Performance enhancement of stepped basin solar still based on OSELM with traversal tree for higher energy adaptive control

Abstract A basin solar still precision design is regularly not reachable. To solve this issue, the basin area is coated with a nanolayer which allows to stimulate and control the multifaceted of the fast evaporations of physiognomies. The use of adaptive neural network-based approaches leads to better design cause permits detecting the conjunction, gigantic period feed, lower performances parameters which can be detrimental to system production. Further, an online Sequential Extreme Learning Machine (OSELM) system can be used to obtain the latest solar still based on adaptive control. Here, the solar still has been created at physical scale activity for haste of energy absorption. The performance of solar still is defined by the uniform occurrence with time series of dynamics transfer from basin liner to saline water. The feasibility scheme to authenticate was studied by applying calculation to the extensive heat transfer process. The furious SiO2/TiO2 nanoparticles used for the stepped basin solar still (SBSS) efficiency shows an increase of performances by 37.69% and 49.21%, respectively using 20% and 30% of SiO2/TiO2 coating. It is comparable higher when equated against an SBSS coating either SiO2 or TiO2, and/or no nanoparticles coatings. The binary search tree enabled to find the optimal cost for the solar still investigated and obtaining a superior design with higher performances.

[1]  Timothy J. Purcell.  Sorting and searching , 2005, SIGGRAPH Courses.

[2]  J. P. Holman,et al.  Experimental methods for engineers , 1971 .

[3]  S. Shanmugan,et al.  Effect of water flow in a solar still using novel materials , 2019, Journal of Thermal Analysis and Calorimetry.

[4]  Oscar Fontenla-Romero,et al.  An online learning algorithm for adaptable topologies of neural networks , 2013, Expert Syst. Appl..

[5]  Jane You,et al.  One-pass online learning: A local approach , 2016, Pattern Recognit..

[6]  Chengbing Wang,et al.  Thermal annealing characteristics of solar selective absorber coatings based on nano-multilayered MoOx films , 2020 .

[7]  A. S. Abdullah,et al.  Improving the trays solar still performance using reflectors and phase change material with nanoparticles , 2020 .

[8]  R. Velraj,et al.  Effect of nano-coated CuO absorbers with PVA sponges in solar water desalting system , 2019, Applied Thermal Engineering.

[9]  Ahmed M. Soliman,et al.  Influence of solvent in the synthesis of nano-structured ZnO by hydrothermal method and their application in solar-still , 2017 .

[10]  M. Feilizadeh,et al.  Nano-coated condensation surfaces enhanced the productivity of the single-slope solar still by changing the condensation mechanism , 2020 .

[11]  Donald E. Knuth,et al.  Sorting and Searching , 1973 .

[12]  M. M. Younes,et al.  Extracting water content from the ambient air in a double-slope half-cylindrical basin solar still using silica gel under Egyptian conditions , 2020 .

[13]  S. Gorjian,et al.  Experimental study on single slope single basin solar still using TiO2 nano layer for natural clean water invention , 2020 .

[14]  Navin Goyal,et al.  Better Analysis of GREEDY Binary Search Tree on Decomposable Sequences , 2016, Theor. Comput. Sci..

[15]  Rui Araújo,et al.  Dissolved oxygen control of the activated sludge wastewater treatment process using stable adaptive fuzzy control , 2012, Comput. Chem. Eng..

[16]  Alfred V. Aho,et al.  Data Structures and Algorithms , 1983 .

[17]  Junfei Qiao,et al.  An online self-adaptive modular neural network for time-varying systems , 2014, Neurocomputing.

[18]  H. Fath,et al.  Influence of partial solar energy storage and solar concentration ratio on the productivity of integrated solar still/humidification-dehumidification desalination systems , 2019, Desalination.

[19]  M. Feilizadeh,et al.  Productivity enhancement of solar stills by nano-coating of condensing surface , 2019, Desalination.

[20]  Saman Rashidi,et al.  Volume of fluid model to simulate the nanofluid flow and entropy generation in a single slope solar still , 2018 .

[21]  Rüdiger Reischuk,et al.  Smoothed analysis of binary search trees , 2005, Theor. Comput. Sci..

[22]  O. Mahian,et al.  A concise review on the role of nanoparticles upon the productivity of solar desalination systems , 2018, Journal of Thermal Analysis and Calorimetry.

[24]  H. Hassan,et al.  Impact of salty water medium and condenser on the performance of single acting solar still incorporated with parabolic trough collector , 2020 .

[25]  Xindong Wu,et al.  Online Learning from Trapezoidal Data Streams , 2016, IEEE Transactions on Knowledge and Data Engineering.

[26]  B. Janarthanan,et al.  Productivity enhancement of solar still by PCM and Nanoparticles miscellaneous basin absorbing materials. , 2017 .

[27]  M. R. K. Estahbanati,et al.  Optimization of geometrical dimensions of single-slope basin-type solar stills , 2017 .

[28]  J. A. Esfahani,et al.  Volume-of-Fluid Model for Simulating Vapor–Liquid Phase Change in a Solar Still , 2018, Journal of Thermophysics and Heat Transfer.

[29]  Chiyang He,et al.  Photocatalytic degradation of sixteen organic dyes by TiO2/WO3-coated magnetic nanoparticles under simulated visible light and solar light , 2018 .

[30]  J. A. Esfahani,et al.  Enhancement of solar still by reticular porous media: Experimental investigation with exergy and economic analysis , 2018 .

[31]  Mark D. Semon,et al.  POSTUSE REVIEW: An Introduction to Error Analysis: The Study of Uncertainties in Physical Measurements , 1982 .

[32]  Junfei Qiao,et al.  Model predictive control of dissolved oxygen concentration based on a self-organizing RBF neural network , 2012 .

[33]  M. M. Younes,et al.  New design of trays solar still with enhanced evaporation methods – Comprehensive study , 2020, Solar Energy.

[34]  M. Rahimi,et al.  Experimental investigation of using an evaporation inhibitor layer in a solar still , 2020 .

[35]  B. Holenda,et al.  Dissolved oxygen control of the activated sludge wastewater treatment process using model predictive control , 2008, Comput. Chem. Eng..

[36]  S. Rashidi,et al.  Exergy and economic analysis for a double slope solar still equipped by thermoelectric heating modules - an experimental investigation , 2017 .

[37]  Narasimhan Sundararajan,et al.  A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks , 2006, IEEE Transactions on Neural Networks.

[38]  J. A. Esfahani,et al.  Partitioning of solar still for performance recovery: Experimental and numerical investigations with cost analysis , 2017 .

[39]  Saman Rashidi,et al.  Optimization of partitioning inside a single slope solar still for performance improvement , 2016 .

[40]  A. Kabeel,et al.  Energy and exergy analysis of solar stills with micro/nano particles: A comparative study , 2018, Energy Conversion and Management.

[41]  M. Feilizadeh,et al.  Efficiency improvement of solar stills through wettability alteration of the condensation surface: An experimental study , 2020 .

[42]  J. A. Esfahani,et al.  Steps optimization and productivity enhancement in a nanofluid cascade solar still , 2018 .

[43]  Edwin Lughofer,et al.  On-line active learning: A new paradigm to improve practical useability of data stream modeling methods , 2017, Inf. Sci..

[44]  Alfredo De Santis,et al.  On Binary Search Trees , 1993, Inf. Process. Lett..

[45]  R. Rana,et al.  Heat transfer coefficients and productivity of a single slope single basin solar still in Indian climatic condition: Experimental and theoretical comparison , 2017, Resource-Efficient Technologies.

[46]  Junfei Qiao,et al.  Nonlinear Model Predictive Control Based on a Self-Organizing Recurrent Neural Network , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[47]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[48]  Á. G. Torre,et al.  Preparation of photocatalytic TiO2 coatings by gel-dipping with polysaccharides , 2012 .