Identification and control of hydrothermal carbonisation process with energy consumption assessment

Abstract

The sustainable and cost-effective production of hydrochar requires energy efficient processing. In this study, data collected from laboratory-scale hydrothermal carbonisation experiments were used to identify dynamic models to predict reactor temperature based on estimated heating power. The performance was then analysed through process simulations with model predictive control. The optimal control model exhibited root mean squared error (RMSE) in the range of 3.19–11.47 °C in model validation with data sets of 31 experiments, whereas for process models it was 1.49–4.79 °C using their training data. In simulations with the optimal control model at the target temperatures of 180–200 °C for 4–6 h, the optimal mean values of mean absolute percentage error (MAPE), overshoot, and specific energy consumption were 0.56%, 2.50 °C, and 40.86 kWh/kg, respectively. Simulations indicated also that strict temperature control settings (average standard deviation s = 1.24 °C at target temperature) increased specific energy consumption slightly (x¯ = 4.4%) when compared with control settings that allowed higher variance around a setpoint (s = 5.11 °C). However, the energy consumption was generally more dependent on the target temperature and processing time, which was also validated based on the measurements of 23 additional experiments. The presented dynamic modelling approach enables accurate real-time process control and predictive energy consumption optimisations for hydrothermal carbonisation.

Keywords

• Energy consumption
• Hydrothermal carbonisation
• Model predictive control
• Process simulation
• System identification