Article pubs.acs.org/IECR
Synthesis of Optimum Water Polygeneration System in Ethylene Glycol Production T. Prihatin,† M. Shuhaimi,*,† M. I. Abdul Mutalib,† and M. D. Bustan‡ †
Process Development Research Group, Chemical Engineering Department, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 31750, Tronoh, Perak, Malaysia ‡ Chemical Engineering Graduate Program, University of Sriwijaya, Bukit Besar, 30139, Palembang, South Sumatera, Indonesia ABSTRACT: Increasing freshwater consumption in industry and the impacts of industrial wastewater discharges bring enormous pressure on the sustainability of water resources for human living. Numerous approaches have been carried out to provide a solution for the global water problem. Polygeneration system is one of the promising strategies that carries great potential for natural resource sustainability, particularly water, in view of energy, environment, and economics. This paper presents a model of water polygeneration system with the objective of minimizing both freshwater consumption and wastewater generation. Water usage in a heat and power utility, a cooling utility, and chemical production are modeled simultaneously. A superstructure comprising three subsystems, i.e. heat and power generations, recirculating cooling water system, and chemical production, with wastewater treatment options, is introduced. To demonstrate the model application, a case study using mixed integer nonlinear programming (MINLP) model on the synthesis of optimum water polygeneration system is developed for an ethylene glycol production.
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INTRODUCTION
There have been different types of polygeneration system design presented in previous works, particularly on the energybased polygeneration system. Gao et al. introduced polygeneration systems based on coal4 and natural gas5 to produce power and methanol. Besides that, Wang et al. introduced a natural gas-based polygeneration system of acetylene production integrated with fuel cell6,7 to produce power and acetylene. Qian et al.8 also developed natural gas-based polygeneration system but to produce power and olefin. They found that the appropriate integration of power and chemical side played a significant role for the system synthesis. The coal-based polygeneration systems combined with CO2 capture have also been an attractive and promising solution due to their benefits with regard to environmental concern.9−11 The study of polygeneration systems has developed in many sectors taking into account more options of fuel feedstocks. Biomass utilization as cofeed with natural gas or coal in the polygeneration system is getting much attention.12−16 These works demonstrated that biomass as a carbon neutral resource is a promising solution that might save energy input and reduce emission. The complexity of integration between the power and chemical productions sides has led to investigations of polygeneration systems based on mathematical programming to find the optimal design. Most progress in the previous works have been done on energy-based polygeneration systems. Liu et al.17 developed a superstructure and a mixed integer linear programming (MILP) model for the investment planning of a
Water issues are no longer a local problem due to the escalating impacts of globalization and excessive water usage on the sustainability of the earth ecosystems. Moreover, intensification of industrial development beyond the borders of traditionally developed countries has led to the fast acceleration of freshwater withdrawals. Industrial water usage accounts for approximately 20% of global freshwater consumption.1 The heavy consumption of water in the process industry is particularly for heating and cooling media, generating steam, cleaning, transporting dissolved substances, as raw material, or as solvent. In addition, industrial water usage creates additional pressure on freshwater resources from the impacts of wastewater discharge. Instead of being seen as a problem, wastewater should be recognized as a water resource. Therefore, minimizing freshwater consumption and wastewater generation in the process industry is the critical concern for the sustainable future water supplies. One of the promising strategies to solve the problem of limited freshwater resources is water polygeneration. A polygeneration system is an integrated approach to an industrial process that generates multiple products from a single or multiple natural resources.2,3 Beginning with the concepts of cogeneration and trigeneration systems,2 polygeneration system has shown a great potential to conserve natural resources, particularly energy and water, and to save environment. Most polygeneration system synthesis which captures the interest of researchers is the integration of power generation and chemical process production. Many of the earlier works were developed through flowsheet simulation based on coal, natural gas, and biomass feed stocks that focused on the evaluation of existing plants and process technologies, the configuration of processes, and the performance and operations of plants.3−8 © 2013 American Chemical Society
Special Issue: PSE-2012 Received: Revised: Accepted: Published: 7066
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Figure 1. Schematic flow diagram of (a) recirculating cooling water, (b) heating and power, and (c) chemical production subsystems.
polygeneration system of methanol and electricity. The more detailed works on the combination of technologies with design and operational variables then had been developed in a mixedinteger nonlinear programming (MINLP) model,18 applied in a multiobjective optimization approach19 and a stochastic programming under uncertainties.20 Through the systematic methods developed in mathematical optimization, the complexity and computational time problems might be reduced. Rubio-Maya et al.21−23 investigated mathematical optimization works on polygeneration systems combining energy and water production as details development for the simulation of polygeneration system of trigeneration and desalination plant.24,25 Since freshwater is the most promising product of a desalination plant, the polygeneration system then becomes a highly attractive solution as the scarcity of energy and water increase, and there is high demand for energy and water for the tourist sector in the Mediterranean. The possible technologies represented by a superstructure model were arranged as a mathematical programming problem. These studies only took into account water production associated with energy, in which heat and power produced by the power generation plant are used for water desalination. It can be stated that recent polygeneration system designs have focused on energy based, fuel feedstocks, greenhouse gas emission, and economic interests. So far, there has been little discussion about polygeneration systems based on water.
Furthermore, simultaneous demand for heating, power, and cooling exists in certain chemical industries. The aim of this study is to investigate a water polygeneration system that coproduces heat and power, cooling water, and chemicals, and incorporates 3R strategy (regenerate, reuse, and recycle) for water recovery. An MINLP model is developed through a superstructure representation. A water polygeneration system case study for ethylene glycol production is presented.
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SUPERSTRUCTURE REPRESENTATION
The appropriate superstructure, of major importance for the estimated representation of alternative structures, is developed to obtain the optimal process flowsheet. One common approach to systematically constructing a superstructure is by combining detailed superstructures for each subsystem that perform multiple tasks or functions and interconnect them. All the feasible interactions are taken into account. In this work, the water polygeneration system consists of three subsystems: heat and power generation, cooling water production, and ethylene glycol (EG) synthesis process. The simplified flow diagram of three subsystems is illustrated in Figure 1. General superstructure for the synthesis of an optimum water polygeneration system in an ethylene glycol production is shown in Figure 2 and the detailed superstructure of several technology options is presented in Figure 3. To illustrate the 7067
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Figure 2. General superstructure representation of the water polygeneration system.
model superstructure, some details of three subsystems of the water polygeneration system are discussed in the following. Heat and Power Generation. To meet heat and power demand in the chemical production plant is a function of heat and power generation plant or utility plant. It includes different types of boilers, steam turbines, electric generators driven by gas turbines or steam turbines, headers at different pressures, and other additional units such as deaerators, condensers, and utility pumps. In the gas turbine, which consists of a compressor and a turbine, ambient air is compressed and raised in temperature as a result of the compression. A portion of the compressed air enters a compressed chamber where fuel is combusted, and the temperature of the gases is raised further to a temperature in excess of 1500 °C. The hot, compressed mixture of air and combustion gases then flows to the inlet of the turbine. In the turbine, the gas is expanded to develop power to drive the compressor. Heat and power generation plant also includes water used in the generation of electric power. The heating process in a boiler turns water into steam. The energy stream is then converted by a steam turbine to turn the turbine-generator producing electricity and power. Very high-pressure steam is also generated in a utility stream boiler. This is expanded in steam turbines at different pressures providing steam at high, medium, or low pressure which is demanded in the chemical process production. The final exhaust from steam turbines is then sent to the condenser to be cooled back into water. Most of the water used in power generation is used in the condenser. The condensed water is pumped back to the steam generator to become steam again while the cooling water is discharged as return flow or is recycled through cooling ponds or cooling towers. Additionally, water is also required for makeup water to replace the water lost as steam and blowdown of boilers. In this paper, as shown in Figure 2, boiler includes an option of conventional boiler (CB) or a cogeneration system that exploits heat from gas turbine exhaust to generate steam in a heat recovery steam generator (HRSG). Steam generated from HRSG might be used directly for process heating or expanded in a steam turbine system to generate additional power. Gas turbines are available through the option of simple (SGT) or
regenerative gas turbines (RGT), while steam turbines options are back-pressure (BPST) or condensing steam turbines (CST). Recirculating Cooling Water System. As shown in Figure 1, cooling water from the cooling tower is pumped as cooling water supply (CWS) to heat exchangers or water using operations (WUO) where waste heat needs to be rejected from the process. The cooling water is in turn heated in the WUO and returned to the cooling tower as hot cooling water return (CWR). The hot water returned to the cooling tower flows down over packing materials and is contacted counter-currently with air. The air is humidified and heated, and rises through the packing. The water is cooled mainly by evaporative cooling. The evaporated water leaves through the top of the cooling tower. Blowdown is necessary to prevent the build-up of contamination in the recirculation. Makeup water is required to compensate for the loss of water from evaporation and blowdown. There are two broad classes of cooling tower, i.e. natural draft and mechanical draft cooling towers. Natural draft is created by the difference in density between the warm humid air within the tower and the denser ambient air, while mechanical draft cooling towers use fans to move the air through the cooling tower. Mechanical draft is commonly used for large power generation plants and complex chemical processes. It consists of two types of design, i.e. forced draft and induced draft cooling towers. In a forced draft design, fans push the air into the bottom of the tower, while induced draft has a fan at the top of the cooling tower to draw air through the tower. In this work, choices of cooling tower include the forced or the induced mechanical drafts types. Ethylene Glycol Production Process. Ethylene glycol is used mainly as antifreeze in automobile radiators, as an engine coolant, and as a raw material for the manufacture of polyester fibers and plasticizer. It is used widely in paints, lacquers, dyes, and inks. Ethylene glycol is a clear, colorless liquid with a syruplike consistency. There are two common methods of ethylene glycol production, i.e hydration of ethylene oxide26 and monoethylene glycol (MEG) only technology.27 Ethylene oxide (EO) is obtained by direct oxidation of ethylene with air or oxygen. In the former method, ethylene glycol is 7068
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factor, ηB, is also included for the energy balance around steam boiler. Mass balance of the steam turbines is given as follows:
produced by the hydrolysis of ethylene oxide in the present of excess water at a temperature of 190−200 °C. Some literature reported that a molar ratio of ethylene oxide to water may reach 1:5 to 1:30.27−30 The ethylene oxide is thermally hydrolyzed to ethylene glycol without a catalyst. Monoethylene glycol (MEG), diethylene glycol (DEG), and polyglycols (TEG) are also produced, as shown in eqs 1−3, with respectively decreasing yield, and are separated from the ethylene glycol by a series of distillation columns at reduced pressure. C2H4O + H 2O → HO‐C2H4‐OH (EO)
∑ ∑ Fj ,st ·yst j
C2H4O + HO‐C2H4‐OH → HO‐C2H4‐O‐C2H4‐OH (EO)
(MEG)
FstB −
(DEG)
(2)
=0 (7)
st
∑ FTst ,i = 0
(8)
i∈I
FTst , i = Wst , i /ΔHst , i
C2H4O + HO‐C2H4‐O‐C2H4‐OH (EO)
k
where yst is a binary variable for steam turbine options, i.e. back pressure and condensing steam turbines. For the energy balance around steam turbine, the power demand, Wst,i, and the steam enthalpy at steam pressure level, ΔHst,i, is correlated to the flow rate intake of the steam turbine, FTst,i, as shown below:31
(1)
(MEG)
∑ ∑ Fk ,st ·yst
−
st
(DEG)
→ HO‐C2H4‐O‐C2H4‐O‐C2H4‐OH (TEG)
(9)
In eq 8, FstB refers to steam flow rate coming from the boiler of the selected steam generation technology. Constraints related to some properties, e.g. to calculate the enthalpy in the header of high pressure, medium pressure, and low pressure as follows:32
(3)
In the second method, ethylene glycol is manufactured by the reaction of ethylene oxide with carbon dioxide to form ethylene carbonate, an intermediate product, which can be hydrolyzed to ethylene glycol, as described in eq 4. This method has now been successfully commercialized by the Shell Group under the process technology name Shell OMEGA (Only MEG Advanced). C2H4O + CO2 → (O‐CH 2CH 2‐O)C
Hnm(HP , Tn) = a1 + a 2Tn + a3Tn2
(10)
Hnm(MP , Tn) = b1 + b2Tn + b3Tn2
(11)
Hnm(LP , Tn) = c1 + c 2Tn + c3Tn2
(12)
where T is temperature (°C) and m is the header at the inlet of the steam turbine exhausting to the header n. The constant value of coefficients for steam enthalpy an, bn, and cn ... n = 1, 2, 3 are presented in Table 1.
(EO)
= O + H 2O → HO‐C2H4‐OH + CO2 (MEG)
(4)
Table 1. Functions for Steam Enthalpy
In this work, the alternative process technologies in reactor and separation units of an ethylene glycol plant are investigated in Aspen HYSYS simulation for superstructure configurations. Ethylene oxide production is based on oxygen or air direct oxidation. Two options of ethylene glycol synthesis are EO hydrolysis and the Shell OMEGA process. For each of the reaction processes, the options of reactor are continuous stirred tank reactor or plug flow reactor. Options for the separation system include direct sequence, indirect sequence, or dividing wall distillation column.
pressure (Mpa) 10.4 4.5 4.0 1.7 0.45
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j
b
k
b
∑ ∑ Fj ,b·Hj. yb − ∑ ∑ Fk ,b·Hk·yb = 0 j
b
k
b
(HP,Tn) = 1411.39 + 5.3140Tn − 2.7918 × 10−3Tn2 (HP,Tn) = 1734.95 + 5.0183Tn − 3.3290 × 10−3Tn2 (MP,Tn) = 1714.85 + 5.3757Tn − 4.0789 × 10−3Tn2 (MP,Tn) = 2152.85 + 305888Tn −2.1697 × 10−3Tn2 (LP,Tn) = 2384.24 + 2.5597Tn − 1.0346 × 10−3Tn2
For gas turbines, mass and energy balances are given by
MATHEMATICAL MODEL The model of the superstructure presented for the optimum water polygeneration system design is formulated as a MINLP problem in which binary variable 0−1 is assigned as a logical constraint for decision making. The constraint is presented as y ∈ Y = {0,1}i and equals 1 if unit i is selected in the optimal structure or 0 if otherwise. Heat and Power Generation Subsystem. Mass and energy balances around steam boiler are expressed as
∑ ∑ Fj ,b·yb − ∑ ∑ Fk ,b·yb = 0
function (kJ/kg) Hmn Hmn Hmn Hmn Hmn
∑ ∑ Fj ,gt ·ygt j
−
gt
k
∑ ∑ Fj ,gt ·Hj. ygt j
gt
∑ ∑ Fk ,gt ·ygt −
=0 (13)
gt
∑ ∑ Fk ,gt ·Hk·ygt k
gt
−W=0 (14)
where ygt is a binary variable representing gas turbine technology options and W is power produced from gas turbine. The constraints of air and exhaust gas enthalpy values in the gas turbine are calculated through eq 15. The coefficients of a, b, and c are given in Table 2. A mass ratio of 0.017 kg fuel/kg air is used to calculate exhaust gas composition.
(5)
Hgtg = aTgt g2 + bTgtg − c
(6)
where F and H are flow rates and enthalpy values of inlet streams j and outlet streams k, respectively, and yb is a binary variable representing the selection of boiler. A boiler efficiency
(15)
Recirculating Cooling Water Subsystem. In the cooling water subsystem, mass balance around the cooling tower is given by 7069
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and can be applied to a wide range of mixtures containing water. The UNIQUAC equation is significantly more detailed and sophisticated than any of the other activity models to represent VLE (vapor−liquid equilibrium) and LLE (liquid− liquid equilibrium). The reactors are operated under isothermal conditions. The constraints for the supply of cooling, heating, and power requirement in the chemical production are formulated as per process demands. For example, the steam supply to a distillation column, STSupply, is equal to the steam demand by the reboiler, STDem.
Table 2. Coefficients for Enthalpy in the Gas Turbine
a b c
compressor inlet
compressor outlet
turbine inlet
turbine outlet
6.47238 × 10−5 0.977148 297.01
1.14274 × 10−4 0.929784 285.84
7.43569 × 10−5 1.06576 354.81
1.26596 × 10−4 0.9531 294.08
∑ ∑ Fj ,cws·ycws − ∑ ∑ Fk ,cws·ycws = 0 j
cws
k
cws
(16)
where the binary variable ycws denotes the selection of cooling tower. The detailed mass balance at specific technology is Fin + Fm − Fout − Fb = 0
STSupply ≥ STDem
Ethylene glycol reactor products will go through a distillation column to produce monoethylene glycol which is purified from the byproducts of water and other higher glycols derivatives. In this separation process, the product recovery and purification also consume water for heating and cooling. The options of a series of direct or indirect sequence distillation columns or a dividing column are available in this work. Cooling water demand at ethylene glycol production, FCW, particularly in the separation process, is correlated mainly to condenser duty, Qcd, as shown in eq 21.
(17)
where Fin is the inlet water flow rate to cooling tower, Fout is outlet water flow rate of cooling tower, Fm is the cooling water makeup, and Fb is the cooling tower blowdown. Water loss through evaporation, Fev, is correlated to blowdown and makeup by the following equations:
Fb = Fev /(πc − 1)
(18)
Fm = Fev[πc /(πc − 1)]
(19)
(20)
FCW =
where πc is the cooling tower cycles of concentration. Ethylene Glycol Production Subsystem. In the ethylene glycol production subsystem, most parameters in the model are estimated from the Aspen HYSYS simulation33 using the UNIQUAC equation. In this work, HYSYS simulation provides mass and energy flow information of the process systems. This fluid package shows a good representation for liquid structure
Q cd cP × ΔT
(21)
where CP = 4.18 kJ/kg °C and ΔT = 20 °C. Objective Function. The objective for the water polygeneration system is to optimize the flow rate of freshwater consumption (FW) at minimum total annualized cost. The issue studied in this work is already complex due to the
Figure 3. Detailed superstructure representation of the water polygeneration system. 7070
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DICOPT++ on PC with Intel i3, 3.30 GHz/4GB RAM. It is a nonconvex MINLP model containing 20 binary variables, 326 continuous variables, and 305 equations. Solution of this formulation took approximately 0.22 s of solver time and 3504 iterations in which were 19 major iterations to achieve the optimum solution. In this work, the base case and the optimized case are the water-based polygeneration system without wastewater recovery and with wastewater recovery, respectively. The MINLP model has been formulated simultaneously for both cases. Based on the superstructure optimization, the optimum solution was obtained as shown in Figure 4. For heat and power generation subsystems, binary variables were used to select between conventional boiler or cogeneration system, simple gas turbine or regenerative gas turbine, and back pressure steam turbine or condensing steam turbine. The optimum solution designated for the power generation unit employs a cogeneration system comprising a gas turbine with HRSG and three steam turbines mix. Simple gas turbine cycle and back-pressure steam turbine are selected to meet the heat and power demands. For recirculating cooling water system, from the binary variables of the forced and the induced mechanical drafts, the latter one is selected as the optimum result. For ethylene glycol production, the options as binary variables for the selected process are hydration of ethylene oxide and Shell OMEGA process technology. In the optimum solution, the ethylene oxide hydrolysis process in which ethylene oxide is produced based on oxygen-direct oxidation is selected with minimum freshwater consumption. For separation unit in the ethylene glycol production, the dividing wall column is selected, resulting in significant reduction in capital cost from the distillation columns arrangement and considerable energy savings in condenser and reboiler loads. The 3R strategy for water recovery is also implemented in the optimum solution. As shown in Figure 4, freshwater is consumed as makeup for demineralized water in the heat and power subsystem, feed to the ethylene glycol reactor, and makeup water for the cooling tower. The cost of water consumption is minimized through water regeneration and recycling from boiler and cooling tower blowdown as well as from wastewater produced in the ethylene glycol plant. Comparison between the water-based polygeneration system without 3R strategy (base case) and with 3R strategy (optimized case) is summarized in Table 4. The optimized case showed a significant reduction of freshwater consumption by almost 50% and total elimination of wastewater discharge. The sensitivity analysis of the system was also performed. The optimized case was modified to analyze the effect of increased power demand and ethylene glycol production on the configuration. Tables 5 and 6 summarize those results, respectively. It can be seen that freshwater flow rate, which is consumed in the polygeneration system, and the objective function become larger with the increase of power demand in the heat and power subsystem. Wastewater generation rates are constant, reaching zero discharge for the total reuse of wastewater. It is shown that higher power demand also led to higher cost, presented here as the objective function k$/yr, in comparison to higher ethylene glycol production.
interactions between the process and the subsystems of heating, power, and cooling. Minimization of freshwater consumption directly reduces freshwater intake by the ethylene glycol production, which is already a sufficient objective to highlight the significance of water conservation. Other economic objectives are not included to avoid any reduction in the significance of the issue on water conservation. The objective function (z) includes the cost of freshwater intake, Cfw, and the cost of water recovery, OPrecovery, as shown in eq 22. min z = [(FW × Cfw) + OPrecovery] × AOT
(22)
The cost of water recovery takes into consideration the pumping cost as well as cost associated with water recycle, reuse, and regeneration (3R). Water recycling involves posttreatment process of the water quality to meet the next wateruser specification, while water reuse does not require additional treatment and possibly sends to the next user in its water effluent condition. The cost is calculated on annual basis assuming AOT, annual operation time, of 8000 h/year. The cost of water recovery is correlated to total power need for pumps operation (Pi) and cost of electricity (Celect) by the following equations:
∑ Pp(i) × Celect
OPrecovery =
(23)
i
In addition, the costs of freshwater (Cfw) and electricity (Celect) are 0.26 $/ton and 0.064 $/kWh, respectively. The power consumed by the pump is a function of the water flow rate to the pump, Fp, pump efficiency, ηp, and density of water, ρ, is34 Pp = 1283
■
0.476 1 ⎡ Fp ⎤ ⎢ ⎥ ηp ⎣ ρ ⎦
(24)
CASE STUDY RESULTS A water polygeneration system superstructure as given in Figure 3 is considered. The utility demands (heat, power, and cooling water) in three subsystems are shown in Table 3. The model is developed in GAMS and solved to optimum using Table 3. Utility Demands
HP heating MP heating LP heating LP heating power demand power demand power demand power demand power demand power demand power demand power demand power demand cooling water cooling water cooling water cooling water
demand
unit
1948 kW 33970 kW 1079 kW 2944 kW 33 kW 300 kW 11 kW 951 kW 262 kW 17 kW 46 kW 1983 kW 2315 kW 3173 t/h 1191 t/h 1175 t/h 852 t/h
distillation distillation distillation cooler BFW pump air blower water pump water pump fan water pump water pump compressor compressor process separation cooler condenser
subsystem EG EG EG EG heat & heat & heat & CWS CWS EG EG EG EG EG EG EG heat &
power power power
■
CONCLUSION This work has presented water polygeneration system as a strategy to address the problem of limited freshwater resources
power 7071
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Figure 4. Optimal configuration derived from the superstructure with 3R strategy.
ment options. The strategy of 3R for water conservation was also embedded in the model. The application of the model was demonstrated on an ethylene glycol production case study, showing almost 50% water savings and total elimination of wastewater generation.
Table 4. Results of Water-Based Polygeneration System
base case optimized case
freshwater consumption (t/h)
wastewater generation (t/h)
objective Function ($/yr)
447.674 219.221
228.453 0
931,161.08 637,587.84
■
Table 5. Results When Power Demand Is Increased on the Optimized Case power demand kW
power demand increased %
freshwater consumption ton/h
wastewater generation ton/h
objective function k $/yr
39941 40739.82 41538.64 42337.38 43136.28 43935.1
0 2 4 6 8 10
219.2 219.9 220.6 221.3 221.9 222.6
0 0 0 0 0 0
637.6 639.3 641.0 642.8 644.5 646.2
Corresponding Author
*E-mail:
[email protected]. Notes
The authors declare no competing financial interest.
■
EG production rate increased %
freshwater consumption ton/h
wastewater generation ton/h
objective function k $/yr
486.00 495.72 505.44 515.16 524.88 534.60
0 2 4 6 8 10
219.,2 219.4 219.5 219.7 219.9 220.1
0 0 0 0 0 0
637.6 637.9 638.3 638.7 639.1 639.5
REFERENCES
(1) The 3rd United Nations World Water Development Report: Water in a Changing World (WWDR-3). http://www.unesco.org/water/ wwap/wwdr/wwdr3/ [Oct. 8, 2009]. (2) POLYSMART (2008); Polygeneration in Europe − A Technical Report; European Commission within the Sixth Framework Programme (2002−2006). (3) Serra, L. M.; Lozano, M. A.; Ramos, J.; Ensinas, A. V.; Nebra, S. A. Polygeneration and efficient use of natural resources. Energy 2009, 34, 575−586. (4) Gao, L.; Jin, H.; Liu, Z.; Zheng, D. Exergy Analysis of coal-based polygeneration system for power and chemical production. Energy 2004, 29, 2359−2371. (5) Gao, L.; Li, H.; Chen, B.; Jin, H.; Lin, R.; Hong, H. Proposal of natural gas-based polygeneration system for power and methanol production. Energy 2008, 33, 206−212. (6) Wang, Z.; Zheng, D.; Jin, H. A novel polygeneration system integrating the acetylene production process and fuel cell. Int. J. Hydrogen Energy 2007, 32, 4030−4039. (7) Wang, Z.; Zheng, D.; Jin, H. Energy integration of acetylene and power polygeneration by flowrate-exergy diagram. Appl. Energy 2009, 86, 372−379. (8) Qian, Y.; Liu, J.; Huang, Z.; Kraslawski, A.; Cui, J.; Huang, Y. Conceptual design and system analysis of a polygeneration system for power & olefin production from natural gas. Appl. Energy 2009, 86, 2088−2095.
Table 6. Results when Ethylene Glycol Production Rate Is Increased on the Optimized Case EG production rate kgmol/h
AUTHOR INFORMATION
in the chemical process industry. Superstructure helps in assessing the potential of different design configurations to formulate a comprehensive model for the optimum water polygeneration system. The model features all possible configurations of heat and power generations, recirculating cooling water system, as well as reaction and separation technologies for chemical production, with wastewater treat7072
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(9) Lin, H.; Jin, H.; Gao, L.; Han, W.; Zhang, N. Thermodynamic and economic analysis of the coal-based polygeneration system with CO2 capture. Energy Procedia I 2009, 4193−4199. (10) Lin, H.; Jin, H.; Gao, L.; Han, W. Techno-economic evaluation of coal-based polygeneration systems of synthetic fuel and power with CO2 recovery. Energy Convers. Manage. 2011, 52, 274−283. (11) Yu, G. W.; Xu, Y. Y.; Hao, X.; Li, Y. W.; Liu, G. Q. Process analysis for polygeneration of Fischer−Tropsch liquids and power with CO2 capture based on coal gasification. Fuel 2010, 89, 1070−1076. (12) Williams, R. H.; Larson, E. D.; Liu, G.; Kreutz, T. G. Fischer− Tropsch fuels from coal and biomass: Strategic advantages of oncethrough (Polygeneration) configurations. Energy Procedia I 2009, 4379−4386. (13) Riccio, G.; Chiaramonti, D. Design and simulation of a small polygeneration plant cofiring biomass and natural gas in a dual combustion micro gas turbine (BIO_MGT). Biomass Bioenergy 2009, 33, 1520−1531. (14) Li, H.; Hong, H.; Jin, H.; Cai, R. Analysis of feasible polygeneration system for power and methanol production taking natural gas and biomass as materials. Appl. Energy 2010, 87, 2846− 2853. (15) Joelsson, J. M.; Gustavsson, L. Reduction of CO2 emission and oil dependency with biomass-based polygeneration. Biomass Bioenergy 2010, 34, 967−984. (16) Song, H.; Starfelt, F.; Daianova, L.; Yan, J. Influence of drying process on the biomass-based polygeneration system of bioethanol, power and heat. Appl. Energy 2011, 90, 32−37. (17) Liu, P.; Gerogioris, D. I.; Pistikopoulos, E. N. Modeling and optimization of polygeneration energy systems. Catal. Today 2007, 127, 347−359. (18) Liu, P.; Pistikopoulos, E. N.; Li, Z. A mixed-integer optimization approach for polygeneration energy systems design. Comput. Chem. Eng. 2009, 33, 759−768. (19) Liu, P.; Pistikopoulos, E. N.; Li, Z. A multi-objective optimization approach to polygeneration energy systems design. AIChE J. 2010, 56, 1218−1234. (20) Liu, P.; Pistikopoulos, E. N.; Li, Z. Decomposition based stochastic programming approach for polygeneration energy systems design under uncertainty. Ind. Eng. Chem. Res. 2010, 49, 3295−3305. (21) Rubio-Maya, C.; Uche, J.; Martinez-Gracia, A. Selection and sizing procedure of polygeneration plants using mathematical programming. Proceedings of ECOS 2009 on 22nd International Conference on Efficiency, Cost, Optimization Simulation and Environmental Impact of Energy Systems, August 31 - September 3, 2009, Brazil. (22) Rubio-Maya, C.; Uche-Marcuello, J.; Martinez-Gracia, A.; Bayod-Rujula, A. A. Design optimization of a polygeneration plant fuelled by natural gas and renewable energy sources. Appl. Energy 2011, 88, 449−457. (23) Rubio-Maya, C.; Uche, J.; Martinez, A. Sequential optimization of a polygeneration plant. Energy Convers. Manage. 2011, 52, 2861− 2896. (24) Uche, J.; Serra, L.; Sanz, A. Integration of desalination with coldheat-power production in the agro-food industry. Desalination 2004, 166, 379−391. (25) Rubio, C.; Uche, J.; Dejo, N. Optimization of desalted water production in a polygeneration scheme for the tourist sector. Desalination 2008, 223, 464−475. (26) Kirk-Othmer. Encyclopedia of Chemical Technology, 5th ed.; Vol. 12; John Wiley & Sons: Hoboken, NJ, 2005. (27) Rekers, D. M.; Smaardjik, A. A. Process for the production of ethylene oxide and ethylene glycol. US Patent 2008018299 A1, 2008. (28) Reman, W. G.; Van Kruchten, E. M. G. A. Process for the preparation of alkylene glycols. US Patent 5488184, 1996. (29) Kakimoto, Y.; Oka, Y. Method for production of ethylene glycol. US Patent 6417411 B2, 2002. (30) Speight, J. G. Chemical Process and Design Handbook; McGrawHill: New York, 2002. (31) Smith, R. Chemical Process Design and Integration; John Wiley & Sons: England, 2005.
(32) Bruno, J. C.; Fernandez, F.; Castells, F.; Grossmann, I. E. A rigorous MINLP model for the optimal synthesis and operation of utility plants. Trans IChemE 1998, 76 (Part A), 246−258. (33) Aspen Technology, Inc. Aspen Hysys Tutorials and Applications, Version 2006, Software Documentation; 2006. (34) Castro, M. M.; Song, T. W.; Pinto, J. M. Minimization of operational costs in cooling water systems. Trans IChemE 2000, 78 (Part A), 192−201.
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dx.doi.org/10.1021/ie302427v | Ind. Eng. Chem. Res. 2013, 52, 7066−7073