Oxygen-rich porous carbon is of great interest for energy storage applications due to its improved local electronic structures compared with unmodified porous carbon. However, a tunable method for the preparation of oxygen-rich porous carbon with a special microstructure is still worth developing. Herein, a novel modification of porous carbon with different microstructures is facilely prepared via low-temperature solvothermal and KOH activation methods that employ the coal tar and eight substances, such as cellulose as carbon source and modifier, respectively. By testing the yield, surface group structure, lattice structures, morphology, thermal weight loss, and specific capacitance of carbonaceous mesophase, cellulose–hydrochloric acid is identified as the additive for the preparation of oxygen-rich coal tar-based porous carbon. The obtained porous carbon displays a specific surface area of up to 859.49 m2 g− 1 and an average pore diameter of 2.39 nm. More importantly, the material delivers a high capacity of 275.95 F g− 1 at 0.3 A g− 1 and maintains a high capacitance of 220 F g− 1 even at 10 A g− 1. When in a neutral electrolyte, it can still retain a reversible capacity of 236.72 F g− 1 at 0.3 A g− 1 and 136.79 F g− 1 at 10 A g− 1. This work may provide insight into the design of carbon anode materials with high specific capacity.

It is difficult to optimize the process parameters of directly preparing carbonaceous mesophase (CMs) by solvothermal method using coal tar as raw material. To solve this problem, a Decision Tree model for CMs preparation (DTC) was established based on the relationship between the process parameters and the yields of CMs. Then, the importance of variables in the preparation process for CMs was predicted, the relationship between experimental conditions and yields was revealed, and the preparation process conditions were also optimized by the DTC. The prediction results showed that the importance of the variables was raw material type, solvothermal temperature, solvothermal time, solvent amount, and additive type in order. And the optimized reaction conditions were as follows: coal tar was pretreated by decompress distillation and centrifugation, the solvent amount was 50.0 ml, the solvothermal temperature was 230 °C, and the reaction time was 5 h. These prediction results were consistent with the actual experimental results, and the error between the predicted yields and the actual yields was about − 1.1%. Furthermore, the prediction error of DTC method was within the acceptable range when the data sample sets were reduced to 100 sets. These results proved that the established DTC for chemical process optimization can effectively lessen the experimental workload and has high application value.