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111 to 120 of 2,224 Results
L3_labeling_primary_biomass_taxonomy.zip
Feb 26, 2026 - Label system, dictionaries, and audit evidence for harmonised over 133,000 feedstock items across major conversion technologies
ZIP Archive - 14.9 MB - MD5: 07c32432a1cf064f5c8feb140213e7a5
L4_final_audit_cross_technology_dictionary.zip
Feb 26, 2026 - Label system, dictionaries, and audit evidence for harmonised over 133,000 feedstock items across major conversion technologies
ZIP Archive - 41.9 MB - MD5: 1671e52b586ffed27fcafabee49aecb4
References.zip
Feb 26, 2026 - Label system, dictionaries, and audit evidence for harmonised over 133,000 feedstock items across major conversion technologies
ZIP Archive - 135.8 MB - MD5: 73fdd2a1c34ffe059be03c81389d15e6
Replication data for: Assessment of Data-Driven Techniques for Flow Rate Predictions in Sub-sea Oil Production
Feb 24, 2026
Neville Aloysius D’Souza; Pfeiffer, Carlos; Mirlekar, Gaurav, 2026, "Replication data for: Assessment of Data-Driven Techniques for Flow Rate Predictions in Sub-sea Oil Production", https://doi.org/10.18710/KIJEWJ, DataverseNO, V1
The data set consists of simulated time‑series measurements from two gas‑lifted subsea oil wells, used to develop and evaluate data‑driven virtual flow metering (VFM) models for oil and gas flow rate prediction. Purpose: To assess a range of machine learning algorithms (10 methods, including LSTM, MLP, XGBoost, SVR, tree‑based and linear methods) f...
00_ReadMe.txt
Feb 24, 2026 - Replication data for: Assessment of Data-Driven Techniques for Flow Rate Predictions in Sub-sea Oil Production
Plain Text - 4.1 KB - MD5: d876283a934a820f7703dd26e950e133
calculate_output.m
Feb 24, 2026 - Replication data for: Assessment of Data-Driven Techniques for Flow Rate Predictions in Sub-sea Oil Production
MATLAB Source Code - 459 B - MD5: bfc538b18e25ef6c92be4f828244e4a7
make_w_gc.m
Feb 24, 2026 - Replication data for: Assessment of Data-Driven Techniques for Flow Rate Predictions in Sub-sea Oil Production
MATLAB Source Code - 1.1 KB - MD5: 61bda33eb19fff36c06dc94044f2e65a
ML_dev_part1.ipynb
Feb 24, 2026 - Replication data for: Assessment of Data-Driven Techniques for Flow Rate Predictions in Sub-sea Oil Production
Jupyter Notebook - 255.9 KB - MD5: 15e085e50a122a36c8cc28635f956db7
ML_dev_part2.ipynb
Feb 24, 2026 - Replication data for: Assessment of Data-Driven Techniques for Flow Rate Predictions in Sub-sea Oil Production
Jupyter Notebook - 240.4 KB - MD5: aec99d2af4b73cb120944552410a6f16
oil_field_model.m
Feb 24, 2026 - Replication data for: Assessment of Data-Driven Techniques for Flow Rate Predictions in Sub-sea Oil Production
MATLAB Source Code - 4.2 KB - MD5: 3afdf981934adb3916a638c92b3e0ef4
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