Artificial intelligence application in a renewable energy-driven desalination system:A critical review

Abstract Artificial intelligence, an emerging technology, widely exists in the field of engineering science and technology. Due to its high efficiency and precision, artificial intelligence is increasingly used in the optimal control of water treatment and seawater desalination. Generally, the design of a desalination system includes four processes: site selection, energy prediction, desalination technology selection and systematic parameter optimization. To a large extent, these choices depend on the experience and relevant criteria of researchers and experts. However, facing the scientific and technological progress and growing expectations, it is impossible to solve such complex nonlinear problems by simple experience and mathematical models, but artificial intelligence is good at this. In this paper, we synthetically analyzed and summarized the application of artificial intelligence in the field of seawater desalination with renewable energy. Artificial intelligence application in desalination is mainly divided into four aspects: expert decision-making, optimization, prediction and control by sequence. The features of artificial intelligence employed in the design of desalination systems not only realize the maximum of efficiency and minimum of cost, but release the human resources. After analyzing the four processes of desalination, it is found that artificial neural network and genetic algorithm are more widespread and mature than other algorithms in dealing with multi-objective nonlinear problems. This paper overviewed the application of artificial intelligence technologies in decision-making, optimization, prediction and control throughout the four processes of desalination designs. Finally, the application and future development prospect of artificial intelligence in the field of seawater desalination are summarized.

[1]  Joseph G Jacangelo,et al.  Emerging desalination technologies for water treatment: a critical review. , 2015, Water research.

[2]  Soteris A. Kalogirou,et al.  Artificial intelligence for the modeling and control of combustion processes: a review , 2003 .

[3]  Ahmad Hajinezhad,et al.  ANN and ANFIS models to predict the performance of solar chimney power plants , 2015 .

[4]  Xiaobing Luo,et al.  Machine learning-optimized Tamm emitter for high-performance thermophotovoltaic system with detailed balance analysis , 2020, Nano Energy.

[5]  Antônio de Pádua Braga,et al.  Radial Basis Functions Networks , 2002 .

[6]  Adrian Gambier,et al.  Control system design of reverse osmosis plants by using advanced optimization techniques , 2009 .

[7]  E. M. Rashad,et al.  A GA-based initialization of PSO for optimal APFS allocation in water desalination plant , 2017, 2017 Nineteenth International Middle East Power Systems Conference (MEPCON).

[8]  R. P. Saini,et al.  A review on Integrated Renewable Energy System based power generation for stand-alone applications: Configurations, storage options, sizing methodologies and control , 2014 .

[9]  Henry Louie,et al.  Operational analysis of hybrid solar/wind microgrids using measured data , 2016 .

[10]  R. Pickhardt.  Nonlinear modelling and adaptive predictive control of a solar power plant , 2000 .

[11]  Krishnendu Chatterjee,et al.  Average case analysis of the classical algorithm for Markov decision processes with Büchi objectives , 2015, Theor. Comput. Sci..

[12]  Shahnaz Danesh,et al.  A hybrid fuzzy multi-criteria decision making approach for desalination process selection , 2013 .

[13]  Iqbal M. Mujtaba,et al.  Generic Model Control (GMC) in Multistage Flash (MSF) Desalination , 2016 .

[14]  Antonio Colmenar-Santos,et al.  Sizing of Wind, Solar and Storage Facilities Associated to a Desalination Plant Using Stochastic Optimization , 2017 .

[15]  Mohammad Hossein Ahmadi,et al.  Smart modeling by using artificial intelligent techniques on thermal performance of flat‐plate solar collector using nanofluid , 2019, Energy Science & Engineering.

[16]  Robert Rallo,et al.  Neural network approach for modeling the performance of reverse osmosis membrane desalting , 2009 .

[17]  Azah Mohamed,et al.  A review on sizing methodologies of photovoltaic array and storage battery in a standalone photovoltaic system , 2016 .

[18]  Vinay Pratap Singh,et al.  A modified controller design based on symbiotic organisms search optimization for desalination system , 2019 .

[19]  Youcef Messlem,et al.  Estimation of the daily global solar radiation based on Box–Jenkins and ANN models: A combined approach , 2016 .

[20]  R. P. Saini,et al.  A review on planning, configurations, modeling and optimization techniques of hybrid renewable energy systems for off grid applications , 2016 .

[21]  Ali Aminian,et al.  Prediction of temperature elevation for seawater in multi-stage flash desalination plants using radial basis function neural network , 2010 .

[22]  Ömer Nezih Gerek,et al.  The effect of model generated solar radiation data usage in hybrid (wind–PV) sizing studies , 2009 .

[23]  Yu Yang,et al.  Sustainably integrating desalination with solar power to overcome future freshwater scarcity in China , 2019, Global Energy Interconnection.

[24]  F. Dweiri,et al.  A multi-criteria decision support system to rank sustainable desalination plant location criteria , 2018, Desalination.

[25]  E. Ghiazza,et al.  Mathematical modelling and expert system integration for optimum control strategy of MSF desalination plants , 1993 .

[26]  Helle Ørsted Nielsen,et al.  Policy Coordination for National Climate Change Adaptation in Europe: All Process, but Little Power , 2020 .

[27]  Fathollah Pourfayaz,et al.  Optimal design of stand-alone reverse osmosis desalination driven by a photovoltaic and diesel generator hybrid system , 2018 .

[28]  Héctor Quintián-Pardo,et al.  A Hybrid Intelligent System to forecast solar energy production , 2019, Comput. Electr. Eng..

[29]  A. A. Alazba,et al.  Application of adaptive neuro-fuzzy inference system (ANFIS) for modeling solar still productivity , 2017 .

[30]  Darwish M.K. Al-Gobaisi.  Conceptual specification for improved automation and total process care in large-scale desalination plants of the future , 1994 .

[31]  A. R. Kurdian,et al.  MODELING, OPTIMIZATION, AND CONTROL OF REVERSE OSMOSIS WATER TREATMENT IN KAZEROON POWER PLANT USING NEURAL NETWORK , 2015 .

[32]  R. Borsani,et al.  Towards improved automation for desalination processes, Part II: Intelligent control , 1994 .

[33]  Majid Jamil,et al.  Fuzzy logic based modeling and estimation of global solar energy using meteorological parameters , 2014 .

[34]  Aly Marei Said,et al.  Modeling solar still production using local weather data and artificial neural networks , 2012 .

[35]  Hisham Ettouney,et al.  Advanced Computational Techniques for Solving Desalination Plant Models Using Neural and Genetic Based Methods , 2007 .

[36]  Ganti Prasada Rao,et al.  Unity of control and identification in multistage flash desalination processes , 1993 .

[37]  D. Kolokotsa,et al.  Design optimization of desalination systems power-supplied by PV and W/G energy sources. , 2010 .

[38]  Ahmed F. Mashaly,et al.  MLP and MLR models for instantaneous thermal efficiency prediction of solar still under hyper-arid environment , 2016, Comput. Electron. Agric..

[39]  Andrea Cipollina,et al.  A neural network-based optimizing control system for a seawater-desalination solar-powered membrane distillation unit , 2013, Comput. Chem. Eng..

[40]  Saeed Shirazian,et al.  Using neural networks coupled with particle swarm optimization technique for mathematical modeling of air gap membrane distillation (AGMD) systems for desalination process , 2017, Neural Computing and Applications.

[41]  Nachiappan Subramanian,et al.  A review of applications of Analytic Hierarchy Process in operations management , 2012 .

[42]  Sabrina Abdeddaim,et al.  Artificial Neural Network power manager for hybrid PV-wind desalination system , 2020, Math. Comput. Simul..

[43]  Mostafa Mjahed,et al.  Improved cooperative artificial neural network ‐ particle swarm optimization approach for solar photovoltaic systems using maximum power point tracking , 2020 .

[44]  Ke Cheng,et al.  A new approach to performance analysis of a seawater desalination system by an artificial neural network , 2007 .

[45]  Abderrahim Abbas,et al.  Model predictive control of a reverse osmosis desalination unit , 2006 .

[46]  Gideon Oron,et al.  The use of computer aided techniques for revere osmosis desalination layout design , 2011 .

[47]  John S. Anagnostopoulos,et al.  Design study of a stand-alone desalination system powered by renewable energy sources and a pumped storage unit , 2010 .

[48]  Iqbal M. Mujtaba,et al.  Neural network based correlation for estimating water permeability constant in RO desalination process under fouling , 2014 .

[49]  A. A. Alazba,et al.  Artificial intelligence for predicting solar still production and comparison with stepwise regression under arid climate , 2017 .

[50]  José Luis Guzmán,et al.  Local model predictive controller in a solar desalination plant collector field , 2011 .

[51]  Ali Zilouchian,et al.  Automation and process control of reverse osmosis plants using soft computing methodologies , 2001 .

[52]  Ahmed F. Mashaly,et al.  Experimental and modeling study to estimate the productivity of inclined passive solar still using ANN methodology in arid conditions , 2018 .

[53]  Mahmoud Al-Ayyoub,et al.  Artificial Intelligence Enabling Water Desalination Sustainability Optimization , 2019, 2019 7th International Renewable and Sustainable Energy Conference (IRSEC).

[54]  E. Ghiazza,et al.  Mathematical modelling and expert systems integration for optimum control strategy of MSF desalination plants , 1993 .

[55]  Adnan Aish,et al.  Artificial neural network approach for predicting reverse osmosis desalination plants performance in the Gaza Strip , 2015 .

[56]  Gengen Zhang,et al.  Stability analysis of discontinuous Galerkin method for stiff Volterra functional differential equations , 2019, Journal of Mathematical Analysis and Applications.

[57]  M. E. El-Hawary.  Artificial neural networks and possible applications to desalination , 1993 .

[58]  Calin Enachescu,et al.  Solar Photovoltaic Energy Production Forecast Using Neural Networks , 2016 .

[59]  Mohamed Khayet,et al.  Artificial neural network modeling and response surface methodology of desalination by reverse osmosis , 2011 .

[60]  Mohamed Khayet,et al.  Artificial neural network model for desalination by sweeping gas membrane distillation , 2013 .

[61]  Evgueniy Entchev,et al.  Adaptive Neuro-Fuzzy Inference System modelling for performance prediction of solar thermal energy system , 2016 .

[62]  Ali Zilouchian,et al.  Prediction of Critical Desalination Parameters Using Radial Basis Functions Networks , 2002, J. Intell. Robotic Syst..

[63]  Olatunde A. Adeoti,et al.  Effect of Training Algorithms on the Performance of ANN for Pattern Recognition of Bivariate Process , 2013 .

[64]  Xiaobing Luo,et al.  Colored radiative cooling: How to balance color display and radiative cooling performance , 2021 .

[65]  A. Salman,et al.  New Computational Intelligence model for predicting evaporation rates for saline water , 2007 .

[66]  Y. A. Liu,et al.  Predictive Modeling of Large-Scale Commercial Water Desalination Plants: Data-Based Neural Network and Model-Based Process Simulation , 2002 .

[67]  Sinan Q. Salih,et al.  Efficiency evaluation of reverse osmosis desalination plant using hybridized multilayer perceptron with particle swarm optimization , 2020, Environmental Science and Pollution Research.

[68]  Seyed M. Buhari,et al.  Water Desalination Fault Detection Using Machine Learning Approaches: A Comparative Study , 2017, IEEE Access.

[69]  Sunanda Sinha,et al.  Review of recent trends in optimization techniques for solar photovoltaic–wind based hybrid energy systems , 2015 .

[70]  K. Palanisamy,et al.  Optimization in microgrids with hybrid energy systems – A review , 2015 .

[71]  Abderrahim Abbas,et al.  Modeling of an RO water desalination unit using neural networks , 2005 .

[72]  K. Sathish Kumar,et al.  A review on hybrid renewable energy systems , 2015 .

[74]  Sanna Syri,et al.  Energy modeling of a solar dish/Stirling by artificial intelligence approach , 2019, Energy Conversion and Management.

[75]  Rahman Saidur,et al.  Application of Artificial Intelligence Methods for Hybrid Energy System Optimization , 2016 .

[76]  Ali Hasan,et al.  Modelling, simulation, optimization and control of multistage flashing (MSF) desalination plants Part II: Optimization and control , 1993 .

[77]  Olivier L. de Weck,et al.  Desalination network model driven decision support system: A case study of Saudi Arabia , 2017 .

[78]  João M. Lemos,et al.  Time scaling internal state predictive control of a solar plant , 2003 .

[79]  Shantha Gamini Jayasinghe,et al.  A review on recent size optimization methodologies for standalone solar and wind hybrid renewable energy system , 2017 .

[80]  Enrique Rosales-Asensio,et al.  Thermal desalination potential with parabolic trough collectors and geothermal energy in the Spanish southeast , 2020 .

[81]  A. A. Alazba,et al.  Comparative investigation of artificial neural network learning algorithms for modeling solar still production , 2015 .

[82]  Junichiro Shiomi,et al.  Machine-Learning-Optimized Aperiodic Superlattice Minimizes Coherent Phonon Heat Conduction , 2020 .

[83]  Joon Ha Kim,et al.  Artificial neural network model for optimizing operation of a seawater reverse osmosis desalination plant. , 2009 .

[84]  J. A. Carta,et al.  Wind-driven SWRO desalination prototype with and without batteries: A performance simulation using machine learning models , 2017, Desalination.

[85]  Makram Anane,et al.  Geospatial and AHP-multicriteria analyses to locate and rank suitable sites for groundwater recharge with reclaimed water , 2015 .

[86]  Xin Wang,et al.  Prediction model to analyze the performance of VMD desalination process , 2020, Comput. Chem. Eng..

[87]  Muhammad Wakil Shahzad,et al.  Exergoeconomic optimization of a forward feed multi-effect desalination system with and without energy recovery , 2021 .

[88]  Jonathan Shek,et al.  Hybrid wind–photovoltaic–diesel–battery system sizing tool development using empirical approach, life-cycle cost and performance analysis: A case study in Scotland , 2015 .

[89]  M. Hajeeh,et al.  Application of the analytical hierarchy process in the selection of desalination plants , 2005 .

[90]  Corrado Sommariva,et al.  Sustainability Ranking of Desalination Plants Using Mamdani Fuzzy Logic Inference Systems , 2020, Sustainability.

[91]  Min-Yuan Cheng,et al.  Symbiotic Organisms Search: A new metaheuristic optimization algorithm , 2014 .

[92]  Yongqing Wang,et al.  Modeling and simulation of VMD desalination process by ANN , 2016, Comput. Chem. Eng..

[93]  Robain De Keyser,et al.  Nonlinear predictive control with dead-time compensator: Application to a solar power plant , 2009 .

[94]  Ajay Kumar Bansal,et al.  BBO-based small autonomous hybrid power system optimization incorporating wind speed and solar radiation forecasting , 2015 .