In this study, a stepwise cyclic voltammetry approach was proposed to clarify the potential dependence of specific capacitance for reduced graphene oxide and ruthenium oxide electrodes. Based on the relationship between specific capacitance and potential, the working voltage window and energy density of RuO2//RuO2 supercapacitors (SCs) were maximized by modulating the mass ratio. The working voltage window of RuO2//RuO2 with a mass ratio of 2.03 (close to optimum mass ratio m+/m-OPTM=2.04
) was extended by about 188 % when compared to that calculated at a mass ratio of 0.56. The former was equivalent to a 304 % increase in energy density. In addition, the source of wasted potential window of electrodes on RuO2//RuO2 was investigated in an effort to design and produce better SCs.
A reasonable arrangement of filling pipelines can solve the problems of low line magnification, a high flow rate, large pipe pressure, etc., in deep well filling slurry transportation. The transportation pressure loss value of filling slurry is the main parameter for the layout design of filling pipelines. At present, pressure loss data are mainly obtained through the loop pipe experiment, which has problems such as a large amount of labor, high cost, low efficiency, and a limited amount of experimental data. In this paper, combined with a new generation of artificial intelligence technology, the random forest machine learning algorithm is used to analyze and model the experimental data of a loop pipe to predict the pressure loss of slurry transportation. The degree of precision reaches 0.9747, which meets the design accuracy requirements, and it can replace the loop pipe experiment to assist with the filling design.
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