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Jianzhao Zhou1, Jingzheng Ren1, Chang He2
1Research Institute for Advanced Manufacturing, Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China.
本研究引入了以知识为导向的机器学习框架,以增强等离子体气化模型. 该方法提高了合成气质量的预测准确性和可解释性,克服了传统方法的局限性.
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