A Combined Computational Fluid Dynamics and Artificial Neural Networks Model for Distillation Point Efficiency
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Mahmood Reza Rahimi
In this work a CFD-ANN model is developed to give the predictions of sieve tray point efficiency. The main objective has been to find the extent to which CFD can be used in combination with artificial neural network as a prediction tool for efficiencies of industrial trays. The model was tested against a wide range of tray geometries, operating conditions and binary systems of materials. CFD model was applied, as a virtual experiment tool for direct prediction of point efficiencies, using tray geometries and operating conditions for any binary system of liquids. The model results were in agreement to experimental data from literatures, shown that CFD-ANN model can be used as a powerful tool in distillation column design and analysis.
©2012 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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Articles in the same Issue
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- Optimization of Pumping Schedules Using the Genealogical Decision Tree Approach
- Response Surface Modeling and Optimization of Immobilized Candida antarctica Lipase-Catalyzed Production of Dicarboxylic Acid Ester
- Search for Optimum Operating Conditions for a Water Purification Process Integrated to a Heat Transformer with Energy Recycling using Artificial Neural Network Inverse Solved by Genetic and Particle Swarm Algorithms
- Generic Mathematical Model for PSA Process
- A Combined Computational Fluid Dynamics and Artificial Neural Networks Model for Distillation Point Efficiency
- Multi-level Reactor Optimisation in the Conceptual Design of Processes with Heterogeneous Catalytic Reactors
- ANN and ANFIS Models for COP Prediction of a Water Purification Process Integrated to a Heat Transformer with Energy Recycling
- Adsorption of Cadmium on Gel Combustion Derived Nano ZnO
- Smith Predictor Based Parallel Cascade Control Strategy for Unstable Processes with Application to a Continuous Bioreactor
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