Measuring the influence of materials composition on nano scale roughness for wood plastic composites by AFM

Published Date
Measurement
September 2016, Vol.91:541–547, doi:10.1016/j.measurement.2016.05.095

Author 

• Shamsul Haq ^,
• Rajeev Srivastava

• Mechanical Engineering Department, Motilal Nehru National Institute of Technology Allahabad, U.P. 211004, India

Received 16 November 2015. Revised 27 May 2016. Accepted 28 May 2016. Available online 30 May 2016. 

Highlights

• •
  Surface smoothness affected by the formulation materials content.
• •
  The Surface roughness of the WPCs improved with increasing wood flour content.
• •
  Recycle PP composites showed a better result in comparison to virgin PP composite.
• •
  Coupled composite have the highest smoothness.
• •
  2D roughness profile, 3D surface variations, and FESEM analysis result also for the same.

Abstract

Composition of mango wood Polypropylene composites (WPCs) are prepared through melt compounding with the help of micro conical twin screw co-rotating extruder then injection molding of WPCs pellets. Polymer matrix composites are formulated for five compositions with a different weight ratio of wood, virgin polypropylene, recycled polypropylene and coupling agent. Average roughness (Ra), mean peak-to-valley height (Rz) and maximum roughness (Rmax) used to evaluate surface characteristics of samples by AFM. Also measures the 3D surface and surface roughness profile to examine the same. Result from this work clearly shows that recycled Polypropylene based composites with and without coupling agent have a smooth surface in comparison with virgin Polypropylene based composites for same operating variable conditions. The surface smoothness of WPCs improved with decreasing of wood flour content for all samples. By addition of coupling agent surface smoothness of the WPCs increases and the value of Ra decrease from 2.17 to 1.04 nm for recycle polypropylene wood based composite. Surface of composites is also examined with the help of FESEM images. FESEM feature proved that MAPP coupled is shown good bonding strength and smoothness in comparison to none coupled composite for the same class.

Keywords

Wood plastic composites

Roughness

3D surface

AFM

FESEM

Abbreviations

WPCs, wood plastic composites

PP, polypropylene

W, wood flour content

r, recycled

v, virgin

MAPP, maleic anhydride grafted polypropylene

CA, coupling agent

h, hours

AFM, atomic force microscopy

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Fig. 1.

 Table 1

Table 1.

 Table 2

Table 2.

Fig. 2.

Fig. 3.

Fig. 4.

• ⁎ 
  Corresponding author.
  
  

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Modeling of coupled heat and mass transfer during drying of tropical woods

Published Date
International Journal of Thermal Sciences
November 2016, Vol.109:299–308, doi:10.1016/j.ijthermalsci.2016.06.012

• Author 

• Merlin Simo-Tagne ^a,,
• Romain Rémond ^b
• Yann Rogaume ^b
• André Zoulalian ^c
• Beguide Bonoma ^d

• aLERMaB, ENSTIB, 27 rue Philippe Séguin, PO Box 1041, F-88051 Epinal, France
• bUniversity of Lorraine, LERMaB, ENSTIB, 27 rue Philippe Séguin, PO Box 1041, F-88051 Epinal, France
• cUniversity of Lorraine, LERMaB, PO Box 239–54560, Vandoeuvre les Nancy, Nancy, France
• dUniversity of Yaoundé I, Higher Teacher Training College, Applied Physic Laboratory, PO Box 47, Yaoundé, Cameroon

Received 14 May 2015. Revised 3 June 2016. Accepted 5 June 2016. Available online 17 June 2016.

Highlights

• •
  Modeling of heat and mass transfer during the drying of tropical woods are proposed.
• •
  Using Fortran 77 language, numerical results are generated.
• •
  Results obtained are compared by these given by Luikov’s model and experiment in constant and variable air characteristics phase.
• •
  Thermophysical coefficients of our model can be easily determined experimentally.
• •
  Numerical simulations obtained give close agreement with experimental results and those of Luikov’s model.

Abstract

We have developed a model on drying of two tropical woods of ayous (Triplochiton Scleroxylon) and frake (Terminalia Superba) coming from Cameroon forests. Some thermophysical parameters used in the model were experimentally obtained in this work while the remaining properties were from literature. A comparison is doing between numerical results of our model, these given by the Luikov’s model and experimental data. Numerical simulation results from the developed model give close agreement with experimental results. We note that Luikov’s model not gives a satisfaction results in the non-hygroscopic domain in the case of frake. The present model can be used to explain the drying phenomenon of these two tropical species and can be applied to others species when necessary thermophysical parameters of these species are known. In a future work, it is important to integrate the influence of anatomical direction on our numerical and experimental results.

Keywords

Drying

Heat and mass transfer

Modeling

Simulation

Experiment

Tropical woods

Central Africa

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Fig. 1.

Fig. 2.

 Table 1

Table 1.

 Table 2

Table 2.

Fig. 3.

Fig. 4.

Fig. 5.

Fig. 6.

Fig. 7.

Fig. 8.

Fig. 9.

 Table 3

Table 3.

Fig. 10.

Fig. 11.

• ∗ 
  Corresponding author. Tel.: +33 613483787.
  
  

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Comparison of solar dryer and solar-assisted heat pump dryer for cassava

Published Date
Solar Energy
15 October 2016, Vol.136:606–613, doi:10.1016/j.solener.2016.07.049

• Author 

• M. Yahya ^a,,
• Ahmad Fudholi ^b,,
• Hadyan Hafizh ^c
• Kamaruzzaman Sopian ^b

• aFakultas Teknologi Industri, Institut Teknologi Padang, Indonesia
• bSolar Energy Research Institute, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia
• cDepartment of Mechanical Engineering, City University College of Science and Technology, 46100 Petaling Jaya, Selangor, Malaysia

Received 26 October 2015. Revised 18 July 2016. Accepted 25 July 2016. Available online 29 July 2016. 

Highlights

• •
  A solar dryer (SD) and solar-assisted heat pump dryer (SAHPD) for cassava were investigated.
• •
  The drying rate of cassava using SAHPD is higher than SD.
• •
  Thermal and pick-up efficiencies were lower in SD compared to SAHPD.
• •
  The solar fraction was higher in SD compared to SAHPD.
• •
  The average COP of the heat pump was 3.38 from 3.23 to 3.47.

Abstract

The performance of a solar dryer (SD) and a solar-assisted heat pump dryer (SAHPD) for drying of cassava chips have been investigated. The SD and SAHPD decreased the mass of cassava from 30.8 kg to 17.4 kg within 13 and 9 h at average temperatures of 40 °C and 45 °C, respectively. The moisture content of cassava decreased from 61% (wet basis) to 10.5%, with a mass flow rate of 0.124 kg/s. The average thermal efficiencies were 25.6% and 30.9% for SD and SAHPD respectively. The average drying rate (DR) and specific moisture extraction rate (SMER) were 1.33 kg/h and 0.38 kg/kW h, respectively, for SD as well as 1.93 kg/h and 0.47 kg/kW h, respectively, for SAHPD. The pick-up efficiencies varied from 3.9% to 65.8% and 15.9% to 70.4% for SD and SAHPD, with average values of 39.3% and 43.6%, respectively. The average solar fractions were 66.7% for SD and 44.6% for SAHPD. The coefficient of performance of the heat pump ranged from 3.23 to 3.47, with an average of 3.38.

Keywords

Drying kinetic

Solar fraction

Pick-up efficiency

Thermal efficiency

Coefficient of performance

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Nomenclature

area of the collector (m²)

specific heat of air (J kg⁻¹ °C⁻¹)

COP

coefficient of performance

electrical energy consumed by the blower (kW)

electrical energy consumed by the compressor (kW)

E_evap

energy used for moisture evaporation (kW)

E_input

energy input to the drying system (kW)

thermal energy released by the condenser (kW)

energy incident in the plane of the solar collector (kW)

useful heat gain by the solar collector (kW)

latent heat of vaporization of water (kJ/kg)

G

solar radiation incident on the collector (W/m²)

I

current (A)

M_C

moisture content

M_f

final moisture content on wet basis (%)

M_i

initial moisture content on wet basis (%)

mass of the bone dry (kg)

initial mass of the product (kg)

mass of water (kg)

mass of water evaporated (kg)

air mass flow rate (kg/s)

mass flow rate of dry air (kg_dry air/s)

drying rate (kg/s)

T_{i, coll}

air temperatures at the inlet (°C)

T_{o, coll}

outlet of the solar collector (°C)

air temperatures at the inlet of the condenser (°C)

temperatures at the outlet of the condenser (°C)

T1

dry bulk ambient temperature or evaporator dry bulk inlet air temperature of heat pump (°C)

T2

wet bulk ambient temperature or evaporator wet bulk inlet air temperature of heat pump (°C)

T3

evaporator dry bulk outlet air temperature of heat pump (°C)

T4

evaporator wet bulk outlet air temperature of heat pump (°C)

T5

condenser dry bulk inlet air temperature of heat pump (°C)

T6

condenser wet bulk inlet air temperature of heat pump (°C)

T7

condenser dry bulk outlet air temperature of heat pump (°C)

T8

condenser wet bulk outlet air temperature of heat pump (°C)

T9

solar collector inlet air temperature (dry bulk) (°C)

T10

solar collector outlet air temperature (dry bulk) (°C)

T11

drying chamber dry bulk inlet air temperature (°C)

T12

drying chamber wet bulk inlet air temperature (°C)

T13

drying chamber dry bulk outlet air temperature (°C)

T14

drying chamber wet bulk outlet air temperature (°C)

t

drying time (s)

SF

solar fractions

SMER

specific moisture extraction rate

voltage (V)

adiabatic saturation humidity of air that enters the drying chamber (kg_water/kg_dryair)

absolute humidity of air that enters the drying chamber (kg_water/kg_dryair)

cos φ

power factor

pick-up efficiency

thermal efficiency

 Table 1

Table 1.

Fig. 1.

Fig. 2.

Fig. 3a.

Fig. 3b.

Fig. 4.

Fig. 5.

 Table 2

Table 2.

Fig. 6.

Fig. 7.

Fig. 8.

Fig. 9.

Fig. 10.

Fig. 11.

Fig. 12.

Fig. 13.

 Table 3

Table 3.

• ⁎ 
  Corresponding authors.

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