Application of GA-Optimized ANNs to Predict the Water Content, CO2 and H2S Absorption Capacity of Diethanolamine (DEA) in Khangiran Gas Sweetening Plant

Tavakoli, Rauf; Bakhshi, Puyan; Mirarab, Meysam; Shahbazi, Khalil

doi:10.1134/s0040579520050449

Cited by 5 publications

(2 citation statements)

References 19 publications

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“…These are the water content, H 2 S, and CO 2 mole fraction, which represent the absorption capacity of the amine circulating in the unit. This objective was achieved by implementing feed-forward ANNs, whose architecture was optimized by exploiting genetic algorithms [8].…”

Section: Introductionmentioning

confidence: 99%

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A Machine Learning-Based Method for Modelling a Proprietary SO2 Removal System in the Oil and Gas Sector

et al. 2022

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The aim of this study is to develop a model for a proprietary SO2 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 SO2 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 SO2 that goes to the Claus unit, the emissions of SO2, 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 SO2 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.

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Section: Introductionmentioning

confidence: 99%

“…In this case, the selected method showed high accuracy in the prediction of the desired targets [9]. The final objective of both these studies was to determine numerically the output values of the equipment, given the expensive and time-consuming experimental measurements otherwise required [8,9].…”

Section: Introductionmentioning

confidence: 99%