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Records with Keyword: Artificial Intelligence
Showing records 1 to 25 of 214. [First] Page: 1 2 3 4 5 Last
Optimal Clustered, Multi-modal CO2 Transport Considering Non-linear Costs – a Path-planning Approach
Kang Qiu, Sigmund Eggen Holm, Julian Straus, Simon Roussanaly
July 9, 2024 (v1)
An important measure to achieve global reduction in CO2 emissions is CO2 capture, transport, and storage. The deployment of CO2 capture requires the development of a shared CO2 transport infrastructure, where CO2 can be transported with different transport modes. Furthermore, the cost of CO2 transport can be subject to significant economies of scale effects with respect to the amount of CO2 transported, also mentioned as clustering effects. Therefore, optimizing the shared infrastructure of multiple CO2 sources can lead to significant reductions in infrastructure costs. This paper presents a novel formulation of the clustered CO2 transport network. The Markov Decision Process formulation defined here allows for more detailed modeling of non-linear, discrete transport costs and increased geographical resolution. The clustering effects are modeled through cooperative multi-agent interactions. A multi-agent, reinforcement learning-based algorithm is proposed to optimize the shared transpo... [more]
Learn-To-Design: Reinforcement Learning-Assisted Chemical Process Optimization
Eslam G. Al-Sakkari, Ahmed Ragab, Mohamed Ali, Hanane Dagdougui, Daria C. Boffito, Mouloud Amazouz
July 9, 2024 (v1)
Subject: Optimization
Keywords: Artificial Intelligence, Carbon Capture, Machine Learning, Optimization, Process Design, Reinforcement Learning, Simulation-based Optimization
This paper proposes an AI-assisted approach aimed at accelerating chemical process design through causal incremental reinforcement learning (CIRL) where an intelligent agent is interacting iteratively with a process simulation environment (e.g., Aspen HYSYS, DWSIM, etc.). The proposed approach is based on an incremental learnable optimizer capable of guiding multi-objective optimization towards optimal design variable configurations, depending on several factors including the problem complexity, selected RL algorithm and hyperparameters tuning. One advantage of this approach is that the agent-simulator interaction significantly reduces the vast search space of design variables, leading to an accelerated and optimized design process. This is a generic causal approach that enables the exploration of new process configurations and provides actionable insights to designers to improve not only the process design but also the design process across various applications. The approach was valid... [more]
Mining Chemical Process Information from Literature for Generative Process Design: A Perspective
Artur M. Schweidtmann
July 9, 2024 (v1)
Keywords: Artificial Intelligence, computer vision, data mining, knowledge graph, natural language processing
Artificial intelligence (AI) and particularly generative AI led to recent breakthroughs, e.g., in generating text and images. There is also a potential of these technologies in chemical engineering, but the lack of structured big domain-relevant data hinders advancements. I envision an open Chemical Engineering Knowledge Graph (ChemEngKG) that provides big open and linked chemical process information. In this article, I present the concept of “flowsheet mining” as the first step towards the ChemEngKG. Flowsheet mining extracts process information from flowsheets and process descriptions found in scientific literature and patents. The proposed technology requires the integration of data mining, computer vision, natural language processing, and semantic web technologies. I present the concept of flowsheet mining, discuss previous literature, and show future potentials. I believe the availability of big data will enable breakthroughs in process design through artificial intelligence.
Artificial Intelligence and Machine Learning for Sustainable Molecular-to-Systems Engineering
Alexander W. Dowling
July 9, 2024 (v1)
Sustainability encompasses many wicked problems involving complex interdependencies across social, natural, and engineered systems. We argue holistic multiscale modeling and decision-support frameworks are needed to address multifaceted interdisciplinary aspects of these wicked problems. This review highlights three emerging research areas for artificial intelligence (AI) and machine learning (ML) in molecular-to-systems engineering for sustainability: (1) molecular discovery and materials design, (2) automation and self-driving laboratories, (3) process and systems-of-systems optimization. Recent advances in AI and ML are highlighted in four contemporary application areas in chemical engineering design: (1) equitable energy systems, (2) decarbonizing the power sector, (3) circular economies for critical materials, and (4) next-generation heating and cooling. These examples illustrate how AI and ML enable more sophisticated interdisciplinary multiscale models, faster optimization algor... [more]
From Then to Now and Beyond: Exploring How Machine Learning Shapes Process Design Problems
Burcu Beykal
July 9, 2024 (v1)
Keywords: Artificial Intelligence, Data-driven analysis, Historical view, Process Synthesis, Surrogate modeling
Following the discovery of the least squares method in 1805 by Legendre and later in 1809 by Gauss, surrogate modeling and machine learning have come a long way. From identifying patterns and trends in process data to predictive modeling, optimization, fault detection, reaction network discovery, and process operations, machine learning became an integral part of all aspects of process design and process systems engineering. This is enabled, at the same time necessitated, by the vast amounts of data that are readily available from processes, increased digitalization, automation, increasing computation power, and simulation software that can model complex phenomena that span over several temporal and spatial scales. Although this paper is not a comprehensive review, it gives an overview of the recent history of machine learning models that we use every day and how they shaped process design problems from the recent advances to the exploration of their prospects.
Chaos-Enhanced Archimede Algorithm for Global Optimization of Real-World Engineering Problems and Signal Feature Extraction
Ahmed Bencherqui, Mohamed Amine Tahiri, Hicham Karmouni, Mohammed Alfidi, Youssef El Afou, Hassan Qjidaa, Mhamed Sayyouri
June 10, 2024 (v1)
Keywords: Archimedean optimization algorithm, Artificial Intelligence, chaotic maps, optimization engineering problems
Optimization algorithms play a crucial role in a wide range of fields, from designing complex systems to solving mathematical and engineering problems. However, these algorithms frequently face major challenges, such as convergence to local optima, which limits their ability to find global, optimal solutions. To overcome these challenges, it has become imperative to explore more efficient approaches by incorporating chaotic maps within these original algorithms. Incorporating chaotic variables into the search process offers notable advantages, including the ability to avoid local minima, diversify the search, and accelerate convergence toward optimal solutions. In this study, we propose an improved Archimedean optimization algorithm called Chaotic_AO (CAO), based on the use of ten distinct chaotic maps to replace pseudorandom sequences in the three essential components of the classical Archimedean optimization algorithm: initialization, density and volume update, and position update. T... [more]
Enhancing Control Room Operator Decision Making
Joseph Mietkiewicz, Ammar N. Abbas, Chidera W. Amazu, Gabriele Baldissone, Anders L. Madsen, Micaela Demichela, Maria Chiara Leva
June 7, 2024 (v1)
Keywords: Artificial Intelligence, control room operators, decision support systems, dynamic influence diagrams, process control, reinforcement learning, situation awareness, task overload, trust in automation
In the dynamic and complex environment of industrial control rooms, operators are often inundated with numerous tasks and alerts, leading to a state known as task overload. This condition can result in decision fatigue and increased reliance on cognitive biases, which may compromise the decision-making process. To mitigate these risks, the implementation of decision support systems (DSSs) is essential. These systems are designed to aid operators in making swift, well-informed decisions, especially when their judgment may be faltering. Our research presents an artificial intelligence (AI)-based framework utilizing dynamic influence diagrams and reinforcement learning to develop a powerful decision support system. The foundation of this AI framework is the creation of a robust, interpretable, and effective DSS that aids control room operators during critical process disturbances. By incorporating expert knowledge, the dynamic influence diagram provides a comprehensive model that captures... [more]
A Comprehensive Review of Microgrid Energy Management Strategies Considering Electric Vehicles, Energy Storage Systems, and AI Techniques
Muhammad Raheel Khan, Zunaib Maqsood Haider, Farhan Hameed Malik, Fahad M. Almasoudi, Khaled Saleem S. Alatawi, Muhammad Shoaib Bhutta
June 7, 2024 (v1)
Keywords: Artificial Intelligence, demand-side management, electric vehicles, energy storage system, microgrid, optimization algorithms, renewable energy resources, smart grid
The relentlessly depleting fossil-fuel-based energy resources worldwide have forbidden an imminent energy crisis that could severely impact the general population. This dire situation calls for the immediate exploitation of renewable energy resources to redress the balance between power consumption and generation. This manuscript confers about energy management tactics to optimize the methods of power production and consumption. Furthermore, this paper also discusses the solutions to enhance the reliability of the electrical power system. In order to elucidate the enhanced reliability of the electrical system, microgrids consisting of different energy resources, load types, and optimization techniques are comprehensively analyzed to explore the significance of energy management systems (EMSs) and demand response strategies. Subsequently, this paper discusses the role of EMS for the proper consumption of electrical power considering the advent of electric vehicles (EVs) in the energy ma... [more]
Study on Micro-Pressure Drive in the KKM Low-Permeability Reservoir
Heng Zhang, Mibang Wang, Wenqi Ke, Xiaolong Li, Shengjun Yang, Weihua Zhu
June 7, 2024 (v1)
Keywords: Artificial Intelligence, artificial lift, low-permeability reservoir, micro-pressure drive development technology
Kazakhstan has abundant resources of low-permeability oil reservoirs, among which the KKM low-permeability oil reservoir has geological reserves of 3844 × 104 t and a determined recoverable reserve of 1670 × 104 t. However, the water flooding efficiency is only 68%, and the recovery efficiency is as low as 32%. The development of the reservoir faces challenges such as water injection difficulties and low oil production from wells. In order to further improve the oil recovery rate of this reservoir, our team developed micro-pressure-driven development technology based on pressure-driven techniques by integrating theories of fluid mechanics and artificial intelligence. We also combined this with subsequent artificial lift schemes, resulting in a complete set of micro-pressure-driven process technology. The predicted results indicate that after implementing micro-pressure-driven techniques, a single well group in the KKM oilfield can achieve a daily oil production increase of 32.08 t, dem... [more]
Exploring the REEs Energy Footprint: Interlocking AI/ML with an Empirical Approach for Analysis of Energy Consumption in REEs Production
Subbu Venkata Satyasri Harsha Pathapati, Rahulkumar Sunil Singh, Michael L. Free, Prashant K. Sarswat
June 7, 2024 (v1)
Keywords: Artificial Intelligence, energy consumption, Machine Learning, processing, rare earths
Rare earth elements (REEs including Sc, Y) are critical minerals for developing sustainable energy sources. The gradual transition adopted in developed and developing countries to meet energy targets has propelled the need for REEs in addition to critical metals (CMs). The rise in demand which has propelled REEs into the spotlight is driven by the crucial role these REEs play in technologies that aim to reduce our carbon footprint in the atmosphere. Regarding decarbonized technologies in the energy sector, REEs are widely applied for use in NdFeB permanent magnets, which are crucial parts of wind turbines and motors of electric vehicles. The underlying motive behind exploring the energy and carbon footprint caused by REEs production is to provide a more complete context and rationale for REEs usage that is more holistic. Incorporating artificial intelligence (AI)/machine learning (ML) models with empirical approaches aids in flowsheet validation, and thus, it presents a vivid holistic... [more]
Artificial Intelligence and Carbon Emissions in Manufacturing Firms: The Moderating Role of Green Innovation
Yixuan Chen, Shanyue Jin
February 10, 2024 (v1)
Subject: Environment
Keywords: Artificial Intelligence, carbon emissions, green management innovation, green product innovation, green technology innovation
Carbon emissions have gained worldwide attention in the industrial era. As a key carbon-emitting industry, achieving net-zero carbon emissions in the manufacturing sector is vital to mitigating the negative effects of climate change and achieving sustainable development. The rise of intelligent technologies has driven industrial structural transformations that may help achieve carbon reduction. Artificial intelligence (AI) technology is an important part of digitalization, providing new technological tools and directions for the low carbon development of enterprises. This study selects Chinese A-share listed companies in the manufacturing industry from 2012 to 2021 as the research objects and uses a fixed-effects regression model to study the relationship between AI and carbon emissions. This study clarifies the significance of enterprise AI technology applications in realizing carbon emissions reduction and explores the regulatory mechanism from the perspective of the innovation effec... [more]
A Novel Hybrid Optimization Approach for Fault Detection in Photovoltaic Arrays and Inverters Using AI and Statistical Learning Techniques: A Focus on Sustainable Environment
Ahmad Abubakar, Mahmud M. Jibril, Carlos F. M. Almeida, Matheus Gemignani, Mukhtar N. Yahya, Sani I. Abba
November 30, 2023 (v1)
Keywords: Artificial Intelligence, boosted tree algorithms, Elman neural network, Fault Detection, Gaussian processes regression, multi-layer perceptron, sustainable development
Fault detection in PV arrays and inverters is critical for ensuring maximum efficiency and performance. Artificial intelligence (AI) learning can be used to quickly identify issues, resulting in a sustainable environment with reduced downtime and maintenance costs. As the use of solar energy systems continues to grow, the need for reliable and efficient fault detection and diagnosis techniques becomes more critical. This paper presents a novel approach for fault detection in photovoltaic (PV) arrays and inverters, combining AI techniques. It integrates Elman neural network (ENN), boosted tree algorithms (BTA), multi-layer perceptron (MLP), and Gaussian processes regression (GPR) for enhanced accuracy and reliability in fault diagnosis. It leverages its strengths for the accuracy and reliability of fault diagnosis. Feature engineering-based sensitivity analysis was utilized for feature extraction. The fault detection and diagnosis were assessed using several statistical criteria includi... [more]
Enhancing Power Generation Stability in Oscillating-Water-Column Wave Energy Converters through Deep-Learning-Based Time Delay Compensation
Chan Roh
July 7, 2023 (v1)
Keywords: Artificial Intelligence, deep learning algorithm, maximum power point tracking, optimal control, oscillating-water-column wave energy converter, output power performance, rated power control, Renewable and Sustainable Energy, time delay
Oscillating-water-column wave energy converters (OWC-WECs) are gaining attention for their high energy potential and environmental friendliness. However, their irregular input energy characteristics pose challenges to achieving stable power generation, particularly due to high peak power compared to average power. This study focuses on stable rating control to enable continuous power generation in the presence of irregular wave energy. It is difficult to precisely configure the existing rated power controllers due to physical time delays; this impacts system stability and utilization. To address this, we propose a rated power controller that compensates for system time delays using a deep learning algorithm. By predicting the valve control angle in advance and analyzing the input data for angle estimation, we successfully compensate for the physical time delay. The performance of the proposed rated power controller, incorporating the deep learning algorithm, is evaluated by analyzing t... [more]
An Artificial Intelligence Method for Flowback Control of Hydraulic Fracturing Fluid in Oil and Gas Wells
Ruixuan Li, Hangxin Wei, Jingyuan Wang, Bo Li, Xue Zheng, Wei Bai
July 7, 2023 (v1)
Keywords: Artificial Intelligence, deep learning neural network, hydraulic fracture, process control
Hydraulic fracturing is one of the main ways to increase oil and gas production. However, with existing methods, the diameter of the nozzle cannot be easily adjusted. This therefore results in ‘sand production’ in flowback fluid, affecting the application of hydraulic fracturing. This is because it is difficult to identify the one-dimensional series signal of fracturing fluid collected on site. In order to avoid ‘sand production’ in the flowback fluid, the nozzle should be properly controlled. Aiming to address this problem, a novel augmented residual deep learning neural network (AU-RES) is proposed that can identify the characteristics of multiple one-dimensional time series signals and effectively predict the diameter of the nozzle. The AU-RES network includes three parts: signal conversion layer, residual and convolutional layer, fully connected layer (including regression layer). Firstly, a spatial conversion algorithm for multiple one-dimensional time series signals is proposed,... [more]
Machine Learning Algorithms and Fundamentals as Emerging Safety Tools in Preservation of Fruits and Vegetables: A Review
Vinay Kumar Pandey, Shivangi Srivastava, Kshirod Kumar Dash, Rahul Singh, Shaikh Ayaz Mukarram, Béla Kovács, Endre Harsányi
July 7, 2023 (v1)
Keywords: Artificial Intelligence, fruit preservation, Machine Learning, nanotechnology
Machine learning assists with food process optimization techniques by developing a model to predict the optimal solution for given input data. Machine learning includes unsupervised and supervised learning, data pre-processing, feature engineering, model selection, assessment, and optimization methods. Various problems with food processing optimization could be resolved using these techniques. Machine learning is increasingly being used in the food industry to improve production efficiency, reduce waste, and create personalized customer experiences. Machine learning may be used to improve ingredient utilization and save costs, automate operations such as packing and labeling, and even forecast consumer preferences to develop personalized products. Machine learning is also being used to identify food safety hazards before they reach the consumer, such as contaminants or spoiled food. The usage of machine learning in the food sector is predicted to rise in the near future as more busines... [more]
Control Strategy Based on Artificial Intelligence for a Double-Stage Absorption Heat Transformer
Suset Vázquez-Aveledo, Rosenberg J. Romero, Moisés Montiel-González, Jesús Cerezo
July 4, 2023 (v1)
Keywords: absorption heat transformer, aqueous lithium bromide, Artificial Intelligence, artificial neural network, fuzzy logic, heat pump
Thermal energy recovery systems have different candidates to mitigate CO2 emissions as recommended by the UN in its list of SDGs. One of these promising systems is thermal absorption transformers, which generally use lithium-water bromide as the working fluid. A Double Stage Heat Transformer (DSHT) is a thermal machine that allows the recovery of thermal energy at a higher temperature than it is supplied through the effect of steam absorption in a concentrated solution of lithium bromide. There are very precise thermodynamic models which allow us to calculate all the possible operating conditions of the DSHT. To perform the control of these systems, the use of Artificial Intelligence (AI) is proposed with two computational techniques—Fuzzy Logic (FL) and Artificial Neural Network (ANN)—to calculate in real-time the set of variables that maximize the product’s Gross Temperature Lift (GTL) and Coefficient of Performance (COP) in a DSHT. The values for Coefficient of Determination (R2), M... [more]
Evaluating the Efficacy of Intelligent Methods for Maximum Power Point Tracking in Wind Energy Harvesting Systems
Dallatu Abbas Umar, Gamal Alkawsi, Nur Liyana Mohd Jailani, Mohammad Ahmed Alomari, Yahia Baashar, Ammar Ahmed Alkahtani, Luiz Fernando Capretz, Sieh Kiong Tiong
June 13, 2023 (v1)
Keywords: Artificial Intelligence, MPPT, wind energy harvesting system
As wind energy is widely available, an increasing number of individuals, especially in off-grid rural areas, are adopting it as a dependable and sustainable energy source. The energy of the wind is harvested through a device known as a wind energy harvesting system (WEHS). These systems convert the kinetic energy of wind into electrical energy using wind turbines (WT) and electrical generators. However, the output power of a wind turbine is affected by various factors, such as wind speed, wind direction, and generator design. In order to optimize the performance of a WEHS, it is important to track the maximum power point (MPP) of the system. Various methods of tracking the MPP of the WEHS have been proposed by several research articles, which include traditional techniques such as direct power control (DPC) and indirect power control (IPC). These traditional methods in the standalone form are characterized by some drawbacks which render the method ineffective. The hybrid techniques com... [more]
Development of Surface Mining 4.0 in Terms of Technological Shock in Energy Transition: A Review
Sergey Zhironkin, Ekaterina Taran
May 23, 2023 (v1)
Subject: Energy Policy
Keywords: Artificial Intelligence, Industry 4.0, Internet of Things, Surface Mining 4.0, technological shock, unmanned equipment
The expansion of end-to-end Industry 4.0 technologies in various industries has caused a technological shock in the mineral resource sector, wherein itsdigital maturity is lower than in the manufacturing sector. As a result of the shock, the productivity and profitability of raw materials extraction has begun to lag behind the industries of its deep processing, which, in the conditions of volatile raw materials markets, can provoke sectoral crises. The diffusion of Industry 4.0 technologies in the mining sector (Mining 4.0) can prevent a technological shock if they are implemented in all segments, including quarrying (Surface Mining 4.0). The Surface Mining 4.0 technological platform would connect the advanced achievements of the Fourth Industrial Revolution (end-to-end digital artificial intelligence technologies, cyber-physical systems and unmanned production with traditional geotechnology) without canceling them, but instead bringing them to a new level of productivity, resource con... [more]
Application of Wearable Gloves for Assisted Learning of Sign Language Using Artificial Neural Networks
Hyeon-Jun Kim, Soo-Whang Baek
April 28, 2023 (v1)
Keywords: Artificial Intelligence, internet of things, LSTM, neural network, RNN, wearable
This study proposes the design and application of wearable gloves that can recognize sign language expressions from input images via long short-term memory (LSTM) network models and can learn sign language through finger movement generation and vibration motor feedback. It is difficult for nondisabled people who do not know sign language to express sign language accurately. Therefore, we suggest the use of wearable gloves for sign language education to help nondisabled people learn and accurately express sign language. The wearable glove consists of a direct current motor, a link (finger exoskeleton) that can generate finger movements, and a flexible sensor that recognizes the degree of finger bending. When the coordinates of the hand move in the input image, the sign language motion is fed back through the vibration motor attached to the wrist. The proposed wearable glove can learn 20 Korean sign language words, and the data used for learning are configured to represent the joint coor... [more]
Artificial Intelligence for Energy Processes and Systems: Applications and Perspectives
Dorian Skrobek, Jaroslaw Krzywanski, Marcin Sosnowski, Ghulam Moeen Uddin, Waqar Muhammad Ashraf, Karolina Grabowska, Anna Zylka, Anna Kulakowska, Wojciech Nowak
April 28, 2023 (v1)
Keywords: Artificial Intelligence, deep learning, energy processes and systems, Machine Learning, neural networks
In recent years, artificial intelligence has become increasingly popular and is more often used by scientists and entrepreneurs. The rapid development of electronics and computer science is conducive to developing this field of science. Man needs intelligent machines to create and discover new relationships in the world, so AI is beginning to reach various areas of science, such as medicine, economics, management, and the power industry. Artificial intelligence is one of the most exciting directions in the development of computer science, which absorbs a considerable amount of human enthusiasm and the latest achievements in computer technology. This article was dedicated to the practical use of artificial neural networks. The article discusses the development of neural networks in the years 1940−2022, presenting the most important publications from these years and discussing the latest achievements in the use of artificial intelligence. One of the chapters focuses on the use of artific... [more]
Understanding & Screening of DCW through Application of Data Analysis of Experiments and ML/AI
Tony Thomas, Pushpa Sharma, Dharmendra Kumar Gupta
April 28, 2023 (v1)
Subject: Environment
Keywords: Artificial Intelligence, experimentation, Machine Learning, oil recovery mechanism, sustainable development, waterflood
An oil recovery technique, different composition waterflooding (DCW), dependent on the varying injected water composition has been the subject of various research work in the past decades. Research work has been carried out at the lab, well and field scale whereby the introduction of different injection water composition vis-a-vis the connate water is seen to bring about improvements in the oil recovery (improvements in both macroscopic and microscopic recoveries) based on the chemical reactions, while being sustainable from ease of implementation and reduced carbon footprint points of view. Although extensive research has been conducted, the main chemical mechanisms behind the oil recovery are not yet concluded upon. This research work performs a data analysis of the various experiments, identifies gaps in existing experimentation and proposes a comprehensive experimentation measurement reporting at the system, rock, brine and oil levels that leads to enhanced understanding of the und... [more]
Artificial Intelligence Methods in Hydraulic System Design
Grzegorz Filo
April 28, 2023 (v1)
Keywords: Artificial Intelligence, artificial neural networks, evolutionary algorithms, fuzzy logic, hydraulic system design
Reducing energy consumption and increasing operational efficiency are currently among the leading research topics in the design of hydraulic systems. In recent years, hydraulic system modeling and design techniques have rapidly expanded, especially using artificial intelligence methods. Due to the variety of algorithms, methods, and tools of artificial intelligence, it is possible to consider the prospects and directions of their further development. The analysis of the most recent publications allowed three leading technologies to be indicated, including artificial neural networks, evolutionary algorithms, and fuzzy logic. This article summarizes their current applications in the research, main advantages, and limitations, as well as expected directions for further development.
Machine Learning Applications in Biofuels’ Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions
Iftikhar Ahmad, Adil Sana, Manabu Kano, Izzat Iqbal Cheema, Brenno C. Menezes, Junaid Shahzad, Zahid Ullah, Muzammil Khan, Asad Habib
April 24, 2023 (v1)
Machine Learning (ML) is one of the major driving forces behind the fourth industrial revolution. This study reviews the ML applications in the life cycle stages of biofuels, i.e., soil, feedstock, production, consumption, and emissions. ML applications in the soil stage were mostly used for satellite images of land to estimate the yield of biofuels or a suitability analysis of agricultural land. The existing literature have reported on the assessment of rheological properties of the feedstocks and their effect on the quality of biofuels. The ML applications in the production stage include estimation and optimization of quality, quantity, and process conditions. The fuel consumption and emissions stage include analysis of engine performance and estimation of emissions temperature and composition. This study identifies the following trends: the most dominant ML method, the stage of life cycle getting the most usage of ML, the type of data used for the development of the ML-based models,... [more]
The Review of Carbon Capture-Storage Technologies and Developing Fuel Cells for Enhancing Utilization
Nehil Shreyash, Muskan Sonker, Sushant Bajpai, Saurabh Kr Tiwary, Mohd Ashhar Khan, Subham Raj, Tushar Sharma, Susham Biswas
April 24, 2023 (v1)
Keywords: Artificial Intelligence, Carbon Capture, CO2 combustion, electrochemical conversion, fuel cell, storage and utilization
The amount of CO2 released in the atmosphere has been at a continuous surge in the last decade, and in order to protect the environment from global warming, it is necessary to employ techniques like carbon capture. Developing technologies like Carbon Capture Utilization and Storage aims at mitigating the CO2 content from the air we breathe and has garnered immense research attention. In this review, the authors have aimed to discuss the various technologies that are being used to capture the CO2 from the atmosphere, store it and further utilize it. For utilization, researchers have developed alternatives to make profits from CO2 by converting it into an asset. The development of newer fuel cells that consume CO2 in exchange for electrical power to drive the industries and produce valuable hydrocarbons in the form of fuel has paved the path for more research in the field of carbon utilization. The primary focus on the article is to inspect the environmental and economic feasibility of n... [more]
AI and Data Democratisation for Intelligent Energy Management
Vangelis Marinakis, Themistoklis Koutsellis, Alexandros Nikas, Haris Doukas
April 24, 2023 (v1)
Keywords: Artificial Intelligence, data democratisation, data sharing, decarbonisation, decision support, energy data spaces, energy management, interoperability
Despite the large number of technology-intensive organisations, their corporate know-how and underlying workforce skill are not mature enough for a successful rollout of Artificial Intelligence (AI) services in the near-term. However, things have started to change, owing to the increased adoption of data democratisation processes, and the capability offered by emerging technologies for data sharing while respecting privacy, protection, and security, as well as appropriate learning-based modelling capabilities for non-expert end-users. This is particularly evident in the energy sector. In this context, the aim of this paper is to analyse AI and data democratisation, in order to explore the strengths and challenges in terms of data access problems and data sharing, algorithmic bias, AI transparency, privacy and other regulatory constraints for AI-based decisions, as well as novel applications in different domains, giving particular emphasis on the energy sector. A data democratisation fr... [more]
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