Design optimisation of constructed wetlands forwastewater treatment

Rosario Pastor a, Chouaib Benqlilou a, Dora Paz b,Geronimo Cardenas b, Antonio Espuna a,*, Luis Puigjaner a

a Chemical Engineering Department, Universitat Politecnica de Catalunya, E.T.S.E.I.B., Diagonal 647,

E-08028 Barcelona, Spainb Estacion Experimental Agroindustrial Obispo Colombes EEAOC, Tucuman, Argentina

Accepted 3 July 2002

Abstract

In this work a combination of mathematical programming based optimisation strategy and

hybrid neural network models is presented in the framework of wastewater minimisation. In

the optimisation strategy, the objective function is composed by three terms: freshwater cost,

wastewater treatment cost and discharge taxes while the constraints are the balance equation

of all the production units. Once all production units that generate wastewater and the diverse

wastewater treatment systems are specified, the model automatically identifies the best

treatment option for each water stream: reuse or recycle with or without regeneration. The

formulated optimisation problem is solved using mathematical programming techniques and

details about the optimum treatment for each stream are obtained. In general, for effluent

treatment from municipal and food-industrial wastewater, the most suitable process identified

is the biological treatment. Among these treatments it has been chosen the constructed

wetlands. The proposed model for representing the dynamic of the wetland is based on a

combination of a first principal model and an artificial neural network. The hybrid model

resulted from combining both modelling strategies has been used to optimise the design of the

wetland. Finally, the methodology is applied to two case studies where the characterisation of

influent and effluent water flows is emphasised.

# 2002 Elsevier Science B.V. All rights reserved.

Keywords: Wastewater treatment; Wetlands; Design optimisation

* Corresponding author

E-mail address: antonio.espuna@ups.es (A. Espuna).

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www.elsevier.com/locate/resconrec

0921-3449/02/$ - see front matter # 2002 Elsevier Science B.V. All rights reserved.

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1. Introduction

In the last years the increasing concern about environmental regulation supported

by a strict legislation on effluents quality and cost involves industrials to look for an

efficient and low cost wastewater treatment.

Constructed wetlands (CW) (Fig. 3) are attractive ecological systems for water

treatment of both municipal and industrial wastewater. This technique has been usedin industrial wastewater treatment cases (Vinces, 1997). The CW has the ability to

treat a variety of wastewater efficiently, removing contaminants like biochemical

oxygen demand (BOD), suspended solids, pathogens, nutrients and heavy metals.

The contaminant removal is based on a complex ecosystem where the unique

required driving force is the Sun. Since this system is practically self-sufficient, the

cost to build and to maintain it is relatively low. Adapting wetland design to the

wastewater involves a trade-off between CW efficiency and sizing.

Mathematical models can help understanding how CW remove pollutants andhow to develop adequate wastewater treatment. Different models have been

proposed to represent the dynamic of the CW processes (Kadlec and Knight,

1996; USEPA, 1993). Nevertheless, the complexity of these treatment systems makes

them very difficult to model, so their optimal design and operation is usually

determined by the experience and intuition of the designer/operator. Artificial neural

network (ANN) has demonstrated to be able to represent highly no-linear multi-

input/multi-output system with an acceptable performance (Tomida et al., 1999;

Gob et al., 1999). Therefore, ANN could be an attractive modelling technique sincesome dynamics of the process under consideration are unknown or difficult to

obtain. This paper describes a way to optimally design a CW for wastewater

treatment of a Citrus plant. Given the medium granularity (n ) and the CW depth,

the area of the wetland is minimised based on the optimal values of bacterial activity

(kT) and operating conditions.

2. Methodology

The purpose of this work is twofold, at first a CW process model is built and

validated; second it is incorporated in the formulation of the CW optimisation

design. The minimisation of the area is considered. In general, biological process

dynamic presents non-linear feature and usually a large uncertainty of some key

process parameters (e.g. process kinetics), in these cases the implementation of an

ANN based model could be adopted accurately as an estimator.A municipal CW case study (Section 2.1) has been used to build and test an ANNs

for the kT. Then, the model is used to build and infer the performance of a wetland

Fig. 1. The wastewater treatment chain of the hotel.

R. Pastor et al. / Resources, Conservation and Recycling 37 (2003) 193�/204194

for a food-industrial treatment wastewater case (Section 2.2). The resulting ANN

model work with a First Principal Model (FPM) in a serial structure as it can be seen

in Fig. 4.The generalisation of the ANN model to an industrial case could be performed

since both processes present similar properties (they share the main design

parameter: BOD (Kadlec and Knight, 1996)). Several studies have demonstrated

that food-industry effluents can be generally treated by biological systems together

with municipal sewage whenever effluents did not contain excessive amount of

Fig. 2. Water and wastewater distributions in the citrus plant.

Fig. 3. Typical scheme of CW.

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contaminants. On the other hand, the recovery of by-products or starting materials

as well as the consumption of freshwater can reduce the contaminant volume in

wastewater (GTZ, 1991). In order to allow food-industry wastewater to be treated as

municipal wastewater, several actions must be taken before treatment:

�/ Identification and strict separation of wastewater according to the process.

�/ Water reuse, to minimise the wastewater volume that requires treatment.

�/ To treat the different water streams in different wastewater systems according

with the nature of contaminants.

Once the wetland model is constructed and validated the optimisation design is

tackled. The aim of the optimisation is to find out the minimum efficient area and

the corresponding operating conditions. These operating conditions are important

for a good performance of the proposed design with the variability of affluent

quality and quantity and the weather conditions. Therefore a control strategy in this

bioprocess has been proposed.

2.1. Case I

The CW considered is a sub-superficial flow type it is the secondary treatment of a

septic tank effluent generated in a Hotel. The final treatment is then achieved by

means of a conventional disinfecting system based on chlorine. This treatment chain

(Fig. 1) was conceived in order to obtain an effluent with sufficient quality for

agricultural irrigation use.

The parameters adopted in the design of the CW for this case study are shown in

Table 1 (Ruiz and Torres, 1999). In Table 2 the average BOD5 in different sampling

points of the CW are presented. It can be seen that the BOD5 measured decrease as

the wastewater moves in the direction of the water flow. Moreover the more

important pollutant removal takes place in the section between the inflow and the

first sampling point (S1).

Fig. 4. Proposed hybrid ANN/FPM model of the CW process.

R. Pastor et al. / Resources, Conservation and Recycling 37 (2003) 193�/204196

2.2. Case II

The Citrus plant considered processes 320 tons of lemon fruit per day, in a 170�/

200 days-season. The main products obtained are concentrated lemon juices of

different qualities, essential oil, and dehydrated lemon peel. During the manufactur-

ing of these products, the amount of wastewater generated is about 246.1 m3/h.

The inflow/outflow water in each production unit was determined and it is shown

in Fig. 2. Furthermore, the main contaminants, chemical oxygen demand, pH and

temperature corresponding to all the streams within the process were analysed.

3. Optimisation strategy for water reuse/recycle

An optimisation strategy for water reuse/recycle developed by Pastor et al. (2001)

is used. This strategy considers using a stream of wastewater without treatment, if its

contaminant concentration permits its reuse. Otherwise, the best treatment system is

selected, taking into consideration the cost and efficiency of contaminant removal.

The expected solution includes the quantities of wastewater that will be reused

without treatment, and the stream of water that will be treated, before being reused.

As an initial point it is considered that every production unit has a specific water use

that has to be taken from the fresh water sources or from one of the sources of

regenerated wastewater. The results obtained are summarised in Table 6. In this

Table 1

CW parameter design values for the hotel

Parameters Values

Number of beds 2

Length (L ) 15 m

Width (W ) 12.5 m

Granular medium 5�/8 mm

Average depth (h ) 0.6 m

Table 2

Average values of BOD5 in the CW beds

Sampling point BOD5, mg O2/l

Inflow 420

S1 270

S2 180

S3 150

Outflow 180

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specific case, the streams 29(b) and 29(c), as indicated in Fig. 2, should be treated

using a CW as will be shown in the following section.

4. Constructed wetlands for wastewater treatment

4.1. Design

The objective of the wetland design is to determine the number of required beds

and their sizing. Furthermore, the porosity n and the vegetation specie will be

selected according to the commonly used values as shown in Tables 3 and 4,

respectively (Pıriz et al., 2000; Brix, 1997).

5. Contaminant elimination

When the CW is designed to work as secondary treatment of a municipal

wastewater, the BOD5 is used for the sizing. The experience has shown that thedesign criteria based on the BOD5 lead to reasonable results for this specific type of

wastewater. In the case of industrial wastewater, the design is carried out in a similar

way but taking into account the predominant contaminant in the effluent. A plug

flow model with a first order kinetics which is based in BOD5 elimination results in

the Eq. (1) (Kadlec and Knight, 1996; USEPA, 1993).

Ce

Co

�e(�kTt) (1)

where Co is the BOD5 concentration of the inflow, Ce the BOD5 concentration of the

outflow, kT the first order kinetic constant, and t the hydraulic retention time.

The hydraulic retention time for a CW with subsurface flow is a function of inflow

and the gap volume in the wetland as is expressed on Eq. (2).

t�Vw

Qm

�nhA

Qm

(2)

where Vw is the gap volume in the wetland, Qm is the inflow of wastewater, n the

Table 3

Characteristics of granular medium used in CW of sub superficial flow

Material Effective size, D10 (mm) Porosity (n ) (%) Hydraulic conductivity, ks (m/d)

Gradine sands 2 28�/32 100�/1000

Gravelly sands 8 30�/35 500�/5000

Fine gravel 16 35�/38 1000�/10 000

Medium gravel 32 36�/40 10 000�/50 000

Small rocks 128 38�/45 50 000�/250 000

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Table 4

Main characteristics of the commonly present species in the CW of sub superficial flow

Specie Optimal environmental temperature, 8C Tolerable pH Speed of growth Normal Spacing, m Roots depth, m

Phragmites australis 12�/33 2.0�/8.0 Very fast 0.6 �/0.6

Scirpus lacustris 16�/27 4.0�/9.0 Moderate 0.3�/0.6 0.6�/0.9

Typha latifolia 10�/30 4.0�/10.0 Fast 0.6 0.3�/0.4

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porosity of granular bed, A is the wetland area, and h the average depth in the

wetland. Combining Eq. (1) and Eq. (2) the resulting expression for the Ce

calculation is obtained and is shown in Eq. (3).

Ce�Coe

(�kT)

�nhA

Qm

�

(3)

6. Hybrid neural network model

In this work a feed forward back-propagation ANN using two layers is adopted.

A tangential function is used as the activation function in the hidden layer while a

linear one is used in the output layer. 14 neurones were used in the hidden layer this

number was selected by trial and error. The data have been obtained directly from

the CW in three different periods of the year in order to ensure that the proposeddesign serve all along the year and can be robust when perturbation affect the

system. The data has been obtained from a CW Hotel wastewater treatment then

have been pre-processed and normalised. For the ANN building the data set has

been split into two subsets, where a 70% was used for training and a 30% for testing

the performance of the ANN. In Fig. 5 it shown the response of the ANN related to

the plant output.

The input to the ANN are the BOD inflow (Co), the BOD outflow (Ce), the

Temperature (T ) and the pH. The output of the ANN model is the first order kineticconstant (kT).

Fig. 5. ANN model of first order kinetic constant (kT).

R. Pastor et al. / Resources, Conservation and Recycling 37 (2003) 193�/204200

The effectiveness and efficiency of CW wastewater treatment is considerably based

on the values of kT. The aim to consider kT is based on the fact that its value changes

notably with the temperature variability around the year and solar light variations.

The implementation of efficient modern control strategies highly depends on the

availability of on-line information about the key biological process components, like:

kinetic constant, root plants activity. Due to the technical and economical features of

on-line sensor for these variables a control strategy could be based on the availableon-line data such as pH and temperature. Since in our case the temperature and pH

has a direct impact on the efficiency of the wetland, thus, controlling the

performance of this wastewater treatment system is reduced to control temperature

and pH. According to Table 4 given the range values of T and pH the type of

vegetation could be selected for the proposed design.

7. Results

Table 5 shows a comparison between the design based on the FPM and on the

hybrid neural network (HNN) model for the Section 2.1. It can be seen that the

efficient area is reduced considerably by using the HNN model and the optimisation

strategy.

The proposed strategy applied to the case described in Section 2.2 leads to a

reduction of the contaminant load of wastewater discharge by application of the

following solutions:

1) Streams 8, 9, 12, 16, 18 can be reused/recycled directly (Fig. 2).

2) Streams 11, 27 and 20 contain soils, oils, emulsion and high contaminant loadare separated to be processed in a specific treatment.

3) The optimisation strategy for water reuse/recycle explained above was applied to

the remained streams. The results obtained using the model are detail in Table 6.

4) Stream 29(b), 29(c) are treated in CW before its discharge in a river.

The aim of the optimisation is the CW area minimisation maintaining the required

efficiency. Since the ANN model extrapolation is inaccurate the Co value has to be

within the range of the ANN data input. If Co value for Section 2.2 is greater than

those of Section 2.1, a previous treatment is needed. The Tables 7 and 8 show the

Table 5

Comparison among FPM and HNN models

Design parameter FPM design HNN design

Area (A ) 374 150.2

Temperature 10 18.00

pH 6.5 6.1

Kinetic constant (kT) 0.46 0.15

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Table 6

Summary of the results in the citric industry

Unit Water consumption Optimisation water consumption flow (t/h) Wastewater Optimisation wastewater

Stream Flow Water Reuse without treat Reuse with treat Stream Flow Discharge

Mixt 9 7.322 �/ �/ 7.286 13 7.322 �/

Green 8 47.558 15.045 26.280 6.233 15 47.558 �/

Screen 2 16(a) 45.000 18.437 18.924 7.638 29(a) 49.066 49.066

Screen 3 16(b) 45.000 31.818 �/ 13.182 29(b) 45.000 20.337

Vincent �/ �/ �/ �/ �/ 29(c) 23.657 23.625

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comparison between the FPM design and HNN results when a separated CW is

designed for streams 29(b), 29(c).

8. Conclusions

The effluents of the food-industry as well as municipal outflows can be processed

through biological treatment systems. There is a strong relationship between the

quantity and the quality (pollutants) of the effluents with the area of the CW.

By means of the application of the optimisation strategies for water reuse/recycle

in Section 2.2, it is possible to reduce substantially the quantity of the effluents. With

the application of strategies of design optimisation to HNN, it is possible to find the

minimum area for maximum efficiency of removal of pollutants. With thecombination of both strategies it is possible to find the maximum reuse/recycle,

the optimal area of treatment, the minimum discharge of wastewater, and

consequently the minimum environmental impact at a minimum cost.

Acknowledgements

R. Pastor kindly acknowledges a fellowship from the Spanish Ministry of Science

and Culture. Travel and living expenses were provided by the Centre per la

Cooperacio i el Desenvolupament (CCD) of UPC. Wetland data supplied by Mr. T.

Pıriz (Catalan Agency of Water) is also thankfully acknowledged.

Table 7

CW parameter design for the treatment of stream (29 b)

FPM design HNN design

Area (A ) 17 659 5753

Temperature 10 18

pH 7 7

Kinetic constant (kT) 0.46 0.4

Table 8

CW parameter design for the treatment of stream (29 c)

FPM design HNN design

Area (A ) 20 514 6680

Temperature 10 14

pH 7 8

Kinetic constant (kT) 0.46 0.13

R. Pastor et al. / Resources, Conservation and Recycling 37 (2003) 193�/204 203

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