The debutanizer column is a key unit in the naphtha fractionation system, responsible for separating light hydrocarbons such as C3~C5, and plays a crucial role in determining gasoline quality. In particular, the bottom C4 content is a critical quality indicator but is difficult to measure online due to the time delay and high cost of traditional laboratory analyses, limiting the efficiency of real-time control. To overcome this challenge, this work proposes a high-accuracy soft sensor model that integrates a deep belief network (DBN) and an improved whale optimization algorithm (IWOA) into an interval type-2 Takagi-Sugeno-Kang fuzzy logic system (IT2 TSK FLS) with an A2-C1 structure. The DBN is employed for deep feature extraction, enhancing data representation and reducing noise in the input process variables. Subsequently, the IWOA enhanced with cosine adjustment and step-size correction mechanisms is applied to optimize both the antecedent membership functions and the consequent parameters of the fuzzy logic system. This joint approach improves the models prediction accuracy and robustness. To evaluate the effectiveness of the proposed model, a comprehensive set of comparison experiments is conducted. The benchmark methods include support vector machines (SVM), long short-term memory (LSTM) networks, gated recurrent units (GRU), and IT2 TSK fuzzy logic systems optimized using backpropagation (BP), particle swarm optimization (PSO), grey wolf optimization (GWO), whale optimization algorithm (WOA), improved WOA (IWOA), and the proposed DBN-IWOA approach.The model achieves lower RMSE and MAE values, along with higher R2 scores, thereby validating its effectiveness and robustness in practical applications. In conclusion, the proposed approach shows strong potential for advancing soft sensor modeling in complex industrial processes, enabling more accurate and efficient real-time quality prediction and process control.