Special Issue "Modelling, Simulation and Optimization of Large Scale Chemical Processes"

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

Deadline for manuscript submissions: closed (21 August 2019) | Viewed by 1513

Special Issue Editor

Prof. Magne Hillestad
E-Mail Website
Guest Editor
Department of Chemical Engineering, Norwegian University of Science and Technology, Norway
Interests: process modelling; simulation, optimization; model predictive control and conceptual design of chemical processes (process technologies); statistical analysis of data, including model regression and design of experiments; processes like gas-to-liquid and biomass-to-liquid process systems; Fischer-Tropsch synthesis; methanol synthesis; methanation; synthesis gas production; membrane processes; polyolefin processes

Special Issue Information

Dear Colleagues,

We are inviting submissions to a Special Issue of the journal ChemEngineering, “Modelling, Simulation and Optimization of Large Scale Chemical Processes”. We are particular interested in efficient and robust techniques and algorithms for simulation and optimization of large scale models describing chemical engineering systems. By “large scale models” we mean detailed models of unit operations combined into a flowsheet model of an entire process. The models may be steady-state or dynamic. Commercial process simulator models represent a large scale model, but also tailor made models implemented in commercial simulators or as standalone programs. A detailed model may be high resolution with respect to describing spatial gradient, but also with respect to distribution of chemical components and other population distributions. In addition, underlying thermodynamic models for calculation of phase and chemical equilibria and physical properties add to the complexity. These models are computationally laborious, and we are interested in efficient and robust methods to use these models in a simulation and/or optimization contexts. In a dynamic simulation context the use of local thermodynamic models has been successfully applied. Similarly, in an optimization context the use of surrogate models has been applied to make the optimization more efficient and robust. To ensure a global optimum is of great interest as well as efficiency. New algorithms and techniques are of great interest, in addition to good and illustrative applications of new and existing techniques.

Prof. Magne Hillestad
Guest Editor

Manuscript Submission Information

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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 semimonthly journal published by MDPI.

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  • Large scale models
  • efficient and robust methods
  • optimization
  • process simulation

Published Papers (1 paper)

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Solubility Models for the Recovery of Rosmarinic Acid from Orthosiphon Aristatus Extract Using Solid Phase Extraction
ChemEngineering 2019, 3(3), 64; https://doi.org/10.3390/chemengineering3030064 - 10 Jul 2019
Cited by 2 | Viewed by 1365
Hildebrand and Hansen solubility parameters, and log P value are widely used to determine the solubility of polymers in solvents. The models were used to explain the recovery of phytochemical, rosmarinic acid from Orthosiphon aristatus extract in C18 solid phase extraction (SPE) using [...] Read more.
Hildebrand and Hansen solubility parameters, and log P value are widely used to determine the solubility of polymers in solvents. The models were used to explain the recovery of phytochemical, rosmarinic acid from Orthosiphon aristatus extract in C18 solid phase extraction (SPE) using the eluent consisting of ethyl acetate and chloroform in the decreasing polarity of solvent system. The experimental recovery of rosmarinic acid appeared to be well explained by the Hansen solubility model. The small difference in the Hansen solubility parameters, particularly for dispersion and hydrogen bonding forces, results in a higher polar solvent system for high rosmarinic acid recovery. The results found that the Hansen solubility model fitted well to the recovery of rosmarinic acid from crude extract with high coefficient of determination (R2 > 0.8), low standard error (4.4%), and p < 0.05. Hildebrand solubility is likely to be the second fit model, whereas log P has poor R2 < 0.7 and higher standard error (7.3%). The Hansen solubility model describes the interaction of solute–solvent in three dimensions (dispersion, polar, and hydrogen bonding forces) which can accurately explain the recovery of rosmarinic acid. Therefore, Hansen solubility can be used to predict the recovery of rosmarinic acid from O. aristatus extract using SPE. Full article
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