Morandise Rubini
email: morandise.rubini@univ-pau.fr

Lisa Feuillerat

Thomas Cabaret

Léo Leroyer

Luc Leneveu

Bertrand Charrier

Comparison of the performances of handheld and benchtop near infrared spectrometers: Application on the quantification of chemical components in maritime pine (Pinus Pinaster) resin

Introduction

Maritime pine (Pinus pinaster) tapping (resin collection) was a prominent activity in France from the 19 th century until the 1970's [1]. Maritime pine covers in the Southwest of France more than one million hectares [START_REF] Lemoine | Tables de production pour le pin maritime dans le sud-ouest de la France[END_REF]. After the 1970s, tapping drastically declines because its lost its profitability, mainly because to the growth of the Chinese market [START_REF] Bussy | La gemme et les produits résineux en France[END_REF][START_REF] Rodríguez-García | Effect of four tapping methods on anatomical traits and resin yield in Maritime pine (Pinus pinaster Ait.)[END_REF]. Recently, tapping reemerged [START_REF] Martos | Construir la tecnología: el caso de la resina de pino en Francia, siglos XVIII y XIX[END_REF] with new, mechanized tapping techniques, in a context with concerns about harvesting in sustainable and environmentally friendly conditions [START_REF] Courau | La relance du gemmage en forêt de Gascogne[END_REF].

Closed cup tapping is one of these techniques. The first works are related to (C. [START_REF] Courau | Procedé de traitement de la résine, dispositif pour la mise en oeuvre de ce procedé et colophane obtenue[END_REF] [START_REF] Courau | Procedé de traitement de la résine, dispositif pour la mise en oeuvre de ce procedé et colophane obtenue[END_REF]. Closed cup tapping became increasingly popular in France and is currently being improved [START_REF] Leneveu | Procédé pour favoriser l'exsudation de l'oleorésine et composition pour mettre en oeuvre ce procédé[END_REF]. Briefly described, a superficial incision using a circular saw removes a section of the bark and of the secondary phloem, where the resin is formed [START_REF] Stoffel | 3D analysis of anatomical reactions in conifers after mechanical wounding: First qualitative insights from X-ray computed tomography[END_REF]. Then, a stimulant paste composed of natural acids is applied to activate resin exudation. Finally, a bottleneck guides the drained resin into an airtight sealed plastic bag, which protects the resin from impurities such as insects, sand, plant debris or rainwater, and limits evaporation.

Resin exudates abundantly [START_REF] Neis | Resin exudation profile, chemical composition, and secretory canal characterization in contrasting yield phenotypes of Pinus elliottii Engelm[END_REF] as a complex mixture of a volatile fraction (turpentine) and a nonvolatile fraction (rosin) [START_REF] Silvestre | Chapter 2 -Terpenes: Major Sources, Properties and Applications[END_REF][START_REF] Silvestre | Chapter 4 -Rosin: Major Sources, Properties and Applications[END_REF]. Turpentine consists of a few unsaturated hydrocarbon monoterpenes, with smaller amounts of other monoterpenes [START_REF] Ghanmi | Étude du rendement et de la composition de l'essence de térébenthine du Maroc: Cas du Pin maritime (Pinus pinaster) et du Pin d'Alep (Pinus halepensis)[END_REF][START_REF] Eberhardt | Monoterpene persistence in the sapwood and heartwood of longleaf pine stumps: Assessment of differences in composition and stability under field conditions[END_REF]. Rosin is mainly composed of diterpenic monocarboxylic acids, plus neutral components [START_REF] Wang | Synthesis of biobased epoxy and curing agents using rosin and the study of cure reactions[END_REF][START_REF] Fao | Gum naval stores: turpentine and rosin from pine resin[END_REF][START_REF] Ghanmi | Évaluation de la qualité de la colophane du pin maritime (Pinus pinaster) et du pin d'Alep (Pinus halepensis) du Maroc Évaluation de la qualité de la colophane du pin maritime (Pinus pinas-ter) et du pin d'Alep (Pinus halepensis) du Maroc[END_REF]. The various components can be analyzed quantitatively using gas chromatography [START_REF] Lombardero | Foliar terpene chemistry of Pinus pinaster and P. radiata responds differently to Methyl Jasmonate and feeding by larvae of the pine processionary moth[END_REF][START_REF] Tümen | Research on the antioxidant, wound healing, and anti-inflammatory activities and the phytochemical composition of maritime pine (Pinus pinaster Ait)[END_REF][START_REF] Gonçalves | Effect of Monochamus galloprovincialis feeding on Pinus pinaster and Pinus pinea, oleoresin and insect volatiles[END_REF][START_REF] Lai | Inheritance of resin yield and main resin components in Pinus elliottii Engelm. at three locations in southern China[END_REF] and or liquid chromatography [START_REF] Lee | High-performance liquid chromatographic determination of dehydroabietic and abietic acids in traditional Chinese medications[END_REF][START_REF] Kersten | Rapid analysis of abietanes in conifers[END_REF]. These methods are used for quality control and to select higher quality resin, but they are time-consuming and hard to use on-site. Considering this, the present study looks for alternative analytical methods, including Near infrared (NIR) spectroscopy in combination with chemometrics [START_REF] Via | Near infrared spectroscopy calibration for wood chemistry: Which chemometric technique is best for prediction and interpretation?[END_REF][START_REF] Sandak | Assessing trees, wood and derived products with near infrared spectroscopy: Hints and tips[END_REF].

NIR spectroscopy is a non-destructive, reliable, and rapid tool for quantification, and can be applied on portable devices [START_REF] Crocombe | Portable Spectroscopy[END_REF]. Indeed, low cost handheld near-infrared spectrometer, such as SCiO (Consumer Physics, Israel), has been reported in the literature as a reliable and inexpensive technology, which facilitates its use on an industrial scale [START_REF] Morillas | Feasibility of a handheld near infrared device for the qualitative analysis of bloodstains[END_REF][START_REF] Wiedemair | Investigations into the Performance of a Novel Pocket-Sized Near-Infrared Spectrometer for Cheese Analysis[END_REF][START_REF] Wilson | A New Handheld Device for the Detection of Falsified Medicines: Demonstration on Falsified Artemisinin-Based Therapies from the Field[END_REF][START_REF] Kosmowski | Evaluation of a miniaturized NIR spectrometer for cultivar identification: The case of barley, chickpea and sorghum in Ethiopia[END_REF][START_REF] Wiedemair | Evaluation of the performance of three hand-held nearinfrared spectrometer through investigation of total antioxidant capacity in gluten-free grains[END_REF][START_REF] Li | Evaluating the performance of a consumer scale SCiO TM molecular sensor to predict quality of horticultural products[END_REF]. The objective of this study is to assess the potential of SCiO as a direct and rapid analysis tool for the quantification of the main chemical components of Pinus pinaster resin with chemometric processing.

Partial Least Squares (PLS) regression was used to develop quantitative predictive models.

Those models were optimized various different spectral preprocessing methods. Then, the performance of SCiO was compared to the baseline of a benchtop spectrometer.

Materials and methods

Sample collection and conservation

One hundred and twenty-five samples of maritime pine (Pinus pinaster) resin were harvested in Biscarrosse (Landes, France) during the summer of 2018. They were collected by tapping one hundred and twenty-five fifty years old trees using a closed cup tapping technique patented by (L. Leneveu, 2002) [START_REF] Leneveu | Procédé pour favoriser l'exsudation de l'oleorésine et composition pour mettre en oeuvre ce procédé[END_REF]. Briefly described, the method consisted in having the resin flow into a sealed plastic bag made of an ethylene vinyl alcohol (EVOH) inner film (70 μm thick) and a low-density polyethylene (LDPE) outer film (45 μm thick).

Once, the samples were stored in a box kept away from light and heat in order avoid chemical alteration.

Reference methods

Proportion of turpentine fraction in Pinus pinaster resin (% turpentine) using a ventilated oven

For convenience, the relative proportion of turpentine and rosin fractions is designated here collectively as the "Proportion of turpentine fraction".

The proportion of turpentine fraction in maritime pine (Pinus pinaster) resin (% turpentine) was quantified using a ventilated oven.

The % turpentine was an important data for this study, because, the chemical composition was determined with two different analytical instruments, and the chemical composition pertains respectively to each fraction. Therefore, % turpentine is an indicator of the actual amount of the chemical components in each of the fractions.

Briefly, 2 g of resin were poured in a aluminium cup. The weight difference before and after drying was used to determine % turpentine. Each sample was placed in a ventilated oven at a temperature close to the boiling point of monoterpenes, for a duration sufficient to provoke its evaporation. Specifically, the samples were placed at 150 °C for 30 min in the ventilated oven.

Chemical composition of the turpentine as determined via Gas

Chromatography coupled to a Flame Ionization Detector (GC-FID) analysis and sample preparation

The chemical composition of the turpentine fraction from maritime pine (Pinus pinaster) resin was quantified using a Perkim-Elmer Clarus 500 gas chromatograph (GC) coupled to a Flame Ionization Detector (FID), and a Perkim-Elmer Elite-5MS capillary column (30 m x 0,25 mm x 0,5 μm film thickness).

To prepare the samples, the plastic bag containing the resin was homogenized manually for several minutes until the turpentine and rosin were completely mixed. Then, 10 mL of the resin were poured in a cylindrical glass vial (h = 50 mm, outer diameter = 22 mm). After three days, 0.5 mL of supernatant containing turpentine was diluted with 1.5 ml of hexane, which was, in turn, stirred in an ultrasonic bath for 1 min, then filtered through a 0.2 μm nylon filter before injecting. 1 μL of this solution.

The experimental conditions developed in the laboratory were as follow: both injector and detector temperatures were set at 290 °C; oven temperature program, 50 to 150 °C (2 °C/min), then 150 to 320 °C (10 °C/min); hydrogen was used as carrier gas. The first ramp was used to elute the volatile components of the turpentine, the second ramp was used to clean the column.

Between each sample injection, two flash cleaning programs with acetone and hexane were applied to remove any residue (50 to 250 °C (15 °C/min)).

For the identification of the components of the turpentine fraction, Kovats's linear retention index was calculated from the injection of a homologous series of hydrocarbons (C8 -C20) and compared with literature data [START_REF] Drive | Identification of Essential Oil Components by Gas Review : Identification of Essential Oil Components by Gas Chromatography / Mass Spectrometry[END_REF], in accordance with ASTM D6730 [START_REF]Standard Test Method for Determination of Individual Components in Spark Ignition Engine Fuels by 100-Metre Capillary (with Precolumn) High-Resolution Gas Chromatography[END_REF]. For the quantitative analysis, the respective rates were expressed as percentages.

Chemical composition of the rosin fraction determined via High-Performance Liquid Chromatography coupled to Diode Array Detectors (HPLC-DAD) and sample preparation

The chemical composition of the rosin fraction from maritime pine (Pinus pinaster) resin was quantified using High-Performance Liquid Chromatography coupled to a Diode Array Detector (HPLC-DAD). A methodology similar to the ones already reported was used [START_REF] Cabaret | Measuring interactions between rosin and turpentine during the drying process for a better understanding of exudation in maritime pine wood used as outdoor siding[END_REF][START_REF] Cabaret | High temperature drying effect against resin exudation for maritime pine wood used as outdoor siding[END_REF]. Briefly, the plastic bag containing the resin was homogenized manually for several minutes until the turpentine and rosin were completely mixed. Then, 1 % w/v of the sample was diluted in Methanol. 10 μL of this solution was injected in a Thermo Scientific Ultimate The methodologies described by [START_REF] Lee | High-performance liquid chromatographic determination of dehydroabietic and abietic acids in traditional Chinese medications[END_REF][START_REF] Kersten | Rapid analysis of abietanes in conifers[END_REF] [START_REF] Lee | High-performance liquid chromatographic determination of dehydroabietic and abietic acids in traditional Chinese medications[END_REF][START_REF] Kersten | Rapid analysis of abietanes in conifers[END_REF] were used for identification and quantification of the chemical components of the rosin fraction.

Near infrared (NIR) acquisition

Near infrared (NIR) acquisition was performed using a benchtop spectrometer and a handheld spectrometer, respectively, MultiPurpose Analyzer I (Bruker, USA) and SCiO (Consumer

Physics, Israel).
MultiPurpose Analyzer I is a Fourier transform near infrared (FT-NIR) spectrometer implemented with an integrating sphere. The spectral range of acquisition was between 12,500 to 4,166 (780 to 2400 nm) with a nominal resolution of 8 . For the spectral acquisition, resin was poured in a cylindrical glass vial (h = 50 mm, outer diameter = 22 mm), and placed over the sphere window, with an average of 16 scans for each spectrum.

The SCiO handheld spectrometer [START_REF] Rosen | Spectrometry system applications[END_REF] operated in a spectral range between 13,514 to 9,346 (740 to 1070 nm) with values every 1 nm and an optical resolution of 30 nm [START_REF] Subedi | Assessment of avocado fruit dry matter content using portable near infrared spectroscopy: Method and instrumentation optimisation[END_REF]. The resin samples were scanned directly through the sealed plastic bag using an accessory provided by Consumer Physics to avoid ambient light interference, and also to ensure a constant distance of 10 mm between the light source and the sample. For each sealed plastic bag, the spectrum was calculated as the median of six measurements.

As the SCiO is a cloud-based device, Consumer Physics proposes a commercial online toolkit, which is required for spectra acquisition and download them. This online toolkit is also useful to create online mathematical models, which can be implemented in a mobile application using the Software Development Kit provided by Consumer Physics. Yet, this application was not used in the present study.

Fig. 1 details the process used during the Multivariate Data Analysis. The same methodology was applied on both spectrometers, in order to compare their quantitative predictive ability.

Preprocessing

During spectral data acquisition, scattering of photons leads to many physical phenomena (lengthening of the optical path, noise) that need to be corrected with spectra preprocessing algorithms [START_REF] Zeaiter | Preprocessing Methods[END_REF] to limit the influence of physical phenomena on the relevant properties [START_REF] Rinnan | Review of the most common preprocessing techniques for near-infrared spectra[END_REF].

Such effects include baseline drift, nonlinearity, curvilinearity, additive and multiplicative effects, and irrelevant variations in the spectra [START_REF] Rinnan | Review of the most common preprocessing techniques for near-infrared spectra[END_REF]. Thus, several preprocessing techniques were applied in order to increase the accuracy of the predictive models: Standard Normal Variate (SNV), Detrend (DT2), combination of SNV and DT2 [START_REF] Barnes | Standard normal variate transformation and detrending of near-infrared diffuse reflectance spectra[END_REF], Multiplicative Scatter Correction (MSC) [START_REF] Helland | Related versions of the multiplicative scatter correction method for preprocessing spectroscopic data[END_REF], and Savitzky-Golay's first and second derivatives [START_REF] Mark | Chapter 57 -Derivatives in Spectroscopy: Part 3-Computing the Derivative (the Savitzky-Golay Method)☆[END_REF].

Subset Selection

Subset selection algorithms were used to extract calibration and independent validation subsets from a dataset [START_REF] Bouveresse | Sélection d'échantillons représentatifs par des méthodes chimiométriques. Application à des modèles d'étalonnage[END_REF]. The DUPLEX algorithm [START_REF] Snee | Validation of Regression Models: Methods and Examples[END_REF] promotes sample representativeness, ensuring that the selected spectra are uniformly distributed in the multidimensional data space and that the dependent variable has the same statistics characteristics (mean, variance, range, etc.).

In the first step, the two spectra most distant from each other are selected and placed in the calibration subset. On the remaining spectra, the following pair of spectra most distant from each other are selected for the validation subset. By alternating subsets, the procedure continues to select a single spectrum for each subset of data. The selected spectrum is the one farthest from the spectra already selected within the subset. Once the required number of samples is reached in the validation subsets, the remaining samples are placed in the calibration subset.

With this procedure, the dataset was split as follow: 70 % in the calibration subset, and 30% in the validation subset.

Modelling Phase

Partial Least Squares (PLS) regression with Nonlinear Iterative Partial Least Squares (NIPALS) algorithm is the favorite for regression in NIR applications, because of its ability to analyze high-dimensional and multi-collinear data [START_REF] Wold | Estimation of Principal Components and Related Models by Iterative Least squares[END_REF][START_REF] Wold | The multivariate calibration problem in chemistry solved by the PLS method[END_REF]. Its purpose is to use independent variables ( ) to predict a dependent variable ( ): the spectra ( ) are used to predict the analytical reference values ( ). To achieve this goal, it will attempt to meet three objectives:

Software

Chemometric models construction was carried out using Matlab 2019a (Mathworks Inc., USA) with the SAISIR toolbox [START_REF] Cordella | SAISIR: A new general chemometric toolbox[END_REF], and all the scripts were designed in-house.

Results and discussion

Chemical composition and % turpentine of maritime pine (Pinus pinaster) resin

Table 1 list the values of the studied parameters of maritime pine (Pinus pinaster) samples: main chemical components of the turpentine and rosin fractions, and proportion of the turpentine fraction.

Concerning the turpentine fraction, the results obtained via GC-FID showed that the analyzed turpentine of Pinus pinaster is mainly composed of unsaturated hydrocarbon monoterpenes, above all α-pinene and β-pinene: the α-pinene content was 66. The turpentine content (% turpentine) was 34.3 ± 6.6 %, also in agreement with the literature: [START_REF] Arrabal | Differentiation among five Spanish Pinus pinaster provenances based on its oleoresin terpenic composition[END_REF] [50] found % turpentine values ranging from 27.7 to 30.3 %.

NIR spectra

As reminded by (Y. Ozaki, 2012) [START_REF] Ozaki | Near-infrared spectroscopy-its versatility in analytical chemistry[END_REF], near infrared can be divided in three regions: region I (800 -1200 nm; 12500 -8500 ), region II (1200 -1800 nm; 8500 -5500 ) and region III (1800 -2500 nm; 5500 -4000 ). Those regions have specific spectral features, covering mainly electronic transitions, overtones and combination bands [START_REF] Wiedemair | Evaluation of the performance of three hand-held nearinfrared spectrometer through investigation of total antioxidant capacity in gluten-free grains[END_REF].

The benchtop MPA I spectrometer covers the 12,500 -4,166 range (780 to 2400 nm), which roughly corresponds to all these three regions. Along the spectra acquired with the MPA I, the first overtone, third overtone, deformation band and combination band;

second overtone and = 0 first and fourth overtone are observed.

The handheld SCiO spectrometer covers a narrow range from 13,514 to 9346 (740 to 1070 nm), which covers the first region (Region I), also called "near infrared", where third overtone and second overtone are observed. In fact, the SCiO's capacity in the visible domain and the NIR domain are limited [START_REF] Li | Evaluating the performance of a consumer scale SCiO TM molecular sensor to predict quality of horticultural products[END_REF]. From this information, it is assumed that MPA I and SCiO spectrometers can consistently detect the same vibrational bands at different overtones.

Table 2 reports the vibrational bands of Pinus pinaster resin. The table was drafted based on the chemical structure of its major components, the spectra shapes, and the literature [START_REF] Schwanninger | A review of band assignments in near infrared spectra of wood and wood components[END_REF][START_REF] Workman | Practical Guide and Spectral Atlas for Interpretive Near-Infrared Spectroscopy[END_REF].

Modelling Phase

Statistics of Partial Least Squares (PLS) regression are presented in Table 3.

PLS regression was applied on calibration subsets to develop quantitative predictive models.

In PLS modeling, Latent Variables (LVs) were calculated and considered as new eigenvectors to reduce the dimensionality and compress the original spectral data. The number of LVs was determined as the one that provided the lowest RMSECV using a leave-one-out crossvalidation. Once developed, the model was applied to validation subsets, which had not been used during the calibration procedures. Then, the predictive ability of each model was assessed by external validation on the validation subsets.

As a rule, a good model should have higher , , and RPD values, and lower RMSEC, RMSECV and RMSEP. Here, the quantitative predictive ability of the model was selected based on highest RPD values.

Benchtop spectrometer: MultiPurpose Analyzer I (Bruker, USA)

Figure 2 (a) shows the raw spectra acquired with the MultiPurpose Analyzer I.

Handheld spectrometer: SCiO (Consumer Physics, Israel)

Figure 3 (a) shows the raw spectra acquired with the SCiO. The raw spectra seem flat and did not exhibit any characteristic vibrational bands except for at 957 nm [START_REF] Neis | Resin exudation profile, chemical composition, and secretory canal characterization in contrasting yield phenotypes of Pinus elliottii Engelm[END_REF]449 ). This is seemingly due to physical phenomena, such as scattering effects and overlapping signals [START_REF] Blanco | NIR spectroscopy: A rapid-response analytical tool[END_REF][START_REF] Ma | Prediction of total protein and intact casein in cheddar cheese using a low-cost handheld short-wave near-infrared spectrometer[END_REF].

In practice, SCiO lacks repeatability and reproducibility. For example, the same sample area can be scanned (without touching or moving the device) several times, causing intensity shift. Similarly, with the PLS regression models developed for the benchtop spectrometer for all parameters, the values of coefficients of determination ( ; ; ) show good agreement between prediction and reference values. RMSEC, RMSECV and RMSEP values were higher than those obtained with the benchtop device, and show some discrepancy, suggesting that PLS regression models cannot be as robust as with the benchtop device.

Looking at RPD statistics, spectral preprocessing is also required to improve the predictive ability of all models. As shown in Table 3, the five parameters can be classified into two groups. In the first group, the RPD ranges from 3.0 to 3.5, which indicate some good quantitative predictive ability for quality control. This group includes all major components of the resin, such as α-pinene, and levopimaric, abietic and neoabietic acids. In the second group, the RPD ranged between 2.5 and 3.0, i.e. β-pinene and % turpentine were less correctly predicted.

Concerning the preprocessing, Detrend (DT2) gives better predictive ability for the mixture of abietic and neoabietic acids (RPD = 3.01). Combination of Standard Normal Variate and Detrend (SNV + DT2) give better predictive ability for % turpentine (RPD = 3.03).

Multiplicative Scatter Correction (MSC) gives better predictive ability for levopimaric acid (RPD = 3.09). Savitzky-Golay second derivatives (SG2) give better predictive ability for αpinene (RPD = 3.16) and β-pinene (RPD = 2.82).

Conclusion

The SCiO handheld NIR spectrometer was studied as a potential tool to quantify the chemical components of maritime pine (Pinus pinaster) resin, in view of its use as a quality control tool for the tapping industry.

This study looked at the performance of the handheld SCiO spectrometer in comparison with a benchtop MPA I spectrometer, and evaluated the performance based on statistical parameters.

Although, SCiO and MPA I work in different NIR regions, PLS regression models can be used to quantify major resin components, such as, α-pinene, β-pinene, levopimaric, abietic and neoabietic acids, and % turpentine. Results shown that spectral preprocessing is mandatory to increase the predictive ability of the PLS regression models.

Considering the selected PLS models: the coefficients of determination ( ; ; )

show good agreement between prediction and reference values for both, SCiO and MPA I spectrometers with values higher than 0.8; RMSEC, RMSECV and RMSEP values were higher for the SCiO compared to the MPA I, and they also show some discrepancy, suggesting that PLS regression models for SCiO cannot be as robust as MPA I models; RPD statistics values were lower for the SCiO compared to the MPA I -the RPD values for MPA I showed an excellent quantitative predictive ability, whereas those for SCiO have a good quantitative predictive ability for quality control purposes.

Considering the overall instrument parameters (wavelength, resolution, size, cost), the SCiO spectrometer showed very good results. Additionally, the SCiO is a handheld spectrometer, and can be easily used on-site. Thus, the SCiO could be a useful tool to predict main chemical components of maritime pine (Pinus pinaster) for quality control in the tapping industry.

  3000 chromatographic unit equipped with a Thermo Scientific LC Acclaim PolarAdvantage II C18 column (5 μm, 150 x 4.6 mm), and a Diode Array Detector. The separation was carried out at 20 °C with a binary gradient mixture of solvent A (methanol + 1.0 % formic acid) and solvent B (water + 1.0 % formic acid). A flow rate of 300 μl/min was used with a gradient program as follow: 0-2 min (60 % of A); 2-17 min. (60-80 % of A); 17-34 min. (80 % of A); 34-48 min. (80-100 % of A); 48-67 min. (100 % of A); equilibrated (60 % of A).

  (i) explain ; (ii) explain ; (iii) find relationships between and . Nonlinear Iterative Partial Least Squares (NIPALS) has been used as algorithm to perform PLS. In order to evaluate the quantitative predictive ability of Partial Least Squares (PLS) models, several merit scores were computed. The coefficient of determination gave a measure of linearity of the prediction based either on the calibration subset ( ; ), or on the validation subset ( ). Root Mean Square Error of Calibration, Cross-Validation and Prediction (respectively, RMSEC, RMSECV and RMSEP) were used to measure the accuracy of the prediction calculated on both the calibration and validation subsets. Another figure of merit, the Ratio of standard error of Performance to standard Deviation (RPD), was computed to classify the applicability of the models. According to (Williams, 2014) [48], higher RPD values were associated with better quantitative prediction. General rules emerge to classify the applicability of the models: with 2.5 ≤ RPD < 3.0, the quantitative model makes approximate quantitative predictions (screening); with 3.0 ≤ RPD < 3.5, good quantitative predictions (quality control); for 3.5 ≤ RPD < 4.1, very good quantitative predictions (process control); and RPD ≥ 4.1 indicates an excellent quantitative predictive model.

7 %

 7 2 ± 9.0 % and the β-pinene content was 18.5 ± 8.5 %.[START_REF] Ghanmi | Étude du rendement et de la composition de l'essence de térébenthine du Maroc: Cas du Pin maritime (Pinus pinaster) et du Pin d'Alep (Pinus halepensis)[END_REF] [START_REF] Ghanmi | Étude du rendement et de la composition de l'essence de térébenthine du Maroc: Cas du Pin maritime (Pinus pinaster) et du Pin d'Alep (Pinus halepensis)[END_REF], comparing the turpentine content of maritime pine (Pinus pinaster) and of Aleppo pine (Pinus halepensis) from Morocco, report similar composition values for α-pinene and β-pinene as main components of turpentine: 78.for α-pinene, and 7.3 % for β-pinene.Concerning the rosin fraction, the results from HPLC-DAD show that the analyzed rosin of Pinus pinaster is mainly composed of diterpenic monocarboxylic acids, such as levopimaric acid, with a content of 62.1 ± 5.9 %.[START_REF] Arrabal | Differentiation among five Spanish Pinus pinaster provenances based on its oleoresin terpenic composition[END_REF] [50] monitored the oleoresin chemical composition of 150 Spanish Pinus pinaster trees aged of 33 years old, from thirty sites. Authors reported similar composition values with levopimaric acid as the main constituent ranging from 41.7 to 45.3 %.

  Figure 2 (a) shows the raw spectra acquired with the MultiPurpose Analyzer I.Figure 2 (b to

(

  [START_REF] Subedi | Assessment of avocado fruit dry matter content using portable near infrared spectroscopy: Method and instrumentation optimisation[END_REF] [START_REF] Subedi | Assessment of avocado fruit dry matter content using portable near infrared spectroscopy: Method and instrumentation optimisation[END_REF] is cited as a first attempt to test the repeatability of SCiO, and two other spectrometers: MicroNIR (Viavi, USA) and F750 (Felix Instruments, USA). There the authors expressed the repeatability as the standard deviation of absorbance of the highest signal across the wavelength range monitored. Standard deviation was computed on 20 repeated measures of a PTFE reference material. Standard deviation was 0.04, 0.22 and 0.40 m Absorbance units for the F750, MicroNIR and SCiO spectrometers respectively. The values obtained with the SCiO were much higher than with the others two spectrometers. It is thus necessary, for each sample, to acquire several spectra, and to use a statistical method (mean, median) in order to have a representative spectrum, and also, to apply preprocessing algorithms. Once the spectra are preprocessed, vibrational bands are highlighted (Figure3: bg).

Figure 1 Figure 2

 12 Figure 1 Detailed overview of the Multivariate Data Analysis.

Figure 3

 3 Figure 3 Different preprocessing algorithms were applied to the raw spectra acquired

Table 1 Values of the parameters of the analyzed samples of maritime pine (Pinus pinaster): main chemical components of the turpentine and rosin fractions, and proportion of the turpentine fraction.

 1

				Number of				Standard
			Parameters		Minimum (%)	Maximum (%)	Mean (%)
				samples				deviation (%)
			α-pinene		44.05	84.37	66.16	9.01
	Turpentine Chemical Composition		β-pinene Myrcene or/and δ-3 carene Limonene	94	3.24 0.04 0.88	37.47 0.52 6.12	18.47 0.28 4.08	8.45 0.11 0.68
			ɣ-terpinene		0.64	2.19	1.42	0.30
			Levopimaric
	Rosin Chemical	Composition	acid Abietic and Neoabietic acids	49	49.66 24.75	75.25 50.34	62.10 37.90	5.93 5.93
	% Turpentine		% Turpentine	112	15	52.8	34.25	6.58

Table 2 Band assignments for the chemical components of maritime pine (Pinus Pinaster) resin.

 2

		Wavenumber		Possible band assignments for:
	Device				Functional group
		(	)	( )		Turpentine constituents	Rosin constituents
		10891	918	methylene (	)
		9889			from tertiary alcohols ( -	)
		8662			= from Carbonyl ( = )
		7211			methyl associated with aromatic ( -	)
	MultiPurpose Analyzer I	6911 6101 5816 5708			aromatic ( ) from vinyl group as ( methyl ( ) methylene (	= )	-)
		5600			methylene (	)
		5222			= (carbonyl) from acid ( =	)
		4535			aromatic (Aryl)
		4420			(	Bending)
		11074	903	methylene (	)
		10787	927	methylene (	)
	SCiO	10449	957	Alkyl Alcohols ( --	)
		9843			from tertiary alcohols as (--	)
		9443			with hydrogen bonding ( --	)

Table 3 Statistics of Partial Least Squares (PLS) regression models.

 3

			SCiO (Consumer Physics, Israel)				MultiPurpose Analyzer I (Bruker, USA)
		Preprocessing
			LVs					LVs
		RAW	0,87 0,83 0,46	3,33	3,71	5,57	1,23	5	0,86 0,87 0,81	3,28	3,25	3,81	2,33
		SNV	10 0,90 0,86 0,64	2,71	3,33	6,57	1,53	4	0,90 0,89 0,89	2,88	2,92	3,10	2,87
		DT2	10 0,93 0,92 0,85	2,36	2,57	4,13	2,28	6	0,94 0,92 0,94	1,99	2,54	2,85	3,63
	α-pinene	SNV + DT2 MSC	12 0,91 0,92 0,89 0,86 0,86 0,81	2,40 3,31	2,57 3,38	3,49 3,96	3,09 2,35	6 4	0,94 0,94 0,92 0,91 0,90 0,91	2,10 2,79	2,13 2,87	2,79 3,03	3,42 2,82
		SG1	0,95 0,92 0,76	1,95	2,47	4,56	1,93	6	0,97 0,97 0,95	1,53	1,66	2,00	4,70
		SG2	0,93 0,92 0,91	2,26	2,47	3,24	3,16	4	0,96 0,95 0,94	1,72	1,91	2,44	4,16
		RAW	10 0,92 0,87 0,47	2,35	3,07	5,49	1,35 10 0,96 0,96 0,94	1,61	1,78	2,19	3,69
		SNV	10 0,90 0,88 0,52	2,70	2,95	7,63	1,11	9	0,97 0,96 0,94	1,47	1,59	2,01	4,12
		DT2	0,89 0,90 0,85	2,71	2,70	3,64	2,52	5	0,88 0,88 0,91	2,71	2,90	3,13	3,11
	β-pinene	SNV + DT2 MSC	0,90 0,89 0,86 0,89 0,87 0,84	2,39 2,58	2,75 2,99	3,99 3,88	2,54 2,42	7 8	0,96 0,94 0,93 0,96 0,95 0,95	1,69 1,62	2,08 1,91	2,38 1,97	3,83 4,45
		SG1	10 0,96 0,95 0,85	1,62	1,91	3,29	2,66	8	0,99 0,98 0,89	0,78	1,13	2,99	3,09
		SG2	0,93 0,92 0,87	2,16	2,45	3,26	2,82	4	0,96 0,96 0,96	1,66	1,79	1,91	4,96
		RAW	0,84 0,86 0,81	3,77	4,27	3,45	1,98	9	0,92 0,92 0,92	2,48	2,67	2,60	3,42
		SNV	0,80 0,84 0,85	3,86	4,22	3,99	2,51	7	0,93 0,93 0,94	2,38	2,76	2,29	3,87
	Levopimaric acid	DT2 SNV + DT2 MSC	0,82 0,84 0,87 0,76 0,78 0,88 0,78 0,81 0,89	3,71 4,01 4,09	4,25 4,09 4,66	3,70 3,77 3,01	2,55 2,85 3,09	9 5 6	0,95 0,94 0,94 0,89 0,90 0,83 0,90 0,90 0,95	1,95 2,90 2,89	2,15 2,88 2,80	2,72 4,26 2,27	3,81 2,24 3,75
		SG1	0,89 0,93 0,79	2,89	3,04	4,14	2,13	6	0,93 0,93 0,94	2,43	2,65	2,38	3,95
		SG2	0,85 0,86 0,86	3,26	3,69	3,93	2,60	4	0,90 0,91 0,94	2,69	2,91	2,72	3,72
		RAW	0,86 0,91 0,86	3,50	3,61	3,00	2,28	8	0,93 0,93 0,94	2,29	2,59	2,34	3,80
	Abietic and neoabietic acids	SNV DT2 SNV + DT2 MSC SG1	0,72 0,73 0,59 0,86 0,88 0,90 0,28 0,29 0,65 0,61 0,61 0,82 0,91 0,91 0,84	4,76 3,23 7,08 6,56 2,67	5,39 3,53 7,44 6,89 2,62	6,63 3,13 7,34 3,98 3,52	1,51 3,01 1,47 2,34 2,51	9 7 7 8 4	0,94 0,94 0,96 0,95 0,95 0,95 0,95 0,96 0,94 0,94 0,95 0,95 0,90 0,91 0,90	2,17 1,87 2,06 2,26 2,88	2,53 1,98 2,18 2,57 3,04	2,06 2,59 2,24 2,17 2,94	4,30 3,99 4,25 3,92 3,19
		SG2	0,85 0,86 0,88	3,22	3,78	3,56	2,87	3	0,90 0,90 0,94	2,66	2,96	2,64	3,83
		RAW	0,89 0,91 0,79	3,09	3,25	3,35	2,04	5	0,91 0,92 0,90	2,63	2,58	2,82	3,16
		SNV	0,85 0,85 0,68	3,37	3,92	6,87	1,46	6	0,89 0,90 0,87	2,98	3,11	3,24	2,74
	% Turpentine	DT2 SNV + DT2 MSC	0,88 0,90 0,90 0,78 0,82 0,89 0,73 0,79 0,78	3,07 3,86 4,98	3,34 4,06 5,25	3,17 3,56 4,67	2,98 3,03 1,99	7 6 6	0,92 0,92 0,90 0,90 0,91 0,88 0,89 0,91 0,92	2,42 2,78 3,09	2,72 3,04 3,42	3,42 3,30 2,96	3,02 2,89 2,88
		SG1	0,90 0,92 0,83	2,87	3,29	3,56	2,48	5	0,89 0,91 0,91	3,03	3,20	2,73	3,44
		SG2	0,87 0,88 0,89	3,05	3,28	3,42	2,99	4	0,89 0,91 0,92	2,86	3,07	2,94	3,45

Acknowledgements

The authors gratefully acknowledge the financial support from the Nouvelle Aquitaine regional council, the Landes departmental council, the Agence Nationale de la Recherche (National Agency for Research) and Xyloforest (ANR-10-EQPX-16). The authors warmly thank Luc Leneveu from the Holiste company, who provided the raw material, and Dr.

Camille Lepoittevin for hosting Lisa Feuillerat during the data acquisition work on the

MultiPurpose Analyzer I spectrometer (INRA, Pierroton, France).

The authors declare no actual or potential conflicts of interests. 311


Sixiang Lyu

Cheng Wang
email: wangcheng3756@163.com

Chuanyu Zhang

Laurent Royon

Xiaofeng Guo
email: xiaofeng.guo@esiee.fr

Experimental characterization of a novel soft polymer heat exchanger for wastewater heat recovery

Keywords: Polymer heat exchanger, wastewater, heat recovery, heat transfer enhancement, oscillation, serpentine-like

Wastewater released from showers, sinks, and washers contains a considerable amount of waste heat that can be recovered by using a heat exchanger. Conventional metal heat exchangers for wastewater heat recovery have common problems of corrosion, fouling and clogging, which makes it necessary to develop a new type of heat exchanger for such low-grade thermal energy recovery applications. This study deals with a novel patented polymer heat exchanger (WO2020049233A1) made of soft polyurethane tubes that are capable of oscillation once subjected to external forces. Laboratory tests coupled with theoretical analyses show a stable global heat transfer coefficient of 100-110 W/m 2 •K, achieving 67-92% of the performance of titanium-, aluminum-, and copper-made heat exchangers with the same configuration. It further reveals that the performance of the soft heat exchanger can be enhanced by 30% when it is under oscillation. In addition, the external convective thermal resistance appears to be the dominant one instead of heat conduction through the wall material. The special operating condition of heat recovery from a sewer pipeline makes the polymer heat exchanger particularly adapted with its equivalent thermal performance but advantages of high flexibility, modularity, and low cost.

INTRODUCTION

The growing world urbanization and climate change bring imminent challenges for the sustainable development of the society. A recent study by U.S. Energy Information Administration [1] predicted an increase in world energy consumption by nearly 50% between 2018-2050, of which almost all the increase comes from non-OECD countries having strong economic growth and rapid urbanization pace. Cities appear to dominate energy consumption and CO2 emissions. In 2013, the world's urban areas accounted for about 64% of global primary energy use and produced 70% of the total CO2 emissions [START_REF]Cities are at the frontline of the energy transition[END_REF]. More recently, the Covid-19 crisis inevitably urge the society to strengthen efforts made to the energy-climate transition.

Accessible, low-cost and stable alternative solutions are key to replace fossil-based energy. One way is to expand the share of renewable energies, such as solar photovoltaic (PV), wind, and bioenergy. However, this usually requires a considerable amount of financial support and their implementation is severely dependent on policy implementation, including the building of new energy infrastructures. Moreover, PV might be in conflict with agriculture when it comes to land occupation, while bioenergy can be in competition with food. The mismatch between demand and renewable production can also be problematic [START_REF] Guo | Comparison of direct and indirect active thermal energy storage strategies for large-scale solar heating systems[END_REF]. An alternative solution is to increase energy efficiency by reusing or converting waste energy (mostly in the form of heat)

to other forms of energy from existing systems.

In recent years, wastewater in urban sewer networks has attracted extensive interest for thermal energy recovery because of its easy accessibility and high abundance. Wastewater released from showers, sinks, and drains contains significant quantities of thermal energy, much higher than organic energy [START_REF] Hao | Energy recovery from wastewater: Heat over organics[END_REF]. An amount of 1.16 kWh thermal energy can be gained, if 1 m 3 of water is cooled down by 1 °C. According to Mazhar et al. [START_REF] Mazhar | A Key Review of Non-Industrial Greywater Heat Harnessing[END_REF], approximately 3.5 kWh of thermal energy per person per day could be harvested and used directly to meet thermal demands. Moreover, reusing heat from wastewater also helps to reduce greenhouse gas emissions. Takashi et al. [START_REF] Takashi | Life cycle inventory analyses for CO2 emission and cost of district heating and cooling systems using wastewater heat[END_REF] conducted a feasibility study in wastewater heat recovery and concluded that 2.5 tons of CO2 can be avoided per 10 000 m 3 of wastewater. Guo and Hendel [7] performed a case study in Paris to evaluate the field performance of a district-scale wastewater heat recovery system with an effluent flow rate of 115 m 3 /h, and reported that up to 75% CO2 could be reduced annually with a primary energy savings of 32%. Cold recovery case studies have also been carried out in Amsterdam drinking water network by van der Hoek et al. [START_REF] Van Der Hoek | Energy recovery from the water cycle: Thermal energy from drinking water[END_REF].

Conventionally, there are two techniques to extract heat from wastewater in urban sewer channels by means of heat exchangers. There are already some existing systems or demonstrating projects using both technologies worldwide enhancement under ultrasonic vibration using a stainless steel circular heater rod, and found a maximum heat transfer increment of 1 557 W/m 2 •K with an increment ratio of 3.01. In terms of energy input, however, their ultrasonic transducer requires a total power of 150 W with a frequency of 40 Hz. Undesirable local heating could be problematic for the use of ultrasounds [START_REF] Legay | Improvement of heat transfer by means of ultrasound: Application to a double-tube heat exchanger[END_REF][START_REF] Legay | Methods to Evaluate Heat Transfer Enhancement in an Ultrasonic Heat Exchanger[END_REF].

To our knowledge, the use of unmodified polymer heat exchanger in sewer wastewater heat recovery has yet not appeared in the literature.

In this paper, we report a newly patented polymer heat exchanger (WO2020049233A1) designed for wastewater heat recovery from sewers [START_REF] Guo | Flexible heat exchanger intended to be positioned in a moving exterior fluid, comprising a collection of flexible temperature probes[END_REF]. Laboratory experiments are carried out to determine its heat transfer characteristics, including heat transfer rate, heat transfer coefficient, and thermal resistance. As a unique advantage, oscillational movement of the soft heat exchanger is also studied to explore the heat transfer enhancement potential compared with standstill position (which is true to all classical rigid heat exchangers). In the absence of established correlation for serpentine-shape cylinder heat convection, theoretical results obtained from ideal models of parallel flow and crossflow heat exchanger with the same dimension and working conditions are used to define the upper and lower performance limits of the studied heat exchanger. Based on the model, the sensibility of heat exchanger performance on material thermal conductivity allows the comparison with metal heat exchangers. The data and results from this paper may offer some reference for the design and use of polymer heat exchanger in wastewater heat recovery or similar applications.

POLYMER HEAT EXCHANGER

Design and dimensions

Figure 1 shows schematically the structure of the novel heat exchanger and the dimension of the tested prototype. The heat exchanger is made of polymer polyurethane (PU), consisting of ten peripheric pipes and one central pipe [START_REF] Guo | Flexible heat exchanger intended to be positioned in a moving exterior fluid, comprising a collection of flexible temperature probes[END_REF]. The pipes are connected at one end by a cap for fluid return and this end is freely immersed in the source-side fluid (wastewater). The peripheric pipes (Port 1) and central pipe (Port 2) at the other end allow the feeding and return of the heat transfer fluid (HTF).

Manufactured by extrusion, this soft polymer heat exchanger holds important advantages of high modularity and flexibility.

Namely, it can be prefabricated then cut into any length according to the capacity requirement or installation environment. The heat exchanger can be operated at four different modes depending on the direction of HTF flow and relative temperatures between the HTF and source-side fluid, as shown in Table 1. Heat recovery refers to the situations when HTF temperature is lower than that of the source-side, while cold recovery refers to the cases when HTF temperature is higher than the source-side fluid.

Figure 1 Basic structure of the soft polymer heat exchanger (left) and dimension of the pipes (right).

Flow patterns

The soft nature of the material, together with the one end in free movement, makes the heat exchanger be easily deformed during operation due to external forces (turbulent flow, natural convection, thermocline, vortices, etc.). The deformation of the heat exchanger changes not only the shape but also the flow pattern between the HTF and source-side fluid.

THEORETICAL MODEL

Overall heat transfer coefficient

The overall heat exchange coefficient ( ) depends on those of peripheric ( ) and central pipes ( ). Each of them depends on internal and external convection and conduction through walls. The thermal resistances for internal convective ( ), external convective ( ), and conductive heat transfer through walls ( ) can be calculated using the Eqs. (1)-(3) [27], hence the total thermal resistance is the sum of them by Eq. ( 4):

= 1 ℎ (1) = 1 ℎ (2) = 2 (3) = + + (4)
The overall heat transfer coefficient of the peripheric pipes or central pipe may be determined as follows [27]:

= 1 ⋅ , (5)
The global heat transfer coefficient of the entire heat exchanger is finally calculated by the sum of the overall heat transfer coefficient of each pipe to the ratio of heat transfer area as shown in Eq. ( 7):

= ⋅ + + ⋅ + (7)
Substituting Eq. ( 5) and ( 6) in ( 7), the Eq. ( 7) becomes:

= 1 ⋅ , + 1 ⋅ , (8)

Internal convective heat transfer coefficient

To determine the internal convective heat transfer coefficient (ℎ ), the Reynolds number of internal tube flow is calculated to determine the flow regime:

= ! (9)
For internal pipe flow, the flow can be considered as laminar flow if < 2300, and turbulent if > 2900 [START_REF] Bergman | Fundamental of Heat and Mass Transfer[END_REF]. It worth noting that for N peripheric pipes, the mean velocity is calculated by the being divided by N (N=10).

In the case of fully developed laminar flow with constant surface temperature, and without considering the entrance development zone, the Nusselt number can be considered as constant:

= 3.66 *+ , -= ./ 012 1 (10)
For turbulent flow, the correlation provided by Gnielinski [27] is used to determine the Nusselt number:

= (+ 8)( -1000)78 ⁄ 1 + 12.7(+ 8 ⁄ ) ; < ⁄ (78 < = ⁄ -1) (11)
This correlation is valid for 0.5 ≤ 78 ≤ 2000 and 3000 ≤ ≤ 5 ⋅ 10 @ , and it can be applied for both constant heat flux and temperature conditions. In the equation, the Darcy friction factor + can be obtained from the Churchill equation (1977) [28] that is valid for all flow regimes:

+ = 8 ⋅ A B CD ;< + EF2.457 ⋅ ln J ; K LM N.O PQ.<R S T U VW ;@ + =RX=Q CD ;@ Y Z;.X [ \ \] (12)
where ^ and are the surface roughness (0.0015 mm for PU) and the inner diameter of the pipe.

Once NuD is obtained, the internal convective heat transfer coefficient of the peripheric or central pipe is determined by Eq. ( 13):

ℎ = _ (13)

External heat transfer coefficient

The external convection coefficient (ℎ ) depends on the relative flow pattern between the HTF and source-side fluid. As the soft heat exchanger is in a serpentine position, the complex flow pattern involves both parallel and crossflow, the latter being more favorable to convective heat transfer. Thus, the external convection coefficient around a serpentine-cylinder should be limited by values obtained by parallel and crossflow correlations.

In parallel flow, the external convective heat transfer coefficient is calculated by considering the peripheric and central pipe to be a single united pipe. This united pipe together with the external water pipeline acts as a concentric tube annulus. The hydraulic diameter of the concentric tube annulus is calculated using Eq. ( 14):

`= 4 7 = < -10 < -< + 10 + (14)
Then, Eq. ( 9) allows determining Reynolds number with ` in place of . The external convective heat transfer coefficient is then determined using:

ℎ = _ ` (15)
In the case of crossflow, the flow structure can no longer be considered as an annulus, Eq. ( 14) for calculating hydraulic diameter is adjusted to Eq. ( 16) as follows [27]:

`= < + 10 < + 10 (16)
To determine the Nusselt number on the external surface with calculated Re of about 52, the correlation for Re < 500 presented by Hsu (1963) is used [30], as shown in Eq. [START_REF] Chen | Recent research developments in polymer heat exchangers -A review[END_REF]. Although Churchill and Bernstein (1977)

The formula for the external convective heat transfer coefficient in crossflow remains the same as described in Eq. ( 15).

Heat transfer rates

The heat transfer rate of the heat exchanger is calculated with the outlet temperatures determined by using the Logarithmic Mean Temperature Difference (LMTD) method. For the peripheric pipe and central pipe, the LMTD is expressed in Eqs. ( 18)- [START_REF] Mamunya | Electrical and thermal conductivity of polymere filled with metal powders[END_REF] [27]:

a, = b, , c -, def,g h -b, , i -, def, i h j , , c -, def,g , , i -, def, i k (18) a, = b, , c -, def,g h -b, , i -, def, c h j , , c -, def,g , , i -, def, c k (19)
It is worth noting the expression of LMTD is based on co-current flow pattern for peripheric pipes and counter-current flow for the central one. This is furtherly illustrated in Figure 4.

Similarly, the heat conductance (UA) can be obtained for the peripheric and central pipe from Eqs. ( 5) and ( 6) as follows:

( ) = 1 , (20)
( ) = 1 , (21)
The outlet temperatures, including the middle and outlet temperature of the HTF fluid (, def,g , , def, c ) and the outlet temperature of the source-side fluid (, , c ), can be derived by solving a system of three equations ( 22)-( 24) with known information of inlet temperatures, flow rates, and heat conductance.

( ) a, = def . ,def b, def,g -, def, i h (22)
( ) a, = def . ,def b, def, c -, def,g h (23)
. , b, , c -, , i h = def . ,def b, def, i -, def, c h

With the solved outlet temperatures, the heat transfer rate is obtained respectively for the peripheric pipe and central pipe using the heat transfer equations ( 25)- [START_REF] Wang | Numerical Study of Heat Transfer Enhancement of Roll-to-Roll Microchannel Heat Exchangers[END_REF]. The global heat transfer rate is the sum of two components in series (Eq. ( 27)).

= . ,def b, def, i -, def,g h

= . ,def b, def,g -, def, c h

= + (26)

EXPERIMENTAL PROCEDURE AND DATA ANALYSIS

Experimental procedure

The polymer heat exchanger is tested in a temperature-regulated water pipeline (with an internal diameter of 90 mm) for source-side fluid under operating mode Case 1 (Table 1), as shown in

Experimental data analysis

The experimental heat transfer rate between the HTF and source-side fluid can be calculated respectively for the peripheric pipes and central pipe using Eqs. ( 25)-( 26), and the global heat transfer rate is the sum of the two components in series by Eq. ( 27). The experimental LMTD is estimated using the same Eqs. ( 18)-( 19) for the peripheric pipe and central pipe, respectively.

The heat conductance (UA) is determined as follows using the previously calculated LMTD:

( ) = a, (28)
( ) = a, (29)
The overall heat transfer coefficient (U) can be calculated individually for peripheric and central pipe:

= ( ) (30)
= ( ) (31)
Finally, the global heat transfer coefficient of the entire heat exchanger is obtained using the Eq. ( 7).

RESULTS AND DISCUSSION

Temperature profiles

The outlet temperatures (, , c , , def, c and , def,g ) of the polymer heat exchanger obtained from the experiment (EXP), parallel flow (PF), and crossflow (CF) theoretical models by the LMTD method are compared at various HTF flow rates, as shown in Figure 5. Constant inlet temperatures , , i and , def, i are averaged at measured values of 30.14 °C and 20.75 °C, respectively. As already illustrated in Figure 4, the HTF temperature lift can be divided into two parts: the temperature lifted in the peripheric pipes (, def,g -, def, i ) and the temperature lifted in the central pipe (, def, c -, def,g ). Results reveal that, with about three-fold heat transfer area, the temperature lift in the peripheric pipes is substantially higher compared to the central pipe regardless of flow patterns. For example, for the HTF flow rate at 8.71 g/s, the experimental results show HTF temperature is increased from 20.75 °C to 24.26 °C after the peripheric pipes and then to 24.62 °C at the outlet of the central pipe. Moreover, since the flow rate of the source-side fluid (60 g/s) is about 4 to 15 times higher than the HTF flow rate, the temperature change in the source-side fluid is significantly smaller than that of HTF. According to experimental results, the maximum temperature difference in the source-side fluid is as low as 0.7 °C while the value is 6.09 °C in HTF. In addition, compared with inlet temperatures, the experimental outlet temperatures in both HTF and source-side fluid (water) are between those estimated by parallel flow and crossflow models, with crossflow giving the highest temperature differences. This confirms the particular parallel-cross mixed flow pattern between the serpentine-shape soft heat exchanger and source-side fluid.

Experimental heat transfer rate

Heat transfer rate has been commonly used to evaluate the heat transfer performance of heat exchangers. With Eqs. ( 25)-( 26), the experimental heat transfer rate of the polymer heat exchanger is calculated, and the results are shown in Figure 6. For both peripheric and central pipes, the heat transfer rate increases with higher HTF flow rates. For the highest flow rate of 15.70 g/s, the total heat transfer rate under the test condition (hot and cold inlets being respectively 30.14 °C and 20.75 °C) can achieve 168 W, leading to a linear value of 102 W/lm (watt per linear meter). Moreover, the peripheric pipes dominate the performance of the heat exchanger, accounting for 87%-95% of the global heat transfer rate in the tested flow range (22 W for the central pipe and 146 W for peripheric ones in the case of 168 W). This is mainly due to the relatively larger heat transfer surface of the peripheric pipe (0.145 m 2 ) than that of the central one (0.052 m 2 ).

It worth noting the pressure loss of the HTF side is also measured for all flow rates and results show a maximal pressure drop of 24 588 Pa for the total length of 1.65 m. Details are given in Appendix A1.

Comparison with theoretical models

The characteristics of heat exchanger in crossflow and parallel flow are studied with the theoretical LMTD model, which is validated by NTU-ε method (details in Appendix A2). The results are compared with the experimental ones, as illustrated in

Thermal resistances

As over 87% of the global heat transfer rate is exchanged through the peripheric pipes, only the thermal resistances of the peripheric part is studied in this section. Figure 9 illustrates the influence of HTF flow rates and flow patterns (parallel flow and crossflow) on the three thermal resistances of peripheric pipes (Rpi, Rpw, and Rpo). Clearly, the total thermal resistance is considerably lower in crossflow than parallel flow, by a factor of about 7. Since the internal Dean flow for the case of crossflow pattern is considered negligible in this study, the only difference between parallel and crossflow lies in the external convention.

It shows that external convective thermal resistance is the principal resistance in both flow patterns, accounting for more than 90% of the total thermal resistance in parallel flow and about 50% in crossflow. This is due to the influence of a considerably lower external convective heat transfer coefficient caused by a larger hydraulic diameter on the external surface in comparison with the internal one. Therefore, the increase of HTF flow rates, which enhances the internal convention only, has little influence on the global thermal resistances in all flow rates and for both flow patterns. More importantly, comparing the three thermal resistances, the wall resistance in peripheric pipes (Rpw) is the lowest. The use of low-conductivity polymer can be potentially competitive to metal-made heat exchangers. Since the situation is slightly different in the central pipe, global heat exchange coefficient should be used to investigate the influence of thermal conductivity.

Comparison with metal heat exchangers

With the theoretical model, the material thermal conductivity is studied to determine its effects on the thermal performance of the heat exchanger in both cases of parallel flow and crossflow. The results obtained with PU (λ = 0.29 W/m•K) are compared with three selected metal heat exchangers, titanium, aluminum, and copper, whose thermal conductivity are 22 W/m•K, 236 W/m•K, and 398 W/m•K, respectively. The global heat transfer coefficient is used as bases for comparison under the same working conditions: def = 10.86 g/s, THTF,in = 20.7 °C, Tw,in = 30.1 °C, = 60 g/s. Three fictive PU with their thermal conductivities multiplied by a factor of 2, 4, and 6 are also analysed.

Figure 10 shows the theoretical results of the global heat transfer coefficient as a function of thermal conductivity in the case of crossflow and parallel flow. The detailed comparison results, as well as their relative performance ratios to each metal heat exchanger, are listed in Table 2 and Table 3. The polymer heat exchanger shows promising performance compared with conventional metal ones with the same dimension and working conditions. For crossflow in Figure 10, the polymer heat exchanger is expected to have a U-value of 209.44 W/m 2 •K, while the best metal performance (Copper) gives 312.18 W/m 2 •K.

For parallel flow, the values are respectively 36.18 W/m 2 •K and 39.37 W/m 2 •K. From the comparison results in Table 2 and Table 3, it is clear that the thermal performance of the heat exchanger made of PU can achieve about 67% and 92% of the titanium, aluminum, or copper heat exchanger in crossflow and parallel flow, respectively. If the thermal conductivity is doped by a factor of 6, reaching 1.74 W/m•K, the heat exchanger performance can achieve about 90% and 99% of the three selected metal materials, respectively in crossflow and parallel flow. As previously discussed, the experimental performance of PUmade heat exchanger is limited by the parallel flow and crossflow models, it is reasonable to conclude that the heat exchanger can achieve a performance of 67%-92% of titanium, aluminum, or copper heat exchangers.

Figure 10 also provides a design guide for polymer heat exchangers in terms of material choice. In the case of crossflow, there is a critical value (about 5.0 W/m•K) of material thermal conductivity that divides the performance curve into two parts.

Below this value, the curve is very sharp, which means that improving thermal conductivity will considerably boost the heat exchanger thermal performance. However, over this value, the curve tends to be flat at 312 W/m•K and thus improving thermal conductivity contributes very little to the heat performance enhancement. In the case of parallel flow, the important critical value is about 1 W/m•K. Above this value, increasing thermal conductivity has little effect on the curve since U-value tends to stay at a constant global heat transfer coefficient of approximately 40 W/m 2 •K. From the heat exchange performance point of view, improving thermal conductivity is worthless after the critical value since the main thermal resistance comes from the convective heat transfer, in particular the external one, rather than the pipe heat conduction (see Figure 9). As the real serpentine shape heat exchanger should be in between parallel and crossflow, the critical heat conductivity should be between 1 and 5 W/m•K. Table 2 Performance comparison between heat exchanger made of different materials in crossflow.

Table 3 Performance comparison between heat exchanger made of different materials in parallel flow.

Performance enhancement by oscillation -preliminary results

As one of the advantages of using soft material, oscillation and its influence on thermal performance is studied experimentally. Manually agitation of the heat exchanger around its longitudinal axis creates serpentine-like movement with transient deformation. As described earlier, the heat exchanger deforms as an external force is applied, either from mechanical shaking or from the natural flow of the source side fluid. Figure 11 shows some preliminary experimental results of the heat transfer rate for a continuous experimental run at four different HTF flow rates. It is found that oscillation significantly improves thermal performance by more than 30% regardless of the HTF flow rates. This is presumably due to the disturbance of the flow characteristics by movement, which perturbs the thermal boundary layer and thus improves the external heat convection. As previously discussed, results from the theoretical model indicate that principal thermal resistance lies in the external convection between the pipe and source-side fluid. Disturbance from manual shaking allows a continuous reduction of the thermal boundary layer since the flow pattern alternates between parallel flow and crossflow. As a result, the crossflow pattern would be more pronounced than in a stationary situation.

The thermal performance improvement by movement can be better illustrated by comparing the experimental results (with and without oscillation) with the theoretical performance limits (parallel flow and crossflow) under the same working conditions, as shown in Figure 12 (left). Compared with the stationary case, a clear increase of 30% in heat transfer rate is observed for the heat exchanger under oscillation, which suggests an enhanced thermal performance closer to that of crossflow (+60%). However, the performance is still within the theoretical performance range limited by parallel flow and crossflow correlations. It offers evidence that more crossflow pattern is presenting in the heat exchanger subjected to oscillational movement. The major heat transfer enhancement under oscillation may be explained by plotting the global heat transfer coefficient against Re in peripheric pipes, as shown in Figure 12 (right). In peripheral pipes, Re ranges from 379 to 873 with corresponding HTF flow rates from 6.35 to 15.59 g/s. A significant rise in the global heat transfer coefficient by about 50% can be observed for the heat exchanger under movement.

Figure 11

Heat transfer rates comparison between fixed and oscillation positions at different HTF flow rates.

CONCLUSIONS AND PERSPECTIVES

In this paper, a patented soft polymer heat exchanger for wastewater heat recovery is studied to explore its heat transfer characteristics, including heat transfer rate, heat transfer coefficient, and thermal resistance. The effect of material thermal conductivity and oscillation on the thermal performance justifies the interests of the novel heat exchanger. Both experiments and theoretical calculations are carried out.

Experimental results show a global heat transfer coefficient between 100-110 W/m 2 •K for an HTF flow rate of 4.27-15.70 g/s and 60 g/s at the source side. It is further found that with a heat transfer area three times larger than the central pipe, the peripheric pipes are largely dominating (over 87%) the performance of the heat exchanger in terms of heat transfer rate. In addition, when subjected to a manually assisted serpentine-like movement, the heat exchanger shows a significantly better performance, with an increase of about 30% compared with a stationary situation.

The theoretical models demonstrate that the performance of the polymer heat exchanger is in between the parallel and crossflow heat exchangers with the same configuration. More specifically, the experimental global heat transfer rate is up to 64% lower than the theoretical value in crossflow while up to 62% higher than those estimated by the parallel flow model.

Hence, it reveals that the actual flow pattern in the experiment is a combination of parallel flow and crossflow. In terms of thermal resistance, theoretical results confirm that the external convective thermal resistance is the dominant one, and the low thermal conductivity of polymer does not significantly deteriorate the global heat transfer coefficient.

With the theoretical models, a sensitivity study on the material thermal conductivity on heat exchanger performance is investigated. The results show that there is a critical value that divides the performance curve of the heat exchanger into two parts. Below this value, improving thermal conductivity will considerably boost the heat exchanger performance while over this value improving thermal conductivity contributes very little to the heat performance enhancement. This critical value is about 5.0 W/m•K in the crossflow heat exchanger and 1.0 W/m•K in the parallel flow heat exchanger. For the specific polymer heat exchanger made of polyurethane in this paper, it can reach 67% and 92% of the titanium, aluminum, and copper heat exchanger performance in crossflow and parallel flow, respectively. It can be concluded that using polymer materials to make heat exchangers can be thermally competitive to the conventional metal-based heat exchangers.

The polymer heat exchanger presented in this paper shows high potentiality to be used in the wastewater heat recovery application, not only because of the merits of using polymer materials but also its unique adaption to the installation environment. Firstly, it is flexible and can be changed to any shape as required. Secondly, due to the low weight, it always floats on the upper part of the wastewater where the water temperature is usually higher than the bottom part. In this way, the heat transfer between wastewater and HTF can be maximized. As mentioned previously, the heat exchanger performance potentially can be further improved in a crossflow environment or under movement. Future works will be focused on the study of heat transfer enhancement by vibration with different frequencies and amplitudes preferably induced by source-side fluid.

Other perspectives include the implementation of the polymer heat exchanger in a real case with specific environments.

APPENDICES A1: Pressure loss

The pressure drop through the HTF side is measured by an AST5100 wet-wet differential pressure transmitter. HTF flowrate ranges from 4.3 to 15.7 g/s, and it the same with the thermal performance part. The pressure loss follows a logarithmic trend with respect to the mass flowrate, and its maximal and minimum values are respectively 7 132 Pa and 24 588 Pa for the total length of 1.65 m.

Figure A1

HTF side pressure loss at the full range of flow rates.

A2: Validation of LMTD with NUT-l method

In order to validate theoretical results calculated from the LMTD method, NTU-ε method is used. The heat transfer rate is calculated separately for the peripheric pipe and central pipe. As the calculation procedures are the same, here taking the peripheric pipe as an example. The heat capacity rates (C) for the two fluids are determined as shown in Eqs. (32)-(33). The heat capacity ratio (m n ) is then the minimum value to the maximum value of the two heat capacity rates (m def , m ). Finally, the heat transfer rate can be evaluated according to Eqs. ( 38)-(39) from the above calculated results. The global heat transfer rate is calculated using Eq. ( 27).

m def = def . ,def (32)
m = . , (33)
= s ⋅ min (m def , m )(, , i -, def, i )

= s ⋅ min (m def , m )(, , i -, def,g )

With the heat transfer rates, the outlet temperatures can be determined as follows:

, def,g = , def, i + m def (40)

, def, c = , def,g + m def (41)

, , c = , , i -m (42)
The comparison results of the HTF outlet temperature in crossflow derived from LMTD and NTU-ε methods are illustrated in Figure A2. The results obtained from LMTD method are numerically in line with that from NTU-ε method. The temperature differences between two methods are only 0.1 °C and 0.05 °C respectively at the HTF middle and outlet locations, which indicates that the LMTD method is highly accurate.

Figure A2

A comparison between the HTF outlet temperatures in crossflow as calculated from LMTD and NTU-ε methods.

Figure 2

 2 illustrates positions of the heat exchanger (only one peripheric pipe is shown instead of ten) in both straight and serpentine positions and their corresponding flow patterns. In the straight position, parallel flow dominates heat transfer between the HTF and source-with peripheric pipes being co-current and the central pipe in counter-current configuration. However, a crossflow is created when the heat exchanger is in the serpentine position, which could enhance the external convection. Moreover, previous studies[START_REF] Wang | Numerical Study of Heat Transfer Enhancement of Roll-to-Roll Microchannel Heat Exchangers[END_REF][START_REF] Bergman | Fundamental of Heat and Mass Transfer[END_REF] have shown that Dean vortices can be formed when a fluid flows in a curved channel, resulting in an enhanced internal convective heat transfer. In sum, the facility of deformation of the novel heat exchanger makes it potentially more performant compared to a rigid heat exchanger under equal material and operating conditions.

Figure 2

 2 Figure 2 Schematic view of the heat exchanger in different positions and their corresponding fluid flow patterns (left: straight position with parallel flow; right: serpentine position with mixed parallel-cross flow).

Figure 3 . 4 .

 34 The heat exchanger is designed to have a length of 1.65 m, slightly shorter than the water pipeline. Both HTF and source-side fluid use water, and the inlet temperature of the source-side and HTF is maintained at 30 °C and 20 °C respectively by two thermostats. HTF is fed into the heat exchanger from the peripheric pipes and returned to the thermostat from the central pipe. At the heat exchanger inlet, outlet, and middle point, as well as the inlet and outlet of the water pipeline, fluid temperatures are measured by K-type thermocouples. The HTF-side pressure loss is measured by an AST5100 wet-wet differential pressure transmitter, with corresponding flow rates measured by Coriolis mass flowmeters (mini CORI-FLOW, Bronkhorst). The heat exchanger model and its corresponding temperature profile in this working mode (Case 1) are illustrated in FigureDuring the experimental runs, the flow rate of the source-side fluid is kept at a constant value of 60 g/s. It is 4 to 15 times higher than the HTF flow rate (4.27-15.7 g/s) and allows a reasonable hypothesis of fixed temperature wall condition during heat convection coefficient estimation. The experiment is kept running continuously with varying HTF flow rates to determine its effects on the thermal performance of the heat exchanger. At each level of the HTF flow rates, four representative data points are chosen for further calculation and analysis. As a preliminary study, the influence of oscillation on heat exchanger performance is also tested. The oscillation is created manually by shaking the heat exchanger around its longitudinal axis. With the other end of the soft heat exchanger in free movement, a transient serpentine form is created with an amplitude of ±35 mm and under a frequency of 1-2 Hz. Approximatively 2-3 sinusoidal cycles constitute the total length of the heat exchanger (1.65 m).The reading of thermocouples and flowmeters are automatically recorded by a computer. The main measurement uncertainties are from the thermocouples and flowmeters and are within ±0.5 °C and ±0.1 g/s, respectively.

Figure 3 Figure 4

 34 Figure 3 Schematic of the experimental setup.

Figure 5 :

 5 Figure 5: Comparison of outlet temperatures derived from experiment and theoretical models.

Figure 6

 6 Figure 6 Experimental heat transfer rates for peripheric and central pipes at different HTF flow rates. (Error bars represent the standard deviation for a set of 4 repetitive test results)

Figure 7 and 8 .

 78 Figure 7 and Figure 8. It worth noting that the aim of theoretical models is to provide upper and lower limits of the experimental performance, instead of validating experimental results.

Figure 7

 7 Figure7shows the global heat transfer rate obtained from the experiment, parallel flow and crossflow theoretical models. It shows that the experimental result is in between the data provided by parallel flow and crossflow correlations. Comparing with the experimental results, the crossflow theoretical model gives 34%-64% higher global heat transfer rate in the studied range of HTF flow rate, of which 64% occurs at the highest HTF flow rate of 15.70 g/s; while the values are 50%-62% lower in the parallel flow. Therefore, it can be concluded that the actual flow in the heat exchanger during experimental runs is neither pure parallel flow nor crossflow, but a combination of the two.

Figure 8

 8 Figure8shows similar trends in terms of the global heat transfer coefficient. Compared with the experimental results, which range from 100 to 110 W/m 2 •K, the theoretical overall heat transfer coefficient in crossflow could double the experimental performance, whereas the coefficient in parallel flow could be reduced to 1/3. In other words, the theoretical model in crossflow and parallel flow prescribe an upper and a lower limit of thermal performance to the actual flow in the heat exchanger. Moreover, as the HTF flow rates increase and the corresponding internal flow regime turns from laminar to turbulent flow, the global heat transfer coefficient keeps stable for all three data series. This implies that the global heat transfer coefficient might be mainly limited by the external convection or the heat conduction through walls, instead of the internal convection.

Figure 7 Figure 8

 78 Figure 7 Comparison between experimental and theoretical global heat transfer rate.Experiments are conducted with serpentine-shape heat exchanger in a straight water pipeline, which is a combination of crossflow and parallel flow. The theoretical results from crossflow and parallel flow convection correlation provide upper and lower limits of the heat exchanger performance.

Figure 9

 9 Figure 9 Thermal resistances of peripheric pipes in the cases of parallel flow and crossflow.

Figure 10

 10 Figure 10 Theoretical relation between global heat transfer coefficient and material thermal conductivity in the case of crossflow (left) and parallel flow (right) (working conditions: def = 10.86 g/s, THTF,in = 20.7 °C, Tw,in = 30.1°C,

Figure 12

 12 Figure 12 Heat transfer improvement by oscillation of the heat exchanger in the source-side fluid: (left) experimental results and theoretical limits and (right) experimental global heat transfer coefficients with respect to Re.

  Transfer Units (NTU) is defined as:, = ( ) min(m def , m )(35)The effectiveness (ε) is then estimated from Eqs. (36)-(37) depending on the flow patterns in the pipe[START_REF] Bergman | Fundamental of Heat and Mass Transfer[END_REF]: -, (1 -m n )w 1 -m n expv-, (1 -m n )w (in crossflow, m n <1, central pipe) (37)

  [g/s] CF, LMTD: T_HTF,m CF, LMTD: T_HTF,out CF, NTU-ε: T_HTF,m CF, NTU-ε: T_HTF,out

to use ultrasonic vibration to enhance heat transfer. Chen et al. [22] investigated the heat transfer

  Hence, facing this new application, it is desirable to find a solution to overcome those problems associated with metal heat exchangers. One promising solution is to use polymer materials instead of metals for manufacturing heat exchangers. Many different polymer materials have been studied for their physicochemical properties, such as polypropylene (PP), polyethylene Therefore, extensive research has been carried out to improve the thermal performance of polymer heat exchangers. The commonly used technique is to dope the polymer thermal conductivity by adding metal, ceramic, or carbon-based particles into the polymer matrix[START_REF] Bart | Innovative Heat Exchangers[END_REF].Breuer and Sundararaj [18] reported that the thermal conductivity of PP could be increased by 120% with 1 wt.% loading of single-walled carbon nanotubes (SWCNTs).

	Nevertheless, polymer materials have significant low thermal conductivities of typically less than 0.5 W/m•K compared to
	most metals which range from 10 W/m•K to 400 W/m•K [15]. to study the effects of mechanical vibration on heat transfer characteristics of laminar flow in a circular heated tube. An
	increased Nusselt number and a maximum heat transfer enhancement of 14.94% was found owing to the vibration. A more
	sophisticated technique is

[START_REF]CELSIUS, Waste heat recovery from sewage water in[END_REF][START_REF]CELSIUS, Excess heat from sewage in Hamburg and Singen[END_REF][START_REF] Reuseheat | [END_REF][START_REF] Veolia | Opening of a new Energido unit in Aix-les-Bains: Veolia transforms into wastewater to produce energy[END_REF]

. The first technique (integrated system) is to install a metal-made tubular heat exchanger at the bottom of the sewer pipeline

[START_REF] Guo | Urban water networks as an alternative source for district heating and emergency heat-wave cooling[END_REF][START_REF] Dürrenmatt | A mathematical model to predict the effect of heat recovery on the wastewater temperature in sewers[END_REF]

.

Several tubular exchangers with long length are often connected in series to achieve the desired heat capacity, due to their relative small heat exchanger surface and low heat transfer coefficient

[START_REF] Guo | Urban water networks as an alternative source for district heating and emergency heat-wave cooling[END_REF]

. Apart from weight and cost, metal heat exchangers also have issues of corrosion and fouling that often cause low efficiency or even failures

[START_REF] Chen | Experimental investigation of plastic finned-tube heat exchangers, with emphasis on material thermal conductivity[END_REF][START_REF] Bart | Innovative Heat Exchangers[END_REF]

. Moreover, in sewer pipes, the efficiency of the heat exchanger will be largely reduced if its surface is surrounded by sediments or biofilms or is not completely submerged

[START_REF] Dürrenmatt | A mathematical model to predict the effect of heat recovery on the wastewater temperature in sewers[END_REF]

.

The second technique (external system) is to pump the wastewater to a heat exchanger installed outside of the sewer. Spiral heat exchangers with advantages of compact design, large exchange surface, and high heat transfer coefficient are usually used in this technique

[START_REF]Alfa Laval brochure-spiral heat exchangers[END_REF]

. Besides, the high-speed flow through spiral heat exchangers can reduce the effect of fouling. However, it requires a dedicated by-pass piping network and high installation and pumping costs

[START_REF] Dürrenmatt | A mathematical model to predict the effect of heat recovery on the wastewater temperature in sewers[END_REF]

. Also, regular maintenance is necessary to avoid clogging by solid matters in wastewater. (PE), polycarbonate (PC), polytetrafluoroethylene (PTFE), etc. [15,17]. These materials have advantages of greater corrosion and fouling resistance, higher geometric flexibility, and they are easier to manufacture with reduced energy of formation and fabrication [15,17]. The use of polymer heat exchangers also helps to reduce transportation and installation costs, which eventually lowers the total investment [14]. Mamuya et al. [19] studied the thermal conductivity of polyvinyl chloride (PVC) filled with copper powders at different filler volume contents and reported 3-4 times thermal conductivity improvements with a filler volume content of 30%. Compared with the thermal performance of metal heat exchangers, Chen et al. [14] have shown that a modified PP heat exchanger with thermal conductivity of 15 W/m•K can achieve 95% of the titanium heat exchanger performance and 84% of the aluminum or copper heat exchanger performance with the same dimensions. In addition to conductivity enhancement of polymers, the operating condition, in particular vibration or other movements, also have potentially high impacts on heat exchanger performance. Recently, vibration effects on heat transfer enhancement have been confirmed numerically or experimentally. Shi et al. [20] numerically studied heat transfer enhancement by Vortex-Induced Vibration (VIV) generated by a cylindrical obstacle in a channel. Their results indicate that VIV can significantly increase the average Nusselt number up to 90.1% over that of a smooth channel. Liu et al. [21] used an experimental approach

Table 1 :

 1 Different operating modes of the polymer heat exchanger.

	Cases	Port 1	Port 2	Relative temperature	Type of recovery
	Case 1	HTF in	HTF out	THTF < Tsource	Heat recovery
	Case 2	HTF out	HTF in	THTF < Tsource	Heat recovery
	Case 3	HTF in	HTF out	THTF > Tsource	Cold recovery
	Case 4	HTF out	HTF in	THTF > Tsource	Cold recovery

ACKNOWLEDGEMENTS

The authors would like to acknowledge research supports provided by Erganeo (France), I-SITE FUTURE (France), ERASMUS program (European Union) as well as China Scholarship Council (P.R. China).