Comparative Study of Linear Co-Volume Based Mixing Rules for Equation of State/ Excess Gibbs Energy (EOS/G^E) Models for CO₂ – MEA and CO₂ – MDEA Systems

With the advent of Equation of State/ Excess Gibbs Energy (EOS/GE) models, the linear co-volume based mixing rules have gained vast importance for predicting multi-component VLE for polar mixtures. Owing to their inherent ease of calculation and good prediction abilities, these mixing rules have been applied in extension, to a variety of systems especially for CO2-H2O-alkanolamine systems. However, no comparative study is available to select appropriate mixing rule for prediction of thermodynamic properties. In this study, pressure prediction of various linear co-volume mixing rules has been compared for CO2– MEA and CO2– MDEA systems, while effects of activity coefficients and process parameters have been kept constant. The infinite pressure mixing rules have heavily under – predicted and approximate zero reference pressure mixing rules have over – predicted, but latter are valid for low and medium pressure ranges. The linear combination of Vidal and Michelsen (LCVM) mixing rule have good predictions at high pressures.</jats:p

Multiscale fault classification framework using kernel principal component analysis and k-nearest neighbors for chemical process system

Process monitoring techniques in chemical process systems help to improve product quality and plant safety. Multiscale classification plays a crucial role in the monitoring of chemical processes. However, there is a problem in coping with high-dimensional correlated data produced by complex, nonlinear processes. Therefore, an improved multiscale fault classification framework has been proposed to enhance the fault classification ability in nonlinear chemical process systems. This framework combines wavelet transform (WT), kernel principal component analysis (KPCA), and K nearest neighbors (KNN) classifier. Initially, a moving window-based WT is used to extract multiscale information from process data in time and frequency simultaneously at different scales. The resulting wavelet coefficients are reconstructed and fed into the KPCA to produce feature vectors. In the final step, these vectors have been used as inputs for the KNN classifier. The performance of the proposed multi-scale KPCA-KNN framework is analyzed and compared using a continuous stirred tank reactor (CSTR) system as a case study. The results show that the proposed multiscale KPCA-KNN framework has a high success rate over PCA-KNN and KPCA-KNN methods

Carbon Capture and Utilization: A Bibliometric Analysis from 2007–2021

It is widely accepted that carbon capture and utilization technologies are an effective way of lowering the amount of greenhouse gases released into the atmosphere. A bibliometric analysis is presented in this article to investigate the development of carbon capture and utilization. The study was conducted to identify the trends in publishing, dominant contributing authors, institutions, countries, potential publishing sources, and the most cited publications in this research area. A total of 4204 articles published between 2007 and 2021 were analyzed, covering 13,272 authors, 727 journals, and 88 countries. The findings indicate that the most productive and influential authors have British and American affiliations. The United States, the United Kingdom, and China have conducted most studies on the aforementioned topic. Imperial College London, United Kingdom, has the highest number of publications in this field of research. Furthermore, the collaborative analysis was developed by creating links between the keywords, published information, authors, institutions, and countries. In addition, the discussion highlights the tremendous development in the research area of carbon capture and utilization, especially with a focus on the exponential rise in the number of yearly publications

Carbon Capture and Utilization: A Bibliometric Analysis from 2007&ndash;2021

It is widely accepted that carbon capture and utilization technologies are an effective way of lowering the amount of greenhouse gases released into the atmosphere. A bibliometric analysis is presented in this article to investigate the development of carbon capture and utilization. The study was conducted to identify the trends in publishing, dominant contributing authors, institutions, countries, potential publishing sources, and the most cited publications in this research area. A total of 4204 articles published between 2007 and 2021 were analyzed, covering 13,272 authors, 727 journals, and 88 countries. The findings indicate that the most productive and influential authors have British and American affiliations. The United States, the United Kingdom, and China have conducted most studies on the aforementioned topic. Imperial College London, United Kingdom, has the highest number of publications in this field of research. Furthermore, the collaborative analysis was developed by creating links between the keywords, published information, authors, institutions, and countries. In addition, the discussion highlights the tremendous development in the research area of carbon capture and utilization, especially with a focus on the exponential rise in the number of yearly publications