Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.7
3.5
Journals
Processes
Special Issues
Modeling, Simulation and Control of Chemical Processes
share announcement

Modeling, Simulation and Control of Chemical Processes

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Chemical Processes and Systems".

Deadline for manuscript submissions: closed (30 April 2019) | Viewed by 79048

Special Issue Editors

Prof. Dr. Jose Carlos Pinto

E-Mail Website
Guest Editor
Programa de Engenharia Quimica / COPPE, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ 21921-972, Brazil
Interests: modeling, simulation and control of chemical reactors; in line monitoring and control of chemical processes; real time optimization of chemical processes; numerical techniques and procedures for real time applications
Special Issues, Collections and Topics in MDPI journals
Dr. Thiago Feital

E-Mail Website
Guest Editor
Optimatech Solutions, Business Incubator, Universidade Federal do Rio de Janeiro, Rua Hélio de Almeida, Building 2, Room 22, Rio de Janeiro, RJ 21941-614, Brazil
Interests: in-line monitoring and control of chemical processes; numerical procedures for fault detection and diagnosis; real time implementations of numerical procedures in industrial environments
Dr. Carlos Castor

E-Mail Website1 Website2
Guest Editor
Programa de Engenharia Quimica / COPPE, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ 21921-972, Brazil
Interests: phenomenological process modeling in high-performance computing platforms, parallel computing and algorithms, model identification

Special Issue Information

Dear Colleagues,

The growing demands for increase of process operation efficiency and safety have encouraged the development and implementation of operation and control procedures based on mathematical models, softsensors, and data-driven techniques. These techniques can be used to provide important pieces of information regarding the current process states, allowing for advanced monitoring, control, and optimization of the plant. This can be particularly important in chemical plants, as many variables that are used to characterize the final product quality and the states of process equipment cannot be measured directly and must be inferred from available signals. On the other hand, the number of signals available at most industrial sites has grown exponentially, demanding the implementation of numerical algorithms that can transform the growing number of measurements into useful information for operators and engineers. Additionally, the digital revolution has been providing cheaper computation platforms and devices with larger storage and memory capabilities that permit the implementation of more complex numerical tasks online, in real time, and with relatively low costs. When one considers all these aspects simultaneously, it becomes easy to realize that there are tremendous incentives for the smart use of mathematical procedures at the plant site.

This Special Issue on “Modeling, Simulation and Control of Chemical Processes” seeks high quality works focusing on the latest novel advances regarding the development, implementation, and use of mathematical procedures for modeling, monitoring, control, and optimization of chemical plants. Topics include, but are not limited to, the following:

  • First-principles modeling of chemical processes and use in real-time applications;
  • Development and implementation of data-driven procedures for monitoring, control, and optimization of chemical processes;
  • Development and implementation of advanced model-based and data-driven control procedures;
  • Use of model-based and data-driven procedures for identification and diagnosis of process faults;
  • Real-time optimization of chemical processes;
  • Industrial experiences related to development, implementation, and use of model-based and data-driven procedures for the improvement of the process operation.

Prof. Dr. José Carlos Pinto
Dr. Thiago Feital
Dr. Carlos Castor
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 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. 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

  • mathematical models
  • data-driven procedures
  • advanced control
  • real time optimization
  • process simulation

Published Papers (16 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

20 pages, 4195 KiB  
Article
An Approach for Feedforward Model Predictive Control of Continuous Pulp Digesters
by Moksadur Rahman, Anders Avelin and Konstantinos Kyprianidis
Processes 2019, 7(9), 602; https://doi.org/10.3390/pr7090602 - 06 Sep 2019
Cited by 12 | Viewed by 6395
Abstract
Kappa number variability at the continuous digester outlet is a major concern for pulp and paper mills. It is evident that the aforementioned variability is strongly linked to the feedstock wood properties, particularly lignin content. Online measurement of lignin content utilizing near-infrared spectroscopy [...] Read more.
Kappa number variability at the continuous digester outlet is a major concern for pulp and paper mills. It is evident that the aforementioned variability is strongly linked to the feedstock wood properties, particularly lignin content. Online measurement of lignin content utilizing near-infrared spectroscopy at the inlet of the digester is paving the way for tighter control of the blow-line Kappa number. In this paper, an innovative approach of feedforwarding the lignin content to a model predictive controller was investigated with the help of modeling and simulation studies. For this purpose, a physics-based modeling library for continuous pulp digesters was developed and validated. Finally, model predictive control approaches with and without feedforwarding the lignin measurement were evaluated against current industrial control and proportional-integral-derivative (PID) schemes. Full article
(This article belongs to the Special Issue Modeling, Simulation and Control of Chemical Processes)
►▼ Show Figures

Figure 1

14 pages, 450 KiB  
Article
Digital Twin for Monitoring of Industrial Multi-Effect Evaporation
by Rafael M. Soares, Maurício M. Câmara, Thiago Feital and José Carlos Pinto
Processes 2019, 7(8), 537; https://doi.org/10.3390/pr7080537 - 15 Aug 2019
Cited by 26 | Viewed by 7921
Abstract
Digital twins are rigorous mathematical models that can be used to represent the operation of real systems. This connection allows for deeper understanding of the actual states of the analyzed system through estimation of variables that are difficult to measure otherwise. In this [...] Read more.
Digital twins are rigorous mathematical models that can be used to represent the operation of real systems. This connection allows for deeper understanding of the actual states of the analyzed system through estimation of variables that are difficult to measure otherwise. In this context, the present manuscript describes the successful implementation of a digital twin to represent a four-stage multi-effect evaporation train from an industrial sugar-cane processing unit. Particularly, the complex phenomenological effects, including the coupling between thermodynamic and fluid dynamic effects, and the low level of instrumentation in the plant constitute major challenges for adequate process operation. For this reason, dynamic mass and energy balances were developed, implemented and validated with actual industrial data, in order to provide process information for decision-making in real time. For example, the digital twin was able to indicate failure of process sensors and to provide estimates for the affected variables in real time, improving the robustness of the operation and constituting an important tool for process monitoring. Full article
(This article belongs to the Special Issue Modeling, Simulation and Control of Chemical Processes)
►▼ Show Figures

Figure 1

14 pages, 1527 KiB  
Article
Development and Application of a Data-Driven System for Sensor Fault Diagnosis in an Oil Processing Plant
by Nayher Clavijo, Afrânio Melo, Maurício M. Câmara, Thiago Feital, Thiago K. Anzai, Fabio C. Diehl, Pedro H. Thompson and José Carlos Pinto
Processes 2019, 7(7), 436; https://doi.org/10.3390/pr7070436 - 10 Jul 2019
Cited by 7 | Viewed by 4495
Abstract
Predictive analytics is usually cited as one of the most important pillars of the digital transformation. For the oil industry, specifically, it is a common belief that issues like integrity and maintenance could benefit from predictive analytics. This paper presents the development and [...] Read more.
Predictive analytics is usually cited as one of the most important pillars of the digital transformation. For the oil industry, specifically, it is a common belief that issues like integrity and maintenance could benefit from predictive analytics. This paper presents the development and the application of a process-monitoring tool in a real process facility. The PMA (Predictive Maintenance Application) system is a data-driven application that uses a multivariate analysis in order to predict the system behavior. Results show that the use of a multivariate approach for process monitoring could not only detect an early failure at a metering system days before the operation crew, but could also successfully identify, among hundreds of variables, the root cause of the abnormal situation. By applying such an approach, a better performance of the monitored equipment is expected, decreasing its downtime. Full article
(This article belongs to the Special Issue Modeling, Simulation and Control of Chemical Processes)
►▼ Show Figures

Figure 1

18 pages, 5180 KiB  
Article
A Lagrangian Particle Algorithm (SPH) for an Autocatalytic Reaction Model with Multicomponent Reactants
by Qingzhi Hou, Jiaru Liu, Jijian Lian and Wenhuan Lu
Processes 2019, 7(7), 421; https://doi.org/10.3390/pr7070421 - 03 Jul 2019
Cited by 4 | Viewed by 2796
Abstract
For the numerical simulation of convection-dominated reacting flow problems governed by convection-reaction equations, grids-based Eulerian methods may cause different degrees of either numerical dissipation or unphysical oscillations. In this paper, a Lagrangian particle algorithm based on the smoothed particle hydrodynamics (SPH) method is [...] Read more.
For the numerical simulation of convection-dominated reacting flow problems governed by convection-reaction equations, grids-based Eulerian methods may cause different degrees of either numerical dissipation or unphysical oscillations. In this paper, a Lagrangian particle algorithm based on the smoothed particle hydrodynamics (SPH) method is proposed for convection-reaction equations and is applied to an autocatalytic reaction model with multicomponent reactants. Four typical Eulerian methods are also presented for comparison, including the high-resolution technique with the Superbee flux limiter, which has been considered to be the most appropriate technique for solving convection-reaction equations. Numerical results demonstrated that when comparing with traditional first- and second-order schemes and the high-resolution technique, the present Lagrangian particle algorithm has better numerical accuracy. It can correctly track the moving steep fronts without suffering from numerical diffusion and spurious oscillations. Full article
(This article belongs to the Special Issue Modeling, Simulation and Control of Chemical Processes)
►▼ Show Figures

Figure 1

11 pages, 1800 KiB  
Article
Improvement of 1,3-Butadiene Separation in 2,3-Butanediol Dehydration Using Extractive Distillation
by Daesung Song, Young-Gak Yoon, Seung-Kwon Seo and Chul-Jin Lee
Processes 2019, 7(7), 410; https://doi.org/10.3390/pr7070410 - 01 Jul 2019
Cited by 3 | Viewed by 6277
Abstract
This study was performed to investigate the extractive distillation for 1,3-butadiene (1,3-BD) purification as a part of the 2,3-butanediol (2,3-BDO) dehydration process. The separation of 1,3-BD from 1-butene produced as a 2,3-BDO dehydration by-product while using distillation is complicated due to the similar [...] Read more.
This study was performed to investigate the extractive distillation for 1,3-butadiene (1,3-BD) purification as a part of the 2,3-butanediol (2,3-BDO) dehydration process. The separation of 1,3-BD from 1-butene produced as a 2,3-BDO dehydration by-product while using distillation is complicated due to the similar volatilities of the two compounds. Thus, an extractive distillation system is proposed for the effective recovery of 1,3-BD, and is compared with a conventional distillation system in terms of its performance and economic feasibility. A higher 1,3-BD recovery rate was achieved while using the proposed system and the relative profitabilities of both separation systems were analyzed according to the market price of 1,3-BD, which is a decisive variable for economic feasibility. Full article
(This article belongs to the Special Issue Modeling, Simulation and Control of Chemical Processes)
►▼ Show Figures

Figure 1

33 pages, 7477 KiB  
Article
Modeling of the Free Radical Copolymerization Kinetics of n-Butyl Acrylate, Methyl Methacrylate and 2-Ethylhexyl Acrylate Using PREDICI®
by Javier A. Gómez-Reguera, Eduardo Vivaldo-Lima, Vida A. Gabriel and Marc A. Dubé
Processes 2019, 7(7), 395; https://doi.org/10.3390/pr7070395 - 26 Jun 2019
Cited by 11 | Viewed by 4294
Abstract
Kinetic modeling of the bulk free radical copolymerizations of n-butyl acrylate (BA) and 2-ethylhexyl acrylate (EHA); methyl methacrylate (MMA) and EHA; as well as BA, MMA and EHA was performed using the software PREDICI®. Predicted results of conversion versus time, composition [...] Read more.
Kinetic modeling of the bulk free radical copolymerizations of n-butyl acrylate (BA) and 2-ethylhexyl acrylate (EHA); methyl methacrylate (MMA) and EHA; as well as BA, MMA and EHA was performed using the software PREDICI®. Predicted results of conversion versus time, composition versus conversion, and molecular weight development are compared against experimental data at different feed compositions. Diffusion-controlled effects and backbiting for BA were incorporated into the model as they proved to be significant in these polymerizations. The set of estimated global parameters allows one to assess the performance of these copolymerization systems over a wide range of monomer compositions. Full article
(This article belongs to the Special Issue Modeling, Simulation and Control of Chemical Processes)
►▼ Show Figures

Figure 1

12 pages, 2012 KiB  
Article
A Mathematical Modeling of the Reverse Osmosis Concentration Process of a Glucose Solution
by Chenghan Chen and Han Qin
Processes 2019, 7(5), 271; https://doi.org/10.3390/pr7050271 - 08 May 2019
Cited by 22 | Viewed by 6977
Abstract
A mathematical modeling of glucose–water separation through a reverse osmosis (RO) membrane was developed to research the membrane’s performance during the mass transfer process. The model was developed by coupling the concentration–polarization (CP) model, which uses one-dimensional flow assumption, with the irreversible thermodynamic [...] Read more.
A mathematical modeling of glucose–water separation through a reverse osmosis (RO) membrane was developed to research the membrane’s performance during the mass transfer process. The model was developed by coupling the concentration–polarization (CP) model, which uses one-dimensional flow assumption, with the irreversible thermodynamic Spiegler–Kedem model. A nonlinear parameter estimation technique was used to determine the model parameters Lp (hydraulic permeability constant), σ (reflection coefficient), and Bs (solute transport coefficient). Experimental data were obtained from the treatment of a pre-treated glucose solution using a laboratory-scale RO system, and studies on the validation of the model using experimental results are presented. The calculated results are consistent with the experimental data. The proposed model describes the RO membrane concentration process and deduces the expression of k (mass transfer coefficient in the CP layer). The verification shows that the expression of k well-describes the reverse osmosis mass transfer of a glucose solution. Full article
(This article belongs to the Special Issue Modeling, Simulation and Control of Chemical Processes)
►▼ Show Figures

Figure 1

15 pages, 3227 KiB  
Article
Internal Model Control for Rank-Deficient System with Time Delays Based on Damped Pseudo-Inverse
by Meiying Jiang, Beiyan Jiang and Qi Wang
Processes 2019, 7(5), 264; https://doi.org/10.3390/pr7050264 - 06 May 2019
Cited by 4 | Viewed by 2665
Abstract
It is a challenge to design a satisfactory controller for a complex multivariable industrial system with minimal offsetting and a slow response. An internal model control method is proposed for rank-deficient systems with a time delay based on a damped pseudo-inverse. An internal [...] Read more.
It is a challenge to design a satisfactory controller for a complex multivariable industrial system with minimal offsetting and a slow response. An internal model control method is proposed for rank-deficient systems with a time delay based on a damped pseudo-inverse. An internal model control was designed to obtain the desired dynamic characteristics of the system by transforming the time-delay system into a system without a time delay, following the Pade approximation approach. By introducing a damping factor, the internal model controller was designed based on a damped pseudo-inverse, since the inverse matrix of the rank-deficient system does not exist. Furthermore, a singular value decomposition was used to analyze the steady-state performance of the system. The selection of the damping factor was also presented, and a μ analysis was made to evaluate the stability of the system. To demonstrate the effectiveness of the proposed method, a crude distillation process with five inputs and four outputs was considered as an example. The simulation results illustrate that not only can the proposed strategy guarantee the system’s stability, but it also has a relatively good dynamic performance. Full article
(This article belongs to the Special Issue Modeling, Simulation and Control of Chemical Processes)
►▼ Show Figures

Figure 1

23 pages, 7390 KiB  
Article
Extracting Valuable Information from Big Data for Machine Learning Control: An Application for a Gas Lift Process
by Ana Carolina Spindola Rangel Dias, Felipo Rojas Soares, Johannes Jäschke, Maurício Bezerra de Souza, Jr. and José Carlos Pinto
Processes 2019, 7(5), 252; https://doi.org/10.3390/pr7050252 - 30 Apr 2019
Cited by 7 | Viewed by 3478
Abstract
The present work investigated the use of an echo state network for a gas lift oil well. The main contribution is the evaluation of the network performance under conditions normally faced in a real production system: noisy measurements, unmeasurable disturbances, sluggish behavior and [...] Read more.
The present work investigated the use of an echo state network for a gas lift oil well. The main contribution is the evaluation of the network performance under conditions normally faced in a real production system: noisy measurements, unmeasurable disturbances, sluggish behavior and model mismatch. The main pursued objective was to verify if this tool is suitable to compose a predictive control scheme for the analyzed operation. A simpler model was used to train the neural network and a more accurate process model was used to generate time series for validation. The system performance was investigated with distinct sample sizes for training, test and validation procedures and prediction horizons. The performance of the designed ESN was characterized in terms of slugging, setpoint changes and unmeasurable disturbances. It was observed that the size and the dynamic content of the training set tightly affected the network performance. However, for data sets with reasonable information contents, the obtained ESN performance could be regarded as very good, even when longer prediction horizons were proposed. Full article
(This article belongs to the Special Issue Modeling, Simulation and Control of Chemical Processes)
►▼ Show Figures

Figure 1

24 pages, 8878 KiB  
Article
Static Light Scattering Monitoring and Kinetic Modeling of Polyacrylamide Hydrogel Synthesis
by Catarina Gomes, Rolando C.S. Dias and Mário Rui P.F.N. Costa
Processes 2019, 7(4), 237; https://doi.org/10.3390/pr7040237 - 24 Apr 2019
Cited by 8 | Viewed by 4017
Abstract
A kinetic model describing aqueous acrylamide homopolymerization and copolymerization of acrylamide with methylene bisacrylamide, leading to hydrogel formation, is presented and applied in the simulation of these reaction processes. This modeling approach is based on population balances of generating functions and, besides the [...] Read more.
A kinetic model describing aqueous acrylamide homopolymerization and copolymerization of acrylamide with methylene bisacrylamide, leading to hydrogel formation, is presented and applied in the simulation of these reaction processes. This modeling approach is based on population balances of generating functions and, besides the crosslinking mechanisms inherent to network formation, other specific kinetic steps important in acrylamide polymerization (e.g., branching due to backbiting) are considered in the simulation tool developed. The synthesis of acrylamide polymers and hydrogels was performed at 26 °C and at 40 °C using two different initiation systems. The formation of such materials was monitored using in-line static light scattering (SLS), and the spatial inhomogeneity of the final hydrogels was also measured using this experimental technique. It is shown that the simulations are helpful in describing information provided by SLS in-line monitoring, namely in the early stages of polymerization with the transition from dilute to semi-dilute regime. Indeed, it finds a plausible match between the critical overlap polymer concentration and gelation, this later leading to the observed spatial heterogeneity of the hydrogels. Usefulness of the kinetic model for defining operation conditions (initial composition, semi-batch feed policies, chain transfer, etc.) in making the shift from gelation to the semi-dilute regime is discussed, and the extension of this approach to processes enabling a higher control of gelation (e.g., controlled radical polymerization) is also prospected. Full article
(This article belongs to the Special Issue Modeling, Simulation and Control of Chemical Processes)
►▼ Show Figures

Graphical abstract

18 pages, 2982 KiB  
Article
Ascertainment of Surfactin Concentration in Bubbles and Foam Column Operated in Semi-Batch
by Rafael Firmani Perna, Maria Carolina Pereira Gonçalves and Cesar Costapinto Santana
Processes 2019, 7(3), 154; https://doi.org/10.3390/pr7030154 - 13 Mar 2019
Cited by 5 | Viewed by 3436
Abstract
This paper describes a mathematical model for the convection, diffusion, and balance phenomena for predicting the depletion curves, i.e., variations in the timed surface-active molecule concentration for fractionation processes in bubbles and foam column, operated in semi-batch. The model was applied for the [...] Read more.
This paper describes a mathematical model for the convection, diffusion, and balance phenomena for predicting the depletion curves, i.e., variations in the timed surface-active molecule concentration for fractionation processes in bubbles and foam column, operated in semi-batch. The model was applied for the purification of the surfactin solution and the results were compared with experimental data. Gibbs adsorption curves were obtained for the biosurfactant at different temperatures, and then adjusted with estimated parameters, according to the Langmuir adsorption model. The gas bubble sizes were optically determined. The isotherm adsorption parameters and bubble average diameter are crucial for the attainment of the depletion curves, generated by the model described. The results demonstrate that the process is most effective when operating a column with reduced gas flow and low initial concentration. A top product with two or thirty times greater concentration than the initial one was achieved and the highest biosurfactant concentrations were attained for higher operating temperatures. It was also observed that bubble diameter increased with a higher gas flow. The adjustment obtained for the adsorption curves of Gibbs was satisfactory. Therefore, there was evidence that surfactin molecules adsorb in monolayers in the liquid–gas interface. Full article
(This article belongs to the Special Issue Modeling, Simulation and Control of Chemical Processes)
►▼ Show Figures

Figure 1

21 pages, 5666 KiB  
Article
An Intelligent Fault Diagnosis Method Using GRU Neural Network towards Sequential Data in Dynamic Processes
by Jing Yuan and Ying Tian
Processes 2019, 7(3), 152; https://doi.org/10.3390/pr7030152 - 12 Mar 2019
Cited by 34 | Viewed by 5267
Abstract
Intelligent fault diagnosis is a promising tool to deal with industrial big data due to its ability in rapidly and efficiently processing collected signals and providing accurate diagnosis results. In traditional static intelligent diagnosis methods, however, the correlation between sequential data is neglected, [...] Read more.
Intelligent fault diagnosis is a promising tool to deal with industrial big data due to its ability in rapidly and efficiently processing collected signals and providing accurate diagnosis results. In traditional static intelligent diagnosis methods, however, the correlation between sequential data is neglected, and the features of raw data cannot be effectively extracted. Therefore, this paper proposes a three-stage fault diagnosis method based on a gate recurrent unit (GRU) network. The raw data is divided into several sequence units by first using a moving horizon as the input of GRU. In this way, we can intercept the sequence to get information as needed. Then, the GRU deep network is established through batch normalization (BN) algorithm to extract the dynamic feature from the sequence units effectively. Finally, the softmax regression is employed to classify faults based on dynamic features. Thus, the diagnosis result is obtained with a probabilistic explanation. Two chemical processes validate the proposed method: Tennessee Eastman (TE) benchmark process as well as para-xylene (PX) oxidation process. In the case of TE, the diagnosis results demonstrate the proposed method is superior to conventional methods. Furthermore, in the case of PX oxidation, the result shows that the proposed method also has an exceptional effect with a little historical data. Full article
(This article belongs to the Special Issue Modeling, Simulation and Control of Chemical Processes)
►▼ Show Figures

Figure 1

19 pages, 3439 KiB  
Article
New Approaches in Modeling and Simulation of CO2 Absorption Reactor by Activated Potassium Carbonate Solution
by Maria Harja, Gabriela Ciobanu, Tatjána Juzsakova and Igor Cretescu
Processes 2019, 7(2), 78; https://doi.org/10.3390/pr7020078 - 04 Feb 2019
Cited by 3 | Viewed by 5199
Abstract
The increase of CO2 concentration in the atmosphere is in strong relation with the human-induced warming up due to industrial processes, transportation, etc. In order to reduce the CO2 content, end of pipe post-combustion methods can be used in addition to [...] Read more.
The increase of CO2 concentration in the atmosphere is in strong relation with the human-induced warming up due to industrial processes, transportation, etc. In order to reduce the CO2 content, end of pipe post-combustion methods can be used in addition to other methods and techniques. The CO2 capture by absorption in potassium carbonate–bicarbonate activated solutions remains a viable method. In this study, a mathematical model for a packed bed reactor has been developed and tested. The mathematical model is tested for an industrial reactor based on CO2 absorption in Carsol solutions. The proposed model was validated by resolving for CO2 and water content, carbonate–bicarbonate, concentrations etc. For each operational parameter the error was calculated. The error for CO2 concentration is up to 4%. The height of the packed reactor is calculated as function of CO2 concentration in the final gas phase. The validated model can also be used for absorbing other CO2 streams taking into account the fact that its efficiency was proved in industrial scale. Future reactors used for CO2 absorption should consist of two parts in order to use partially regenerated solutions in the first part, with significant energy savings in the operational costs. Full article
(This article belongs to the Special Issue Modeling, Simulation and Control of Chemical Processes)
►▼ Show Figures

Graphical abstract

17 pages, 2845 KiB  
Article
Modeling/Simulation of the Dividing Wall Column by Using the Rigorous Model
by Chi Zhai, Qinjun Liu, Jose A. Romagnoli and Wei Sun
Processes 2019, 7(1), 26; https://doi.org/10.3390/pr7010026 - 08 Jan 2019
Cited by 5 | Viewed by 5326
Abstract
Dividing wall column (DWC) is an atypical distillation column with an internal, vertical WE partition wall that effectively accommodates two conventional distillation columns into one to improve the thermodynamic efficiency. In previous studies, different equivalent models by combining conventional columns are adopted to [...] Read more.
Dividing wall column (DWC) is an atypical distillation column with an internal, vertical WE partition wall that effectively accommodates two conventional distillation columns into one to improve the thermodynamic efficiency. In previous studies, different equivalent models by combining conventional columns are adopted to approximate the DWC modeling, which may not well describe the integration of the DWC; moreover, the computational cost increases when multiple columns are implemented to represent one DWC. In this paper, a rigorous mathematical model is proposed based on the mass balance, the energy and phase equilibrium of the DWC, where decision variables and state variables are equally treated. The model was developed in the general process modeling system (gPROMS). Based on the rigorous model, the influences of liquid split ratio and vapor split ratio are discussed, and it is shown that the heat duty is sensitive to changes on the liquid and vapor split ratio. Inappropriate liquid and vapor split ratio will increase the mixing effects at both ends of the dividing wall, and adversely affect the thermodynamic efficiency. Hence, the degree of mixing is defined to characterize the column efficiency. Furthermore, the middle component split ratio at the top of the pre-fractionator has an optimal point for better energy saving with certain liquid and vapor split ratios, and can be used as an indicator for the energy performance. Finally, the model was tested and validated against literature data by using the ternary benzene–toluene–xylene mixture system as a case study. Full article
(This article belongs to the Special Issue Modeling, Simulation and Control of Chemical Processes)
►▼ Show Figures

Figure 1

15 pages, 3052 KiB  
Article
A Data-Driven Reaction Network for the Fluid Catalytic Cracking of Waste Feeds
by José Ignacio Alvira, Idoia Hita, Elena Rodríguez, José M. Arandes and Pedro Castaño
Processes 2018, 6(12), 243; https://doi.org/10.3390/pr6120243 - 27 Nov 2018
Cited by 14 | Viewed by 5095
Abstract
Establishing a reaction network is of uttermost importance in complex catalytic processes such as fluid catalytic cracking (FCC). This step is the seed for a faithful reactor modeling and the subsequent catalyst re-design, process optimization or prediction. In this work, a dataset of [...] Read more.
Establishing a reaction network is of uttermost importance in complex catalytic processes such as fluid catalytic cracking (FCC). This step is the seed for a faithful reactor modeling and the subsequent catalyst re-design, process optimization or prediction. In this work, a dataset of 104 uncorrelated experiments, with 64 variables, was obtained in an FCC simulator using six types of feedstock (vacuum gasoil, polyethylene pyrolysis waxes, scrap tire pyrolysis oil, dissolved polyethylene and blends of the previous), 36 possible sets of conditions (varying contact time, temperature and catalyst/oil ratio) and three industrial catalysts. Principal component analysis (PCA) was applied over the dataset, showing that the main components are associated with feed composition (27.41% variance), operational conditions (19.09%) and catalyst properties (12.72%). The variables of each component were correlated with the indexes and yields of the products: conversion, octane number, aromatics, olefins (propylene) or coke, among others. Then, a data-driven reaction network was proposed for the cracking of waste feeds based on the previously obtained correlations. Full article
(This article belongs to the Special Issue Modeling, Simulation and Control of Chemical Processes)
►▼ Show Figures

Figure 1

14 pages, 3444 KiB  
Article
The Influence of O2 on Decomposition Characteristics of c-C4F8/N2 Environmental Friendly Insulating Gas
by Song Xiao, Shuangshuang Tian, Xiaoxing Zhang, Yann Cressault, Ju Tang, Zaitao Deng and Yi Li
Processes 2018, 6(10), 174; https://doi.org/10.3390/pr6100174 - 29 Sep 2018
Cited by 11 | Viewed by 3153
Abstract
The c-C4F8 gas is considered to have great potential as a gaseous medium for gas-insulated equipment, due to its good insulation properties and its relatively low greenhouse gas potential (GWP) relative to SF6. However, the decomposition is an [...] Read more.
The c-C4F8 gas is considered to have great potential as a gaseous medium for gas-insulated equipment, due to its good insulation properties and its relatively low greenhouse gas potential (GWP) relative to SF6. However, the decomposition is an important indicator of its use in equipment. In this paper, the decomposition characteristics of c-C4F8 and the influence by oxygen have been explored through experiments and theoretical calculations. Firstly, the breakdown test of mixed gas was carried out and the precipitated elements of the electrodes and breakdown products of gas mixture were analyzed by X-ray photoelectron spectroscopy (XPS) and gas chromatography mass spectrometry (GC-MS). At the same time, the differences in decomposition products have also been studied when a small amount of O2 was present. The path and mechanism of c-C4F8 decomposition is then discussed, based on density functional theory (DFT). The results show that the black powdery substance descends on the electrode surface after the breakdown of the mixture of c-C4F8/N2 gas containing O2, and its main constituent elements are C, O and F. O2 can promote the decomposition of c-C4F8. The mixture with O2 produced a large number of additional toxic and corrosive COF2 in addition to generating more CF4, C2F4, C2F6, C3F6 and C3F8. The GWP values of the products are lower than SF6. Comprehensive insulation properties and decomposition characteristics, c-C4F8 should not be mixed with dry air for use, and the oxygen content should be strictly controlled in c-C4F8 mixed gas. Full article
(This article belongs to the Special Issue Modeling, Simulation and Control of Chemical Processes)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-16
Back to TopTop