Process Modelling and Simulation

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Process Control and Monitoring".

Deadline for manuscript submissions: closed (28 February 2019) | Viewed by 89113

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Special Issue Editors

Department of Systems Engineering and Automatic Control, University of Valladolid, 47011 Valladolid, Spain
Interests: process modelling and simulation; predictive control; process optimization
1. Process Systems Enterprise Ltd., London W6 7HA, UK
2. Centre for Process Systems Engineering, Imperial College London, London SW7 2AZ, UK
Interests: architecture and design of general-purpose process modelling tools, numerical methods for large-scale simulation and optimisation, and molecular modelling
Department of Systems Engineering and Automatic Control, University of Valladolid, 47011 Valladolid, Spain
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

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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. 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 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.

Keywords

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

Published Papers (15 papers)

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Editorial

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3 pages, 161 KiB  
Editorial
Special Issue on “Process Modelling and Simulation”
by César de Prada, Constantinos C. Pantelides and José Luis Pitarch
Processes 2019, 7(8), 511; https://doi.org/10.3390/pr7080511 - 05 Aug 2019
Cited by 3 | Viewed by 2440
Abstract
Collecting and highlighting novel developments that address existing as well as forthcoming challenges in the field of process modelling and simulation was the motivation for proposing this special issue on “Process Modelling and Simulation” in the journal Processes [...] Full article
(This article belongs to the Special Issue Process Modelling and Simulation)

Research

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14 pages, 3971 KiB  
Article
Mathematical Modelling Forecast on the Idling Transient Characteristic of Reactor Coolant Pump
by Xiuli Wang, Yajie Xie, Yonggang Lu, Rongsheng Zhu, Qiang Fu, Zheng Cai and Ce An
Processes 2019, 7(7), 452; https://doi.org/10.3390/pr7070452 - 15 Jul 2019
Cited by 2 | Viewed by 3429
Abstract
The idling behavior of the reactor coolant pump is referred to as an important indicator of the safe operation of the nuclear power system, while the idling transition process under the power failure accident condition is developed as a transient flow process. In [...] Read more.
The idling behavior of the reactor coolant pump is referred to as an important indicator of the safe operation of the nuclear power system, while the idling transition process under the power failure accident condition is developed as a transient flow process. In this process, the parameters such as the flow rate, speed, and head of the reactor coolant pump are all nonlinear changes. In order to ensure the optimal idling behavior of the reactor coolant pump under the power cutoff accident condition, this manuscript takes the guide vanes of the AP1000 reactor coolant pump as the subject of this study. In this paper, the mathematical model of idling speed and flow characteristic curve of reactor coolant pump under the power failure condition were proposed, while the hydraulic modeling database of different vane structure parameters was modeled based on the orthogonal optimization schemes. Furthermore, based on the mathematical modeling framework of multiple linear regressions, the mathematical relationship of the hydraulic performance of each guide vane in different parameters was predicted. The derived model was verified with the idling test data. Full article
(This article belongs to the Special Issue Process Modelling and Simulation)
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15 pages, 6342 KiB  
Article
Wave Characteristics of Coagulation Bath in Dry-Jet Wet-Spinning Process for Polyacrylonitrile Fiber Production Using Computational Fluid Dynamics
by Son Ich Ngo, Young-Il Lim and Soo-Chan Kim
Processes 2019, 7(5), 314; https://doi.org/10.3390/pr7050314 - 25 May 2019
Cited by 4 | Viewed by 4006
Abstract
In this work, a three-dimensional volume-of-fluid computational fluid dynamics (VOF-CFD) model was developed for a coagulation bath of the dry-jet wet spinning (DJWS) process for the production of polyacrylonitrile (PAN)-based carbon fiber under long-term operating conditions. The PAN-fiber was assumed to be a [...] Read more.
In this work, a three-dimensional volume-of-fluid computational fluid dynamics (VOF-CFD) model was developed for a coagulation bath of the dry-jet wet spinning (DJWS) process for the production of polyacrylonitrile (PAN)-based carbon fiber under long-term operating conditions. The PAN-fiber was assumed to be a deformable porous zone with variations in moving speed, porosity, and permeability. The Froude number, interpreted as the wave-making resistance on the liquid surface, was analyzed according to the PAN-fiber wind-up speed ( v P A N ). The effect of the PAN speed on the reflection and wake flow formed by drag between a moving object and fluid is presented. A method for tracking the wave amplitude with time is proposed based on the iso-surface of the liquid volume fraction of 0.95. The wave signal for 30 min was divided into the initial and resonance states that were distinguished at 8 min. The maximum wave amplitude was less than 0.5 mm around the PAN-fiber inlet nozzle for v P A N = 0.1–0.5 m/s in the resonance state. The VOF-CFD model is useful in determining the maximum v P A N under an allowable air gap of the DJWS process. Full article
(This article belongs to the Special Issue Process Modelling and Simulation)
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26 pages, 2605 KiB  
Article
Modeling On-Site Combined Heat and Power Systems Coupled to Main Process Operation
by Cristian Pablos, Alejandro Merino and Luis Felipe Acebes
Processes 2019, 7(4), 218; https://doi.org/10.3390/pr7040218 - 16 Apr 2019
Cited by 6 | Viewed by 3117
Abstract
Many production processes work with on-site Combined Heat and Power (CHP) systems to reduce their operational cost and improve their incomes by selling electricity to the external grid. Optimal management of these plants is key in order to take full advantage of the [...] Read more.
Many production processes work with on-site Combined Heat and Power (CHP) systems to reduce their operational cost and improve their incomes by selling electricity to the external grid. Optimal management of these plants is key in order to take full advantage of the possibilities offered by the different electricity purchase or selling options. Traditionally, this problem is not considered for small cogeneration systems whose electricity generation cannot be decided independently from the main process production rate. In this work, a non-linear gray-box model is proposed in order to deal with this dynamic optimization problem in a simulated sugar factory. The validation shows that with only 52 equations, the whole system behavior is represented correctly and, due to its structure and small size, it can be adapted to any other production process working along a CHP with the same plant configuration. Full article
(This article belongs to the Special Issue Process Modelling and Simulation)
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15 pages, 4767 KiB  
Article
Evaluation of the Influences of Scrap Melting and Dissolution during Dynamic Linz–Donawitz (LD) Converter Modelling
by Florian Markus Penz, Johannes Schenk, Rainer Ammer, Gerald Klösch and Krzysztof Pastucha
Processes 2019, 7(4), 186; https://doi.org/10.3390/pr7040186 - 31 Mar 2019
Cited by 6 | Viewed by 4546
Abstract
The Linz–Donawitz (LD) converter is still the dominant process for converting hot metal into crude steel with the help of technically pure oxygen. Beside hot metal, scrap is the most important charging material which acts as an additional iron source and coolant. Because [...] Read more.
The Linz–Donawitz (LD) converter is still the dominant process for converting hot metal into crude steel with the help of technically pure oxygen. Beside hot metal, scrap is the most important charging material which acts as an additional iron source and coolant. Because of the irrevocable importance of the process, there is continued interest in a dynamic simulation of the LD process, especially regarding the savings of material and process costs with optimized process times. Based on a thermodynamic and kinetic Matlab® coded model, the influences of several scrap parameters on its melting and dissolution behavior were determined, with a special focus on establishing the importance of specific factors on the crude steel composition and bath temperature after a defined blowing period to increase the accuracy of the process model. The calculations reported clearly indicate that the dynamic converter model reacts very sensitively to the chemical composition of the scrap as well as the charged scrap mass and size. Those results reflect the importance of experiments for validation on the diffusive scrap melting model in further research work. Based on that, reliable conclusions could be drawn to improve the theoretical and practical description of the dissolution and melting behavior of scrap in dynamic converter modelling. Full article
(This article belongs to the Special Issue Process Modelling and Simulation)
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23 pages, 29225 KiB  
Article
A Systematic Grey-Box Modeling Methodology via Data Reconciliation and SOS Constrained Regression
by José Luis Pitarch, Antonio Sala and César de Prada
Processes 2019, 7(3), 170; https://doi.org/10.3390/pr7030170 - 23 Mar 2019
Cited by 20 | Viewed by 4371
Abstract
Developing the so-called grey box or hybrid models of limited complexity for process systems is the cornerstone in advanced control and real-time optimization routines. These models must be based on fundamental principles and customized with sub-models obtained from process experimental data. This allows [...] Read more.
Developing the so-called grey box or hybrid models of limited complexity for process systems is the cornerstone in advanced control and real-time optimization routines. These models must be based on fundamental principles and customized with sub-models obtained from process experimental data. This allows the engineer to transfer the available process knowledge into a model. However, there is still a lack of a flexible but systematic methodology for grey-box modeling which ensures certain coherence of the experimental sub-models with the process physics. This paper proposes such a methodology based in data reconciliation (DR) and polynomial constrained regression. A nonlinear optimization of limited complexity is to be solved in the DR stage, whereas the proposed constrained regression is based in sum-of-squares (SOS) convex programming. It is shown how several desirable features on the polynomial regressors can be naturally enforced in this optimization framework. The goodnesses of the proposed methodology are illustrated through: (1) an academic example and (2) an industrial evaporation plant with real experimental data. Full article
(This article belongs to the Special Issue Process Modelling and Simulation)
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33 pages, 1116 KiB  
Article
Incremental Parameter Estimation under Rank-Deficient Measurement Conditions
by Kris Villez, Julien Billeter and Dominique Bonvin
Processes 2019, 7(2), 75; https://doi.org/10.3390/pr7020075 - 02 Feb 2019
Cited by 5 | Viewed by 3120
Abstract
The computation and modeling of extents has been proposed to handle the complexity of large-scale model identification tasks. Unfortunately, the existing extent-based framework only applies when certain conditions apply. Most typically, it is required that a unique value for each extent can be [...] Read more.
The computation and modeling of extents has been proposed to handle the complexity of large-scale model identification tasks. Unfortunately, the existing extent-based framework only applies when certain conditions apply. Most typically, it is required that a unique value for each extent can be computed. This severely limits the applicability of this approach. In this work, we propose a novel procedure for parameter estimation inspired by the existing extent-based framework. A key difference with prior work is that the proposed procedure combines structural observability labeling, matrix factorization, and graph-based system partitioning to split the original model parameter estimation problem into parameter estimation problems with the least number of parameters. The value of the proposed method is demonstrated with an extensive simulation study and a study based on a historical data set collected to characterize the isomerization of α -pinene. Most importantly, the obtained results indicate that an important barrier to the application of extent-based frameworks for process modeling and monitoring tasks has been lifted. Full article
(This article belongs to the Special Issue Process Modelling and Simulation)
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13 pages, 3585 KiB  
Article
Numerical Simulation of Water Absorption and Swelling in Dehulled Barley Grains during Canned Porridge Cooking
by Lei Wang, Mengting Wang, Mingming Guo, Xingqian Ye, Tian Ding and Donghong Liu
Processes 2018, 6(11), 230; https://doi.org/10.3390/pr6110230 - 20 Nov 2018
Cited by 8 | Viewed by 3672
Abstract
Understanding the hydration behavior of cereals during cooking is industrially important in order to optimize processing conditions. In this study, barley porridge was cooked in a sealed tin can at 100, 115, and 121 °C, respectively, and changes in water uptake and hygroscopic [...] Read more.
Understanding the hydration behavior of cereals during cooking is industrially important in order to optimize processing conditions. In this study, barley porridge was cooked in a sealed tin can at 100, 115, and 121 °C, respectively, and changes in water uptake and hygroscopic swelling in dehulled barley grains were measured during the cooking of canned porridge. In order to describe and better understand the hydration behaviors of barley grains during the cooking process, a three-dimensional (3D) numerical model was developed and validated. The proposed model was found to be adequate for representing the moisture absorption characteristics with a mean relative deviation modulus (P) ranging from 4.325% to 5.058%. The analysis of the 3D simulation of hygroscopic swelling was satisfactory for describing the expansion in the geometry of barley. Given that the model represented the experimental values adequately, it can be applied to the simulation and design of cooking processes of cereals grains, allowing for saving in both time and costs. Full article
(This article belongs to the Special Issue Process Modelling and Simulation)
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26 pages, 2466 KiB  
Article
Toward a Comprehensive and Efficient Robust Optimization Framework for (Bio)chemical Processes
by Xiangzhong Xie, René Schenkendorf and Ulrike Krewer
Processes 2018, 6(10), 183; https://doi.org/10.3390/pr6100183 - 03 Oct 2018
Cited by 9 | Viewed by 4221
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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25 pages, 2856 KiB  
Article
Model Development and Validation of Fluid Bed Wet Granulation with Dry Binder Addition Using a Population Balance Model Methodology
by Shashank Venkat Muddu, Ashutosh Tamrakar, Preetanshu Pandey and Rohit Ramachandran
Processes 2018, 6(9), 154; https://doi.org/10.3390/pr6090154 - 01 Sep 2018
Cited by 19 | Viewed by 4331
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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26 pages, 827 KiB  
Article
GEKKO Optimization Suite
by Logan D. R. Beal, Daniel C. Hill, R. Abraham Martin and John D. Hedengren
Processes 2018, 6(8), 106; https://doi.org/10.3390/pr6080106 - 31 Jul 2018
Cited by 195 | Viewed by 25941
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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12 pages, 1177 KiB  
Article
Sequential Parameter Estimation for Mammalian Cell Model Based on In Silico Design of Experiments
by Zhenyu Wang, Hana Sheikh, Kyongbum Lee and Christos Georgakis
Processes 2018, 6(8), 100; https://doi.org/10.3390/pr6080100 - 24 Jul 2018
Cited by 12 | Viewed by 3801
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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18 pages, 2478 KiB  
Article
Modelling Condensation and Simulation for Wheat Germ Drying in Fluidized Bed Dryer
by Der-Sheng Chan, Jun-Sheng Chan and Meng-I Kuo
Processes 2018, 6(6), 71; https://doi.org/10.3390/pr6060071 - 09 Jun 2018
Cited by 8 | Viewed by 5984
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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27 pages, 5814 KiB  
Article
Toward a Distinct and Quantitative Validation Method for Predictive Process Modelling—On the Example of Solid-Liquid Extraction Processes of Complex Plant Extracts
by Maximilian Sixt, Lukas Uhlenbrock and Jochen Strube
Processes 2018, 6(6), 66; https://doi.org/10.3390/pr6060066 - 01 Jun 2018
Cited by 39 | Viewed by 7568
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
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11 pages, 3784 KiB  
Discussion
Data-Mining for Processes in Chemistry, Materials, and Engineering
by Hao Li, Zhien Zhang and Zhe-Ze Zhao
Processes 2019, 7(3), 151; https://doi.org/10.3390/pr7030151 - 11 Mar 2019
Cited by 39 | Viewed by 6112
Abstract
With the rapid development of machine learning techniques, data-mining for processes in chemistry, materials, and engineering has been widely reported in recent years. In this discussion, we summarize some typical applications for process optimization, design, and evaluation of chemistry, materials, and engineering. Although [...] Read more.
With the rapid development of machine learning techniques, data-mining for processes in chemistry, materials, and engineering has been widely reported in recent years. In this discussion, we summarize some typical applications for process optimization, design, and evaluation of chemistry, materials, and engineering. Although the research and application targets are various, many important common points still exist in their data-mining. We then propose a generalized strategy based on the philosophy of data-mining, which should be applicable for the design and optimization targets for processes in various fields with both scientific and industrial purposes. Full article
(This article belongs to the Special Issue Process Modelling and Simulation)
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