Model Of Separation Process Research Articles

Dynamic Modelling and Simulation of A Three-Phase Gravity Separator

Many studies have investigated the crude oil separation process’s separation mechanisms, size, and design, employing horizontal 3-Phase Gravity Separators in depth. There are, however, very few articles on their dynamics, modelling, and simulation. Understanding its dynamic behaviour will aid in designing and tuning the device that can manage water level, oil level, and gas pressure in response to feeding variations. This Scientific Paper gives a complete mathematical analysis, modelling, and simulation of a crude oil separation process using a horizontal 3-Phase Gravity Separator using Mathworks Matlab R2016b-x64 and Aspen Hysys V10. Bishoy’s Equations, which were constructed, will assist in operating this gadget, locating various variables, and observing the effect of modifying variables on the system’s variables. The rationale for this study was developed in response to the small number of articles discovered, which may be a covert issue held up by large oil companies, as well as the complicated equations related to this process that remain unsolved, and to monitor what is happening in this complex dynamic process. As a result, this Research Paper is unique and novel, and it can be compared to the work done by some authors who did not solve the obtained differential equations and did not go further in Aspen Hysys Modeling and Simulation, whereas this paper provides everything related to a three-phase gravity separator, including changing of variables and observing the effect on the system when those variables were modified. Furthermore, separators are designed with internal baffles to promote laminar flow and increase separator efficiency. However, it has been assumed that there are no baffles here, which is a significant problem, but with the help of these equations, the horizontal three-phase gravity separator can be operated at its maximum efficiency. The equations determined the following variables: The height of gas, water, oil, the height of oil when it jumped the weir, the pressure of the gas (in and out), water pressure (in and out), oil pressure (in and out), and the effect of increasing (control valve’s stem position) and decreasing Qin (inlet volumetric flowrate) on these variables have all been studied. This article discovered that increasing the control valve stem position and decreasing the inflow volumetric flowrate of both oil and water was highly unsafe and caused significant variations in the system’s heights and pressures using Matlab. The Aspen Hysys analysis optimally separates the oil, gas, and water to determine material, energy streams properties, and compositions. As a result, this complex dynamic behaviour was observed, and no additional articles were discovered that addressed this subject. This process monitoring will determine the best conditions for flawless separation, with the selectivity of the desired product or products as the primary goal. This research can revolutionize the way people think about oil and gas extraction and processing and benefit colossal oil and gas firms in Europe, Asia, and Africa.

Mass and Heat Transfer of Pressure Swing Adsorption Oxygen Production Process with Small Adsorbent Particles

Rapid-cycle pressure swing adsorption (PSA) with small adsorbents particles is intended to improve mass transfer rate and productivity. However, the mass transfer mechanisms are changed with reduction of particle size during rapid-cycle adsorption process. A heat and mass transfer model of rapid-cycle PSA air separation process employing small LiLSX zeolite particles is developed and experimentally validated to numerically analyze the effects of mass transfer resistances on the characteristics of cyclic adsorption process. Multicomponent Langmuir model and linear driving force model are employed for characterizing the adsorption equilibrium and kinetic. The results of numerical analysis demonstrate that the dominant mass transfer resistance of small adsorbents particles is a combination of film resistance, axial dispersion effect and macropore diffusion resistance. The oxygen purity, recovery and productivity of the product are overestimated by ~2–4% when the effect of axial dispersion on mass transfer is ignored. As particle size decreases, the front of nitrogen-adsorbed concentration and gas temperature become sharp, which effectively improves the performance. However, the adverse effect of axial dispersion on the mass transfer becomes significant at very small particles conditions. It is nearly identical shapes of nitrogen concentration and gas temperature profiles after adsorption and desorption steps. The profiles are pushed forward near the production end with an increase in bed porosities. The optimal oxygen recovery and productivity are achieved with a particle diameter of 0.45 mm and bed porosity of 0.39 during the PSA process.

Modeling and Optimization of a Suspension Crystallization Separation Process for para-Xylene Purification

The morphology and DSC methods were used for measuring the crystallization kinetics of para-xylene. Solubility data from DSC experiments were fitted using the activity coefficient model and classical solid–liquid phase equilibrium theory, while the rates of nucleation, growth, aggregation, and breakage (functions of supersaturation) were built from morphological experiments. The particle size distribution was modeled via a population balance equation and validated by experimental data on a suspension crystallization separation process. The effects of the crystallizer operating temperature and residence time on product yield were also investigated; although the rates of nucleation, growth, breakage, and aggregation increase with decreasing temperature and increasing residence time, the breakage and aggregation are dominant at low temperatures and long residence times. Therefore, the crystallization process should be operated at an optimal temperature and residence time. The models built for this work can be used further in additional process design and optimization.

Modeling and Optimization of the Para-Xylene Continuous Suspension Crystallization Separation Process via a Morphology Technique and a Multi-Dimensional Population Balance Equation

In this study, we carried out a para-xylene crystallization experiment at constant temperature and concentration levels. Throughout the process, the kinetics of nucleation, growth, breakage, and aggregation of para-xylene particles were measured and built using a morphological approach. An additional a three-stage continuous suspension crystallization separation experiment was carried out, the process for which was simulated using the population balance model based on correlated kinetic equations. The population balance equation was solved using an extended moment of classes algorithm, and the solving process was implemented in MATLAB. In this case, the predicted particle size distribution of the products matched well with the experiment. In order to provide references for the optimization of the industrial para-xylene crystallization process, a three-stage suspension crystallization separation experiment was designed and conducted, in which each crystallizer had a distinct operating temperature and mean residence time. The effects of operating parameters on the final product were investigated further. The proposed models and algorithms can also be applied in other cases and provide an alternative approach for optimizing continuous crystallization processes.

Computational simulation of molecular separation in liquid phase using membrane systems: Combination of computational fluid dynamics and machine learning

A new methodology was developed in this work based on combination of machine learning and computational fluid dynamics (CFD) for hybrid modeling of membrane separation processes. The developed methodology was tested for a separation case in removing organic molecules from aqueous solution using a polymeric membrane in cylindrical shape. The CFD simulation was carried out via finite element technique. The results of the CFD outputs were inputted several machine learning techniques to build the hybrid model of the process. The data of mass transfer in form of concentration was extracted which are more than 2000 data points. The dataset includes two inputs r and z, in addition to one output (C). A number of different models are selected for this purpose, including Random Forests, Gradient Boosting models and Extremely Randomized Trees (Extra Trees). During the course of this research, it was confirmed that these models are capable of performing very well in this dataset when their hyper-parameters are optimally chosen. The R2 parameter for all three methods was higher than 0.99, indicating that the performance was very favorable. It is noteworthy that the RMSE metric decreased to 2.43 and the MAE metric has also decreased to 1.06 by optimization.

Multi-scale design of MOF-based membrane separation for CO2/CH4 mixture via integration of molecular simulation, machine learning and process modeling and simulation

Metal-organic framework (MOF) membranes have demonstrated high efficiency for CO2 capture due to their wide range of pore sizes, high surface area, high porosity, and open metal sites. In this work, we propose a multi-scale design framework of MOF-based membrane separation for CO2/CH4 mixture via integration of molecular simulation, machine learning, and process modelling and simulation. The GCMC and MD molecular simulation is first used to evaluate adsorption isotherms, isosteric adsorption heat, self-diffusivity, activation energy, permeability, and selectivity of a MOF-based membrane (e.g., IRMOF-1) for CO2/CH4 separation at different operating conditions. It is found that the simulated isotherms at 298 K are consistent with experimental results. CO2 permeability can range from 4.090 × 104 to 3.818 × 105 barrer with variation in operating conditions. The same phenomenon is also reflected in the selectivity. We then establish prediction models of adsorption capacity and self-diffusivity using machine learning methods such as artificial neural network (ANN), which are used to calculate the permeability. The mean squared error is 0.0086, and the coefficient of determination is 0.9822. The proposed ANN models are integrated with the tanks-in-series model of a hollow fiber membrane separation process, which is simulated using the finite volume method. Three case studies illustrate the feasibility and superiority of the proposed integrated framework.

Model of the aggregation process of fine ferromagnetic particles

The most important result of the effect of interparticle interactions of ferromagnetic particles in suspensions is the formation of aggregates and modification of their physical properties, which allows changing the kinetics of separation processes. The interest of the authors to this problem is dictated by both its theoretical content and purely applied aspects within mineral processing. Based on the block-structural approach to technological equipment modeling within the framework of the ideal mixing model, a mathematical model of processes occurring in the working volumes of apparatuses using magnetic forces to intensify the separation of mineral suspensions was created. The use of the MATHCAD software has allowed obtaining analytical solutions of the differential equations system of the model and predictions of the kinetics of aggregation of ferromagnetic particles and physical properties of the formed aggregates, such as coarseness, density, deposition velocity in the gravity field as functions of medium parameters and external magnetic fields. The analytical solutions are necessary for developing simulation models of magnetic separation processes in strong, weak, uniform and non-uniform magnetic fields, magnetic deslammers and filters.

Reactive transport in membrane separation modeling: A perspective

Reactive transport modeling refers to the coupling of chemical equilibrium reactions with rate-dependent phenomena such as transport and phase change. The concept originated and is widely used in geosciences but has been infiltrating other fields. Reactive transport can contribute to model prediction and fundamental understanding of transport phenomena by illuminating the interrelations between species distribution of solutes and their displacement. Despite many potential benefits, the Chemical Environmental Engineering field has been slow at adopting the reactive transport approach. Here we focus on reactive transport modeling of membrane separation processes in aqueous solutions for environmental applications. We briefly review the progress made in the last decades for selected technologies and discuss the potential in further developing reactive transport modeling for each case. We then discuss several common misunderstandings and knowledge gaps while highlighting the challenges and opportunities of implementing the reactive transport approach in membrane transport modeling and simulation.

An accelerated approach for mechanistic model based prediction of linear gradient elution ion-exchange chromatography of proteins

With growing demands for therapeutic monoclonal antibodies, in silico downstream process development based on mechanistic modeling of chromatography separation process is being increasingly used for process optimization and process characterization. Application of mechanistic modeling in biopharmaceutical industry has been sparse due to the significant investment of time and resources that are required for performing model calibration. Mechanistic modeling of the chromatography process involves a large number of mass transport and binding parameters and their initial input values are required for simulations. These input values of column parameters can be easily obtained either from experiments or from empirical correlations available in literature. On the other hand, obtaining the model input valves for binding kinetic parameters is usually a cumbersome process as it involves performing batch experiments which are not only tedious but also require significant quantities of purely isolated main product and its related impurities, which is challenging as the product related impurities are typically present in smaller quantities and hence are difficult to obtain as pure species. In the present work, a mechanistic model that is based on the general rate model coupled with extended Langmuir binding model has been used for prediction of linear gradient elution peaks of monoclonal antibody on cation exchanger chromatography. The present work describes an accelerated approach for obtaining the input values for binding kinetic parameters in the extended Langmuir binding model from the two Yamamoto coefficient A and B values obtained by Yamamoto method directly from the model calibration linear gradient elution runs of different gradient slopes and at low to moderate protein loadings. The equations that can relate the two coefficients to the extended Langmuir model equation binding kinetic parameters were derived. Therefore, once A and B are determined, the binding kinetic parameter values were determined straightforward, thereby avoiding the problem of multiple solutions for the model parameters. The estimated binding parameters were successfully validated from isocratic elution experiments performed at low loading. What we demonstrate is that the proposed approach allows us to estimate binding kinetic parameters in a significantly more efficient and accelerated manner than presently used approaches, thereby accelerating development and implementation of mechanistic modeling for process chromatography.

Study on the Formation and Separation Process of Droplets in the Medical Piezoelectric Atomization Device Induced by Intra-hole Fluctuation

Traditional atomization devices always exhibit many drawbacks, such as non-uniform atomization particle sizes, instability of transient atomization quantity and uncontrollability of precise energy, which seriously restrict further practical application of atomization inhalation therapy. The formation and separation process of droplets belongs to a microphenomenon of atomization. The investigation of the droplet formation and separation process will be favorable for understanding the atomization mechanism. In present work, the Conservative Level Set Method (CLSM) is successfully applied on the simulation of the formation and separation of droplets in a medical piezoelectric atomization device induced by intra-hole fluctuation. The intra-hole fluctuation mechanism is systematically explored and analyzed, and also the expression of the volume change in the micro cone hole is built and evaluated. Both the control equation and simulation model of droplet formation and separation process have been well established by meshing the simulation model, and thereby the process of droplet formation and separation is simulated. The corresponding results demonstrate that the breaking time of droplets is decreased with the inlet velocity and liquid temperature rising, while enhanced with the liquid concentration increasing. Meanwhile, the volume of droplet is decreased with the inlet velocity and liquid concentration increasing, but increased with the liquid temperature rising. The velocity of droplet is enhanced with the inlet velocity and liquid temperature rising, and reduced with the increase of liquid concentration. When the large side diameter of micro-cone hole is set as 79 μm, the breaking time of the droplet reaches a minimum value of 38.7 μs, whereas the volume and the velocity of droplet reach a maximum value of 79.8 pL and 4.46 m/s, respectively. This study provides theoretical guidance for the design of medical piezoelectric atomization devices and contributes to the promotion of inhalation therapy in practical use.

Modeling, simulation, and techno-economic optimization of argon separation processes

High-purity argon is conventionally recovered from air by cryogenic distillation, which is a mature, efficient but high-cost technology. In recent years, membrane gas separation technology is starting to emerge illustrating the potential to achieve lower production costs compared to distillation, without compromising the final product quality. In this work, a flowsheet of a conventional cryogenic distillation system for the separation of an oxygen-argon mixture was built and simulated in the gPROMS™ modeling environment. Then, a detailed mathematic modeling framework of a hollow fiber membrane system for the same separation was developed. The predictive power of the model is in a good agreement with theoretically expected results for a wide range of operating conditions. The results indicate that commercially available polymeric membranes are not capable of achieving a high-purity argon product while innovative materials, such as carbon membranes, turn out to be more efficient. Finally, the performance of the membrane process was compared with the cryogenic distillation process using an optimization-based techno-economic analysis. The comparison revealed that the distillation process has a lower operational cost compared to a membrane process for a final product purity of 99.9 mol%.

Mathematical Modeling of Phase Separation and Branching Process of the Film Structure during Binary Thin Film Deposition

In this study, we applied a mathematical model to explore the mechanism and factors leading to phase separation and the formation of branching structures with nanocolumns extending from larger clusters formed on the substrate of a grown film. The mathematical model simulated the growth of a thin film over time by using partial differential equations, including the processes of adsorption, phase separation, and diffusion due to the curvature of the thin film surface. The modeling results revealed the possible mechanism that could lead to the formation of the described branching structures. That mechanism can be divided into two main steps. The first step is the growth of a relatively large cluster (of a component that makes up the branching phase) on the substrate during the initial growth stages. The second step is the division process of that large cluster into smaller clusters in the later growth stages. The model parameters influencing the growth conditions that lead to the formation mechanism of the branching structures were determined, and their influences on the phase structure were analyzed.

Artificial Intelligence in Fractional-Order Systems Approximation with High Performances: Application in Modelling of an Isotopic Separation Process

This paper presents a solution for the modelling, implementation and simulation of the fractional-order process of producing the enriched 13C isotope, through the chemical exchange between carbamate and carbon dioxide. To achieve the goal of implementation and simulation of the considered process, an original solution for the approximation of fractional-order systems at the variation of the system’s differentiation order is proposed, based on artificial intelligence methods. The separation process has the property of being strongly non-linear and also having fractional-order behaviour. Consequently, in the implementation of the mathematical model of the process, the theory associated with the fractional-order system’s domain has to be considered and applied. For learning the dynamics of the structure parameters of the fractional-order part of the model, neural networks, which are associated with the artificial intelligence domain, are used. Using these types of approximations, the simulation and the prediction of the produced 13C isotope concentration dynamics are made with high accuracy. In order to prove the efficiency of the proposed solutions, a comparation between the responses of the determined model and the experimental responses is made. The proposed model implementation is made based on using four trained neural networks. Moreover, in the final part of the paper, an original method for the online identification of the separation process model is proposed. This original method can identify the process of fractional differentiation order variation in relation to time, a phenomenon which is quite frequent in the operation of the real separation plant. In the last section of the paper, it is proven that artificial intelligence methods can successfully sustain the system model in all the scenarios, resulting in the feasible premise of designing an automatic control system for the 13C isotope concentration, a method which can be applied in the case of other industrial applications too.

Predictive molecular thermodynamic models for ionic liquids

AbstractPredictive molecular thermodynamic models can bridge the gap between the microscopic molecular level and macroscopic system scale. Over the past few decades, ionic liquids (ILs), as a class of green solvents and functional materials, have received widespread research interest in the field of chemical processing owing to their unique physicochemical merits. This review aims to provide an easy‐to‐read and exhaustive reference regarding state‐of‐the‐art predictive thermodynamic models for ILs, with more focuses on UNIFAC‐ and COSMO‐based models and various applications involving phase equilibrium prediction, guidance for IL screening and design, and building equilibrium stage models for separation processes. This review provides a theoretical perspective on the structure–property relationships between molecular structures and phase behaviors for the systems and the constituted components covering a multi‐scale viewpoint from molecular level to industrial scale. Moreover, the predictive capacities of different thermodynamic models are comprehensively compared.

Direct single shooting for dynamic optimization of differential-algebraic equation systems with optimization criteria embedded

Differential-algebraic equation systems with embedded optimization criteria (DAEOs) arise in a number of applications, most prominently dynamic models of separation processes based on phase equilibrium and microbial transformation processes modeled via the dynamic flux balance analysis approach.We consider the optimization of DAEOs. To this end, we first reformulate the DAEO using first-order optimality conditions yielding a nonsmooth DAE system. This solution approach tracks a stationary point of the embedded optimization problem that may not be globally or locally optimal on the total time horizon for non-convex problems.We use adjoint sensitivity analysis for the resulting nonsmooth DAEs for gradient-based optimization in a direct single-shooting approach. We apply the solution method to the optimization of a single flash unit, calculation of an optimal start-up scenario of a rectification column, and the optimization of biomass growth in a microbial transformation process.

Adaptive Soft-Sensor Modeling of SMB Chromatographic Separation Process Based on Dynamic Fuzzy Neural Network and Moving Window Strategy

Adaptive Soft-Sensor Modeling of SMB Chromatographic Separation Process Based on Dynamic Fuzzy Neural Network and Moving Window Strategy

ANFIS soft sensing model of SMB chromatographic separation process based on new adaptive population evolution particle swarm optimization algorithm

Aiming at predicting the purity of the extract and raffinate components in the simulated moving bed (SMB) chromatographic separation process, a soft-sensor modeling method was proposed by adoptig the hybrid learning algorithm based on an improved particle swarm optimization (PSO) algorithm and the least means squares (LMS) method to optimize the adaptive neural fuzzy inference system (ANFIS) parameters. The hybrid learning algorithm includes a premise parameter learning phase and a conclusion parameter learning phase. In the premise parameter learning stage, the input data space division of the SMB chromatographic separation process and the initialization of the premise parameters are realized based on the fuzzy C-means (FCM) clustering algorithm. Then, the improved PSO algorithm is used to calculate the excitation intensity and normalized excitation intensity of all the rules for each individual in the population. In the conclusion parameter learning phase, these linear parameters are identified by the LMS method. In order to improve population diversity and convergence accuracy, the population evolution rate function was defined. According to the relationship between population diversity, population fitness function and particle position change, a new adaptive population evolution particle swarm optimization (NAPEPSO) algorithm was proposed. The inertia weight is adaptively adjusted according to the evolution of the population and the change of the particle position, thereby improving the diversity of the particle swarm and the ability of the algorithm to jump out of the local optimal solution. The simulation results show that the proposed soft-sensor model can effectively predict the key economic and technical indicators of the SMB chromatographic separation process so as to meet the real-time and efficient operation of the SMB chromatographic separation process.

Development and research of mathematical models and control algorithms for the separation of bulk materials

Analysis of the separation process showed that this object is multidimensional, stochastic, nonlinear and has a number of other “inconvenient” properties. All stages of the separation system functioning have a significant impact on the properties of the finished product. Therefore, to improve the separation technology, constant automatic control over all stages of this technological process and an effective control system for this object are required. The creation of such control systems requires the development of methods for their identification, modeling of processes occurring during separation, creation of operational control algorithms implemented by software and hardware systems. At present, the resources for increasing the efficiency of control over the separation of bulk materials by means of automating their main stages have been exhausted, and the way out is seen in the creation and implementation of new generation systems - systems of intelligent functioning and control. The intelligence of the control processes for the separation of bulk materials is determined by the general logic of functioning, as well as the efficiency of making managerial decisions, which is significantly increased if the developed mathematical models and control algorithms that correspond to the separation processes under study are used, which make it possible to evaluate the quality of the decision made on the model. The article provides a literary review of works on the development of methods and models for effective management of production systems based on the use of modern information technologies, which showed that there are many unresolved issues. This work is aimed at solving some of them. The relevance of the topic and the objectives of the research are determined. The research carried out made it possible to develop mathematical models of separation processes of various bulk materials and algorithms for identifying these processes. Based on the results obtained, an algorithm for making decisions by an intelligent software and hardware control system was developed.

Solid waste shape description and generation based on spherical harmonics and probability density function

Transport and separation processes of solid waste can only be modelled successfully with discrete element methods in case the shape of the particles can be described accurately. The existing techniques for morphological data acquisition, such as computed tomography, laser scanning technique, optical interferometer, stereo photography and structured light technique, are laborious and require a large amount of realistic solid waste samples. Therefore, there is a pressing need for an alternative method to describe the shape of solid waste particles and to generate multiple variations of particles with almost similar shapes. In this paper, a new method to describe solid waste particles is proposed that is frequency-based and uses spherical harmonics (SHs). Additionally, a new shape generation method is introduced that uses the shape description of a single particle to generate an array of related shapes based on a probability density function with a dimensionless control factor η. The newly proposed methods were successfully applied to describe the complex shapes of pieces of metal and plastic scrap. The shapes of these pieces of scrap can be described adequately with 15° of SH expansion and the overall divergence is within 0.1 mm. Five different values for η were tested, which generated shapes with the same distribution as the original particle. Rising levels of η cause the morphological variation of the generated particles to increase. These new methods improve the modelling of transportation and separation processes.

Modeling of oil, gas and reservoir water separation processes A. N. Parshukov

In this paper, a mathematical model of an oil and gas separator as an object of control is obtained. This model describes the dynamic process of degassing in the separator and is intended for automatic control of this process. The model is based on the material balance linearized equations for the liquid and gas phases. The results of numerical simulation confirm the sensitivity of the process behavior to automatic control.