IoT-Based Monitoring in Carbon Capture and Storage Systems

被引：10

作者：

Chawla A. ^{[1
]}

Arellano Y. ^{[2
]}

Johansson M.V. ^{[2
]}

Darvishi H. ^{[1
,3
]}

Shaneen K. ^{[1
]}

Vitali M. ^{[4
]}

Finotti F. ^{[2
]}

Rossi P.S. ^{[1
,2
]}

机构：

[1] Norwegian University of Science and Technology (NTNU), Norway

[2] SINTEF Energy Research, Norway

[3] Electrical Engineering Institute of the Swiss Federal Institute of Technology (EPFL), Switzerland

[4] Marche Polytechnic University, Italy

来源：

IEEE Internet of Things Magazine | 2022年 / 5卷 / 04期

关键词：

Cost effectiveness - Data fusion - Digital storage - Global warming - Internet of things;

D O I：

10.1109/IOTM.001.2200175

中图分类号：

学科分类号：

摘要：

Carbon capture and storage (CCS) is critical for climate-change policies and strategies targeting global warming within the Paris Agreement. The overarching technological requirements are well described in the strategic plans, yet several barriers exist to the technology's wide-spread deployment, including improved cost-effectiveness and enhanced process integration. For the safe and reliable operation of large-scale CCS systems, the development of effective Internet of things (IoT)-based monitoring tools to ensure flow assurance of CO2 throughout the CCS value chain is crucial. Further, reliable sensor measurements related to different transport parameters such as temperature, pressure, and flow across the process are essential to develop these methods. However, sensors are prone to errors due to inherent issues or environmental conditions which result in performance degradation of the overall monitoring system. Developing techniques for detecting anomalies in the measurements, identifying the faulty sensors and accommodating them with appropriately estimated data is one of the paramount requirements for the reliable operation of the CCS systems. In such context, the present article provides an overview of CCS's monitoring and control requirements, emphasizing data-fusion synergies. This work investigates the state-of-the-art methods for sensor validation and proposes a roadmap to further deploy metering technologies for industrial needs. © 2018 IEEE.

引用

页码：106 / 111

页数：5

共 15 条

[1]

Aasen A., Et al., Thermodynamic Models to Accurately Describe the PVTxy-behavior of Water/Carbon Dioxide Mixtures, Fluid Phase Equilibria, 442, pp. 125-139, (2017)

[2]

Kocbach J., Et al., Where Do We Stand on Flow Metering for CO2 Handling and Storage?, 38th Int?l. North Sea Flow Measurement Wksp, (2020)

[3]

Span R., Wagner W., A New Equation of State for Carbon Dioxide Covering the Fluid Region from the Triple-Point Temperature to 1100 K at Pressures Up to 800 MPa, J. Physical and Chemical Reference Data, 25, pp. 1509-1596, (1996)

[4]

Darvishi H., Et al., Sensor-Fault Detection, Isolation and Accommodation for Digital Twins via Modular Data-Driven Architecture, IEEE Sensors J, 21, 4, pp. 4827-4838, (2021)

[5]

Yin S., Et al., A Review on Basic Data-Driven Approaches for Industrial Process Monitoring, IEEE Trans. Industrial Electronics, 61, 11, pp. 6418-6428, (2014)

[6]

Wen L., Et al., A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method, IEEE Trans. Industrial Electronics, 65, 7, pp. 5990-5998, (2018)

[7]

Iqbal R., Et al., Fault Detection and Isolation in Industrial Processes Using Deep Learning Approaches, IEEE Trans. Industrial Informatics, 15, 5, pp. 3077-3084, (2019)

[8]

Tian J., Et al., Motor Bearing Fault Detection Using Spectral Kurtosis-Based Feature Extraction Coupled with K-Nearest Neighbor Distance Analysis, IEEE Trans. Industrial Electronics, 63, 3, pp. 1793-1803, (2016)

[9]

Vafamand A., Moshiri B., Vafamand N., Fusing Unscented Kalman filter to Detect and Isolate Sensor Faults in DC Microgrids with CPLs, IEEE Trans. Instrumentation and Measurement, 71, 2022, pp. 1-8

[10]

Chen Z., Li W., Multisensor Feature Fusion for Bearing Fault Diagnosis Using Sparse Autoencoder and Deep Belief Network, IEEE Trans Instrumentation and Measurement, 66, 7, pp. 1693-1702, (2017)

← 1 2 →