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1 to 10 of 134,632 Results

Replication data for: Real-Time Optimization and Estimation for CO2 Purification Process Feb 24, 2026 - University of South-Eastern Norway Hvidsten, Jan Inge; Mirlekar, Gaurav, 2026, "Replication data for: Real-Time Optimization and Estimation for CO2 Purification Process", https://doi.org/10.18710/UFXAVM, DataverseNO, V1 The dataset represents dynamic simulation and estimation data for a CO2 scrubbing process in a bio‑CO2 purification plant, used to design and test a real‑time optimization and Kalman filter–based control framework for solvent flow. Purpose: To estimate unmeasured mass flows (CO2 inlet gas flow and rich solvent flow) in a CO2 scrubber using first‑ a...
00_ReadMe.txt Feb 24, 2026 - Replication data for: Real-Time Optimization and Estimation for CO2 Purification Process Plain Text - 3.4 KB - MD5: dbfc3c3b6015107309c71c89a07b6dcc
DataFile.pdf Feb 24, 2026 - Replication data for: Real-Time Optimization and Estimation for CO2 Purification Process Adobe PDF - 626.5 KB - MD5: 1f3dc4cda3f8cf07b511f22967cf4f43
Python scripts for Soft Ordering 1-D CNN to Estimate the Capacity Factor of Windfarms for Identifying the Age-Related Performance Degradation. Feb 24, 2026 - University of Agder Mathew, Manuel Sathyajith; Kandukuri, Surya Teja; Omlin, Christian, 2026, "Python scripts for Soft Ordering 1-D CNN to Estimate the Capacity Factor of Windfarms for Identifying the Age-Related Performance Degradation.", https://doi.org/10.18710/LNXUIR, DataverseNO, V1 The supplementary materials provide the complete codebase for the Soft Ordering 1-D Convolutional Neural Network (1-D CNN) model for estimating the capacity factor of wind farms, as presented in "Soft Ordering 1-D CNN to Estimate the Capacity Factor of Windfarms for Identifying the Age-Related Performance Degradation" (PHME 2024). This research was...
00_ReadMe.md Feb 24, 2026 - Python scripts for Soft Ordering 1-D CNN to Estimate the Capacity Factor of Windfarms for Identifying the Age-Related Performance Degradation. Markdown Text - 5.8 KB - MD5: 1100ca3456432e3de7660367408bf319 Description of the dataset.
00_ReadMe.txt Feb 24, 2026 - Python scripts for Soft Ordering 1-D CNN to Estimate the Capacity Factor of Windfarms for Identifying the Age-Related Performance Degradation. Plain Text - 6.5 KB - MD5: e5cab828e3e375849aec71ffd9f6c8a6 Description of the dataset.
main.py Feb 24, 2026 - Python scripts for Soft Ordering 1-D CNN to Estimate the Capacity Factor of Windfarms for Identifying the Age-Related Performance Degradation. Python Source Code - 14.2 KB - MD5: d69365b0d4241ba0018aae90f31927d5 Main script file
model.py Feb 24, 2026 - Python scripts for Soft Ordering 1-D CNN to Estimate the Capacity Factor of Windfarms for Identifying the Age-Related Performance Degradation. Python Source Code - 4.0 KB - MD5: a3d78306f675376e4895213660a14255 Model configuration
preprocess_data.py Feb 24, 2026 - Python scripts for Soft Ordering 1-D CNN to Estimate the Capacity Factor of Windfarms for Identifying the Age-Related Performance Degradation. Python Source Code - 5.2 KB - MD5: e006c550ef9aee497f120f172918073f Data preprocessing
PyTools.py Feb 24, 2026 - Python scripts for Soft Ordering 1-D CNN to Estimate the Capacity Factor of Windfarms for Identifying the Age-Related Performance Degradation. Python Source Code - 3.0 KB - MD5: bf4e23a1c30e805ca2d7d0be1c6a98a1 Helper tools

Replication data for: Real-Time Optimization and Estimation for CO2 Purification Process

Feb 24, 2026 - University of South-Eastern Norway

Hvidsten, Jan Inge; Mirlekar, Gaurav, 2026, "Replication data for: Real-Time Optimization and Estimation for CO2 Purification Process", https://doi.org/10.18710/UFXAVM, DataverseNO, V1

The dataset represents dynamic simulation and estimation data for a CO2 scrubbing process in a bio‑CO2 purification plant, used to design and test a real‑time optimization and Kalman filter–based control framework for solvent flow. Purpose: To estimate unmeasured mass flows (CO2 inlet gas flow and rich solvent flow) in a CO2 scrubber using first‑ a...

00_ReadMe.txt

Feb 24, 2026 - Replication data for: Real-Time Optimization and Estimation for CO2 Purification Process

Plain Text - 3.4 KB -

DataFile.pdf

Feb 24, 2026 - Replication data for: Real-Time Optimization and Estimation for CO2 Purification Process

Adobe PDF - 626.5 KB -

Python scripts for Soft Ordering 1-D CNN to Estimate the Capacity Factor of Windfarms for Identifying the Age-Related Performance Degradation.

Feb 24, 2026 - University of Agder

Mathew, Manuel Sathyajith; Kandukuri, Surya Teja; Omlin, Christian, 2026, "Python scripts for Soft Ordering 1-D CNN to Estimate the Capacity Factor of Windfarms for Identifying the Age-Related Performance Degradation.", https://doi.org/10.18710/LNXUIR, DataverseNO, V1

The supplementary materials provide the complete codebase for the Soft Ordering 1-D Convolutional Neural Network (1-D CNN) model for estimating the capacity factor of wind farms, as presented in "Soft Ordering 1-D CNN to Estimate the Capacity Factor of Windfarms for Identifying the Age-Related Performance Degradation" (PHME 2024). This research was...

00_ReadMe.md

Feb 24, 2026 - Python scripts for Soft Ordering 1-D CNN to Estimate the Capacity Factor of Windfarms for Identifying the Age-Related Performance Degradation.

Markdown Text - 5.8 KB -

Description of the dataset.

00_ReadMe.txt

Feb 24, 2026 - Python scripts for Soft Ordering 1-D CNN to Estimate the Capacity Factor of Windfarms for Identifying the Age-Related Performance Degradation.

Plain Text - 6.5 KB -

Description of the dataset.

main.py

Feb 24, 2026 - Python scripts for Soft Ordering 1-D CNN to Estimate the Capacity Factor of Windfarms for Identifying the Age-Related Performance Degradation.

Python Source Code - 14.2 KB -

Main script file