Supercritical water gasification of organic solid waste: H2 yield and cold gas efficiency optimization considering modeling uncertainties

Abstract

Supercritical water gasification (SCWG) is one of the typical hydrothermal treatment technologies for organic solid waste. However, the current SCWG optimization methods perform deterministic optimization without considering the uncertainty of the model for calculating the objective function, which leads to low reliability of the optimization results. Therefore, an optimization framework that considers the prediction uncertainties of SCWG data-driven models is proposed to optimize the H2 yield and cold gas efficiency of organic solid waste SCWG. An ensemble prediction model integrating random forest, gradient boosting regression, and K-nearest neighbor algorithms by the stacking learning method are built to predict SCWG gas yields. The cold gas efficiency prediction model is constructed based on the gas yield prediction models. The SCWG optimization models are constructed by combining the H2 yield and cold gas efficiency prediction models. The uncertainties in the H2 yield and cold gas efficiency prediction models are analyzed and integrated into the optimization models. The case studies were conducted to test the proposed framework. The optimization results were verified by the results of similar experimental conditions. It demonstrates that the proposed framework can obtain the robust results of the organic solid waste SCWG optimization, which can provide a reference for SCWG optimization.