aquafit4use - description of mathemetical integrated models results/aquafit4use... · description...

AquaFit4Use is co-financed by the European Union’s 7th

Framework Programme

The project for sustainable water use in chemical, paper, textile and

food industries

Description of the mathematical integrated models for the simulation of water systems in industry

By Izaro Lizzarralde (Ceit)

2nd April 2012

Description of mathematical integrated models Izaro Lizzarralde, CEIT, 2

nd April 2012

Executive summary This report is a result of the project AquaFit4Use, a large-scale European research project co-financed by the 7

th framework programme of the European Union on water

treatment technologies and processes. The increasing costs associated with water supply and the disposal of wastewater has stimulated industries to seek more efficient water management systems. However this is not a straightforward task due to the multiple alternatives of water reuse within the industries. Mathematical modeling and simulation can be a very valuable tool to study different possible alternatives whilst assessing optimum solutions for water management in industry. In this context, in this report two integrated models are presented that represent the integrated water networks of two actual cases (CHS and Holmen Paper Madrid). These integrated models have been built by using the mathematical model library previously built to reproduce relevant treatments in industry (WP 2.2; D2.2.1). Models to describe the industrial process steps have also been used in order to describe the water networks as a whole. These integrated models have been implemented in the Water Quality Management Tool WQMT, which enables the representation, simulation and optimization of industrial water networks, and simulations for the current water networks have been carried out. The simulations for the case studies show promising results. It was found that the models built in this work reproduce adequately the real water networks, with respect to the experimental data available. Therefore, it can be concluded that integrated models for the accurate simulation of water systems in paper and food industry have been built.


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CONTENT

EXECUTIVE SUMMARY ............................................................................................................... 2

1 INTRODUCTION .................................................................................................................... 4

1.1 OBJECTIVES ........................................................................................................................................ 4

1.2 DEVIATIONS FROM ORIGINAL DOW ....................................................................................................... 4

1.2.1 Description of work related to deliverable as given in DoW ....................................................... 4

1.2.2 Time deviations from original DoW ............................................................................................ 5

1.2.3 Content deviations from original DoW ....................................................................................... 5

1.2.4 Problems occurred ..................................................................................................................... 5

2 METHODS .............................................................................................................................. 6

2.1 CONSTRUCTION OF MATHEMATICAL MODEL LIBRARY .............................................................................. 6

2.2 IMPLEMENTATION OF MATHEMATICAL MODEL LIBRARY IN SIMULATION PLATFORM ..................................... 6

2.3 CONNECTION OF UNIT PROCESSES ....................................................................................................... 7

2.4 CALIBRATION OF INTEGRATED WATER NETWORKS ................................................................................. 7

3 RESULTS AND ACHIEVEMENTS .......................................................................................... 8

3.1 MAJOR RESULTS AND ACHIEVEMENTS ................................................................................................... 8

3.2 CHS CASE STUDY ............................................................................................................................ 8

3.2.1 Definition of the case study ........................................................................................................ 8

3.2.2 Modelling and calibration of the existing water network ............................................................. 9

3.2.2.1 Sanitary water ..................................................................................................................................................... 10

3.2.2.2 Equipment and factory cleaning ......................................................................................................................... 10

3.2.2.3 Vegetable washing .............................................................................................................................................. 11

3.2.2.4 Blanching/Cooling .............................................................................................................................................. 11

3.2.2.5 Jar washing ......................................................................................................................................................... 12

3.2.2.6 Potato processing ................................................................................................................................................ 12

3.2.3 Verification of the case study ................................................................................................... 13

3.3 HOLMEN CASE STUDY ........................................................................................................................ 14

3.3.1 Definition of the case study ...................................................................................................... 14

3.3.2 Modelling and calibration of the existing water network ........................................................... 15

3.3.2.1 Drum Pulper ....................................................................................................................................................... 15

3.3.2.2 Loop 1 & Loop 2 ................................................................................................................................................ 16

3.3.2.3 Paper Machine .................................................................................................................................................... 19

3.3.3 Verification of the case study ................................................................................................... 20

4 EXPLOITATION AND DISSEMINATION OF MAJOR RESULTS ......................................... 22

5 PARTNER CONTRIBUTION / PROGRESS OF WORK ....................................................... 23

5.1 FIRST PERIOD .................................................................................................................................... 23

5.1.1 Partner 1: CEIT ........................................................................................................................ 23

5.1.2 Partner 2: ATM ......................................................................................................................... 23

5.1.3 Partner 3: UCM ........................................................................................................................ 23

5.1.4 Partner 4: Holmen .................................................................................................................... 23

5.1.5 Partner 5: CHS ......................................................................................................................... 23

6 CONCLUSIONS ................................................................................................................... 24

6.1 MAJOR ACHIEVEMENTS ...................................................................................................................... 24

6.2 FUTURE WORK ................................................................................................................................... 24

6.2.1 Within AquaFit4Use ................................................................................................................. 24

7 LITERATURE ....................................................................................................................... 25


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1 Introduction In recent years, the rising costs associated with the water supply and disposal of industrial water wastewater have motivated industries to seek more efficient water management approaches. Thus, the availability of alternative water sources such as reclaimed municipal wastewater or recycled process water can encourage more efficient water use practices that translate into significant cost savings for many industries (Lens, 2002). In this context, mathematical modelling and dynamic simulation of the processes in a water circuit can be a very useful tool in the selection of best option for the water management in an industry taking into account processes stability, water quality required for different water uses and in the effluent, and operational costs. For this reason, the main goal of SP2 subproject is the development of simulation tools able to reproduce the water networks and the verification/validation of these models on real scenarios provided by the industrial partners. Specifically, WP 2.3 is focused on modelling a number of complete water networks based on the mathematical model library developed in WP2.2. In this report the two water networks studied (CHS and Holmen)are described and the capability of the model library to reproduce these water networks is checked.

1.1 Objectives

The objective has been to construct an integrated water-network using the model library presented in the I2.2.2.2 to represent real water networks for food and paper industries. In order to fulfil this general objective, two specific water networks describing the CHS-Gutarra water network and the Holmen Paper Madrid water networks have been built.

1.2 Deviations from original DoW

1.2.1 Description of work related to deliverable as given in DoW

This task is devoted to the experimental calibration and validation of some water systems constructed in WP 2.3.1 and some case-study processes selected from the ATM S.A. The calibration will be carried out by ATM under supervision of CEIT. Several industrial users as Conservas de Hijos de M Sanchez Besarte S.A and Nestlé Waters will provide information on water networks and specific production processes in order to accomplish this task under different production conditions in food industry. The calibration procedures used in this WP will be based on the methods and on the tools developed in WP 2.4 The description of model parameter uncertainty will be used in WP 2.4 for the analysis of the uncertainty propagation and risk in the whole water system. Finally, the analysis of the identification results will be used, in an iterative way, for the progressive improvement of the Unit-Process models when required.


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1.2.2 Time deviations from original DoW

This report was due in January, it is handed in March

1.2.3 Content deviations from original DoW

The case studies selected for circuit definition and calibration have been Conservas de Hijos de M Sanchez Besarte S.A and Holmen Paper Madrid, since data for this mills were available.

1.2.4 Problems occurred

No major problems encountered


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2 Methods As mentioned in the previous section, the mathematical modelling and numeric simulation of the different unit-processes in a water circuit is a very useful tool for the study of their behaviour. The construction of a mathematical model that describes a whole water network follows four steps:

• first, the development of process models for each individual unit included in that circuit (Unit Process models);

• second, the implementation of the mathematical models in the simulation platform (WQMT);

• third, the connection of these Unit Process models to simulate the behaviour of the entire water circuit (building up the network in WQMT);

• and finally the calibration of the integrated model based on the experimental data available.

2.1 Construction of mathematical model library

Due to the need of reproducing water networks as a whole, an integrated modelling methodology has been used for the construction of the unit-process models in an standardized way based on three principles:

(i) definition of a common state vector (I 2.2.2.2) to guarantee mass and thermal energy continuity and enable an easy connection among different technologies. The components considered within the vector are those necessary to describe the relevant compounds in paper, food and textile industries ();

(ii) next to mass and thermal energy balances, all the unit process models include investment and operational costs equations as function of operational variable (i.e. treated flow, organic load or additive requirements) that will allow the estimation of the total exploitation cost of the water network;

(iii) finally, upper and lower bounds values of the components are fixed to ensure water quality for machinery requirements, product quality, workers safety and legislation limits. This way a specific technology will not be allowed to be used in water streams with concentrations out of the bounds specified for that technology.

The unit process models developed within the framework of the project are presented in the internal report I 2.2.2.2 and D 2.2.1. Besides the mathematical model library to describe the most relevant treatments unit; process models are also built to describe the internal production processes using the same modelling methodology for each industrial case.

2.2 Implementation of mathematical model library in simulation platform

The mathematical model libraries including the common treatment library and the production processes specific for the production line have been implemented in the WQMT. Network blocks are modeled using nonlinear steady-state models, using the Modelica language.


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2.3 Connection of unit processes

Due to the fact that all the mathematical models in the library have the same internal structure and are written based on the same common state vector, the link between different unit processes is straightforward.

2.4 Calibration of integrated water networks

The mathematical model library has been calibrated in W.P 2.2. However a tailor-made calibration is required for each case study, that considers the influence of the raw materials processed and the actual applied process conditions. The calibration of the integrated model has been carried out by calibrating each block in the network individually. The calibration has been carried out following the water flow, i.e the first processes using fresh water has been calibrated first, followed by the process receiving the output of the calibrated process. The final process to be calibrated is the one that discharges the water to the municipal wastewater or other external destinations. In order to calibrate each process the relevant parameters affecting the state components have been changed to mimic the experimental data available. Using this modeling methodology the integrated models to represent the Holmen Paper Case study and the CHS Gutarra case study have been built and are presented in this report.


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3 Results and achievements

3.1 Major results and achievements

The integrated models for CHS and Holmen water networks have been built and calibrated. The ability of the models to reproduce real water networks has been shown.

3.2 CHS CASE STUDY

3.2.1 Definition of the case study

Gutarra CHS is a food processing company located in Villafranca (Navarre, Spain) which produces canned vegetables. Due to the nature of the industrial activity there is a wide seasonal variety and a wide range of products processed. The case study (Figure 1) compiles a high number of treatments and specific processes in the production line. Water withdrawn from Yesa reservoir passes through a sand filter and a chlorination tank before it is used for processing food. A fraction of water can be used directly for washing, scaling, cooling, as preserving liquid, jar washing or potato processing. However, a fraction of water has to be softened so that it can be used for some specific vegetables. After being used in the production line, water is collected and passes through two rotating sieves, 4 moving bed bioreactors and then released to the canal.

Figure 1 Water existing in CHS


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Due to the wide variety of the production it is a difficult task to define a unique scenario for the case study analysis. However, in the analysis carried out in WP 4.2 it was seen that some of the products have similar water consumption and have a similar effect on the process water, so they can be analyzed with the same scenario. In this WP 4.2 two main scenarios were defined for this case study:

• Medium load scenario (most of the products)

• High load scenario (main product-thistle)

The major water consuming processes in both scenarios are: the vegetables washing, blanching and the factory and equipment cleaning.

3.2.2 Modelling and calibration of the existing water network

All the processes in the water network have been modelled using empirical models to describe the changes in the variables that are measured (Chemical Oxygen Demand,COD, and and Total Suspended Solids, TSS). Equations that describe the outflow for the processes in CHS are gathered in Table 1.

Table 1 Equations for processes in CHS

Output

Particulate components

Xb in

RM_VSSVSS_Xb

in,bout,bQ

1000·RM·f·fXX +=

Xi in

RM_VSSVSS_Xi

in,iout,iQ

1000·RM·f·fXX +=

Xii in

RM_Xii

in,iiout,iiQ

1000·RM·fXX +=

Colloidal components

Cb, Ci in

RM_Col

in,bout,bQ

1000·RM·fCC +=

Dissolved components

Sf, in

RM_VDSVDS_Sf

in,fout,fQ

1000·RM·f·fSS +=

Sa, in

RM_VDSVDS_Sa

in,aout,aQ

1000·RM·f·fSS +=

Si in

RM_VDSVDS_Si

in,iout,iQ

1000·RM·f·fSS +=

The parameters used in these equations are gathered in table 2 and have been calibrated for each process in the water network.


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Table 2 Parameters used in the models for processes in CHS

Model parameters Description Unit

RM Raw Material processed in the mill Ton

f_VSS_RM Volatile suspended matter content of raw material g COD/kg RM

f_Xb_VSS Biodegradable fraction in suspended matter g VSS/g COD

f_Xi_VSS Inorganic matter fraction in suspended matter g VSS/g COD

f_Xii Inert matter content of raw material g/ kg RM

f_VCS_RM Volatile colloidal matter content of raw material g COD/kg RM

f_VDS_RM Volatile dissolved matter content of raw material g COD/kg RM

f_Sa_VDS VFA fraction in soluble COD g VDS/g COD

f_Sf_VDS Soluble and fermentable fraction in soluble COD g VDS/g COD

f_Si_VDS Inorganic matter fraction in dissolved COD g VDS/g COD

3.2.2.1 Sanitary water Sanitary water has been calibrated to achieve water with urban wastewater characteristics. This is supposed to be independent from the raw material that is being processed.

Model parameters Description Unit Value

f_VSS_RM Volatile suspended matter content of raw material g COD/kg RM 500

f_Xb_VSS Biodegradable fraction in suspended matter g VSS/g COD 0.7

f_Xi_VSS Inorganic matter fraction in suspended matter g VSS/g COD 1-f_Xb_VSS

f_Xii Inert matter content of raw material g/ kg RM 0.001

f_VCS_RM Volatile colloidal matter content of raw material g COD/kg RM 52.8

f_Cb_VCS 0.7

f_Ci_VCS 1-f_Cb_VCS

f_VDS_RM Volatile dissolved matter content of raw material g COD/kg RM 1000

f_Sa_VDS VFA fraction in soluble COD g VDS/g COD 0.2

f_Sf_VDS Soluble and fermentable fraction in soluble COD g VDS/g COD 0.5

f_Si_VDS Inorganic matter fraction in dissolved COD g VDS/g COD 1- (f_Sa_VDS+

f_Sf_VDS)

3.2.2.2 Equipment and factory cleaning


RM Raw Material processed in the mill Ton 10

f_VSS_RM Volatile suspended matter content of raw material g COD/kg RM 0.1





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f_Cb_VCS 0.7

f_Ci_VCS 1-f_Cb_VCS

f_VDS_RM Volatile dissolved matter content of raw material g COD/kg RM 0.1



f_Si_VDS Inorganic matter fraction in dissolved COD g VDS/g COD 1-

(f_Sa_VDS+ f_Sf_VDS)

Table 3 Model parameters for Equipment and factory cleaning

3.2.2.3 Vegetable washing The objective of washing is to remove and separate unwanted components to ensure that the surface of the food is in suitable condition for further processing. Unwanted components can include foreign materials from the product.








f_Cb_VCS 0.7

f_Ci_VCS 1-f_Cb_VCS






3.2.2.4 Blanching/Cooling Blanching is used for the exposure of the entire product to high temperature for a short period of time. It generally comprises three steps such as preheating, blanching and cooling. The primary function of this operation is to inactive or retard bacterial and enzyme action, which could otherwise affect the nutrient content, colour, flavour of texture during subsequent processing and storage.




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f_Cb_VCS 0.7

f_Ci_VCS 1-f_Cb_VCS






3.2.2.5 Jar washing








f_Cb_VCS 0.7

f_Ci_VCS 1-f_Cb_VCS






3.2.2.6 Potato processing







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f_Cb_VCS 0.7

f_Ci_VCS 1-f_Cb_VCS






3.2.3 Verification of the case study

The model of the CHS case study has been calibrated for both possible scenarios. Measurement of COD and TSS trough the water network have been provided by CHS. . The data have been used for the calibration of the case study model. Table 5 shows the comparison of available data and simulated data.

Table 4

Medium load High load

Experimental Simulated Experimental Simulated

Flow COD TSS COD TSS Flow COD TSS COD TSS

Chorination 482 <100 0 45 0.003 449 <100 0 45 0.003

Water Softener 67 <100 0 45 0.003 63 <100 0 45 0.003

Reverse Osmosis 33 <100 0 45 0.003 33 <100 0 45 0.003

Cleaning mill 126 No info No info 45 19.88 138 No info No info 45 18.23

Vegetable washing 69 321 111 338 119 43 828 36 869 40.69

Blanching 89 1750 59 1669 60 40 2662 35 2286 35.69

Potato process 131 1737 135 1703 297 131 1939 135 1700 278

Rotative Sieves 0.5 --- 108000 --- 109020 0.5 --- No info --- 67749

Equalization tank 467 617 410 758 5.7 434 961 548 863 5.1

Effluent 467 455 373 587 537 434 732 479 669 607

The results presented in Table 5 show that there is a fair accordance between the simulated data and experimental data available in terms of COD and TSS. It can be seen that there is a mismatch within simulated and experimental data in the effluent of the equalization tank in terms of TSS. The value of the effluent in the equalization tank is the same as in the rotating sieves (the


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value given in the experimental data mass continuity is not fulfilled in rotating sieves). Thus the value of the mathematical model has been considered to be right, and this has shown the potential of the model to detect some errors in experimental measurements, even if this is not the main objective of the modelling.

3.3 Holmen Case Study

3.3.1 Definition of the case study

In Holmen Paper Madrid, which consumes around 10,000 m3 of fresh water a day, water is no

longer regarded as a consumable, commodity or utility but as a highly valuable asset. This is the reason why the study of different scenarios in which wastewater is treated and internally reused in the mill instead of being discharged to the municipal WWTP is being a matter of high interest to the mill. The case study (Figure 2) includes four specific parts of the paper production (Drum pulper, Loop 1, Loop 2 and a paper machine), a heat exchanger and cooling tower, three Dissolved Air Flotation units (DAFs) and one thickener and finally the wastewater treatment plant existing in the mill (four Moving Bed Bioreactors (MBBRs) and one DAF). The internal circuit of the production line is closed, minimizing water consumption in the drum pulper, loop1 and loop2 (Blanco et al., 2009). Therefore, the only point at which water consumption can be reduced is the paper machine. There, water is spread as showers and it may be in contact with personnel, this is the reason why workers safety is the most demanding limitation for water quality for reuse. Moreover, conductivity has to be lower than 500 µS/cm and the water should be solids free. The point from which water can be withdrawn is the effluent of the WWTP, but further treatments are needed to meet the quality requirements. The points from 1 to 14 marked in the water network are the sampling points (SP) in which experimental data are available. Data corresponding to the real plant have been provided by Holmen Paper Madrid and Complutense University of Madrid.

Figure 2 Existing water network


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3.3.2 Modelling and calibration of the existing water network

The model of the water network has been constructed and implemented in the WQMT. The model is based on the mathematical model library presented in WP2.2 and additional models that describe the processes of the production line in a paper mill (black box models where the outflows are expressed as function of water inflow and operational parameters). The models for the processes are presented below.

3.3.2.1 Drum Pulper The first process of the production line is the drum pulper, where recycled paper is disintegrated with water and additives to obtain pulp in suspension.

Lower Limit Upper Limit

TSS --- 1000 g/m3

Table 5 Water quality requirements for water inflow


RM Raw Material entering the paper mill Ton/d 1800

f_Col_RM Colloidal matter content of raw material gCOD/kg RM 0.67

f_VSS_RM Volatile suspended matter content of raw material gCOD/kg RM 135

f_VDS_RM Volatile dissolved matter content of raw material gCOD/kg RM 2.5

f_Xb_VSS Fraction of biodegradagle COD of VSS gCOD/gCOD 0.7

f_Xi_VSS Fraction of inert COD of VSS gCOD/gCOD 1-f_Xb_VSS

f_Xii_RM Inorganic suspended matter content of raw matter g/kg RM 32.1

f_sulph_RM Sulphate content of raw matter gCOD/gCOD 0.015

f_inorgdissolved Dissolved inorganic matter content of raw matter g/kgRM 2.95·10-5

Table 6 Model parameters for Drum Pulper


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Equations that describe the outflow for the drum pulper are gathered in Table 8

Output


Xb in

RM_VSSVSS_Xb

in,bout,bQ

1000·RM·f·fXX +=

Xi in

RM_VSSVSS_Xi

in,iout,iQ

1000·RM·f·fXX +=

Xii in

RM_Xii

in,iiout,iiQ

1000·RM·fXX +=

Particulate salt in

inorg

in,jout,jQ

1000·RM·fXX +=


Cb, Ci in

RM_Col

in,bout,bQ

1000·RM·fCC +=

Dissolved

components

Sf, Sa, Si in

RM_VDS

in,jQ

1000·RM·fS +

Dissolved ions in

lvedinorgdisso

in,jout,jQ

1000·RM·fSS +=

SO4=

in

RM_sulph

in,4soout,4soQ

1000·RM·fSS +=

Table 7 Model for Drum Pulper

3.3.2.2 Loop 1 & Loop 2 After the pulping phase, the product is subjected to a series of mechanical separation. This separation phase is represented by Loop 1 and Loop 2.


TSS --- 1000 g/m3

Table 8 Water quality requirements for Loop 1


Process flow Flow rate for the flow that continues in production line m3/d 20000

f_Cnss Fraction of colloidal matter in process flow --- 0.1

f_Xnss Fraction of suspended matter in process flow --- 0.001

f_SO4 Fractions of sulphates in process flow --- 0.6

f_sol Fraction of soluble matter in process flow --- 0.8

Table 9 Model Parameters for Loop 1


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Process flow Outflow

Flow rate Q flowocessPrQ process = processinout QQQ −=


TSS ( )process

ininnss_Xprocess

Q

Q·TSS·f1TSS −=

out

ininnss_Xout

Q

Q·TSS·fTSS =

Xk process

in

in,k

process,k TSSTSS

XX = out

in

in,k

out,k TSSTSS

XX =


TCS ( )process

ininnss_Cprocess

Q

Q·TCS·f1TCS −=

out

ininnss_Cout

Q

Q·TCS·fTCS =

Ck processin

in,k

process,k TCSTCS

CC = out

in

in,k

out,k TCSTCS

CC =


Sk in,ksolprocess,k S·fS =

out

process,kprocessin,kin

out,kQ

S·QS·QS

−=

SSO4= in,4so4soprocess,4SO S·fS =

out

process,4SOprocessin,4SOin

out,4SOQ

S·QS·QS

−=

Table 10 Model for Loop 1


TSS --- 1000 g/m3

Table 11 Water quality requirements for Loop 2


Process flow Flow rate in the production line m3/d 2000

f_process_Cnss Fraction of colloidal matter released to process flow --- 0.1

f_process_Xnss Fraction of suspended matter released to process flow --- 0.2

f_process_SO4 Fractions of sulphates released to process flow --- 0.6

f_process_sol Fraction of soluble matter released to process flow --- 0.2

Recycle flow Flow rate recycled from Loop 2 to production line m3/d 15000

f_recycle_Cnss Fraction of colloidal matter in recycled flow --- 0.1

f_recycle_Xnss Fraction of suspended matter in recycled flow --- 0.1

Table 12 Model parameters for Loop 2


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Process flow Recycle flow Outflow

Flow rate Q flowocessPrQprocess = flowcycleReQrec = recprocessinout QQQQ −−=


TSS process

ininXnss_ocessPrprocess

Q

Q·TSS·fTSS = ( )

rec

ininXnss_ocessPrXnss_cRerec

Q

Q·TSSf1·fTSS −= ( )( )

out

ininXnss_ocessPrXnss_cReout

Q

Q·TSSf1·f1TSS −−=

Xk process

in

in,k

process,k TSSTSS

XX = rec

in

in,k

rec,k TSSTSS

XX = out

in

in,k

out,k TSSTSS

XX =


TCS process

ininCnss_ocessPrprocess

Q

Q·TCS·fTCS =

( )rec

ininCnss_ocessPrCnss_cRerec

Q

Q·TCSf1·fTCS −=

( )( )out

ininCnss_ocessPrCnss_cReout

Q

Q·TCSf1·f1TCS −−=

Ck processin

in,k

process,k TCSTCS

CC = rec

in

in,k

rec,k TCSTCS

CC = out

in

in,k

out,k TCSTCS

CC =


Sk in,ksol_processprocess,k S·fS = processin


out,kQQ

S·QS·QS

−

−=

processin


out,kQQ

S·QS·QS

−

−=

SSO4= in,4so4so_processprocess,4SO S·fS = processin

process,4soprocessin,4soin

out,4soQQ

S·QS·QS

−

−=

processin

process,4soprocessin,4soin

out,4soQQ

S·QS·QS

−

−=

Table 13 Model for Loop2


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Page | 19

3.3.2.3 Paper Machine The last stage of the production line is the manufacture of the paper sheet. Pulp is introduced in the paper machine, where the pulp in suspension is distributed homogeneously, obtaining a continuous paper band.

Variable Lower limit Upper limit

TSS --- 150 g/m3

Conductivity --- 3000 µS/cm

COD --- 700 g/m3

BOD --- 100 g/m3

Legionella --- 100 CFU/100mL

Table 14 Water quality requirements for paper machine


k_evap Fraction of evaporated water --- 0.06

Paper Paper production Ton/day 1500

f_Xb_paper Mass of Xb in final product --- 20

f_Xi_paper Mass of Xi in final product --- 9

f_Xii_paper Mass of Xii in final product --- 6

Table 15 Model parameters for paper machine

Steam flow Outflow

Flow rate Q )QQ·(kQ in_freshinevapsteam += steaminout QQQ −=


Xb 0 ( )

out

Paper_Xbfresh,bfreshin,bin

out,bQ

1000·Paper·fX·QX·QX

−+=

Xi 0 ( )

out

Paper_Xifresh,ifreshin,iin

out,iQ


−+=

Xii 0 ( )

out

Paper_Xiifresh,iifreshin,iiin

out,iiQ


−+=


Ck 0 out

fresh,kfreshin,kin

out,kQ

C·QC·QC

+=


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Page | 20


Sk 0 out

fresh,kfreshin,kin

out,kQ

S·QS·QS

+=

Table 16 Model for Paper Machine

The stoichiometric, kinetic and operational parameters for the MBBR have been adopted from literature (I 2.2.2.2). Default values for the DAF model parameters have been proposed in the model library taken from literature (I 2.2.2.2). However, in this case study the operational parameters for each DAF have been estimated considering the real experimental data collected in the sampling points from 1 to 14. Since the gravity table and processes for the production line have been designed for this case study specifically, all the model parameters have been estimated to fix simulated data to the experimental data available.

3.3.3 Verification of the case study

Once constructed, the integrated model that describes the Holmen Paper water circuit has been experimentally verified by monitoring throughout the whole circuit the following measurements: soluble chemical oxygen demand (COD), total suspended solids (TSS), sulphates, conductivity, cationic demand and temperature. The comparison between real plant and the simulation results is done in this section. Figure 3 shows that the simulation reproduces in an adequate grade the soluble COD and the TSS throughout the water network. However, there is a mismatch regarding soluble COD between experimental and simulated data after the gravity table. The experimental data showed an increase in soluble COD in contrast to simulated data in the clarified outflow of the gravity table (SP10). This is possibly caused by the partial solubilisation of the particulate matter in the gravity table. This error is transmitted to SP 11, the outflow of the DAF; it can be noticed that the difference between experimental and simulated data in SP 10 and 11 is similar.

0

500

1000

1500

2000

2500

3000

3500

4000

1 2 3 4 5 6 7 8 9 10 11 12 13 14

Sampling point

So

lub

le C

OD

(g

CO

D/m

3) Experimental

Simulated

0

5000

10000

15000

20000

25000

30000

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Figure 3 (a) COD and (b) TSS monitoring through circuit

In figure 4 it can be seen that, in general, the model is able to reproduce the water network behaviour in terms of sulphates and conductivity. In the case of the sulphates there is a difference in the simulated and experimental data in SP 10, the outflow of the gravity table. This reduction in the real network could be explained by precipitation of sulphates in the thickener. However, the model considers the thickener as an ideal splitter and does not predict precipitation in the thickener. This error is again propagated to the outflow of the DAF. There is no experimental data available for sulphates in the wastewater treatment plant.


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0

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Figure 4 (a) Sulphates and (b) Conductivity monitoring through circuit

There is a lack of data available for SP 12, SP 13 and SP14 regarding cationic demand, however, as shown in Figure 5, the model is able to reproduce reasonably well the behaviour of the production line in terms of cationic demand. Lastly, the model is able to predict temperature in a satisfactory grade.

0.00

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Figure 5 (a) Cationic demand and (b) Temperature monitoring through circuit

Based on these results, it can be said that the integrated model library can be used to reproduce the water network in the paper mill. However it does not reproduce some effects happening in the gravity table, leading to the conclusion that this process model in particular could be enhanced by considering partial solubilisation of the particulates and considering the precipitation of sulphates. Nevertheless, it has to be taken into account that the overall objective of the model library presented is to make a first approach to reduce the economical cost of the mill and not to accurately reproduce the process performance. The calibration of the case study is satisfactory.


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4 Exploitation and dissemination of major results de Gracia M., Goyenechea B., Claeys F.H.A and Lizarralde I. “Application of the WQMT to CHS case study” Oral Presentation in Water&Industy conference, Valladolid, May 1-4, 2011. Lizarralde I., Claeys F.H.A., Ordóñez R., de Gracia M., Sancho L. and Grau P. “New Mathematical Model Library for Industrial Water Network Optimization” Oral Presentation in Water&Industy conference, Valladolid, May 1-4, 2011. Lizarralde I., Claeys F.H.A., Ordóñez R., de Gracia M., Sancho L. and Grau P. “Water Network Optimization in a Paper Mill Based on a New Library of Mathematical Models” Oral Presentation in Watermatex conference, San Sebastián, June 20-22, 2011.


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5 Partner contribution / progress of work

5.1 First period

5.1.1 Partner 1: CEIT

CEIT has constructed and calibrated the integrated model corresponding to the Holmen water network and to the CHS water network.

5.1.2 Partner 2: ATM

ATM has constructed and calibrated the integrated model corresponding to the Holmen water network and to CHS water circuit.

5.1.3 Partner 3: UCM

UCM has provided data from the real plant for the verification and calibration of the integrated model constructed

5.1.4 Partner 4: Holmen

Holmen has provided data from the real plant for the verification and calibration of the integrated model constructed

5.1.5 Partner 5: CHS

Holmen has provided data from the real plant for the verification and calibration of the integrated model constructed


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6 Conclusions

6.1 Major achievements

• Based on the mathematical model library presented in I 2.2.2.2 and D2.2.1 an integrated mathematical model to reproduce real water networks have been built.

• The case studies for Holmen Paper Madrid and Gutarra CHS have been simulated showing the capability of the models to reproduce real water networks.

• From these studies, the capability of the models to reproduce real water networks have been shown, showing the validity of the WQMT to reproduce real water networks.

• These calibrated models can now be used to perform optimization studies as it will be shown in D 2.3.2

6.2 Future work

6.2.1 Within AquaFit4Use

The calibrated integrated models described in this report will be used for the optimization and uncertainty studies that will be presented in D2.3.2 and D2.4.1.


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7 Literature Claeys, F.H.A. Benedetti, L. Lizarralde, I. and de Gracia M. (2011) WQMT: A software framework

for representing and solving water quality issues in industry. Presented for Watermatex ’11, June 20-22, San Sebastián, SPAIN.

Fritzson P. Principles of Object-Oriented Modeling and Simulation with Modelica. Wiley-IEEE

Press, February 2004. ISBN 0-471-47163-1. Henze, M. Harremoës, P. la Cour Janses, J. Arvin, E. Waterwater Treatment, Biological and

chemical Processes. Springer, Heidelberg, 1995 Henze M., Van Loosdrecht M., Ekama G.A. and Brdjanovic D. Biological Wastewater Treatment:

Principles, Modelling and Design. IWA Publishing 2008. Lens, P. Hulshoff, P. and Asano, T. Water Recycling and Resource Recovery, IWA Publishing

2002. Purchas, D. B, Solid/liquid separation equipment scale-Up, Uplands Press Ltd 1977.


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Annex-Abreviations Symbol Description Unit

1 Sa Soluble biodegradable COD (volatile fatty acids) (< 0.1 µm) gCOD/m3

2 Sf Soluble biodegradable COD (0.1 µm-0.45 µm) gCOD/m3

3 Si Soluble inert COD (< 0.45 µm) gCOD/m3

4 Sh Protons mol H/m3

5 Soh Hydroxyl ions mol OH/m3

6 Spo4-3 Phosphate mol P/m3

7 Shpo4= Hydroxyl phosphate mol P/m3

8 Sh2po4- Dihydroxyl phosphate mol P/m3

9 Snh4+ Ammonium mol Nm3

10 Snh3 Ammonia mol N/m3

11 Sco2 Dissolved carbon dioxide mol C/m3

12 Shco3- Bicarbonate mol C/m3

13 Sco3= Carbonate mol C/m3

14 SNa+ Sodium ion mol Na/m3

15 Sk+ Potassium ion mol k/m3

16 SMg2+ Magnesium ion mol Mg/m3

17 SCa2+ Calcium ion mol Ca/m3

18 SBa2+ Barium ion mol Ba/m3

19 SMn2+ Manganese ion mol Mn/m3

20 SSr2+ Strontium ion mol Sr/m3

21 SFe2+ Ferrous ion mol Fe/m3

22 SAl3+ Aluminum ion mol Al/m3

23 SCl- Chloride mol Cl/m3

24 SSO4= Sulfate mol S/m3

25 Sno3- Nitrate mol N/m3

26 Cb Colloidal biodegradable COD (0.45 µm-7 µm) gCOD/m3

27 Ci Colloidal inert COD (0.45 µm-7 µm) gCOD/m3

28 Xb Particulate biodegradable COD (7 µm-11 µm) gCOD/m3

29 Xi Particulate inert COD (7 µm-11 µm) gCOD/m3

30 Xii Particulate inorganic matter (0.45 µm-11 µm) g/m3

31 XCaCO3 Calcium carbonate mol CaCO3/m3

32 XMgCO3 Magnesium carbonate mol MgCO3/m3

33 XFeCO3 Iron (II) carbonate mol FeCO3/m3

34 XBaCO3 Barium carbonate mol BaCO3/m3

35 XMnCO3 Manganese carbonate mol MnCO3/m3

36 XSrCO3 Strontium carbonate mol SrCO3/m3

37 XCaSO4 Calcium sulfate mol CaSO4/m3

38 XBaSO4 Barium sulfate mol BaSO4/m3

39 XSrSO4 Strontium sulfate mol SrSO4/m3

40 XCaPO4 Calcium phosphate mol Ca3(PO4)2/m3

41 XBaPO4 Barium phosphate mol Ba3(PO4)2/m3

42 XSrPO4 Strontium phosphate mol Sr3(PO4)2/m3

43 XMgPO4 Magnesium phosphate mol Mg3(PO4)2/m3

44 XMnPO4 Manganese phosphate mol Mn3(PO4)2/m3

45 XAlPO4 Aluminum phosphate mol AlPO4/m3

46 XSiO2 Silica mol SiO2/m3

47 SVi Virus units/m3

48 XEcoli Bacteria E-coli units/m3

49 XLegionela Legionella units/m3

50 XCyst Cyst-Giardia units/m3

51 XCryst Crystosporidium units/m3

52 XEgg Nematode Eggs units/m3 53 Temp Temperature ºC


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Symbol Description Unit

54 TSS Total suspended solids g/m3

55 TCS Total colloidal solids g/m3

56 TDS Total dissolved solids g/m3

57 COD Chemical oxygen demand gCOD/m3

58 COD_sol Soluble COD gCOD/m3

59 BOD Biochemical oxygen demand gCOD/m3

60 cond Conductivity (µS/cm)1

61 CD Cationic demand

62 sulph Sulphates gSO4=/m3

63 pH pH

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