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Applied Sciences
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Models for Predicting the Thermodynamic Data Necessary for Process Design
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Models for Predicting the Thermodynamic Data Necessary for Process Design

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Chemical and Molecular Sciences".

Deadline for manuscript submissions: closed (30 June 2019) | Viewed by 9679

Special Issue Editor

Dr. Ilya Polishuk

E-Mail Website
Guest Editor
Department of Chemical Engineering, Ariel University, Ariel 40700, Israel
Interests: thermodynamic properties; transport properties; phase equilibria; surface tension; equations of state; SAFT

Special Issue Information

Dear Colleagues,

It is my pleasure to invite scientific contributions to the Special Issue of Applied Sciences dedicated to models for predicting thermodynamic data. As is well known, various thermodynamic data, such as phase equilibria, volumetric and auxiliary properties are necessary for all kinds of process design. However, their experimental measurements are often expensive and time-consuming. Therefore, theoretical methods capable of the reliable estimation of these data play the principal role in various disciplines such as chemical, mechanical, material and nuclear engineering, biotechnology, etc. This issue will be dedicated to the recent advances in development of various thermodynamic models such as cubic equations, the SAFT models, lattice theory, and other approaches such as artificial neural networks, along with their methods of implementation to pure compounds and mixtures, such as the group-contribution and other parametrization approaches, and mixing rules.

Assoc. Prof. Dr. Ilya Polishuk
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (3 papers)

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Research

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24 pages, 9298 KiB  
Article
Thermal and Mechanical Analysis of a 72/48 Switched Reluctance Motor for Low-Speed Direct-Drive Mining Applications
by Esmail Elhomdy, Zheng Liu and Guofeng Li
Appl. Sci. 2019, 9(13), 2722; https://0-doi-org.brum.beds.ac.uk/10.3390/app9132722 - 05 Jul 2019
Cited by 6 | Viewed by 3735
Abstract
In the process of electric motor design, it is essential to predict and provide an accurate thermal and mechanical model. The aim of this research is to improve the thermal and mechanical performance—which is implemented into a 72/48 switched reluctance motor (SRM) with [...] Read more.
In the process of electric motor design, it is essential to predict and provide an accurate thermal and mechanical model. The aim of this research is to improve the thermal and mechanical performance—which is implemented into a 72/48 switched reluctance motor (SRM) with 75 kW—of a low-speed direct-drive mining system (pulverizer). Thermal analysis of the SRM requires a deep understanding of the coolant behavior and the thermal mechanism in the motor. Computational fluid dynamics (CFD) based finite element analysis (FEA) was carried out in order to precisely visualize and estimate fluid state and temperature distribution inside the motor. Several different coolant configurations were carried out, with the purpose of determining an appropriate one for uniform temperature distribution in the SRM. The natural frequencies are presented with the developed finite element mechanical, structural model. To adapt in the mining application, the cooling jacket configurations with 17 channels and the shaft with spoke was found to be optimal for the SRM, which may raise the natural frequency and reduce the weight and temperature of the motor. The simulations results showed a good agreement with experimental results regarding temperature distribution within the motor. Full article
(This article belongs to the Special Issue Models for Predicting the Thermodynamic Data Necessary for Process Design)
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16 pages, 3874 KiB  
Article
Soft Sensor with Adaptive Algorithm for Filter Gain Correction in the Online Monitoring System of a Polluted River
by Przemysław Hawro, Tadeusz Kwater, Robert Pękala and Bogusław Twaróg
Appl. Sci. 2019, 9(9), 1883; https://0-doi-org.brum.beds.ac.uk/10.3390/app9091883 - 08 May 2019
Cited by 6 | Viewed by 2335
Abstract
This paper proposes the realization of a soft sensor using an adaptive algorithm with proportional correction of the gain coefficient for monitoring river water quality. This algorithm makes it possible to monitor online signals of an object described by nonlinear ordinary differential equations. [...] Read more.
This paper proposes the realization of a soft sensor using an adaptive algorithm with proportional correction of the gain coefficient for monitoring river water quality. This algorithm makes it possible to monitor online signals of an object described by nonlinear ordinary differential equations. Simulation studies of a biochemically polluted river, for which the water quality was represented by biochemical oxygen demand (BOD) indices and the dissolved oxygen (DO) deficit, were carried out. The algorithm concept uses only online measurements of the object, and adaptive changes in the gain coefficient are determined based on the adaptation error adopted for this purpose. Simulation results indicated the correct functioning of the soft sensor even for inaccurately identified parameters of the mathematical model and for unknown values and intensity of disturbances affecting the object. The quality of the signals monitored via a soft sensor implemented in this way was determined with the root-mean-squared error (RMSE) and mean percentage error (MPE) indicators and compared with the Kalman filter. Full article
(This article belongs to the Special Issue Models for Predicting the Thermodynamic Data Necessary for Process Design)
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Review

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19 pages, 2089 KiB  
Review
A Way towards Reliable Predictive Methods for the Prediction of Physicochemical Properties of Chemicals Using the Group Contribution and other Methods
by Robert J. Meier
Appl. Sci. 2019, 9(8), 1700; https://0-doi-org.brum.beds.ac.uk/10.3390/app9081700 - 24 Apr 2019
Cited by 5 | Viewed by 3198
Abstract
Physicochemical properties of chemicals as referred to in this review include, for example, thermodynamic properties such as heat of formation, boiling point, toxicity of molecules and the fate of molecules whenever undergoing or accelerating (catalytic) a chemical reaction and therewith about chemical equilibrium, [...] Read more.
Physicochemical properties of chemicals as referred to in this review include, for example, thermodynamic properties such as heat of formation, boiling point, toxicity of molecules and the fate of molecules whenever undergoing or accelerating (catalytic) a chemical reaction and therewith about chemical equilibrium, that is, the equilibrium in chemical reactions. All such properties have been predicted in literature by a variety of methods. However, for the experimental scientist for whom such predictions are of relevance, the accuracies are often far from sufficient for reliable application We discuss current practices and suggest how one could arrive at better, that is sufficiently accurate and reliable, predictive methods. Some recently published examples have shown this to be possible in practical cases. In summary, this review focuses on methodologies to obtain the required accuracies for the chemical practitioner and process technologist designing chemical processes. Finally, something almost never explicitly mentioned is the fact that whereas for some practical cases very accurate predictions are required, for other cases a qualitatively correct picture with relatively low correlation coefficients can be sufficient as a valuable predictive tool. Requirements for acceptable predictive methods can therefore be significantly different depending on the actual application, which are illustrated using real-life examples, primarily with industrial relevance. Furthermore, for specific properties such as the octanol-water partition coefficient more close collaboration between research groups using different methods would greatly facilitate progress in the field of predictive modelling. Full article
(This article belongs to the Special Issue Models for Predicting the Thermodynamic Data Necessary for Process Design)
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