Following the global consensus on responding to climate change, reducing coal usage has become a critical task given that it is primary contributor to greenhouse gas (GHG) emissions in the steel industry. Given this context, the development of graphics processing units has led to an increasing research interest in applying various deep-learning technologies to enhance process efficiency in steel manufacturing operations. To this end, this study proposes an automatic control logic for supplying coal to rollers during briquette production. The proposed system employs convolutional neural network-long short-term memory for time-series prediction and you look only once for object detection to stabilize coal briquette quality in the FINEX process, replacing the existing manually controlled task in the hot metal production process. Actual process applications were verified by using these models to detect roller currents used as key control factors and briquettes produced below the roller and establish a correlation with compressive strength, which is the main quality indicator of briquettes. Based on these models, an automatic control logic was constructed and tested in actual processes, and the obtained results confirmed a quality improvement effect of ~30 %. The results of this study are expected to contribute to improving coal usage efficiency and reducing GHG emissions in the FINEX process.