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Artificial neural network based predictive negative hydrogen ion helicon plasma source for fusion grade large sized ion source

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Abstract

Ion source operation and control is a challenging task, and the manual search of the optimized input parameter by the operator becomes a time consuming and cumbersome process. HELicon Experiment for Negative Ion Source (HELEN-I) with single driver developed at the Institute for Plasma Research (IPR) for the production negative hydrogen ion involves a multitude of interacting systems (such as Radio Frequency (RF) power, gas feed pressure, magnetic fields). This paper presents an Artificial Intelligence (AI) driven multiple-input and multiple-output model for HELEN-I to get optimized values of ion saturation current (mA) and high plasma density of order 1018/m3. The developed Artificial Neural Network (ANN) based model predicts the outputs of the HELEN-I and is further utilizes a heuristic global optimization Particle Swarm Optimization (PSO) algorithm to select optimal source parameters to predict the desired outputs without conducting real-time experiments. The experimental studies further validate the results obtained by the proposed approach.

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source parameter optimization of HELEN-I

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sourceparameters of HELEN-I

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Authors and Affiliations

  1. Pandit Deendayal Petroleum University, Gandhinagar, Gujarat, India

    Vipin Shukla, Vivek Pandya & A. Maulik

  2. Institute for Plasma Research, HBNI, Gujarat, Gandhinagar, 382428, India

    Mainak Bandyopadhyay & A. Pandey

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  1. Vipin Shukla
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  3. Vivek Pandya
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  5. A. Maulik
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Shukla, V., Bandyopadhyay, M., Pandya, V. et al. Artificial neural network based predictive negative hydrogen ion helicon plasma source for fusion grade large sized ion source. Engineering with Computers 38, 347–364 (2022). https://doi.org/10.1007/s00366-020-01060-5

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