Deciphering Refinery Water System Design and Optimization

Subscriber access provided by READING UNIV

Article

Deciphering Refinery Water System Design and Optimization: Novel Superstructure and Generalized Mathematical Model Chun Deng, Wei Jiang, and Xiao Feng ACS Sustainable Chem. Eng., Just Accepted Manuscript • DOI: 10.1021/ acssuschemeng.7b03754 • Publication Date (Web): 18 Dec 2017 Downloaded from http://pubs.acs.org on December 24, 2017

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

ACS Sustainable Chemistry & Engineering is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 52 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ACS Sustainable Chemistry & Engineering

Deciphering Refinery Water System Design and Optimization: Novel Superstructure and Generalized Mathematical Model Chun Denga*, Wei Jianga, Xiao Fengb a

State Key Laboratory of Heavy Oil Processing, China University of Petroleum-Beijing, 18 Fuxue Road, Changping, Beijing, 102249, China b

School of Chemical Engineering & Technology, Xi’an Jiaotong University, No.28, Xianning West Road, Xi'an, Shaanxi, 710049, China

Abstract: The up-to-date approaches to optimizing water system only include freshwater, regenerated water and wastewater and ignore other types of water in refinery, i.e. desalted water, deaerated water, circulated cooling water, steam with different pressure levels and condensate water. Therefore, the existing mathematical model for water system optimizaiton is not directly applicable for the optimization of practical refinery water system. To overcome the limitation and bridge the theory and application, we firstly presented a generalized model of water-using process including multiple types of water and a general superstructure for the optimization of refinery water system. The superstructure consists of water-using processes including multiple types of water in the main production units (i.e. crude oil distillation, fluid catalytic cracking), water pretreatment systems (i.e. freshwater station, desalted water station, steam power station) and wastewater treatment system. The flowrate balance equations for those components of refinery water system and the correlation for all types of water are formulated. The replacement ratio of altered type of water is introduced in the flowrate balance equations for water reuse/recycling and it avoids the imprecise data extraction of limiting water quality for the inlets of water-using processes. We *

Corresponding author. E-mail address: [email protected] (Chun Deng).

1

ACS Paragon Plus Environment

ACS Sustainable Chemistry & Engineering 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

presented two mathematical models with different objective functions (minimum flowrate of water resource (Scenario 1) and minimum partial annualized cost (Scenario 2)). The proposed models are applied for the optimization of water system of a large-scale refinery in China. Results show that water system with minimum flowrate of water source can be obtained in Scenario 1. In Scenario 2, the profit of water conversation for five strategies cannot offset the investment cost of added pipelines, and their actual replacement ratios are zero. It leads to an economic and simpler water system with slightly higher flowrate of water resource.

Keywords: multiple types of water; mathematical programming; water network; wastewater minimization; superstructure;

2


Page 2 of 52

Page 3 of 52 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Introduction

Because of the growth of population and rapid progress of urbanization and industrialization, the amount of freshwater intake has been increasing. It is reported by Organization for Economic Co-operation and Development (OECD), the global water demand is projected to increase by 55% globally between 2000 and 20501. The mainly increment of water demand will come from manufacturing, electricity and domestic. In the face of these competing demands, there will be little scope for increasing water for irrigation. The water-energy-food nexus will become great challenge for the entire world. The industrial water management and reclamation is vital for the sustainable development of manufacture industries. Massive pinch techniques, algebraic and mathematical optimization approaches are proposed for the synthesis of water network for wastewater minimization. Typical reviews2-5 and books6-8 are available for good reference. Water-using processes include two main categories9-10, i.e. Fixed Contaminant load (FC) and Fixed Flow rate (FF) water-using processes. For the FC process, water-using process (e.g., washing, scrubbing, and extraction) is characterized by mass transfer operation where a fixed load of contaminant is transferred from contaminant-rich stream to water and it acts as a mass separating agent. In addition, the FF water-using process (e.g., boilers, cooling towers, reactors) is specified as water sinks/sources that consume/generate a fix flowrate of water. The primary concern of the FF water-using process is the flow rate of water. The problem for the synthesis of water network is mainly categorized into FC problem and FF problem. As reported, the limiting data for single contaminant FC and FF processes are interchangeable. The comprehensive review for the synthesis of water network with FC or FF problem via pinch and algebraic techniques can be found in the literature3. Only freshwater, regenerated water and wastewater are considered for FC or FF water-using units. In 1980s, Takama and his collaborators11-12 proposed a superstructure for the optimization of water system consisting of water-using and water-regeneration/treatment processes and developed corresponding non-linear programming model. Huang et al.13 improved the water network superstructure proposed by Takama et al.12, which contained multiple fresh water sources and 3



considered water generation and loss. Bagajewicz and Savelski14 introduced the linear models for the design of water system according to the deduced the necessary conditions15. Gunaratnam et al.16 proposed the superstructure of total water network and the water-using sub-system and the effluent treatment sub-system are addressed simultaneously. Karuppiah and Grossmann17 proposed the superstructure of integrated water systems and developed the strategy for the global optimization and it is extended via Yang et al.18. Meyer and Floudas19 introduced the superstructure for generalized pooling problem and the distributed wastewater treatment system is optimized. Considerable work is conducted via Feng and her coworkers, includes superstructure for regeneration recycling20, regeneration reuse with single contaminant21 and multiple contaminants22, single and two outflow water-using process23 and model for water-using process24. Faria and Bagajewicz25 introduced the evolution of the synthesis of water network and proposed the complete water system, which includes the pretreatment subsystem, water-using subsystem and wastewater treatment subsystem. The previous work can be categorized into optimization of water system with FC processes. Besides, many efforts have been conducted on the optimal synthesis of water network with FF processes. Ng et al.26-27 introduced the automated targeting model for single-contaminant water network with direct reuse/recycle26 and single-pass and/or partitioning regeneration scheme27. Property integration which provides more wide consideration on water quality (i.e. pH, conductivity, COD, hardness, tocity and color) is novelly proposed via El-Halwagi and his co-workers28-30. Considerable work has been conducted on the synthesis of property-based water network31-39. Yang et al.40 recently proposed a two-stage stochastic mixed-integer linear programing model for the optimization of life cycle water-use in hydraulic fracturing. Later, Yang et al.41 presented a mixed-integer linear programming model for optimizing capital investment decisions for water use for shale gas production. Gao and You42 firstly developed a mixed integer linear fractional programming model to maximize profit per unit flowrate of freshwater and both economic performance and water-use efficiency are optimized simultaneously. Lira-Barragan et al.43 proposed a mathematical model for the optimal reuse of flowback water in hydraulic fracturing considering seasonal and environmental constraints and the uncertainty for the water reuse system for shale gas production is recently addressed44. The approaches of water system optimization has been applied for the optimization of 4


Page 4 of 52

Page 5 of 52 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


practical industrial water network, such as catalyst plant45, chemical plant46, refinery plant14, 25, 47, semiconductor plant48, papermaking plant49, polyvinyl chloride manufacturing process50, coal-based chemical plant51. However, the types of water are just simply classified into fresh water, regenerated water and waste water in previous literatures, as shown in Figure 1(a). Huang et al.13 categorized the water sources into primary water (so-called freshwater)and secondary water (i.e. effluent of water-using process). In fact, the types of water also include desalted water, circulating cooling water, steam with different pressure levels and condensate water etc., as shown in Figure 1(b). In addition, the up-to-date model for water system optimization can only obtain the minimum fresh water consumption of the extracted or selected water-using processes, but cannot obtain that of the whole water system. For example, the saved flowrate of desalted water needs be converted to the flowrate of fresh water via a certain specific ratio46. It indicates that the correlations exist between different types of water. The reduction of other types of water will influence the minimum flowrate of fresh water of the whole water system. Fresh water is produced for desalted water in desalted water station. The desalted water could supply for boiler to generate steam. If the water quality of some outlet of process is so high that could be reused as the substitute of desalted water and/or fresh water, the flowrate of fresh water for the whole water system will decrease accordingly, and the decrement of the flowrate of desalted water also leads to the flowrate reduction of fresh water.

(a) Conventional superstructure of water-using process

5



(b) Generalized superstructure of water-using process Figure 1. Model of water-using process with multiple types of water To overcome the limitations of previous approaches, this paper classified the types of water into fresh water, regenerated water, wastewater, desalted water, circulating cooling water, steam with different pressure levels and condensate water and proposed a generalized model of water-using process considering multiple types of water. In addition, a general schematic diagram of refinery water system is proposed and it includes water-using processes, pretreatment systems (i.e. freshwater station, desalted water station, steam power station) and wastewater treatment system. The mathematical model for the optimization of refinery water system is presented accordingly. Two objectives, i.e., minimum flowrate of water source and minimum annualized cost are addressed. The water system of a large-scale refinery in China is analyzed to show the applicability of the proposed approach.

Problem Statement

A general schematic diagram for refinery water system is given in Figure 2. The system includes a number of main production units (PUi, i∈PU), such as crude oil distillation unit (CDU), fluid catalytic cracking unit (FCCU), kerosene diesel hydrotreating unit (KDHT), continuous 6


Page 6 of 52

Page 7 of 52 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


catalytic reforming unit (CCR), hydrogen production (HP), sour water stripping unit (SWSU), etc. Each unit contains many water-using processes that utilizes diverse types of water (i.e. freshwater, desalted water, cooling water) and steam with all pressure levels (it can also be considered as a type of water) and generate many types of wastewater (saline, oil and/or sulfur wastewater). The network also includes several assisted units (AU), i.e. fresh water station (FWS), desalted water station (DWS), circulating water station (CWS), power station (PS), wastewater treatment station (WTS) etc. AUs are mainly used to provide diverse types of water (i.e. freshwater, desalted water, cooling water) and steam with different pressure levels (i.e. 3.5 MPa, 1.0 MPa and 0.1 MPa) for PUs, or treat wastewater for reuse/recycling or discharge. In addition, water resources, i.e., municipal water, river and lake water, and rain water, will go through pretreatment processes, like filtration and sedimentation in fresh water station (FWS). This paper aims to develop a general superstructure and the associated mathematical model for refinery water system optimization.

Figure 2. Schematic diagram of a general refinery water system

7



Page 8 of 52

Mathematical Model

According to the proposed schematic diagram and the problem statement in the previous section, we present the model formulation for the optimization of refinery water network. A list of indices, sets, parameters and variables is given in the Notation, where all the parameters are denoted as upper-case symbols and all the variables are denoted as lower-case symbols. Constraints The flowrate balance for all the assisted units (i.e. FWS, DWS, PS, CWS and WTS) and main production units (i.e. CDU, FCCU, CRU, HP) can be generalized as, TypeIn

∑

TypeOut

+fsGain fsTypeIn ,in ,in =

∑

f sTypeOut + fsLoss ,out ,out

∀s ∈ AU U PU

(1)

where the subscripts, in and out, denote the inlet and outlet. The superscripts Gain and Loss denote the water gain and loss, respectively. TypeIn and TypeOut denote the inlet and outlet type of water stream, which are different for each assisted units ( ∀s = r ∈ AU ) and production unit ( ∀s = p ∈ PU ). Fresh water station (FWS) is utilized for the pretreatment of water resources (i.e. municipal water, river and lake water, and rain water). The freshwater production ratio ( α FWS ) of FWS can be used to correlate the inlet and outlet flowrates of FWS as expressed as equation (2). The pretreated water (outlet water of FWS) is so-called freshwater and can be used for production, living, fire protection and construction. The total outlet flowrate of water resource equals the summation of the flowrates of production, life, fire protection and construction. It can be calculated via equation (1) and the set of TypeIn and TypeOut is expressed as equation (3). TypeOut

∑

TypeOut Resource fFWS,out = αFWS ⋅ fFWS,in

8


(2)

Page 9 of 52 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


∀s = r = FWS ∀TypeIn = {Resource}

(3)

∀TypeOut = {Prod, Live,Fire,Const}

where

α FWS denotes the water production ratio of the pretreatment unit. TypeIn includes

resource. TypeOut includes Prod, Live, Fire, Const, which denote the water stream for production, living, fire and construction. The water gain and loss for FWS are neglected. Desalted water station (DWS) receives production water and reclaim steam condensate to produce desalted water. It is another important water pretreatment unit. There are several industrial combined pretreatment techniques, i.e. ion exchange + ultrafiltration + reverse osmosis. The regeneration or cleaning process for those pretreatment units will generate saline or oily wastewater. The water flowrate balance is expressed as equation (1) and the set of TypeIn and TypeOut is shown in equation (4).

∀ s = r = DWS ∀ TypeIn = {Rain, Circu,Prod,Cond}

(4)

∀ TypeOut = {Circu, Desalt,Oil,Discharge} where Rain, Circu, and Cond denote the rain water, circulated cooling water and condensate water, respectively. Desalt, Oil and Discharge denotes desalted water, oily water and discharged water, respectively. The water gain and loss for DWS are neglected. Since the cleaning and/or regeneration process in the DWS will generate wastewater, the flowrate of produced desalted water is less than the feed flowrate. The water production ratio of the DWS is defined as feed flowrate divided by flowrate of produced desalted water and it can be calculated via equation (5). Rain Prod Cond Desalt fDWS,in + fDWS,in + fDWS,in = fDWS,out ⋅αDWS

where

α DWS denotes the water production ratio of the DWS and it is usually greater than one. 9


(5)


Page 10 of 52

In the power station (PS), the desalted water is fed to deaerator and the steam is typically used to heat the desalted water and remove the contained oxygen. The boilers can convert the deaerated water to high-pressure steam. The water flowrate balance is shown in equation (1) and the set of TypeIn and TypeOut is expressed as equation (6).

∀s = r = PS ∀TypeIn = { Circu,Desalt,Steam3.5}

Circu, Deaerat,Steam9.5,Steam1.0,  ∀TypeOut =   Steam0.45, Cond,Oil,Reuse,Discharge 

(6)

where Deaerat, denotes deaerated water and Steam 9.5 MPa, 3.5 MPa and 1.0 MPa represent the steam with different pressure levels. The blowdown of the boiler can be discharged to wastewater treatment plant or reused via other process if the quality is within its limitation. The high-pressure steam (Steam9.5) production ratio ( α PS ) of PS can be used to correlate the inlet desalted water flowrate and outlet flowrate of Steam9.5 for PS and it can be expressed as equation (7). Steam9.5 Desalt fPS,out = αHP ⋅ fPS,in

where,

αHP

(7)

denotes the high-pressure steam production ratio for desalted water in the PS.

Circulating water station (CWS) supplies the cool circulating water (its temperature is around 25℃) for the whole refinery. The circulating cooling water is the most important and wide-used cooling utility and utilized for the cooling of process streams (i.e. top stream of distillation column, outlet stream of compressor). The temperature of the returned circulating water is increased to be around 35℃ and it will be cooled down in the cooling towers via evaporation. The entrainment will lead to the water loss. In addition, part of the returned circulating water (i.e. 5%) will go through the filtration units before it fed to the cooling towers. The backwash of those filtration 10


Page 11 of 52 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


units will generate wastewater and the blow-down wastewater of the cooling pool will be discharged to wastewater treatment plant. CWS receives production water, regenerated water and reused water from other process as the supplement flowrate for the water loss (i.e., evaporation, entrainment), and wastewater discharge. The water flowrate balance is shown in equation (1) and the set of TypeIn and TypeOut is expressed as equation (8).

∀ s = r = CWS ∀ TypeIn = { Circu,Prod,Reuse}

(8)

∀ TypeOut = {Circu,Discharge,Loss} where Loss denotes the evaporation and entrainment loss of the CWS. The correlation among flowrates of supplemented water, evaporation, entrainment, and air flowrate of CWS can be shown in equation (9)52.

Prod f RCS,in =

f

Evap RCS,out

Evap f RCS,out ⋅N

N −1

Drift Loss + f RCS,out = f RCS,out

Evap Circu f RCS,out = f RCS,out ⋅

Drift Circu f RCS,out = f RCS,out ⋅

Air f RCS,in =

cp ∆H r

⋅ ∆T

(9)

DF 100

Evap f RCS,out

H out − H in

where, Evap, Drift and Air denote water evaporation, entrainment, and inlet air flowrate. N represents the concentration multiple or rate. cp denotes water heat capacity. ∆Hr is the latent heat of evaporation. ∆T denotes temperature difference between inlet and outlet of CWS. DF is the drift parameter. Hin and Hout denote inlet and outlet air humidity of CWS. Wastewater Treatment Station (WTS) receives production water, living wastewater, oily wastewater, saline wastewater and other wastewater (i.e. stripping water) for treatment. There are 11



Page 12 of 52

several combined industrial treatment techniques, i.e. sedimentation + activated sludge + ultrafiltration + reverse osmosis. The treatment process will generate product and concentrated streams. The water flowrate balance is expressed as equation (1) and the set of TypeIn and TypeOut is expressed as equation (10).

∀s = r = WTS ∀TypeIn = {Prod,Life,Oil,Saline,Others}

(10)

∀TypeOut = {Reuse,Discharge} where Life, Saline and Others denote living, saline and another wastewater. WTS can be simplified into three-level treatment model as shown in Figure 3, i.e. sedimentation, activated sludge and advanced treatment techniques (i.e. ultrafiltration + reverse osmosis). The model for the treatment process can be formulated as equation (11).

Figure 3. Schematic diagram of wastewater treatment station

12


Page 13 of 52 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


prod = α T1 ⋅ f TU1

TypeIn

∑

resd = (1 − α T1 ) ⋅ f TU1

TypeIn f WTS,in TypeIn

∑

TypeIn f WTS,in

prod in reuse = f TU2 + f TU1 f TU1 prod in = α T2 ⋅ f TU2 f TU2 resd in = (1 − α T2 ) ⋅ f TU2 f TU2

(11)

prod in reuse = f TU3 + f TU2 f TU2 prod in = α T3 ⋅ f TU3 f TU3 resd = (1 − α T3 ) ⋅ f TinU3 f TU3

Reuse reuse reuse prod f WTS,out = f TU1 + f TU2 + f TU3 Discharge resd resd resd f WTS,out = f TU1 + f TU2 + f TU3

where

prod prod prod , f TU2 , f TU3 f TU1

respectively.

denote the flowrates of product stream of each treatment unit,

resd resd resd , f TU2 , f TU3 fTU1

denote the flowrates of concentrated stream of each treatment

reuse reuse , f TU2 f TU1

denote the flowrates of reused water streams of first two

unit, respectively.

treatment units, respectively.

αT1 , αT2 , αT3

denote water treatment production ratios of each

treatment unit, respectively. Main production units (PUs) (i.e. CDU, FCCU, KDHT, CCR, HP, SWSU) include multiple water-using processes that consume and generate all types of water and wastewater. The water flowrate balance of each PU can be shown in equation (1) and the set of TypeIn and TypeOut is expressed as equation (12).

∀s = p = PU Circu,Prod,Desalt,Deaerat,Steam9.5,    ∀TypeIn = Steam3.5,Steam1.0,Steam0.45,  Cond,Sulfur,Reuse,Others,Strip    Circu,Deaerat,Cond,Steam9.5,    ∀TypeOut = Steam3.5,Steam1.0,Steam0.45,Oil,  Sulfur,Saline,Reuse,Discharge    where Strip denotes stripping water. Sulfur denotes sulfur-containing wastewater. 13


(12)


Page 14 of 52

In addition, as shown in Figure 2, there are main pipelines for each type of water (i.e. fresh water, desalted water, deaerated water, circulating cooling water, 9.5 MPa steam, 3.5 MPa steam, 1.0 MPa steam, 0.45 MPa steam, and stripping water). The flowrate balance of all types of water can be expressed as equation (13).

∑f s

TypeIn s,in

= ∑ fsTypeOut + f TypeOut,Loss ,out

∀s ∈ AU U PU

(13)

s

where the superscript TypeOut, Loss denote the water loss during transportation. The set of TypeIn and TypeOut can be expressed as equation (14).

∀s ∈ AU U PU  Prod,Desalt,Deaerat,Steam9.5,  ∀TypeIn=TypeOut=   Steam3.5,Steam1.0,Steam0.45,Strip 

(14)

For conventional water-using process shown in Figure 1(a), the summation of its inlet flowrate and water gain is equal to the summation of its outlet flowrate and water loss and the water balance is given by equation (15). However, the types of conventional water-using process only contain fresh water, regenerated water or effluent of other water-using process j. The discharge of process j can be sent to other water-using process k, or sent to wastewater treatment unit as wastewater.

fi,in + fi Gain = fi,out + fi Loss

∀i ∈ Ni

(15)

where the subscript i denotes ith water-using process. The proposed generalized water-using process model shown in Figure 1(b) considers multiple types of water, i.e. fresh water, regenerated water, desalted water, circulating cooling water, steam with all pressure levels and wastewater. The water flowrate balance is expressed as equation (16). 14


Page 15 of 52 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


TypeIn

∑

TypeOut

f i TypeIn + fi Gain =

∑

f i TypeOut + fi Loss

∀i ∈ N i

(16)

Typically, the contaminant mass balance or property balance should be considered for each contaminant or property around inlet and outlet of water-using process. However, it is hard to determine exact limitations or lower and upper bounds of quality (contaminant concentration, i.e. suspend solid, oil, sulfide, ammonia-nitrogen, chloridion, or property, i.e. pH, COD, toxicity, color) for inlet of water-using process, although several efforts have been made towards the data extraction53. For the electrostatic desalting process in the CDU, freshwater pipeline and stripping water pipeline are both available. If the qualities (i.e. sulfide, ammonia-nitrogen, chloridion, pH) of the stripping water are within the specified bounds, the electrostatic desalting process can use the stripping water fully and the valve for freshwater pipeline would be closed. If something happens in the stripping column and qualities of stripping water are out of specified bounds, the electrostatic desalting process will use freshwater fully or partially. The same concept is adopted in the paper. If qualities of water streams available for reuse are within the inlet quality bounds for certain water-using process and those water streams can be taken as altered water. For instance, stripping water, as altered water, can be fed to the electrostatic desalting process in CDU. The altered type of water, i.e. Strip (denotes stripping water), is defined as “AlterTypeIn” and the equation (16) can be rewritten as equation (17). TypeIn

( ∑ fi TypeIn )'+

AlterTypeIn

∑

TypeOut

fi AlterTypeIn + fi Gain =

∑

fi TypeOut + fi Loss

∀i ∈ Ni

(17)

where the superscript AlterTypeIn denotes the water type that can alter the original water type for water-using process i. 15



Page 16 of 52

Equation (17) minus equation (16) can derive equation (18). TypeIn

( ∑ fi

AlterTypeIn TypeIn

)'+

∑

TypeIn

fi

AlterTypeIn

=

∑f

TypeIn

i

∀i ∈ Ni

(18)

TypeIn

where

∑

fi TypeIn denotes the original flowrate before optimization, i.e. freshwater,

TypeIn

(

∑

fi TypeIn ) ' represents the flowrate after optimization.

With the consideration of quality fluctuation of water sources, i.e. stripping water, the original type of water (i.e. freshwater) needs to be mixed to ensure the normal operating of water-using process. The correlation between the flowrate of altered water and that of original type of water can be expressed as equation (19). AlterTypeIn

∑

where k =

fi AlterTypeIn = k ⋅ Fi TypeIn

∀i ∈ Ni

(19)

f i AlterTypeIn is defined as water replacement ratio of water-using process i for altered Fi TypeIn

type of water and its upper bound is specified as k UP . Once k equals 0, it represents that the water-using process i fully use the original type of water. If k equals 1, it indicates that the original type of water is completely replaced by the altered type of water. The influence of k on the flowrate of freshwater, cost and number of pipelines will be analyzed in the case study. Objective functions In the refinery plant, the most important criterion for the evaluation of the water system is flowrate of water resource intake per ton of processing crude oil. The processing amount of crude oil is assumed to be constant. We first consider a model with an objective to minimize the flowrate of water resource. The problem is denoted as (FF), and the objective function is given by equation 16


Page 17 of 52 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(20). Resource min ff = fFWS,in

(20)

To evaluate the economic performance, we also consider the problem with the objective of minimum total annual costs, and the problem is denoted as (TAC). The problem (TAC) contains cost of water resource, operation costs of AUs and investment costs of AUs, investment cost of new pipelines. The objective function of problem (TAC) is given by equation (21). new min TAC = cwater + octreatment + Af ⋅ (icpipe + ICtreatment )

where

cwater

denotes annualized cost of water resource,

costs of AUs,

new icpipe

octreatment

(21)

denotes annual operation

denotes investment costs of new added pipelines, ICtreatment denotes

investment costs of AUs.

Af is annualized factor for the investment cost and it can be

expressed as equation (22).

Af =

fi ⋅ (1+ fi)ny (1+ fi)ny −1

(22)

where fi denotes interest rate (4% in this paper), ny denotes depreciation year (20 years in the paper). The problem (TAC) contains all elements of total annualized costs. However, the optimization of steam system and cooling water system is not considered here and thus the operation costs of PS and CWS are kept unchanged. In addition, the capacities for AUs (i.e. FWS, DWS, PS, CWS and WTS) will not change greatly for the refinery water system optimization. For instance, the most part of desalted water generated in DWS is for stream production. The total steam intake is kept unchanged and the capacity of PS is fixed. The capacity of CWS depends on the flowrate of circulating cooling water and it is assumed to be constant. The optimization of 17



Page 18 of 52

refinery water system will influence the design capacities of FWS and WTS. However, the spare capacities of FWS and WTS should be assigned for expansion. Thus, all the investment costs of AUs are fixed. Therefore, we can minimize the Partial annualized cost, PAC) instead of TAC as the objective function. The problem (PAC) can be given by equation (23). new ′ min PAC = cwater + octreatment + Af ⋅ icpipe

(23)

where octreatment denotes the annual operation costs of AUs except for PS. ′ The costs of water resource can be expressed as equation (24). Resource cwater = ewater ⋅ H ⋅ fFWS,in

where ewater denotes the unit price of water resource,

(24)

H denotes annual operation hours.

The annual operation costs of AUs is given by equation (25).

octreatment = ocFWS + ocCWS + ocPS + ocWTS + ocRCS

(25)

where ocFWS , ocCWS , ocPS , ocWTS , ocRCS denote the operation costs of FWS, DWS, PS, WTS, RCS, respectively. The operation cost of FWS, CWS and WTS can be generally expressed as equation (26). TypeIn

ocs = es ⋅ H ⋅

∑

∀s ∈ {FWS,CWS,WTS}

f sTypeIn ,in

(26)

TypeIn

where es denotes the unit price of the unit s.

∑

denotes the inlet flowrates of all f sTypeIn ,in

types of water of unit s. The operation cost of PS mainly depends on the fuel consumption (i.e. fuel oil, natural gas). It can be expressed as equation (27). HP ocPS = eFuel ⋅ H ⋅ R ⋅ f PS,out

18


(27)

Page 19 of 52 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


where eFuel denotes the unit price of fuel.

R denotes fuel consumptions of unit steam

HP production. f PS,out denotes the produced steam amount.

The annual operation cost of RCS is mainly associated with pump power, flowrates of inlet air, circulated water, make-up freshwater and discharged wastewater. It can be expressed as equation (28). air Circu ) +110 × ( fRCS,in ) ocCWS = 2.4094 ×10−3 × ( pC ) + 44 × ( fRCS Fresh Discharge +2275.132 × (fRCS,in )+1138× ( fRCS,out )

pC =

Circu fRCS,in ⋅ Head ⋅ ρ

(28)

ηM ⋅ηP

air where f RCS denotes the inlet air flowrate of RCS. pC represents the power of pump. Head

denotes the pump head.

ρ

is the density of water,

η M and η P denote the mechanical

efficiency and pump efficiency, respectively. The investment costs of all the AUs are parameters and the summation is given by equation (29).

ICtreatment = ICFWS + ICDWS + ICPS + ICWTS + ICRCS

(29)

where ICtreatment denotes the total investment cost of all the AUs. ICFWS , ICDWS , ICPS ,

ICWTS , ICRCS denote the investment costs of FWS, DWS, PS, WTS, RCS, respectively. The investment cost of added pipelines is given by equation (30) steam

water

steam water ICpipe = ∑ ICpipe + ∑ ICpipe

(30)

steam water where ICpipe and ICpipe denote the investment costs of steam and water pipeline.

The cost of steam pipeline can be expressed as equation (31)54. steam steam steam 0.48 steam ICpipe = ( A1 ⋅ wtpipe + A2 ⋅ (Dout ) + A3 + A4 ⋅ Dout ) ⋅ Lsteam

19


(31)


steam

where A1 denotes pipe cost per unit weight. wtpipe

Page 20 of 52

is pipe weight per unit length. A2

steam represents the installation cost. Dout is the external diameter of pipe. A3 denotes the

steam right-of-way costs of pipe. A4 denotes the insulation cost of pipe. L is the length of steam

pipe. The inner diameter of pipeline can be calculated by equation (32).

steam Dinner =

4 ⋅ f steam u steam ⋅ ρ steam ⋅ π

(32)

steam denotes the inner diameter. f is the flow rate of steam. u represents the flow where Dinner

velocity. ρ denotes the density of steam. Steam with low and very low pressures (i.e. 1.0 MPa and 0.1 MPa steam) are transported in stainless steel pipes of schedule 40. The external diameter and weight can be calculated by equation (33). steam steam Dout = 1.052 ⋅ Dinner + 0.005251 steam steam 2 steam wtpipe = 644.3 ⋅ ( Dinner + 0.4611 ) + 72.5 ⋅ Dinner

(33)

Steam with high and medium pressures (i.e. 9.5 MPa and 3.5 MPa steam) are transported in stainless steel pipes of schedule 80. The external diameter and weight can be calculated by equation (34). steam steam Dout = 1.101⋅ Dinner + 0.006349 steam steam 2 steam wtpipe = 1330 ⋅ ( Dinner + 0.9268 ) + 75.18 ⋅ Dinner

(34)

The total costs of water pipeline can be calculated by equation (35) and (36)55 as stainless steel pipes of schedule 80. water water β ICpipe = α ⋅ (Dinner ) ⋅ Lwater

20


(35)

Page 21 of 52 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


water Dinner = 0.363(

water

f

ρ water

) 0.45 ⋅ ( ρ water ) 0.13

(36)

water water is the where ICpipe denotes the cost of water pipe. α and β are coefficients. Dinner

inner diameter of pipe, Lwater represents the length of water pipe. f water is the flowrate of pipe.

ρ water denotes the density of water. Model summary We consider two models (P1 and P2) for the optimization of refinery water system. P1 – Flowrate of water resource as objective function min

Resource ff = f FWS,in given in equation (20)

s.t. mass balance constraints for AUs (1)-(11) mass balance constraints for PUs (12) mass balance constraints for each type of water (13)(14) mass balance constraints for each water-using process (18) mass balance constraints for altered type of water (19) It is worthy to mention that there is no binary variables and nonlinear term in the model P1 and it leads to a linear programming (LP) problem. P2 – Partial annualized cost as objective function

′ + Af ⋅ icpipe given in equation (23) min PAC = cwater + octreatment new

s.t. mass balance constraints for AUs (1)-(11) mass balance constraints for PUs (12) mass balance constraints for each type of water (13)(14) mass balance constraints for each water-using process (18) mass balance constraints for altered type of water (19) cost constraint (22) and (24)-(36) steam 0.48 In the model P2, the non-linear terms ( ( Dout ) ,

steam 2 water β ) , ( Dinner ) , f steam , ( Dinner

( f water )0.45 ) result in a NLP problem. All the models are coded in GAMS 24.2.2 on a PC with Intel® Core™ i5-3330 3.2 GHz and 4.00 GB RAM, running Windows 10, 64-bit operating system. 21



The LP problem is solved using CPLEX solver, and the NLP model is solved using BARON solver for global optimization. The absolute optimality tolerance for all solvers is set as 10-6.

Case Study

To illustrate the application of the proposed models, we analyze the water system for a certain large-scale refinery in China. The production units can be categorized into PUs and AUs. The PUs include CDU, FCCU, gas fractionation unit (GFU), alkylation, MTBE, wax oil hydrotreating unit (WHU), kerosene diesel hydrotreating unit (KDHU), gasoline diesel hydrotreating unit (GDHU), hydrogen production (HP), continuous catalytic reforming unit (CRU), aromatics, delayed coking unit (DCU), desulfurization unit (DSU), sulfur recovery unit (SRU), sour water stripping unit (SWTU), acid regeneration unit (ARU), and storage transportation station (STUS). The AUs includes FWS, CWS, WTS, PS and DWS. The input data are shown in the Table S1 and S2. The parameters are shown in the Table S3. We will present the two scenarios, namely the scenario with the objective of minimum fresh water consumption (model P1) and the objective of minimum PAC (model P2).

Scenario 1: minimize the flowrate of water resource To minimize the flowrate of water resource, the current refineries have made significant effort to achieve the target. In this scenario, we will demonstrate the advantage of the propose model that the minimum flowrate of water resource can be obtained at one step. In addition, we will investigate the correlation between the minimum flowrate of water resource and different water replacement ratios and perform the economic analysis. 22


Page 22 of 52

Page 23 of 52 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


It is easy to test the quality of water sources (i.e. production water, desalted water, blowdown of cooling tower and boiler, stripping water, regenerated water) and the tested quality for several water sources (i.e. fresh water, stripping water) are list in Table 1. However, it is very complex to determine the exact quality bounds for water sinks or inlet of water-using process. Several possible considerations (i.e. solubility, fouling, corrosion) for limiting data for maximum inlet and outlet contaminant concentrations are presented by Wang and Smith56. Foo53 presented the guidelines to data extracting for flowrate and water quality. The historical quality data for the feed water source can be good reference for data extraction53 while it is not always available for each water source. The limiting data for water qualities is extracted based on the engineering experience for the water system integration of chemical46 and catalyst plant57. It is very imprecise for the limiting water quality data extraction based on the engineering experience. We avoid the estimation on the limiting data of maximum inlet and outlet qualities for water-using units in this paper. One exception is that, the maximum inlet quality for make-up water for circulating cooling water system is regulated via national and enterprise standards58 and they are listed in Table 1. Table 1. Water quality of several water sources and quality limits of make-up water for circulating cooling water system COD

Turbidity

Conductivity

Dissolved solid

(mg/L)

(NTU)

(µs/cm)

(mg/L)

21

0

14

7

pH

Boiler blowdown

8.2

23



Page 24 of 52

Make-up water for circulating cooling

8~8.7

0~150

0~20

0~5000

-

regenerated water

6.0~9.0

80

10

-

1000

Stripping water

8.18

882.7

4.57

119.2

26

freshwater

7.49

-

1

1164.67

-

Desalted water

8.57

-

-

2.9

-

water system

On the basis of the application of water conservation strategies for other refinery plants, the typical water conservation potential can be identified as listed in Table 2. Note that, the boiler blow-down in CDU and SRU can be directly reused as make-up water for circulating cooling water system in CWS. The pH, COD concentration and turbidity of boiler blow-down are within the quality limits of make-up water of CWS regulated via standards58. Sour water streams are typical generated from various units, such as CDU, FCCU, DCU, CRU, WHU, KDHU and hydrocracking unit (HCU). They are categorized into two types, non-hydrogenation (i.e. CDU, FCCU, DCU) and hydrogenation (CRU, WHU, KDHU and HCU). The concentrations of sulfur and NH3-N for the sour water generated from hydrogenation units are typically higher than those from non-hydrogenation units. There are two stripping columns and they are used to treat two types of sour water separately to produce striping water. Stripping water I is produced from sour water from non-hydrogenation units and Stripping water II is produced from sour water from hydrogenation units. Currently, 135 t/h of stripping water I is reused to electrostatic desalting for crude oil and it is the most successful case for water regeneration reuse/recycle in refinery plant. 24


Page 25 of 52 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


In addition, the Stripping water II is widely reused for water injection processes for hydrogenation units and there are many industrial application cases. However, the engineers concern about the pipeline corrosion caused by Stripping water II which replaces the original desalted water and they would adjust the replacement ratio of Stripping water II if necessary. We adopt the same concept and introduce the replacement ratio of altered water and it is defined in Equation (19). All the water conversation strategies as shown in the third column of Table 2 for the refinery are examined with practical engineers. The types of altered water, saved water and the maximum potential flowrates are presented in the fourth, fifth and sixth columns of Table 2. Note that, diverse types of water will be saved, and it demonstrates that the water-using unit model with multiple types of water proposed in the paper is practical. Table 2. Potential water conservation strategies for this case study Maximum potential Type of

Type of

Water conservation

Strategy

flowrate of saved

units No.

altered

saved

strategies

water water

water (t/h)

Boiler blow-down can be reused as 1

Boiler

Production

CDU

6.49 make-up water for

blow-down

water

condensate

Condensate

cooling system Condensate water 2

MTBE

2.43 can be recycled to

water 25


water


Page 26 of 52

condensate water pipeline Stripping water II Stripping 3

WHU

Desalted

can be reused as

47.27 water II

water

condensate

Condensate

injection water Condensate water can be recycled to 4

WHU

1 condensate water

water

water

Stripping

Desalted

pipeline Stripping water II 5

KDHT

17.91

can be reused as water II

water

water can be reused

condensate

Production

as make-up water for

water

water

Stripping

Desalted

injection water Process condensate

6

HP

33.6

cooling system Stripping water II 7

CRU

can be reused as

4 water II

water

Stripping water I can

Stripping

Desalted

be reused as

water I

water

injection water

8

Aromatics

0.1

26


Page 27 of 52 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


injection water Stripping water I can 9

DCU

Stripping

Condensate

water I

water

Stripping

Desalted

water I

water

Boiler

Production

be reused as

12.5

injection water Stripping water I can 10

DSU

be reused as washing

6

water Boiler blowdown can be reused as 11

1.5

SRU make-up water for

blow-down

water

Regenerated

Production

cooling system Regenerated water can be reused as 12

CWS

70.44 make-up water for

water

water

cooling system Next, with the given input data shown in Table S1 and S2 and parameters in Table S3, model P1 is utilized to determine the minimum flowrate of water resource via setting different replacement ratios of altered water. Totally, there are 12 replacement ratios and each potential water-using unit has one replacement ratio. They can be different from each other and are assumed to be the same for simplicity. The upper bounds of replacement ratios of altered water (kUP) vary from 0 to 1 with the step size of 0.1. The LP model (P1) has 583 continuous variables and 108 27



constraints. The model is solved in 0.01 CPUs via GAMS 24.2.2 using CPLEX solver (the PC specification: Intel® Core™ i5-3330 3.2 GHz and 4.00 GB RAM, Windows 10, 64-bit operating system). The computational results of minimum flowrate of water resource for the whole refinery water system is shown as Table S4 and all 12 practical replacement ratios of altered water reach the upper bounds. The variation of minimum flowrate of water resource with the upper bounds of replacement ratio of altered water is illustrated in Figure 4. As shown, the minimum flowrate of water resource is linearly reduced with the increase of upper bounds for the replacement ratios of altered water. When all the replacement ratios of altered water are zero, it denotes the current water system without any optimization. The flowrate of water resource is 504.405 t/h. When all the replacement ratios of altered water reach ones, it indicates that the original type of water is completely replaced by the altered type of water and the flowrate of water resource is reduced to 293.637 t/h. Due to the correlation equations for different types of water incorporated in the equations (2)-(6), the flowrate of water resource utilized in the whole refinery can be determined in one step via solving the model P1. For this case, 469.360 t/h of rain water is utilized as important water resource and it is assumed to be constant. The water conversation ratio reaches 21.6% (=[(504.405+469.360)-(293.637+469.360)]/(504.405+469.360).

28


Page 28 of 52

Page 29 of 52

Minimum flowrate of water resource (Scenario 1) Minimum flowrate of water resource (t/h)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


550.00 500.00 450.00 400.00 350.00 300.00 250.00 0

0.2

0.4

0.6

0.8

1

Upper bounds for replacement ratio of altered water

Figure 4. Variation of minimum flowrate of water resource with the upper bounds for replacement ratio of altered water Table 3. Comparison between flowrates of production water, desalted water and reused water before and after optimization Before optimization (k = 0) (t/h)

After optimization (k = 1) (t/h)

Production

Desalted

Reused

Production

Desalted

Reused

water

water

water

water

water

water

CDU

0.03

13.79

135

0.03

13.79

135

FCCU

0.25

43.85

0.25

43.85

GFU Alkylation

5.29

5.29

MTBE

0.61

0.02

0.61

0.02

WHU

0.26

47.27

0.26

0 (↓)

29



KDHU

0.26

GDHU

0.26

HP

0.26

CRU

17.91

0.26

166.63

23

0.26

108.82

0.26

96.84

Page 30 of 52

0 (↓) 23 163.63 (↓)

108.82

92.84 8.91

(↓)

15.79

4.03

0.1

1.51(↓)

Aromatics

8.91

0.1

DCU

28.29

4.03

DSU

0.1

7.51

SRU

1.63

1.63

STWU

0.81

0.81

ARU

0.84

0.84

STUS

3.28

3.28

28.5

28.5

FWS CWS

213.88

WTS

39.73

PS DWS

586.611

101.85(↓)

698.641(↑)

39.73 317.11

317.11 70.857(↓)

157.095

245.727( Summation

456.495

720.35

642.07(↓)

881.931

993.961(↑)

↓) The flowrates of production water, desalted water and reused water before and after optimization is compared as shown in Table 3. Note that, the flowrate of reused water for CWS is increased from 586.611 t/h to 698.641 t/h with the introduction of the reused water from CDU 30


Page 31 of 52 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(6.49 t/h), HP (33.6 t/h), SRU (1.5 t/h) and regenerated water (70.44 t/h). The flowrate of production water for CWS is reduced from 213.88 t/h to 101.85 t/h. In addition, due to the reuse of condensate water from MTBE (2.43 t/h) and WHU (1 t/h) and the reuse of stripping water in WHU, KDHU, CRU, Aromatics and DSU, the flowrates of desalted water for these units are decreased and then the flowrate of production water of DWS is reduced from 157.095 t/h to 70.857 t/h accordingly. Economic analysis is performed to examine the contribution of elements of total annualized cost and they are calculated via equations (21)-(37). The Annualized cost distribution of each element of TAC is illustrated in Figure 5. Note that, the cost of water resource is 1.94 million RMB/y, occupying only 0.87% of TAC which is 222 million RMB/y. The annualized operation cost and annualized investment cost are 199 million RMB/y and 21.0 million RMB/y and they take up 89.68% and 9.45% of TAC. As illustrated in Figure 5, the operation cost takes major part of TAC. Next the elements of operation cost are calculated as shown in Figure 6. PS takes up the largest part of the annualized operation cost and it is attributed to the massive fuel consumption during steam production and the value is 110 million RMB/y. But in this case, we do not optimize the steam system and thus the operation cost of PS is fixed and we do not need to take it into consideration the optimization model. The second largest part of the annualized operation cost is CWS due to the power consumption for the high circulation flowrates of CWS and its value reaches 64.0 million RMB/y. The investment costs for each element are shown in Figure 7. The smallest part of investment cost is FWS and it is 3.00 million RMB. Because the pretreatment process for fresh water is simple and the investment cost is low. The largest part of investment

31



Page 32 of 52

cost is WTS and the value is 210 million RMB and it is attributed to the complex supporting facility for wastewater treatment process. The investment costs of AUs are fixed parameters and they are not changeable for optimization. Next, we can switch the objective function TAC to PAC in the following economic analysis. The cost distribution of PAC is illustrated in Figure 8. The proportion of items distribution of PAC shown in Figure 8(a) is similar to the proportion of TAC shown in Figure 5. But the total value of PAC is only 92.6 million RMB/y and it is much smaller than TAC which is 222 million RMB/y.

Cost distribution of TAC (Scenario 1) 9.45%

0.87%

Water Operation Investment

89.68%

Figure 5. Cost distribution of TAC (Scenario 1)

32


Page 33 of 52 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Annual operation cost of AUs (Scenario 1) (106 RMB/year) 120

109.88

100 80 64.00 60 40 13.11

20

11.65

0.58 0 FWS

DWS

PS

WTS

CWS

Figure 6. Annual operation cost of AUs (Scenario 1)

Investment cost of AUs (106 RMB) (Scenario 1) 250 210 200 150 100 50 3

20

15

20

17.55

0 FWS

DWS

PS

WTS

CWS

Pipe

Figure 7. Comparison of each element of investment cost of AUs

33



Page 34 of 52

Cost distribution of PAC (Scenario 1) 1.39% 2.09%


96.51%

(a) Scenario 1

Cost distribution of PAC (Scenario 2) 1.15% 2.22%


96.63%

(b) Scenario 2 Figure 8. Cost distribution of PAC

Scenario 2: minimize the partial annualized cost (PAC) Model P2 is utilized to determine the minimize the partial annualized cost (PAC) via setting different replacement ratio upper bound of altered water (kUP) with the given input data shown in Table S1 and S2 and parameters in Table S3. Similarly, the upper bounds of replacement ratios of 34


Page 35 of 52 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


altered water (kUP) vary from 0 to 1 with the step size of 0.1. The NLP model (P2) has 670 continuous variables and 195 constraints. The model is solved in 0.02 CPUs via GAMS 24.2.2 using BARON solver (the PC specification: Intel® Core™ i5-3330 3.2 GHz and 4.00 GB RAM, Windows 10, 64-bit operating system). The cost distribution of PAC is shown in Figure 8(b). Compared with Figure 5, the proportion of investment cost for PAC is reduced according to the removal of investment of AUs from the TAC. It leads to the increment of proportion of cost of water resource and annualized operation cost in PAC. Next, the comparisons for the flowrates of water resource, PAC, total operation costs of AUs investment costs and numbers of added pipelines for Scenarios 1 and 2 are illustrated in Figures 9-13. The actual replacement ratios of 12 potential water conversation strategies for Scenarios 1 and 2 are shown in Table S5 and S6. As shown, all the actual replacement ratios reach the upper bounds for Scenario 1 with the minimum flowrate of water resource as the objectives. It is obvious that the maximum flowrate reduction of water resource can be achieved when the upper bounds of replacement ratios are achieved. As shown in Figure 9, the flowrates of water resource for Scenario 1 is less than those for Scenario 2 when the upper bounds of replacement ratios are greater than zero. When the upper bounds of replacement ratios are set as one, the flowrate of water resource is determined as 311.177 t/h, which is slightly greater than 293.637 t/h in Scenario 1.

The

water

conversation

ratio

in

Scenario

2

reaches

19.8%

(=[(504.405+469.360)-(311.177+469.360)]/(504.405+469.360). For Scenario 2 with the minimum partial annualized cost (PAC) as the objectives, the PAC of Scenario 2 is mostly less than that of Scenario 1 as shown in Figure 10. The total operation cost of AUs for Scenario 2 is slightly less

35



than that for Scenario 1 as shown in Figure 11. The investment costs for Scenario 1 is always greater than those for Scenario 2 as shown in Figure 12 because the more added pipelines for Scenario 1. The profit of water recovery for strategies No. 2, 4, 8, 9, 11 (Table S6) cannot offset the investment cost for the added pipelines. Their replacement ratios are always kept zero and therefore the maximum connection numbers for Scenario 2 is 7 as shown in Figure 13. In addition, as shown in Table S6, the replacement ratio for strategies No. 1 reaches 0.6, that for No. 7 reaches 0.3 and that for No. 10 reaches 0.2 and the upper bounds can be reached. The profits of water recovery for those strategies can balance the investment cost for the added pipelines. Moreover, for the strategies No. 3, 5, 6, 12 (Table S6), the profits of water recovery for those strategies can always exceed the investment cost of added pipelines and the replacement ratios can always reach the upper bounds. Note that, as shown in Figure 13, the number of added pipelines for Scenarios 1 is increased to be 12 when the replacement ratios are greater than 0.1 and it is kept unchanged with the increment of upper bounds for replacement ratios. The number of added pipelines for Scenario 2 is increased gradually with the increment of replacement ratios from 0 to 0.3. It is kept being 6 when the replacement ratios between 0.3 and 0.5. Once the replacement ratios are greater than 0.6, the number of added pipelines for Scenario 2 is increased to be 7 and kept unchanged.

36


Page 36 of 52

Page 37 of 52 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Flowrate of water resource (t/h) 550 500 450 400 350 300 250 0

0.2 0.4 0.6 0.8 Upper bounds for replacement ratio of altered water Scenario 1 Scenario 2

1

Figure 9. Comparison on the flowrate of water resource for Scenarios 1 and 2

Partial annualized cost (PAC) (106 RMB/y) 96.00 95.50 95.00 94.50 94.00 93.50 93.00 92.50 92.00 0

0.2

0.4

0.6

0.8

Upper bounds for replacement ratio of altered water Scenario 1 Scenario 2

1

Figure 10. Comparison on the PAC for Scenarios 1 and 2

Annual operation cost (106 RMB/y) 92.50 92.00 91.50 91.00 90.50 90.00 89.50 89.00 0


37


1


Page 38 of 52

Figure 11. Comparison on the total operation cost of AUs for Scenarios 1 and 2

Investment cost comparison (106 RMB) 1.40 1.20 1.00 0.80 0.60 0.40 0.20 0.00 0

0.2

0.4

0.6

0.8

1

Upper bounds for replacement ratio of altered water Scenario 1 Scenario 2 Figure 12. Comparison on the investment cost for Scenarios 1 and 2

Number of added pipelines 14 12 10 8 6 4 2 0 0


1

Figure 13. Comparison on the numbers of added pipelines for Scenarios 1 and 2

Conclusion

This paper proposed a generalized model of water-using process in refinery considering multiple types of water (i.e. desalted water, condensate water, steam with different pressure levels, circulated cooling water, sour water and stripping water). A general schematic diagram of refiney 38


Page 39 of 52 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


water system is presented and it includes the water-using processes in the main production units (PUs) (i.e. CDU, FCCU, KDHT, CCR, HP, etc.) and assisted units (AUs), such as FWS, DWS, CWS, PS, which can be considered as the pretreatment system and wastwater treatment system. The flowrate balance equations for the whole refinery water system are fomulated and the correlation for all types of water is integrated in the fomulated mathematical model. The cost equations for the operation, investment, pipeline and water resource are introduced for economic analysis. To avoid the imprecise data extration for limiting water qualities and make the approach easier to be applied in practice, one simple concept is proposed for water reuse/recycle. The strategies for water conversation can be identified via engineering experience, application practice and test of water quality and the potential flowrate for alterative water for the replacement of original water can be figured out concequently. The replacement ratios can be adjusted according to the quality of altered water. The water system of a large-scale refinery in China is analyzed. Two objectives (minimum flowrate of water source (Scenario 1) and minimum partical annualized cost (Scenario 2)) are analyzed and compared. The minimum flowrate of water source can be achieved for Scenario 1. Several unprofitable stratigies are avoid in Scenario 2 and it yields a economic and simpler water system with slightly higher flowrate of water source. The conversation ratios of water resource for Scenarios 1 and 2 reach 21.6 % and 19.8%. Case study shows that the proposed approach is applicable for the grossroot design and retrofit analysis for refiney water system. It provides a solution for sustainable development for refineries. Future work will be conducted on the data extraction on limiting qualities.

39



Notation

Variables

f

flowrate，t/h

IC

Investment cost

OC

operation cost

D

diameter

wt

weight

Sets

i

the ith water-using process

s

the sth production unit

Superscripts AlterType

the altered type of water

Circu

circulating cooling water

Cond

condensate water

Const

construction water

Deaerat

deaerated water

Desalt

desalted water

Discharge

water discharge

Fire

fire water

Prod

production water

40


Page 40 of 52

Page 41 of 52 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Gain

water gain

Life

life water/ life wastewater

Loss

water loss

Oil

oily wastewater

Others

other water

Rain

rain water

Reuse

reuse water

Saline

saline wastewater

Resource

fresh water/water resource

SteamXX

steam with pressure level of XX MPa

Strip

stripping purifying water

Sulfur

sulfur containing wastewater

TypeIn

the type of water of inlet

TypeOut

the type of water of outlet

water

All type of water

steam

All type of steam

Subscript DWS

desalted water station

FWS

fresh water station

in

inlet

out

outlet

41



PS

power station

PU

production unit

RCS

circulating cooling water station

STS

storage transportation station

UIn

water-using unit of inlet

UOut

water-using unit of outlet

WTS

wastewater treatment station

pipe

pipe

inner

Inner diameter

out

Outside diameter

Supporting Information

Tables S1- S6 are listed in the Supplementary file as Supporting Information.

Acknowledgements

National Natural Science Foundation of China (No. 21576287) is gratefully acknowledged. The research is also supported by Science Foundation of China University of Petroleum, Beijing (No. 2462015BJB02, 2462015YQ0305 and C201606).

Literature Cited

1.

OECD, OECD Environmental Outlook to 2050. OECD Publishing.

42


Page 42 of 52

Page 43 of 52 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


2.

Bagajewicz, M., A review of recent design procedures for water networks in refineries and

process

plants.

Comput.

Chem.

Eng.

2000,

24

(9-10),

2093-2113.

DOI:

10.1016/S0098-1354(00)00579-2. 3.

Foo, D. C. Y., State-of-the-Art Review of Pinch Analysis Techniques for Water Network

Synthesis. Ind. Eng. Chem. Res. 2009, 48 (11), 5125-5159. DOI:10.1021/ie801264c. 4.

Jezowski, J., Review of Water Network Design Methods with Literature Annotations. Ind.

Eng. Chem. Res. 2010, 49 (10), 4475-4516. DOI:10.1021/ie901632w. 5.

Klemeš, J. J., Industrial water recycle/reuse. Current Opinion in Chemical Engineering

2012, 1 (3), 238-245. DOI:10.1016/j.coche.2012.03.010. 6.

Mann, J. G.; Liu, Y. A., Industrial water reuse and wastewater minimization. McGraw-Hill

Professional: New York, 1999. DOI: 10.1016/S0304-3894(00)00165-5 7.

Smith, R., Chemical process design and integration. 2nd ed.; John Wiley & Sons Ltd:

Chichester, UK, 2005. pp 581-623 8.

Foo, D. C. Y., Process integration for resource conservation. CRC Press: Boca Raton,

Florida, US, 2012. 9.

Hallale, N.,

A new graphical targeting method for water minimisation. Advances in

Environmental Research 2002, 6 (3), 377-390. DOI: 10.1016/S1093-0191(01)00116-2. 10. Manan, Z. A.; Tan, Y. L.; Foo, D. C. Y., Targeting the minimum water flow rate using water cascade analysis technique. Aiche J. 2004, 50 (12), 3169-3183. DOI: 10.1002/aic.10235. 11. Takama, N.; Kuriyama, T.; Shiroko, K.; Umeda, T., Optimal planning of water allocation in industry. J. Chem. Eng. Jpn. 1980, 13 (6), 478-483. DOI: 10.1252/jcej.13.478.

43



12. Takama, N.; Kuriyama, T.; Shiroko, K.; Umeda, T., petroleum

refinery.

Comput.

Chem.

Eng.

1980,

Page 44 of 52

Optimal water allocation in a

4

(4),

251-258.

DOI:

10.1016/0098-1354(80)85005-8. 13. Huang, C. H.; Chang, C. T.; Ling, H. C.; Chang, C. C.,

A mathematical programming

model for water usage and treatment network design. Ind. Eng. Chem. Res. 1999, 38 (7), 2666-2679. DOI: 10.1021/ie990043s. 14. Bagajewicz, M.; Savelski, M.,

On the use of linear models for the design of water

utilization systems in process plants with a single contaminant. Chem. Eng. Res. Des. 2001, 79 (A5), 600-610. DOI: 10.1205/02638760152424389. 15. Savelski, M. J.; Bagajewicz, M. J.,

On the optimality conditions of water utilization

systems in process plants with single contaminants. Chem. Eng. Sci. 2000, 55 (21), 5035-5048. DOI: 10.1016/S0009-2509(00)00127-5. 16. Gunaratnam, M.; Alva-Argáez, A.; Kokossis, A.; Kim, J. K.; Smith, R., Automated design of total water systems. Ind. Eng. Chem. Res. 2005, 44 (3), 588-599.. DOI: 10.1021/ie040092r. 17. Karuppiah, R.; Grossmann, I. E., Global optimization for the synthesis of integrated water systems in chemical processes. Comput. Chem. Eng. 2006, 30 (4), 650-673. DOI: 10.1016/j.compchemeng.2005.11.005. 18. Yang, L.; Salcedo-Diaz, R.; Grossmann, I. E.,

Water Network Optimization with

Wastewater Regeneration Models. Ind. Eng. Chem. Res. 2014, 53 (45), 17680-17695. DOI: 10.1021/ie500978h. 19. Meyer, C. A.; Floudas, C. A.,

Global optimization of a combinatorially complex

44


Page 45 of 52 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


generalized pooling problem. Aiche J. 2006, 52 (3), 1027-1037. DOI: 10.1002/aic.10717. 20. Feng, X.; Bai, J.; Wang, H. M.; Zheng, X. S., Grass-roots design of regeneration recycling water

networks.

Comput.

Chem.

Eng.

2008,

32

(8),

1892-1907.

DOI:

10.1016/j.compchemeng.2007.10.006. 21. Bai, J.; Feng, X.; Deng, C., Optimal design of single-contaminant regeneration reuse water networks with process decomposition. Aiche J. 2010, 56 (4), 915-929. DOI: 10.1002/aic.12037. 22. Hu, N.; Feng, X.; Deng, C., Optimal design of multiple-contaminant regeneration reuse water networks with process decomposition. Chemical Engineering Journal 2011, 173 (1), 80-91. DOI: 10.1016/j.cej.2011.07.040. 23. Liu, Y.; Feng, X.; Chu, K. H.; Deng, C., Optimization of water network with single and two outflow water-using processes. Chemical Engineering Journal 2012, 192 (0), 49-57. DOI: 10.1016/j.cej.2012.03.042. 24. Deng, C.; Feng, X.; Wen, Z.,

Optimization of water network integrated with process

models. Clean Techn Environ Policy 2013, 15 (3), 473-487. DOI: 10.1007/s10098-013-0609-3. 25. Faria, D. C.; Bagajewicz, M. J., On the Appropriate Modeling of Process Plant Water Systems. Aiche J. 2010, 56 (3), 668-689. DOI: 10.1002/aic.11983. 26. Ng, D. K. S.; Foo, D. C. Y.; Tan, R. R.,

Automated Targeting Technique for

Single-Impurity Resource Conservation Networks. Part 1: Direct Reuse/Recycle. Ind. Eng. Chem.

Res. 2009, 48 (16), 7637-7646. DOI: 10.1021/ie900120y. 27. Ng, D. K. S.; Foo, D. C. Y.; Tan, R. R.,

Automated Targeting Technique for

Single-Impurity Resource Conservation Networks. Part 2: Single-Pass and Partitioning

45



Page 46 of 52

Waste-Interception Systems. Ind. Eng. Chem. Res. 2009, 48 (16), 7647-7661. DOI: 10.1021/ie900127r. 28. El-Halwagi, M. M.; Glasgow, I. M.; Qin, X. Y.; Eden, M. R.,

Property integration:

Componentless design techniques and visualization tools. Aiche J. 2004, 50 (8), 1854-1869. DOI: 10.1002/aic.10305. 29. Shelley, M. D.; El-Halwagi, M. M., Component-less design of recovery and allocation systems: a functionality-based clustering approach. Comput. Chem. Eng. 2000, 24 (9-10), 2081-2091. DOI: 10.1016/S0098-1354(00)00578-0. 30. El-Halwagi, M. M., Sustainable Design Through Process Integration: Fundamentals and

Applications to Industrial Pollution Prevention, Resource Conservation, and Profitability Enhancement.

ButterworthHeinemann:

Waltham,

2011;

p

448.

DOI:

10.1016/B978-1-85617-744-3.00034-5. 31. Rubio-Castro, E.; Ponce-Ortega, J. M.; Serna-González, M.; El-Halwagi, M. M.; Pham, V., Global optimization in property-based interplant water integration. Aiche J. 2012, 59 (3), 813–833. DOI: 10.1002/aic.13874. 32. Hortua, A. C.; El-Halwagi, M. M.; Ng, D. K. S.; Foo, D. C. Y., Integrated Approach for Simultaneous Mass and Property Integration for Resource Conservation. ACS Sustainable

Chemistry & Engineering. 2013, 1 (1), 29–38. DOI: 10.1021/sc300008n. 33. Ponce-Ortega, J. M.; Mosqueda-Jiménez, F. W.; Serna-González, M.; Jiménez-Gutiérrez, A.; El-Halwagi, M. M.,

A property-based approach to the synthesis of material conservation

networks with economic and environmental objectives. Aiche J. 2011, 57 (9), 2369-2387. DOI:

46


Page 47 of 52 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


10.1002/aic.12444. 34. Kheireddine, H.; Dadmohammadi, Y.; Deng, C.; Feng, X.; El-Halwagi, M., Optimization of Direct Recycle Networks with the Simultaneous Consideration of Property, Mass, and Thermal Effects. Ind. Eng. Chem. Res. 2011, 50 (7), 3754-3762. DOI: 10.1021/ie1012272. 35. Ponce-Ortega, J. M.; El-Halwagi, M. M.; Jimenez-Gutierrez, A., Global optimization for the synthesis of property-based recycle and reuse networks including environmental constraints.

Comput. Chem. Eng. 2010, 34 (3), 318-330. DOI: 10.1016/j.compchemeng.2009.10.005. 36. Ng, D. K. S.; Foo, D. C. Y.; Tan, R. R.; El-Halwagi, M., Automated targeting technique for concentration- and property-based total resource conservation network. Comput. Chem. Eng. 2010,

34 (5), 825-845.DOI: 10.1016/j.compchemeng.2010.01.018. 37. Chen, C.-L.; Lee, J.-Y.; Ng, D. K. S.; Foo, D. C. Y.,

A unified model of property

integration for batch and continuous processes. Aiche J. 2010, 56 (7), 1845-1858. DOI: 10.1002/aic.12116. 38. Ponce-Ortega, J. M.; Hortua, A. C.; El-Halwagi, M.; Jimenez-Gutierrez, A.,

A

Property-Based Optimization of Direct Recycle Networks and Wastewater Treatment Processes.

Aiche J. 2009, 55 (9), 2329-2344. DOI: 10.1002/aic.11828. 39. Ng, D. K. S.; Foo, D. C. Y.; Rabie, A.; Ei-Halwagi, M. M., Simultaneous synthesis of property-based water reuse/recycle and interception networks for batch processes. Aiche J. 2008,

54 (10), 2624-2632. DOI: 10.1002/aic.11578. 40. Yang, L. L.; Grossmann, I. E.; Manno, J., Optimization Models for Shale Gas Water Management. Aiche J. 2014, 60 (10), 3490-3501. DOI: 10.1002/aic.14526.

47



41. Yang, L.; Grossmann, I. E.; Mauter, M. S.; Dilmore, R. M., Investment optimization model for freshwater acquisition and wastewater handling in shale gas production. Aiche J. 2015, 61 (6), 1770-1782. DOI: 10.1002/aic.14804. 42. Gao, J.; You, F., Optimal Design and Operations of Supply Chain Networks for Water Management in Shale Gas Production: MILFP Model and Algorithms for the Water-Energy Nexus. Aiche J. 2015, 61 (4), 1184-1208. DOI: 10.1002/aic.14705. 43. Fernando Lira-Barragan, L.; Maria Ponce-Ortega, J.; Serna-Gonzalez, M.; El-Halwagi, M. M., Optimal Reuse of Flowback Wastewater in Hydraulic Fracturing Including Seasonal and Environmental Constraints. Aiche J. 2016, 62 (5), 1634-1645. DOI: 10.1002/aic.15167. 44. Lira-Barragán, L. F.; Ponce-Ortega, J. M.; Guillén-Gosálbez, G.; El-Halwagi, M. M., Optimal Water Management under Uncertainty for Shale Gas Production. Ind. Eng. Chem. Res. 2016. DOI: 10.1021/acs.iecr.5b02748. 45. Foo, D. C. Y.; Manan, Z. A.; Tan, Y. L., Use cascade analysis to optimize water networks.

Chem. Eng. Prog. 2006, 102 (7), 45-52. 46. Zheng, P. Y.; Feng, X.; Qian, F.; Cao, D. L., Water system integration of a chemical plant.

Energy Conv. Manag. 2006, 47 (15-16), 2470-2478. DOI: 10.1016/j.enconman.2005.11.001. 47. Tovar-Facio, J.; Lira-Barragan, L. F.; Napoles-Rivera, F.; Bamufleh, H. S.; Ponce-Ortega, J. M.; El-Halwagi, M. M., Optimal Synthesis of Refinery Property-Based Water Networks with Electrocoagulation Treatment Systems. Acs Sustainable Chemistry & Engineering 2016, 4 (1), 147-158. DOI: 10.1021/acssuschemeng.5b00902. 48. Alwi, S. R. W.; Manan, Z. A.,

SHARPS: A new cost-screening technique to attain

48


Page 48 of 52

Page 49 of 52 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


gost-effective minimum water network. Aiche J. 2006, 52 (11), 3981-3988. DOI: 10.1002/aic.10986. 49. Tan, Y. L.; Manan, Z. A.,

Retrofit of water network with optimization of existing

regeneration units. Ind. Eng. Chem. Res. 2006, 45 (22), 7592-7602. DOI: 10.1021/ie060629w. 50. Chan, J. H.; Foo, D. C. Y.; Kumaresan, S.; Aziz, R. A.; Abu-Hassan, M. A., An integrated approach for water minimisation in a PVC manufacturing process. Clean Techn Environ Policy 2008, 10 (1), 67-79. DOI: 10.1007/s10098-007-0127-2. 51. Zhu, Q.; Zhang, B.; Chen, Q.; Pan, M.; Ren, J.; He, C., Optimal Synthesis of Water Networks for Addressing High-Concentration Wastewater in Coal-Based Chemical Plants. ACS

Sustainable

Chemistry

&

Engineering

2017.

5

(11),

10792–10805.

DOI:

10.1021/acssuschemeng.7b02758. 52. Jones, R. L. A water chemistry model for open recirculating cooling water systems. University of Houston, 1991. 53. Foo, D. C. Y.; Manan, Z. A.; El-Halwagi, M. M., Correct identification of limiting water data for water network synthesis. Clean Techn Environ Policy 2006, 8 (2), 96-104. DOI: 10.1007/s10098-006-0037-8. 54. Stijepovic, M. Z.; Linke, P.,

Optimal waste heat recovery and reuse in industrial zones.

Energy 2011, 36 (7), 4019-4031. DOI: 10.1016/j.energy.2011.04.048. 55. Alnouri, S. Y.; Linke, P.; El ‐ Halwagi, M.,

Optimal interplant water networks for

industrial zones: addressing interconnectivity options through pipeline merging. Aiche J. 2014, 60 (8), 2853-2874. DOI: 10.1002/aic.14516.

49



56. Wang, Y. P.; Smith, R., Wastewater minimisation. Chem. Eng. Sci. 1994, 49 (7), 981-1006. DOI: 10.1016/0009-2509(94)80006-5. 57. Feng, X.; Wang, N.; Chen, E., Water system integration in a catalyst plant. Chem. Eng. Res.

Des. 2006, 84 (A8), 645-651. DOI: 10.1205/cherd.05191. 58. China chemical industry standard, Reusing wastewater quality standard for industrial circulating cooling water. 2007; Vol. HG/T3923.

50


Page 50 of 52

Page 51 of 52 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Synopsis: The optimization of refinery water system leads to water conservation for sustainability.



Schematic diagram of general refinery water system 152x78mm (300 x 300 DPI)


Page 52 of 52

Deciphering Refinery Water System Design and Optimization

Recommend Documents