Modeling, simulation and optimization of large-scale commercial desalination plants

This dissertation introduces desalination processes in general and multistage flash (MSF)and reverse osmosis (RO) in particular (Chapter 2). It presents the fundamental andpractical aspects of neural networks and provides an overview of their structures,topology, strengths, and limitations (Chapter 3). This study includes the neural networkapplications to prediction problems of large-scale commercial MSF and RO desalinationplants in conjunction with statistical techniques to identify the major independent variablesto optimize the process performance.In contrast to several recent studies, this work utilizes actual operating data (notsimulated) from a large-scale commercial MSF desalination plant (48 million gallons perday capacity, MGPD) and RO plant (15 MGPD) located in Kuwait and the Kingdom ofSaudi Arabia, respectively. We apply Neural Works Professional IIlPlus (NeuraIWare,1993) and SAS (SAS Institute Inc., 1996) software to accomplish this task (Chapter 4).This dissertation demonstrates how to apply modular and equation-solving approaches forsteady-state and dynamic simulations of large-scale commercial MSF desalination plantsusing 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 (Chapter 5).This work illustrates the development of an optimal operating envelope for achieving astable operation of a commercial MSF desalination plant using the SPEEDUP modeldeveloped in Chapter 5 (Chapter 6). We then discuss modellinearization around nominaloperating conditions and arrive at pairing schemes for manipulated and controlledvariables by interaction analysis (Chapter 7). Finally, this dissertation describes ourexperience in applying a commercial software, DynaPLUS, for combined steady-state anddynamic simulations of a commercial MSF desalination plant (Chapter 8).This dissertation is unique and significant in that it reports the first comprehensive study ofpredictive modeling, simulation, and optimization of large-scale commercial desalination