[26] Anil Kilickaplan, Dmitrii Bogdanov, Onur Peker, Upeksha Caldera, Arman Aghahosseini, Christian Breyer, An energy transition pathway for. Turkey to ...
INDIAN INSTITUTE OF TECHNOLOGY ROORKEE
A review of the Artificial Neural Network based modelling and simulation approaches applied to Reverse Osmosis Desalination techniques
Under Kind Guidance of: Dr. Gaurav Manik, Assistant Professor Department of Polymer and Process Engineering IIT Roorkee, Saharanpur Campus, India
Presented by: Rajesh Mahadeva, Research Scholar (17924009) Department of Polymer and Process Engineering IIT Roorkee, Saharanpur Campus, India Member of IDA: [#165460]
Index Introduction Recent developments Desalination status in the Globe Desalination plant and process Desalination technologies Modelling and simulation using ANN RO desalination technologies using ANN by researchers Future recommendations Conclusion and future scope References Appendix (I-VIII)
2
Introduction
Source: Ahmed El Mekawy et. al, “The near-future integration of microbial desalination cells with reverse osmosis technology.”
3
Recent development
4
Literature statistics of desalination units and their capacity (m3/d) across the world
5
Areas of physical and economical water scarcity
6
IDA: International Desalination Association
American Membrane Technology Association (AMTA), Australian Water Association (AWA), Caribbean Desalination Association (CaribDA), European Desalination Society (EDS), Japan Desalination Association (JDA), Indian Desalination Association (InDA) etc. 7
Desalination • Desalination is a process that extracts mineral components from saltwater. • Desalination refers to the removal of salts and minerals from a target substance. • Saltwater is desalinated to produce water suitable for human consumption or irrigation. • Currently, approximately 1% of the world’s population is dependent on desalination water to meet daily needs, but the UN (United Nations, Human Rights) expert that 14% of the world’s population will encounter water scarcity 2025.
8
Desalination plant and process
.
9
Desalination technologies
10
Expansion of RO in Global desalination market
Source: Kurihara et al., SWRO-PRO System in “Mega-ton Water System” for Energy Reduction and Low Environmental Impact, Water 10, 48, (2018) 3-15.
11
Worldwide desalination facility by country
12
Structure of RO Membrane and Elements
13
Modelling and simulation using Artificial Neural Network (ANN)
Artificial neural networks (ANNs) are computational models inspired by the human brain. Artificial Neural Networks (ANN) is the foundation of Artificial Intelligence (AI) and solves problems that would prove impossible or difficult to solve by human or statistical standards. ANN has self-learning capabilities that enable it produce better results as more data becomes available. Based on these strategies, ANN may help in solving and optimizing desalination problems more effectively. 14
Applications of ANN………… Aerospace − Autopilot aircrafts, aircraft fault detection. Automotive − Automobile guidance systems. Military −
Weapon orientation and steering, target tracking, object discrimination, facial recognition, signal/image identification.
Electronics − Code sequence prediction, IC chip layout, chip failure analysis, machine vision, voice synthesis. Financial −
Real estate appraisal, loan advisor, mortgage screening, corporate bond rating, portfolio trading program, corporate financial
analysis, currency value prediction, document readers, credit application evaluators. Industrial − Manufacturing process control, product design and analysis, quality inspection systems, welding quality analysis, paper quality prediction, chemical product design analysis, dynamic modelling of chemical process systems, machine maintenance analysis, project bidding, planning, and management. Medical −
Cancer cell analysis, EEG and ECG analysis, prosthetic design, transplant time optimizer.
Speech −
Speech recognition, speech classification, text to speech conversion.
Telecommunications −
Image and data compression, automated information services, real-time spoken language translation.
Transportation −
Truck Brake system diagnosis, vehicle scheduling, routing systems.
Software −
Pattern Recognition in facial recognition, optical character recognition, etc.
Time Series Prediction − ANNs are used to make predictions on stocks and natural calamities. Signal Processing − Control −
Neural networks can be trained to process an audio signal and filter it appropriately in the hearing aids.
ANNs are often used to make steering decisions of physical vehicles.
Anomaly Detection −
As ANNs are expert at recognizing patterns, they can also be trained to generate an output when something unusual occurs that misfits the pattern.
15
Summary of RO using ANN by researchers Authors Niemi et al. AI-Sayji et al.
[90] [112]
Year 1995 1997
RO using Artificial Neural Network applications A method for the simulation of membrane processes developed Used back-propagation in conjunction with statistical techniques in order to predict output variables of both MSF and RO plants
Jafar et al. Jafar et al. Jafar et al. Zilouchian et al. AI-Sayji et al. Murthy et al. Sroka et al. Abbas et al. Zhao et al. Al-Alawi et al.
[121] [122] [70] [123] [114] [72] [124] [73] [116] [125]
1998 2000 2001 2001 2002 2004 2004 2005 2005 2007
Modelling of RO using intelligent control (NN, FLC and GA) Modelling of RO using NN and FLC Modelling of RO desalination plant RO using NN, GA and PR Modelling of RO and MSF Prediction of RO performance Biological treatment of meat industry wastewater using RO Modelling of an RO water desalination unit Predicting RO/NF water quality Optimum operation of an integrated hybrid renewable energy-based water and power supply system (IRWPSS)
Gao et al. Libotean et al. Lee et al. Quintanilla et al. Libotean et al. Purkait et al. Khayet et al. Moradi et al. Garg et al. Barello et al. Aish et al.
[98] [118] [75] [99] [74] [126] [76] [127] [128] [78] [129]
2007 2008 2009 2009 2009 2009 2011 2013 2014 2014 2015
An integrative system of air-conditioning and desalination driven by heat pumps is presented Based on quantitative structure–property relations (QSPR) Optimizing operation of SWRO desalination plant QSAR for rejection of neutral organic compounds by NF and RO Modelling the performance of RO membrane desalting Modeling for membrane-based treatment of leather plant effluent Methodology of desalination plant by RO Mathematical models for RO membrane performances Evaluating a small-scale brackish water reverse osmosis (RO) process using parameter optimization Modelling of RO desalination process Modelling of RO desalination plant in the Gaza Strip
Reyna et al.
[130]
2015
Modeling data from a membrane-based wastewater treatment (WWT) pilot plant
Madaeni et al.
[131]
2015
Used back-propagation concept were utilized to develop data-driven models for predicting reverse osmosis (RO) plant
Salami et al. Iranmanesh et al.
[80] [132]
2016 2016
Mathematical modeling to simulate osmosis membrane RO membranes performances, including separation factor, pure solvent flux and total flux
Cabrera et al. Garcia et al. Roehl et al.
[81] [82] [120]
2017 2017 2018
Simple seawater reverse osmosis plant Seawater reverse osmosis desalination plants Modelling of a three stage RO system
16
RO using ANN focus and progress with time
No of Research contributions using ANN
. Research contributions using ANN
10
10
8
7 6
6
4
4
3 2
0 1995-1998
1999-2003
2004-2007
2008-2011
2012-2018
Years
Future Benefits of proposed Techniques: Low cost Time saving Pure water Less Instruments required 17
Conclusion and Future scope Water being necessary for consumption, household usage, industry production, and agriculture, its demand will rise with economic and population growth. Desalination of seawater and brackish water is accepted as an alternative source to fulfil growing water demand. The benefits of desalination are straightforward: more water and improved quality. The drawback, unfortunately, is the cost. Present-day involvement indicates that ANN is an important and appropriate tool to assist desalination plant operations. The potential benefits of ANN offering great ideas to support operators for decision making.
18
Conclusion and Future scope Research and development: Find alternatives to the current methods for desalination process. Find chemical additives for polymer material alternatives. Find alternatives to energy used for the desalination process. RO is most efficient in terms of separation performance as well as salt rejection and has lower energy consumption for water treatment. RO applications in desalination further involves sub-technologies such as Seawater RO (SWRO), Brackish water RO (BWRO), Low-pressure RO (LPRO), RO Electro deionization (RO-EDI) and RO Demineralizer (RO-DM). Hybrid technologies based on RO such as RO-MSF, RO-FO, NF-ROMED, UF-SWRO, etc. have been also developed for further enhancing productivity of fresh water.
19
Conclusion and Future scope The overall costs for RO desalination processes have fallen considerably over the last three decades.
20
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Appendix-I (Global list of desalination plants)
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Appendix-I (Global list of desalination plants)
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Appendix-I (Global list of desalination plants)
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Appendix-I (Global list of desalination plants)
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Appendix-I (Global list of desalination plants)
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Appendix-II (Desalination components)
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Appendix-III (India leading players)
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Appendix-IV (Global water risk)
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Appendix-V (World population)
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Appendix-VI (Experimental data used for ANN modelling)
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Appendix-VII (Desalination plant using different instruments)
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Appendix-VIII (Desalination, SCI, High Impact, Journals)
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Thank you….
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