Development and performance assessment of a novel multigeneration clean energy system based on biomass-LNG using a dynamic model and supervised learning algorithms

Abstract

Combining process integration mechanisms with artificial intelligence (AI) can be used as a tool for maximum use of biomass capacity and energy production. This study proposes a continuous multi-generation hybrid energy production system based on biomass-LNG using the carbon capture, utilization, and storage (CCUS) method. In this system, using the Gibbs free energy minimization approach, a process based on the fluidized bed gasifier was designed, which in addition to producing hydrogen and electricity from biomass, also produces liquid CO2 and precipitated calcium carbonate (PCC) from liquid natural gas and limestone for the first time. This process was also examined from thermal (HHV) and thermodynamic (Exergy) standpoints. The simulation of the gasification unit was able to predict the percentage of exhaust gases from the gasifier with errors of RMSE = 3.8 and RMSE = 3.2 for almond shell and rice shell biomass, respectively. Following that, a dataset with more than 14 million records was generated. Then, machine learning methods were employed to investigate and improve the process's simulation characteristics. The evaluation of 15 machine learning algorithms in the construction of the predictive model showed that ANN, Extra Tree Regressor, and Random Forest are the most accurate algorithms in predicting the desired values (such as hydrogen, work, PCC, liquid CO2, exergy, and HHV) with R2 > 0.99. The dataset analysis showed that the current process could produce 130 kg/h of hydrogen, 375 kWh of work, 2600 kg/h of PCC, and 531 kg/h of liquid carbon dioxide on average. As a final step, a WPF desktop application was developed that simplifies the use of ML models so that the impact of changing the variables can easily be calculated.