Research on Design Method for Optimal Flow Rate of U-shaped Geothermal Well Based on Exergy Analysis

Closed-loop U-shaped geothermal wells show great potential owing to their special well-depth structure, which can provide a good flow rate and heat extraction. However, no advanced process parameter optimization method is available for U-shaped geothermal wells. To effectively describe the heat transfer processes of U-shaped geothermal wells, an analytical solution that couples transient heat conduction in the surrounding soil (or rocks) with the quasisteady heat transfer process in boreholes was developed. This modelling approach depends on many common elements, such as the thermophysical properties of the working fluid and series of resistances for various elements in the wellbore. Subsequently, based on the exergy analysis method, the optimal operating flow rate was defined and a design method for the optimal flow rate was developed. Results indicate that to obtain the maximum exergy efficiency, different optimal flow rates for the U-shaped geothermal well are achieved at different stages of the heating period. This findings of this study expand the research ideas of the process parameter optimization of U-shaped geothermal wells and provide a theoretical basis for developing an optimal circulating-flow-rate design for U-shaped geothermal wells.

A Novel Multi-Objective Process Parameter Interval Optimization Method for Steel Production

Customized small batch orders and sustainable development requirements pose challenges for product quality control and manufacturing process optimization for steel production. Building a multi-quality objective process parameter optimization method that converts the original single target optimization into multi-objective interval capability optimization has become a new method to ensure product quality qualification rate and reduce production costs. Aiming at the multi-quality objective control problem of plate products, we proposed a novel multi-objective process parameter interval optimization model (MPPIO) with equipment process control capability and parameter sensitive analysis. The multi-output support vector regression method was used to establish a multi-quality objective prediction model, which was settled as a verification model for the process parameter optimization results based on the particle swarm optimization algorithm (PSO). The process control capability functions of key parameters were fitted based on the real data in production. With these functions, each optimized particle of the classical PSO was converted into the particle beam of the MIPPO. The iteration process was weight controlled by calculating the Morris sensitivity between each input parameter and output index in the multi-quality objective prediction model, and finally the processing control window of each key parameter was determined according to the process parameter optimization results. The experimental results show that the MPPIO model can obtain the optimal parameter optimization results with the maximum processing capacity and meet the customized processing range requirements. The MPPIO model can reduce the difficulty of control and save production costs while ensuring the product properties is qualified.