Artificial Intelligence and Data-Driven Modeling in Ironmaking — Potential and Limitations

Understanding relationships among multimodal data extracted from a smartphone-based electrochemiluminescence (ECL) sensor is crucial for the development of low-cost point-of-care diagnostic devices. In this work, artificial intelligence (AI) algorithms such as random forest (RF) and feedforward neural network (FNN) are used to quantitatively investigate the relationships between the concentration of Ru ( bpy ) 3 2 + luminophore and its experimentally measured ECL and electrochemical data. A smartphone-based ECL sensor with Ru ( bpy ) 3 2 + /TPrA was developed using disposable screen-printed carbon electrodes. ECL images and amperograms were simultaneously obtained following 1.2-V voltage application. These multimodal data were analyzed by RF and FNN algorithms, which allowed the prediction of Ru ( bpy ) 3 2 + concentration using multiple key features. High correlation (0.99 and 0.96 for RF and FNN, respectively) between actual and predicted values was achieved in the detection range between 0.02 µM and 2.5 µM. The AI approaches using RF and FNN were capable of directly inferring the concentration of Ru ( bpy ) 3 2 + using easily observable key features. The results demonstrate that data-driven AI algorithms are effective in analyzing the multimodal ECL sensor data. Therefore, these AI algorithms can be an essential part of the modeling arsenal with successful application in ECL sensor data modeling.

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Applications of AI and Data-Driven Modeling in Energy Production and Marketing Processes

10.2118/207153-ms ◽

2021 ◽

Author(s):

Joy Ugoyah ◽

Anita Mary Igbine

Keyword(s):

Artificial Intelligence ◽

Oil And Gas ◽

Health And Safety ◽

Oil And Gas Industry ◽

Data Driven ◽

Gas Industry ◽

Energy Needs ◽

Safety Risks ◽

Data Driven Modeling ◽

Applications Of Ai

Abstract Faster and more accurate decisions are what the Oil and Gas industry needs with the world's fast-evolving energy needs and economy. The area of Artificial intelligence and Data-driven modelling is relatively new and has not found popular application in the industry. AI is an emerging technology that can be used to predict event outcomes and automate anomaly-detection processes. The various applications of AI in different industries were researched into. This paper highlighted important processes that can be improved with the application of Artificial Intelligence through data-driven modelling. It also highlights areas in the various industries where AI intelligence is already being applied and ways it can be improved. AI and data-driven modelling has the potential to improve exploration accuracy, reduce production down-time, reduce cost of maintenance, and reduce health and safety risks. This body of information can serve as a guideline for adopting AI in the oil and gas industry. A trend of industry-tailored intelligence solutions would be more effective in the evolving energy industry.

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“Zhores” — Petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in Skolkovo Institute of Science and Technology

Open Engineering ◽

10.1515/eng-2019-0059 ◽

2019 ◽

Vol 9 (1) ◽

pp. 512-520 ◽

Cited By ~ 25

Author(s):

Igor Zacharov ◽

Rinat Arslanov ◽

Maksim Gunin ◽

Daniil Stefonishin ◽

Andrey Bykov ◽

...

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Predictive Analytics ◽

Science And Technology ◽

Data Driven ◽

Scientific Instrument ◽

Science And Engineering ◽

Data Intensive ◽

Cluster Properties ◽

Data Driven Modeling

AbstractThe Petaflops supercomputer “Zhores” recently launched in the “Center for Computational and Data-Intensive Science and Engineering” (CDISE) of Skolkovo Institute of Science and Technology (Skoltech) opens up new exciting opportunities for scientific discoveries in the institute especially in the areas of data-driven modeling, machine learning and artificial intelligence. This supercomputer utilizes the latest generation of Intel and NVidia processors to provide resources for the most compute intensive tasks of the Skoltech scientists working in digital pharma, predictive analytics, photonics, material science, image processing, plasma physics and many more. Currently it places 7th in the Russian and CIS TOP-50 (2019) supercomputer list. In this article we summarize the cluster properties and discuss the measured performance and usage modes of this new scientific instrument in Skoltech.

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Faculty Opinions recommendation of Data-driven modeling of sleep EEG and EOG reveals characteristics indicative of pre-Parkinson's and Parkinson's disease.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.718519461.793499389 ◽

2014 ◽

Author(s):

Mario Manto ◽

Florian Bodranghien

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Sleep Eeg ◽

Data Driven ◽

Data Driven Modeling

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Dynamic-Data Driven Modeling of Uncertainties and 3D Effects of Porous Shape Memory Alloys

10.21236/ada597368 ◽

2014 ◽

Author(s):

Craig C. Douglas

Keyword(s):

Shape Memory Alloys ◽

Shape Memory ◽

Data Driven ◽

Dynamic Data ◽

3D Effects ◽

Data Driven Modeling

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The Oxford Handbook of Ethics of AI

10.1093/oxfordhb/9780190067397.001.0001 ◽

2020 ◽

Cited By ~ 1

Keyword(s):

Artificial Intelligence ◽

Ethical Issues ◽

Rapid Development ◽

Data Driven ◽

International Human Rights Law ◽

Human Knowledge ◽

Human Rights Law ◽

Ai Ethics ◽

And Behavior ◽

Control Complex

This book explores the intertwining domains of artificial intelligence (AI) and ethics—two highly divergent fields which at first seem to have nothing to do with one another. AI is a collection of computational methods for studying human knowledge, learning, and behavior, including by building agents able to know, learn, and behave. Ethics is a body of human knowledge—far from completely understood—that helps agents (humans today, but perhaps eventually robots and other AIs) decide how they and others should behave. Despite these differences, however, the rapid development in AI technology today has led to a growing number of ethical issues in a multitude of fields, ranging from disciplines as far-reaching as international human rights law to issues as intimate as personal identity and sexuality. In fact, the number and variety of topics in this volume illustrate the width, diversity of content, and at times exasperating vagueness of the boundaries of “AI Ethics” as a domain of inquiry. Within this discourse, the book points to the capacity of sociotechnical systems that utilize data-driven algorithms to classify, to make decisions, and to control complex systems. Given the wide-reaching and often intimate impact these AI systems have on daily human lives, this volume attempts to address the increasingly complicated relations between humanity and artificial intelligence. It considers not only how humanity must conduct themselves toward AI but also how AI must behave toward humanity.

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