Sustainability

Research

38 pages, 4020 KiB

Open AccessArticle

Artificial Intelligence/Machine Learning in Energy Management Systems, Control, and Optimization of Hydrogen Fuel Cell Vehicles

by Mojgan Fayyazi, Paramjotsingh Sardar, Sumit Infent Thomas, Roonak Daghigh, Ali Jamali, Thomas Esch, Hans Kemper, Reza Langari and Hamid Khayyam

Sustainability 2023, 15(6), 5249; https://doi.org/10.3390/su15065249 - 15 Mar 2023

Cited by 14 | Viewed by 4819

Abstract

Environmental emissions, global warming, and energy-related concerns have accelerated the advancements in conventional vehicles that primarily use internal combustion engines. Among the existing technologies, hydrogen fuel cell electric vehicles and fuel cell hybrid electric vehicles may have minimal contributions to greenhouse gas emissions [...] Read more.

Environmental emissions, global warming, and energy-related concerns have accelerated the advancements in conventional vehicles that primarily use internal combustion engines. Among the existing technologies, hydrogen fuel cell electric vehicles and fuel cell hybrid electric vehicles may have minimal contributions to greenhouse gas emissions and thus are the prime choices for environmental concerns. However, energy management in fuel cell electric vehicles and fuel cell hybrid electric vehicles is a major challenge. Appropriate control strategies should be used for effective energy management in these vehicles. On the other hand, there has been significant progress in artificial intelligence, machine learning, and designing data-driven intelligent controllers. These techniques have found much attention within the community, and state-of-the-art energy management technologies have been developed based on them. This manuscript reviews the application of machine learning and intelligent controllers for prediction, control, energy management, and vehicle to everything (V2X) in hydrogen fuel cell vehicles. The effectiveness of data-driven control and optimization systems are investigated to evolve, classify, and compare, and future trends and directions for sustainability are discussed. Full article

(This article belongs to the Special Issue Circular Economy and Artificial Intelligence)

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17 pages, 1003 KiB

Open AccessArticle

Economic Efficiency of the Implementation of Digital Technologies in Energy Power

by Victoria Galkovskaya and Mariia Volos

Sustainability 2022, 14(22), 15382; https://doi.org/10.3390/su142215382 - 18 Nov 2022

Cited by 1 | Viewed by 1501

Abstract

The present research prioritized the most important direction of energy power transformation—energy sector digitalization—and its contribution to the achievement of the sustainable development goals focused on climate change mitigation and responsible consumption and production. The authors evaluated the economic efficiency of the implementation [...] Read more.

The present research prioritized the most important direction of energy power transformation—energy sector digitalization—and its contribution to the achievement of the sustainable development goals focused on climate change mitigation and responsible consumption and production. The authors evaluated the economic efficiency of the implementation of digital tools due to the decrease in energy production costs. The evaluation was performed using the energy cost calculation method before and after the implementation of digital technology, and the digital technology cost calculation method based on the technology readiness level, introduced for the first time. The study demonstrates the advantages of energy power digitalization worldwide, including the achievement of the declared goals of sustainable development and the transition to low-carbon energy. The scientific results obtained are valuable for scholars who carry out research in the area of the efficiency of economic digitalization, since they hold for all countries and types of energy generation. The directions of further research include the development of an analytical notation to show that the digital technology cost also depends on the speed and volume of its distribution, as well as the level of technological development of material and technical resources in the industry and in the country, and also the labor and capital availability. Full article

(This article belongs to the Special Issue Circular Economy and Artificial Intelligence)

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14 pages, 2552 KiB

Open AccessArticle

Application of Machine Learning Algorithms for Sustainable Business Management Based on Macro-Economic Data: Supervised Learning Techniques Approach

by Muhammad Anees Khan, Kumail Abbas, Mazliham Mohd Su’ud, Anas A. Salameh, Muhammad Mansoor Alam, Nida Aman, Mehreen Mehreen, Amin Jan, Nik Alif Amri Bin Nik Hashim and Roslizawati Che Aziz

Sustainability 2022, 14(16), 9964; https://doi.org/10.3390/su14169964 - 12 Aug 2022

Cited by 7 | Viewed by 3105

Abstract

Macroeconomic indicators are the key to success in the development of any country and are very much important for the overall economy of any country in the world. In the past, researchers used the traditional methods of regression for estimating macroeconomic variables. However, [...] Read more.

Macroeconomic indicators are the key to success in the development of any country and are very much important for the overall economy of any country in the world. In the past, researchers used the traditional methods of regression for estimating macroeconomic variables. However, the advent of efficient machine learning (ML) methods has led to the improvement of intelligent mechanisms for solving time series forecasting problems of various economies around the globe. This study focuses on forecasting the data of the inflation rate and the exchange rate of Pakistan from January 1989 to December 2020. In this study, we used different ML algorithms like k-nearest neighbor (KNN), polynomial regression, artificial neural networks (ANNs), and support vector machine (SVM). The data set was split into two sets: the training set consisted of data from January 1989 to December 2018 for the training of machine algorithms, and the remaining data from January 2019 to December 2020 were used as a test set for ML testing. To find the accuracy of the algorithms used in the study, we used root mean square error (RMSE) and mean absolute error (MAE). The experimental results showed that ANNs archives the least RMSE and MAE compared to all the other algorithms used in the study. While using the ML method for analyzing and forecasting inflation rates based on error prediction, the test set showed that the polynomial regression (degree 1) and ANN methods outperformed SVM and KNN. However, on the other hand, forecasting the exchange rate, SVM RBF outperformed KNN, polynomial regression, and ANNs. Full article

(This article belongs to the Special Issue Circular Economy and Artificial Intelligence)

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21 pages, 5339 KiB

Open AccessArticle

Intelligent Driver Assistance and Energy Management Systems of Hybrid Electric Autonomous Vehicles

by Ziad Al-Saadi, Duong Phan Van, Ali Moradi Amani, Mojgan Fayyazi, Samaneh Sadat Sajjadi, Dinh Ba Pham, Reza Jazar and Hamid Khayyam

Sustainability 2022, 14(15), 9378; https://doi.org/10.3390/su14159378 - 31 Jul 2022

Cited by 7 | Viewed by 1930

Abstract

Automotive companies continue to develop integrated safety, sustainability, and reliability features that can help mitigate some of the most common driving risks associated with autonomous vehicles (AVs). Hybrid electric vehicles (HEVs) offer practical solutions to use control strategies to cut down fuel usage [...] Read more.

Automotive companies continue to develop integrated safety, sustainability, and reliability features that can help mitigate some of the most common driving risks associated with autonomous vehicles (AVs). Hybrid electric vehicles (HEVs) offer practical solutions to use control strategies to cut down fuel usage and emissions. AVs and HEVs are combined to take the advantages of each kind to solve the problem of wasting energy. This paper presents an intelligent driver assistance system, including adaptive cruise control (ACC) and an energy management system (EMS), for HEVs. Our proposed ACC determines the desired acceleration and safe distance with the lead car through a switched model predictive control (MPC) and a neuro-fuzzy (NF) system. The performance criteria of the switched MPC toggles between speed and distance control appropriately and its stability is mathematically proven. The EMS intelligently control the energy consumption based on ACC commands. The results show that the driving risk is extremely reduced by using ACC-MPC and ACC-NF, and the vehicle energy consumption by driver assistance system based on ACC-NF is improved by 2.6%. Full article

(This article belongs to the Special Issue Circular Economy and Artificial Intelligence)

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15 pages, 5361 KiB

Open AccessArticle

Renewable Thermal Energy Driven Desalination Process for a Sustainable Management of Reverse Osmosis Reject Water

by Kawtar Rahaoui, Hamid Khayyam, Quoc Linh Ve, Aliakbar Akbarzadeh and Abhijit Date

Sustainability 2021, 13(19), 10860; https://doi.org/10.3390/su131910860 - 30 Sep 2021

Cited by 1 | Viewed by 2048

Abstract

A sustainable circular economy involves designing and promoting products with the least environmental impact. This research presents an experimental performance investigation of direct contact membrane distillation with feed approaching supersaturation salinity, which can be useful for the sustainable management of reverse osmosis reject [...] Read more.

A sustainable circular economy involves designing and promoting products with the least environmental impact. This research presents an experimental performance investigation of direct contact membrane distillation with feed approaching supersaturation salinity, which can be useful for the sustainable management of reverse osmosis reject water. Traditionally, reject water from the reverse osmosis systems is discharged in the sea or in the source water body. The reinjection of high salinity reject water into the sea has the potential to put the local sea environment at risk. This paper presents a design of a solar membrane distillation system that can achieve close to zero liquid discharge. The theoretical and experimental analysis on the performance of the lab scale close to zero liquid discharge system that produces supersaturated brine is studied. The lab-based experiments were conducted at boundary conditions, which were close to the real-world conditions where feed water temperatures ranged between 40 °C and 85 °C and the permeate water temperatures ranged between 5 °C and 20 °C. The feed water was supplied at salinity between 70,000 ppm to 110,000 ppm, similar to reject from reverse osmosis. The experimental results show that the maximum flux of 17.03 kg/m²·h was achieved at a feed temperature of 80 °C, a feed salinity of 10,000 ppm, a permeate temperature of 5 °C and at constant feed and a permeate flow rate of 4 L/min. Whereas for the same conditions, the theoretical mass flux was 18.23 kg/m²·h. Crystal formation was observed in the feed tank as the feed water volume reduced and the salinity increased, reaching close to 308,000 ppm TDS. At this condition, the mass flux approached close to zero due to crystallisation on the membrane surface. This study provides advice on the practical limitations for the use of membrane distillation to achieve close to zero liquid discharge. Full article

(This article belongs to the Special Issue Circular Economy and Artificial Intelligence)

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17 pages, 5435 KiB

Open AccessArticle

Cascade Adaptive MPC with Type 2 Fuzzy System for Safety and Energy Management in Autonomous Vehicles: A Sustainable Approach for Future of Transportation

by Duong Phan, Ali Moradi Amani, Mirhamed Mola, Ahmad Asgharian Rezaei, Mojgan Fayyazi, Mahdi Jalili, Dinh Ba Pham, Reza Langari and Hamid Khayyam

Sustainability 2021, 13(18), 10113; https://doi.org/10.3390/su131810113 - 09 Sep 2021

Cited by 9 | Viewed by 2239

Abstract

A sustainable circular economy involves designing and promoting new products with the least environmental impact through increasing efficiency. The emergence of autonomous vehicles (AVs) has been a revolution in the automobile industry and a breakthrough opportunity to create more sustainable transportation in the [...] Read more.

A sustainable circular economy involves designing and promoting new products with the least environmental impact through increasing efficiency. The emergence of autonomous vehicles (AVs) has been a revolution in the automobile industry and a breakthrough opportunity to create more sustainable transportation in the future. Autonomous vehicles are supposed to provide a safe, easy-to-use and environmentally friendly means of transport. To this end, improving AVs’ safety and energy efficiency by using advanced control and optimization algorithms has become an active research topic to deliver on new commitments: carbon reduction and responsible innovation. The focus of this study is to improve the energy consumption of an AV in a vehicle-following process while safe driving is satisfied. We propose a cascade control system in which an autonomous cruise controller (ACC) is integrated with an energy management system (EMS) to reduce energy consumption. An adaptive model predictive control (AMPC) is proposed as the ACC to control the acceleration of the ego vehicle (the following vehicle) in a vehicle-following scenario, such that it can safely follow the lead vehicle in the same lane on a highway. The proposed ACC appropriately switches between speed and distance control systems to follow the lead vehicle safely and precisely. The computed acceleration is then used in the EMS component to find the optimal engine torque that minimizes the fuel consumption of the ego vehicle. EMS is designed based on two methods: type 1 fuzzy logic system (T1FLS) and interval type 2 fuzzy logic system (IT2FLS). Results show that the combination of AMPC and IT2FLS significantly reduces fuel consumption while the ego vehicle follows the lead vehicle safely and with a minimum spacing error. The proposed controller facilitates smarter energy use in AVs and supports safer transportation. Full article

(This article belongs to the Special Issue Circular Economy and Artificial Intelligence)

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16 pages, 3473 KiB

Open AccessArticle

The Impact of 4IR Digital Technologies and Circular Thinking on the United Nations Sustainable Development Goals

by Mohamed Sameer Hoosain, Babu Sena Paul and Seeram Ramakrishna

Sustainability 2020, 12(23), 10143; https://doi.org/10.3390/su122310143 - 04 Dec 2020

Cited by 76 | Viewed by 12688

Abstract

As we stand at the cusp of the fourth industrial revolution, digital technologies such as artificial intelligence, machine learning, the Internet of Things, Big Data, Blockchain, Robotics, 3D technologies, and many more have become the means and solutions to many of the world’s [...] Read more.

As we stand at the cusp of the fourth industrial revolution, digital technologies such as artificial intelligence, machine learning, the Internet of Things, Big Data, Blockchain, Robotics, 3D technologies, and many more have become the means and solutions to many of the world’s problems. Most recently, these technologies have assisted in the global fight of the COVID-19 pandemic and other societal problems. Together with these innovative techniques, the concept of circular economy and its relevant tools such as life cycle costing, life cycle impact assessment, materials passports, and circularity measurements have been implemented in a number of sectors in different countries for the transition from a linear “take, make, and dispose” model towards a more circular model, which has shown positive results for the environment and economy. In this article, with the help of implementation, prototyping, and case studies, we explore how these technological advancements and innovative techniques are used in different sectors such as information and communications technology, the built environment, mining and manufacturing, education, healthcare, the public sectors, and others to provide an opportunity to understand and resolve the agreed upon framework in 2015 by 193 countries, that is, the 17 United Nations Sustainable Development Goals. Full article

(This article belongs to the Special Issue Circular Economy and Artificial Intelligence)

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Review

Jump to: Research, Other

18 pages, 1026 KiB

Open AccessReview

The Role of Artificial Intelligence within Circular Economy Activities—A View from Ireland

by Muhammad Salman Pathan, Edana Richardson, Edgar Galvan and Peter Mooney

Sustainability 2023, 15(12), 9451; https://doi.org/10.3390/su15129451 - 12 Jun 2023

Cited by 4 | Viewed by 3946

Abstract

The world’s current linear economic model is unsustainable. This model encourages improper use of limited natural resources and causes abundant waste production resulting in severe harm to the environment. A circular economy (CE) is a sustainable, restorative, and regenerative alternative to the current [...] Read more.

The world’s current linear economic model is unsustainable. This model encourages improper use of limited natural resources and causes abundant waste production resulting in severe harm to the environment. A circular economy (CE) is a sustainable, restorative, and regenerative alternative to the current linear economy and is gaining popularity worldwide. Amongst various digital technologies, Artificial intelligence (AI) is a crucial enabler for CE and can aid significantly with the adoption and implementation of CE in real-world applications. In this paper, we describe the intersection of AI and CE and policies around implementing CE principles using AI. As a means of grounding the discussion, we discuss some initiatives taken by the Irish government to adopt circularity and explore the role AI plays in these. We present a number of practical examples of AI and CE from Ireland. We argue that digitalisation has potential in CE and it has a major role to play in the transition towards CE. We close the paper by reflecting on future steps around practical implementations of AI-based CE processes. Full article

(This article belongs to the Special Issue Circular Economy and Artificial Intelligence)

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27 pages, 3224 KiB

Open AccessReview

Towards Sustainable Fuel Cells and Batteries with an AI Perspective

by Brindha Ramasubramanian, Rayavarapu Prasada Rao, Vijila Chellappan and Seeram Ramakrishna

Sustainability 2022, 14(23), 16001; https://doi.org/10.3390/su142316001 - 30 Nov 2022

Cited by 15 | Viewed by 3467

Abstract

With growing environmental and ecological concerns, innovative energy storage systems are urgently required to develop smart grids and electric vehicles (EVs). Since their invention in the 1970s, rechargeable lithium-ion batteries (LIBs) have risen as a revolutionary innovation due to their superior benefits of [...] Read more.

With growing environmental and ecological concerns, innovative energy storage systems are urgently required to develop smart grids and electric vehicles (EVs). Since their invention in the 1970s, rechargeable lithium-ion batteries (LIBs) have risen as a revolutionary innovation due to their superior benefits of high operating potential and energy density. Similarly, fuel cells, especially Proton Exchange Membrane Fuel Cells (PEMFC) and Solid-Oxide Fuel Cells (SOFC), have been developed as an energy storage system for EVs due to their compactness and high-temperature stability, respectively. Various attempts have been made to explore novel materials to enhance existing energy storage technologies. Materials design and development are significantly based on trial-and-error techniques and require substantial human effort and time. Additionally, researchers work on individual materials for specific applications. As a viewpoint, we present the available sustainable routes for electrochemical energy storage, highlighting the use of (i) green materials and processes, (ii) renewables, (iii) the circular economy approach, (iv) regulatory policies, and (v) the data driven approach to find the best materials from several databases with minimal human involvement and time. Finally, we provide an example of a high throughput and machine learning assisted approach for optimizing the properties of several sustainable carbon materials and applying them to energy storage devices. This study can prompt researchers to think, advance, and develop opportunities for future sustainable materials selection, optimization, and application in various electrochemical energy devices utilizing ML. Full article

(This article belongs to the Special Issue Circular Economy and Artificial Intelligence)

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Graphical abstract

29 pages, 3292 KiB

Open AccessReview

Facemask Global Challenges: The Case of Effective Synthesis, Utilization, and Environmental Sustainability

by Kamyar Shirvanimoghaddam, Bożena Czech, Ram Yadav, Cemile Gokce, Laura Fusco, Lucia Gemma Delogu, Açelya Yilmazer, Graham Brodie, Amani Al-Othman, Adil K. Al-Tamimi, Jarret Grout and Minoo Naebe

Sustainability 2022, 14(2), 737; https://doi.org/10.3390/su14020737 - 10 Jan 2022

Cited by 12 | Viewed by 4799

Abstract

Coronavirus disease (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused a rapidly spreading pandemic and is severely threatening public health globally. The human-to-human transmission route of SARS-CoV-2 is now well established. The reported clinical observations and symptoms of [...] Read more.

Coronavirus disease (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused a rapidly spreading pandemic and is severely threatening public health globally. The human-to-human transmission route of SARS-CoV-2 is now well established. The reported clinical observations and symptoms of this infection in humans appear in the range between being asymptomatic and severe pneumonia. The virus can be transmitted through aerosols and droplets that are released into the air by a carrier, especially when the person coughs, sneezes, or talks forcefully in a closed environment. As the disease progresses, the use and handling of contaminated personal protective equipment and facemasks have become major issues with significant environmental risks. Therefore, providing an effective method for treating used/contaminated facemasks is crucial. In this paper, we review the environmental challenges and risks associated with the surge in facemask production. We also discuss facemasks and their materials as sources of microplastics and how disposal procedures can potentially lead to the contamination of water resources. We herein review the potential of developing nanomaterial-based antiviral and self-cleaning facemasks. This review discusses these challenges and concludes that the use of sustainable and alternative facemask materials is a promising and viable solution. In this context, it has become essential to address the emerging challenges by developing a new class of facemasks that are effective against the virus, while being biodegradable and sustainable. This paper represents the potentials of natural and/or biodegradable polymers for manufacturing facemasks, such as wood-based polymers, chitosan, and other biodegradable synthetic polymers for achieving sustainability goals during and after pandemics. Full article

(This article belongs to the Special Issue Circular Economy and Artificial Intelligence)

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25 pages, 4012 KiB

Open AccessReview

Strategies for Improving the Sustainability of Data Centers via Energy Mix, Energy Conservation, and Circular Energy

by Matteo Manganelli, Alessandro Soldati, Luigi Martirano and Seeram Ramakrishna

Sustainability 2021, 13(11), 6114; https://doi.org/10.3390/su13116114 - 28 May 2021

Cited by 27 | Viewed by 9313

Abstract

Information and communication technologies (ICT) are increasingly permeating our daily life and we ever more commit our data to the cloud. Events like the COVID-19 pandemic put an exceptional burden upon ICT. This involves increasing implementation and use of data centers, which increased [...] Read more.

Information and communication technologies (ICT) are increasingly permeating our daily life and we ever more commit our data to the cloud. Events like the COVID-19 pandemic put an exceptional burden upon ICT. This involves increasing implementation and use of data centers, which increased energy use and environmental impact. The scope of this work is to summarize the present situation on data centers as to environmental impact and opportunities for improvement. First, we introduce the topic, presenting estimated energy use and emissions. Then, we review proposed strategies for energy efficiency and conservation in data centers. Energy uses pertain to power distribution, ICT, and non-ICT equipment (e.g., cooling). Existing and prospected strategies and initiatives in these sectors are identified. Among key elements are innovative cooling techniques, natural resources, automation, low-power electronics, and equipment with extended thermal limits. Research perspectives are identified and estimates of improvement opportunities are mentioned. Finally, we present an overview on existing metrics, regulatory framework, and bodies concerned. Full article

(This article belongs to the Special Issue Circular Economy and Artificial Intelligence)

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19 pages, 269 KiB

Open AccessReview

Information and Communication Technology Solutions for the Circular Economy

by Konstantinos Demestichas and Emmanouil Daskalakis

Sustainability 2020, 12(18), 7272; https://doi.org/10.3390/su12187272 - 04 Sep 2020

Cited by 91 | Viewed by 11585

Abstract

The concept of circular economy (CE) is becoming progressively popular with academia, industry, and policymakers, as a potential path towards a more sustainable economic system. Information and communication technology (ICT) systems have influenced every aspect of modern life and the CE is no [...] Read more.

The concept of circular economy (CE) is becoming progressively popular with academia, industry, and policymakers, as a potential path towards a more sustainable economic system. Information and communication technology (ICT) systems have influenced every aspect of modern life and the CE is no exception. Cutting-edge technologies, such as big data, cloud computing, cyber-physical systems, internet of things, virtual and augmented reality, and blockchain, can play an integral role in the embracing of CE concepts and the rollout of CE programs by governments, organizations, and society as a whole. The current paper conducts an extensive academic literature review on prominent ICT solutions paving the way towards a CE. For the categorization of the solutions, a novel two-fold approach is introduced, focusing on both the technological aspect of the solutions (e.g., communications, computing, data analysis, etc.), and the main CE concept(s) employed (i.e., reduce, reuse, recycle and restore) that each solution is the most relevant to. The role of each solution in the transition to CE is highlighted. Results suggest that ICT solutions related to data collection and data analysis, and in particular to the internet of things, blockchain, digital platforms, artificial intelligence algorithms, and software tools, are amongst the most popular solutions proposed by academic researchers. Results also suggest that greater emphasis is placed on the “reduce” component of the CE, although ICT solutions for the other “R” components, as well as holistic ICT-based solutions, do exist as well. Specific important challenges impeding the adoption of ICT solutions for the CE are also identified and reviewed, with consumer and business attitude, economic costs, possible environmental impacts, lack of education around the CE, and lack of familiarization with modern technologies being found among the most prominent ones. Full article

(This article belongs to the Special Issue Circular Economy and Artificial Intelligence)

Other

Jump to: Research, Review

13 pages, 5883 KiB

Open AccessSystematic Review

Transitioning to a Circular Economy: A Systematic Review of Its Drivers and Barriers

by Jovan Tan, Fabien Jianwei Tan and Seeram Ramakrishna

Sustainability 2022, 14(3), 1757; https://doi.org/10.3390/su14031757 - 03 Feb 2022

Cited by 31 | Viewed by 6590

Abstract

Advancing societal’s progress to achieve circularity is imperative as our linear (take, make, waste) economic model is highly unsustainable. It depletes our natural resources and substantially contributes to pollution and global greenhouse gas emissions. Our continued participation in the linear economy will also [...] Read more.

Advancing societal’s progress to achieve circularity is imperative as our linear (take, make, waste) economic model is highly unsustainable. It depletes our natural resources and substantially contributes to pollution and global greenhouse gas emissions. Our continued participation in the linear economy will also expose businesses to volatile resource prices and supply disruptions resulting from the scarcity of critical materials and geopolitical factors. Hence, there are compelling reasons for businesses to transit and participate in the circular economy. However, anecdotal evidence suggests limited practical implementations. Therefore, this systematic review aims to determine the most significant drivers and barriers that influence business leaders to transform their businesses for participation in the circular economy. By clarifying the most influential factors and their characteristics, we can introduce effective measures to encourage or mitigate them. This review takes a transdisciplinary approach to discuss salient and consequential ideas with depth and completeness. Its associated practical and managerial implications are also thoroughly discussed. Full article

(This article belongs to the Special Issue Circular Economy and Artificial Intelligence)

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