Thermal control of coke furnace by data-driven approach

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To achieve efficient and stable coke furnace operation, we developed an operation guidance system to reduce the variance of net coking time (NCT). A control algorithm that predicts the future NCT by locally weighted regression using the individual chamber database and adjusts the fuel gas flow rate was constructed. After a simulation validation using a newly developed one-dimensional transient model, the operation guidance system based on the developed control algorithm was implemented in a real plant. As a result, the root mean square (RMS) of the control error of NCT was reduced by 0.12 h, and the combustion chamber temperature was reduced by 28.8°C. The developed operation guidance system contributed to the reduction of fuel consumption and CO2 emission of the coke furnace.