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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Abbreviations
- 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
References
Abedinia O, Zareinejad M, Doranehgard MH, Fathi G, Ghadimi N (2019) Optimal offering and bidding strategies of renewable energy based large consumer using a novel hybrid robust-stochastic approach. J Clean Prod 215(2019):878–889. https://doi.org/10.1016/j.jclepro.2019.01.085
Bagal HA, Soltanabad YN, Dadjuo M, Wakil K, Ghadimi N (2018) Risk-assessment of photovoltaic-wind-battery-grid based large industrial consumer using information gap decision theory. Sol Energy 169(2018):343–352
Bilal A, Jamil B, Haque NU, Ansari MA (2019) Investigating the effect of pumice stones sensible heat storage on the performance of a solar still. Groundw Sustain Dev 9(2019):1–13. https://doi.org/10.1016/j.gsd.2019.100228
Dumka P, Mishra DR (2019) Performance evaluation of single slope solar still augmented with the ultrasonic fogger. Energy 10(2019):1–13. https://doi.org/10.1016/j.energy.2019.116398
Dumka P, Sharma A, Kushwah Y, Singh A, Raghav DR, Mishra, (2019) Performance evaluation of single slope solar still augmented with sand-filled cotton bags. J Energy Storage 25(2019):1–8. https://doi.org/10.1016/j.est.2019.100888
Elbar ARA, Yousef MS, Hassan H (2019) Energy, exergy, exergoeconomic and enviroeconomic (4E) evaluation of a new integration of solar still with photovoltaic panel. J Clean Prod 233(10):665–680. https://doi.org/10.1016/j.jclepro.2019.06.111
Gao W, Darvishan A, Toghani M, Mohammadi M, Abedinia O, Ghadimi N (2019) Different states of multi-block based forecast engine for price and load prediction. Int J Electr Power Energy Syst 104(2019):423–435. https://doi.org/10.1016/j.ijepes.2018.07.014
Ghadimi N, Akbarimajd A, Shayeghi H, Abedinia O (2018) Two stage forecast engine with feature selection technique and improved meta-heuristic algorithm for electricity load forecasting. Energy 161(2018):130–142. https://doi.org/10.1016/j.energy.2018.07.088
Kabeela AE, Khairat MM, Dawood KR, Nabil T, Elnaghi B, Ahmed elkassar, (2019) Enhancement of single solar still integrated with solar dishes: an experimental approach. Energy Convers Manag 196(15):165–174. https://doi.org/10.1016/j.enconman.2019.05.112
Khodaei H, Hajiali M, Darvishan A, Sepehr M, Ghadimi N (2018) Fuzzy-based heat and power hub models for cost-emission operation of an industrial consumer using compromise programming. Appl Therm Eng 137:395–405. https://doi.org/10.1016/j.applthermaleng.2018.04.008
Kumbhar SV (2019) Double slope solar still distillate output data set for conventional still and still with or without reflectors and PCM using high TDS water samples. Data Brief 24:1–14. https://doi.org/10.1016/j.dib.2019.103852
Mamdani EH, Assilian S (1975) An experiment in linguistic synthesis with a fuzzy logic controller. Int J Man Mach Stud 7(1):1–13
Marimuthu M, Geetha P, Deepiha P, Sridharan M (2015) MATLAB simulation of transparent glass PV/T hybrid water collectors. Int Conf Intell Syst Control (ISCO). https://doi.org/10.1109/ISCO.2015.7282327
Mohammadzadeh A, Kayacan E (2019) A non-singleton type-2 fuzzy neural network with adaptive secondary membership for high dimensional applications. Neurocomputing 338:63–71. https://doi.org/10.1016/j.neucom.2019.01.095
Mohammadzadeh A, Kaynak O (2019) A novel general type-2 fuzzy controller for fractional-order multi-agent systems under unknown time-varying topology. J Franklin Inst 356(10):5151–5171. https://doi.org/10.1016/j.jfranklin.2019.05.006
Narayana LR, Ramachandra RV (2018) Experimental study on performance of passive and active solar stills in Indian coastal climatic condition V. Front Energy 14:105–113. https://doi.org/10.1007/s11708-018-0536-4
Porta-Gándara MA, Fernández-Zayas JL, Chargoy-del-Valle N (2020) Solar still distillation enhancement through water surface perturbation. Sol Energy 196(2020):312–318. https://doi.org/10.1016/j.solener.2019.12.028
Reddy KS, Sharona H, Krithika D, Philip L (2018) Performance, water quality and enviro-economic investigations on solar distillation treatment of reverse osmosis reject and sewage water. Sol Energy 173(2018):160–172. https://doi.org/10.1016/j.solener.2018.07.033
Rizwan M, Jamil M, Kirmani S, Kothari DP (2014) Fuzzy logic based modeling and estimation of global solar energy using meteorological parameters. Energy 70(2014):1–7. https://doi.org/10.1016/j.energy.2014.04.057
Saeedi M, Moradi M, Hosseini M, Emamifar A, Ghadimi N (2018) Robust optimization based optimal chiller loading under cooling demand uncertainty. Appl Therm Eng 148(2018):1081–1091. https://doi.org/10.1016/j.applthermaleng.2018.11.122
Sathish D, Veeramanikandan M, Tamilselvan R (2019) Design and fabrication of single slope solar still using metal matrix structure as energy storage. Mater Today 16(8):1–5. https://doi.org/10.1016/j.matpr.2019.07.709
Sivakumar P, Christraj W, Sridharan M, Jayamalathi N (2012a) Performance improvement study of solar water heating system. ARPN J Eng Appl Sci 7(1):45–49
Sivakumar P, Christraj W, Sridharan M, Jayamalathi N (2012b) Performance comparison of differently configured solar water heaters. Eur J Sci Res 91(1):23–31
Sridharan M (2020a) Predicting performance of double pipe parallel and counter flow heat exchanger using fuzzy logic. ASME J Therm Sci Eng Appl 12(3):031006. https://doi.org/10.1115/1.4044696
Sridharan M (2020b) Application of fuzzy logic expert system in predicting cold and hot fluid outlet temperature of counter-flow double-pipe heat exchanger. Advanced analytic and control techniques for thermal systems with heat exchangers. Academic Press, London, pp 307–323. https://doi.org/10.1016/B978-0-12-819422-5.00014-1
Sridharan M (2020c) Application of generalized regression neural network in predicting the performance of natural convection solar dryer. J Sol Energy Eng 142(3):1–7. https://doi.org/10.1115/1.4045384
Sridharan M (2020d) Application of generalized regression neural network in predicting the performance of solar photovoltaic thermal water collector. Ann Data Sci. https://doi.org/10.1007/s40745-020-00273-1
Sridharan M (2020e) Applications of artificial intelligence techniques in heat exchanger systems. Advanced analytic and control techniques for thermal systems with heat exchangers. Academic Press, London, pp 325–334. https://doi.org/10.1016/B978-0-12-819422-5.00015-3
Sridharan M, Anabayan K (2014) Performance analysis on concrete photovoltaic/thermal water collectors. Int J Eng Comput Sci 4(6):12440–12443
Sridharan M, Jayaprakash G (2020) Verification and validation of solar photovoltaic thermal water collectors performance using fuzzy logic. ASME J Verif Valid Uncertain 4(4):0410051–0410058. https://doi.org/10.1115/1.4045895
Sridharan M, Shenbagaraj S (2020) Application of generalized regression neural network in predicting the thermal performance of solar flat plate collector systems. J Therm Sci Eng Appl 13(2):1–11. https://doi.org/10.1115/1.4047824
Sridharan M, Vimal M (2017) Performance improvement analysis on Pv/T water collectors connected in series and parallel. Int J Adv Res Methodol Eng Technol 1(3):283–288
Sridharan M, Jayaprakash G, Chandrasekar M, Vigneshwar P, Paramaguru S, Amarnath K (2018) Prediction of solar photovoltaic/thermal collector power output using fuzzy logic. J Sol Energy Eng 140(6):061013. https://doi.org/10.1115/1.4040757
Sridharan M, Devi R, Dharshini CS, Bhavadarani M (2019) IoT based performance monitoring and control in counter flow double pipe heat exchanger. Internet Things 5(2019):34–40. https://doi.org/10.1016/j.iot.2018.11.002
Suganthi L, Iniyan S, Samuel AA (2015) Applications of fuzzy logic in renewable energy systems. A review. Renew Sustain Energy Rev 48(2015):585–607. https://doi.org/10.1016/j.rser.2015.04.037
Suresh KB, Chinnathambi S, Sridharan M (2014) Performance enhancement study on single basin double slope solar still using flat plate collector. Int J Innov Res Sci Eng Technol 3(2014):1303–1308
Vafaei LE, Sah M (2019) Predicting fresh water of single slope solar still using a fuzzy inference system. In: Aliev R, Kacprzyk J, Pedrycz W, Jamshidi M, Sadikoglu F (eds) 13th International Conference on theory and application of fuzzy systems and soft computing ICAFS-2018. ICAFS 2018. Advances in intelligent systems and computing. Springer, Cham. https://doi.org/10.1007/978-3-030-04164-9_46
Vigneswaran VS, Kumaresan G, Dinakar BV, Karthick Kamal K, Velraj R (2019) Augmenting the productivity of solar still using multiple PCMs as heat energy storage. J Energy Storage 26(12):1–6. https://doi.org/10.1016/j.est.2019.101019
Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353
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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