an integrated approach for water minimisation in a pvc

ORIGINAL PAPER

An integrated approach for water minimisation in a PVCmanufacturing process

Jun Hoa Chan Dominic Chwan Yee Foo Sivakumar Kumaresan Ramlan Abdul Aziz Mohd Ariffin Abu-Hassan

Received: 23 July 2007 / Accepted: 12 October 2007 / Published online: 30 November 2007

Springer-Verlag 2007

Abstract This paper presents a water minimisation study

carried out for a polyvinyl chloride (PVC) resins manu-

facturing plant. Due to the complexity of the mixed batch

and continuous polymerisation process, an integrated pro-

cess integration approach, which consists of process

synthesis, analysis and optimisation was used for this work.

A simulation model was first developed in a batch process

simulation software, SuperPro Designer V6.0, based on the

operating condition of a PVC manufacturing process. The

batch simulation model captured the essential information

needed for a water minimisation study, e.g. process

duration, water mass flow, etc. Data extracted from the

simulation model was later used in the water minimisation

study, utilising the widely established process synthesis

technique of water pinch analysis. Two water saving sce-

narios were presented. Scenario 1 reports a fresh water and

wastewater reduction of 28.5 and 90.1% respectively, for

the maximum water recovery scheme without water stor-

age system. In Scenario 2, higher fresh water and

wastewater reduction are reported at 31.7 and 100%

respectively, when water storage tank is installed in the

water network.

Keywords Process integration Water minimisation Pinch analysis Process modelling Batch processes Polymer manufacturing

Introduction

Recent development has seen the shift of the process

industries from conventional end-of-pipe waste treatment

to more sustainable pollution prevention or waste mini-

misation practises. Some of the factors that drive this shift

include environmental sustainability and stringent emission

legislations, as well as increasing cost of resources and

waste treatment. One of the active areas for waste mini-

misation activities has been that of in-plant water recovery.

The benefits of implementing water recovery are twofold.

Apart from the reduction of fresh water (and its associated

treatment), smaller volume of wastewater is generated.

The seminal work on insight-based techniques for water

network synthesis was initiated by Wang and Smith (1994),

who presented a two-stage pinch analysis technique for

fixed load problems, based on the more generalised mass

exchange network synthesis problems (El-Halwagi and

J. H. Chan

GTC Process Technology (Singapore) Pte. Ltd,

3 Science Park Drive, #01-08/09,

The Franklin 118223, Singapore

e-mail: [email protected]

D. C. Y. Foo (&)School of Chemical and Environmental Engineering,

University of Nottingham Malaysia, Broga Road,

43500 Semenyih, Selangor, Malaysia


S. Kumaresan

School of Engineering and Information Technology,

University Malaysia Sabah, Locked Bag 2073,

88999 Kota Kinabalu, Sabah, Malaysia


R. A. Aziz

Chemical Engineering Pilot Plant,

Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia


M. A. Abu-Hassan

Chemical Engineering Department,

Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia


123

Clean Techn Environ Policy (2008) 10:6779

DOI 10.1007/s10098-007-0127-2

Manousiouthakis 1989). In the first synthesis stage, limiting

composite curve is used to locate the minimum fresh water

and wastewater flowrate targets prior to detailed network

design (second stage of network synthesis). Flowrate con-

straints and multiple water sources were considered in their

later work (Wang and Smith 1995a). The basic concept

underlying these works is that all processes that use water

are modelled as mass transfer operations.

However, later works (e.g. Dhole et al. 1996; Hallale

2002; Manan et al. 2004) showed that representing all

processes that use water with mass transfer model might

not be always adequate. Some processes that use water e.g.

boiler blowdown, cooling tower make-up and reactor

effluent are typical examples where water quantity is more

important than the quality (always termed as fixed flowrate

problems). New graphical and numerical approaches have

been later developed to handle the fixed flowrate problems.

Some promising techniques in locating the minimum water

targets for the fixed flowrate problems include the graphi-

cal tools of water surplus diagram (Hallale 2002), material

recovery pinch diagram (El-Halwagi et al. 2003; Prakash

and Shenoy 2005a), source composite curve (Bandyopad-

hyay et al. 2006; Bandyopadhyay 2006) and the improved

limiting composite curve (Agrawal and Shenoy 2006), as

well as the numerical targeting tools such as cascade

analysis techniques (Manan et al. 2004; Almutlaq et al.

2005; Foo et al. 2006a; Almutlaq and El-Halwagi 2007;

Foo 2007a, b).

Apart from the targeting stage, numerous techniques

have also been proposed for the systematic design of water

network, i.e. second stage of the synthesis task. This

includes water grid diagram (Wang and Smith 1994), load

table (Olesen and Polley 1996, 1997), water main method

(Kuo and Smith 1998; Castro et al. 1999; Feng and Seider

2001), water source diagram procedure (Gomes et al. 2007)

for fixed load problems; as well as source sink mapping

diagram (El-Halwagi 1997), nearest neighbour algorithm

(Prakash and Shenoy 2005a) as well as the load problem

table (Aly et al. 2005) for fixed flowrate problems. Once

the flowrate targets are established, the water network is

designed to achieve the minimum targets using any of the

above-mentioned design tools. Subsequently, a preliminary

synthesised network can be evolved to yield a simplified

network (Prakash and Shenoy 2005b; Ng and Foo 2006).

In contrast to the active evolvement of water network

synthesis techniques in continuous processes, water mini-

misation for batch processes has received far less attention

due to its nature of complexity. Only recently, similar work

has received considerable attention for batch water net-

work. Wang and Smith (1995b) initiated the work in batch

water network synthesis that focused on mass transfer-

based processes that use water. Majozi et al. (2006)

developed a similar technique to account for batch

processes where no water intake and discharge are per-

mitted during the operation. The shortcoming of both these

approaches is their limitation in handling fixed load prob-

lems. To overcome the limitation of the fixed load

problems, Foo et al. (2005) proposed the use of water

cascade analysis technique that was originally developed

for water network for continuous processes (Manan et al.

2004; Foo et al. 2006a) in handling the fixed flowrate

problems. Other works on batch water network synthesis

are mainly dominated by various mathematical optimisa-

tion approaches (Almato et al. 1997, 1999a, 1999b;

Puigjaner et al. 2000; Kim and Smith 2004; Li and Chang

2006; Majozi 2005a, 2005b, 2006; Shoaib et al. 2007).

It should be noted that most of the reported works on

batch water network synthesis have utilised hypothetical

examples for demonstration. To date, limited numbers of

industrial cases have been reported, in both pinch-based

and mathematical-based works. This includes production

of concentrated fruit juice (Almato et al. 1997) and agro-

chemicals (Majozi et al. 2006). More industrial case studies

are needed to help in the development of new water min-

imisation techniques that are of practical use. This is the

subject of this paper. El-Halwagi (1997) describes that a

process integration study consists of three elements: i.e.

process synthesis, analysis and optimisation. In this work,

an integrated process integration approach has been used.

The process synthesis element consists of water network

synthesis, via pinch analysis technique while process

analysis and optimisation were carried out by batch process

simulation and modelling studies.

In the following section, the water cascade analysis

technique (Manan et al. 2004; Foo et al. 2006a) for locating

the minimum targets for a water network is first described.

This is followed by the description of a PVC manufactur-

ing case study. A simulation model that was developed

based on the plant operation data is next described. Batch

process simulation overcomes the constraint of the mixed-

batch and continuous operation mode in the polymer

manufacturing process. Data extracted from the simulation

model are later used in the water minimisation study car-

ried out using pinch analysis technique.

Water cascade analysis technique

The generic water cascade table (WCT) in Table 1 sum-

marises how WCA is carried out for water targeting in the

reuse/recycle scheme (Manan et al. 2004; Foo et al. 2006a).

The first step in conducting a WCA is to locate the various

water sinks and sources at their respective concentration

levels. As shown in the first two columns of Table 1, the

concentration levels (Ck) are arranged in an ascending

order (k = 1, 2, , n), and the flow of water sink (Fj) and

68 J. H. Chan et al.

123

source (Fi) are summed at their respective concentration

level k in columns 3 and 4. Column 5 represents the net

flow, (RiFi - RjFj) between water sources and sinks ateach concentration level k; with positive indicating surplus,

negative indicating deficit. Next, the net water flow sur-

plus/deficit is cascaded down the concentration levels to

yield the cumulative surplus/deficit flow (FC, k) in column 6

with an assumed zero fresh water flow (FFW = 0). This

assumed flow is to facilitate the search for the minimum

water flow and will later be replaced once the rigorous

fresh water target is located.

Two important parameters to fulfil in water network

targeting are the water flow and impurity load constraints

(Foo et al. 2006a). Water flow constraints are fulfilled once

the above-described flow cascading is carried out. The next

step involves setting up the cumulative impurity load cas-

cade (Cum. Dm) to fulfil the load constraint. Impurity loadin column 7 (Dmk) is obtained via the product of cumula-tive flow (FC, k) and the concentration difference across

two subsequent concentration levels (Ck+1 - Ck). Cascad-

ing the impurity load down the concentration levels of

column 8 yields the cumulative load (Cum. Dmk), which isessentially the numerical equivalent of graphical targeting

tool of water surplus diagram (Hallale 2002). A feasible

water network is characterised by the presence of only

positive values of Cum. Dm in column 8. A negative Cum.Dmk means the impurity load is transferred from lower tohigher concentration level, which is infeasible. In such a

case, an interval fresh water flow (FFW, k, column 9) is

calculated by dividing Cum. Dmk by the concentrationdifference between level k (Ck) and the fresh water con-

centration (CFW) i.e.,

FFW;k Cum. DmkCk CFW : 1

The absolute value of the largest negative FFW, k will then

replace the earlier assumed zero fresh water flow in the

cumulative flow (column 6) to obtain a new set of feasible

flow cascade and hence a feasible load cascade. This new

fresh water flow represents the minimum fresh water flow

(FFW) of the network; while the final row in column 6

represents the wastewater flow (FWW) generated from the

network. The network pinch concentration is the impurity

concentration with zero Cum. Dmk, while the water sourcethat exists at the pinch is called the pinch-causing source

(Manan et al. 2004; Foo et al. 2006a). Note that once the

flow and impurity load cascading are carried out using the

minimum fresh water flow, the final column of Table 1 is

omitted.

The above-described procedure for locating the mini-

mum water targets was original developed for water

networks operated in continuous mode (Manan et al. 2004;

Foo et al. 2006a), in which water flowrate is used instead of

water flow. Foo et al. (2005) later extended the targeting

tool for batch water network known as time-dependent

water cascade analysis. By segregating water sinks and

sources into the time interval when they exist, cascade

analysis is carried out in each time interval to locate the

minimum water targets. More recently, the targeting

technique was extended to multiple fresh resources (Foo

2007a) and threshold (Foo 2007b) problems.

Case study on PVC manufacturing

Figure 1 shows the process flow diagram for PVC resins

manufacture. It consists of ten parallel batch polymerisa-

tion reactors, blow-down vessel, and two parallel trains of

downstream processing equipment. Each downstream

processing train consists of a stripper, two decanters and a

fluid bed dryer. The plant is operated in a semi-batch mode

where upstream polymerisation reactors are scheduled to

match the downstream processing trains that are operated

in continuous mode. The polymerisation process is carried

Table 1 The generic water cascade table (WCT)

k Ck RjFj RiFi RiFi - RjFj FC,k Dmk Cum. Dmk FFW,k

FFW

k Ck (RjFj)k (RiFi)k (RiFi - RjFj)kFC,k Dmk

k + 1 Ck+1 (RjFj)k+1 (RiFi)k+1 (RiFi - RjFj)k+1 Cum. Dmk+1 FFW,k+1FC,k+1 Dmk+1

..

. ... ..

. ... ..

. ... ..

. ... ..

.

n - 2 Cn-2 (RjFj)n-2 (Ri Fi)n-2 (RiFi - RjFj)n-2FC,n-2 Dn-2

n - 1 Cn-1 (RjFj)n-1 (RiFi)n-1 (RiFi -Rj Fj) n-1 Cum. Dm n-1 FFW,n-1FC,n-1 = FWW Dmn-1

n Cn Cum. Dmn FFW,n

An integrated approach for water minimisation in a PVC manufacturing process 69

123

out subsequently in ten parallel batch reactors. Raw

materials for the polymerisation process include deminer-

alised water, mixture of fresh vinyl chloride monomer

(VCM) and recycled VCM (mainly from blowdown vessel

and stripper), initiators and suspending agents, which are

charged into the reactor in sequence.

The suspending agent is diluted to approximately

4.0 wt% and is charged into the reactor together with

demineralised water. This is mainly due to the high vis-

cosity of the suspending agent solution. Upon completion

of the suspending agent charge, primary and secondary

initiators are added into the reactor. It shall also be noted

that, due to piping constraints, demineralised water can

only be charged into the reactors one at a time. Charging

procedure in each of the reactor will only start after the end

of charging in an earlier reactor. The same situation occurs

to the charging of VCM and the blowdown operation after

the reaction ends.

Upon completion of water charge, the reactor is purged

by nitrogen and then vacuumed before charging of VCM is

commenced. This is to ensure that no oxygen is present in

the reactor, as reaction between VCM and oxygen might

result in an explosion. Upon the completion of VCM

charge, heating is commenced to raise the reactor to the

operating temperature and pressure. Exothermic polymer-

isation process next commences for a duration of 7.5 h.

Continuous cooling is needed to remove excess heat and to

maintain the reactor temperature. A huge amount of chilled

water is added towards the end of the polymerisation

process to maintain the reactor temperature. PVC polymer

is the main product at the end of the polymerisation pro-

cess. Other by-products of the reaction are the unreacted

VCM, initiators, suspending agent and some trace amount

of contaminant. The final reactor content is in slurry form

(mainly consists of PVC and water).

The blowdown vessel receives batches of slurry, which

are intermittently discharged from the reactors. The

blowdown vessel acts as buffer storage so that slurry can be

continuously fed to the two parallel downstream processing

trains. As the slurry enters the blowdown vessel, the major

portion of the unreacted VCM trapped in the PVC particles

flashes off and is vented to the gas holder for recovery. The

VCM vapour is next compressed and condensed into liquid

form before it is sent for reuse in the polymerisation

reactors.

As the effluent from the blowdown vessel enters the

downstream processing trains, the stripper column further

removes the unreacted VCM from the PVC slurry. Steam is

introduced at the base of the stripping column and the

VCM is removed as an overhead product to the gas holder.

The stripped slurry is then directed to two parallel

decanters for water removal. Approximately 80% of water

is removed by these decanters from the PVC slurry. Wet

cake from the decanters is dried in the fluidised bed dryer.

Dried PVC resins are transported to the shifter. Fine resins

that pass through the shifter are sold as product, while the

course resins are ground and sold as low-grade resins.

Process simulation model

Figure 2 shows the base case process simulation model that

has been developed based on the operating condition of the

PVC manufacturing process using SuperPro Designer V6.0

(Intelligen 2005). The annual operating time of the process

model is taken as 7920 h. In the modelling environment of

SuperPro Designer, a few operations take place sequen-

tially in a single unit procedure (Intellegen 2005). For

instance, vessel procedure P-1 in Fig. 2 is used to model

the batch polymerisation procedure that takes place in

vessel K-207A. The polymerisation procedure consists of

sequential operations of raw material charges, vacuum

operation, material heating, polymerisation as well as

product blowdown. The modelling of these single opera-

tions is described next.

The raw material charged into the reactor during the

start of procedure P-1 includes demineralised water

(CHARGE-H2O), suspending agent (CHARGE-PVA),

primary initiator (CHARGE-Init1) and secondary initiator

(CHARGE-Init2). Note that the model was developed

based on the operating condition of the manufacturing

process, where the charging of demineralised water for a

subsequent reactor is scheduled to start after the water

charging of the preceding reactor ends. Suspending agent

and initiators are charged into the reactor during the

charging of water.

The vacuum operations (VACUUM) in procedure P-1

include a vacuum air purge, followed by a nitrogen gas

purge and finally another vacuum purge. The vacuum

operations were carried out before the charging of VCM

(CHARGE-VCM), to avoid the mixing of VCM with air,

which will lead to explosion.

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123

During the heating operation, hot water is used to raise

the reactor temperature (HEAT-1) to 60C where thepolymerisation process (REACT-1) is carried out. The

polymerisation reaction operation takes an overall duration

of 7.5 h. Chilled water is added to the reactor (CHARGE-

Chilled H2O) in the last half-an-hour just before the

polymerisation reaction ends. Stoichiometric reaction

model is used and the reaction stoichiometric is defined as

the conversion of one mass of VCM to one mass of PVC,

as follows:

nVCM ! PVCn: 2Upon the completion of the polymerisation process, the

slurry reactor content is discharged (TRANS-OUT) to the

blowdown procedure P-14 (in vessel V-101). Similar to the

case of demineralised water charge, blowdown operation of

a subsequent reactor is also scheduled to start after the

completion of a preceding reactor, due to the piping con-

straint in the PVC plant. The last operation in the vessel

procedure is the cleaning process. The scheduling summary

for all reaction procedures (P-1 to P-10) is displayed in the

process Gantt chart as shown in Fig. 3.

From the blowdown vessel, the slurry product enters

into two parallel identical and continuous downstream

processing trains, which individually consist of a stripper,

slurry tank, two parallel identical decanters and a fluid-bed

dryer. Thus, no scheduling is needed in these processes.

The modelling specifications of each unit procedure in

these downstream processing trains are next described.

Stripper procedures (P-16 and P-17) are used for VCM

removal. Steam at 110C is introduced into the strippers toachieve a VCM removal of 99.9%, before the PVC slurry

enters into two parallel identical decanters (P-18 and P-19,

each represents two identical decanters). Solid removal in

the decanters is specified as 99% for PVC, with water loss

of 21%. Effluent emits from these decanter units (WW A-D)

is the main wastewater of the process, while the wet filter

cake is sent to fluidised-bed dryers for moisture removal. In

the fluidised-bed dryers (P-20 and P-21), hot air at 95C isused to dry the wet filter cake. Evaporation rate of water and

the final solid temperature are set at 916 kg H2O/h and 60Crespectively. As a result, 99.8% of the moisture (the com-

ponent of water and chilled water) is removed.

From the simulation model, the minimum cycle time for

the process is determined to be 11.78 h corresponds to the

completion of the last reactor (P-10/K-207J). Based on the

annual operating time of 7,920 h, this translates into 671

batches/year. It should also be noted that all vessel pro-

cedures (P-1 to P-10) are observed to have different cycle

times (see Gantt chart in Fig. 3). This is mainly due to the

scheduling of the TRANS-OUT operations in each of the

reactor procedures (P-1 to P-10). Since TRANS-OUT

operation in each of the reactor is scheduled to start after

the TRANS-OUT operation of the preceding reactor ends,

this creates a lag time for the product to be discharged from

the subsequent reactor before the preceding reactor com-

pletes its TRANS-OUT operation. A good example is

shown in Fig. 3. As shown, the polymerisation reaction

Fig. 2 Simulation flowsheet of PVC manufacturing


123

(REACT-1) in P-2/K-207B is completed at 9.62 h. How-

ever, due to the TRANS-OUT operation of P-1/K-207A

being still in operation, TRANS-OUT operation in P-2/K-

207B cannot be started until the TRANS-OUT operation in

P-1/K-207A ends at 9.78 h.

Water minimisation study

This section presents water minimisation study carried out

for the PVC manufacturing process. Figure 4 shows the

water usage diagram for the process. This includes the PVC

manufacturing as well as the utility sections.

As shown in Fig. 4, the process receives fresh water

supply from the state water supplier. Since the PVC man-

ufacturing process requires feed water that is free of

hardness, the fresh water is further treated in a deionised

plant (DI plant). Feed water to the utility section (boiler

and cooling tower make-up) and general cleaning purposes

tolerate a lower quality of water, and hence treatment in the

DI plant has been avoided.

The only water source for the process originates from

the continuous discharge of the four parallel decanters

(WW A-D), which are used to dewater the slurry effluent

from the strippers. The total flowrate of these water sources

are determined to be 9,243.9 kg/h. Since the four water

sources have the same characteristics, they are treated as a

single water source. The main impurity that is of concern

for water reuse/recycling activity is the suspended solid

content in the wastewater at a concentration of 50 ppm.

Water sinks that are considered for water reuse/recycle

include the PVC manufacturing process (mainly for reactor

feed), utility section (boiler and cooling tower make-up)

and miscellaneous usage (e.g. floor cleaning, etc.).

Limiting water data extraction

Data extraction is considered to be the most essential step

of a water minimisation study. Incorrect extraction of data

leads to sub-optimal water network with reduced water

saving targets (Foo et al. 2006b). For a batch water net-

work, apart from the limiting water flow and concentration,

the occurrences of the processes that use water are to be

identified. In this work, the start and end time as well as the

duration for each processes that use water are identified

with the aid of the base case simulation model.

Table 2 summarises the limiting data for a single batch

of polymerisation process. However, if one were to use this

set of limiting data for the analysis, one may lose the

greater water saving potential. This is because the raw data

extracted from the simulation study does not reflect how

the batch polymerisation process are carried out in actual

operation. For most industrial batch processes, the

Fig. 3 Process Gantt chart for base case simulation


123

operations are repeated (as cyclic mode) once the equip-

ment has completed its earlier operation. Figure 5 shows

such a situation, where three consequent batches of pro-

cesses occur simultaneously within the time frame of a

single batch N. As shown, batch (N - 1) completed its

operations after 4.5 h batch N commences its operation;

while batch (N + 1) begins its operations 4.5 h before

batch N ends. The revised limiting data is summarised in

Table 3.

It should also be noted that some operations in Fig. 5

and Table 3 are carried out in sequence. For instance,

reactor feed operation (SK1) for one reactor takes 20 min

to complete (CHARGE-H2O as in Fig. 3), hence a total of

200 min or 3.33 h would be needed for 10 reactors to

complete their reactor feed operations carried out

subsequently (Fig. 5). The same situation occurs to line

flushing (SK2), reactor cleaning (SK5) and flushing (SK7),

in which each individual operation for each reactor takes

0.5 h to complete.

Two water saving scenarios with and without water

storage system were analysed using the time-dependent

water cascade analysis technique (Foo et al. 2005). The

first stage of the analysis corresponds to the allocation of

the water sinks and sources in their respective time inter-

vals. The time interval table in Table 4 is utilised for this

preliminary analysis. As shown, there are 13 time intervals

and all water sinks are located in their respective time

intervals according to their occurrence time. The sum of

the individual water flow of the water sinks gives the total

water requirement of each time interval. The flowrate of

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Table 2 Raw data extracted from base case simulation

Sinks Operation Flowrate Concentration Start time End time Mass flow

SKj (kg/h) Cj (ppm) tS (h) tT (h) Fj (kg)

1 Reactor feed 31,531.53 10 0 3.33 105,000

2 Line flushing 3,125.00 10 9.28 14.28 15,625

3 Boiler make-up 1 13,200.00 10 0 0.25 3,300

4 Boiler make-up 2 13,200.00 10 8 8.25 3,300

5 Reactor cleaning 1,200 20 9.78 14.78 6,000

6 Cooling tower 6,300.00 50 0 14.78 93,114

7 Flushing 2,000.00 200 9.28 14.28 10,000

8 Floor cleaning 1 6,000.00 500 0 0.25 1,500

9 Floor cleaning 2 6,000.00 500 8 8.25 1,500

RjSKj 82,556.53 239,339

Sources Operation Flowrate Concentration Start time End time Mass flow

SRi (kg/h) Ci (ppm) tS (h) tT (h) Fi (kg)

RjSRi Decanter 9,243.90 50 0 14.78 136,625


123

the continuous water source (SR1 from decanter) is also

scaled according to the duration of each time interval. Both

water saving scenarios are analysed as follows.

Scenario 1: water network without water storage system

In Scenario 1, no water storage tank is installed for the batch

water network. Hence, water saving can only be carried out

via direct integration, i.e. when both water sinks and sources

present together within the same time interval.

Table 5 shows two examples of flow targeting for time

interval 00.25 h and 9.289.78 h. Due to the overlapping

between two consequent batches, water sinks from the

earlier batch (SK2, SK5, SK6 and SK7) and current batch

(SK1, SK3, SK6 and SK8) operations are observed

between 0 and 0.25 h. The water source for this interval is

available at 2,311 kg and 50 ppm ( column 3 in Table 4).

Without considering for water reuse/recycle, this interval

requires 17,414 kg of fresh water and generates 2,311 kg

of wastewater. As shown in Table 5, maximum water

recovery enables the fresh water (FFW) to be reduced to

15,103 kg and wastewater is completely eliminated

(FWW = 0 kg). Another characteristic to be noted in

Table 5 is that water targeting this time interval exhibits a

threshold problem. Hence, flow adjustment is needed for

the flow cascade, as reflected in the adjusted cumulative

flow (Adj. FC) column in Table 5. Detailed description for

the threshold problem is outlined in Foo (2007b).

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Fig. 5 The overlap of three consequent batches of polymerisationprocesses (only water sinks are shown)

Table 3 Limiting data that takes into consideration repeated batch processes

Number of

batches

Sinks SKj Operation Flowrate

(kg/h)

Concentration

Cj (ppm)Start time

tS (h)End time

tT (h)Mass flow

Fj (kg)

N - 1 2a Line flushing 3,125.00 10 0 4 12,500

5a Reactor cleaning 1,200 20 0 4.5 5,400

6a Cooling tower 6,300.00 50 0 4.5 28,350

7a Flushing 2,000.00 200 0 4 8,000

1b Reactor feed 31,531.53 10 0 3.33 105,000

2b Line flushing 3,125.00 10 9.28 14.28 15,625

3b Boiler make-up 1 13,200.00 10 0 0.25 3,300

4b Boiler make-up 2 13,200.00 10 8.00 8.25 3,300

N 5b Reactor cleaning 1,200 20 9.78 14.78 6,000

6b Cooling tower 6,300.00 50 0 14.78 93,114

7b Flushing 2,000.00 200 9.28 14.28 10,000

8b Floor cleaning 1 6,000.00 500 0 0.25 1,500

9b Floor cleaning 2 6,000.00 500 8.00 8.25 1,500

1c Reactor feed 31,531.53 10 10.28 13.61 105,000

N + 1 3c Boiler make-up 1 13,200.00 10 10.28 10.53 3,300

6c Cooling tower 6,300.00 50 10.28 14.78 28,350

8c Floor cleaning 1 6,000.00 500 10.28 10.53 1,500

RjSKj 152,213 431,739

Sources SKi Operation Flowrate (kg/h) Concentration Ci (ppm) Start time tS (h) End time tT (h) Mass flow Fi (kg)

Continuous 1 Decanter 9,243.90 50 0 14.78 136,625

RjSKi 9,243.90 136,625


123

Table 4 Time interval table for case study on PVC manufacturing

SKj Flowrate (kg/h) Start time, tS (h)

0 0.25 3.33 4 4.5 8 8.25 9.28 9.78 10.28 10.53 13.61 14.28

2a 3,125 781 9,625 2,094

5a 1,200 300 3,696 804 600

6a 6,300 1,575 19,404 4,221 3,150

7a 2,000 500 6,160 1,340

1b 31,532 7,883 97,117

2b 3,125 1,563 1,563 781 9,625 2,094

3b 13,200 3,300

4b 13,200 3,300

5b 1,200 600 300 3,696 804 600

6b 6,300 1,575 19,404 4,221 3,150 22,050 1,575 6,489 3,150 3,150 1,575 19,404 4,221 3,150

7b 2,000 1,000 1,000 500 6,160 1,340

8b 6,000 1,500

9b 6,000 1,500

1c 31,532 7,883 97,117

3c 13,200 3,300

6c 6,300 1,575 19,404 4,221 3,150

8c 6,000 1,500

SRi Flowrate

1 9,244 2,311 28,471 6,193 4,622 32,354 2,311 9,521 4,622 4,622 2,311 28,471 6,193 4,622

Table 5 Cascade analysis for water flow targeting (00.25 h)

Ci(ppm)

RjFj(kg)

RiFi(kg)

RiFi - RjFj(kg)

FC(kg)

Dm(mg)

Cum. Dm(mg)

FFW,k(kg)

FC(kg)

Adj. FC(kg)

Dm(mg)

Cum. Dm(mg)

0

0 15,102 FFW = 15,103 151,032

10 11,964 -11,964 151,032

-11,964 -119,641 3,138 3139 31,390

20 300 -300 -119,641 -5,982 182,422

-12,264 -367,924 2,838 2839 85,171

50 3150 2,311 -839 -487,565 -9,751 267,593

-13,103 -1,965,474 1,999 2000 300,000

200 500 -500 -2,453,039 -12,265 567,593

-13,603 -4,080,947 1,499 1500 450,000

500 1,500 -1,500 -6,533,986 -13,068 1,017,593

-15,103 -15,095,605,921 -1 FWW = 0 0

1,000,000 -15,102,139,907 -15,102 1,017,593

Table 6 Cascade analysis for water flow targeting (9.289.78 h)

Ci(ppm)

RjFj(kg)

RiFi(kg)

RiFi - RjFj(kg)

FC(kg)

Dm(mg)

Cum. Dm(mg)

FFW,k(kg)

FC(kg)

Dm(mg)

Cum. Dm(mg)

0

0 FFW = 1,250 12,500

10 1,563 -1,563 12,500

-1,563 -15,625 -313 -3,125

20 0 -15,625 -781 9,375

-1,563 -46,875 -313 -9,375

50 3,150 4,622 1,472 -62,500 -1,250 0

-91 -13,583 1,159 173,918 (PINCH)

200 1,000 -1,000 -76,083 -380 173,918

-1,091 -327,165 159 47,835

500 0 -403,248 -806 221,753

-1,091 -1,090,004,725 FWW = 159 159,370,275

1,000,000 -1,090,407,973 -1,090 159,592,028


123

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3h

10

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1

3.6

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13

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1

4.2

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14

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1

4.7

8h

Ci

(pp

m)

RiF

i-

RjF

j

(kg

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FC

(kg

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RiF

i-

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15

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31

26

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0

4.5

0h

4.5

0

8.0

0h

8.0

0

8.2

5h

8.2

5

9.2

8h

Ci

(pp

m)

RiF

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RiF

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j

(kg

)

FC

(kg

)

RiF

i-

RjF

j

(kg

)

FC

(kg

)

(a)

0

9.2

8h

0

15

,10

31

26

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56

,48

62

,27

80

26

40

0

10

-1

1,9

64

-1

06

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2,0

94

00

-3

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00

3,1

39

20

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34

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32

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80

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60

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0

20

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00

-3

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80

4-

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00

00

2,8

39

16

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73

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(pp

m)

RiF

i-

RjF

j

(kg

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FC

(kg

)

RiF

i-

RjF

j

(kg

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FC

(kg

)

RiF

i-

RjF

j

(kg

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FC

(kg

)

RiF

i-

RjF

j

(kg

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FC

(kg

)

RiF

i-

RjF

j

(kg

)

FC

(kg

)

RiF

i-

RjF

j

(kg

)

FC

(kg

)

(b)

9.2

8

14

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h

0

1,2

50

16

10

98

51

12

0,1

96

6,4

86

2,2

78

RF

FW

=2

95

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4

10

-1

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3-

1,5

63

-1

1,9

64

-1

06

,74

2-

2,0

94

0

-3

13

48

-2

11

31

3,4

54

4,3

93

2,2

78

20

0-

60

0-

30

0-

3,6

96

-8

04

-6

00

-3

13

-5

53

-2

41

39

,75

83

,58

91

,67

8

50

1,4

72

1,4

72

-8

39

-1

0,3

37

-2

,24

9-

1,6

78

1,0

00

10

00

20

00

6,1

60

1,3

40

0

20

0-

1,0

00

-1

,00

0-

50

0-

6,1

60

-1

,34

00

00

15

00

00

0

50

00

0-

1,5

00

00

0

00

00

00

RF

WW

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0


123

On the other hand, only three water sinks from current

operation are observed between 9.28 and 9.78 h (observed

from Table 4). As shown in Table 6, targeting for this time

interval yields fresh water and wastewater flows of 1,250

and 159 kg, respectively, with a pinch concentration

observed at 50 ppm.

Similar water flows targeting were carried out for all

other time intervals, and the summary is shown in Table 7

(only net water flow and cumulative flow are shown). The

final column of Table 7 shows that the total fresh water and

wastewater flows for the network are determined to be

308,609 and 13,495 kg respectively. This corresponds to a

reduction of 28.5% fresh water and 90.1% of wastewater,

compared to current water network without any water

reuse/recycle practice.

Scenario 2:water network with water storage system

In Scenario 1, due to the absence of a water storage tank,

wastewater are discharged at certain time intervals without

being further utilised. This includes time intervals of 4.50

8.00, 8.259.28 and 9.289.78 h. By utilising a water

storage tank, wastewater at these time intervals may be

stored for further usage at time intervals that exhibit water

deficit. Table 8 shows how this indirect integration scheme

is materialised. The last row of Table 8 displays the water

flow (FST) that enters or leaves the water storage. As

shown, water is stored between intervals that generate

wastewater (given as positive FST value) and utilised at

other intervals (given as negative FST value) to reduce their

fresh water demand. As such, the fresh water flow is

reduced to 295,114 kg (31.7% reduction), while wastewa-

ter flow is completely removed. A cumulative water mass

chart is shown in Fig. 6, indicating that a tank with a

capacity of 12 tons is needed to achieve the maximum

water recovery as in Scenario 2. The flow sheet for the final

water reuse/recycling scheme is shown in Fig. 7.

Fig. 6 Cumulative mass in water storage tank

Fig. 7 Flow sheet after water reuse/recycling


123

The decision as to which scenarios would be imple-

mented eventually will rely on the decision of the plant

authority after a detailed economic assessment of each

scenario, which is beyond the scope of this work.

Conclusion

A water minimisation scheme was developed for a PVC

manufacturing case study. An integrated approach that

consists of process synthesis, analysis and optimisation

steps was utilised to identify the true minimum water tar-

gets for the water network that is operated in mixed batch

and in continuous mode. Two water saving scenarios were

presented to analyse the maximum water saving potential

of the process.

Acknowledgment The technical assistance of Mr. Yoeng CheeNyok is gratefully acknowledged.

References

Agrawal V, Shenoy UV (2006) Unified conceptual approach to

targeting and design of water and hydrogen networks. AIChE J

52(3):10711081

Almato M, Sanmart E, Espuna A, Puigjaner L (1997) Rationalizing

the water use in the batch process industry. Comput Chem Eng

21:S971S976

Almato M, Espuna A, Puigjaner L (1999a) Optimisation of water use

in batch process industries. Comp Chem Eng 23:14271437

Almato M, Sanmat E, Espuna A, Puigjaner L (1999b) Economic

optimization of the water reuse network in batch process

industries. Comput Chem Eng 23:S153S156

Almutlaq AM, Kazantzi V, El-Halwagi MM (2005) An algebraic

approach to targeting waste discharge and impure fresh usage via

material recycle/reuse networks. Clean Technol Environ Policy

7(4):294305

Almutlaq AM, El-Halwagi MM (2007) An algebraic targeting

approach to resource conservation via material recycle/reuse.

Int J Environ Poll 29(1/2/3):418

Aly S, Abeer S, Awad M (2005) A new systematic approach for water

network design. Clean Technol Environ Policy 7(3):154161

Bandyopadhyay S, Ghanekar MD, Pillai HK (2006) Process water

management. Ind Eng Chem Res 45:52875297

Bandyopadhyay S (2006) Source composite curve for waste reduc-

tion. Chem Eng J 125:99110

Castro P, Matos H, Fernandes MC, Nunes CP (1999) Improvements

for mass-exchange networks design. Chem Eng Sci 54:1649

1665

Dhole VR, Ramchandani N, Tainsh RA, Wasilewski M (1996) Make

your process water pay for itself. Chem Eng 103:100103

El-Halwagi MM, Manousiouthakis V (1989) Synthesis of mass

exchange networks. AIChE J 35(8):12331244

El-Halwagi MM (1997) Pollution prevention through process inte-

gration: systematic design tools. Academic Press, San Diego

El-Halwagi MM, Gabriel F, Harell D (2003) Rigorous graphical

targeting for resource conservation via material recycle/reuse

networks. Ind Eng Chem Res 42:43194328

Feng X, Seider WD (2001) New structure and design method for

water networks. Ind Eng Chem Res 40:61406146

Foo DCY, Manan ZA, Tan YL (2005) Synthesis of maximum water

recovery network for batch process systems. J Clean Prod

13(15):13811394

Foo DCY, Manan ZA, Tan YL (2006a) Use cascade analysis to

optimize water networks. Chem Eng Prog 102(7):4552

Foo DCY, Manan ZA, El-Halwagi MM (2006b) Correct identification

of limiting water data for water network synthesis. Clean

Technol Environ Policy 8(2):96104

Foo DCY (2007a) Water cascade analysis for single and multiple

impure fresh water feed. Chem Eng Res Des 85(A8):11691177

Foo DCY (2007b) Flowrate targeting for threshold problems and

plant-wide integration for water network synthesis. J Environ

Manage (in press)

Gomes JFS, Queiroz EM, Pessoa FLP (2007) Design procedure for

water/wastewater minimization: single contaminant. J Clean

Prod 15:474485

Hallale N (2002) A new graphical targeting method for water

minimisation. Adv Environ Res 6(3):377390

Intelligen Inc. (2005) SuperPro Designer1 users guide. Intelligen

Inc., Scotch Plains, New Jersey

Kim J-K, Smith R (2004) Automated design of discontinuous water

systems. Trans Inst Chem Eng, Part B 82(B3):238248

Kuo W-CJ, Smith R (1998) Designing for the interactions between

water use and effluent treatment. Trans Inst Chem Eng, Part A,

287301

Li BH, Chang C (2006) A mathematical programming model for

discontinuous water-reuse system design. Ind Eng Chem Res

45:50275036

Majozi T, Brouckaert CJ, Buckley CA (2006) A graphical technique

for wastewater minimisation in batch processes. J Environ

Manage 78:317329

Majozi T (2005a) Wastewater minimisation using central reusable

water storage in batch plants. Comp Chem Eng 29:16311646

Majozi T (2005b) An effective technique for wastewater minimisa-

tion in batch processes. J Clean Prod 13(15):13741380

Majozi T (2006) Storage design for maximum wastewater reuse in

multipurpose batch plants. Ind Eng Chem Res 45:59365943

Manan ZA, Tan YL, Foo DCY (2004) Targeting the minimum water

flowrate using water cascade analysis technique. AIChE J

50(12):31693183

Ng DKS, Foo DCY (2006) Evolution of water network with improved

source shift algorithm and water path analysis. Ind Eng Chem

Res 45:80958104

Olesen SG, Polley GT (1996) Dealing with plant geography and

piping constraints in water network design. Trans Inst Chem

Eng, Part B 74:273276

Olesen SG, Polley GT (1997) A simple methodology for the design of

water networks handling single contaminants. Trans Inst Chem

Eng, Part A 75:420426

Prakash R, Shenoy UV (2005a) Targeting and design of water

networks for fixed flowrate and fixed contaminant load opera-

tions. Chem Eng Sci 60(1):255268

Prakash R, Shenoy UV (2005b) Design and evolution of water

networks by source shifts. Chem Eng Sci 60(7):20892093

Puigjaner L, Espuna A, Almato M (2000) A software tool for helping

in decision-making about water management in batch process

industries. Waste Manage 20:645649

Shoaib AM, Aly SM, Awad ME, Foo DCY, El-Halwagi MM (2007)

A hierarchical approach for the synthesis of batch water network.

Comp Chem Eng (in press)

Wang YP, Smith R (1994) Wastewater minimisation. Chem Eng Sci

49:9811006

Wang YP, Smith R (1995a) Wastewater minimization with flowrate

constraints. Trans Inst Chem Eng, Part A 73:889904

Wang YP, Smith R (1995b) Time Pinch Analysis. Trans Inst Chem

Eng, Part A 73:905914


123

An integrated approach for water minimisation in a PVC manufacturing processAbstractIntroductionWater cascade analysis techniqueCase study on PVC manufacturing Process simulation modelWater minimisation studyLimiting water data extractionScenario 1: water network without water storage systemScenario 2:water network with water storage system

ConclusionAcknowledgmentReferences

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an integrated approach for water minimisation in a pvc

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