File Name: natural gas sweetening process design and simulation .zip
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Chemical Engineering Process Simulation is ideal for students, early career researchers, and practitioners, as it guides you through chemical processes and unit operations using the main simulation softwares that are used in the industrial sector. This book will help you predict the characteristics of a process using mathematical models and computer-aided process simulation tools, as well as model and simulate process performance before detailed process design takes place. Content coverage includes steady and dynamic simulations, the similarities and differences between process simulators, an introduction to operating units, and convergence tips and tricks. You will also learn about the use of simulation for risk studies to enhance process resilience, fault finding in abnormal situations, and for training operators to control the process in difficult situations. This experienced author team combines industry knowledge with effective teaching methods to make an accessible and clear comprehensive guide to process simulation. Process engineers, chemical engineers, chemical engineering students graduates and post-graduates. Professor Ir.
Deep eutectic solvents DESs have received significant attention as potential extracting agents in recent years due to their favorable characteristics including low cost, easy preparation and environmentally safe starting materials. Experimentally screening for highly efficient DESs meeting various requirements for natural gas sweetening remains a challenging task. Reliable thermophysical properties were determined experimentally, and a detailed equilibrium-based model was developed for one of the glycol-based DESs i. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: All relevant data are within the manuscript and its Supporting Information files.
Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: Split-flow gas sweetening is known to consume less energy than a conventional gas sweetening process when the inlet sour gas contains a high concentration of acid gases. The input of parameters such as the concentration of sour gases CO2, H2S in the feed gas has been examined. View on Springer. Save to Library.
Richard Ochieng, Abdallah S. We show how a multiobjective bare-bones particle swarm optimization can be used for a process parameter tuning and performance enhancement of a natural gas sweetening unit. This has been made through maximization of hydrocarbon recovery and minimization of the total energy of the process as the two objectives of the optimization. A trade-off exists between these two objectives as illustrated by the Pareto front. This algorithm has been applied to a sweetening unit that uses the Benfield HiPure process. Detailed models of the natural gas unit are developed in ProMax process simulator and integrated to the multi-objective optimization developed in visual basic environment VBA. In this study, the solvent circulation rates, stripper pressure and reboiler duties are considered as the decision variables while hydrogen sulfide and carbon dioxide concentrations in the sweetened gas are considered as process constraints.
PDF | On Dec 4, , Immanuel Sebastine Ribwar ABDULRAHMAN published Natural gas sweetening process design and simulation: a Case study of.
There exists large space to save energy of high-sulfur natural gas purification process. The multi-objective optimization problem has been investigated to effectively reduce the total comprehensive energy consumption and further improve the production rate of purified gas. A steady-state simulation model of high-sulfur natural gas purification process has been set up by using ProMax. Seven key operating parameters of the purification process have been determined based on the analysis of comprehensive energy consumption distribution. To solve the problem that the process model does not converge in some conditions, back-propagation BP neural network has been applied to substitute the simulation model to predict the relative parameters in the optimization model.
In this research, firstly, a commercial gas sweetening unit with methyl-di-ethanol-amine MDEA solution is simulated and comprised in an integrated model in accordance with Aspen HYSYS software. According to the simulation results, the considerable energy potential contributed to the pressure difference between absorber and regenerator columns causes this energy driving force to be applied in power recovery turbine PRT. In the last step, the amount of waste hydraulic energy is calculated, and its recovery methods are investigated.
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