Yes / Despite the fact of being intensive energy consumption, MSF is a mature technology that characterised by a high production capacity of high-quality water. The multistage flash (MSF) desalination process is one of the prominent thermal desalination used in the industry of seawater desalination to produce high quantity and high quality of freshwater. However, this process consumes large amount of energy and faces thermal limitations due to its high degree of exergy destruction at several units of the process. Therefore, the research of MSF is still existed to elevate the performance indicators and to resolve the concern of high energy consumption. To rectify these limitations, it is important to determine the units responsible in dissipating energy. This study aims to model an industrial MSF process validated against real data and then investigate the exergy destruction and thermodynamic limitations of the process. As a case study, Azzour MSF seawater desalination plant, located in Al Khiran in Kuwait is under the focus. A comprehensive model is developed by analysing several published models. Specifically, the calculation of exergy destruction has embedded both physical and chemical exergies that identified as a strong point of the model developed. As expected, the highest exergy destruction (55.5%) occurs within the heat recovery section followed by the brine heater with exergy destruction of 28.26% of the total exergy destruction. This study identifies the sections of the industrial process that cause the highest energy losses.

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 gallonsper 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. / Ph. D.

Yes / Multistage Flash (MSF) desalination process is still a dominant process, especially in the Gulf region, to produce high quality freshwater. Although there has been energy price surge in recent years, MSF process will continue to operate in that region for some foreseeable future. The key challenge is how to make such processes still profitable. Understanding the dynamics of any processes under uncertainty and disturbances is very important to make a process operationally feasible and profitable. The main aim of this work is to understand the dynamics of industrial scale MSF process using high fidelity and reliable process model. For this purpose, a detailed dynamic model for the MSF process incorporating key and new features is developed and validated against the actual data of a large-scale seawater desalination plant. The model is then used to study the behaviour of large scale MSF processes for disturbances in steam temperature, feed temperature and the recycle brine flow rate. The simulation results show that the last stage requires a longer time to settle compared to the preceding stages. In addition, steam temperature shows insignificant influence on the performance ratio compared to the inlet seawater temperature and recycle brine flow rate. Furthermore, it is found that the productivity of plant can increase in the winter compared to that in the summer. However, this benefit comes at the expense of increased steam consumption in the winter, resulting in a low performance ratio.

Among many seawater desalination processes, the multistage flash (MSF) desalination process is a major source of fresh water around the world. The most costly design and operation problem in seawater desalination is due to scale formation and corrosion problems. Fouling factor is one of the many important parameters that affect the operation of MSF processes. This thesis therefore focuses on determining the optimal design and operation strategy of MSF desalinations processes under fouling which will meet variable demand of freshwater. First, a steady state model of MSF is developed based on the basic laws of mass balance, energy balance, and heat transfer equations with supporting correlations for physical properties. gPROMS software is used to develop the model which is validated against the results reported in the literature. The model is then used in further investigations. Based on actual plant data, a simple dynamic fouling factor profile is developed which allows calculation of fouling factor at different time (season of the year). The role of changing brine heater fouling factor with varying seawater temperatures (during the year) on the plant performance and the monthly operating costs for fixed water demand and fixed top brine temperature are then studied. The total monthly operation cost of the process are minimised while the operating parameters such as make up, brine recycle flow rate and steam temperature are optimised. It was found that the seasonal variation in seawater temperature and brine heater fouling factor results in significant variations in the operating parameters and operating costs. The design and operation of the MSF process are optimized in order to meet variable demands of freshwater with changing seawater temperature throughout the day and throughout the year. On the basis of actual data, the neural network (NN) technique has been used to develop a correlation for calculating dynamic freshwater demand/consumption profiles at different times of the day and season. Also, a simple polynomial based dynamic seawater temperature correlation is developed based on actual data. An intermediate storage tank between the plant and the client is considered. The MSF process model developed earlier is coupled with the dynamic model for the storage tank and is incorporated into the optimization framework within gPROMS. Four main seasons are considered in a year and for each season, with variable freshwater demand and seawater temperature, the operating parameters are optimized at discrete time intervals, while minimizing the total daily costs. The intermediate storage tank adds flexible scheduling and maintenance opportunity of individual flash stages and makes it possible to meet variable freshwater demand with varying seawater temperatures without interrupting or fully shutting down the plant at any-time during the day and for any season. Finally, the purity of freshwater coming from MSF desalination plants is very important when the water is used for industrial services such as feed of boiler to produce steam. In this work, for fixed water demand and top brine temperature, the effect of separation efficiency of demister with seasonal variation of seawater temperatures on the final purity of freshwater for both cleaned and fouled demister conditions is studied. It was found that the purity of freshwater is affected by the total number of stages. Also to maintain the purity of freshwater product, comparatively large number of flash stage is required for fouled demister.

No description available.

Yes / Solar energy, amongst all renewable energies, has attracted inexhaustible attention all over the world as a supplier of sustainable energy. The energy requirement of major seawater desalination processes such as multistage flash (MSF), multi-effect distillation (MED) and reverse osmosis (RO) are fulfilled by burning fossil fuels, which impact the environment significantly due to the emission of greenhouse gases. The integration of solar energy systems into seawater desalination processes is an attractive and alternative solution to fossil fuels. This study aims to (i) assess the progress of solar energy systems including concentrated solar power (CSP) and photovoltaic (PV) to power both thermal and membrane seawater desalination processes including MSF, MED, and RO and (ii) evaluate the economic considerations and associated challenges with recommendations for further improvements. Thus, several studies on a different combination of seawater desalination processes of solar energy systems are reviewed and analysed concerning specific energy consumption and freshwater production cost. It is observed that although solar energy systems have the potential of reducing carbon footprint significantly, the cost of water production still favours the use of fossil fuels. Further research and development on solar energy systems are required to make their use in desalination economically viable. Alternatively, the carbon tax on the use of fossil fuels may persuade desalination industries to adopt renewable energy such as solar.

Among many seawater desalination processes, the multistage flash (MSF) desalination process is a major source of fresh water around the world. The most costly design and operation problem in seawater desalination is due to scale formation and corrosion problems. Fouling factor is one of the many important parameters that affect the operation of MSF processes. This thesis therefore focuses on determining the optimal design and operation strategy of MSF desalinations processes under fouling which will meet variable demand of freshwater. First, a steady state model of MSF is developed based on the basic laws of mass balance, energy balance, and heat transfer equations with supporting correlations for physical properties. gPROMS software is used to develop the model which is validated against the results reported in the literature. The model is then used in further investigations. Based on actual plant data, a simple dynamic fouling factor profile is developed which allows calculation of fouling factor at different time (season of the year). The role of changing brine heater fouling factor with varying seawater temperatures (during the year) on the plant performance and the monthly operating costs for fixed water demand and fixed top brine temperature are then studied. The total monthly operation cost of the process are minimised while the operating parameters such as make up, brine recycle flow rate and steam temperature are optimised. It was found that the seasonal variation in seawater temperature and brine heater fouling factor results in significant variations in the operating parameters and operating costs. The design and operation of the MSF process are optimized in order to meet variable demands of freshwater with changing seawater temperature throughout the day and throughout the year. On the basis of actual data, the neural network (NN) technique has been used to develop a correlation for calculating dynamic freshwater demand/consumption profiles at different times of the day and season. Also, a simple polynomial based dynamic seawater temperature correlation is developed based on actual data. An intermediate storage tank between the plant and the client is considered. The MSF process model developed earlier is coupled with the dynamic model for the storage tank and is incorporated into the optimization framework within gPROMS. Four main seasons are considered in a year and for each season, with variable freshwater demand and seawater temperature, the operating parameters are optimized at discrete time intervals, while minimizing the total daily costs. The intermediate storage tank adds flexible scheduling and maintenance opportunity of individual flash stages and makes it possible to meet variable freshwater demand with varying seawater temperatures without interrupting or fully shutting down the plant at any-time during the day and for any season. Finally, the purity of freshwater coming from MSF desalination plants is very important when the water is used for industrial services such as feed of boiler to produce steam. In this work, for fixed water demand and top brine temperature, the effect of separation efficiency of demister with seasonal variation of seawater temperatures on the final purity of freshwater for both cleaned and fouled demister conditions is studied. It was found that the purity of freshwater is affected by the total number of stages. Also to maintain the purity of freshwater product, comparatively large number of flash stage is required for fouled demister.

No description available.

The rapid increase in the demand on fresh water due the increase in the world population and scarcity of natural water puts more stress on the desalination industrial sector to install more desalination plants around the world. Among these desalination plants, multistage flash desalination process (MSF) is considered to be the most reliable technique of producing potable water from saline water. In recent years, however, the MSF process is confronting many problems to cut off the cost and increase its performance. Among these problems are the non-condensable gases (NCGs) and the accumulation of fouling which they work as heat insulation materials. As a result, the MSF pumps and the heat transfer equipment are overdesigned and consequently increase the capital cost and decrease the performance of the plants. Moreover, improved process control is a cost effective approach to energy conservation and increased process profitability. Thus, this study is motivated by the real absence of detailed kinetic fouling model and implementation of advance process control (APC). To accomplish the above tasks, commercial modelling tools can be utilized to model and simulate MSF process taking into account the NCGs and fouling effect, and optimum control strategy. In this research, gPROMS (general PROcess Modeling System) model builder has been used to develop the MSF process model. First, a dynamic mathematical model of MSF is developed based on the basic laws of mass balance, energy balance and heat transfer. Physical and thermodynamic properties of brine, distillate and water vapour are included to support the model. The model simulation results are validated against actual plant data published in the literature and good agreement with these data is obtained. Second, the design of venting system in MSF plant and the effect of NCGs on the overall heat transfer coefficient (OHTC) are studied. The release rate of NCGs is studied using Henry’s law and the locations of venting points are optimised. The results reveal that high concentration of NCGs heavily affects the OHTC. Furthermore, advance control strategy namely: generic model control (GMC) is designed and introduced to the MSF process to control and track the set points of the two most important variables in the MSF plant; namely the Top Brine Temperature (TBT) which is the output temperature of the brine heater and the Brine Level (BL) in the last stage. The results are compared to conventional Proportional Integral Derivative Controller (PID) and show that GMC controller provides better performance over conventional PID controller to handle a nonlinear system. In addition, a new control strategy called hybrid Fuzzy-GMC is developed and implemented to control the same aforementioned loops. Its results reveal that the new control outperforms the pure GMC in some areas. Finally, a dynamic fouling model is developed and incorporated into the MSF dynamic process model to predict fouling at high temperature and high velocity. The proposed dynamic model considers the attachment and removal mechanisms of calcium carbonate and magnesium hydroxide with more relaxation of the assumptions. Since the MSF plant stages work as a series of heat exchangers, there is a continuous change of temperature, heat flux and salinity of the seawater. The proposed model predicts the behaviour of fouling based on the physical and thermal conditions of every single stage of the plant.

The rapid increase in the demand on fresh water due the increase in the world population and scarcity of natural water puts more stress on the desalination industrial sector to install more desalination plants around the world. Among these desalination plants, multistage flash desalination process (MSF) is considered to be the most reliable technique of producing potable water from saline water. In recent years, however, the MSF process is confronting many problems to cut off the cost and increase its performance. Among these problems are the non-condensable gases (NCGs) and the accumulation of fouling which they work as heat insulation materials. As a result, the MSF pumps and the heat transfer equipment are overdesigned and consequently increase the capital cost and decrease the performance of the plants. Moreover, improved process control is a cost effective approach to energy conservation and increased process profitability. Thus, this study is motivated by the real absence of detailed kinetic fouling model and implementation of advance process control (APC). To accomplish the above tasks, commercial modelling tools can be utilized to model and simulate MSF process taking into account the NCGs and fouling effect, and optimum control strategy. In this research, gPROMS (general PROcess Modeling System) model builder has been used to develop the MSF process model. First, a dynamic mathematical model of MSF is developed based on the basic laws of mass balance, energy balance and heat transfer. Physical and thermodynamic properties of brine, distillate and water vapour are included to support the model. The model simulation results are validated against actual plant data published in the literature and good agreement with these data is obtained. Second, the design of venting system in MSF plant and the effect of NCGs on the overall heat transfer coefficient (OHTC) are studied. The release rate of NCGs is studied using Henry’s law and the locations of venting points are optimised. The results reveal that high concentration of NCGs heavily affects the OHTC. Furthermore, advance control strategy namely: generic model control (GMC) is designed and introduced to the MSF process to control and track the set points of the two most important variables in the MSF plant; namely the Top Brine Temperature (TBT) which is the output temperature of the brine heater and the Brine Level (BL) in the last stage. The results are compared to conventional Proportional Integral Derivative Controller (PID) and show that GMC controller provides better performance over conventional PID controller to handle a nonlinear system. In addition, a new control strategy called hybrid Fuzzy-GMC is developed and implemented to control the same aforementioned loops. Its results reveal that the new control outperforms the pure GMC in some areas. Finally, a dynamic fouling model is developed and incorporated into the MSF dynamic process model to predict fouling at high temperature and high velocity. The proposed dynamic model considers the attachment and removal mechanisms of calcium carbonate and magnesium hydroxide with more relaxation of the assumptions. Since the MSF plant stages work as a series of heat exchangers, there is a continuous change of temperature, heat flux and salinity of the seawater. The proposed model predicts the behaviour of fouling based on the physical and thermal conditions of every single stage of the plant.