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Topic Review
Data Envelopment Analysis (DEA)
Data Envelopment Analysis (DEA) is a non-parametric methodology for measuring the efficiency of Decision Making Units (DMUs) using multiple inputs to outputs configurations. This is the most commonly used tool for frontier estimations in assessments of productivity and efficiency applied to all fields of economic activities.
  • 10.9K
  • 28 Jan 2022
Topic Review
Data Fusion in Process Analytical Technology
The release of the FDA’s guidance on Process Analytical Technology has motivated and supported the pharmaceutical industry to deliver consistent quality medicine by acquiring a deeper understanding of the product performance and process interplay. The technical opportunities to reach this high-level control have considerably evolved since 2004 due to the development of advanced analytical sensors and chemometric tools.
  • 255
  • 22 Feb 2023
Topic Review
Data Management in Smart Grids
Given the importance of data for smart grids, proper management is required throughout its life cycle, ensuring added value, sustainability, and efficiency for stakeholders, and providing information and knowledge about the energy system’s operation and consumption practices. Therefore, data architecture facilitates the capture, storage, and processing of information to support data analysis models in smart microgrids. 
  • 495
  • 22 Jan 2024
Topic Review
Data Science for Industry 4.0 and Sustainability
The industrial, scientific, and technological fields have been subject to a revolutionary process of digitalization and automation called Industry 4.0. Its implementation has been successful mainly in the economic field of sustainability, while the environmental field has been gaining more attention from researchers. 
  • 303
  • 03 Nov 2023
Topic Review
Data-Driven Algorithms Applied to Wave Climate Assessment
With the increasing capabilities of computational systems, reliable algorithms can be applied to a wide variety of case studies, including those found within the wave energy sector. Some of the most advanced tools are supported by Big Data and employ artificial intelligence (AI) towards estimating local wave energy resource, forecasting operational or extreme wave conditions, or filling data-gaps in wave field measurements.
  • 459
  • 30 Jun 2023
Topic Review
Data-Driven Decision-Making
Decision-making for manufacturing and maintenance operations is benefiting from the advanced sensor infrastructure of Industry 4.0, enabling the use of algorithms that analyze data, predict emerging situations, and recommend mitigating actions. 
  • 652
  • 22 Apr 2021
Topic Review
Data-Driven Fault Diagnosis Methods for Nuclear Power Plants
Fault diagnosis plays an important role in complex and safety-critical systems such as nuclear power plants (NPPs). With the development of artificial intelligence (AI), extensive research has been carried out for fast and efficient fault diagnosis based on data-driven algorithms.
  • 693
  • 23 Feb 2023
Topic Review
Data-Driven Methodologies Used for Wind Turbines O&M Tasks
 Wind power is one of the most sustainable and eco-friendly energy sources. With the rapid development of wind turbines (WTs), there is an increasing need to lower the Cost of Energy (COE) of wind power. Predictive maintenance techniques that leverage past failures to learn from and forecast failure and the remaining usable life of various wind turbines can significantly reduce operation and maintenance (O&M) expenses. Recent advancements in data-driven models for condition monitoring and predictive maintenance of wind turbines’ critical components (e.g., bearing, gearbox, generator, blade pitch) are reviewed. The entry categorizes these models according to data-driven procedures, such as data descriptions, data pre-processing, feature extraction and selection, model selection (classification, regression), validation, and decision making. The findings after reviewing extensive relevant articles suggest that (a) SCADA (supervisory control and data acquisition) data are widely used as they are available at low cost and are extremely practical (due to the 10 min averaging time), but their use is in some sense nonspecific. (b) Unstructured data and pre-processing remain a significant challenge and consume a significant time of whole machine learning model development. (c) The trade-off between the complexity of the vibration analysis and the applicability of the results deserves further development, especially with regards to drivetrain faults. (d) Most of the proposed techniques focus on gearbox and bearings, and there is a need to apply these models to other wind turbine components. 
  • 293
  • 13 Mar 2023
Topic Review
Data-Driven Methods in Power Grids
Applications of data-driven methods in power grids are motivated by the need to predict and mitigate intermittency in a (future) grid that is expected to lean heavily on renewables.
  • 557
  • 22 Jun 2022
Topic Review
Data-Driven Modeling in Drilling in Well Operations
Swab and surge pressure fluctuations are decisive during drilling for oil. The axial movement of the pipe in the wellbore causes pressure fluctuations in wellbore fluid; these pressure fluctuations can be either positive or negative, corresponding to the direction of the movement of the pipe. For example, if the drill string is lowering down in the borehole, the drop is positive (surge pressure), and if the drill string is pulling out of the hole, the drop is negative (swab pressure). The intensity of these pressure fluctuations depends on the speed of the lowering down (tripping in) or withdrawing the pipe out (tripping out). High tripping speed corresponds to higher pressure fluctuations and can lead to fracturing the well formation. Low tripping speed leads to a slow operation, causing non-productive time, thus increasing the overall well budget. 
  • 657
  • 22 Apr 2022
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