Optimal Design of Multi-stage Flash (MSF), Reverse Osmosis (RO) and Hybrid MSF-RO Seawater Desalination Processes Using Differential Evolution Algorithm.

Optimal design of multi-stage flash (MSF), reverse osmosis (RO) and hybrid MSF-RO Seawater. Amit Kumar from the Department of Chemical Engineering for his valuable contribution in my research work.

Chapter 2: Optimal Design of Multi-stage Flash (MSF) Seawater Desalination Processes __________________________________________ 53

Summary _____________________________________________ 132

Chapter 3: Optimal Design of Reverse Osmosis (RO) Seawater Desalination Processes with Retentate Reprocessing ________________ 135

Summary _____________________________________________ 216

Optimal design of Reverse Osmosis (RO) Seawater Desalination Processes with permeate Reprocessing and Recycle ________________ 219

Summary _________________________________________________ 270

Chapter 5: Optimal Design of Hybrid MSF-RO seawater desalination Processes ____________________________________________________ 273

Summary _____________________________________________ 363

Future Work ______________________________________________ 378

List of Tables

Symbols

WMD flow rate of total potable water product stream in the MSF process, m/h3 WMF feed flow in the MSF process, m/h3. WMRWBD Flow rate of brine stream exiting mixer M1 and entering separator S2 in MSF-BR process, m3/h.

Greek symbols

Introduction and Literature Review

Motivation for research in Seawater Desalination systems

Potable water world data
Water classification
Composition and Properties of Seawater

This is due to the reason that water is required in a number of human biological processes and thus plays an important role in the development and care of people. As shown, the water available on earth can be broadly categorized into salt water (97.5%) and fresh water (2.5%).

Figure 1.1: Distribution of various water sources on earth (Source: Cipollina et al., 2009) SW

Technologies for Potable water generation from seawater

MEE: Multiple Efficient Evaporation SE: Single Efficient Evaporation TVC: Thermal Vapor Compression MVC: Mechanical Vapor Compression ADVC: Adsorption Vapor.

Figure 1.3: Taxonomy of seawater desalination.

MSF MED

History of industrial desalination processes

The first commercial land-based seawater desalination plant was installed by the Ottomans in Jeddah, Saudi Arabia. With the improvement in underwater tube technology, the first evaporator with a total capacity of about 45000 m3/day was built in 1950 at Curacao city in Kuwait. Later, with rapid progress in MSF desalination technology, the first 0.4 million liters per day (MGD) plant was installed in 1957 in Kuwait.

Subsequently, Kuwait gained extensive experience in the design, commissioning, operation and maintenance of MSF desalination plants and continues to lead in the field of desalination. 2000 Design and construction of MSF high performance system with 43 stages, 17280, and a performance ratio of 1.

Table 1.4: Timeline of thermal and membrane desalination processes (Source: El-Dessoukey and Ettouney, 2002)

Thermal Processes

Multi-stage flash desalination

The VC process therefore enables efficient energy recovery with energy recovery from the heat of condensation to evaporate brine. Thereby, MEE enables the recovery of condensation heat from the salt-free distillate vapor to further heat the concentrated brine in later effects. Subsequent effects are operated at lower pressures to improve vapor recovery from the concentrated brine streams.

Therefore, no pumps are required for fluid transport in the forward configurations of the MEE. However, a pump will be needed to draw the concentrated brine from the final effect of the MEE.

Membrane Processes

Reverse Osmosis

Greater operating costs for an RO process are due to the pump that supplies the feed at higher pressure to the membrane modules. This is due to the primary requirement to overcome the osmotic pressure of feed water with high salinity ppm). Both these membranes are held alternately to each other in the ED system and electrical current is passed through the fluid.

Ultimately, cations migrate to the cathode and anions to the anode to generate two water channels across a combination of two adjacent membranes. The main operating costs for the ED processes are energy consumption, which is proportional to the concentration of salts to be removed.

State of the Art in Process Optimization of MSF, RO and hybrid MSF-RO processes

MSF desalination processes

Overview
Simulation based analysis

Hybrid MSF-RO desalination processes

The authors concluded that the optimal solution was strongly influenced by the initial guess values of the independent variables. Based on the superstructure optimization, the authors determined that the TRO-RSR process is the optimal process. It was found that GA converged quickly and efficiently to the optimal solution for a generation size of 8. The parameters of the GA algorithm had a significant effect on the quality of the obtained solutions. 2008) used a solution-diffusion model to simulate the performance of RO membranes.

Sarif et.al (2008) formulated a mathematical model of MINLP for the optimization of seawater RO desalination process. The authors studied seven different hybrid MSF-RO process configurations to minimize water production costs.

Table 1.5: A summary of prominent literatures on the process systems engineering of desalination processes

Possible scope for further research

Extensive research has been conducted into the NLP optimization of MSF, RO and hybrid MSF-RO processes. The optimal design of hybrid MSF-RO processes has been investigated using Excel solver (GRG) and GAMS CONOPT (SQP) platforms. In summary, a comparative assessment of alternative hybrid MSF-RO processes has not been addressed in the literature to date.

In other words, alternative hybrid MSF-RO processes have not been studied using non-deterministic methods in the literature. Thus, limited insights can be obtained from available literature regarding the best configurations under MSF, RO and hybrid MSF-RO processes.

Objectives of the Thesis

Thereby, the performance of DE algorithm and MATLAB built-in optimization functions were elaborated in the results and discussion section. Using identified optimal process configurations of MSF (MSF-BR) and RO processes that have the lowest freshwater production costs, 20 different hybrid MSF-RO process configurations were constructed. Thereby, Chapter 5 addresses the modeling, simulation, optimization methodology, results and sensitivity aspects of hybrid MSF-RO processes.

Thereby, the chapter summarizes the ranking of various optimal MSF, RO and hybrid MSF-RO processes based on the minimum freshwater production costs. In conclusion, the chapter deals with the scope and possibilities for future work with optimization of MSF, RO and hybrid MSF-RO processes.

Optimal Design of MSF Seawater Desalination Processes

Optimal Design of Multi-stage Flash (MSF) Seawater Desalination Processes

Problem Statement

MSF Design Configurations

Modeling of MSF Desalination

Mathematical modeling and optimization
Model Basis and Assumptions
Process description
Parameters
Model Equations
Inequality constraints
Parameters
Dependent variables
Model equations
Independent variables
Dependent variables
Parameters
Independ e nt variables
Model Equations

Such models are essential for MSF desalination process design, simulation and optimization studies. This heat recovery improves the efficiency of the process due to the increased temperature of the seawater feed. The main objective of the MSF-M process is to reduce energy losses in the cooling seawater flow.

The remaining elements of the system are similar to those for the MSF-OT process. The brine recirculation stream (WMR) enters the brine heater tubes, where the heating steam (WS) is condensed on the outer surface of the tubes.

Fig. 2.1 represents a schematic of the single stage flash with cooling water desalination unit

Objective function

An extremely high value of the penalty parameter ensures the achievement of feasible solutions after applying the termination criteria.

Parametric and Design Specifications

Model Summary

Simulation methodology

The primary objective of the thesis is to survey various alternative optimization techniques for MSF plant optimization. However, the same can be better defined by the solutions obtained from non-deterministic optimization techniques, where in the standard deviation of the optimal solutions can be evaluated to derive from the quality of the obtained solutions. The effectiveness of DE with respect to other deterministic (SQP and MS-SQP) and non-deterministic (GA and SA) optimization methods is investigated for the optimality of MSF plants.

As previously discussed, optimization methodology involves the application of one of the following: DE, GA, SA, SQP, MS-SQP, DE-SQP, and GA-SQP. The basic principle of the DE algorithm refers to the creation of new candidate solutions by combining the parent individual and several other individuals from the same population.

Figure 2.5: Overall solution methodology for MSF process optimization.

Create a new population by adopting mutation, cross-over and selection operations in sequence

Sequential quadratic programming (SQP)

SQP optimization methods are considered one of the best non-linear programming (NLP) methods (Tolle, 1995) as they outperform any other NLP method in terms of efficiency, accuracy and percentage of successful solutions over a large number of test problems. Similar to the Newton's method adopted for unconstrained optimization methods, a typical SQP method involves the implementation of Broyden-Fletcher-Goldfarb-Shanno (BFGS) quasi-Newton method for evaluating the Hessian of the Lagrangian function. The working principle of the SQP is to solve a series of quadratic programming (QP) subproblems where solutions are used to form a search direction using line search procedure.

Since the objective function of the MSF desalination model is non-convex and non-linear (Marcovecchio, Mussati et al. 2005), SQP ensures a local minimum for an initial vector supplied to the algorithm. Thus, the solution quality of SQP could not be judged due to the difficulty involved in determining alternative optimal solutions by manipulating the initial vector.

Multi-start SQP

Other syntaxes in the SQP toolbox job command are similar to those presented for the GA algorithm in the previous sub-section. Standard options of MATLAB (2013 a version) are applicable for controlling the value of the SQP solution termination criteria function, user-supplied and approximate derivatives, algorithm settings, and output function. Using this optimization technique, DE was first run to reach the best solution for the MSF plant.

Finally, the best solution obtained with DE was used as an initial vector value for the SQP to achieve any further improvements in the obtained optimal solutions. For the DE-SQP method, the methods presented in previous subsections of DE and SQP apply.

GA-SQP

Simulation of MSF-BR model

Differential Evolution algorithm

The effectiveness of the developed MSF model and optimization model needs to be established to proceed towards detailed calculation and parametric analysis. Therefore, the DE algorithm is deduced to be effective in proceeding towards desalination network optimization. Appendix F summarizes the DE algorithm code used for test function evaluation and the results obtained in the model code validation study of the DE algorithm.

Results and discussions

Efficacy of various optimization methods
CPU Time
Optimality of independent and dependent variables
Optimality of cost function values
Optimality of DE algorithm parameters (NG and NP)

Therefore, it can be concluded that the DE offers the lowest freshwater production cost for MSF-BR. It can be observed that marginal improvements in solutions can be obtained for the MSF-BR plant configuration compared to the literature using the DE algorithm. Furthermore, compared to the literature, a marginal improvement in freshwater production costs with marginal variations in the cost components can be observed in Table 2.13 for the MSF-BR process.

2.12 (c) and 2.12 (d), for the MSF-BR process configuration, it can be observed that the DCLIC is 2% lower for the DE-based optimal solution compared to that reported in the literature. Therefore, it is assumed that the optimal population and generation size is 270 and 800, respectively, for the optimal design of the MSF-BR process setup using the DE algorithm.

Figure 2.10: Bar chart depicting the performance of optimization methods for MSF desalination processes

Optimal (cost+penalty) ($/m3)

Optimal penalty ($/m3)

No. of generations (NG)

Effect of feed concentration on optimal freshwater production cost
Effect of top brine temperature on thermal performance
Effect of chemical cost multiplier on optimal freshwater production cost
Effect of steam cost multiplier on optimal freshwater production cost
Effect of Labour cost multiplier on optimal freshwater production cost
Effect of power cost multiplier on optimal freshwater production cost
Effect of spares cost multiplier on optimal freshwater production cost
Effect of top brine temperature on freshwater production cost
Optimality of other dependent variables
Summary

By varying the feed concentration in this range, the optimal cost of freshwater production was evaluated for the MSF-OT, MSF-M and MSF-BR processes. These slopes refer to the sensitivity of water production cost to changes in nutrient concentration. 2.16 (c) plots the variation of optimal water production cost as a function of the chemical cost multiplier for MSF-OT, MSF-M, MSF-BR, and MSF-BR literature data.

2.17 (a) shows that the optimal water production costs estimated for MSF-OT, MSF-M, MSF-BR, and MSF-BR literature data are sensitive to labor cost multiplier for all processes. 2.17 (c) illustrates the variation of minimum water production cost as a function of parts cost multiplier for MSF-BR, MSF-OT, MSF-M, and MSF-BR literature data.

Figure 2.15: Effect of DE algorithm parameters (NG and NP) on the solution optimality for MSF-M

Optimal Design of RO Seawater Desalination Processes with Retentate Reprocessing

Optimal Design of Reverse Osmosis (RO) Seawater Desalination Processes with Retentate Reprocessing

Problem statement

RO Design Configurations

Modeling of RO Desalination process

Single stage RO unit (SRO) .1 Process description

Notation for Various Parameters and Variables