MSF MED
1.3 State of the Art in Process Optimization of MSF, RO and hybrid MSF-RO processes
1.3.3 Hybrid MSF-RO desalination processes
Al-Mutaz et.al (1989) reported an optimal MSF-RO hybrid process for coupling with nuclear power plants. The hybrid MSF-RO process is analyzed to provide good features such as low power demand, improved water quality and lower running cost. The authors arrived at the optimal process configuration by carrying extensive simulation studies while integrating with the nuclear power plant. For the hybrid MSF-BR-RO process, the authors reported an optimal set of independent variables of P1F = 81.06 bar Uj = 2.56 kW/m2k, UR = 3.01 kW//m2K, UB = 2.61 kW/m2K, AM = 1.696 M cm2. The optimal cost function values are CMSF = 1.08 $/m3 and CRO = 1.1 $/m3.
Helal et al. (2003) studied various hybrid MSF-RO process configurations for seawater desalination. The authors studied seven different hybrid MSF-RO process configurations to minimize the water production cost. Simple thermodynamic models have been used for both MSF and RO processes. Excel SOLVER was used as a tool for the optimization of various networks (Hybrid process configurations 1 – 5 and MSF-OT- SRO process). Among all processes, hybrid 2 and 4 processes provided best performance in terms of the optimal water production cost (CH2 = CH4 = 0.84$/m3). For the hybrid 2 process, the optimal set of independent variables are LT = 9.14, U j= 3.25 kW/m2. K, UR = 2.94 kW//m2. K, UB = 3.50 kW/m2 K, NR = 14,
1
PF = 67.89 bar, NM1 = 2983, C1RP = 750. For other hybrid MSF-RO processes, the optimal costs correspond to CH1 = 0.86 $/m3, CH3 = 0.85 $/m3, CH5 = 0.9 $/m3. For the hybrid MSF-OT-SRO process, the optimal freshwater production cost is 0.88 $/m3.
Introduction and Literature Review
Marcovecchio et al. (2005) formulated a NLP optimization model for hybrid MSF-RO seawater desalination system. The authors used MSF-OT process in the hybrid process configurations.
The mathematical formulation had non-linear and non-convex constraints. The NLP problem for one configuration had 2533 constraints and 2530 optimization variables. The NLP model was solved using CONOPT solver in GAMS modeling environment. It was analyzed by the authors that the optimal hybrid configuration was strongly dependent on the seawater conditions (salinity and temperature) and freshwater demand. For the MSF-OT-TRO-RSR hybrid process configuration, the authors reported an optimal independent variable set values of WMF = 9.073 Ggr/h, WL = 0.75 m, WW = 21.52, BVPH = 2.95 m/s, NM1 = 3024, NM2 = 2444. Corresponding optimal cost value CH = 1.259 $/m3.
Using MINLP process model that was developed for the optimization of hybrid MSF-RO desalination process, Francois et al. (2006) detailed upon technical, economic and environmental performance in a multi-objective optimization approach. The MINLP formulation was further decomposed into two sub-problems that were solved using MILP solvers. Subsequently, the MINLP model was solved for an industrial case study (30,000 m3/day of potable water production with 100 ppm maximum salinity). The authors inferred that the mathematical formulation is flexible to accommodate dual purpose (water and power production schemes) optimization approaches and can also address simpler configurations such as single membrane or distillation plant. The authors reported optimal set of independent variables for hybrid 2 process configuration and referred to the values of Am1 = 35.2 m2, P1F = 70 bar, W1RF = 3 m3/h, W1RR = 65000 ppm. Corresponding optimal cost value refers to CH = 1.38 $/m3.
Abdulrahim and Alasfour (2010) presented a rigorous NLP formulation for the multi-objective optimization of hybrid MSF-RO desalination system. The authors adopted MSF-OT
Chapter 1 configuration in the hybrid process configuration. Four objectives were considered during the MOO study namely maximization of distillate production, minimization of product cost, maximization of gain ratio and minimization of energy destruction. The optimization results referred to the optimality of geometric design of each stage, heat transfer surface area and brine velocity for MSF-BR system and operating pressure, permeate flow rate and concentration for the RO process. For the MSF-BR process, it was analyzed that the single objective function enabled its improvement but deteriorated other objective functions. The authors concluded that generalization of obtained results are not possible and various cases (single, two and three objective functions) should be regarded as separate formulations to deduce inferences. The author reported the optimal set of independent variables for hybrid 2 MSF-RO process. These correspond to the values of W1RF = 7671.7 m3/h, TSea = 31.55 °C for an optimal cost CH = 1.091
$/m3, GOR = 8.569 and WMD = 1414.8 m3/h.
Using MINLP process model that was developed for the optimization of RO desalination process Marcovecchio et al. (2011) detailed upon technical, economic and environmental performance optimality using multi-objective optimization model. The MINLP mathematical formulation was solved using DICOPT++. The authors indicted that feed salinity strongly influences optimal design. For feed salinity below 38000 ppm, hybrid MSF process was not optimal and the RO process (TRO-RSR) was the best. Above 38000 ppm feed salinity, hybrid desalination process was found to be optimal. The MSF-BR process was also studied by the authors as a stand alone process whose optimality was not better than the TRO-RSR process.
A summary of results obtained from the above research articles is presented in Table 1.9 of the
Introduction and Literature Review
Table 1.9: Literature data for hybrid MSF-RO desalination process optimization.
S.No Author Year Configuratio ns
Model summary
Software Method Optimal independent variables
Optimal objective function value EQ IEC IV OF
1 Al-Mutaz et al 1989 MSF-BR-RO 69 5 -
Min water cost
- - 1
PRF = 81.06 bar , Uj=2.56 kW/m2k, UR = 3.01 kW//m2K, UB = 2.61 kW/m2K, AM = 1.696 M cm2
CM = 1.08
$/m3, CRO = 1.1 $/m3
2 Helal et. al. 2003
Hybrid 1, Hybrid 2, Hybrid 3, Hybrid 4, Hybrid 5, MSF-OT-SRO
90 15 13 Min
cost
SOLVE R tool
Newton- Raphsons
LT = 9.14, Uj = 3.25 kW/m2. K, UR
= 2.94 kW//m2. K, UB = 3.50 kW/m2 K, NR = 14, P1RF = 67.89
bar, NM1 = 2983, C1RP = 750
CH1 = 0.86, CH2 = 0.84, CH3 = 0.85, CH4 = 0.84, CH5 = 0.9, CHM = 0.88
$/m3 3 Marcovecchio
et. al. 2005 MSF-OT-
TRO-RSR 55 10 8 Min
cost -
GAMS, GRG, CONOPT
WMF = 9.073 Ggr/h, WL = 0.75 m, WW = 21.52, BVPH = 2.95 m/s, NM1 = 3024, NM2 = 2444
Cost = 1.259
$/m3
4 Francois et al 2006 Hybrid 2 80 52 51 Min
Cost MINLP
linear programmi ng solver
AT = 35.2 m2, P1F = 70 bar,
1
WRF = 3 m3/h, C1RR = 65000
ppm
Cost = 1.38
$/m3
5 Abdulrahim
and Alasfour 2010 Hybrid 2 70 16 15 Multi
OF MOO GA 1
WRF = 7671.7 m3/h, TSea = 31.55
°C
Cost = 1.091
$/m3 , GOR = 8.569
6 Marcovecchio
et al. 2011
Hybrid MSF- RO processes 1 -20
78 14 4
Min water cost
MINLP DICOPT++
1
WRF = 4790.1 m3/h, W1RP = 1540.7 m3/h, C1RR = 55810 ppm,
1
CRP = 437.8. ppm
Cost of TRO- RSR = 0.7410
$/m3
Nomenclature: WRORF = Flow rate of stream entering SWIP, m3/h, CRORF = Concentration of stream entering SWIP, ppm, NM1 = Number of membranes in stage 1 of RO process, P1F = Pressure of stream entering stage 1 of RO process, bar, AM = Total membrane area, m2, C1RP = Concentration of permeate stream from stage 1 in RO process, ppm, C1RR = Concentration of retentate stream from stage 1 in RO process, ppm, r1 = split ratio 1, AT = Total area of hybrid process, Mm2.
Introduction and Literature Review 1.3.4 Summary
Extensive research has been carried out for the NLP optimization of MSF, RO and hybrid MSF-RO processes. Rigorous NLP formulations have been addressed in the literature for the optimization of MSF, RO and hybrid MSF-BR processes using deterministic methods (GRG and SQP methods). Few studies have targeted MINLP formulations and few authors have targeted the application of non-deterministic optimization algorithms (GA) for the optimal design of MSF, RO and hybrid MSF-RO processes. Further analysis of the literature approaches will be addressed in the next section i.e., possible scope for further research.