Designing Single Slop Single Basin Solar Water Distillation Plant Performance Monitoring and Prediction System
Rajeev Raghuvanshi1, Dhanraj Verma2, Manojkumar Deshpande3, Md. Ilyas4
1Rajeev Raghuvanshi, PhD in Computer Science & Engineering, College of Engineering, Dr. APJ Abdul Kalam University, Indore.
2Dr. Dhanraj Verma, Professor in Department of Computer Science & Engineering , College of Engineering, Dr. APJ Abdul Kalam University, Indore .
3Prof. Manojkumar Deshpande, Professor & Associate Dean at MPSTME, SVKM’s NMIMS-Mumbai, Shirpur Campus.
4Md. Ilyas, Assistant Professor in Department of Computer Science & Engineering, Prestige Institute of Engineering Management & Research Indore.
Manuscript received on October 12, 2019. | Revised Manuscript received on 22 October, 2019. | Manuscript published on November 10, 2019. | PP: 2445-2451 | Volume-9 Issue-1, November 2019. | Retrieval Number: A4558119119/2019©BEIESP | DOI: 10.35940/ijitee.A4558.119119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The issue of the water crisis is rising day by day, due to global warming and other environmental effects. That is not only the issue for India, but it is also for the entire world. However, the solar-based water distillation plants are not much efficient but we can use this method for producing pure and drinkable water. In this paper, we proposed to design a solar water distillation plant using the single slop method. In addition to that for monitoring and measuring the performance of the distillation plant a data mining based prediction system is implemented. The experiments are performed on the real-world implemented single slop solar water distillation plant-based observations. The observations are collected using the IoT (Internet of things) device for each five-minute time difference for each sample collection. The data samples are collected between 10:00 AM to 4:00 PM for 7 days. Additionally by using the collected samples the data mining model is trained and tested on the prepared syntactic dataset. The experimental results demonstrate accurate predictions for the solar distillation water plant. After this implementation and system model, the future directions of the research are also provided.
Keywords: Machine Learning, Data Mining, Solar Energy, Water Distillation, Performance Prediction and Monitoring.
Scope of the Article: Machine Learning