Type of Document Dissertation Author Al-Shayji, Khawla Abdul Mohsen Author's Email Address khw95@vt.edu URN etd-32898-13261 Title Modeling, Simulation, and Optimization of large-Scale Commercial Desalination Plants Degree PhD Department Chemical Engineering Advisory Committee
Advisor Name Title Liu, Y. A. Committee Chair Conger, William L. Committee Member Sherali, Hanif D. Committee Member Vanlandingham, Hugh F. Committee Member Velander, William H. Committee Member Keywords
- Artificial Intelligence
- Multistage Flash
- Desalination
- Reverse Osmosis
- Modeling
- Simulation
- Optimization
- Linearization
- Neural Network
- Interaction Analysis
Date of Defense 1998-04-17 Availability unrestricted Abstract This dissertation introduces desalination processes in
general and multistage flash (MSF) and reverse osmosis
(RO) in particular. It presents the fundamental and
practical aspects of neural networks and provides an
overview of their structures, topology, strengths, and
limitations. This study includes the neural network
applications to prediction problems of large-scale
commercial MSF and RO desalination plants in
conjunction with statistical techniques to identify the
major independent variables to optimize the process
performance.
In contrast to several recent studies, this work utilizes
actual operating data (not simulated) from a large-scale
commercial MSF desalination plant (48 million gallons
per day capacity, MGPD) and RO plant (15 MGPD)
located in Kuwait and the Kingdom of Saudi Arabia,
respectively. We apply Neural Works Professional
II/Plus (NeuralWare, 1993) and SAS (SAS Institute
Inc., 1996) software to accomplish this task.
This dissertation demonstrates how to apply modular
and equation-solving approaches for steady-state and
dynamic simulations of large-scale commercial MSF
desalination plants using ASPEN PLUS (Advanced
System for Process Engineering PLUS) and SPEEDUP
(Simulation Program for Evaluation and Evolutionary
Design of Unsteady Processes) marketed by Aspen
Technology, Cambridge, MA.
This work illustrates the development of an optimal
operating envelope for achieving a stable operation of a
commercial MSF desalination plant using the SPEEDUP
model. We then discuss model linearization around
nominal operating conditions and arrive at pairing
schemes for manipulated and controlled variables by
interaction analysis. Finally, this dissertation describes
our experience in applying a commercial software,
DynaPLUS, for combined steady-state and dynamic
simulations of a commercial MSF desalination plant.
This dissertation is unique and significant in that it reports
the first comprehensive study of predictive modeling,
simulation, and optimization of large-scale commercial
desalination plants. It is the first detailed and comparative
study of commercial desalination plants using both
artificial intelligence and computer-aided design
techniques. The resulting models are able to reproduce
accurately the actual operating data and to predict the
optimal operating conditions of commercial desalination
plants.
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