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A Machine Learning-Based Method for Modelling a Proprietary SO 2 Removal System in the Oil and Gas Sector

Author

Listed:
  • Francesco Grimaccia

    (Department of Energy, Politecnico di Milano, 20156 Milan, Italy)

  • Marco Montini

    (Eni S.p.A., Strada Statale 9, Via Emilia 1, San Donato Milanese, 20097 Milan, Italy)

  • Alessandro Niccolai

    (Department of Energy, Politecnico di Milano, 20156 Milan, Italy)

  • Silvia Taddei

    (Eni S.p.A., Strada Statale 9, Via Emilia 1, San Donato Milanese, 20097 Milan, Italy)

  • Silvia Trimarchi

    (Department of Energy, Politecnico di Milano, 20156 Milan, Italy)

Abstract

The aim of this study is to develop a model for a proprietary SO 2 removal technology by using machine learning techniques and, more specifically, by exploiting the potentialities of artificial neural networks (ANNs). This technology is employed at the Eni oil and gas treatment plant in southern Italy. The amine circulating in this unit, that allows for a reduction in the SO 2 concentration in the flue gases and to be compliant with the required specifications, is a proprietary solvent; thus, its composition is not publicly available. This has led to the idea of developing a machine learning (ML) algorithm for the unit description, with the objective of becoming independent from the licensor and more flexible in unit modelling. The model was developed in MatLab ® by implementing ANNs and the aim was to predict three targets, namely the flow rate of SO 2 that goes to the Claus unit, the emissions of SO 2 , and the flow rate of steam sent to the regenerator reboiler. These represent, respectively, the two physical outputs of the unit and a proxy variable of the amine quality. Three different models were developed, one for each target, that employed the Levenberg–Marquardt optimization algorithm. In addition, the ANN topology was optimized case by case. From the analysis of the results, it emerged that with a purely data-driven technique, the targets can be predicted with good accuracy. Therefore, this model can be employed to better manage the SO 2 removal system, since it allows for the definition of an optimal control strategy and the maximization of the plant’s productivity by not exceeding the process constraints.

Suggested Citation

  • Francesco Grimaccia & Marco Montini & Alessandro Niccolai & Silvia Taddei & Silvia Trimarchi, 2022. "A Machine Learning-Based Method for Modelling a Proprietary SO 2 Removal System in the Oil and Gas Sector," Energies, MDPI, vol. 15(23), pages 1-15, December.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:23:p:9138-:d:991391
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    References listed on IDEAS

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    1. Yang Liu & Changchun Zou & Qiang Chen & Jinhuan Zhao & Caowei Wu, 2022. "Optimization of Critical Parameters of Deep Learning for Electrical Resistivity Tomography to Identifying Hydrate," Energies, MDPI, vol. 15(13), pages 1-17, June.
    2. Alessandro Niccolai & Alberto Dolara & Emanuele Ogliari, 2021. "Hybrid PV Power Forecasting Methods: A Comparison of Different Approaches," Energies, MDPI, vol. 14(2), pages 1-18, January.
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