Results 21 to 30 of about 41,161 (71)
Forecasting the production of Distillate Fuel Oil Refinery and Propane Blender net production by using Time Series Algorithms [PDF]
arXiv, 2022 Oil production forecasting is an important step in controlling the cost-effect and monitoring the functioning of petroleum reservoirs. As a result, oil production forecasting makes it easier for reservoir engineers to develop feasible projects, which helps to avoid risky investments and achieve long-term growth.
arxiv
When Computing Power Network Meets Distributed Machine Learning: An Efficient Federated Split Learning Framework [PDF]
arXiv, 2023 In this paper, we advocate CPN-FedSL, a novel and flexible Federated Split Learning (FedSL) framework over Computing Power Network (CPN). We build a dedicated model to capture the basic settings and learning characteristics (e.g., training flow, latency and convergence).
arxiv
Data-Based Design of Multi-Model Inferential Sensors [PDF]
arXiv, 2023 This paper deals with the problem of inferential (soft) sensor design. The nonlinear character of industrial processes is usually the main limitation to designing simple linear inferential sensors with sufficient accuracy. In order to increase the inferential sensor predictive performance and yet to maintain its linear structure, multi-model ...
arxiv
Reinforcement Learning Based Gasoline Blending Optimization: Achieving More Efficient Nonlinear Online Blending of Fuels [PDF]
arXiv, 2023 The online optimization of gasoline blending benefits refinery economies. However, the nonlinear blending mechanism, the oil property fluctuations, and the blending model mismatch bring difficulties to the optimization. To solve the above issues, this paper proposes a novel online optimization method based on deep reinforcement learning algorithm (DRL).
arxiv
IOP Conference Series: Materials Science and Engineering, 2020 In the present study, the effectiveness of a procedure of electrocoagulation for removing chemical oxygen demand (COD) from the wastewater of petroleum refinery has been evaluated.
Sajjad S. Alkurdi, A. Abbar
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Emotion Profile Refinery for Speech Emotion Classification [PDF]
arXiv, 2020 Human emotions are inherently ambiguous and impure. When designing systems to anticipate human emotions based on speech, the lack of emotional purity must be considered. However, most of the current methods for speech emotion classification rest on the consensus, e.g., one single hard label for an utterance.
arxiv
METER-ML: A Multi-Sensor Earth Observation Benchmark for Automated Methane Source Mapping [PDF]
arXiv, 2022 Reducing methane emissions is essential for mitigating global warming. To attribute methane emissions to their sources, a comprehensive dataset of methane source infrastructure is necessary. Recent advancements with deep learning on remotely sensed imagery have the potential to identify the locations and characteristics of methane sources, but there is
arxiv
Monitoring of Air Pollution by Moss Bags around an Oil Refinery: A Critical Evaluation over 16 Years
Atmosphere, 2020 The present study analyzes the results of a biomonitoring campaign, carried out by means of Hypnum cupressiforme Hedw. moss bags around an oil refinery, located in the southwestern part of Sardinia island (Italy).
A. D. Agostini, P. Cortis, A. Cogoni
semanticscholar +1 more source
EPTCS 209, 2016, pp. 17-23, 2016 "Big data" has become a major area of research and associated funding, as well as a focus of utopian thinking. In the still growing research community, one of the favourite optimistic analogies for data processing is that of the oil refinery, extracting the essence out of the raw data.
arxiv +1 more source
University of Thi-Qar journal, 2019 The rainfall Intensity-Duration-Frequency (IDF) relationship is one of the most commonly used tools in water resources engineering, either for planning, designing and operating of water resource projects.
Ahmed A. Dakheel
semanticscholar +1 more source