Special Issue "Process Modelling and Simulation"

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Computational Methods".

Deadline for manuscript submissions: 31 January 2019

Special Issue Editors

Guest Editor
Prof. Dr. Cesar de Prada

Department of Systems Engineering and Automatic Control, University of Valladolid, Valladolid 47011, Spain
Website | E-Mail
Interests: process modelling and simulation; predictive control; process optimization
Guest Editor
Prof. Dr. Costas Pantelides

Process Systems Enterprise Ltd., London W6 7HA, United Kingdom
Centre for Process Systems Engineering, Imperial College London, London SW7 2AZ, United Kingdom
Website | E-Mail
Interests: architecture and design of general-purpose process modelling tools, numerical methods for large-scale simulation and optimisation, and molecular modelling
Guest Editor
Dr. Jose Luis Pitarch

Department of Systems Engineering and Automatic Control, University of Valladolid,Valladolid 47011, Spain
Website | E-Mail
Interests: real-time optimization; grey-box modelling; fuzzy systems; artificial intelligence; stability analysis; disturbance-invariant control; scheduling

Special Issue Information

Dear Colleagues,

Models are increasingly used for a large variety of purposes in the process industry, including process design, process control, supervisory systems, process optimization, etc. and are becoming irreplaceable components for any analysis or decision making process. Nevertheless, as models are today ubiquitous in many applications, the problems and alternatives related to their development, validation, reduction or efficient use become more apparent. Massive amounts of data available today online open the door for new applications. However, transforming data into useful models and information in the context of the process industry or bio-systems, agro-food, etc., requires specific approaches and considerations, in particular new modelling methodologies incorporating the complex, stochastic, hybrid and distributed nature of many processes.

The same can be said about the tools and software environments used to describe and solve the models and facilitate their exploitation. More and more, they cannot be considered merely as simulation tools, but as a set of tools built around the models facilitating topics such as experiment design, parameter estimation, model initialization, model validation, analysis, discretization, optimization, model reduction and surrogate model extraction, multiscale operation, distributed computation, co-simulation, HIL, etc. Moreover, advances both in modelling tools and in the underlying hardware and software infrastructure are allowing increasing re-use of models along the entire process lifecycle, from early stage process design to process operations, automation and control.

This Special issue of Processes, dedicated to “Process Modelling and Simulation”, aims to collect novel developments in the field in order to address the challenges brought by the use of models in its different facets, and to reflect the state of the art in methods, tools and applications. Topics include, but are not limited to:

  • Modelling methodologies
  • Model development
  • Model online adaptation
  • Parameter estimation
  • Process-model uncertainty quantification
  • Simulation environments
  • Grey-box models
  • Hybrid modelling
  • Multiscale systems
  • Numerical methods
  • Distributed models
  • Model reduction
  • Data driven models
  • Applications illustrating recent advances and/or outstanding research gaps in the above areas

We hope that you consider contributing with the results of your research to this Special Issue.

Prof. Dr. Cesar de Prada
Prof. Dr. Costas Pantelides
Dr. Jose Luis Pitarch
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. Processes is an international peer-reviewed open access monthly 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 850 CHF (Swiss Francs). Please note that for papers submitted after 31 December 2018 an APC of 1100 CHF applies. 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

  • Modelling
  • Simulation
  • Grey box models
  • Stochastic models
  • Hybrid systems
  • Data driven modelling
  • Parameter estimation
  • Model reduction
  • Distributed modelling and Simulation

Published Papers (6 papers)

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Research

Open AccessFeature PaperArticle Toward a Comprehensive and Efficient Robust Optimization Framework for (Bio)chemical Processes
Processes 2018, 6(10), 183; https://doi.org/10.3390/pr6100183
Received: 6 September 2018 / Revised: 21 September 2018 / Accepted: 26 September 2018 / Published: 3 October 2018
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Abstract
Model-based design principles have received considerable attention in biotechnology and the chemical industry over the last two decades. However, parameter uncertainties of first-principle models are critical in model-based design and have led to the development of robustification concepts. Various strategies have been introduced
[...] Read more.
Model-based design principles have received considerable attention in biotechnology and the chemical industry over the last two decades. However, parameter uncertainties of first-principle models are critical in model-based design and have led to the development of robustification concepts. Various strategies have been introduced to solve the robust optimization problem. Most approaches suffer from either unreasonable computational expense or low approximation accuracy. Moreover, they are not rigorous and do not consider robust optimization problems where parameter correlation and equality constraints exist. In this work, we propose a highly efficient framework for solving robust optimization problems with the so-called point estimation method (PEM). The PEM has a fair trade-off between computational expense and approximation accuracy and can be easily extended to problems of parameter correlations. From a statistical point of view, moment-based methods are used to approximate robust inequality and equality constraints for a robust process design. We also apply a global sensitivity analysis to further simplify robust optimization problems with a large number of uncertain parameters. We demonstrate the performance of the proposed framework with two case studies: (1) designing a heating/cooling profile for the essential part of a continuous production process; and (2) optimizing the feeding profile for a fed-batch reactor of the penicillin fermentation process. According to the derived results, the proposed framework of robust process design addresses uncertainties adequately and scales well with the number of uncertain parameters. Thus, the described robustification concept should be an ideal candidate for more complex (bio)chemical problems in model-based design. Full article
(This article belongs to the Special Issue Process Modelling and Simulation)
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Open AccessFeature PaperArticle Model Development and Validation of Fluid Bed Wet Granulation with Dry Binder Addition Using a Population Balance Model Methodology
Processes 2018, 6(9), 154; https://doi.org/10.3390/pr6090154
Received: 15 June 2018 / Revised: 2 August 2018 / Accepted: 21 August 2018 / Published: 1 September 2018
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Abstract
An experimental study in industry was previously carried out on a batch fluid bed granulation system by varying the inlet fluidizing air temperature, binder liquid spray atomization pressure, the binder liquid spray rate and the disintegrant composition in the formulation. A population balance
[...] Read more.
An experimental study in industry was previously carried out on a batch fluid bed granulation system by varying the inlet fluidizing air temperature, binder liquid spray atomization pressure, the binder liquid spray rate and the disintegrant composition in the formulation. A population balance model framework integrated with heat transfer and moisture balance due to liquid addition and evaporation was developed to simulate the fluid bed granulation system. The model predictions were compared with the industry data, namely, the particle size distributions (PSDs) and geometric mean diameters (GMDs) at various time-points in the granulation process. The model also predicted the trends for binder particle dissolution in the wetting liquid and the temperatures of the bed particles in the fluid bed granulator. Lastly, various process parameters were varied and extended beyond the region studied in the aforementioned experimental study to identify optimal regimes for granulation. Full article
(This article belongs to the Special Issue Process Modelling and Simulation)
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Open AccessFeature PaperArticle GEKKO Optimization Suite
Processes 2018, 6(8), 106; https://doi.org/10.3390/pr6080106
Received: 1 July 2018 / Revised: 19 July 2018 / Accepted: 23 July 2018 / Published: 31 July 2018
PDF Full-text (827 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
This paper introduces GEKKO as an optimization suite for Python. GEKKO specializes in dynamic optimization problems for mixed-integer, nonlinear, and differential algebraic equations (DAE) problems. By blending the approaches of typical algebraic modeling languages (AML) and optimal control packages, GEKKO greatly facilitates the
[...] Read more.
This paper introduces GEKKO as an optimization suite for Python. GEKKO specializes in dynamic optimization problems for mixed-integer, nonlinear, and differential algebraic equations (DAE) problems. By blending the approaches of typical algebraic modeling languages (AML) and optimal control packages, GEKKO greatly facilitates the development and application of tools such as nonlinear model predicative control (NMPC), real-time optimization (RTO), moving horizon estimation (MHE), and dynamic simulation. GEKKO is an object-oriented Python library that offers model construction, analysis tools, and visualization of simulation and optimization. In a single package, GEKKO provides model reduction, an object-oriented library for data reconciliation/model predictive control, and integrated problem construction/solution/visualization. This paper introduces the GEKKO Optimization Suite, presents GEKKO’s approach and unique place among AMLs and optimal control packages, and cites several examples of problems that are enabled by the GEKKO library. Full article
(This article belongs to the Special Issue Process Modelling and Simulation)
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Open AccessArticle Sequential Parameter Estimation for Mammalian Cell Model Based on In Silico Design of Experiments
Processes 2018, 6(8), 100; https://doi.org/10.3390/pr6080100
Received: 18 May 2018 / Revised: 20 July 2018 / Accepted: 20 July 2018 / Published: 24 July 2018
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Abstract
Due to the complicated metabolism of mammalian cells, the corresponding dynamic mathematical models usually consist of large sets of differential and algebraic equations with a large number of parameters to be estimated. On the other hand, the measured data for estimating the model
[...] Read more.
Due to the complicated metabolism of mammalian cells, the corresponding dynamic mathematical models usually consist of large sets of differential and algebraic equations with a large number of parameters to be estimated. On the other hand, the measured data for estimating the model parameters are limited. Consequently, the parameter estimates may converge to a local minimum far from the optimal ones, especially when the initial guesses of the parameter values are poor. The methodology presented in this paper provides a systematic way for estimating parameters sequentially that generates better initial guesses for parameter estimation and improves the accuracy of the obtained metabolic model. The model parameters are first classified into four subsets of decreasing importance, based on the sensitivity of the model’s predictions on the parameters’ assumed values. The parameters in the most sensitive subset, typically a small fraction of the total, are estimated first. When estimating the remaining parameters with next most sensitive subset, the subsets of parameters with higher sensitivities are estimated again using their previously obtained optimal values as the initial guesses. The power of this sequential estimation approach is illustrated through a case study on the estimation of parameters in a dynamic model of CHO cell metabolism in fed-batch culture. We show that the sequential parameter estimation approach improves model accuracy and that using limited data to estimate low-sensitivity parameters can worsen model performance. Full article
(This article belongs to the Special Issue Process Modelling and Simulation)
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Open AccessArticle Modelling Condensation and Simulation for Wheat Germ Drying in Fluidized Bed Dryer
Processes 2018, 6(6), 71; https://doi.org/10.3390/pr6060071
Received: 10 April 2018 / Revised: 1 June 2018 / Accepted: 5 June 2018 / Published: 9 June 2018
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Abstract
A low-temperature drying with fluidized bed dryer (FBD) for wheat germ (WG) stabilization could prevent the loss of nutrients during processing. However, both evaporation and condensation behaviors occurred in sequence during FBD drying of WG. The objective of this study was to develop
[...] Read more.
A low-temperature drying with fluidized bed dryer (FBD) for wheat germ (WG) stabilization could prevent the loss of nutrients during processing. However, both evaporation and condensation behaviors occurred in sequence during FBD drying of WG. The objective of this study was to develop a theoretical thin-layer model coupling with the macro-heat transfer model and the bubble model for simulating both the dehydration and condensation behaviors of WG during low-temperature drying in the FBD. The experimental data were also collected for the model modification. Changes in the moisture content of WG, the air temperature of FBD chamber, and the temperature of WG during drying with different heating approaches were significantly different. The thermal input of WG drying with short heating time approach was one-third of that of WG drying with a traditional heating approach. The mathematical model developed in this study could predict the changes of the moisture content of WG and provide a good understanding of the condensation phenomena of WG during FBD drying. Full article
(This article belongs to the Special Issue Process Modelling and Simulation)
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Graphical abstract

Open AccessFeature PaperArticle Toward a Distinct and Quantitative Validation Method for Predictive Process Modelling—On the Example of Solid-Liquid Extraction Processes of Complex Plant Extracts
Processes 2018, 6(6), 66; https://doi.org/10.3390/pr6060066
Received: 2 May 2018 / Revised: 14 May 2018 / Accepted: 23 May 2018 / Published: 1 June 2018
Cited by 1 | PDF Full-text (5814 KB) | HTML Full-text | XML Full-text
Abstract
Physico-chemical modelling and predictive simulation are becoming key for modern process engineering. Rigorous models rely on the separation of different effects (e.g., fluid dynamics, kinetics, mass transfer) by distinct experimental parameter determination on lab-scale. The equations allow the transfer of the lab-scale data
[...] Read more.
Physico-chemical modelling and predictive simulation are becoming key for modern process engineering. Rigorous models rely on the separation of different effects (e.g., fluid dynamics, kinetics, mass transfer) by distinct experimental parameter determination on lab-scale. The equations allow the transfer of the lab-scale data to any desired scale, if characteristic numbers like e.g., Reynolds, Péclet, Sherwood, Schmidt remain constant and fluid-dynamics of both scales are known and can be described by the model. A useful model has to be accurate and therefore match the experimental data at different scales and combinations of process and operating parameters. Besides accuracy as one quality attribute for the modelling depth, model precision also has to be evaluated. Model precision is considered as the combination of modelling depth and the influence of experimental errors in model parameter determination on the simulation results. A model is considered appropriate if the deviation of the simulation results is in the same order of magnitude as the reproducibility of the experimental data to be substituted by the simulation. Especially in natural product extraction, the accuracy of the modelling approach can be shown through various studies including different feedstocks and scales, as well as process and operating parameters. Therefore, a statistics-based quantitative method for the assessment of model precision is derived and discussed in detail in this paper to complete the process engineering toolbox. Therefore a systematic workflow including decision criteria is provided. Full article
(This article belongs to the Special Issue Process Modelling and Simulation)
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