AbstractHydrocracking process is an essential and widely used means to remove impurities such as sulfur, nitrogen, and oxygen from heavy oil in refineries. The sulfur content in the tail oil of the hydrogenation process is a key quality index that must be strictly monitored and controlled. However, the traditional measurement ways of the sulfur content suffer from either large delays or high investment and maintenance costs. To this end, a predictive model named “adaptively regularized dynamic polynomial partial least squares (ARDPPLS)” is proposed to develop soft analyzer for sulfur content estimation. The ARDPPLS takes complicated characteristics of the hydrogenation process into consideration, including process dynamics, nonlinearities, and transportation delays of materials and energies. Specifically, the dynamics and nonlinearities are modeled by the finite impulse response paradigm and polynomial fitting, respectively, and the delays of explanatory variables are automatically determined using differential evolution. In particular, a Bayesian inference‐based adaptive regularization scheme for the polynomial partial least squares is designed to tackle overfitting that results from high‐order variable augmentation and high‐order polynomials. The performance of the ARDPPLS is evaluated by a real‐world industrial wax oil hydrocracking process, showing the effectiveness of the ARDPPLS.