Source Tracing of Raw Material Components in Wood Vinegar Distillation Process Based on Machine Learning and Aspen Simulation

As a kind of high-oxygen organic liquid produced during biomass pyrolysis, wood vinegar possesses significant industrial value due to its rich composition of acetic acid, phenols, and other bioactive compounds. In this study, we explore the application of advanced machine learning models in optimizing the dual-column distillation process for wood vinegar production, such as Random Forest algorithms. Through the integration of Aspen Plus simulation and deep learning, an adaptive control strategy is proposed to enhance the separation efficiency of key components under varying feed conditions. The experimental results demonstrate that the Random Forest model exhibits superior predictive accuracy to traditional decision tree methods, and an R2 of 0.9728 can be achieved for phenol concentration prediction. This AI-driven system can provide real-time process optimization, enhancing energy efficiency, stabilizing component yields, and contributing to the advancement of sustainable practices within the biomass chemical industry. These findings are anticipated to offer valuable insights into the integration of green chemistry principles with intelligent control systems to facilitate the achievement of Industry 4.0 objectives in bio-based production.