Chemical engineer

Summary

In the field of engineering, a chemical engineer is a professional, equipped with the knowledge of chemical engineering, who works principally in the chemical industry to convert basic raw materials into a variety of products and deals with the design and operation of plants and equipment.MobyDick Dictionary of Engineering", McGraw-Hill, 2nd Ed. In general, a chemical engineer is one who applies and uses principles of chemical engineering in any of its various practical applications; these often include

Design, manufacture, and operation of plants and machinery in industrial chemical and related processes ("chemical process engineers")

Development of new or adapted substances for products ranging from foods and beverages to cosmetics to cleaners to pharmaceutical ingredients, among many other products ("chemical product engineers")

Development of new technologies such as fuel cells, hydrogen power and nanotechnology, as well as working in fields wholly or partially

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The zein solutions containing different concentrations of cuminaldehyde (5%, 10%, and 20%, w/w) were electrospun. The morphology and average diameter of fibers were evaluated by scanning electron microscopy and the optimized fiber (20% cuminaldehyde) was chosen. Proton nuclear magnetic resonance spectroscopy and encapsulation efficiency showed the presence and desirable content of loaded cuminaldehyde in cuminaldehyde-loaded zein fiber, respectively. Chemical evaluations using Fourier transform infrared spectroscopy, X-ray diffraction, and confocal Raman spectroscopy assigned the domination of helical domains, amorphousness, and acceptive distribution to the resultant fiber, respectively. Modeling of Z19 and Z22 as well as molecular docking confirmed the results of chemical evaluations. Furthermore, the enhanced onset thermostability of cuminaldehyde (from 80 to 99 degrees C) was observed after the incorporation into zein fiber. Finally, the diffusion controlledrelease of cuminaldehyde from a hydrophobic surface, its low-risk toxicity, and potent antibacterial activity of cuminaldehyde-loaded zein fiber against Staphylococcus aureus and Escherichia coli suggested its potentiality to be used in food and therapeutic applications. (c) 2021 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

ELSEVIER2021

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In the context of process and product design, predictive models are increasingly employed. Decomposition properties of chemicals may be experimentally determined through calorimetric measurements, and a few molecular structure-based models- which correlate the molecular structure of compounds with their decomposition properties- are also available. The aim of this paper is to improve predictive modeling of decomposition characteristics derived from Differential Scanning Calorimetry (DSC) (Baati et al., 2016), through the implementation of pattern recognition as a primary classification. For this purpose, the entire decomposition peaks of the molecules are represented and treated with image processing algorithms to identify the different patterns. Predictive modeling is then performed within the categories and compared to a global model prediction. Firstly, DSC thermograms (or curves) are analyzed to identify similar decomposition patterns in order to develop a clustering based on their overall thermal behavior instead of their structural similarities. Secondly, the reparation of the structural groups in the clusters is evaluated in order to determine the most influential functional groups on the thermal decomposition behavior. From this analysis, a systematic classification is developed to assign molecules of unknown thermal behavior to a particular cluster. Thirdly, predictive models of thermal characteristics are constructed within the different classes allowing predicting the entire DSC curve. The primary classification based on the pattern recognition increases the predictive performance of the regressions models (C) 2017 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

Institution of Chemical Engineers2017

Correlation between flocculation and adsorption of cationic polyacrylamides on precipitated calcium carbonate

Hamideh Ahmadloo, Ineide Cruz Pinheiro, David Hunkeler, Christine Wandrey

The study of each stage of the flocculation process is essential to better understand and predict flocculation mechanisms. The adsorption of cationic polyacrylamide derivatives (C-PAM) onto precipitated calcium carbonate (PCC) has been investigated systematically as a function of the C-PAM characteristics including molar mass, chain architecture, and charge density. The adsorption results show that, for C-PAM of similar molar mass, highly branched architectures reach the equilibrium faster than linear C-PAM. Similarly, the flocculation rate is higher for the branched C-PAM, which may be indicative of the predominance of the bridging mechanism. In terms of the molar mass, lower molar mass leads to lower adsorption rates and slower flocculation. Adsorption isotherms of C-PAM onto precipitated calcium carbonate could be described by the Langmuir isotherm model. The maximum amount of C-PAM that adsorbs on the particle surface as a monolayer, obtained from adsorption tests through the Langmuir isotherm linear fit, could be correlated with the structure of the aggregates, obtained from flocculation experiments. Moreover, a good correlation was obtained between the adsorption results and the kinetics of the first stage of the flocculation process dominated by particle aggregation. (c) 2014 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

Inst Chemical Engineers2015