The production of metallurgical coke in heat recovery coke production plants converts all heat generated from the combustion of distilled gases during coking pro-duction to steam and electricity, thus eliminating the need of processing hazardous by-products chemical substances. The gases, after inertization inside the ovens, are directed to the desulphurization plant called flue gas desulphurization (FGD) which uses lime slurry to remove SOx compounds (SO2 and SO3) and bag filters to remove the generated residue, lime ash. Due to the high cost of lime, it is important to model the process and evaluate which are the main variables that affects its result, thus allowing action in the process to make it more competitive and environmentally sus-tainable. The purpose of this work was to develop a mathematical model using arti-ficial neural networks to determine the main variables that affect lime consumption in the desulphurization process. Literature reveals that the main parameters that in-fluence sulfur removal efficiency, thus reducing specific lime consumption, are ap-proach to adiabatic saturation temperature and Ca/S ratio in the process. This study indicated that consumption is mainly related to the inlet and outlet SDA gas temper-atures, oxygen concentration in stack and lime slurry density according to the feed-foward backpropagation neural network sensitivity analysis. MLP 14-19-2 and hy-perbolic tangent transfer function in the intermediate layer and logistics in the output layer. Thus, the evaluation reinforced the effect of the increase of the gas outlet tem-perature on the specific lime consumption according to the literature, but also added new parameters: gas inlet temperature, O2 (g) concentration in the outlet of stack and lime slurry density.