# Characterization of an Absorption Machine Using Artificial Neural Networks

A. Ferre, M. Castilla, J. A. Carballo, J. D. Álvarez

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A. Ferre, M. Castilla, J. A. Carballo, J. D. Álvarez. Characterization of an Absorption Machine Using Artificial Neural Networks. 19th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2023, León, Spain. pp.279-291, 10.1007/978-3-031-34107-6_22. hal-05498075

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# Characterization of an Absorption Machine Using Artificial Neural Networks

A. Ferre<fnref n="1" />, M. Castilla<fnref n="1" />✉, J. A. Carballo<fnref n="2" />✉, and J. D. Álvarez<fnref n="1" />✉✉

**Abstract.** To fulfil current energy objectives established by governments and public institutions is mandatory to develop zero-emission buildings or refurbish existing ones using, to this aim, systems based on renewable energy sources. Absorption machines are becoming an increasingly important alternative to conventional vapour-compression chillers due to the possibility of being powered with heat from renewable energy sources, such as solar energy, biomass or others. Thus, this kind of system can be the keystone of the air-conditioning system in residential, public or commercial buildings. Most current approaches to model absorption machines are made on stationary conditions, with less attention paid to the dynamic phenomena inherent to renewable energy sources. Besides that, the lack of internal measures in this kind of plant makes the development of a dynamic model a challenging task. This work presents a dynamic model of a LiBr Absorption Machine developed through Artificial Neural Networks. Comparison with real data shows an appropriate behaviour of the obtained model. Simulations were done to evaluate the performance of external temperatures and mass flow rates; moreover, the Coefficient Of Performance (COP) is also included in the model predictions.

**Keywords:** Artificial Neural Network · Absorption machine · Time series forecasting model

## 1 Introduction

In the European markets reference indices, the rise of certain fossil fuels, such as gas and oil, is pushing on the price of electricity. This increase in the cost at the continental level is due to various factors that do not have to be mutually exclusive: i) the fact that it has been possible to reach the production peak of the leading gas suppliers at the European level, ii) to various geopolitical tensions that affect supply, the war in Ukraine, and, iii) logistics and transportation

Grant TED2021-131655B-I00 funded by AEI/10.13039/501100011033 and by the "European Union Next GenerationEU".

© IFIP International Federation for Information Processing 2023

Published by Springer Nature Switzerland AG 2023

I. Maglogiannis et al. (Eds.): AIAI 2023, IFIP AICT 676, pp. 279–291, 2023.

https://doi.org/10.1007/978-3-031-34107-6_22

<footnote n="1">¹ CIESOL Joint Centre University of Almería-CIEMAT, University of Almería, Almería, Spain</footnote>

<footnote>{mcastilla,jhervas}@ual.es</footnote>

<footnote n="2">² Plataforma Solar de Almería, Almería, Spain</footnote>

<footnote>joseantonio.carballo@psa.es</footnote>
problems due to the energy demand rebound once the pandemic caused by Covid-19 has been left behind.

The main sectors causing energy consumption at a global level are the industrial, transport and residential sectors [9]. In developed countries, also known as first-world countries, the energy consumption associated with residential buildings and the tertiary sector is approximately one-third of the total energy consumed [9]. The building sector is crucial for achieving the EU's energy and environmental goals. At the same time, better and more energy-efficient buildings improve the quality of citizens' life while bringing additional benefits to the economy and society. To boost the energy performance of buildings, the EU has established a legislative framework that includes the Energy Performance of Buildings Directive 2010/31/EU (EPBD) and the Energy Efficiency Directive 2012/27/EU. Therefore, the European Climate, Infrastructure and Environment Executive Agency (CINEA) is running parts of Horizon 2020 and Horizon Europe (HORIZON) Programme in transport and energy, as well as innovation of energy-efficient technologies and solutions for buildings, heating and cooling, etc.

In buildings, the Heating, Ventilation and Air-Conditioning (HVAC) system can account for 38% of buildings consumption, equivalent to 12% of final energy [6]. Thus, as for new buildings or older ones that will be refurbished is worth keeping in mind this fact if the building's CO₂ emissions want to be reduced, become it in a Zero-Emission Building (ZEB). Absorption machines arise as an alternative to traditional HVAC with the main advantage that they can be fed by heat from renewable energy sources, such as solar energy, biomass or others. On the other hand, their main disadvantages are: i) the complexity of this kind of system versus traditional HVAC and ii) it needs advanced control systems to manage it [3, 16]. Advanced control systems are needed since the absorption machine has, besides that renewable energy sources, other conventional sources, such as a gas heater, to support its operation when the renewable source is unavailable. Thus, during the operation of the plant is necessary to switch among energy sources depending on the weather conditions, so the control problem becomes a challenging task.

These advanced control systems are based on dynamic models of the system. Thus, through these models is possible to forecast the dynamic behaviour of the plant under certain situations. Most current approaches to model absorption machines are made on stationary conditions [17], with less attention paid to the dynamic phenomena inherent to renewable energy sources. On the other hand, recent works have developed models based on thermodynamic equations [12]. The lack of internal measures in this kind of system makes necessary the use of state predictors to complete the dynamic model and set it to adjust its predictions to the real values of the plant outputs.

This work proposes a black-box model of an absorption machine where no physical equations are needed nor any physical knowledge about the machine's working. The black-box model is an artificial neural network (ANN) that has been set through a large amount of data from a real absorption machine placed at a bioclimatic building. Validation results show that the ANN model can forecast
the absorption machine's behaviour under several operation situations. Thus, it
can be used later for the development of control systems.

The rest of the paper is structured as follows: Sect. 2 describes both the working of the absorption machine and the framework in which this system is used, that is, the bioclimatic building at which it is placed. Section 3.1 introduces the time forecasting models and, more specifically, the ANN and its structures. Section 4 presents the main results obtained through the training and validation of the ANN model and, finally, in Sect. 5, the main conclusions of this work are drawn, and future works are listed.

## 2 Scope of the Research: CIESOL Building

The CIESOL building (https://ciesol.com/) is a solar energy research centre located within the campus of the University of Almería, in the southeast of Spain. One of the main features of this building is that it has been built according to bioclimatic architecture criteria. Therefore, it has some passive characteristics that allow it to take advantage of the typical climate of its location in order to reduce energy consumption, such as the use of different enclosures based on its orientation or the setback of the windows facing east and south. In addition, the CIESOL building also has some active strategies which make use of solar energy, such as the Heating, Ventilation and Air Conditioning (HVAC) system based on solar cooling. This system is composed of a flat-plate solar collectors field, two hot-water storage tanks, a gas boiler, an absorption machine with its refrigeration tower, etc.

The HVAC system based on solar cooling has two operation modes: winter and summer. In winter mode, a heat-exchanger is used to transfer the energy produced in the heat production circuit to the building's consumption circuit, and thus, the energy is distributed among all the buildings' enclosures through a set of fan-coil units. On the contrary, in summer mode, the heat-exchanger is replaced by an absorption machine, see Fig. 1.

### 2.1 Description of the Absorption Machine

Generally, within residential and commercial building sectors is very common the use of air-to-air heat pumps instead of absorption machines. In broad terms, the main objective of both of them is to transfer heat from one place to another but they operate in different ways. More in detail, air-to-air heat pumps use electricity to transfer heat from one environment to another through a process of compression and expansion of a refrigerant. They can extract heat from the outside air and transfer it to the inside of a building (heating) or vice-versa (cooling). On the contrary, absorption machines use a chemical process to absorb or release heat. Absorption machines are more common in industrial and large building HVAC applications, as they can take advantage of other heat sources, such as waste heat or solar thermal energy.

Concretely, in the CIESOL building, a *Yazaki WFC SC20* model [1] absorption machine is being used. In this case, it uses as a source energy the heat
produced by a hot water circuit from a solar collectors field. Moreover, the fluid used in the refrigeration cycle is a solution of water and lithium bromide (LiBr) where the water is the refrigerant and the LiBr represents the absorbent. The main characteristics of the *Yazaki WFC SC20* model are summarized in Table 1.

<table><caption>**Table 1.** Main characteristics of the *Yazaki WFC SC20* absorption machine model. Source: [1].</caption><thead><tr><td>Description</td><td>Unit</td><td>Value</td></tr></thead><tbody><tr><td>Cooling capacity</td><td>kW</td><td>70.3</td></tr><tr><td>Chilled water circuit - Water flow rate</td><td>m³/h</td><td>11.0</td></tr><tr><td>Chilled water circuit - Inlet/outlet temperature</td><td>°C</td><td>12.5/7.0</td></tr><tr><td>Hot water circuit - Inlet cooling capacity</td><td>kW</td><td>100.0</td></tr><tr><td>Hot water circuit Water flow rate</td><td>m³/h</td><td>17.28</td></tr><tr><td>Hot water circuit - Inlet/outlet temperature</td><td>°C</td><td>88.0/83.0</td></tr></tbody></table>

The absorption cycle that takes place in that machine is composed of four stages [15], see Fig. 2. In the first stage, named *generation*, hot-water from the flat-plate solar collectors field enters the generator where it increases the temperature of a dilute solution producing steam from the water in this solution. Then, water vapour passes to the condenser while the concentrate solution obtained in the previous phase is moved to the absorber. Afterwards, in the second stage, denoted as *condensation*, water vapour is condensed in the condenser, precipitating into the evaporator where the pressure is lower. As a result of this stage, a certain amount of heat is released, and thus, it is necessary the use of an additional cooling system, as a refrigeration tower or buried wells. These additional systems do not require a considerable energy input since their operation temperature range is usually very close to ambient temperature. In the third stage,

<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Operation scheme of the HVAC system based on solar cooling. Source: [4]</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Operation scheme of the *Yazaki WFC SC20* absorption machine</figcaption></figure>

*evaporation*, the fluid obtained from the condensation stage absorbs the heat of the fluid to be cooled in the evaporator. Finally, the evaporator fluid enters into the absorber and, in the fourth stage, named *absorption*, the concentrate solution coming from the generator absorbs the fluid from the evaporator, becoming a dilute solution which is sent back to the generator by means of a pump that increases its pressure, closing the cycle. Therefore, for a correct operation of the absorption machine, it can be seen that the main energy input is required by the generation stage, where it is necessary both to increase and to maintain the temperature of the solution within a certain range. From an optimal management point of view, one of the main problems is the lack of internal sensors in the absorption machine. Hence, it is very difficult to predict its behaviour, and thus, to optimize its operation. For this reason, this paper presents a black-box model based on Artificial Neural Networks (ANNs) of the absorption machine.

## 3 Time-Series Forecasting Models

In the simulation field, the process of predicting future events based on past and present data is known as forecasting. Specifically, forecasting based on a succession of data measured at certain times and ordered chronologically is called time-series forecasting. In other words, time-series forecasting is a modeling technique that uses historical and current data to predict values over a period of time. A time series forecasting model is usually modeled like a sequence of time-dependent random variables. Due to the temporal dependencies in time series data, different validation techniques are needed than traditional modeling, for example, cross-validation, time series split cross-validation and blocked cross-validation.
Nevertheless, the most relevant aspect of times series modeling is understand-
ing your data model and knowing which process needs to be modeled using this
data. For that, common problems in time series forecasting include generalizing
from a single data source and difficulty in obtaining appropriate measurements
and accurately identifying the correct model to represent the data. Currently
exist many different types of time-series models (Autoregressive (AR), Moving
average (MA), Autoregressive moving average (ARMA), Autoregressive inte-
grated moving average (ARIMA) model, Seasonal autoregressive integrated mov-
ing average (SARIMA), Vector autoregressive (VAR), Vector error correction
(VECM), Vector Autoregression Moving-Average (VARMA), Vector Autoregres-
sion Moving-Average with Exogenous Regressors (VARMAX), Simple Exponen-
tial Smoothing (SES), Holt Winter's Exponential Smoothing (HWES) [7]. Time
series forecasting is a scientific simulation field that is attracting great interest as
a result of the great number of possible applications and thanks to the increasing
amount of data available due to the improvement of data acquisition and storage
technologies.

## 3.1 Artificial Neural Networks

Currently, thanks to advances in computing, apart from the above modeling
techniques, time series forecasting modeling based on machine learning is tak-
ing on great relevance. Among the best-known machine learning techniques that
can be used to extrapolate time-series data, it can be found random forest,
gradient-boosting regressor, and neural networks. Neural network or artificial
neural network is one of the most interesting techniques due to it has proved
to be suitable for handling such complex problems, particularly when the phys-
ical phenomena inside the system are difficult to model [11,14], for example,
the absorption process that could be very complex and unpredictable when the
boundary conditions are variable.

For all that, models based on neural networks are well suited for time series
forecasting of chiller operation, as the scientific literature shows. For example,
in [10] a novel, the model-based control strategy for absorption cooling systems
was based on a chiller model that used artificial neural networks. This model
takes into account inlet and outlet temperatures as well as the flow rates of the
external water circuits. The configuration showed excellent agreement between
the prediction and the experimental data. Later, was published a methodology to
model and evaluate the energy performance and outlet temperatures of absorp-
tion chillers [11]. The methodology can be applied to systems that can be found
on the market and there is no need to create a physical model of the system.

Recently, it has been published a comparative that aims to predict the coef-
ficient of performance and thermal energy consumption of an absorption chiller
network using three widely-used machine learning methods of the artificial neu-
ral network, support vector machine, and genetic programming, [14]. The highest
prediction accuracy among the mentioned methods was obtained by the artificial
neural network.
A successful neural network modeling application is described in [5], in where a steady state modeling based on the artificial neural network of a double effect absorption chiller using steam as the heat input is developed. The model predicts the chiller performance based on the chilled water inlet and outlet temperatures, cooling water inlet and outlet temperatures, and steam pressure. Another successful neural network modeling application is presented in [8]. In this paper, it was simulated with good results a dual-effect absorption chiller in MATLAB using a neural network. The neural network was used to convert a dual-effect absorption chiller model that is in TESS library of the TRNSYS software into a function in MATLAB software.

# 4 Results and Discussion

## 4.1 Inputs, Outputs and Training for the ANN

As commented before, the first step before calculating the ANN model is to understand the process and decide which signals will be the inputs and outputs of the model. Thus, after several analyses and model tests, the list of inputs is: i) the inlet flow to the generator: Q5 signal, ii) the inlet water temperature from the solar field or gas heater to the absorption machine: s11 signal, iii) the inlet water temperature to the absorption machine from the building: s18 signal and, iv) the inlet water temperature from the additional cooling system, that is, the refrigeration tower: s20 signal.

On the other hand, the chosen outputs are: i) the return temperature from the absorption machine to the solar field: s12 signal, ii) the outlet chilled water temperature from the absorption machine to the different building rooms: s19 signal, and iii) the outlet water temperature from the absorption machine to the refrigeration tower: s21 signal.

Besides that, the outputs are fed back as inputs delayed one or two samples to test if better predictions are obtained from the model. The location of the selected inputs and outputs regarding the absorption machine can be observed in Fig. 2.

The training parameters are also crucial for achieving high accuracy in learning and prediction. One popular training algorithm for neural networks is the Levenberg-Marquardt algorithm [13], its implementation in MATLAB has been used in this work. This algorithm combines the advantages of the Gauss-Newton and steepest descent methods to achieve fast convergence with high accuracy.

The training has been configured with 1000 epochs of training and a learning rate of $10^{-3}$. The number of epochs determines the number of times the entire dataset is used to update the network weights, while the learning rate controls the size of the weight updates at each iteration.

## 4.2 Datasets Construction

To develop an appropriate ANN model, a key point is the availability of suitable datasets. In this case, historic data from the CIESOL building has been
used. In addition, it should be noted that the absorption machine is only used during the summer season, but the building is closed due to summer vacation during the month of August. Hence, the data from the month of June has been chosen for training purposes; specifically, the data available from June 2014 has been selected. This dataset has a sample time equal to 1 min. This dataset has been split into three subsets to train, validate and test the ANN model with a proportion of 70%, 15% and 15% of the data, respectively.

Furthermore, a second dataset corresponding to June 2015 has been prepared and used as an independent dataset for testing purposes. The results obtained with this second dataset are included and discussed in Sect. 4.4.

## 4.3 Architecture and Structure Selection

To select an appropriate architecture for the ANN model it is necessary to take into account that, for the system presented in this paper, it should be able to learn long temporal dependencies among the involved variables. Hence, a recurrent architecture denoted as Nonlinear Autoregressive with eXogenous input (NARX) have been chosen [2]. This type of ANN architecture has as inputs two tapped delay lines: one for the input signals and the other for the outputs signals. Besides, it also has a hidden layer with a certain number of neurons which should be carefully chosen. To do that, an analysis of the performance of ANN models obtained while increasing the number of neurons in the hidden layer has been done based on Mean Squared Error (MSE). Equation 1 shows how the MSE is calculated, whereas Fig. 3 depicts how this index changes, Y axis in logarithm scale, with the number of neurons in the ANN hidden layer, X axis.

$$MSE = \frac{1}{n} \sum_{i=1}^{n} (Y_i - \hat{Y}_i)^2 \quad (1)$$

Before the calculation of the ANN model, the datasets were preprocessed: a low-pass filter was applied, and the gaps in the data were filled. The MSE value falls down drastically until three neurons, see Fig. 3. From this number of neurons, the MSE value keeps almost constant. However, with the second testing dataset belonging to June 2015, the MSE value is higher than with the June 2014 dataset. For this reason, looking for a trade-off between simplicity and performance of the model, the number of neurons has been set to six.

It is worth mentioning that although the internal structure of an absorption machine does not differ so much from one model to another, see Fig. 2, the ANN model must be recalculated if the absorption machine would be another commercial model or proceed from another manufacturer. The ANN structure may be the same, but the weights must be set with new data.

## 4.4 Results Obtained from the Second Testing Dataset

Firstly, an evaluation of the results provided for each output by the absorption machine ANN model using the testing portion of the first dataset (June 2014)
<figure><img src="image_5.png" /><figcaption>Fig. 3. MSE per number of neurons in the ANN</figcaption></figure>

has been done. Then, the proposed model has also been tested using the second dataset (June 2015). For goodness-of-fit evaluation, an analysis based on some statistical indexes has been performed. More specifically, the following indexes have been considered: *Rng* (range of the dataset), *N* (number of samples), *MAE* (Mean Absolute Error), *MRE* (Mean Relative Error), *S*<sub>n</sub> (Standard deviation) and *NMAE* (Normalized Mean Absolute Error). The formulas associated with these indexes have not been included in this paper mainly due to the lack of space. In Table 2, a summary of the quantitative analysis is shown:

<table><caption>Table 2. Summary results obtained for both datasets.</caption><thead><tr><th rowspan="2">Parameter</th><th colspan="3">1<sup>st</sup> Dataset: June 2014</th><th colspan="3">2<sup>nd</sup> Dataset: June 2015</th></tr><tr><th>s12 [°C]</th><th>s19 [°C]</th><th>s21 [°C]</th><th>s12 [°C]</th><th>s19 [°C]</th><th>s21 [°C]</th></tr></thead><tbody><tr><td>Rng</td><td>52.87</td><td>25.70</td><td>9.71</td><td>55.33</td><td>25.06</td><td>9.15</td></tr><tr><td>N</td><td>31396</td><td>31396</td><td>31396</td><td>30194</td><td>30194</td><td>30194</td></tr><tr><td>MAE</td><td>0.042</td><td>0.043</td><td>0.043</td><td>0.091</td><td>0.040</td><td>0.039</td></tr><tr><td>MRE</td><td>6.51 · 10<sup>-4</sup></td><td>0.004</td><td>0.002</td><td>0.0014</td><td>0.0032</td><td>0.0013</td></tr><tr><td>S<sub>n</sub></td><td>7.70</td><td>7.31</td><td>1.54</td><td>7.84</td><td>8.50</td><td>2.02</td></tr><tr><td>NMAE</td><td>0.08%</td><td>0.17%</td><td>0.44%</td><td>0.17%</td><td>0.16%</td><td>0.42%</td></tr></tbody></table>

Furthermore, Fig. 4 depicts the results obtained from the ANN absorption machine prediction model for each one of the three outputs using the second testing dataset from June 2015. The upper graph in Fig. 4 shows the evolution of the return temperature to the heat production circuit for both real data gathered at the CIESOL building (in blue) and the results provided by the absorption machine ANN model (in orange). Besides, the middle graph depicts
the evolution of s19 signal, that is, the outlet chilled water temperature, which flows from the absorption machine to the building's rooms. Finally, in the bottom graph, the outlet temperature from the absorption machine to the refrigeration tower is shown. As can be observed in this figure, the ANN model is able to perfectly capture the dynamics of the absorption machine in all the cases. On the other hand, as it can be observed in Table 2, the results obtained are acceptable since the NMAE is less than 1% in all cases.

<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Results obtained from the second testing dataset, June 2015 (Color figure online)</figcaption></figure>

Finally, the last graph shows a comparison of the Coefficient Of Performance (COP) between the one calculated through the real measurements from the absorption machine and the other calculated through the values predicted by these variables with the ANN model. The COP is defined as the relationship between the power (kW) that is drawn out of the absorption machine, as cooling or heat, and the power (kW) that is supplied to it. It can be calculated with the following equation:

$$COP = \frac{Q4 \cdot C_p \cdot (s18 - s19)}{Q5 \cdot C_p \cdot (s11 - s12)} \quad (2)$$

where Q4 is the outlet flow from the generator and Cₚ is the water specific heat which value has been set to 4.18 J/(kg·K). The comparison results are showed in Fig. 5 where zoom-in has been made to appreciate better the accuracy of the predicted COP.
<figure><img src="image_7.png" /><figcaption>Fig. 5. Coefficient OF Performance (COP) results, June 2015</figcaption></figure>

# 5 Conclusions and Future Works

As commented before, the behavior of an absorption machine is complex, with long temporal dependencies among the involved variable. In addition, it should be considered that the absorption machine, which is the object of this work, is a commercial machine with a lack of internal sensors. For all that, it is very difficult to predict its behavior, and thus, control its operation from an optimal management point of view. However, thanks to the existence of a time series data set and the capabilities of the neural networks for handling complex problems when the physical phenomena inside the system are difficult, it has been decided to develop a black-box model based on ANNs of the absorption machine that allows optimizing the control of the absorption machine. ANN architecture called NARX has been chosen due to its ability to learn long temporal dependencies among variables.

After carrying out tasks to understand the process to be modeled and different studies about the influence of the available variables on the data set, a list of inputs and outputs of the model is defined. These variables allow to achieve the main objective of developing a black box model for optimization tasks of the operation based on the installation state. With the data set divided for training, validation, and test tasks and with the model structure defined, the next step consists of the optimization of the model hyper-parameters, which are the number of neurons in the hidden layer. The analysis of the performance of ANN models obtained while increasing the number of neurons in the hidden layer revealed that six neurons are a good trade-off between the simplicity and performance of the model. Finally, the validation and testing process of the model with the second testing dataset revealed that the ANN model is able to perfectly capture the dynamics of the absorption machine with acceptable results.
Future works include increase and analyze in depth the data set to improve its variability, analyze the use of other ANN structures, sensitivity study of the input variables with the aim of using less and improve the training configuration. Finally, the most important future work: develop a optimal controller based on the black box model developed in this work and measure its performance.

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