Abstract
The solar distillation process utilizes the abundantly available solar energy to separate pure water from the contaminants. The process takes place in a device called the solar distillation still (SDS). The thermal performance delivered by the SDS mainly depends on the distillate formation rate inside the basin. The distillate formation inside the SDS depends on its basin temperature (BT), basin water temperature (BWT), glass cover inside temperature (GCIT) and glass cover outside temperature (GCOT). The thermal performance delivered by the SDS is non-linear and fluctuating. The variation in thermal performance is mainly due to the sudden changes in ambient conditions (solar irradiance and wind speed). The fluctuation in performance demands a forecast model with higher prediction accuracy to monitor the deviations in the system performance. In this study, a fuzzy inference system (FIS) is proposed for predicting the thermal performance delivered by the SDS. The real-time experimental results are used to train and test the model. The FIS proposed in this study is simple, robust, stable and effective in comparison with the available quantitative models. The newly proposed FIS is capable of predicting the performance of SDS with an accuracy of 94.5%.
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- SDS:
-
Solar distillation still
- C-SDS:
-
Conventional solar distillation still
- SECD:
-
Solar energy conversion device
- BT:
-
Basin temperature
- BWT:
-
Basin water temperature
- GCIT:
-
Glass cover inside temperature
- GCOT:
-
Glass cover outside temperature
- AI:
-
Artificial intelligence
- FL:
-
Fuzzy logic
- FIS:
-
Fuzzy logic expert system
- FIS:
-
Fuzzy inference system
- PCM:
-
Phase change material
- MMS:
-
Metal matrix structure
- L:
-
Low
- LM:
-
Low medium
- M:
-
Medium
- MH:
-
Medium high
- H:
-
High
- DO:
-
Distillate output
- SE:
-
Still efficiency
- m:
-
Mass of evaporated water (kg/s)
- L:
-
Latent heat of vaporization (kJ/kg-K)
- A:
-
Area of glass cover (m2)
- t:
-
Time (seconds)
- Q:
-
Total heat stored (W)
- Cp :
-
Specific heat (J / kg-K)
- T:
-
Temperature (°C)
- Ta :
-
Ambient temperature (°C)
- G:
-
Solar irradiance (W/m2)
- ƞ:
-
Efficiency (%)
- τglass :
-
Transmissivity of glass cover
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Appendices
Appendix 1
See Table 7.
Appendix 2
A typical triangular membership function plot with three linguistic variables
A typical trapezoidal membership function plot with three linguistic variables
A typical Gauss-Bell membership function plot with three linguistic variables
A typical Gauss2 membership function plot with three linguistic variables
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Sridharan, M. Application of Mamdani fuzzy inference system in predicting the thermal performance of solar distillation still. J Ambient Intell Human Comput 12, 10305–10319 (2021). https://doi.org/10.1007/s12652-020-02810-5
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DOI: https://doi.org/10.1007/s12652-020-02810-5
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