Simulation Of Process Research Articles

Post-combustion carbon capture process modeling, simulation, and assessment of synergistic effect of solvents

Post-combustion carbon capture appears to be a promising solution to reduce the emission of carbon dioxide (CO2) from power plants that generate electricity using either coal or natural gas. In addition, carbon capture process efficiency, capacity, and energy consumption have become challenging against the performance of the capture process. However, synergistic effect due to solvents blend has gained attention to reduce the process energy consumption and enhance process efficiency. In this study, blends of methyldiethanolamine (MDEA) and piperazine (PZ) at different concentrations were investigated using a validated post-combustion capture process model using Aspen HYSYS. Results were compared with 30 wt% monoethanolamine (MEA) as reference case. The effective process variables are concentration of solvents, the amount of water and solvent in the makeup section, viscosity of solvent, energy consumed in different process stages, and the amount of lean solvent flow rate. These variables were studied against fixed process variables using rate-based model. The study shows that using (43 wt% MDEA/7 wt% PZ) for post-combustion carbon capture needs 2.53 MJ/kgCO2 regeneration energy for 88.5% process efficiency compared to 4.003 MJ/kgco2 for 30 wt% MEA without the need for any process modifications. In addition, it was found that solvents synergistic effect contributes to resolving the drawbacks of post-combustion capture that will enable the high utilization of the process and contribution to reduce the consequences effect of climate change. Therefore, the study will help policymakers, industries and encourage researchers towards the large-scale commissioning of blended solvent -based post-combustion capture process.

Process design and simulation of methyl isobutyl ketone (MIBK) dehydration by batch distillation: A study on unit configuration and operational policies

Heteroazeotropic batch distillation of methyl isobutyl ketone-water binary mixture is investigated, seeking a reliable operation for dehydration of methyl isobutyl ketone (MIBK). The dynamic batch distillation module (BatchSep) of the commercial package Aspen Plus V.12.1® is applied for simulations. An initially fed unit at atmospheric pressure is simulated from the heating-up step until it reaches the desired MIBK purity of 99.8 wt%. Three configurations, namely conventional batch distillation unit (Mode I), batch distillation unit with decanter (Mode II), and a simple distillation unit (Mode III), are compared in a wide range of operating conditions. The effects of condenser temperature and the number of theoretical stages are also examined. According to the results, applying a decanter (Mode II) with a return fraction of over 0.75 for the MIBK-rich phase and below 0.4 for the aqueous phase provides higher MIBK recovery than the maximum achievable value in a conventional unit (Mode I), with almost no increase in process time. A perfect decanter offers an almost complete MIBK recovery, which is about 5% over the maximum value by conventional units (Mode I). Moreover, cutting the reflux (Mode III) offers the fastest way to the desired product but provides the lowest MIBK recovery value. The aqueous phase return fraction does not significantly impact MIBK recovery, but if it exceeds 0.5, it remarkably affects the process time/energy cost. When maximum MIBK recovery rate/minimum energy cost is desired, applying a decanter (Mode II) with a return fraction above 0.5 for the MIBK-rich phase and below 0.55 for the aqueous phase yields a higher production rate and a lower energy cost per unit quantity of product compared to the best achievable values for a conventional unit (Mode I). A perfect decanter improvesproductionrateandenergyefficiencyby8 %overthe best case in a conventionalunit (ModeI). Also, operation without reflux (Mode III) is preferred over a conventional operation (Mode I) with a total return fraction over 0.7 due to its superior energy efficiency and production rate.

Modeling of laser-assisted micro-milling Inconel718

Nickel-based superalloy Inconel718 is a typical difficult-to-machine material with the characteristic of high strength and hardness. Demands for micro parts of nickel-based superalloy Inconel718 are growing in aerospace, biomedical and other fields. Laser-assisted micro-milling (LAMM) can realize precision machining of the workpiece because of its lower cutting force. However, the machining mechanism and the change law of cutting force during LAMM of nickel-based superalloy Inconel718 are still unclear. There is a crucial need for modeling of LAMM Inconel718. This paper explores LAMM of nickel-based superalloy Inconel718 and realizes the simulation of LAMM process. A three-dimensional transient heat transfer finite element (FE) model is established to predict the temperature distribution of a Gaussian heat source in LAMM. Multiple sets of simulations of laser pre-heating with various laser power are conducted to analyze the temperature fields of workpiece and determine the appropriate laser parameters. A novel 3D model of the full machining process of LAMM Inconel 718 is constructed by ABAQUS with multi-subroutine, a fully coupled thermal-mechanical process is achieved. A modified Johnson-Cook constitutive model is used to reflect the size effect and flow stress of LAMM Inconel 718. Finally, the validity of the models is verified by experiments. Based on the verified model, the appropriate laser pre-heating parameters are obtained, and the prediction of cutting forces of LAMM Inconel718 is achieved.

The effect of aquathermolysis catalyst on the processing of Yarega oil

This study focuses on the synthesis of oil-soluble precursor catalysts using iron and nickel and evaluates their performance through laboratory simulations of aquathermolysis processes under varying temperature and duration conditions. The investigation highlights that the initial oil sample exhibits high resin content (27 %) and asphaltenes (∼4 %). Gas phase analysis reveals a significant increase in the levels of n-C1-C5 hydrocarbons and C4–C10 isomers (up to 3.5–4 times compared to control experiments), indicating substantial alterations in the oil's composition. After thermal steam treatment (TST), the viscosity of all samples decreases in comparison to the initial oil. For instance, when using an iron talate catalyst, the viscosity drops to 575 mPa s, which is approximately 10.5 times lower than the initial oil viscosity of 6027 mPa s. Additionally, experiments involving iron-based catalysts consistently exhibit reduced viscosity compared to control experiments, achieving a 2.2-fold decrease after a 72-h duration (from 1928 mPa s to 743 mPa s).Economic evaluations were conducted, demonstrating an additional oil production increase of 2855 tons. The net discounted income over a span of 4 years, resulting from the application of this catalytic aquathermolysis technology, totaled 40,652,750 russian rubles.

Improvement of a pharmaceutical powder mixing process in a tote blender via DEM simulations

An industrial-scale pharmaceutical powder blending process was studied via discrete element method (DEM) simulations. A DEM model of two active pharmaceutical ingredient (API) components and a combined excipient component was calibrated by matching the simulated response in a dynamic angle of repose tester to the experimentally observed response. A simulation of the 25-minute bin blending process predicted inhomogeneous API distributions along the rotation axis of the blending container. These concentration differences were confirmed experimentally in a production-scale mixing trial using high-performance liquid chromatography analysis of samples from various locations in the bin. Several strategies to improve the blend homogeneity were then studied using DEM simulations. Reversing the direction of rotation of the blender every minute was found to negligibly improve the blending performance. Introducing a baffle into the lid at a 45° angle to the rotation axis sped up the axial mixing and resulted in a better final blend uniformity. Alternatively, rotating the blending container 90° around the vertical axis five minutes prior to the process end was predicted to reduce axial segregation tendencies.

Crystallisation Dynamics in Large-Scale Extrusion Additive Manufacturing: An Analysis with and without Temperature Modification.

Large-Scale Material Extrusion (LS-MEX) is increasingly being used in small-scale production and prototyping due to its ability to create components in new temporal and spatial dimensions. However, the use of this manufacturing process poses microscopic and macroscopic challenges not encountered in previous small-scale production systems. These challenges arise primarily from the prolonged retention of heat in the material, which leads to insufficient strength in the extruded strands at the macrostructural level. As a result, the component can collapse, a phenomenon known as 'slumping'. Thermal energy also influences microstructural changes, such as crystallisation kinetics, which affect properties such as the strength and stiffness of the final product. The duration and dynamics of thermal energy are influenced by manufacturing parameters and the possible use of additional peripheral equipment, which affects component quality. In this study, the influence of thermal energy on structural processes through simulations of polyamide 6 with 40% carbon fibres (PA6 wt.%40 CF) is investigated. The results show that by adjusting the process parameters and using modification units, the thermal profile of the material can be accurately controlled, which allows the microstructural processes to be precisely controlled. This leads to the targeted modification of the macroscopic material properties. The focus of this work is on the combination of numerical simulations of the LS-MEX process with semi-empirical methods for the analysis of crystallisation processes. The application of the Nakamura model, which is used throughout similar investigations, allows a detailed description and prediction of the crystallisation kinetics during the manufacturing process. The study shows that the absolute degree of crystallisation can be determined with simplified assumptions using a combination of thermal simulations and semi-empirical approaches. It was found that the absolute degree of crystallisation increases from the outer interface of the strand to the print bed across the cross-section. This can be attributed to the specific thermal boundary conditions and the resulting temperature profiles at different points.

Transfer Learning-Based Artificial Neural Network for Predicting Weld Line Occurrence through Process Simulations and Molding Trials

Optimizing process parameters to minimize defects remains an important challenge in injection molding (IM). Machine learning (ML) techniques offer promise in this regard, but their application often requires extensive datasets. Transfer learning (TL) emerges as a solution to this problem, leveraging knowledge from related tasks to enhance model training and performance. This study explores TL’s viability in predicting weld line visibility in injection-molded components using artificial neural networks (ANNs). TL techniques are employed to transfer knowledge between datasets related to different components. Furthermore, both source datasets obtained from simulations and experimental tests are used during the study. In order to use process simulations to obtain data regarding the presence of surface defects, it was necessary to correlate an output variable of the simulations with the experimental observations. The results demonstrate TL’s efficacy in reducing the data required for training predictive models, with simulations proving to be a cost-effective alternative to experimental data. TL from simulations achieves comparable predictive metric values to those of the non-pre-trained network, but with an 83% reduction in the required data for the target dataset. Overall, transfer learning shows promise in streamlining injection molding optimization and reducing manufacturing costs.

Numerical Simulation of Solidification Process in an SCR Wheel and Belt Continuous Caster

Numerical Simulation of Solidification Process in an SCR Wheel and Belt Continuous Caster

Accuracy vs memory advantage in the quantum simulation of stochastic processes

Many inference scenarios rely on extracting relevant information from known data in order to make future predictions. When the underlying stochastic process satisfies certain assumptions, there is a direct mapping between its exact classical and quantum simulators, with the latter asymptotically using less memory. Here we focus on studying whether such quantum advantage persists when those assumptions are not satisfied, and the model is doomed to have imperfect accuracy. By studying the trade-off between accuracy and memory requirements, we show that quantum models can reach the same accuracy with less memory, or alternatively, better accuracy with the same memory. Finally, we discuss the implications of this result for learning tasks.

Hazard Assessment of Debris Flow: A Case Study of the Huiyazi Debris Flow

The Bailong River Basin is situated at the northeastern edge of the Qinghai–Tibet Plateau and the western transition zone of the Loess Plateau, characterized by steep terrain and heavy rainfall. This area experiences frequent occurrences of debris flows, posing serious threats to towns and construction projects. Focusing on the Huaiyazigou debris flow in the Bailong River Basin, numerical simulations of debris flow processes were conducted using Digital Surface Model (DSM) data with a resolution of 5 m × 5 m for various recurrence periods. The simulation results indicate that the debris flow develops rapidly along the gully after formation, decelerating and beginning to deposit upon reaching the cement plant area near the mouth of the gully, eventually merging into the Bailong River. The primary destructive modes of debris flow disasters encompass impact and burial. When encountering buildings, their flow characteristics manifest as deposition and diversion. A debris flow hazard classification model, based on intensity and recurrence periods, was established according to Swiss and Austrian standards, dividing the hazard into low, medium, and high levels. This method generated a debris flow hazard zone map, offering guidance for risk prevention and monitoring. This research demonstrates that using high-precision Digital Surface Models (DSM) can accurately represent the digital information of debris flow gully terrains and buildings. During the simulation process, it realistically reflects the characteristics of the debris flow movement, allowing for the more precise delineation of hazard zones.

Implementing the Blowers-Masel Approximation to Scale Activation Energy Based on Reaction Enthalpy in Mean-Field Microkinetic Modeling for Catalytic Methane Partial Oxidation.

Mean-field microkinetic modeling is a powerful tool for catalyst design and the simulation of catalytic processes. The reaction enthalpies in a microkinetic model often need to be adjusted when changing species' binding energies to model different catalysts, when performing thermodynamic sensitivity analyses, and when fitting experimental data. When altering reaction enthalpies, the activation energies should also be reasonably altered to ensure realistic reaction rates. The Blowers-Masel approximation (BMA) relates the reaction barrier to the reaction enthalpy. Unlike the Brønsted-Evans-Polani relationship, the BMA requires less data because only one parameter, the intrinsic activation energy, needs to be determined. We validate this application of BMA relations to model surface reactions by comparing against density functional theory data taken from the literature. By incorporating the BMA rate description into the open-source Cantera software, we enable a new workflow, demonstrated herein, allowing rapid screening of catalysts using linear scaling relationships and BMA kinetics within the process simulation software. For demonstration purposes, a catalyst screening for catalytic methane partial oxidation on 81 hypothetical metals is conducted. We compared the results with and without BMA-corrected rates. The heat maps of various descriptors (e.g., CH4 conversion, syngas yield) show that using BMA rates instead of Arrhenius rates (with constant activation energies) changes which metals are most active. Heat maps of sensitivity analyses can help identify which reactions or species are the most influential in shaping the descriptor map patterns. Our findings indicate that while using BMA-adjusted rates did not markedly affect the most sensitive reactions, it did change the most influential species.

Effect of Micro-Cracks on Chloride Ion Diffusion in Concrete Based on Stochastic Aggregate Approach

For concrete structures in offshore areas, chloride ion erosion is one of the main factors affecting durability. It is crucial to evaluate the chloride ion permeability resistance of concrete structures. In this paper, a finite element simulation of the chloride ion diffusion process in concrete is conducted. A mass diffusion finite element model based on a random aggregate approach is established to investigate the influences of an aggregate, the interface transition zone, and micro-cracks on the chloride ion diffusion coefficients in concrete. The results show that the mass diffusion finite element analysis based on the exponential function model and the power function model can effectively simulate the chloride ion diffusion process in concrete. In addition, the data reveal that volume fraction and distribution aggregates considerably affect chloride ion diffusivity in concrete. Also, the interface transition zone significantly accelerates chloride ion diffusion in concrete. Moreover, this acceleration effect exceeds the barrier effect of an aggregate.

Bio2Py: An API for integrating Python with BioWin for enhanced data acquisition in wastewater treatment simulations

Commercial simulators for modeling wastewater treatment processes (WWTPs), such as BioWin, play a crucial role for optimizing the design, operation, and control of treatment facilities. These simulators offer fast and user-friendly interfaces and can be employed to enhance the efficiency of wastewater treatment plants. However, a notable limitation of BioWin and similar platforms is the lack of direct integration with Python®, a programming language renowned for its versatility and extensive data analysis and machine learning libraries. Bridging this gap constitutes an opportunity to significantly improve the simulation process by leveraging Python's capabilities. The Python package Bio2Py (BioWin to Python) is presented as a compelling solution, reconciling BioWin's specialized functionality and Python's automation and data processing strengths. By utilizing Python's PyAutoGUI package, Bio2Py automates various aspects of the simulation process, such as loading influent data, running simulations, and saving simulation results without manual intervention. The integration of BioWin with Python not only speeds up the workflow by automating repetitive tasks but also opens new possibilities, such as parameter calibration for specific wastewaters and using BioWin as a data generator for machine learning applications. This brief communication presents the Bio2Py API, illustrating its use and applicability through a simple implementation example. Bio2Py's ability to automate and optimize various aspects of wastewater treatment process simulation, including data acquisition, and its potential use for process variable optimization and parameter tuning, positions it as a valuable tool for engineers and researchers who work in this field.

Process simulation and optimization for post-combustion CO2 capture pilot plant using high CO2 concentration flue gas as a source

This work mainly focuses on process simulation and optimization of a high-concentrated CO2 chemical absorption process using aqueous amine solutions based on both pilot plant data and process modeling. To optimize operating parameters for reducing energy consumptions, this study explores the influence of process structure and operational parameters on the product purity, capture rate, and energy consumption, which would provide theoretical and technical support for optimizing high-concentration CO2 capture. At first, the rate-based absorption/desorption models are established and well-verified by a total of 25 sets of pilot plant data, which shows high accuracy for reliable model predictions. Under an annual absorption capacity of 50 tons at a 90% capture rate and 95% product purity, the optimal case using Monoethanolamine (MEA) solution shows an increased CO2 capture rate of 12.37% and a decreased unit energy consumption of 22.5%. After flowsheet optimization, a new system of blended MEA, Methyl diethanolamine (MDEA), and 2-Amino-2-methyl-1-propanol (AMP) solution is designed to capturing high-concentrated CO2 under the same conditions. Compared with the MEA system, its specified energy consumption is reduced by 10.35% and its CO2 capture capacity increases by 11.6 tons annually.

Experimental Analysis of Heat Transfer Post Quenching of Medium Carbon Steel

<div>Transient temperature analysis is involved in the thermal simulation of the heat treatment process, in which the hot metal temperature changes with respect to time from an initial state to the final state. The critical part of the simulation is to determine the heat transfer coefficient (HTC) between the hot part and the quenching medium or quenchant. In liquid quenching, the heat transfer between the hot metal part and water becomes complicated and it is difficult to determine HTC. In the current experimentation a medium carbon steel EN 9 rod with a diameter of 50 mm and length 100 mm was quenched in water and ethylene glycol mixture with different concentrations. A part model was created; meshed and actual boundary conditions were applied to conduct computational fluid dynamics (CFD) analysis. In order to validate CFD analysis the experimental trials were conducted. Experimental results showed a reduced cooling rate for the specimen, and also a reduction in heat-carrying capacity of the mixture, which is attributed to the addition of ethylene glycol.</div>

Numerical simulation and optimization of Pervaporation process based on Heat-Mass-Flow coupling

Pervaporation (PV) has the advantages of low energy consumption and high separation efficiency, but the trade-off effect between flux and separation factor restricts its large-scale application. Based on the conservation equations of mass, momentum and heat, a transient model of the whole PV process coupled with heat-mass-flow was established in this paper. The effects of feed temperature, feed concentration and feed flow rate on heat and mass transfer in PV process were analyzed. TEOS crosslinked PDMS/PTFE-PVDF membrane was prepared by scraping coating method. The reliability of the model was verified by taking n-butanol/water separation system as an example. The concentration polarization and temperature boundary layer under different operating conditions are discussed. The effect of operating conditions on the separation performance was evaluated by response surface method, and the optimal operating conditions are obtained. The results show that the concentration polarization of the membrane surface becomes more obvious with the increase of feed concentration. The temperature boundary layer decreases with the increase of feed flow rate. Surprisedly, there is a significant temperature gradient in the membrane, and the maximum temperature difference between the two sides of the membrane is as high as 1.02 K. Diffusion in the membrane is the rate controlling factor of PV process. The contribution of operating conditions to the separation performance is in the order of feed temperature > feed concentration > feed flow rate. The best operating conditions are: feed temperature 350.2 K, feed concentration 3.8 wt% n-butanol/water and feed flow rate 23.6 L/h. This work provides a theoretical basis for the design of PV membrane materials and the optimization of PV operating conditions.

Impacts of Fengyun-4A and Ground-Based Observation Data Assimilation on the Forecast of Kaifeng’s Heavy Rainfall (2022) and Mechanism Analysis of the Event

The advancement of Numerical Weather Prediction (NWP) is pivotal for enhancing high-impact weather forecasting and warning systems. However, due to the high spatial and temporal inhomogeneity, the moisture field is difficult to describe by initial conditions in NWP models, which is the essential thermodynamic variable in the simulation of various physical processes. Data Assimilation techniques are central to addressing these challenges, integrating observational data with background fields to refine initial conditions and improve forecasting accuracy. This study evaluates the effectiveness of integrating observations from the Fengyun-4A (FY-4A) Advanced Geosynchronous Radiation Imager (AGRI) and ground-based microwave radiometer (MWR) in forecasts and mechanism analysis of a heavy rainfall event in the Kaifeng region of central China. Our findings reveal that jointly assimilating AGRI radiance and MWR data significantly enhances the model’s humidity profile accuracy across all atmospheric layers, resulting in improved heavy rainfall predictions. Analysis of the moisture sources indicates that the storm’s water vapor predominantly originates from westward air movement ahead of a high-altitude trough, with sustained channeling towards the rainfall zone, ensuring a continuous supply of moisture. The storm’s development is further facilitated by a series of atmospheric processes, including the interplay of high and low-level vorticity and divergence, vertical updrafts, the formation of a low-level jet, and the generation of unstable atmospheric energy. Additionally, this study examines the influence of Tai-hang Mountain’s terrain on precipitation patterns in the Kaifeng area. Our experiments, comparing a control setup (CTL) with varied terrain heights, demonstrate that reducing terrain height by 50–60% significantly decreases precipitation coverage and intensity. In contrast, increasing terrain height enhances precipitation, although this effect plateaus when the elevation increase exceeds 100%, closely mirroring the precipitation changes observed with a 75% terrain height increment.

The effects of phase interfaces in SmFeN/YSZ composite thermal barrier materials on electromagnetic wave-absorbing properties

Yttria-stabilized zirconia (YSZ) oxide ceramic powder is a thermal-barrier material used to fabricate thermal barrier coatings (TBC). In this study, to improve the electromagnetic wave (EMW) absorbing properties of TBCs, microwave-absorbing samarium iron nitrogen (SmFeN) particles were used to prepare SmFeN/yttria-stabilized zirconia (YSZ) composites. To study the effect of the SmFeN/YSZ interfaces on the EMW-absorbing properties of the SmFeN/YSZ composite, SmFeN/YSZ interfaces were built in different ways in composite materials having the same mass ratio of 1:1, for example, SmFeN and YSZ particles homogeneously mixed together, SmFeN/YSZ interfaces perpendicular and parallel to the EMW incident direction, and different interfaces between YSZ and SmFeN. Thus, the microwave absorbing performance of the samples, including the electromagnetic parameters, attenuation coefficient, eddy-loss current (C0), impedance matching, and minimum reflection loss (RLmin) values were examined and analyzed. The results demonstrate that interfaces perpendicular to the direction of the EMWs can increase the contact area and enhance the interfacial absorption effect. Further, when the wave-absorbing agent was placed in the middle of the composite, its wave-absorbing effect was the best among all the samples, yielding RLmin = −27.824 dB and effective absorption bandwidth (EAB) = 8.5453 GHz (effective absorption frequency range 7.9256–16.4709 GHz for a 7.2-mm-thick layer). Crucially, the frequency and bandwidth at which the best absorption occurred could be tuned by adjusting the thickness of the coating. This is because the interfaces in the samples resulted in polarization enhancement and anomalous dispersion, leading to large dielectric losses. Overall, the use of a coaxial measurement technique is highly efficient for EMW absorbing body design and process simulations.

Parametric optimisation of laser welding of stainless steel 316L

This paper presents numerical and experimental studies focused on the optimisation of laser welding parameters for AISI 316L stainless steel. The focus of the numerical studies was to obtain the mathematical model with the least time complexity and the highest fidelity. Based on the comparison of different mathematical models, a combination of two models, double ellipsoidal and conical models, was found to be optimal for the numerical simulation of the laser welding process. The studies were also complemented by material characterization studies for validation purpose. A pulse duration of 8 milliseconds and a current of 400 amperes with an average power of 380W were found to be the optimum parameters for laser welding of standard gauge 12 sheet of stainless steel AISI 316L. In addition, the effect of duty factor of the pulsed laser beam on the weld profile was also investigated and was found to be a major contributor to the optimisation process. The properties of the sample welded with the optimised set of parameters were also compared with the base metal, and based on the mechanical characterisation studies, it was found that the yield strength and hardness of the welded sample were improved, but the overall ductility was slightly reduced as compared to the base metal. The average weld zone size was also reduced by increasing the power density due to multiple reflections of the beam.

UNIFAC PREDICTION OF VAPOR LIQUID EQUILIBRIA INVOLVING GAMMA-VALEROLACTONE DERIVATIVE SYSTEMS

Researchers have studied the hydrogenation of Gamma-Valerolactone (GVL) over Ru/C to produce 2-methyltetrahydrofuran (2-MTHF), a biomass-based platform chemical with potential as a biofuel and green solvent. Other byproducts of this reaction include 2-butanol (2-BuOH), 2-pentanol (2-PeOH), and 1,4-pentanediol (1,4-PDO). In this study, UNIFAC activity coefficient models were used to predict the vapor-liquid equilibrium of several systems included in the process to explain the phase behavior which is important in purification process. The accuracy of the UNIFAC model is tested by comparing the experimental boiling points for the binary system of 2-propanol + 1-butanol and the vapor-liquid equi-librium of the GVL + 2-MTHF system. From this comparison, the Root Mean Square Deviation (RMSD) values for the boiling point measurements are obtained to be 3.153%, and for the liquid and vapor phases in the vapor-liquid equilibrium measurements, the RMSD values are 1.934% and 0.298% respectively. These RMSD values indicate the level of accuracy of the UNIFAC model in representing the experimental data for both boiling point measurements and vapor-liquid equilibrium phase behavior. The prediction results of vapor-liquid equilibrium data for GVL derivative systems showed that the 2-BuOH + 2-MTHF system does form an azeotrope when the mol fraction of 2-BuOH is 0.0031 at 79.780C. The calculation was performed using ChemCAD commercial software for chemical process modeling and simulation