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Energy 32 (2007) 983998

Step-by-step process integration method for the improvements and

optimization of sodium tripolyphosphate process plant

Predrag Raskovic

Faculty of Technology, University of Nis, Bulevar OsloboXenja 124, 18000 L eskovac, Serbia

Abstract

Improvement and optimization of a complex chemical plant motivated by energy savings is presented in this paper. The referenceobject of the research is the sodium tripolyphosphate plant in IHP-Prahovo, the biggest factory for producing chemical products in

Serbia and Montenegro. The research is done by using a step-by-step process integration approach, which combines several computer-

based simulation/optimization methodologies. The evaluation of obtained results indicates considerable possibilities for plant efficiency

improvement.

r 2006 Elsevier Ltd. All rights reserved.

Keywords: Energy efficiency; Process integration; Sodium tripolyphosphate; Software tool

1. Introduction

The improvement of energy system efficiency is covering

a variety of actions and approaches. These operations ingeneral can be described by the tasks of changing the

manufacture procedures and improvement of control, unit

operations and system integration. One of the methods,

which play an important role in the effort to improve the

energy efficiency of industrial plants is Process Integration(PI).

PI can be defined very widely as design, operation and

management of industrial processes with system-oriented

and integrated methods, models and tools. Such definition

insinuates that proper use of PI methods includes on one

hand the fundamental knowledge of thermodynamics,

chemical reactions, process control, unit operations, and

on the other hand, an understanding and experience in

optimizations methods and technical objectives.

Major projects in the chemical industry are rarely

motivated by energy savings. If the energy is not taken

into consideration before late in the design project, the

possibilities of its savings will be reduced. The restrictions

in choices (originate from the fact that other parts of the

projects with influence on the energy consumption have

been fixed) do not enable a procedure, which handles the PI

task in a general manner. Instead of that, in the case of

chemical industry, when reduction of the energy consump-tion is investigated the optimal objectives for PI are often a

subjective decision.

The PI task for the improving energy efficiency of

sodium tripolyphosphate manufacture, one of the most

complex processes in the chemical industry, possesses the

lot of qualifications described in previous paragraphs. In

the literature review the author has found only one paper

concerning exergy analysis of sodium trypolyphosphate

manufacture [1], unfortunately with different production

route and without use of computer-aided methods. In spite

of this difference, that work was of great help.

This paper will be dedicated to a more detailed

description of process and plant configuration, as well as

the explanation of PI methods and software tools, used for

the energy efficiency improvement. Detailed description of

the mathematical models and the research results will be

presented in future papers.

2. Properties and the use of sodium tripolyphosphate

Sodium tripolyphosphate (STPP)Na5P3O10, is a solidinorganic compound, which belong to the group of

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doi:10.1016/j.energy.2006.10.004

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condensed inorganic phosphates. The general term con-

densed inorganic phosphates is applied to phosphorus

compounds in which various numbers of PO4 groups are

linked together by oxygen bridges. Condensed inorganic

phosphates can be classified into three classes: cyclic, linear

and cross-linked condensed phosphates. Linear condensed

phosphates, also called polyphosphates have the general

elementary composition [PnO3n+1](n+2) and the most

important of them are presented in Table 1.

Sodium tripolyphosphate have the stable form as the

hexahydrated salt. Identity and physical/chemical proper-

ties of sodium tripolyphosphate are presented in Table 2.STPP is widely used in regular and compact laundry

detergents, automatic dishwashing detergents, toilet and

surface cleaners, where it provides a number of functions

including:

sequestration of water hardness enabling surfactantsto function effectively,

pH buffering, dirt emulsification and prevention of deposition, hydrolysis of grease, dissolving-dispersing dirt particles.

Generally speaking, the use of STPP in detergents could

be replaced only by a great number of different chemicals,

as no one substitute offers all its functions.

The amount of STPP which was used in household

cleaning products in Europe in 2000, is estimated to be

about 300 000 t, since its consumption in household

detergents varies considerably between different countries

in Europe. It should be pointed out that in some countries

the STPP use in detergents is almost exclusively concen-

trated on automatic dishwashing products while in other

countries the use in laundry detergents is the overwhelming

application. The United States Food and Drug Administra-

tion lists STPP as generally recognized as safe, no

environmental risk related to its use in detergents is

indicated in soil, air or sewage treatment plants.1

Sodium tripolyphosphate exists in two major crystalline

forms, known as PhaseI (or FormI) and PhaseII (or FormII). Phase I material is formed if the process temperature ismaintained above 450 1C, while Phase II material is formed

at temperatures below 450 1C. The final product of STTP

in practice, according to its composition, is named as: LowTerm(Form I is the major component in product), M iddleTerm(Form I and Form II has nearly the same percentagein product) and High Term (Form II is the major

component in product). The price of the product is directlyproportional to the percentage of Form II. Typical

composition of STPP includes 4% impurity due to

presence of sodium pyrophosphate, sodium orthophosphateand sodiummetaphosphate.

A production plant for STTP, based on wet manufactur-

ing route2, essentially consists of:

neutralization part (Wet process part) where phosphoricacid is neutralized to an orthophosphate solution,

heating part (Dry process part) where, by drying andcalcination, the solution is converted into sodium

tripolyphosphate.

The raw material for sodium tripolyphosphate manu-

facture is obtained by recovering the phosphate values

from phosphoric ore, in the form of phosphoric acidH3PO4. In this process, phosphate rock is acidulated with

sulfuric acid H2SO4, either alone, or in conjunction with

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Nomenclature

A area (m2)c velocity (m/s)E exergy (W)

G mass flow rate (kg/s)h specic enthalpy (J/kg)HRAT heat recovery approach temperature (1C)Hd lower heating value (kJ/kg)I {i} process unitsJ {j} process streamsK thermal conductivity (W/mK)p pressure (Pa)Q:

heat flow (W)

t temperature (1C)T temperature (K)W

:

work flow(power) (W)

Greek symbols

d thickness (m)

l coefficient of excess air ()

x height (m)

Subscripts

cal calcinerFl fuelFU firing unitIN inletOUT outletORT orthophosphate

1A primary environmental concern of sodium phosphates is their release

into water. Phosphate may be a limiting nutrient in some aquatic

environments and in some countries the use of phosphate in detergents has

been discouraged to prevent unsustainable plant growth and oxygen

starvation (eutrophication) of lakes and waterways.2The alternative one, named as thermal route, is not used in the

production of STPP for detergent applications.

P. Raskovic / Energy 32 ( 2007) 983998984


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phosphoric acid. The result of this operation is formation

of precipitate, which contains the calcium values as calciumsulfate, and recovering the impure phosphoric acid, termedwet acid. The resulting wet acid is contaminated [2] with:

process impurities originate from the reagents used in

the production of phosphoric acid, like sulfuric acid andprocess water, common impurities in the phosphate rock, like alumi-

num (Al3+), iron (Fe3+), magnesium (Mg2+), calcium

(Ca2+), potassium (K+), strontium (Sr2+), chlorides

(Cl) and fluoride (F),

common trace elements in the phosphate ore, like arsenic,cadmium, mercury, uranium, copper, zinc and lead.

Such wet acid is unsuitable for use in sodium tripolypho-

sphate production, because the presence of soluble

cationic/anionic impurities and fluoride contamination,

which would end up in the final product.

3. Process description of sodium tripolyphosphate

manufacture

The reference object, in this paper, is the sodium

tripolyphosphate manufacture in IHP-Prahovo, the

biggest factory for producing base chemical products in

Serbia and Montenegro. This company production plant

essentially consists of two parts:

wet process part, where the raw material is primarilytreated with chemical reactions and

dry (final) process part, based on thermal treating ofsemifinished product from previous part.

The Wet process plant (Fig. 1) comprising:

Primary treatment stagewhere the wet acid is intro-duced in storage tanks in order of removing process

impurities by precipitation. Major fraction of the sludge

formed in this operation is calcium sulfate dihydrate(gypsum) CaSO4d2H2O. The process of sludge thickening

is done by the use of centrifugal pumps.

Defluorization stageoperation for separating the in-soluble fluorides (like hydrofluoric acid HF or sodiumfluoride NaF, present in the wet acid as fluorosilicate acidH2SiF6) from wet acid by adding sodium carbonate

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Table 2

Identity and physical/chemical properties of sodium tripolyphosphate

Formula

Symonimus Sodium triphosphate; Triphosphoric acid, pentasodium salt; Sodium Phosphate Tripoly; STPP;

Tripolyphosphate de sodium; Pentasodium triphosphate; Pentasodium Tripolyphosphate;

Natriumtripolyphosphat; Pentanatriumtriphosphat (German); Trifosfato de pentasodio(Spanish);Triphosphate de pentasodium (French)

Macro-molecular description Solid, inorganic

Physical state/particle size Slightly hygroscopic granules

Molecular weight 367.86 (g/mol)

Melting point Decomposes at 620 1C 1

Vapor pressure at 25 1C Negligible

Water solubility other* At 20 1C 140 (g/kg)

At 25 1C 145 (g/l)

At 40 1C 160 (g/kg)

At 100 1C 325 (g/l)

Density 0.45 1.15 (g/cm3) at ambient temp. 4

pH-value At 25 1C : 9.010 in 1% aqueous solution

PKa H5P3O102H++H4P3O10

(pK1 1)

H4P3O10

2H++H3P3O102 (pK2 1:1)

H3P3O1022H++H2P3O10

3 (pK3 2:3)

H2P3O1032H

++HP3O104 (pK4 6:3)

HP3O1042

H

+

+P3O105

(pK5 8:

9)

Table 1

Condensed phosphates

Number of P

atoms

Type Example CAS no.

1 Monophosphates/

Orthophosphates

Na3PO4 7601-54-9

NaH2PO4 7558-79-4

Na2HPO4 7558-80-7

2 Biphosphates/

Pyrophosphates

Na4P2O7 7722-88-5

Na3HP2O7 14691-80-6

Na2H2P2O7 7758-16-9

NaH3P2O7 13847-74-0

3 Triphosphates Na5P3O10 7758-29-4

Na4HP3O10 24616-37-3

Na3H2P3O10

4 Tetraphosphates Na6P4O13

P. Raskovic / Energy 32 ( 2007) 983998 985


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Fig. 1. Process flow diagram of wet process plant sodium tripolyphosphate manufacture.



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Na2CO3, which reacts with fluoride as

H2SiF6 Na2CO3 ! Na2SiF6 H2O CO2 (1)

2HF Na2CO3 ! 2NaF H2O CO2 (2)

The result of these reactions is made up of sodium

fluorosilicate Na2SiF6 and sodium fluoride NaF, whichprecipitates in the solution and substantially reduces thefluoride concentration of the wet acid.

Filtration stageoperation for removing the residualcontaminants from acid, by its passing through the filter

(impregnated by cellulose fiber). This production step

ensures the complete removal of Na2SiF6 and NaF from

acid.

Decolorization stageadsorption process for removingthe organic compound from the acid, by passing through

active coal layer. The adsorbate is desorbed in regeneration

stage by the use of NaOH solution.

Extraction stagein this stage, the acid is introduced on

the top of an extraction column and extracted in counter-flow by an organic extraction solution (water insoluble).

Extraction column allow efficient liquid to liquid contact

between the wet acid and the organic extracting solution

(made up of alkyl phosphate, tri-n-butylphosphate(C4H9)3PO4 (TBP)). The TBP is diluted with an organic

solvent (low boiling petroleum hydrocarbons-kerosene)

that has limited solubility in water to improve phase

separation. In the extraction stage of the process,

phosphorous pentoxide P2O5 values, present in the wetacid, are loaded into the organic extract, leaving behind the

bulk of the mineral impurities in a resulting raffinate. The

raffinate is an aqueous solution, which can be employed inproducing fertilizer. The extraction step is normally carried

out at temperatures 5055 1C.

Washing stageoperation, which is used to remove thesoluble impurities in TBP (H2SO4, H2SiF6 and Fe salt), by

adding water in mixers.

Reextractionstageprocess for separating TBP from theacid by neutralization (adding the sodium dihydrogenphosphate NaH2PO4) and precipitation. In this phase wetacid is transformed in the primal salt. From the top of

decanter 87% of the light phase (TBP) flows to a storage

tank and the rest of this phase goes to the Regenerationstage. On the other side 20% of the hard phase from thebottom of decanter flows to centrifugal separators(Centrifugation stage) in order to separate the residualTBP from this compound. Remaining 80% of this phase is

going back to the Reextraction stage.Neutralization stageoperation of mixing NaH2PO4

with sodium carbonate Na2CO3 in order to form a sodium

orthophosphate solution having a Na:P mole ratio of1.67:1. This reaction is presented as

3NaH2PO4 Na2CO3 ! 2Na2HPO4 NaH2PO4

H2O CO2 3

Neutralization result is the formation of an aqueous

mixture named orthophosphate solution, containing mono-

sodium orthophosphate and disodium orthophosphate in amole ratio of about 1:2.

Evaporation stagewhere the concentration of salt inorthophosphate solution is rising up from 43% to 50%.

The orthophosphate solution also called make up liquor

is stored in storage tank, which presents the link to the

heating part of the process.The dry process plant (Fig. 2) comprising:

Drying stagethe orthophosphate solution is first driedin spray drier. Atomization of the feed is done in multiple

nozzles at the top of the drier, where the solution is injected

using the high-pressure rotary pumps (pE100 bars). Solu-tion in finely atomized state is heated by a parallel

downward flow of hot (flue) gases from a heavy oil-firing

unit, which are employed in drier as the mixture of

recirculated and fresh flue gases from mixing chambers.

After drying, orthophosphate solution is transformed in

orthophosphate powder (mixture 2Na2HPO4/NaH2PO4).Dehydration (calcining) stagewhere sodium tripoly-

phosphate is produced from the sodium orthophosphate

mixture. The moisturized material from spray drier is

calcined in rotary kiln (calciner) in order to made reaction:

4Na2HPO4s 2NaH2PO4s ! 2Na4P2O7s

Na2H2P2O7s 3H2O 4

at temperature within the range from about 300 to 600 1C.

The temperature is selected in order to produce the desired

Form I and/or Form II content in the final STPP product.

Heating agents in calciner is the flue gas from another

heavy oil firing unit.

Cooling stagewhere the product is cooled in arecuperative water-cooled rotary tubular cooler for solids.Final product stagethe product coming from the

previous stage is generally in agglomerated form. The

agglomerates are sized by crushing and screening to a

desired size range or distribution (granules size about

f 1 mm).

The flue gas streams, emanating from either the spray

dryer or the calciner, are passing through a solid recovery

zone (cyclone battery of centrifugal separators) to remove

the bulk of any orto/sodium tripolyphosphate powder that

has been entrained in the streams. Such treated streams are

then passing through a scrubber, containing a liquid

scrubbing solution, which cleans the flue gas. The scrubber

also takes up remaining amounts of finally powdered

sodium tripolyphosphate, which were not caught in the

centrifugal separators.

4. Step-by-step PI approach

The improvement and the optimization of a chemical

process plant, motivated by energy savings, cover a wide

range of alternatives. In other word, definition of the

problems, improvements, optimization of objectives as well

as final evaluations, involve a large number of choices.

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In order to constrain the problem and limit the

improvement/optimization possibilities, Step-by-Step PIApproach (SSPIA) is developed. Simplified flow chart ofthe approach is presented in Fig. 3.

This approach is based on the combined use of

mathematical and thermodynamic optimization techniques

[3], which are arranged for the improvement/optimization

of reference, sodium tripolyphosphate plant. The prefix,

step-by-step, denotes the presence of more than one phasein the route of this approach. The results obtained by the

use of SSPIA yielded a wide range of possible improve-

ments and acceptable solutions, which could be fit into the

existing plant design.

4.1. Process modeling phase

In the common practice, study for energy efficiency

improvement in chemical industry does not necessarily

depend on the complete mapping of the factory. In the

effort of limiting the range of research, it has been

recognized, that the most significant part in plant produc-

tion cost is the heavy oil consumption (approximately

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Fig. 2. Process flow diagram of dry process partsodium tripolyphosphate manufacture.



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7000 t crude oil per year). As more than 90% of the oil

consumption is executed in the final part of process (dry

process part), the PI task is focused on it.In the first stage of this SSPIA phase, the physical model

of dry process plant (Fig. 4) is defined, by the use

component-oriented approach, as the network of inter-

connected component (process units). This kind of

approach usually generates many equations, but it is very

user friendly and enables quick representation of the plant

for possible reconfigurations. In order to create appro-

priate mathematical model of the plant, some assumptions

were added after an analysis of the available parameters.

The most significant are the following:

connections between components are assumed to bewithout losses, model is steady state, because the processes in the plant

can be defined as time independent,

some of the streams are the ideal mixtures of air, dry fluegases, water vapor in flue gases and sodium tripolypho-

sphate powder. For such streams, balance equations are

created for each component,

bulk mass flow and the water vapor content in flue gasesare the functions of mass flow rate of stream and fuel

specification, respectively.

The method, chosen for handling the inter-connections,

was direct componentcomponent connections. The nat-

ural consequence of that selection is demand for mathe-

matical model of each component by the using mass end

energy balance equations, which are in general formpresented as:

Mass balance equation:Xj2IN i;j

Gj X

j2OUT i;j

Gj 0; 8i 2 I . (5)

Energy balance equation:

Xj2IN i;j

Gj hj c2

2 gx

Xj2OUT i;j

Gj hj c2

2 gx

W i:

Qi:

08 i 2 I , 6

where

W i 0; 8i 2 CL ; CBA; CBK; SP; ST ; AT ; KC ;f

CC ; L A; L Kg,

Qi 0; 8i 2 CL ; CBA; CBK; SP; STf g,

Qi fK; A; d;DT 8i 2 AT ; KC ; CCf g,

Qi fGFl ; Hd; l; ZFU 8i 2 L A; L Kf g,

Qi fGORT;

pH;

T cal 8i 2 KCf g.

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Fig. 3. Flowchart of step-by-step process integration approach.



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4.2. Process simulation phase

The second phase of SSPIA, Processsimulation phase, canbe noted as the most difficult and the most important part in

the research route. Difficult, because in this phase, two

different software tools were developed; important, since the

results obtained by the use of these software tools, point out

the distance of mathematical models from real thermody-

namic parameters of the plant. This phase has two stages.

In the first stage, numerical model of plant (based on

sequential modular approach) is implemented in the

spreadsheet software tool named ExP, developed onM icrosoft Excel programming platform. Thermodynamicproperties of the vapor present in the flue gas streams are

calculated by the use of water/steam properties simulator,

settled in the Excel Add-in component. Validation of theresult, obtained by simulation (presented in the Table 3), is

achieved by comparison with operating data from technical

documentation. The research in this phase enabled some

very important conclusion and accomplishments:

Results of software tool confirmed the proposedmathematical models of process units and enabled the

construction of mass and energy flows (Fig. 5) diagrams

of the plant.

Simulation results were the base for polynomial fittingof some equations concerning chemical reaction in

calciner and fire units, as well as thermodynamic

properties of the flue gas streams.

The participation of kinetic and potential energy instreams (except input stream of orthophosphate solu-

tion) is negligible in respect to overall energy balance.

Previously, this software tool was used for exergy-

efficiency analysis of the plant, but its sequential modular

nature has limited the range of possible improvements.

Still, its results encouraged the author for the next more

complicated research steps.

In the next stage, the mathematical model is integrated in

software tool named Prahovo Sim, by equation-orientedapproach. Numerical model of this software can be

classified as the non-linear optimization problem

(DOF40)3, but it could be easily transformed for solvingthe non-linear simulation problems (DOF 0).

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Fig. 4. Physical model of dry process part.

3DOFnumber of degrees of freedom.



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The solving procedure for this problem is based on

Sequential L inear Programming (SLP ) method [4,5]. Thismethod belongs to the group of mathematical program-

ming strategies for constrained optimization problems. The

SLP method was developed in the early 1960s and in the

literature is also known as Kelleys cutting plane method or

as Stewart and Griffiths method of approximate program-ming. Although SLP is considered unattractive by theore-ticians its concept has proven to be quite powerful and

efficient for engineering design. As the name implies, SLP

use linear programming search technique. This technique

can be applied to non-linear mathematical models, very

often in energy project designs. The general formulation of

a SLP problem could be expressed as

optimize fx; x x1; x2; . . . ; xNf g, (7)

subject to gjxp0; j 1; 2; . . . ; P, (8)

zjx 0;

j P 1;

. . .

;

P M , (9)

where f(x) is a non-linear objective function, gj(x) andzj(x) are non-linear inequality and equality constraints,respectively.

The idea of SLP is based on linear approximations of the

objective function and constraints, usually by using the first

term of a Taylor series expansion. In that way the

mathematical model is transformed to a linear problem,which can be solved by the Simplex method.In the numerical model of Prahovo Sim, equality

constraints are presented as the mass and energy balance

equations of process units, since inequality constrain is the

ratio of flue gasses mass flow rates in splitter SP (Fig. 4). Inorder to simulate the existing plant ratio of flue gasses mass

flow rates in splitter is fixed to 0.667, which is the usual

value in existing operating regime. The variables that

describe a numerical model are divided into two groups:

decision variables (variables that are under the control of

the decision maker) and design variables (which are varied

by the simulation/optimization process). Software is

developed on Visual Studio C++ programming platform,

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Table 3

Stream parameters obtained by sequential modular simulation by ExP

Stream Gdry (kg/s) Gwet (kg/s) SG (kg/s) t (1C) c (m/s) x (m) G c2/2 (kW) G x (kW) G h (kW) SE (kW)

1 1.93 1.93 3.86 80.00 70.00 30.00 801.96 9.45 1.13 812.54

2 12.89 0.53 13.42 523.30 8.80 32.00 8999.55 0.52 4.21 9004.29

3 1.78 0.01 1.79 10.00 0.50 33.00 38.19 0.00 0.58 38.77

4 1.35 0.07 1.42 80.00 0.30 5.00 133.42 0.00 0.07 133.49

5Aa 14.67 2.37 17.04 160.00 14.70 5.00 8985.93 1.84 0.84 8988.61

5Bb 0.58 0.03 0.61 160.00 14.70 5.00 113.89 0.07 0.03 113.98

6 6.89 0.23 7.12 790.03 1.40 32.00 6837.45 0.01 2.23 6839.69

7 6.00 0.30 6.30 213.53 18.00 30.00 2161.72 1.02 1.85 2164.60

8 3.77 0.02 3.79 10.00 22.00 30.00 77.87 0.91 1.11 79.89

9 3.04 0.01 3.05 10.00 22.00 30.00 62.58 0.74 0.89 64.21

10 0.16 10.00 6759.79

11 0.09 0.00 0.09 220.00 0.12 12.00 19.68 0.00 0.01 19.69

12 2.00 0.10 2.10 109.01 0.30 3.00 265.80 0.00 0.06 265.86

13 1.45 0.01 1.46 10.00 0.50 2.00 30.41 0.00 0.03 30.44

14B 0.092 0.00 0.09 220.00 14.00 5.00 20.44 0.01 0.00 20.45

14A 9.10 0.45 9.55 220.00 14.00 5.00 3337.96 0.94 0.47 3339.36

15 7.65 0.17 7.82 525.51 1.40 1.60 4890.99 0.01 0.12 4891.12

16 1.74 0.00 1.74 350.00 0.00 3.00 607.64 0.00 0.05 607.69

17 5.69 0.02 5.71 10.00 22.00 3.00 121.45 1.38 0.17 123.00

18 2.14 0.01 2.15 10.00 22.00 3.00 45.75 0.52 0.06 46.33

19 0.11 10.00 4768.124

20A 9.10 0.45 9.55 213.53 18.00 20.00 3283.35 1.55 1.87 3286.77

20B 0.003 0.00 0.00 213.53 18.00 20.00 0.73 0.00 0.00 0.73

21A 3.09 0.15 3.25 213.53 18.00 20.00 1121.63 0.53 0.64 1122.79

21B 0.003 0.00 0.00 213.53 18.00 20.00 0.73 0.00 0.00 0.73

22 0.56 0.03 0.59 160.00 0.12 12.00 111.27 0.00 0.03 111.30

23A 14.67 2.37 17.04 159.04 14.70 20.00 8914.02 1.84 0.17 8916.03

23B 0.013 0.00 0.01 159.04 14.70 20.00 2.60 0.00 0.00 2.61

24 17.76 1.44 19.20 65.63 7.00 32.00 5024.34 0.47 6.03 5033.73

25 0.02 28.86 28.88 80.00 2.00 26.00 9665.75 0.06 7.36 9673.17

26 0.00 27.78 27.78 40.00 5.00 31.00 4651.11 0.35 8.45 4659.91

27 0.00 13.89 13.89 25.00 1453.47 1453.47

28 0.00 13.89 13.89 36.20 2104.81 2104.81

29 1.74 0.00 1.74 50.00 87.33 87.33

aA-flue gas in the stream.bB-orto/tripolyphosphate powder in the stream.



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Fig. 5. Mass (a) and energy flow (b) diagrams of dry process part.



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with user-friendly interfaces. The Prahovo Sim flowchart ispresented on Fig. 6. As it can be seen from the figure,

software consists of three parts:

Input part, part under the control of operator, which isused for input of decision variables and preassigned

parameters.

Solver part, which comprises: the linearization ofconstraints, definitions of starting values of design

variables, move limits, stopping criteria and Simplexsolver. This stage is under the programmer control,

invisible for operator. During the execution solver

checks the convergence and feasibility of the solutions,

so in a case of failure the program is automatically

returned in the Input part. Output part, where the simulation/optimization results

are first checked (percent error due to non-linearity of

the problem) and then classified for analyses, either in

the appropriate software menu, or in the ole-comconnected M icrosoft Excel Worksheets.

In the stage of data validation, the simulation results arecompared with real plant parameters (presented in the

Table 4). For that purpose, temperature and pressure

values, obtained from plant-operating measurements, have

become the decision variables in the input part of the

software. The result obtained from simulationheavy oil

consumption4is then compared with operating plant

data. Data validations, based on oil consumption, are

presented in Fig. 7. The author concluded that the average

error of about 5.84% can be good starting point for the

next, optimization phase.

4.3. Optimization of the reference plant

The optimization phase is realized in two research levels.

On the first level, the SLP software tool Prahovo Sim,amplified by two new numerical modules, got his final form

Prahovo Target.On the second level Prahovo Target is rearranged for

application to three different scenarios (plant configura-

tions):

NR scenarioconfiguration without heat recovery op-tion.

FRG scenariosconfiguration with heat recovery optionaccomplished by the flue gas recirculation.

HENS scenariosconfiguration where heat recoveryoption are realized by the use of recuperative heat

exchangers network.

Scenarios are realized on the seven different flowsheets

(named model) of the plant as:

Model 1.0 for NR scenario. Model 1.1 (scenario with one stage gas recirculation,existing plant configuration), and Models 2.0, 2.1, 2.2

(scenarios with multistage gas recirculation) for FRGscenarios.

Models 3.0 and 3.1 for HENS scenarios.

The flow sheets of some models are presented in Fig. 8,

and important facts about their mathematical models are

presented in Table 5.

Scenario for the Model 1.0 is simplified configuration of

existing plant in which the recirculation of flue gasses is

excluded. This scenario can be named the base scenario,

since there is no heat recovery operation. According to the

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Fig. 6. Flowchart of Prahovo Sim.

4There is a wide range of data validation but author decided to present

only one, in his opinion the most important.



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number of inequality constrains its mathematical model is

transformed into non-linear simulation.

Second scenario for the Model 1.1 is existing plant

configuration, explained in previous part of the paper. This

time the fixed value, defined by the ratio of mass flow rates

in splitter, is substituted by additional inequality constrain.

Such exchange transformed this problem into a non-linear

optimization task.

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Table 4

Plant operating parameters

Date Sort p1 (bar) t1 (1C) t2 (1C) t5 (1C) t6 (1C) t14 (1C) t15 (1C) t16 (1C) t24 (1C) t28 (1C) t29 (1C) Prod. (t/h) Fuel cons. (kgG/tpr)

18. 1 M T-1 100 98.8 395 160.6 685 237.5 432.5 349.9 50.8 14.9 6.5 5.5 175

29.1 M T-2 90 108.4 488 160.4 802 233 402 349.2 56.8 17.8 6.6 5.9 175

30. 6 M T-3 80 92 418 151 653 244 530 347.6 67.8 53.6 30.2 4.5 150

26.7 M T-4 100 101.5 501.7 160.8 846.7 240 443.3 371.5 66 45.5 32.3 6.8 170

1.8 M T-5 98.6 101 415.7 160 715.7 240.7 417.1 364.6 63.6 40.9 33.6 5.6 170

8.8 M T-6 100 102 420 159.2 725 240 421.7 368.5 62.2 41.3 33.2 5.5 170

27.7 H T-1 100 94 429.4 158.8 551.7 260.4 611.3 402.3 63.8 55.6 27.7 5 185

19.8 H T-2 110 93.2 422.9 154.8 759.8 236.1 631.9 445.3 63.2 65.1 45.9 5.1 185

5.9 H T-3 111.7 89.3 368.3 158.7 750.4 173.3 591.7 360.2 63.1 47.9 29.7 6.2 185

21.7 L T-1 100 100 608.3 157.3 720 175 480 346.3 59 45.3 32 6.3 140

25.7 L T-2 100 97 458 153.6 686 211 519 376 62 59 33.2 5.06 140

25.9 L T-3 95 94.4 407.5 135.6 752.5 233.1 574.4 364.5 60.5 66.9 41 4.3 140

MTmiddle term sodium tripolyphosphates.

LTlow term sodium tripolyphosphates.

HThigh term sodium tripolyphosphates.

Fig. 7. Data validation.



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Scenario for the Model 2.0 is developed by considering

the new possibilities of the flue gasses recirculation in

existing plant. In order to remove the moisture from fluegasses, the gas fluid separators modules are included in

flow sheet (new splitter and mixture chambers, too).

Although a lot of energy could be regenerated in these

units, their main purpose is to remove the vapor from the

flue gases stream. Mathematical model of this process unit

is expressed by the same equitation as for the other units (5,

6). The outlet temperature of condensate and dry gasses is

the dew point of refined flue gasses. Model 2.0 has two

modifications (Model 2.1 and Model 2.2) which represent

their simplifications but with the same DOF 1.Scenario for the Model 3.0 (and its simplified modifica-

tion Model 3.1) is developed on the basis of Model 1.0, and

rearranged for PI by the use of pinch design method.

PI of existing dry process plant, by the use of pinch

design method, is limited for only 3 streams. Targettemperature of these streams can be changed in order to

create heat exchanger network for possible heat recovery.

This operation would be in accordance with global mass

and energy balance equation, and the final result of such PI

can be qualified as correct, from physical point of view.

However, in this way the energy efficiency of the plant will

be improved just slightly.

The situation is quite different for the others; let me call

them promising5 streams. If the target temperature of

these streams is changed (and these streams are involved in

pinch design task), the result of such PI would be

mathematically correct and quite better, according to theenergy efficiency improvement. On the other side, that

change will disturb the global mass and energy balance of

the plant, thus from the physical point of view, the

obtained results can be qualified as incorrect.6

In order to exceed this limitation, new heat exchangers

are added on every promising stream which exists in the

Model 1.0. Such added heat exchangers present physically

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Fig. 8. Flowsheets of plant configuration scenarios.

Table 5

Important facts about SLP mathematical models of different plantconfigurations

Model 1.0 Model 1.1 Model 2.0 Model 3.0

Variables 62 71 83 84

Decision variable 31 33 28 34

Design variables 31 41 56 50

Equality constrains 31 40 55 50

Inequality constrains 0 1 1 0

5Promising, from pinch design point of view. Streams, which can be

used for the creation of heat exchangers network, such as primary and

secondary air streams in combustion chambers of firing units.6The main reason for this is the fact that pinch design method does not

respond to complex chemical operation in examined energy system

(combustion, spray drying, calcining, scrubbing).



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fictive elements, the balance equations of which are settled

in the mathematical model of the plant (HENS scenarios).They enable possible heating/cooling of those streams and

changing of their target temperatures, this time in

accordance with mass and energy balance. In SLP soft-

ware, target temperatures of such streams become the new

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Fig. 9. Flowchart of Prahovo Target.



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decision variables, since the rest of the program stay the

same as for the other scenarios.

Another update of HENS scenario was done by plugging

the pinch design module [6] in SLP software tool (PrahovoSim). After simulation (the problem DOF 0) by SLPsoftware, the data (which includes all promising streams)

for pinch task are automatically generated. New addednumerical module enables the execution of targeting,

network design and network optimization, by choosing

different possibilities for external heating/cooling and

different HRAT.The operating cost in first two scenarios (NR and FRG)

is primarily depending on heavy oil fuel consumption. In

the case of HENS scenario the optimal solution (heat

exchanger network) indicates the need for new utilities (hot

and cold), which means the new energy source/sink in

existing plant. As in the first part of research, the intention

was to compare the solutions of all scenarios on the base of

heavy oil fuel consumption, new utilities are excluded from

the final HENS solution by moving the composite curves in

threshold problem statement. Operating and investment

costs for such states are loaded and classified in inter-

mediate (Targeting results) database. The solutions, thistime with real heat exchangers network, which had the best

energy performance, is then presented in the grid and

process flow diagrams.

A more detailed flowchart of Prahovo Target is pre-sented in Fig. 9. As may be seen from the picture, the

numerical module for calculation of exergy rates is

common to all scenarios, and enabled their evaluation by

exergy efficiency analysis in the last phase of the research.

Pinch design module is used only in the case of HENSscenarios.

4.4. Evaluation of possible improvements

The development of all phases, by presented step-by-step

PI approach, makes a wide range for possible project

improvements with respect to energy, environmental and

economic objectives. The assessment of the improvement

potential is generated by:

identifying the most important design variables throughsensitivity analyses,

investigating the effect of these variables on theperformance,

evaluating the improvement potential.

As the authors intention is to promote the general ideas

and methods of sodium tripolyphosphate project design,

detailed presentation and description of obtained results

are excluded in this paper.

Instead of that one general evaluation of energy

efficiency improvement potential, with respect to retrofit

project cost and fuel consumption is presented on

Fig. 10. Values presented in the diagram were calcu-

lated by comparing the oil consumption and total cost

(which include the investment cost) of existing plant

configuration (Model 1.1) versus consumption and

cost in the new-formed scenarios. Comparison was done

on the basis of the same important energy inputs, and

the same physicochemical properties of raw and final

materials. Presented results lead to three important

conclusions:

The most effective gas recirculation is the one in existingplant configuration (Model 1.1). Scenarios without that

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Fig. 10. Evaluation of energy efficiency improvement potential.



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recirculation possibility (Models 1.0, 2.1) indicate

increase of fuel consumption and cost.

The results clearly indicate better possibilities for theproject improvements in the case of HENS models than

in the case of FRG and NR models.

Investment cost for NR and FGR cases can be rated as

negligible in comparison to the case of HENS cases.

5. Conclusion

The need of increasing production and energy efficiency

of plants as well as the improvements of their environ-

mental performance are the most important future

challenges for chemical industry. The paper presents an

approach, which collects a few process integration methods

for achieving these goals. The use of a computer-based

simulation/optimization methodology has been applied to

energy efficiency improvement of the sodiumtripolypho-

sphate production plant. Preliminary evaluation of ob-

tained results indicates considerable possibilities for plant

efficiency improvement.

References

[1] Atanasova L, Rasheva D. In: Hirs GG, editor. Exergy analysis of

sodium tripolyphosphate manufacture. Proceedings of the interna-

tional conference ECOS 2000. Efficiency, costs, optimization, simula-

tion and environmental aspects of energy and process systems, vol. 3.

the Netherlands: University of Twente Enschede; 2000. p. 13218.

[2] Becker P. Phosphates and phosphoric acid: raw materialstechnol-ogy, and economics of the wet-process. New York: Marcel Dekker Inc;

1989.

[3] Gundersen T. Process integration PRIMER, Trondheim, Norway.

SINTEF energy research, 2000. See also: /http://www.tev.ntnu.no/

iea/pi/primer.htmlS.

[4] Bojic M, Lukic N, Trnobranski K. Linear programming applied to an

industrial building with several available hot refuse flows. Energy

1995;20(10):106774.

[5] Bojic M, Raskovic P, Radojkovic N. An influence of boiler-recovery-

heat exchanger to boiler sizea tool shop example. Facta UnivSer

Mech Eng 1997;1(4):44757.

[6] Raskovic P. In: Signe Kjelstrup, Johan E. Hustad, Truls Gundersen,

Audun Rsjorde George Tsatsaronis, editors. Process integration

case study sodiumtripolyphosphate manufacture. Proceedings of

the international conference ECOS 2005; 18th international con-ference on efficiency, cost, optimization, simulation and environmental

impact of energy systems, vol. 1. Trondheim, Norway; 2005.

p. 493501.

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