Bayesian-optimized Neural Networks and their application to model gas-to-liquid plants

Basha, Nour; Ibrahim, Gasim; Choudhury, Hanif A.; Challiwala, Mohamed S.; Fezai, Radhia; Malluhi, Byanne; Nounou, Hazem; Elbashir, Nimir O.; Nounou, Mohamed

doi:10.1016/j.jgsce.2023.204964

Cited by 2 publications

(2 citation statements)

References 26 publications

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“…The exit stream is fed together with oxygen to the ATR reactor (3) to produce syngas with a H 2 :CO ratio of 2.05 to 1. A cooler (4) is used to adjust the feed to a flash unit (5) where water is separated. CO 2 is then separated (6) using amines [36] and the remaining gas enters a compressor (7) to achieve a pressure of 45 bar.…”

Section: Application To the Gtl Processmentioning

confidence: 99%

“…One option that has gained recent interest to overcome such limitations is the use of supercritical fluids. [5][6][7] Several criteria have been developed for a proper choice of the supercritical fluid, which include lower critical properties than those of the FTS, stability of the supercritical media, and a proper affinity for aliphatic hydrocarbons. [8] Additional criteria for the selection of supercritical fluids have been suggested by Elmalik et al [6] In particular, the use of supercritical media has been shown to have a positive effect on the performance of FTS.…”

Section: Introductionmentioning

confidence: 99%

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Probabilistic assessment of the safety profile of the Fischer–Tropsch process with a supercritical solvent

Herrera‐Ovando,

Jiménez‐Gutiérrez,

Kazantzis

et al. 2023

Can J Chem Eng

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Inherent safety assessment during the design stage of chemical processes is typically conducted based on average values for design parameters. Under those conditions, the single‐point deterministic process performance assessment may be affected by the phenomenon known as the ‘flaw of averages’ in the presence of irreducible sources of uncertainty (performance evaluated at average conditions does not represent average performance). In this work, an inherent process safety assessment developed under a probabilistic formulation is presented. An evaluation of the proposed approach is performed in the case of a gas‐to‐liquid process system using a supercritical solvent for Fischer–Tropsch reactor systems. The pertinent uncertainty analysis is carried out using Monte Carlo simulation techniques to account for the propagation of uncertainty through the inherent process safety model and the derivation of probability distribution profiles for the associated metrics, thus statistically characterizing ranges of potential performance outcomes. The response variables were the autothermal reactor and the syngas flows. The results show that the input variables associated to the autothermal flow potentially generate the most hazardous conditions for the process. The results also show how the metrics are affected when uncertainty is explicitly taken into account at the design stage of the process, offering a more nuanced assessment and characterization of the inherent process safety profile.

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Section: Application To the Gtl Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Probabilistic assessment of the safety profile of the Fischer–Tropsch process with a supercritical solvent

Herrera‐Ovando,

Jiménez‐Gutiérrez,

Kazantzis

et al. 2023

Can J Chem Eng

View full text Add to dashboard Cite

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Synergistic Feature Engineering and Ensemble Learning for Early Chronic Disease Prediction

Al-Jamimi

2024

IEEE Access

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Chronic diseases, a global public health challenge, necessitate the deployment of cutting-edge predictive models for early diagnosis and personalized interventions. This study presents an advanced methodology for early prediction of chronic diseases, including heart attack, diabetes, breast cancer, and kidney disease, leveraging a synergistic combination of cutting-edge techniques. Recognizing the challenge posed by extensive medical datasets with numerous features, we introduce a novel approach that begins with Feature Engineering using Recursive Feature Elimination (RFE) in conjunction with a Support Vector Machine (SVM). The presented methodology identifies and removes irrelevant features to simplify data complexity. The refined dataset is then input into the robust eXtreme Gradient Boosting (XGBoost) classifier, known for its efficiency and adeptness in predicting complex relationships within the data. The chosen ensemble learning algorithm demonstrates significant prowess in inducing intricate patterns crucial for chronic disease prediction. To enhance model performance, an essential phase of optimization is introduced through hyperparameter tuning using Bayesian optimization. This strategically navigates the hyperparameter space, maximizing the efficiency of the search process and fine-tuning the model for optimal predictive accuracy. The proposed approach showcases a substantial improvement in the early prediction of chronic diseases, demonstrating the effectiveness of the proposed approach.

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Bayesian-optimized Neural Networks and their application to model gas-to-liquid plants

Cited by 2 publications

References 26 publications

Probabilistic assessment of the safety profile of the Fischer–Tropsch process with a supercritical solvent

Probabilistic assessment of the safety profile of the Fischer–Tropsch process with a supercritical solvent

Synergistic Feature Engineering and Ensemble Learning for Early Chronic Disease Prediction

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