International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Volume 7 Issue 3
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Machine Learning-Enhanced Simulation of Thermal Plasmas for Medical Waste Gasification in a Cylindrical Reactor

Author(s) Dr. Mr. SUGENG Sugeng Rianto RIANTO, Ach. Agus Dardiri, Achmad Hidayat
Country Indonesia
Abstract This study presents a hybrid modeling approach that integrates physics-based simulations and machine learning to analyze the behavior of thermal plasmas in a cylindrical reactor for medical waste gasification. Plasma gasification is a promising method for hazardous waste disposal due to its high temperature and environmental safety, but the simulation of such systems remains computationally expensive due to the complexity of the underlying magnetohydrodynamic (MHD) equations. A finite element framework is employed to solve the governing equations of heat transfer and fluid flow, including temperature distribution and velocity fields within the plasma reactor. Several numerical challenges, such as boundary instability, solver divergence, and stiff temperature gradients, are addressed through a combination of mathematical simplifications and adaptive boundary conditions. To enhance simulation efficiency and enable rapid parametric studies, machine learning surrogates—including Gaussian Process Regression and neural networks—are trained on high-fidelity simulation data. These models predict thermal and velocity profiles with high accuracy at a fraction of the computational cost. The integrated framework demonstrates strong potential for use in design optimization, real-time control, and large-scale simulations of plasma-based waste treatment systems.
Keywords Plasma gasification; thermal plasma; CFD; machine learning; surrogate modeling; medical waste; finite element simulation; Gaussian Process Regression; neural networks; magnetohydrodynamics (MHD)
Field Physical Science
Published In Volume 7, Issue 3, May-June 2025
Published On 2025-06-23
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E-ISSN 2582-2160
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