scholarly journals Appendix C: Hints When Using Aspen Plus®

2006 ◽  
pp. 259-268
Keyword(s):  
Aspen Plus

Моделирование процесса мембранного разделения газов и паров в среде ASPEN PLUS

2019 ◽  
Vol 9 (1) ◽  
pp. 3-8
Author(s):  
А. А. Козлова ◽  
М. М. Трубянов ◽  
А. А. Атласкин ◽  
Н. Р. Янбиков ◽  
М. Г. Шалыгин
Keyword(s):  
Aspen Plus

Simulation of biomass gasification in bubbling fluidized bed reactor using aspen plus®

2021 ◽  
Vol 235 ◽  
pp. 113981
Author(s):  
M. Puig-Gamero ◽  
D.T. Pio ◽  
L.A.C. Tarelho ◽  
P. Sánchez ◽  
L. Sanchez-Silva
Keyword(s):  
Fluidized Bed ◽  
Aspen Plus ◽  
Fluidized Bed Reactor ◽  
Biomass Gasification ◽  
Bubbling Fluidized Bed

scholarly journals Simulation of Palm Kernel Shell Gasification for Small Scale Power Generation Using Aspen Plus Software

2021 ◽  
Vol 1051 (1) ◽  
pp. 012054
Author(s):  
N A Najwa Annuar ◽  
N Kamarulzaman ◽  
Z F M Shadzalli ◽  
I H I Abdullah ◽  
P Y Liew ◽  
...  
Keyword(s):  
Power Generation ◽  
Palm Kernel ◽  
Aspen Plus ◽  
Small Scale ◽  
Palm Kernel Shell

scholarly journals Thermodynamics and Machine Learning Based Approaches for Vapor–Liquid–Liquid Phase Equilibria in n-Octane/Water, as a Naphtha–Water Surrogate in Water Blends

Processes ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 413
Author(s):  
Sandra Lopez-Zamora ◽  
Jeonghoon Kong ◽  
Salvador Escobedo ◽  
Hugo de Lasa
Keyword(s):  
Machine Learning ◽  
Liquid Phase ◽  
Phase Equilibria ◽  
Aspen Plus ◽  
Phase Region ◽  
Two Phase ◽  
Technical Literature ◽  
Phase Liquid ◽  
Liquid Vapor ◽  
Vapor Liquid

The prediction of phase equilibria for hydrocarbon/water blends in separators, is a subject of considerable importance for chemical processes. Despite its relevance, there are still pending questions. Among them, is the prediction of the correct number of phases. While a stability analysis using the Gibbs Free Energy of mixing and the NRTL model, provide a good understanding with calculation issues, when using HYSYS V9 and Aspen Plus V9 software, this shows that significant phase equilibrium uncertainties still exist. To clarify these matters, n-octane and water blends, are good surrogates of naphtha/water mixtures. Runs were developed in a CREC vapor–liquid (VL_ Cell operated with octane–water mixtures under dynamic conditions and used to establish the two-phase (liquid–vapor) and three phase (liquid–liquid–vapor) domains. Results obtained demonstrate that the two phase region (full solubility in the liquid phase) of n-octane in water at 100 °C is in the 10-4 mol fraction range, and it is larger than the 10-5 mol fraction predicted by Aspen Plus and the 10-7 mol fraction reported in the technical literature. Furthermore, and to provide an effective and accurate method for predicting the number of phases, a machine learning (ML) technique was implemented and successfully demonstrated, in the present study.

Separation of Dimethylformamide/H2O Mixtures Using Pervaporation-Distillation Hybrid Process

2011 ◽  
Vol 233-235 ◽  
pp. 866-869 ◽  
Author(s):  
Guang Lu Han ◽  
Qi Zhang ◽  
Jing Zhong ◽  
Hui Shao ◽  
Huan Ru Zhang
Keyword(s):  
Aqueous Solution ◽  
Separation Factor ◽  
Aspen Plus ◽  
Composite Membranes ◽  
Hybrid Process ◽  
Hybrid Processes ◽  
Operation Temperature ◽  
The Cost

Three kinds of commercial PVA composite membranes with different crosslinking degrees (PVA-1, PVA-2 and PVA-3) were used to separate DMF/H2O mixtures. Their pervaporation performance was investigated at different operation temperatures. The results showed that PVA-1 was the most suitable one for separating DMF/H2O mixtures. When operation temperature was 60°C and downstream pressure was lower than 6kPa, flux reached to 0.59 kg·m-2·h-1 and separation factor was 33 for PVA-1 membranes. Aspen Plus® was applied to simulate the normal distillation for retentate from pervaporation unit. Comparing with the two-effect distillation, the cost of concentrating DMF could be reduced 16.2% to 19.2% for DMF aqueous solution with different composition by hybrid processes. The cost would be the lowest for a hybrid process that concentrated the feed into 50wt% by pervaporation firstly, then concentrated retentate to 99.6wt% by two-effect distillation

scholarly journals Multiplicity Analysis in Reactive Distillation Column Using ASPEN PLUS

2006 ◽  
Vol 14 (3) ◽  
pp. 301-308 ◽  
Author(s):  
Bolun YANG ◽  
Jiang WU ◽  
Guosheng ZHAO ◽  
Huajun WANG ◽  
Shiqing LU
Keyword(s):  
Distillation Column ◽  
Reactive Distillation ◽  
Aspen Plus

scholarly journals Simulation of Corn Stover and Distillers Grains Gasification with Aspen Plus

2009 ◽  
Vol 52 (6) ◽  
pp. 1989-1995 ◽  
Author(s):  
A. Kumar ◽  
H. Noureddini ◽  
Y. Demirel ◽  
D. D. Jones ◽  
M. A. Hanna
Keyword(s):  
Corn Stover ◽  
Aspen Plus ◽  
Distillers Grains

scholarly journals Technological Solutions and Tools for Circular Bioeconomy in Low-Carbon Transition: Simulation Modeling of Rice Husks Gasification for CHP by Aspen PLUS V9 and Feasibility Study by Aspen Process Economic Analyzer

Energies ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 2006
Author(s):  
Diamantis Almpantis ◽  
Anastasia Zabaniotou
Keyword(s):  
Fluidized Bed ◽  
Rice Husk ◽  
Aspen Plus ◽  
Economic Assessment ◽  
Product Yield ◽  
Steam Gasification ◽  
Low Carbon ◽  
Simulation Tools ◽  
Fluidized Bed Gasifier ◽  
Annual Earnings

This study explored the suitability of simulation tools for accurately predicting fluidized bed gasification in various scenarios without disturbing the operational system, and dedicating time to experimentation, in the aim of benefiting the decision makers and investors of the low-carbon waste-based bioenergy sector, in accelerating circular bioeconomy solutions. More specifically, this study aimed to offer a customized circular bioeconomy solution for a rice processing residue. The objectives were the simulation and economic assessment of an air atmospheric fluidized bed gasification system fueled with rice husk, for combined heat and power generation, by using the tools of Aspen Plus V9, and the Aspen Process Economic Analyzer. The simulation model was based on the Gibbs energy minimization concept. The technological configurations of the SMARt-CHP technology were used. A parametric study was conducted to understand the influence of process variables on product yield, while three different scenarios were compared: (1) air gasification; (2) steam gasification; and (3) oxygen-steam gasification-based scenario. Simulated results show good accuracy for the prediction of H2 in syngas from air gasification, but not for the other gas components, especially regarding CO and CH4 content. It seems that the RGIBBS and Gibbs free minimization concept is far from simulating the operation of a fluidized bed gasifier. The air gasification scenario for a capacity of 25.000 t/y rice husk was assessed for its economic viability. The economic assessment resulted in net annual earnings of EUR 5.1 million and a positive annual revenue of EUR 168/(t/y), an excellent pay out time (POT = 0.21) and return of investment (ROI = 2.8). The results are dependent on the choices and assumptions made.

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