Prediction of Optimal Production Time during Underground CH<sub>4</sub> Storage with Cushion CO<sub>2</sub> Using Reservoir Simulations and Artificial Neural Networks

Helland, Johan Olav; Friis, Helmer André; Assadi, Mohsen; Klimkowski, Łukasz; Nagy, Stanisław

doi:10.1021/acs.energyfuels.3c03382

Cited by 2 publications

(1 citation statement)

References 33 publications

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“…eq is the basis of the MLP method. In this equation, g denotes the output vector, w denotes the weight vector of the coefficients, x k corresponds to the reference vector, θ denotes the threshold limit g = f false( normalwx normali k + θ false) …”

Section: Deep Learning Methods Selection and Optimizationmentioning

confidence: 99%

Intelligent Soft Measurement of Catalyst Activity during a Polyethylene Reaction Based on Mechanistic Modeling and Deep Learning

Tian,

Ji,

Wang

et al. 2024

Ind. Eng. Chem. Res.

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The polymerization reaction is the core part of the polymerization process, where the catalyst has an important influence on the polymerization products. Catalyst activity, an essential operational indicator, is not directly monitored, resulting in the operator being unable to understand the operating conditions of the reactor fully. In this paper, an intelligent soft-measurement model based on mechanistic modeling and deep learning is proposed for predicting catalyst activity parameters in polyethylene reaction processes. First, a steady-state model of the polyethylene production process is established and utilized to collect data unavailable in the industry. A K-mean clustering method is used to cluster typical working conditions, and the collected data is used as inputs for mechanism modeling. Second, the primitive reactions in the polyethylene production process are studied to understand the mechanism of the polymerization process and to find the parameters that have the greatest impact on the catalyst activity. Then, the artificial neural network (ANN) deep learning method is selected, and its structure is optimized. The parameters and data provided by the mechanism model are integrated to constitute a complete data set. The catalyst activity characterized by the finger forward factor is predicted using the artificial neural network deep learning model. Finally, the self-built soft measurement model is validated with a maximum relative error of 0.44%. The validation results show that this method can reasonably predict the catalyst activity and guide the production process.

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Section: Deep Learning Methods Selection and Optimizationmentioning

confidence: 99%

Intelligent Soft Measurement of Catalyst Activity during a Polyethylene Reaction Based on Mechanistic Modeling and Deep Learning

Tian,

Ji,

Wang

et al. 2024

Ind. Eng. Chem. Res.

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show abstract

Investigation of wettability and IFT alteration during hydrogen storage using machine learning

Maleki,

Dehghani,

Akbari

et al. 2024

Heliyon

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Prediction of Optimal Production Time during Underground CH₄ Storage with Cushion CO₂ Using Reservoir Simulations and Artificial Neural Networks

Cited by 2 publications

References 33 publications

Intelligent Soft Measurement of Catalyst Activity during a Polyethylene Reaction Based on Mechanistic Modeling and Deep Learning

Intelligent Soft Measurement of Catalyst Activity during a Polyethylene Reaction Based on Mechanistic Modeling and Deep Learning

Investigation of wettability and IFT alteration during hydrogen storage using machine learning

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Prediction of Optimal Production Time during Underground CH4 Storage with Cushion CO2 Using Reservoir Simulations and Artificial Neural Networks

Cited by 2 publications

References 33 publications

Intelligent Soft Measurement of Catalyst Activity during a Polyethylene Reaction Based on Mechanistic Modeling and Deep Learning

Intelligent Soft Measurement of Catalyst Activity during a Polyethylene Reaction Based on Mechanistic Modeling and Deep Learning

Investigation of wettability and IFT alteration during hydrogen storage using machine learning

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Product

Resources

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Prediction of Optimal Production Time during Underground CH₄ Storage with Cushion CO₂ Using Reservoir Simulations and Artificial Neural Networks