Artificial Intelligence for Renewable Energy and Climate Change E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 565

Veröffentlichungsjahr: 2022

Beschreibung

ARTIFICIAL INTELLIGENCE FOR RENEWABLE ENERGY AND CLIMATE CHANGE

Written and edited by a global team of experts in the field, this groundbreaking new volume presents the concepts and fundamentals of using artificial intelligence in renewable energy and climate change, while also covering the practical applications that can be utilized across multiple disciplines and industries, for the engineer, the student, and other professionals and scientists.

Renewable energy and climate change are two of the most important and difficult issues facing the world today. The state of the art in these areas is changing rapidly, with new techniques and theories coming online seemingly every day. It is important for scientists, engineers, and other professionals working in these areas to stay abreast of developments, advances, and practical applications, and this volume is an outstanding reference and tool for this purpose.

The paradigm in renewable energy and climate change shifts constantly. In today’s international and competitive environment, lean and green practices are important determinants to increase performance. Corresponding production philosophies and techniques help companies diminish lead times and costs of manufacturing, improve delivery on time and quality, and at the same time become more ecological by reducing material use and waste, and by recycling and reusing. Those lean and green activities enhance productivity, lower carbon footprint and improve consumer satisfaction, which in reverse makes firms competitive and sustainable.

This practical, new groundbreaking volume:

• Features coverage on a wide range of topics such as classical and nature-inspired optimization and optimal control, hybrid and stochastic systems
• Is ideally designed for engineers, scientists, industrialist, academicians, researchers, computer and information technologists, sustainable developers, managers, environmentalists, government leaders, research officers, policy makers, business leaders and students
• Is useful as a practical tool for practitioners in the fields of sustainable and renewable energy sustainability
• Includes wide coverage of how artificial intelligence can be used to impact the struggle against global warming and climate change

Leseprobe

Table of Contents

Cover

Title Page

Copyright

Preface

Section I: RENEWABLE ENERGY

1 Artificial Intelligence for Sustainability: Opportunities and Challenges

1.1 Introduction

1.2 History of AI for Sustainability and Smart Energy Practices

1.3 Energy and Resources Scenarios on the Global Scale

1.4 Statistical Basis of AI in Sustainability Practices

1.5 Major Challenges Faced by AI in Sustainability

1.6 Major Opportunities of AI in Sustainability

1.7 Conclusion and Future Direction

References

2 Recent Applications of Machine Learning in Solar Energy Prediction

2.1 Introduction

2.2 Solar Energy

2.3 AI, ML and DL

2.4 Data Preprocessing Techniques

2.5 Solar Radiation Estimation

2.6 Solar Power Prediction

2.7 Challenges and Opportunities

2.8 Future Research Directions

2.9 Conclusion

Acknowledgement

References

3 Mathematical Analysis on Power Generation – Part I

3.1 Introduction

3.2 Methodology for Derivations

3.3 Energy Discussions

3.4 Data Analysis

Acknowledgement

References

Supplementary

4 Mathematical Analysis on Power Generation – Part II

4.1 Energy Analysis

4.2 Power Efficiency Method

4.3 Data Analysis

Acknowledgement

References

Supplementary - II

5 Sustainable Energy Materials

5.1 Introduction

5.2 Different Methods

5.3 X-Ray Diffraction Analysis

5.4 FTIR Analysis

5.5 Raman Analysis

5.6 UV Analysis

5.7 SEM Analysis

5.8 Energy Dispersive X-Ray Analysis

5.9 Thermoelectric Application

5.10 Limitations and Future Direction

5.11 Conclusion

Acknowledgement

References

6 Soft Computing Techniques for Maximum Power Point Tracking in Wind Energy Harvesting System: A Survey

6.1 Introduction

6.2 Other MPPT Control Methods

6.3 Conclusion

References

Section II: CLIMATE CHANGE

7 The Contribution of AI-Based Approaches in the Determination of CO2 Emission Gas Amounts of Vehicles, Determination of CO2 Emission Rates Yearly of Countries, Air Quality Measurement and Determination of Smart Electric Grids’ Stability

7.1 Introduction

7.2 Materials

7.3 Artificial Intelligence Approaches

7.4 Experimental Analysis

7.5 Discussion

7.6 Conclusion

References

8 Performance Analysis and Effects of Dust & Temperature on Solar PV Module System by Using Multivariate Linear Regression Model

8.1 Introduction

8.2 Literature Review

8.3 Experimental Setup

8.4 Results Discussion

8.5 Future Research Directions

8.6 Conclusion

References

9 Evaluation of In-House Compact Biogas Plant Thereby Testing Four-Stroke Single-Cylinder Diesel Engine

9.1 Introduction

9.2 Literature Review

9.3 Methodology

9.4 Analysis of Compact Biogas Plant

9.5 Analysis of Single-Cylinder Diesel Engine on Dual Fuel

9.6 General Comments

9.7 Conclusion

9.8 Future Scope

References

10 Low-Temperature Combustion Technologies for Emission Reduction in Diesel Engines

10.1 Introduction

10.2 Scope of the Current Article

10.3 HCCI Technology

10.4 Partially Premixed Compression Ignition (PPCI)

10.5 Exhaust Gas Recirculation (EGR)

10.6 Reactivity Controlled Compression Ignition (RCCI)

10.7 LTC Through Fuel Additives

10.8 Emulsified Fuels (Water-in-Diesel Emulsion Fuel)

10.9 Conclusion and Future Scope

Acknowledgement

References

11 Efficiency Optimization of Indoor Air Disinfection by Radiation Exposure for Poultry Breeding Rational for Microclimate Systems Modernization for Livestock Premises

11.1 Introduction

11.2 Materials and Methods

11.3 Results

11.4 Discussion

11.5 Conclusions

References

12 Improving the Efficiency of Photovoltaic Installations for Sustainable Development of the Urban Environment

12.1 Introduction

12.2 Background

12.3 Main Focus of the Chapter

12.4 Solutions and Recommendations

Acknowledgements

References

13 Monitoring System Based Micro-Controller for Biogas Digester

13.1 Introduction

13.2 Related Work

13.3 Methods and Material

13.4 Results

13.5 Conclusion

Acknowledgements

References

14 Greenhouse Gas Statistics and Methods of Combating Climate Change

Introduction

Conclusion

References

About the Editors

Index

Also of Interest

Wiley End User License Agreement

List of Tables

Chapter 5

Table 5.1 Crystalline size of Zn-CuO.

Table 5.2 Mode of vibration for different methods.

Chapter 6

Table 6.1 Ziegler Nichols method.

Table 6.2 MATLAB built-in transfer functions.

Table 6.3 Comparing neural networks and fuzzy systems.

Table 6.4 The two passes in the hybrid learning algorithm.

Table 6.5 Advantages and disadvantages of MPPT techniques.

Chapter 7

Table 7.1 Condition category of the environment according to gas concentration r...

Table 7.2 Features that make up dataset #2 and obtained from each vehicle.

Table 7.3 Sample representation of string format features in dataset #2.

Table 7.4 Sample representation of numerical value features in dataset #2.

Table 7.5 Change of countries’ CO2 emission amounts by years.

Table 7.6 Features that make up the electrical grid dataset.

Table 7.7 The architectural structure of the CNN model was designed for this stu...

Table 7.8 The architectural structure of the LSTM model was designed for this st...

Table 7.9 The architectural structure of the LSTM & CNN model was designed for t...

Table 7.10 The architectural structure of the RNN model was designed for this st...

Table 7.11 Analysis results of dataset #1 with machine learning methods (%).

Table 7.12 Analysis results of dataset #1 with deep learning models (%).

Table 7.13 Analysis results of dataset #2 with machine learning methods (%).

Table 7.14 Analysis results of dataset #2 with deep learning models (%).

Table 7.15 Literature studies and analysis results performed using dataset #4.

Chapter 8

Table 8.1 Temperature coefficient of various PV cell technologies [10].

Table 8.2 Conversion efficiencies of various numerous PV module technologies [10...

Table 8.3 Summary of PV modules cleaning.

Table 8.4 Calculation for typical set of parameters during phase I.

Table 8.5 ANOVA table: Conversion efficiency v/s exposure day, ambient temperatu...

Table 8.6 Calculation for typical set of parameters during phase II.

Table 8.7 ANOVA table: conversion efficiency v/s exposure day, ambient temperatu...

Table 8.8 Calculation for typical set of parameters during phase III.

Table 8.9 ANOVA table: conversion efficiency v/s exposure day, ambient temperatu...

Table 8.10 Input parameters and responses for full phase during 1 Jan to 25 June...

Table 8.11 ANOVA table: conversion efficiency v/s exposure day, ambient temperat...

Table 8.12 Results of best subset regression analysis.

Table 8.13 Regression outputs summary.

Chapter 9

Table 9.1 Components of biogas [18].

Table 9.2 C/N ratios of commonly used materials.

Table 9.3 Required equipments for a compact biogas plant.

Table 9.4 Testing data of 50 kg animal dung along with 500 ltrs water.

Table 9.5 Testing data for kitchen waste.

Table 9.6 Testing data of fruits waste.

Table 9.7 Time taken to consume 10 cc of fuel (10 ml) at different load and cons...

Table 9.8 Testing results by using diesel fuel on 4-stroke single-cylinder diese...

Table 9.9 Results of diesel testing.

Table 9.10 Heat balance sheet for 2 kg load.

Table 9.11 Heat balance sheet for 4 kg load.

Table 9.12 Heat balance sheet for 6 kg load.

Table 9.13 Heat balance sheet for 8 kg load.

Table 9.14 Heat balance sheet for 10 kg load.

Table 9.15 Time taken to consume 10 cc of fuel (10 ml) at different loads and co...

Table 9.16 Testing results by using dual fuel on 4-stroke single-cylinder diesel...

Table 9.17 Result table of dual fuel testing.

Table 9.18 Heat balance sheet for 2 kg load.

Table 9.19 Heat balance sheet for 4 kg load.

Table 9.20 Heat balance sheet for 6 kg load.

Table 9.21 Heat balance sheet for 8 kg load.

Table 9.22 Heat balance sheet for 10 kg load.

Chapter 10

Table 10.1 Comparison of HCCI with SI engine.

Table 10.2 Comparison of HCCI with CI engine.

Chapter 11

Table 11.1 Measurement of temperature, air velocity, humidity.

Table 11.2 Comparison between results of theoretical studying and natural measur...

Chapter 13

Table 13.1 System cost.

Chapter 14

Table 14.1 Initial data.

Table 14.2 Correlation analysis.

Table 14.3 Regression analysis of the data.

List of Illustrations

Chapter 1

Figure 1.1 History of AI through time [7].

Figure 1.2 Cost savings expected from energy and utilities sector by integrating...

Figure 1.3 Energy and utilities organizations implementing AI [7].

Figure 1.4 Benefits of AI [7].

Figure 1.5 AI for sustainability globally until 2030 [17].

Figure 1.6 Talent- & Business-related challenges of AI [13].

Figure 1.7 Prediction models for AI [7].

Chapter 2

Figure 2.1 Different types of solar radiation.

Figure 2.2 Relation between DL, ML and AI.

Figure 2.3 Pyranometer with data logger.

Figure 2.4 Sunshine recorder.

Figure 2.5 Solar radiation at different day times.

Figure 2.6 Sunshine recorder burnt strip.

Figure 2.7 Architecture of ANN.

Figure 2.8 ML steps involved in solar radiation prediction.

Figure 2.9 Solar PV panel deposited with dust and other impurities.

Figure 2.10 Schematic of Solar PV power plant.

Figure 2.11 Rooftop solar PV power plant.

Chapter 3

Figure 3.1 Classical thermocouple with a voltmeter.

Figure 3.2 Thermocouple with a voltmeter, an ammeter, and an SPDT switch.

Figure 3.3 Thermocouple with a voltmeter, an ammeter, and a DPDT switch.

Figure 3.4 Voltage – Temperature relations. (a): for (3.13), (b): for (3.16), (c...

Figure 3.5 Voltage – Temperature relations. (a): for (3.14), (b): for (3.17), (c...

Figure 3.6 Voltage – Temperature relations. (a): for (3.15), (b): for (3.18), (c...

Chapter 4

Figure 4.1 Temperature-Voltage graph for Range 1: 650°C to 700°C

Figure 4.2 Temperature-Voltage graph for Range 2: 700°C to 750°C.

Figure 4.3 Temperature-Voltage graph for Range 3: 750°C to 800°C.

Figure 4.4 Temperature-Voltage graph for Range 4: 800°C to 850°C.

Figure 4.5 Temperature-Voltage graph for Range 5: 850°C to 900°C.

Figure 4.6 Temperature-Voltage graph for Range 6: 900°C to 950°C.

Figure 4.7 Temperature-Voltage graph for Range 7: 950°C to 1000°C.

Figure 4.8 Temperature-Voltage graph for Range 8: 100°C to 1050°C.

Chapter 5

Figure 5.1 Comparative work of three methods.

Figure 5.2 XRD analysis for Zn-CuO nanoparticles for different methods.

Figure 5.3 FTIR Analysis of Zn-CuO nanoparticles.

Figure 5.4 Variation in Raman shift at Ag and Bg of Zn-CuO nanoparticles.

Figure 5.5 UV Absorbance spectra for Zn-CuO nanoparticles.

Figure 5.6 Band gap of Zn-CuO, (a) Co-precipitation method, (b) Microwave-assist...

Figure 5.7 The Zn-CuO Morphology of 5μm, 1m (a, b) Co-precipitation method, (b, ...

Figure 5.8 EDAX spectra of Zn-CuO (a) Co-precipitation method, (b) Microwave-ass...

Figure 5.9 Zn-CuO (a) Thermal conductivity, (b) Electric conductivity, (c) Seebe...

Chapter 6

Figure 6.1 Maximum power point algorithms.

Figure 6.2 The block diagram of the TSR control.

Figure 6.3 Block diagram of the TSR control.

Figure 6.4 The block diagram of optimal torque control MPPT method.

Figure 6.5 The block diagram of a wind energy system with the power signal feedb...

Figure 6.6 Characteristics of turbine mechanical power as a function of the roto...

Figure 6.7 Hill climb search MPPT control strategy.

Figure 6.8 Block diagram of PID controller [11].

Figure 6.9 Triangle membership function (a=4, b=7, c=9).

Figure 6.10 Trapezoidal Membership Function (a=2, b=5, c=8, d=9).

Figure 6.11 Gaussian membership function (σ=2, c=5).

Figure 6.12 Generalized bell membership function (a=2, b=4, c=6).

Figure 6.13 Sigmoidal Membership Function (a=1, c=5).

Figure 6.14 Basic structure of fuzzy control system.

Figure 6.15 Flow chart of FLC design [22].

Figure 6.16 Neuron of nerve cell [25].

Figure 6.17 McCulloch-Pitts model of an artificial neuron.

Figure 6.18 A taxonomy of feed-forward and recurrent/feedback network architectu...

Figure 6.19 RBF structure.

Figure 6.20 Modified BP-based RBF network training flowchart.

Figure 6.21 A two–input first-order Sugeno fuzzy model with two rules.

Figure 6.22 Equivalent ANFIS architecture [36].

Figure 6.23 Flowchart of implementation of ANFIS.

Chapter 7

Figure 7.1 Graphical representation of fuel types in dataset #2.

Figure 7.2 Features that make up the electrical grid dataset.

Figure 7.3 SVM classification process; (a) binary classification process and (b)...

Figure 7.4 Decision tree method’s root-leaf link.

Figure 7.5 Working principle of the RF method [35].

Figure 7.6 The general layer structure of CNN models.

Figure 7.7 The process steps and design of the LSTM model.

Figure 7.8 Confusion matrices obtained by machine learning methods of dataset #1...

Figure 7.9 Training-test success graphs obtained for dataset #1 of CNN and LSTM ...

Figure 7.10 Confusion matrices obtained for dataset #1 of CNN and LSTM & CNN mod...

Figure 7.11 The relationship graphs with the attribute of the vehicles and the a...

Figure 7.12 Comparison of fuel types according to fuel consumption and CO2 emiss...

Figure 7.13 Confusion matrices obtained for dataset #2 of machine learning; (a) ...

Figure 7.14 Training-test success graphs obtained for dataset #2 of deep learnin...

Figure 7.15 Canada’s CO2 emissions rates by years.

Figure 7.16 Turkey’s CO2 emissions rates by years.

Figure 7.17 The top 10 countries according to the amount of CO2 emission based o...

Figure 7.18 The top 10 countries according to the amount of CO2 emission based o...

Figure 7.19 Graphical statistics of the three countries with the highest CO2 emi...

Figure 7.20 Training-test graphics of the RNN model in measuring the stability o...

Figure 7.21 Confusion matrix graphic of the RNN model in measuring the stability...

Chapter 8

Figure 8.1 Tentative year-wise cumulative targets to be attained by 2022 [6].

Figure 8.2 Configuration of grid-connected SPV system [10].

Figure 8.3 Different PV module technologies [10].

Figure 8.4 Soiling on top surface of array resulting in mismatch losses [12].

Figure 8.5 Detailed dimensional drawing of module [37].

Figure 8.6 Photographic view of installed PV array.

Figure 8.7 PV grid-connected inverter.

Figure 8.8 Overview of SR20-D2 pyranometer [38].

Figure 8.9 Pyranometer.

Figure 8.10 Digital thermometer.

Figure 8.11 Lightning arrester.

Figure 8.12 Variation in ambient temperature and conversion efficiency w.r.t. ex...

Figure 8.13 Variation in capacity utilization factor and performance ratio w.r.t...

Figure 8.14 Normal probability plot of residuals.

Figure 8.15 Plot for residual v/s fitted values (efficiency).

Figure 8.16 Plot for residual v/s order of data.

Figure 8.17 Counter plot of conversion efficiency v/s ambient temp. & exposure d...

Figure 8.18 Variation in ambient temperature and conversion efficiency w.r.t. ex...

Figure 8.19 Variation in capacity utilization factor and performance ratio w.r.t...

Figure 8.20 Normal probability plot of residuals.

Figure 8.21 Plot for residual v/s fitted values (efficiency).

Figure 8.22 Plot for residual v/s order of data.

Figure 8.23 Counter plot of conversion efficiency v/s ambient temp. & exposure d...

Figure 8.24 Variation in ambient temperature and conversion efficiency w.r.t. ex...

Figure 8.25 Variation in capacity utilization factor and performance ratio w.r.t...

Figure 8.26 Normal probability plot of residuals.

Figure 8.27 Plot for residual v/s fitted values (efficiency).

Figure 8.28 Plot for residual v/s order of data.

Figure 8.29 Counter plot of conversion efficiency v/s ambient temp. & exposure d...

Figure 8.30 Normal probability curve of residuals.

Figure 8.31 Plot for residual v/s order of data.

Figure 8.32 Plot for residual v/s fitted values (efficiency).

Figure 8.33 Counter plot of conversion efficiency v/s ambient temp. & exposure d...

Figure 8.34 Comparison between experimental and predicted efficiencies.

Figure 8.35 Comparison between experimental and predicted efficiencies (without ...

Chapter 9

Figure 9.1 Path of anaerobic digestion.

Figure 9.2 Anaerobic process microbiology consists of four steps [18].

Figure 9.3 Projected trends in the generation of municipal solid waste.

Figure 9.4 Shows the overview of the life cycle analysis of a biomethanization.

Figure 9.5 Three ranges of temperature at which digestion process can occur and ...

Figure 9.6 Assembled compact biogas plant.

Figure 9.7 Different views of compact biogas plant.

Figure 9.8 Compact biogas plant with empty gasholder at the experiment start (al...

Figure 9.9 Compact biogas plant with gasholder full of biogas after 4-5 weeks (a...

Figure 9.10 Gas quantity in gas holder (all dimensions in mm).

Figure 9.11 Gas quantity calculated manually (all dimensions in mm).

Figure 9.12 Biogas production w.r.t. no. of days by using animal dung.

Figure 9.13 Biogas production w.r.t. no. of days by using kitchen waste.

Figure 9.14 Biogas production w.r.t. no. of days by using fruits waste.

Figure 9.15 Biogas productions w.r.t. no. of days by using animal dung, kitchen ...

Figure 9.16 Production of biogas per day for animal dung.

Figure 9.17 Production of biogas on alternate day for kitchen waste.

Figure 9.18 Production of biogas on alternate day for fruits waste.

Figure 9.19 pH value variation w.r.t. no. of days by using animal dung.

Figure 9.20 pH value variation w.r.t. no. of days by using fruits waste.

Figure 9.21 pH value variation w.r.t. no. of days by using kitchen waste.

Figure 9.22 pH value variation w.r.t. no. of days by using animal dung, kitchen ...

Figure 9.23 Temperature variation w.r.t. no. of days by using animal dung.

Figure 9.24 Temperature variation w.r.t no. of days by using kitchen waste.

Figure 9.25 Temperature variation w.r.t. no. of days by using fruits waste.

Figure 9.26 Temperature variation w.r.t. no. of days by using animal dung animal...

Figure 9.27 Biogas production at different quantities of kitchen waste.

Figure 9.28 Biogas production at different quantities of fruits waste.

Figure 9.29 Schematic diagram of 4-stroke single-cylinder diesel engine.

Figure 9.30 Brake power (BP) vs. Mass flow rate of diesel testing

Figure 9.31 Connection of dual fuel engine.

Figure 9.32 Schematic diagram of dual fuel mode using diesel and biogas.

Figure 9.33 BP vs Bth.

Figure 9.34 IP vs Ith.

Figure 9.35 Volm efficiency vs. Brake power.

Figure 9.36 BSFC vs. BP.

Figure 9.37 Indicated power vs. ISFC.

Chapter 10

Figure 10.1 Emission standards getting more stringent year by year [12].

Figure 10.2 Increase in energy demand [7].

Figure 10.3 Sources of particulate matter in New Delhi sector-wise [16].

Figure 10.4 Various AQI levels and their effects [17].

Figure 10.5 Effect of low-temperature combustion shown graphically.

Chapter 11

Figure 11.1 Impact of average temperature and humidity index (THI) on daily cow ...

Figure 11.2 Layout of the farm “K. A. Timiryazev RGAU-MSKhA” used the natural ve...

Figure 11.3 Farm layout. Measurement points of air gas composition.

Figure 11.4 Theoretical study of air velocity in plane 0YX.

Figure 11.5 Theoretical study of volume concentration of ammonia and hydrogen su...

Figure 11.6 Results of practical measurement of ammonia and hydrogen sulfide con...

Figure 11.7 Results of practical measurement of H2S and NH3 contents from 6:00 a...

Figure 11.8 State of ventilation system.

Figure 11.9 Air exchange rate on NH3 and H2S.

Chapter 12

Figure 12.1 Examples of solar concentrators. (low-potential: (a) - phocon, (b) -...

Figure 12.2 Cases of shading of photovoltaic systems with a sun tracking system.

Figure 12.3 Partial mutual shading of modules in solar power plants with a multi...

Figure 12.4 Shunting by diodes of solar cells with partial shading (a) - block s...

Figure 12.5 Options for switching photovoltaic modules a - row; b - block (1 - p...

Figure 12.6 The inclusion of a shunt diode to the photovoltaic modules.

Figure 12.7 Switching of individual matching converters (a) and micro-inverters ...

Figure 12.8 Current-voltage characteristics of the PV module arrays.

Figure 12.9 Block diagram of a method for matching arrays of PV modules.

Figure 12.10 Options for partial shading of a photovoltaic installation: (a) - u...

Figure 12.11 Results of modeling power losses of a photovoltaic installation dep...

Figure 12.12 Results of modeling power losses of a photovoltaic installation dep...

Figure 12.13 Results of calculating the values of power losses of arrays of phot...

Figure 12.14 Incorporating DCA into an array of solar cells ((a) - in-line, (b) ...

Figure 12.15 Plots of power loss of a photoelectric installation against partial...

Figure 12.16 Volt-ampere and power characteristics of an array of photovoltaic m...

Figure 12.17 Diagram of the daily energy output of arrays of photovoltaic module...

Chapter 13

Figure 13.1 Output vs. pressure differential [12].

Figure 13.2 Raspberry Pi 2 Model B 1G.

Figure 13.3 Cases of shading of photovoltaic systems with a sun tracking system.

Figure 13.4 System implementation.

Figure 13.5 The graphical interface that represents the evolution of different p...

Figure 13.6 Interface accessed by phone with temperature evolution graph.

Chapter 14

Chart 14.1 Percentage distribution of greenhouse gases.

Figure 14.1 Countries as emitters of greenhouse gases.

Figure 14.2 The behavior pattern of greenhouse gas emissions in CO2 equivalent, ...

Charts 14.2 Main sources of gas emissions.

Guide

Cover

Table of Contents

Title Page

Copyright

Preface

1 Artificial Intelligence for Sustainability: Opportunities and Challenges

About the Editors

Index

Also of Interest

End User License Agreement

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Scrivener Publishing

100 Cummings Center, Suite 541J

Beverly, MA 01915-6106

Publishers at Scrivener

Martin Scrivener ([email protected])

Phillip Carmical ([email protected])

Artificial Intelligence for Renewable Energy and Climate Change

Edited by

Pandian Vasant

Gerhard-Wilhelm Weber

Joshua Thomas

José Antonio Marmolejo-Saucedo

and

Roman Rodriguez-Aguilar

This edition first published 2022 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA

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Preface

Our novel book offer was accepted by Scrivener Publishing for its broadly appealing, meaningful and timely properties and its promise on a worldwide stage. Thereto, also its analytical and applied characteristics contributed. This compendium and handbook together with our inquiries on renewable energy, on generation and distribution, supply and use of power are remarkable incentives which could become scholarly resources that investigate the efficient usage of contemporary electrical resources and natural renewable sources. These ought to have a positive effect on sustainable development in our urban and rural regions, in sea and sky, hence, for agility and communication, mobility and collaboration, for peace inside and between our peoples and countries, and all of our living beings and everything in the world.

The dynamics of Renewable Energy and Climate Change, and Supply Chain Management (SCM) has transformed and accelerated over the years; paradigms shifted and new ones have emerged and been added to respond to those switching and changing accompanied by an increasing economic, ecologic and social concerns and pressures. In today’s international and competitive environment, Lean and Green practices in economies are most important determinants to increase the performances. Corresponding production philosophies and techniques help companies to advance their performances in terms of diminishing lead times and costs of manufacturing, to improve delivery on time and quality, at the same time becoming more ecological by reducing material use and waste, and by recycling and reusing. Those lean and green activities enhance productivity, lower carbon footprint and improve consumer satisfaction, which in reverse makes firms competitive and sustainable.

In previous decades, interest and awareness about clean energy have exponentially grown, together with a steadily growing concern about our natural neighborhoods and habitats, and about our well-being and health, our resources and atmosphere, our spaces and times. This progress has also been due to a reduction in the cost of both installed capacity of converters and generated energy. Such successes stories were possible by advances in modern technologies for the converter production, enhancements in efficiency in use of energy incoming, optimization of converter operation and data analysis based on records during the system operations with the chances of production planning.

From our editorial sides, this handbook was hoped to become a most valuable reference book from 2021 onwards in the areas of modern renewable energy and climate change by the help of Operational Research (OR), Artificial Intelligence (AI), Creative Arts and Sciences. Given all the hard work from the authors’ and our sides, the research and application project has become successful in gathering and integrating emerging findings and outcomes of the state-of-the-art and inventions about and electronic and electrical, energetic and informational, recoverable and renewable, creational and recreational sources and their swift and mobile usages with care and responsibility. Now, the thus given work and compendium of scientific investigation on questions of renewable power and energy supply for cities and rural areas is a remarkable scholarly treasure which discloses the efficient employment of those modern-times resources that have a helpful influence on sustainable development of our megalopolises and our countryside, hence on migratory movements into big cities and eventually on social peace in and among our peoples.

For this book’s international orientation it has turned into a unique resource that outlines newest progress achieved around the globe in related domains of OR, AI, renewable power, electronical, informational and transformational technologies. It is on the way of becoming accepted and classical at a worldwide stage because of its comprehensive contents ensured by analytical, applied and life-friendly approaches towards the whole creation.

Artificial Intelligence for Renewable Energy and Climate Change, is a hand-picked collection of creative research on and with methods and applications of mathematics, machine and deep learning (ML and DL) in the realms of business and management, economics and finance, natural sciences and engineering. While featuring topics including data-hybridization, computational modeling, and artificial neural networks, this book is designed and suitable for engineers and IT experts, analysts and data scientists, engineers and investigators, academicians and philanthropes, policy makers and caretakers, experienced ones and the youth, for whoever seeks for contemporary research on Intelligent Optimization in emerging smart-technological, energetic, environmental and creative industries.

The rise in population and the concurrently growing consumption rate necessitates the evolution of clean energy systems to adopt current analytic and computational technologies such as big-data, IoTs and 5G technology to increase production at a faster and smoother scale. While existing technologies may help in energy processing, there is a need for studies that seek to understand how modern approaches like OR, AI, ML, DL, hybrid technologies and advancements in mathematics can aid to sustain clean energy and climate change processes while utilizing energy sources efficiently and productively.

This book on Artificial Intelligence for Renewable Energy and Climate Change is an essential publication that examines the benefits and barriers of implementing computational models to clean energy systems, global warming, climate change, and energy sources as well as how these models can produce more cost-effective and sustainable solutions. Featuring coverage on a wide range of topics such as classical and nature-inspired optimization and optimal control, hybrid and stochastic systems, this book is ideally designed for engineers, scientists, industrialist, academicians, researchers, computer and information technologists, sustainable developers, managers, environmentalists, government leaders, research officers, policy makers, business leaders and students. This book aims to become a delight for practitioners in the fields of sustainable and renewable energy sustainability and their outstanding impacts on how to face global warming and climate change.

Invited subjects of this compendium welcome but are not limited to the following:

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Artificial intelligence,

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Machine intelligence, Deep intelligence,

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Metaheuristic algorithms,

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Hydropower, Renewable electricity, Solar PV, Bio power,

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Geothermal power, Ocean power, Wind power,

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Bio-gas, Hydrogen,

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Global warming, Climate change,

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Renewable energy,

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Hybrid technology,

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CO

2

minimization,

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Evolutionary algorithms, Swarm intelligence,

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Computational intelligence, Soft computing,

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Operational research, Data mining, Hybrid optimization,

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Bioenergy Recycling, Biofuel supply chains,

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Energy management policy, Energy efficiency, Energy-saving technology,

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Small hydropower plants, Thermal treatments,

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Remote sensing,

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Optimization theory and applications,

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Optimal control theory and applications,

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Stochastic optimal control theory and applications.

This book is grouped into 2 parts, namely: Section I – Renewable Energy, and Section II - Climate Change.

Subsequently, we provide a short introduction of the fourteen chapters of this work.

In the first chapter “Artificial Intelligence for Sustainability: Opportunities and Challenges”, the author Amany Alshawi focuses on AI in the sustainable practices across the environmental sector and related industries. AI uses tools and methods such as ML effective monitoring and prediction. A main establishment is on AI opportunities and multiple multilevel systemic approaches to systems in designing new techniques while considering the psychological and sociological factors as well as the economic values.

The second chapter “Recent Applications of Machine Learning In Solar Energy Prediction”, authored by N. Kapilan, R. P. Reddy and Vidhya P, among the renewable energy sources addresses solar energy which is preferred as its potential is high. But there are few challenges due to variability and uncertainty with solar radiation which results in lower energy conversion, intermittent power supply. They discuss basic concepts of solar energy, energy conversion methods and different types of ML used in solar photovoltaic systems, ML algorithms, challenges and opportunities in solar energy production.

In the third chapter called “Mathematical Analysis on Power Generation – Part I”, the author G. Udhaya Sankar addresses Seebeck and Peltier effects which are used to generate current and heat in a thermocouple, respectively; current from heat and heat from current. It is possible to produce light from electricity and electricity from light. They are the basics for theory of photovoltaic theory. The author derives a formula providing a linear relationship between product of current with voltage and heat in a thermocouple.

The fourth chapter named “Mathematical Analysis on Power Generation – Part II”, authored by G. Udhaya Sankar, states that is no exact analytic formula which is applicable for conversion of voltage to temperature for thermocouple-temperature sensors, even though there are theories and equations for thermocouples. However, it is possible to get approximate localized as well as approximate globalized polynomial formulas.

The fifth chapter called as “Sustainable Energy Materials” by G. Udhaya Sankar refers to Co-Precipitation, Microwave assisted Solvothermal and Sol-Gel methods which are chosen for investigation of the Zn-CuO nanoparticles in energy harvesting. The synthesized nanoparticles are examined by different characterization. Energy harvesting applications depend on thermal conductivity, electrical conductivity, Seebeck coefficient, power factor, and figure of merit.

In the sixth chapter “Optimization of Hybrid Wind and Solar Renewable Energy System by Iteration Method”, the authors Diriba Kajela Geleta and Mukhdeep Singh Manshahia is concerned with integration of knowledge, techniques and methodologies from many complementary AI tools for solving complex problems. A combination of intelligent controllers with adaptiveness appears as most promising in practical implementation and control of electrical drives. The authors briefly describe different MPPT techniques with main focus on FL, ANN and Neuro fuzzy methods.

The seventh chapter named “The Contribution of AI-Based Approaches in The Determination of CO2 Emission Gas Amounts of Vehicles, Determination of CO2 Emission Rates Yearly of Countries, Air Quality Measurement and Determination of Smart Electric Grids Stability”, authored by Mesut Togacar, states that it is now possible to provide automatic control of systems which can harm the environment with AI-based technologies. Four datasets are used. He employs long short-term memory (LSTM), bidirectional LSTM, convolutional neural network (CNN), CNN-based LSTM model, and recurrent neural network (RNN).

In the eighth chapter called “Performance Analysis and Effects of Dust & Temperature on Solar PV Module System By Using Multivariate Linear Regression Model”, the authors Sumit Sharma, Ashish Nayyar and Vivek Pandey experimentally collected operating and electrical performance parameters of SPV array. The data were used for the calculation of the CUF, PR, and power conversion efficiency of the SPV systems. A multivariate linear regressions (MLR) model is established to estimate the system’s output performance with the consideration of conversion efficiency as the dependent variable, and ambient temperature and dust exposure day as the independent variables.

In the ninth chapter “Evaluation of In-House Compact Biogas Plant Thereby Testing Four Stroke Single Cylinder Diesel Engine”, the authors Pradeep Kumar Meena, Sumit Sharma, Amit Pal and Samsher investigate the usefulness of wastages caused by metropolitans and cities to produce biogas. They perform a comprehensive analysis of compact biogas plant in terms of its temperature, pH value, and efficiency for different kinds of wastages such as kitchen waste, fruits waste, animal dung and sugar as a catalyst, in view to increase production efficiency of biogas. Furthermore, a 4-stroke single cylinder diesel engine is tested by using dual fuel.

In the tenth chapter called “Low-Temperature Combustion Technologies for Emission Reduction in Diesel Engines”, the authors Amit Jhalania, Sumit Sharma, Pushpendra Kumar Sharma and Digambar Singh state that Diesel engines are lean burn engines; hence CO and HC emissions do not occur in substantial amounts in diesel exhaust. Emissions of serious concern in compression ignition engines are particulate matter and nitrogen oxides because of elevated temperature conditions of the combustion zone. They critically review the literature on low-temperature combustion conditions using various conventional and alternative fuels.

The eleventh chapter called as “Efficiency Optimization of Indoor Air Disinfection by Radiation Exposure for Poultry breeding Rational for Microclimate Systems Modernization for Livestock Premises” by Dovlatov Igor Mamedjarevich and Yurochka Sergey Sergeevich state that in I, II and III climatic zones of Russia, where the winter temperature reaches -18°C and the average wind velocity makes up to 3.6 m/s, the majority of farmers use no means of forced ventilation in cowsheds because they are energy-consuming. Due to the gas composition, the cows‘ productivity and the milk quality fall. In the course of both theoretical and experimental researches on a dairy farm in Moscow region, diverse results were obtained.

The twelfth chapter named “Improving the Efficiency of Photovoltaic Installations for Sustainable Development of the Urban Environment”, authored by Pavel Kuznetsov, Leonid Yuferev and Dmitry Voronin, presents research carried out at existing solar power plants and laboratory renewable energy sources purposed to increase the energy efficiency of photovoltaic installations with parallel and mixed switching of photocells, operating under uneven illumination, parallel voltage arrays of photovoltaic modules due to voltage equalization. They see the creation of a methodology for parametric optimization of power plants operating from renewable energy sources that contribute to the sustainable development of the urban environment in the context of digital transformation as an extremely important area of further research.

The thirteenth chapter named “Monitoring System Based Micro-Controller for Biogas Digester”, authored by Ahmed Abdelouareth and Mohamed Tamal, report that telemetry provides an additional ability to the measurement system by taking advantage of the transmission of the measured values through the local network or the Internet. They propose to control the process by measuring the state parameters of an anaerobic- methane is generated by the fermentation of waste deposited in the reactor. The system is all open-source hardware and software.

In the fourteenth chapter “Greenhouse Gas Statistics and Methods of Combating Climate Change”, the author Tatyana G. Krotova informs that economic growth in China has caused an unexampled surge in carbon dioxide emissions. The author sets the task to find out the sources of environmental pollution entailing climate warming and simulates an econometric model to calculate the values of statistical indicators required for the level-down of environmental pollution.

We as the editors wish that the chosen fields and selected topics of this book represent a core selection of global investigations coping with upcoming and complicated, occasionally long-lasting challenges and needs of Intelligent Optimization and their domains in Renewable Energy and Climate Change, with approaches, techniques and results of Operational Research and Artificial Intelligence. We are very grateful to the publishing house of Wiley - Scrivener Publishing for the honor of hosting and featuring our compendium as a pioneering scientific enterprise. Special gratitude is extended to the Editors of the publishing house, and also to Editorial Managers and Staff for their continuous advice and guidance in each and every respect. We say thanks to all the authors for their diligent efforts and generous readiness to share their emerging insights, ideas and smartest inventions with our worldwide community. Now we really wish that the authors’ investigations will catalyze, crystalize and initiate cooperation and fruitful progress on a worldwide and exquisite level, as a powerful service to humanity and our entire creation.

The Guest Editors:

Pandian Vasant

MERLIN Research Centre, Ton Duc Thang University, Vietnam E-mail: [email protected]

Gerhard-Wilhelm Weber

Poznań University of Technology, Poland E-mail: [email protected]

J. Joshua Thomas

UOW Malaysia; KDU Penang University College, Malaysia E-mail: [email protected]

José Antonio Marmolejo-Saucedo

Facultad de IngenierÞa, Universidad Panamericana, Ciudad de México, Mexico E-mail: [email protected]

Roman Rodriguez-Aguilar

Facultad de IngenierÞa, Universidad Panamericana, Ciudad de México, Mexico E-mail: [email protected]

Section IRENEWABLE ENERGY

1Artificial Intelligence for Sustainability: Opportunities and Challenges

Amany Alshawi*

Communication and Information Technology Research Institute, King Abdulaziz City for Science and Technology (kacst), Riyadh, Saudi Arabia

Abstract

This chapter focuses on Artificial Intelligence (AI) and its application in sustainable practices across the environmental sector and all related industries. AI uses tools and methods such as machine learning for effective monitoring and prediction. The AI shift from the traditional energy resources such as coal, natural gas and fossil fuel-derived energy to the more green, sustainable and less carbon emitting energy resource will be the highlight of this chapter. The study will focus on AI and how it will facilitate and foster environmental governance. It has been established that there will be challenges as well as opportunities for businesses as well as societies in the incorporation of AI for sustainability. The research will highlight the opportunities which AI has in the current scenarios and the challenges it needs to address. The main establishment will be the opportunities which AI is going to offer. It will further offer multiple multilevel systemic approaches to the systems in designing new techniques while considering the psychological and sociological factors as well as the economic values.

Keywords: Artificial intelligence, renewable energy, machine learning, sustainable, sustainability, carbon

1.1 Introduction

Artificial intelligence (AI) has been transforming the manner of business operations in the industrial sector. Its encouraging prospect has reached the point where it can solve major societal issues. One of the major issues is related to the sustainability of the environment and the natural resources. Dereliction of the natural atmosphere and climate change have been increasingly complicated concepts which need extensive research as well as innovative and groundbreaking solutions [1]. With the aim of catalyzing the revolutionary research and practical implementation of AI is bringing more environmental sustainability, it is well supported and claimed by researches that AI can provide sustenance to culturally motivated administrations as well as corporate practices in reducing the consumption and disruption of the natural resource and energy resources while carrying out day-to-day functions and other matters of human life [2].

The exact and meaningful significance of AI is not only related to the fact that it will enable society to reduce its consumption of energy, water, and land and create moderation in the intensity of usage. AI will further focus on how to facilitate and foster environmental governance. It has been established that there will be challenges as well as opportunities for businesses as well as societies in incorporation of AI in sustainability practices [3]. Some comprehensive challenges to note before going into the details are the excessive dependence on the previous data collected through the machine learning models as well as the lack of trust in the human behavioral reactions to AI-based intermediations. It came to light that the increase in cybersecurity risks has been one of the major issues increasing the adverse effects of AI applications on sustainability practices. The complications in analyzing the impacts of interventions posed by AI have further complicated the implementation of AI strategies in the sustainability practices across businesses [4].

While it has been established that there are many opportunities which AI is going to offer, it will further offer multiple multilevel systemic approaches to the systems in designing new techniques while considering the psychological and sociological factors as well as the economic values. The main aim of having long-term effects of AI-based solution in deriving sustainability as well as their immediate effects still poses questions over the opportunities that it offers [5].

1.2 History of AI for Sustainability and Smart Energy Practices

The history of AI for sustainability and smart energy practices has to be mentioned. AI-driven advancement has been happening for the last 50 years and has been an active part of the construction of experimental machines to conduct various types of intelligent performance in the energy and manufacturing industry [6]. The history of AI and its practicability in the world was explored by Alan Turing, who is seen as the pioneer of AI. He invented the imitation game, which was later termed the Alan Turning Test. A brief history of AI is shown in Figure 1.1.

Figure 1.1 History of AI through time [7].

AI suffered a bit of a cold period between 1975 and 1980. This also led to the reduction in the funding opportunities for all AI projects. The period 1990-2015 explored the usage of AI in the logistics planning in the US military as well as vertical markets, innovative and creative web designs, face recognition, and integrated online communication applications. Recently, AI has been taken to the next level by integrating it with sustainability practices [5].

1.3 Energy and Resources Scenarios on the Global Scale

The world has undergone a huge change in terms of energy resources, distribution, and usage. There has been a lot of pressure on companies to reduce their carbon emissions and devise proper ways to effectively and sustainably manage the power supply-demand balance in the energy sector. The main aim has been to make sure the shift is made from traditional energy resources such as coal, natural gas and fossil fuel-derived energy to the more green, sustainable and less carbon-emitting energy resource [8].

It has been clear that the world has been deriving most of its energy needs from the traditional energy resources such as coal, crude oil, fossils and natural gas, etc. The share of renewable energy resources in the usage of energy consumption at the global scale was only 17.48% in 2016. The numbers have increased only slightly in the last four years. With the increase in global per capita income, the increased dependency on fossil fuels for energy usage, such as transportation, which lead to global warming and climate change, have raised more concerns [9].

The greenhouse gas (CO2) emissions have increased as per the data collected by researchers; from 2015-2020, an average increase of 0.22% has been recorded. Global demand for the usage of the primary energy resources has increased by 2.3% since 2018. This has been supported by the fact that countries like China, USA and India are consuming 70% of the world’s energy demand. The cumulative share of renewable energy is estimated to grow from 0.25% to about 45% by 2040. The recent development has not brought much encouragement, since energy crises over the globe have been increasing. Over 120 million people lost direct access to electricity in 2018-2019 [10].

By taking into consideration the vagueness linked to the future of technological advancements, it can be predicted that we will be witnessing much more progress as well as opportunities in the sector for bringing in more sustainability using AI-based solutions. The case of electrical access can be largely resolved by integrating solutions such as AI smart grid that will reduce energy costs and avoid sustained electricity blackouts by linking demand and supply for the countries struggling with an electric crisis [11]. In conclusion, despite the fact that AI will be facing a lot of challenges to be applied in the sustainability practices, it will have many opportunities to be applied across the globe, making it one of the tools for creating cheaper, trustworthy, clean and carbon-free energy resources.

1.4 Statistical Basis of AI in Sustainability Practices

1.4.1 General Statistics

In recent times many companies have been jumping on the bandwagon of implementing AI-based practices in their business solutions. One of the major statistical factors to consider in this case is the cost effectiveness in the energy and utilities sector. Figure 1.2 highlights the reduction in costs owing to AI-based strategies being employed in the industrial sectors [12].

Although there are numerous challenges which have been faced by enterprises in terms of maintaining cost margins and cutting spending, it is important to make sure that all applications offer security and trust in the business processes, variations in the commodity prices, transforming regulatory policies, and altering demand. Figure 1.3 below shows the energy and utilities organizations in countries implementing AI.

Figure 1.2 Cost savings expected from energy and utilities sector by integrating AI [13].

The statistical analysis of AI in recent times showcases many benefits which make it a very effective choice to be implemented in the business sector. The fact that it acts as a catalyst in employing automation and increasing the efficiency of the supply chain processes while bringing more sustainability is one of the major reasons behind its widespread usage. Many industrial sectors are still lagging behind in the wide-scale implementation of AI-based strategies, as is the case with the water utilities [14]. The deployment of automation services and their benefits are shown by Figure 1.4 below.

It has already been established that not all companies embark upon the journey of AI in bringing more sustainability. Companies need to effectively understand the AI initiative before trying it out. Companies have been dealing with a black box issue to a great degree in recent times in the implementation of the AI-based strategies [15].

Furthermore, deeper analysis showed that AI methods will offer environmental advantages much more than just the GHG emissions solutions. This will include the estimation of the environmental effects of the water quality, air pollution, deforestation and biodiversity. It has been established that AI is capable of analyzing satellite data and ground-based sensors data for effective monitoring of real-time forest situations. This allows it to provide early warnings related to the deforestation attempts. In prospect, it is expected to save 32 million hectares of forest from deforestation by 2030. Alongside that, AI-based sensors have been providing safer and accurate numbers for establishing air quality indexes in cities and populated areas to deal with air pollution. It has been estimated that this will save up to 150 million USD on the global scale by 2030. Add to this the significant impact of this achievement in reducing healthcare costs [16]. Figure 1.5 shows how utilizing AI for sustainability will increase GDPs, GHG, and net jobs around the world by 2030.

Figure 1.3 Energy and utilities organizations implementing AI [7].

1.4.2 Environmental Stress–Based Statistics

AI can provide sustenance to culturally motivated administrations as well as corporate practices in reducing the consumption and disruption of natural resources and energy consumption. Due to the growth and advancements in scientific analysis, data gathering and modelling has increased over the last few decades, allowing researchers to better evaluate and predict the effects of human growth and associated activities. The outcomes are worrisome, since the increase in the total human footprint is a clear indication of the fact that environmental stress has been rising. The major reasons behind the increase in environmental stress are as follows:

Figure 1.4 Benefits of AI [7].

Figure 1.5 AI for sustainability globally until 2030 [17].

1.4.2.1 Climate Change

The increase in the levels of greenhouse gases is worrying scientists and environmental activists across the globe. The major reason behind that increase is attributed to the unsustainable practices carried out by all sectors and most specifically the industrial sector. The level has reached astounding numbers in comparison to previous years. If the recent Paris Agreement terms are taken into consideration, it is estimated that global average temperatures will rise by 3°C in 2100 in comparison to preindustrial levels. This will be further accompanied by the 1.5C threshold which will be required to avoid the acutest effects of climate change in the future. The integration of AI-based practices will be a way to counter these issues in an effective manner [18].

1.4.2.2 Biodiversity

One of the other major issues which has increased environmental stress and highlighted the need for the persistent use of AI practices in a sustainable manner is the loss of biodiversity. The Earth has lost a lot in terms of biodiversity. The increased number of extinctions of various species has led to a decrease in populations. A sharp decline of 60% has been estimated since 1970. This is alarming since this will be impacting the overall cycle of life as well as sustainability.

1.4.2.3 Deforestation

Scientists have been worried about deforestation, which has been impacting sustainable practices in recent times. Recent deforestation rates have been decreasing. This has impacted the overall deforestation in the areas of the Amazon Basin. Deforestation has been reduced by 8% and is projected to further decrease, which will be accompanied by major regional rainfalls. This will further lead to the condition of a shift, which will form a “savannah state”. This state will have more serious and wide-ranging effects on Earth’s atmospheric practices. This will impact the overall biodiversity as well as the sustainability of Earth to a great degree [19].

1.4.2.4 Changes in Chemistry of Oceans

The changes and shifts in the chemistry of the oceans has been one of the other leading reasons for the increasing environmental stress levels. The changes in the chemistry of the oceans have produced the most drastic shift in possibly 300 million years. The subsequent acidification and mounting temperatures of the ocean will make an unparalleled impression on corals and fish stocks.

1.4.2.5 Nitrogen Cycle

The world has been dealing with the crisis of the nitrogen cycle in recent times to a great degree. The biggest and speediest influence on the nitrogen cycle has been seen in the last few decades in comparison to the last 2.5 billion years. This can be attributed to the increasing effect of the prevalent nitrogen and phosphate pollution which has been occurring due the usage of fertilizers into the crops and the dumping of chemicals in the seas by factories. The polluted water is used for consumption by sea life as well as for many human activities, which has impacted fish stocks and developed dead zones for sea life and ocean biodiversity in over 10% of the world’s oceans. This is an alarming situation and needs to be tackled in the best manner possible. The use of AI for sustainable practices and predictions will help but it will face many challenges [20].

1.4.2.6 Water Crisis

The challenges faced by the global water cycle are unprecedented in recent times due to the actions carried out by the industrial sector. The lack of integration of sustainable practices and modern tools and technologies has been a major factor affecting these practices. The harsh impacts due to over-extraction and unrestrained pollution are the leading contributors to the water crisis on the global scale. AI technologies will be of great aid in this case since it is predicted that the global climate will suffer a further 40% shortfall in the amount of freshwater which will be required to aid the entire global economy in the coming decade. The statistics are alarming to a great degree and need solutions to be introduced at the earliest time.

1.4.2.7 Air Pollution