Artificial neural network analysis of liquid desiccant dehumidifier performance in a solar hybrid air-conditioning system

Accepted Manuscript Artificial neural network analysis of liquid desiccant dehumidifier performance in a solar hybrid air-conditioning system Abdulrahman Th. Mohammad, Sohif Bin Mat, M.Y. Sulaiman, K. Sopian, Abduljalil A. Al-abidi PII:

S1359-4311(13)00420-1

DOI:

10.1016/j.applthermaleng.2013.06.006

Reference:

ATE 4857

To appear in:

Applied Thermal Engineering

Received Date: 8 April 2013 Accepted Date: 5 June 2013

Please cite this article as: A.T. Mohammad, S.B. Mat, M.Y. Sulaiman, K. Sopian, A.A. Al-abidi, Artificial neural network analysis of liquid desiccant dehumidifier performance in a solar hybrid air-conditioning system, Applied Thermal Engineering (2013), doi: 10.1016/j.applthermaleng.2013.06.006. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Artificial neural network analysis of liquid desiccant dehumidifier performance in a solar hybrid air-conditioning system

Abduljalil A. Al-abidia

Solar Energy Research Institute, University Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia

b

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a

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Abdulrahman Th. Mohammada,b,*, Sohif Bin Mata, M. Y. Sulaimana, K. Sopiana and

Department of Mechanical Engineering, Baqubah Technical Institute, Foundation of Technical

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Education, Baghdad, Iraq

*Corresponding author; Abdulrahman Th.Mohammad Tel: + 60173672679, Fax: + 60389214593

Abstract

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E-mail:[email protected]

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A new solar hybrid liquid desiccant air conditioning system has been tested and simulated to investigate the technical feasibility of cooling systems for greenhouse applications using weather

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data for Malaysia. In this paper, experimental tests are carried out to investigate the performance of a counter flow dehumidifier using lithium chloride (LiCl) solution as the desiccant. A single and multilayer artificial neural network is used to predict the performance of the dehumidifier. Five parameters are used as inputs to the ANN, namely: air and desiccant flow rates, air inlet humidity ratio, and air and desiccant inlet temperatures. The outputs of the ANN are the temperature, humidity ratio, moisture removal rate, and the effectiveness. ANN predictions for

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these parameters are compared with the experimental values. The results show that the optimum testing model for moisture removal rate in the dehumidifier was the 5-5-5-1 structure with R2 = 0.91, whereas the optimum testing model for effectiveness was the 5-11-11-1 structure with

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R2 = 0.79. The maximum temperature and humidity ratio difference between the ANN model and experimental are 1.2oC and 1.9 g/kg, respectively.

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Keywords: desiccant, dehumidifier, effectiveness, ANN.

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Nomenclature

E

Sum of square error

ma

Air mass flow rate at the inlet of the dehumidifier (g sec-1)

P

Total pressure of air above the solution(Pa)

Partial pressure of water vapor in the solution(Pa)

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P wz

W

Weight matrix

Win

Inlet air humidity ratio(kg/ kg dry air)

Outlet air humidity ratio(kg/kg dry air)

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Wout

Synaptic weights

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wj

W out _ eq

Humidity of the air in equilibrium with the desiccant solution(kg/kg dry air)

Greek symbols

∆mcon

Moisture removal rate in dehumidifier (g sec-1)

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Effectiveness of the dehumidifier

υ

Neuron

ϕ (υ )

Sigmoid function

θ

Bias

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ε

ANN

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Subscripts

Artificial neural network

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learngdm Learning gradient descent with momentum weight/bias learning function Mean square error

Tansig

Tan-sigmoid transfer function

traingdm

Training gradient descent with momentum

1. Introduction

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Dehumidification is the process of removing water vapor from moist air, which occurs in a

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liquid or solid desiccant dehumidifier. The effectiveness and condensation rate is mostly used as an index to determine the performance of the liquid desiccant dehumidifier. Based on previous

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studies, modeling techniques for prediction of dehumidifier performance can be classified into: theoretical analysis model, experimental model and artificial intelligence model. Theoretical analysis models alone can hardly predict the real results, and should be verified by the experimental results. Many studies have investigated the theoretical analysis and computer simulation of the heat and mass transfer process in a liquid desiccant dehumidifier. A theoretical model that uses NTU as its input parameter was developed by Liu et al. [1] to simulate the heat

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and mass transfer process in a cross-flow liquid desiccant dehumidifier/regenerator. Many researchers have performed analytical models on the fluid flow and the conjugation of the heat and mass transfer cross membrane. A mathematical model was used by Zhang et al. [2] to study

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the fluid flow and conjugate the heat and mass transfer in a hollow fiber membrane module used for liquid desiccant dehumidification. The module-like shell and tube heat exchanger, the air stream flows in the shell tube, while the desiccant stream flows in the tube side of the module.

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The model can express the Nusselt and Sherwood numbers, which can provide the fundamentals for future system and structural optimization.

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Experimental models can be developed by using conventional approaches such as statistical techniques. Many studies have been carried out on the performance of a packed tower under various parameters such as the inlet mass flow rate, temperature, and humidity of the air, as well as the inlet mass flow rate, temperature, and concentration of the desiccant solution,

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Chung et al. [3] and Kessling et al. [4]. A study was conducted by Moon et al. [5] to present new mass transfer performance data of a cross-flow liquid desiccant dehumidification system, and a new empirical correlation was developed for the dehumidification effectiveness, which fitted the

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experimental data within ±10%. Sanjeev et al. [6] developed an experimental setup to study the performance of the liquid desiccant dehumidification system under different operating conditions

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(hot water temperature, inlet air conditions, and solution concentration). Lithium chloride and calcium chloride were used as desiccants in the system. The results show that the effectiveness of the dehumidifier was found to range between 0.25 and 0.44, while that of the regenerator was between 0.07 and 0.80.

Liu et al. [7] compared the mass transfer performance of lithium chloride and lithium bromide aqueous solutions. The experimental results show that the dehumidifcation performance

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of the LiCL was better than LiBr in the condition of having the same desiccant flow rate. Liu et al. [8] investigated a heat and mass transfer model, which was validated using experiments and numerical results to analyze the uncoupled heat and mass transfer process, relative humidity, and

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the enthalpy difference between the solution and air. Liu et al. [9] conducted an experimental study on the mass transfer performance of cross flow configuration dehumidifier. Celdek structured packing was used in the dehumidifier and lithium bromide was used as the liquid

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desiccant.

Artificial intelligence models have become the preferred trend for some researchers to predict the

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performance of heat and mass transfer processes in the dehumidifier because of their ability to learn and adapt to change with little human interaction. In order to see how capable the artificial neural network is as a technique for modeling the liquid desiccant dehumidification, several studies are reviewed. An ANN-based model was investigated by Gandhidasan and Mohandes

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[10] to simulate the relationship between the inlet and outlet parameters of a packed bed-liquid desiccant dehumidifier with a height of 0.6 m and polypropylene packing material with a specific area of 210 m2/m3, using lithium chloride as the liquid desiccant. Eight parameters were used as

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inputs for the ANN model; air input parameters (flow rate, temperature, and humidity ratio), solution input parameters (flow rate, temperature, and concentration), dimensionless temperature

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ratio, and inlet temperature of the cooling water. Water condensation rate, solution concentration, and solution temperature were used as output parameters. El-shafei et al. [11] applied a multilayer artificial neural network model to study the performance of a solar liquid dehumidification/regeneration system. Harpreet [12] developed the Mamdani fuzzy models to predict the water condensation rate from the air to the liquid desiccant in an air dehumidification process. Mohammad et al. [13] proposed ANN model for predicting the performance of a liquid

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desiccant dehumidifier in terms of the water condensation rate and dehumidifier effectiveness. MATLAB code was designed to study feed forward back propagation. The results show that the maximum percentage difference between the ANN and experimental values for the water

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condensation rate and dehumidifier effectiveness were 8.13% and 9.0485%, respectively. In the current study, an experimental analysis was performed to evaluate the performance of a lithium chloride liquid desiccant counter-flow dehumidifier at varying solution and air inlet conditions.

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Moisture removal rate and effectiveness were used as performance indicators. An artificial neural network was employed to predict the performance of the desiccant dehumidifier. The

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results show that the optimum testing model for moisture removal rate in the dehumidifier was the 5-5-5-1 structure with R2 = 0.91, whereas the optimum testing model for effectiveness was the 5-11-11-1 structure with R2 = 0.79.

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2. System description

The schematic diagram of the proposed system is shown in Fig.1. The proposed system consists of three main components: dehumidifier, regenerator, and refrigerant cycle. The refrigerant cycle

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is used to pre-cool the liquid desiccant on the dehumidification side and pre-heat it on the regeneration side. There are also some auxiliary fittings such as two centrifugal fans, two

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solution pumps, two valves, and two flow meters. The dehumidifier and the regenerator are the core of the system. The structured packing

has a section area of 0.24 m2 and a height of 0.85 m. Two solution sumps, with a capacity of 40 L each, are located at the bottom of the system (one for storing the weak solution and the other for storing the strong solution). A pump was used to circulate the hot, strong desiccant solution through the evaporator to pre-cool it before entering the dehumidifier using the refrigerating

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output from the vapor compression unit. Ambient air is drawn by a centrifugal fan into the bottom of the dehumidifier and is in direct contact with the strong solution in a counter-flow configuration. Once the strong solution has attracted moisture from the air, the processed air is

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dehumidified, and its temperature depends on the temperature of the desiccant solution.

At the same time, the desiccant solution becomes weak and has to be reactivated. The weak solution in the dehumidifier sump is pumped into the condenser, where it is pre-heated by the

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exhaust heat from the condenser of the vapor compression unit before entering the regenerator. Ambient air is also pre-heated in the heating coil (state A4) by solar-heated water (states W1 to

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W2) using the evacuated tube solar collectors, which are located about 20 m from the system. The total area of solar collector was 34 m2 and can produce hot water at about the maximum temperature of 95°C. After pre-heating, the air stream enters the regenerator (state A5) to carry over the moisture from the hot desiccant solution. The hot and wet air leaving the regenerator is

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subtracted into the ambient air. Air filters are used at the entrance to the dehumidifier/regenerator to prevent dust from entering the system. Two eliminators are used at the outlet of the absorber

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/regenerator to prevent the carryover of the desiccant solution particles into the ducts.

3. Measurements and instrumentation

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To measure the air dry bulb temperature and relative humidity at different points in the system, five thermocouples with an accuracy of ±0.1°C, and TR-RH2W humidity sensors with an accuracy of ± 3% RH are installed at points (A1, A2, A3, A4, and A5). Two anemometers (AM402) are installed at points (A1 and A3) to measure the air velocity, and then calculate the air flow rate entering the dehumidifier and regenerator. The tested desiccant parameters included temperature and flow rate. Four thermocouples with an accuracy of ±0.1°C are installed at points

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(S1, S2, S3, and S4) to measure the temperature before and after entering the dehumidifier and regenerator. A glass flow meter with an accuracy of ±10 l/h is installed to measure the mass flow rate of the desiccant solution entering the dehumidifier. The temperatures of the pre-cooling and

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pre-heating of the evaporator and condenser are measured by four thermocouples installed at points (R1, R2, R3, and R4). Two thermocouples are used to measure water temperature before and after the heating coil at points (W1 and W2), and one glass flow meter is used to control the

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solar hot water flow rate from the storage tank.

All these sensors are coupled with a PC system to store the data in the computer. An ADAM-

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4018 data logger was used to collect sensor data. This data-acquisition system has a capability of two single-ended (six differential) voltage channels. Various single-channel inputs are available in the ADAM data acquisition: thermocouple, mV, V, and mA. The data acquisition system samples data every 10 minutes. The main characteristics of the different measuring devices are

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shown in Table 1.

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4. Liquid desiccant dehumidifier performance parameters

The performance of the dehumidifier is evaluated by calculating the moisture removal rate and

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its effectiveness. Many researchers have conducted theoretical models, and experimented on the heat and mass transfer of liquid desiccant dehumidifiers. Nelson and Goswami [14], and Oberg and Goswami [15] evaluated the performance of dehumidifiers in terms of their moisture removal rate. The water transferred from the air to the liquid desiccant was defined by Yin et al. [16] as follows:

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∆m

= ma × ( win − wout)

con

(1)

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Where ma is the air mass flow rate at the dehumidifier inlet, win is the inlet air humidity ratio, and wout is the outlet air humidity ratio.

The effectiveness of the dehumidifier is evaluated as the ratio between the change in actual humidity in the air and the maximum change in humidity possible and can be represented

− wout

w −w

out −eq

in

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ε = win

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as follows [17]:

(2)

Where wout-eq is the humidity ratio of the air in equilibrium with the desiccant solution under the following operating conditions, as shown by Kinsara et al. [18]:

out − eq

= 0.62185 × P wz

(P − P ) wz

(3)

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w

Where P is the total pressure of air above the solution, and Pwz is the partial pressure of water vapor in the solution.

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5. Artificial Neural Network model

A neural network consists of a large number of simple processing elements called neurons.

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Neurons are connected to each other by directed communication links, which are associated with weights [19]. A typical artificial network has an input layer, an output layer, and at least one hidden layer. The number of hidden layers has no theoretical limit. The number of input and output nodes is determined by the nature of the modelling problem, the method of input data representation chosen, and the form of network output required. Fig. 2 shows the model of a neuron. The three basic elements of the neural model are as follows: (1) A set of synapses or connecting links, each of which is characterized by a weight or strength of its own. (2) An adder 9

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for summing up the input signals, weighted by the respective synaptic strength of the neuron. (3) An activation function for limiting the amplitude of the output of a neuron (a squashing function) Haykin [20].

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Normalization process is performed before the training and testing phase begins. It is possible to normalize the quantitative variable to some standard range such as [0 1] or [-1 1]. The neural model also includes an externally applied bias θ , which increases or decreases the net

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input of the activation function, depending on whether its value, is positive, or negative. Each neuron consists of two parts, the net function and the activation function. The activation function

{y ;1 ≤ j ≤ m} are combined inside the neuron. A neuron can j

be described in a mathematical model as: m

υ = ∑ wj y + θ j

j =1

{w ;1 ≤ j ≤ m} is the synaptic weights and θ j

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Where

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determines how the network inputs

(4)

is the bias, which is used to model the

threshold. The threshold is a factor used to calculate the activation of the given neural network (the activation is based on threshold):

(5)

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y = ϕ (υ )

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The logistic function is the most popular activation function used in the concentration of neural networks, and is defined as follows:

ϕ (υ ) = 1

(6)

( −υ )

1 + exp

The learning cycle, which consists of input presentation, error calculation and alteration, is iterated using a set of different input/output examples until the global root mean-square error of the network reaches an acceptably low level.

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6. Error Back-Propagation Training of Neural Network

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A key step in applying ANN model is to choose the weight matrices. Assuming a layered ANN structure, the weights feeding into each layer of neurons form a weight matrix of that layer (the input layer does not have a weight matrix because it does not contain neurons). The values

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of these weights are determined using the error back-propagation training method.

Given a set of training samples {(x(k)); 1 ≤ k ≤ K}, error back-propagation training

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begins by feeding all k input through the neural network and the corresponding output {z(k); 1≤ k ≤ K} is computed Yu and Jeng [21]. The sum of the square error is computed as follows: K

E=∑ k =1

[e(k )]

2

K

=∑ k =1

[d (k )− z(k )]

2

k =1

[d (k )− f (W x(k ))]

[W W W 0

1

2

2

]

.......W k , x is the input vectors =

(7)

[x x x ....... x ] 0

1

2

k

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Where W is the weight matrix =

K

=∑

and d is the desired target value. The objective is to adjust the weight matrix W to minimize error E. However, the aforementioned calculations cause a nonlinear least square optimization

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problem. There are numerous nonlinear optimization algorithms available to solve this problem. Basically, these algorithms adopt a similar iterative formulation:

W (t + 1) = W (t ) + ∆W (t )

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(8)

Where ∆W (t ) is the correction made to the current weights W (t ) .

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7. Normalization

Before training the neural networks, the input vectors and the target vectors were

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normalized from 0 to 1 as the standard range. Equation 9 from Sanjay et al. [22] was used to normalize the input and output data. The dmin and dmax values for normalization are given shown in Table 2.

i

=

0.8 (d − d ) + 0.1 d max − d min i min

(9)

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x

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Where dmax is the maximum value of the input/output data, dmin is the minimum value of the input/output data, and di is the ith input/output data.

The prediction performance of the neural networks was evaluated based on the mean square error (MSE) in percentage between the predicted and the experimental values according to

N

∑ X predicted − X exp erimental i =1 N

2

(10)

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MSE =

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the following expression:

8. Results and discussion

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8.1 Predicting the performance of the liquid desiccant system using ANN

Zhang et al. [23] reported that the number of nodes in the hidden layer is a function of the

number of input nodes, which is denoted as i or j = n/2, 1*n, 2*n, 2*n+1 where n is the number of input nodes. Therefore, the hidden layer has two configurations. The first contains a single hidden layer with four structures (5-2-1, 5-5-1, 5-10-1, 5-11-1), and the second contains multiple

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hidden layers with four structures (5-2-2-1, 5-5-5-1, 5-10-10-1, 5-11-11-1). ANN relies on trial and error and other factors, such as network structure, amount of training data, amount of testing data, training function, learning function, and performance function.

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A total of 16 ANN structures and a MATLAB algorithm were utilized to predict the performance of the liquid desiccant dehumidifier. The algorithm was trained through the back propagation technique with traingdm, learngdm, MSE, and tansig as the training, learning,

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performance, and transfer functions, respectively. A detailed flow chart is shown in Fig.3. Sixteen structures were utilized to predict the performance of the dehumidifier (eight structures

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were utilized to predict the MRR and eight to predict the effectiveness). Five parameters were employed in the input layer (air mass flow rate, inlet air temperature, inlet air humidity ratio, desiccant mass flow rate, and inlet desiccant temperature), and one parameter was employed in the output layer (MRR or effectiveness). The ANN model can identify the outlet parameters of

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air, such as air temperature and air humidity ratio for the dehumidifier, as well as the performance of the dehumidifier in terms of MRR and effectiveness, which are important in

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studying the enhancement of heat and mass transfer in a liquid desiccant system.

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8.2 ANN model of the dehumidifier

The 5-5-5-1 network structure yielded the best model for the prediction of MRR, with the best validation performance of 0.011054 at epoch 9 as shown in Fig.4 (a). The 5-11-11-1 network structure yielded the best model for the prediction of dehumidifier effectiveness, with the best validation performance of 0.0077333 at epoch 6 as shown in Fig.4 (b).

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The results of comparing predicted air temperature and humidity ratio in the outlet dehumidifier with the experimental findings are shown in Fig.5. The maximum temperature and humidity ratio difference between the ANN and experimental models are 1.2 °C and 1.9 g/kg,

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respectively. The predicted and measured air temperature and humidity ratio are almost precisely the same, but some disparity exists in a few points. The disparity is probably a result of the accuracy of the ANN model and experimental error.

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The comparison between the 5-5-5-1 and 5-11-11-1 structures and the experimental data is shown in Table 3. The maximum percentage difference between ANN and experimental MRR

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is 11.96% with R2 = 0.98 for training and R2 = 0.91 for testing. The maximum percentage difference between ANN and experimental effectiveness is -14.07% with R2 = 0.91 for training and R2 = 0.79 for testing. The predicted results of the neural network coincide with the experimental data. Fig. 6 presents the neural network graphs of the 5-5-5-1 and 5-11-11-1

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structures, respectively. The training and testing values with the experimental values of MRR and effectiveness are also shown in the figures. The R2 for all the structures of the neural network during training, validation, testing, and the total are listed in Table 4. The R2 values vary from

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0.71 to 0.98 during the training phase, from 0.74 to 0.98 during the validation phase, and from 0.71 to 0.98 during the testing phase. The total R2 values ranged between 0.70 and 0.97.

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The mean square error defined as a network performance index. The neural network training process was terminated when the maximum number of epochs (15000) was reached or when the minimum MSE of the validating sets was attained. The graphs in Figs.7 and 8 were generated by considering the water condensation rate and predicted effectiveness values that appear in the testing phase of the artificial neural network for the single hidden layer and two hidden layers, respectively. The testing patterns of the eight neural structures generated a line similar to the

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experimental line, with good examination of the plot between the (5-5-5-1) structure and the experimental values for MRR and between the (5-11-11-1) structure and experimental values for

8.3

Effect dehumidifier performance

of

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effectiveness. Examination of the plots revealed a significant difference in the MSE values.

input

parameters

on

the

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Figs. 9, 10, and 11 illustrate the effect of inlet air temperature, inlet air humidity ratio, and inlet solution temperature on the moisture removal rate and effectiveness of the dehumidifier. Fig. 9

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shows that increasing the inlet air temperature increases the solution temperature, which in turn increases the partial pressure of the desiccant solution. Hence, the amount of MRR and the effectiveness of the dehumidifier are reduced.

Fig. 10 shows that increasing the inlet air humidity ratio increases the MRR in the

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dehumidifier, and produces a slight change in effectiveness. The partial water vapor pressure of the air increases by increasing the humidity ratio, which increases the mass transfer potential.

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However, the inlet humidity ratio does not significantly affect dehumidifier effectiveness.

Fig. 11 shows the influence of the liquid desiccant inlet temperature on MRR and

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dehumidifier effectiveness. MRR declines rapidly as the solution temperature increases. Increasing the solution temperature increases the vapor pressure of the solution and reduces the mass transfer potential between the moisture air and liquid desiccant. Increasing the solution temperature increases the equilibrium humidity ratio on the solution surface and the humidity ratio of the outlet of air from the dehumidifier. Lastly, the effectiveness of the dehumidifier increases slightly as a result of the offsetting effects between the numerator and denominator of Equation 2. 15

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9. Conclusions

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An experimental analysis was performed to evaluate the performance of a lithium chloride liquid desiccant counter-flow dehumidifier at varying solutions and air inlet conditions. Moisture removal rate and effectiveness were employed as performance indicators. An artificial neural

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network was used to predict the performance of the desiccant dehumidifier. A comparison between the artificial model and experimental data was performed. Based on an experimental

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and ANN results, the following conclusions can be drawn:

The effect of the main factors, such as air temperature, air humidity ratio, and solution temperature,

on

liquid

desiccant

dehumidification

was

quantified

through

experimentation. The performance of the dehumidifier was consistent with that obtained

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in the other studies reported in the literature.

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y comparing the experimental values and predicted values through ANN, the maximum

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difference in temperature and humidity ratio in the outlet of the dehumidifier was found to be 1.2 oC and 1.9 g/kg, respectively. These results indicate that the accuracy of the

•

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ANN model was satisfactory and coincided with the experimental data. The maximum percentage difference between

ANN and experimental values of MRR and the effectiveness of the dehumidifier was found to be 11.9 % and -14.07 %, respectively.

•

The dependence of prediction accuracy on the number of layers and nodes for the moisture removal rate and effectiveness indicates that accuracy could be improved

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further by expanding the experimental database for network training. However, such expansion would increase processing time. •

The optimum testing model for MRR in the

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dehumidifier was the 5-5-5-1 structure with R2 = 0.91, whereas the optimum testing model for effectiveness was the 5-11-11-1 structure with R2 = 0.79.

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Acknowledgement

The authors are grateful to the staff of Solar Energy Research Institute/University Kebangsaan Malaysia

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for their help.

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dehumidification and desiccant regeneration, Sol. Energy 72 (2002) 351–361. [15]Oberg V., Goswami D. Y., Experimental study of the heat and mass transfer in a packed bed liquid desiccant air dehumidifier, Solar Energy Eng. 120 (1998) 289–297.

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[16] Yin Y., Zhang X., Chen Z., Experimental study on dehumidifier and regenerator of liquid desiccant cooling air conditioning system, Build. Environment. 42 (2007) 2505-2511.

[17]Dai Y.J., Wang R.Z., Zhang H.F., Yu J.D., Use of liquid desiccant cooling to improve the

[18]Kinsara A. A., Elsayed M. M.,

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performance of vapor compression air conditioning, Appl. Therm. Eng. 21(2001)1185-1202. AI-Rabghi O. M.,

Proposed energy-efficient air-

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conditioning system using liquid desiccant, Appl. Therm. Eng. 16 (1996) 791-806. [19]Sivanandam S. N., Sumathi S., Deepa S. N., Introduction to neural networks using matlab 6, In Tata McGraw-Hill Publishing Company Limited, New Delhi, 2006. [20]Haykin S., Neural Networks and Learning Machines, third ed., New jersey, 2008.

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[21]Yu Hen Hu, Jeng-Neng Hwang , Handbook of neural network signal processing, EPublishing Inc, Washington, pp 6-10, 2001. [22]Sanjay C., Jyothi C., Chin C.W.,

A study of surface roughness in drilling using

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mathematical analysis and nueral networks, Int. J. Advanced Manufacturing Technol. 29 (2006)

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[23]Zhang G., Patuwo B.E., Hu M.Y. 1998. Forecasting with artificial nueral networks, the state of the art. Int. J. Forcast.14 (1998) 35-62.

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Fig. 1.

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Fig. 2.

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Fig.3.

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Fig.4.

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Fig.5.

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Fig.6.

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Fig.10.

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Fig. 11.

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Fig. 1. Schematic diagram of the system a) Photograph of the liquid desiccant unit b) Experimental facility. Fig. 2. A simplified Artificial Network.

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Fig.3. Flow chart of Artificial Neural Network program. Fig.4. Best validation performances in dehumidifier a) MRR b) ε.

Fig.5. Comparison of ANN model with experimental for dehumidifier a) air outlet temperature b) air outlet humidity ratio.

Fig.6. Neural Network Training Regression a) Epoch 9, Validation stop for MRR (5-5-5-1 structure) b) Epoch 6,

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Validation stop for ε (5-11-11-1 structure).

Fig. 7. Investigation of the similarity of line pattern between experimental and ANN of MRR of dehumidifier in the testing phase (a) Single hidden layer (b) Two hidden layer. Fig.8. Investigation of the similarity of line pattern between experimental and ANN of ε of dehumidifier in the

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testing phase (a) Single hidden layer (b) Two hidden layers. Fig.9. Influence of the inlet air temperature on the MRR and the dehumidifier effectiveness.

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Fig.10. Influence of the inlet air humidity ratio on the MRR and the dehumidifier effectiveness. Fig. 11. Influence of the inlet solution temperature on the MRR and the dehumidifier effectiveness.

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Table 1

Parameters

Devices

Accuracy

Operational range

Air velocity

Anemometer AM-402

±2% m/s

0.4-30 m/s

Air and solution temperature

Thermocouples Type K

±0.1 oC

Air relative humidity

Humidity sensors TR-RH2W

± 3% RH

Solution and water flow rate

Glass flow meter

±10 l/h

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Specifications of the different measuring devices

Parameters

Symbol Ta

Desiccant inlet temperature

Ts

Air flow rate

ma

Desiccant flow rate

ms

ranges

0

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27 to 34.5

0

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27.5 to 38.5

Kg s-1

0.08 to 0.125

Kg s-1

0.07 to 0.1

Win

KgH20/kgda

mde

g s-1 %

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Effectiveness

Unit

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Air inlet temperature

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Values of normalization

Water condensation rate

10-95 %RH

200-2600 LPH

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Air inlet humidity ratio

0 - 1370 oC

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0.020 to 0.025 0.1 to 1.2 0.32 to 0.6

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Table 3

ANN

MRR (g /s):

ε (%):

Structure 5-5-5-1

Structure:5-11-11-1

Experimental

Difference

ANN

Experimental

%

Difference %

0.536069

-1.51655

0.228203

0.260325

-14.07560

0.035443

0.040769

-7.43072

0.159331

0.140314

11.93534

0.840546

0.852241

-1.39136

0.460766

0.509122

-10.49470

0.512064

0.554578

-8.30242

0.287034

0.292767

-1.997320

0.991234

0.925955

6.585601

0.521861

0.519329

0.485304

0.574858

0.523406

8.950390

0.488886

0.420900

13.90630

0.160392

0.141200

11.96547

0.655914

0.650076

0.890130

0.516200

0.484665

6.109024

0.484689

0.429233

11.44151

0.728998

0.766693

-5.170700

0.523755

0.506071

3.376439

0.531539

0.532299

-0.142980

0.408588

0.396931

2.852920

0.634952

0.683850

-7.701000

0.486023

0.456648

6.04389

0.249158

0.224786

9.782058

0.336711

0.347747

-3.27756

0.314924

0.280758

10.84873

0.434143

0.439654

-1.26949

0.154882

0.144212

6.888564

0.345669

0.428137

-2.06695

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0.528061

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Comparison of testing ANN results with the experimental data in dehumidifier

0.427488

0.424523

0.693517

0.224778

0.237741

-5.76695

0.565709

0.624710

-10.4296

0.228581

0.336404

-4.1710

0.801449

0.716167

10.64094

0.344613

0.366495

-6.34981

0.523987

0.534379

-1.98315

0.36592

0.374488

-2.34132

1.231208

1.091747

11.3272

0.430422

0.437572

-1.66123

0.884682

0.795982

10.02611

0.556232

0.533211

4.138731

1.201267

1.299486

-8.17628

0.442269

0.443310

-0.23525

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Table 4 Values of the ANN structures in the training, validation, testing phases and the all Phases Training

Validation

Testing R2

Total

0.97

0.98

0.95

0.97

5 - 5 -1

0.96

0.96

0.98

5 - 10 - 1

0.98

0.98

0.95

5 - 11 - 1

0.98

0.98

5 - 2 - 2 -1

0.97

0.98

5 - 5 -5 - 1

0.98

0.98

5 -10 -10 -1

0.94

0.91

5 -11 -11 -1

0.97

0.90

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5 -2 - 1

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MRR (g /s)

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Structure No.

0.96 0.97

0.95

0.97

0.97

0.97

0.91

0.96

0.92

0.93

0.97

0.95

0.79

0.78

0.72

0.74

0.81

0.74

0.85

077

0.79

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Effectiveness (%)

0.71

5 - 5 -1

0.74

5 - 10 - 1

0.84

5 - 11 - 1

0.77

0.88

0.72

0.77

5 - 2 - 2 -1

075

0.92

0.91

0.80

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5 -2 - 1

5 - 5 -5 - 1

0.77

0.81

0.71

0.72

5 -10 -10 -1

0.79

0.75

0.73

0.70

5 -11 -11 -1

0.91

0.81

0.79

0.86

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Highlights •

Experimental tests are carried out to investigate the performance of a counter flow dehumidifier. Single and multilayer artificial neural network used to predict the performance of

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dehumidifier. •

Outputs of the ANN are the temperature, humidity ratio, moisture removal rate, and effectiveness.

The results show that the optimum testing model for MRR was the 5-5-5-1 structure with

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R2 = 0.91.

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The optimum testing model for effectiveness was the 5-11-11-1 structure with R2 = 0.79.

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•