Coal coking is an efficient and environmentally friendly technology for energy utilization that yields various industrial materials. However, the intricate nature of the coal coking reaction poses a challenge for chemical theories to fully capture and derive its reaction process. To address this challenge, a novel dual-way inference modeling method was proposed to simulate the coal coking process. This modeling method not only predicts H2 and CH4 concentrations, but also infers optimal operating conditions based on the desired H2 and CH4 concentrations. In this work, three representative machine learning methods (XGBoost, Random Forest, and ANN) are compared with dual-way inference modeling method, and the key factors affecting H2 and CH4 concentrations are thoroughly explored. The experimental results show that the primary factor influencing CH4 concentration is C, H2 concentration is predominantly affected by fixed carbon, while C plays the most important role for CH4 + H2. The dual-way inference modeling method achieved excellent performance in predicting H2 and CH4 concentrations and operating conditions. In the prediction task of H2, CH4, and H2 + CH4 concentrations, the coefficient of R2 achieved 0.9767, 0.9936, and 0.9851, respectively. The result for predicting coking temperature based on the desired H2 and CH4 concentrations indicate a positive correlation between coking temperature and both H2 and CH4 concentrations. In general, as coking temperature increases, the concentrations of both H2 and CH4 will increase.
