Modeling, Simulation, and Optimization of large-Scale Commercial Desalination Plants

Type of Document Dissertation
Author Al-Shayji, Khawla AbdulMohsen
Author's Email Address khw95@vt.edu
URN etd-32898-13261
Title Modeling, Simulation, and Optimization of large-Scale Commercial Desalination Plants
Degree Doctor of Philosophy
Department Chemical Engineering
Advisory Committee
Advisor Name Title
Y.A. Liu Committee Chair
Hanif D. Sherali Committee Member
Hugh F. Vanlandingham Committee Member
William H. Velander Committee Member
William L. Conger Committee Member
Keywords

* Linearization
* Optimization
* Simulation
* Modeling
* Reverse Osmosis
* Desalination
* Multistage Flash
* Artificial Intelligence
* 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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