# Thermo-Hydric Study of Wood-Based Materials under Thermal Comfort Conditions

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## Abstract

**:**

## 1. Introduction

- Developing new general methods;
- Utilizing alternative energy sources and minimizing heat losses to the atmosphere;
- Implementing low-cost methods, such as artificial intelligence or simulations, for potential energy savings;
- Coordinating processes for efficient energy use;
- Emphasizing environmentally friendly solutions and reducing greenhouse gas emissions.

- Theoretical foundations and mathematical modelling: Describing phenomena within materials by extrapolating prior research to our context. A new formulation is developed to describe moisture transfer phenomena (diffusivity) in wood-based materials under specific thermal comfort conditions, subsequently used in the simulation.
- Numerical simulation and analysis: Visualize these phenomena and determine the required drying time to reach specific MC values (12% and 8%). Wood-based materials are assumed to be porous media, and diffusivity is derived from the first approach.
- Experimental study: Tracking MC evolution in two types of wood-based materials (melamine and MDF samples) over drying time for each extreme condition of the thermal comfort zone. The drying time for specific MC values is deduced.
- Artificial neural network (ANN) implementation: Combined with experimental data to build a generalized model calculating drying time under any environmental condition in the thermal comfort zone. The numerical model in MATLAB calculates the required time to reach 12% and 8% MC values. Drying time results are compared by calculating the percent error (PE) and subsequently discussed.

## 2. Detailed Problem Definition and Research Justification

#### 2.1. Phenomenon Description

#### 2.2. Thermal Comfort

#### 2.3. Conceptual Formulation Overview

#### 2.3.1. Moisture Content (MC)

_{wet}is the mass of the specimen at a given moisture content and m

_{dry}is the mass of the oven-dry specimen.

#### 2.3.2. Equilibrium Moisture Content (EMC)

_{W}is the molecular weight of water (18 g·mol

^{−1}), R is the universal gas constant (8.314 J·K

^{−1}·mol

^{−1}), A is the natural logarithm (ln) of the Gibbs free energy as the relative humidity approaches zero, and M

_{V}is a material constant that approaches the fiber saturation point for desorption (%).

#### 2.4. Determination of Test Parameters

## 3. Theoretical Foundations and Mathematical Modelling

#### 3.1. Literature Review

#### 3.2. Development of the Mathematical Model

^{3}/M ratio represents 1/ρ considered constant.

## 4. Numerical Simulation and Analysis

#### 4.1. General Description

#### 4.2. Governing Equations

- (1)
- The mass transfer of moisture occurs in vapor form.
- (2)
- Drying is merely driven by natural heat and mass transfer (natural drying).
- (3)
- The diffusion is based on an approximative equation.

#### 4.2.1. Heat Transfer in Porous Media

#### 4.2.2. Moisture Transport in Porous Media (Diffusion Model)

#### 4.2.3. Input Parameters

#### 4.3. Geometry and Mesh Generation

^{3}for melamine and 200 mm × 200 mm × 12.7 mm for MDF. This geometry is consistently applied across all study sections to facilitate the comparison of results. The predefined mesh, featuring a free tetrahedral geometry, is automatically generated by COMSOL (Figure 3).

#### 4.4. Simulation Results and Discussion

- (1)
- Heat and moisture distribution: To show the transfer phenomena inside the material, the cross-section of the samples is presented according to the width and thickness. Figure 5 and Figure 6 show the evolution of combined heat and mass transfer in melamine and MDF, respectively. The heat transfer illustrates the temperature distribution and its evolution over time, from 0 to 2 h (depending on the condition), until it reaches a steady state. Notably, MDF reaches this state more rapidly than melamine due to the differences in their heat capacities within the temperature range of 292.15 °K and 300.15 °K (CP (melamine) > CP (MDF)). Furthermore, the boundaries of the specimen are influenced before the core. Moisture transfer occurs faster in MDF than in melamine and requires more time than heat transfer alone. When moisture transfer is combined with heat transfer, the drying process accelerates significantly compared to moisture transfer alone (more than 240 h). Therefore, temperature emerges as the pivotal factor in drying, particularly in drying time.

- (2)
- Moisture content Graph: The simulation provides a detailed view of the phenomenon. Figure 7a,b illustrate the drying process for condition (5) (Table 1), divided into two stages. The first stage is characterized by a rapid drop in moisture content within a short period (2 h). This phenomenon results from the combined effects of heat and moisture transfer. The second stage features a gradual decrease in moisture content as the temperature stabilizes throughout the specimen until it reaches the equilibrium moisture content (EMC) or the desired values. The overall curve shape is consistent across all conditions, with differences primarily observed in the duration of each stage and the final moisture content. A comprehensive comparison of all graphs is presented in Section 7.2.

- The factors influencing the simulation are associated with the materials (such as the particles’ heterogeneity, nature, or size, and additives), the approximation equations, and the phenomenon’s complexity.
- Simulation allows us to visualize moisture content evolution within the materials and identify the stages of the phenomenon.
- Temperature plays the primary role in drying, especially for drying time.
- Like most wood-based materials, melamine and MDF exhibit comparable general behavior in response to their environment.

## 5. Experimental Study

#### 5.1. Methodology of the Experimental Study

- Step 1: Drying until obtaining the anhydride mass, m
_{dry.}All samples were placed in a kiln dryer at 80 °C and measured their weight daily using a precision balance until it became stable (no weight variation). The final weight represented the anhydride mass m_{dry}(0% moisture). - Step 2: Attaining maximum moisture level (saturated moisture). After the anhydrite samples were obtained, they were placed in a conditioning chamber under extreme humidity conditions (23 °C, 95%) and obtained the maximum wetted mass m
_{wet}by measuring the weight until it became stable. This step allowed us to determine the maximum wetted mass (saturated moisture). - Step 3: Applying test parameters. The final step involved applying the test parameters outlined in Table 1 and calculating the time required to reach 12% or 8% moisture content levels. During this step, moisture content variations were measured over time. This experience aimed to establish the necessary drying time for each sample.

- i: Test number (1 to 2). For the average, the letter M is used.
- X: Material (M for MDF, P for melamine).
- j: Number of test conditions (1 to 6).

#### 5.2. Test Devices

- Kiln dryer: A kiln dryer assures the experience parameters of the first step at 80 °C.
- Weighing (precision balance): The precision balance measures the weight accurately, with a precision of 0.01 g and a maximum capacity of 1000 g.
- Conditioning chamber: The conditioning chamber ensures the experience parameters of the second and third steps with real-time control.

#### 5.3. Materials

**Melamine:**- The test samples were 200 mm (length) × 100 mm (width) × 15.8 mm (thickness);
- Samples number: 12 (1P1, 1P2, 1P3, 1P4, 1P5, 1P6, 2P1, 2P2, 2P3, 2P4, 2P5, 2P6);
- Density: 728 kg/m
^{3}(calculated).

**MDF:**- The test samples were 200 mm (length) × 200 mm (width) × 12.7 mm (thickness);
- Samples number:12 (1M1, 1M2, 1M3, 1M4, 1M5, 1M6, 2M1, 2M2, 2M3, 2M4, 2M5, 2M6);
- Density: 728 kg/m
^{3}(calculated).

#### 5.4. Experimental Results and Discussion

- Drying (desorption) and wetting (adsorption) are inverse processes.
- The graphs present a pseudo-exponential form.
- The sample exposed to higher temperatures dried faster.
- The sample exposed to lower air humidity also dried faster.
- MDF dries, under most conditions, faster than melamine.

## 6. ANN Design and Implementation

#### 6.1. General Description

- Firstly, determine the ANN architecture.
- Secondly, train the model, and validate its final configuration by calculating the mean squared error (MSE) and the coefficient of regression R.
- Thirdly, once the neural network predicts the time required to reach moisture content (MC) levels of 12% and 8%, compare these predictions with the experimental results by calculating the errors.

#### 6.2. Model Building

#### 6.3. Performance Parameters

#### 6.4. ANN Results and Discussion

## 7. Comparative Analysis

#### 7.1. Comparison Criterion

#### 7.2. Experience vs. Simulation

#### 7.3. Experience vs. ANN

#### 7.4. Interpretation of Comparison Results

#### 7.5. Comparative Analysis with Previous Studies

## 8. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

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**Figure 1.**Example of equilibrium moisture content (%) for wood as a function of temperature (°C) and relative humidity (%) [9].

**Figure 2.**Acceptable operating temperature and humidity ranges for individuals wearing typical summer and winter clothing during light activity [19] (Copyright notice for ASHRAE Standards, ©ASHRAE, www.ashrae.org (accessed on 30 Novembre 2023). (1995) ASHRAE Standard (Addendum 55a-1995).

**Figure 10.**Comparison between the experimental results and the simulation: (1) 23 °C and 65%; (2) 26 °C and 57%; (3) 27 °C and 20%; (4) 20 °C and 31%; (5) 19 °C and 65%; (6) 23 °C and 63%.

Test # | Initial Test Conditions | Initial EMC Melamine (%) | Initial EMC MDF (%) | Corrected Test Conditions | EMC Melamine (%) | EMC MDF (%) |
---|---|---|---|---|---|---|

1 | 23 °C and 77% | 15.0 | 14.0 | 23 °C and 65% | 12.3 | 11.5 |

2 | 26 °C and 57% | 11.1 | 10.2 | 26 °C and 57% | 11.1 | 10.2 |

3 | 27 °C and 20% | 5.8 | 4.9 | 27 °C and 20% | 5.8 | 4.9 |

4 | 20 °C and 31% | 7.5 | 6.6 | 20 °C and 31% | 7.5 | 6.6 |

5 | 19 °C and 87% | 18.5 | 17.5 | 19 °C and 65% | 12.5 | 11.7 |

6 | 23 °C and 63% | 11.9 | 11.0 | 23 °C and 63% | 11.9 | 11.0 |

**Table 2.**Methods used in the literature to determine the moisture distribution M and/or diffusion D for wood particleboards.

Year | Author (s) | Expression | Advantage | Limits |
---|---|---|---|---|

1949 | Crank and Park [25] | D = (0.049 a_{2})/t_{0.5} | Easy in application and calculus. | Significant error. Many missed parameters. |

1956 | Crank [24] | D = (0.1976 l2)/t_{0.5} | Easy in application and calculus. | Significant error. Many missed parameters. |

1966 | Luikov [26] | $\frac{{M}_{c}-{M}_{s}}{{M}_{0}-{M}_{e}}=\frac{1}{2}{Ki}_{m}\left(1+\epsilon {P}_{n}{K}_{0}{L}_{u}\right)$ $\frac{{T}_{s}-{T}_{c}}{{T}_{a}-{T}_{0}}=\frac{1}{2}{Ki}_{m}\epsilon {K}_{0}{L}_{u}$ Kim, ε, P _{n}, K_{0} and L_{u}: Variable parameters | Analytic resolution. General formula considering both mass and heat transfer. | Depending on a lot of parameters. Difference from specimen surface to center. |

1974 | Simpson [24] | $\frac{\partial S}{\partial T}=D\frac{{\partial}^{2}S}{\partial {X}^{2}}$ $\frac{{\stackrel{`}{S}}_{m}-{S}_{m}}{\u2206T}=D\left(\frac{{S}_{m+1}-2{S}_{m}+{S}_{m-1}}{{\left(\u2206X\right)}^{2}}\right)$ S: Averages of dealing with the concentration depending on the diffusion coefficient. ${\stackrel{`}{S}}_{m}$ and ${S}_{m}$ are the values of S at the points X = m(ΔX) at T = (n + 1) ΔT and T = n (ΔT), respectively, and Sm + 1 and Sm − 1 are the values of S at X = (m + 1)(ΔX) and X = X = (m − 1)(ΔX) at T = n (ΔT). | Series of approximations that are repeated until certain experimental and calculated values agree. Good match with experience results. | Large lumber of successive approximations. A high-speed computer is necessary to apply the method. |

1977 | Whitaker [31] | $\left(\frac{\partial S}{\partial t}\right)=\nabla .(\mathrm{D}\nabla \mathrm{S})$ D: drying diffusion coefficient S: fractional moisture saturation $D={K}_{\epsilon}+\left[\frac{\partial \xi}{\partial S}\right]\frac{{D}_{eff}^{\left(1\right)}}{{\epsilon}_{\gamma}}$ | Analytic resolution. Based on simultaneous heat, mass, and momentum transfer in porous media. | Many assumptions (neglected parameters) and approximations to solve the problem. A lot of calculations. |

1984 | Siau [22] | $\stackrel{`}{D}=\frac{{E}^{2}{L}^{2}}{5.10t}$ L = thickness in direction of flow, cm; t = time, s. E = dimensionless term. D’ = diffusion coefficient which includes diffusion coefficient D and surface emission coefficient S | Analytic equation. Simple to calculate. | Includes unknown variable S (surface emission coefficient). |

1986 | Stanish [32] | $\frac{\partial}{\partial t}\left({\rho}_{a}\right)=-\frac{\partial}{\partial z}\left({n}_{a}\right)$ (The conservation equation for air) $\frac{d}{{d}_{t}}\left|\genfrac{}{}{0pt}{}{{\rho}_{a}}{\genfrac{}{}{0pt}{}{{\rho}_{m}}{T}}\right|=\left|\genfrac{}{}{0pt}{}{\mathcal{A}}{\genfrac{}{}{0pt}{}{\mathcal{M}}{\mathcal{C}}}\right|$ | Simulated and experimental results revealed minor changes in the model parameter set. | Computational resolution by finite elements. |

1989 | Liu [29] | $\mathrm{D}=\frac{-0.165{\left(\raisebox{1ex}{$\mathrm{a}$}\!\left/ \!\raisebox{-1ex}{$2$}\right.\right)}^{2}}{\raisebox{1ex}{$0.701{\mathrm{d}\mathrm{t}}_{0.5}$}\!\left/ \!\raisebox{-1ex}{$\mathrm{d}\mathrm{E}$}\right.+2.05{\mathrm{t}}_{0.5}}$ $\mathrm{S}=\frac{0.701D}{\left(\raisebox{1ex}{$\mathrm{a}$}\!\left/ \!\raisebox{-1ex}{$2$}\right.\right)\left[\mathrm{D}\mathrm{t}/{\left(\raisebox{1ex}{$\mathrm{a}$}\!\left/ \!\raisebox{-1ex}{$2$}\right.\right)}^{2}-0.196\right]}$ $\frac{{t}_{0.5}}{0.2}\left(\frac{L}{2}\right)=\frac{\left(L/2\right)}{D}+\frac{3.5}{S}$ | Use of surface emission coefficient S with diffusion D to obtain global phenomena understanding. | Significant error. Many missed parameters. |

1991 | Simpson et Liu [30] | D = A exp (B m) A and B are coefficients determined by non-linear regression | Treats high-level moisture. Easy to apply. | Proceeding by two approximations. A and B numerical value changing with approximation. Adjusted parameters by experience. |

1993 | Cloutier and Fortin [24] (based on [25,30,31] works) | ${\left[{K}_{x}\right]}_{xi,tj}=\frac{{\left[\frac{\partial I}{\partial t}\right]}_{xi,tj}}{{\left[\frac{\partial \psi}{\partial x}\right]}_{xi,tj}}$ $I={\int}_{xq=0}^{x}Cdx$ [Kx]xi,tj = effective water conductivity in the direction of flow x at position xi and time tj; [∂I/∂t]xi,tj = −(flow through plane xi at time tj); [∂ψ/∂x]xi,tj = ψ gradient at xi and tj. C: moisture concentration ψ: water potential (See Figure 1) | Combining analytic and experimental resolution. Less error and more precision than another formula. | Many notions and equations are to be considered. Needs a small gap between points, so more computing resources. |

1992 | L. Cai and S. Deku [35] | D’ = b0 + b1M + b2M2 + b3M3 | The diffusion D’ was expressed as a function of water content M with a third-degree polynomial. Use of the FEM. | Neglects the resistance of the surface in the unsteady regime. Error about 10% between experiment and FEM. |

1994 | L. Cai, F. and Wang [37] | (1) The diffusion coefficients of moisture movement perpendicular (d) and parallel (d) (2) Usins Liu [29] and Siau [22] equations to determine the diffusion coefficient (3) Regression equation Y = b + aX a,b depending on parameters on materials and direction (4) Determinate a simple relationship between moisture M, Diffusion D, and time t, for each case (example: M = 1.4774 t 0.178 with correlation coefficient R = 0.9710) | Treat both steady-state and unsteady-state. Determines diffusion coefficient D for both parallel and perpendicular orientations. Determines the linear relationship between diffusion D and moisture M for each case. | No general equation (equation for each case). A lot of approximation. Uses experience and graphic analysis to determine diffusion d for the steady state (giving the results by direct values). |

1996 | Wu and Suchsland [13] | Determinate the moisture content MC in two ways and compare between them: Predicting: Equation (5) and D $D=\left(\frac{\pi {L}^{2}}{4}\right){\left[\frac{{E}_{2}{-E}_{1}}{\sqrt{{t}_{2}}-\sqrt{{t}_{1}}}\right]}^{2}$ ${E}_{i}={(W}_{i}-{W}_{1})/({W}_{E}-{W}_{1})$ W _{i} = specimen weight at time ‘‘i”W _{1} = initial specimen weight,W _{E} = specimen weight at equilibrium, L = half specimen thickness.Measuring: Using Simpson’s [27] equation to determine the diffusion coefficient Comparing the two approaches | Good agreement between predicted and measured MC for three different panels. Demonstrates the backer can reduce the extent of the moisture differential and swelling stress imbalance. | The moisture profile is supposed uniform across the specimen thickness. Using experimental results that cannot fully create the assumed conditions. |

2001 | Wu Q, and Xiong M [36] | K (p,x) = 0.00431 − 0.00107p + 0.00192x + 0.0005p^{2} − 0.0192x^{2} + 0.000206.x.p | No big calculation and complex formula. Dedicated to MDF and particle boards. | A small range of temperature and humidity. |

Parameter | Type | Definition | Value/Expression | Reference |
---|---|---|---|---|

T | Boundary conditions | Temperature (°K) | 292.15–300.15 | Table 1 |

RH | Boundary conditions | Relative humidity | 20–65% | Table 1 |

T_ini | Initial conditions | Initial temperature (°K) | 291.15 | Present work |

MC_ini | Initial conditions | Initial moisture content | 18% | Present work |

MC_fin | Boundary conditions | Final moisture content | EMC | Present work |

D | Diffusion (m^{2}/s) | Equation (15) | Present work | |

CP_Melamine | Specific heat capacity of melamine (J/kg·K) | $6\xb7714\times T-604\xb753$ | [40,41] | |

CP_MDF | Specific heat capacity of MDF (J/kg·K) | $3.867\times T+103\xb71$ | [42,43] |

Test # | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

Min | Max | Min | Max | Min | Max | Min | Max | Min | Max | Min | Max | Min | Max | Min | Max | Min | Max | |

Element size (mm) | 14 | 100 | 10.8 | 60 | 8 | 38 | 5.6 | 30 | 3.6 | 20 | 2 | 16 | 0.8 | 11 | 0.3 | 7 | 0.04 | 4 |

Calculation time | 14 min 21 s | 4 min 58 s | 6 min 20 s | 4 min 57 s | 7 min 53 s | 11 min 22 s | 45 min 31 s | 1 h 50 min 40 s | 2 h 15 min 21 s | |||||||||

Relative error | 21.67% | 19.22% | 16.90% | 15.28% | 11.69% | 4.70% | 2.50% | 0.6% | 0.5% |

Material | Dimensions (L × W × T) [mm] | Sample Numbers | Density (kg/m^{3}) |
---|---|---|---|

Melamine | 200 × 100 × 15.8 | 1P1, 1P2, 1P3, 1P4, 1P5, 1P6, 2P1, 2P2, 2P3, 2P4, 2P5, 2P6 | 728 (calculated) |

MDF | 200 × 200 × 12.7 | 1M1, 1M2, 1M3, 1M4, 1M5, 1M6, 2M1, 2M2, 2M3, 2M4, 2M5, 2M6 | 728 (calculated) |

MDF | Melamine | ||||||
---|---|---|---|---|---|---|---|

First Test | Second Test | First Test | Second Test | ||||

Sample | Weight (g) | Sample | Weight (g) | Sample | Weight (g) | Sample | Weight (g) |

1M1 | 355.18 | 2M1 | 370.91 | 1P1 | 222.45 | 2P1 | 227.16 |

1M2 | 362.58 | 2M2 | 371.37 | 1P2 | 237.39 | 2P2 | 233.99 |

1M3 | 359.39 | 2M3 | 378.33 | 1P3 | 228.89 | 2P3 | 217.08 |

1M4 | 369.35 | 2M4 | 371.08 | 1P4 | 239.87 | 2P4 | 235.74 |

1M5 | 372.93 | 2M5 | 375.71 | 1P5 | 222.88 | 2P5 | 235.27 |

1M6 | 378.50 | 2M6 | 385.99 | 1P6 | 236.58 | 2P6 | 224.70 |

Time (Days) | MC (%) Melamine | MC (%) MDF |
---|---|---|

0.00 | 0.0% | 0.0% |

1.00 | 5.3% | 6.6% |

4.00 | 9.0% | 14.8% |

5.00 | 10.2% | 15.4% |

7.00 | 11.4% | 16.1% |

12.00 | 14.9% | 17.3% |

18.00 | 16.9% | 17.9% |

19.00 | 17.5% | 18.1% |

20.00 | 17.7% | 18.1% |

21.00 | 17.9% | 18.2% |

Test # | Test Conditions | Melamine (Days) | MDF (Days) | ||
---|---|---|---|---|---|

12% | 8% | 12% | 8% | ||

1 | 23 °C and 65% | 30 | 66 | 25 | 58 |

2 | 26 °C and 57% | 13 | 33 | 7 | 19 |

3 | 27 °C and 20% | 10.4 | 14.3 | 7.3 | 10.4 |

4 | 20 °C and 31% | 5.4 | 12.6 | 7.4 | 16 |

5 | 19 °C and 65% | 21 | 70 | 18 | 60 |

6 | 23 °C and 63% | 23 | 68 | 18 | 54 |

Material | ANN Architecture | MSE Training | MSE Validation | R | Linear Regression Model |
---|---|---|---|---|---|

Melamine | [3-10-7-1] | 0.17 | 0.13 | 0.99956 | Output = 1*Target + 0.011 |

MDF | [3-10-10-1] | 0.08 | 0.33 | 0.99924 | Output = 1*Target + 0.071 |

Condition | 1 | 2 | 3 | 4 | 5 | 6 | |
---|---|---|---|---|---|---|---|

Melamine | Minimum error | 0.3% | 1.7% | 0.5% | 2.7% | 0.2% | 0.9% |

Maximum error | 18.6% | 14.6% | 20.7% | 25.4% | 20.8% | 22.2% | |

Average errors | 9.6% | 8.9% | 10.8% | 10.1% | 13.1% | 14.2% | |

MDF | Minimum error | 1.1% | 1.2% | 0.6% | 0.2% | 0.9% | 0.8% |

Maximum error | 15.7% | 15.5% | 24.6% | 10.3% | 19.8% | 22.2% | |

Average errors | 8.3% | 10.7% | 10.7% | 4.4% | 13.5% | 14.2% |

Condition | 1 | 2 | 3 | 4 | 5 | 6 |
---|---|---|---|---|---|---|

Melamine (8%) Experience | 66 | 33 | 14.3 | 12.6 | 70 | 68 |

Melamine (8%) ANN | 66.3 | 33.51 | 14.26 | 12.76 | 67.66 | 69.04 |

PE | 0.46% | 1.55% | 0.27% | 1.27% | 3.35% | 1.53% |

Average | 1.40% | |||||

Melamine (12%) Experience | 30 | 13 | 10.4 | 5.4 | 21 | 23 |

Melamine (12%) ANN | 29.05 | 12.69 | 9.91 | 5.42 | 20.68 | 21.86 |

PE | 3.17% | 2.39% | 4.70% | 0.37% | 1.52% | 4.94% |

Average | 2.85% | |||||

MDF (8%) Experience | 58 | 19 | 10.4 | 16 | 60 | 54 |

MDF (8%) ANN | 58.44 | 19.5 | 10.6 | 16.15 | 61.24 | 54.11 |

PE | 0.77% | 2.64% | 1.88% | 0.94% | 2.07% | 0.21% |

Average | 1.42% | |||||

MDF (12%) Experience | 25 | 7 | 7.3 | 7.4 | 18 | 18 |

MDF (12%) ANN | 24.79 | 7.18 | 7.51 | 7.65 | 17.58 | 17.73 |

PE | 0.85% | 2.60% | 2.81% | 3.43% | 2.31% | 1.48% |

Average | 2.25% |

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## Share and Cite

**MDPI and ACS Style**

Haddouche, M.; Martini, F.; Chaouch, M.; Ilinca, A.
Thermo-Hydric Study of Wood-Based Materials under Thermal Comfort Conditions. *Materials* **2024**, *17*, 1177.
https://doi.org/10.3390/ma17051177

**AMA Style**

Haddouche M, Martini F, Chaouch M, Ilinca A.
Thermo-Hydric Study of Wood-Based Materials under Thermal Comfort Conditions. *Materials*. 2024; 17(5):1177.
https://doi.org/10.3390/ma17051177

**Chicago/Turabian Style**

Haddouche, Mohamed, Fahed Martini, Mounir Chaouch, and Adrian Ilinca.
2024. "Thermo-Hydric Study of Wood-Based Materials under Thermal Comfort Conditions" *Materials* 17, no. 5: 1177.
https://doi.org/10.3390/ma17051177