the dynamic interplay of miRNAs, TFs and target genes were investigated. Finally, a new panel of candidate diagnostic biomarkers (STAT3 and ICAM1) was identified to have discriminatory capability for patients with or without MI, especially the patients with or without recurrent events. The results of the present study not only shed new light on the understanding underlying regulatory mechanisms involved in MI progression, but also contribute to the discovery of true diagnostic biomarkers for MI.</p></div

Hierarchical clustering on standardized expression values of the top 100 SDE genes.

<p>Two main clusters were identified, and each cluster included its corresponding gene profiles (grey lines) and mean expression values (dark dots).</p