Special Issue "Control and Optimization of Chemical and Biochemical Processes"

A special issue of ChemEngineering (ISSN 2305-7084).

Deadline for manuscript submissions: 1 December 2018

Special Issue Editors

Guest Editor
Prof. Dr. Stefano Curcio

Department of Computer Engineering, Modeling, Electronics and Systems (D.I.M.E.S.), Laboratory of Transport Phenomena and Biotechnology, University of Calabria, Cubo-39c, Via P. Bucci, 87036 Rende, Italy
Website | E-Mail
Interests: Chemical Engineering; Modeling, Simulation and Control; Food Engineering and Biotechnology; Membrane Science and Technology; Environmental Science Engineering; Energy; Materials Science
Guest Editor
Dr. Sudip Chakraborty

Department of Computer Engineering, Modeling, Electronics and Systems (D.I.M.E.S.), Laboratory of Transport Phenomena and Biotechnology, University of Calabria, Cubo-42a, Via P. Bucci, 87036 Rende, Italy
E-Mail
Interests: Chemical Engineering; Membrane Science and Technology; Environmental Science Engineering; Materials Science; Membrane reactors

Special Issue Information

Dear Colleagues,

The special issue will cover different topics including modeling, simulation, design, control and optimization of both chemical and biochemical processes. A more efficient analysis of several problems, ranging from the design of novel materials to the optimization of industrial plants, will be provided. One of the major advantages of the described techniques will be represented by the possibility of obtaining accurate predictions of the analyzed processes over a wide range operating conditions. Computer simulations, or computer experiments, are indeed less impaired by non-linearity, having many degrees of freedom or lacking in symmetries than analytical approaches. As a result, computer simulations establish their greatest value for those systems where the gap between theoretical predictions and laboratory measurements is large. Instead of constructing layers of different approximations of a basic law (formulated as equations), a numerical approach simulates directly the original problem with its full complexity without making many assumptions.

The modeling, simulation, control and optimization techniques described in this special issue can be, therefore, used as an exploratory tool in “computer experiments” under conditions, which would not be feasible or too expensive in real experiments in the laboratory.

One of the major impacts of the present proposal is to show the true interactions and interconnectivities among different topics belonging to nanotechnology, chemistry, energy and (bio-) chemical engineering research fields.

Main goals:

- A wide spectrum of different problems and of innovative and unconventional modeling, simulation, control and optimization techniques will be covered.

- It will be shown how various kinds of advanced models can be exploited either to predict the behavior of real processes, thus optimizing their performance or to achieve the formulation of novel products/materials.

- The control/optimization of novel processes/materials will be achieved in a faster and more reliable way.

Prof. Dr. Stefano Curcio
Dr. Sudip Chakraborty
Guest Editors

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 papers will be 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. ChemEngineering is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) is waived for well-prepared manuscripts submitted to this issue. 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.

Keywords

  • Hybrid modeling
  • Multi-scale simulation
  • Efficient models
  • Simulation, modeling, optimization and control
  • Membrane reactors in chemical & bioprocess engineering

Published Papers (2 papers)

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Research

Open AccessArticle The Effect of Off-Spec Canola Biodiesel Blending on Fuel Properties for Cold Weather Applications
ChemEngineering 2018, 2(3), 30; https://doi.org/10.3390/chemengineering2030030
Received: 12 February 2018 / Revised: 2 June 2018 / Accepted: 29 June 2018 / Published: 2 July 2018
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Abstract
Biodiesel is a renewable and reduced-emission alternative fuel produced mainly from the alcoholysis of vegetable oils and/or animal fats. It is mainly used in blends with diesel fuel to reduce emissions, enhance lubrication and lower sulfur content. Being able to accurately determine the
[...] Read more.
Biodiesel is a renewable and reduced-emission alternative fuel produced mainly from the alcoholysis of vegetable oils and/or animal fats. It is mainly used in blends with diesel fuel to reduce emissions, enhance lubrication and lower sulfur content. Being able to accurately determine the physicochemical properties of blended fuel is important for optimal injection, combustion, and lubricating performance in diesel engines. Also, fuel properties vary as the ratio of biodiesel-diesel changes, affecting the final fuel quality. In this study, a wide range and narrow intervals of (0, 2, 4, 6, 8, 10, 12, 15, 18, 20, 25, 35, 50, 75 and 100% by volume) off-quality canola-based biodiesel blends were prepared at ambient conditions and used to study the blended fuel properties (density, kinematic viscosity, flash point, cloud point and pour point). This is particularly important for examining the effect of a biodiesel content of more than 20%—the industry maximum blend content—on cold flow properties, fuel stability, energy value, and emissions. It was found that the kinematic viscosity and density increased linearly as the concentration of the biodiesel in the blend increases. The pour point and cloud point temperature showed a small increase up to 35% blending ratio and a rapid increase in temperature for biodiesel concentrations higher than 35%. Also, the flash point remained almost constant at an average value of 73 °C for blends less than 20%, above which the values for the flash point increased exponentially with biodiesel concentration. Furthermore, predictive correlations were developed for all tested fuel properties from regressing corresponding experimental data. All models exhibited excellent agreement with experimental data with an average absolute deviation of less than 5%. Full article
(This article belongs to the Special Issue Control and Optimization of Chemical and Biochemical Processes)
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Open AccessArticle Development and Analyses of Artificial Intelligence (AI)-Based Models for the Flow Boiling Heat Transfer Coefficient of R600a in a Mini-Channel
ChemEngineering 2018, 2(2), 27; https://doi.org/10.3390/chemengineering2020027
Received: 6 April 2018 / Revised: 7 June 2018 / Accepted: 11 June 2018 / Published: 13 June 2018
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Abstract
Environmental friendly refrigerants with zero ozone depletion potential (ODP) and zero global warming potential (GWP) are in great demand across the globe. One such popular refrigerant is isobutane (R600a) which, having zero ODP and negligible GWP, is considered in this study. This paper
[...] Read more.
Environmental friendly refrigerants with zero ozone depletion potential (ODP) and zero global warming potential (GWP) are in great demand across the globe. One such popular refrigerant is isobutane (R600a) which, having zero ODP and negligible GWP, is considered in this study. This paper presents the two most popular artificial intelligence (AI) techniques, namely support vector regression (SVR) and artificial neural networks (ANN), to predict the heat transfer coefficient of refrigerant R600a. The independent input parameters of the models include mass flux, saturation temperature, heat flux, and vapor fraction. The heat transfer coefficient of R600a is the dependent output parameter. The prediction performance of these AI-based models is compared and validated against the experimental results, as well as with the existing correlations based on the statistical parameters. The SVR model based on the structural risk minimization (SRM) principle is observed to be superior compared with the other models and is more accurate, precise, and highly generalized; it has the lowest average absolute relative error (AARE) at 1.15% and the highest coefficient of determination (R2) at 0.9981. ANN gives an AARE of 5.14% and a R2 value of 0.9685. Furthermore, the simulated results accurately predict the effect of input parameters on the heat transfer coefficient. Full article
(This article belongs to the Special Issue Control and Optimization of Chemical and Biochemical Processes)
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