The close relationship between steady-state prediction outputs and actual inputs results in the existence of model uncertainty in the steady-state prediction equation for integrating processes. This paper establishes a steady-state prediction model that can reflect the dynamic execution process of the manipulated variables. Based on integration of the steady-state optimization layer and dynamic optimization layer, the input increment sequences of multi-step prediction are regarded as the decision variables. A quadratic programming model with inputs, outputs, and input increment constraints was developed, which simultaneously solved the problems of steady-state optimization and dynamic control of integration process, as well as the sub-optimal solution of the steady-state targets in each cycle. Simulation examples illustrate that the optimal setpoints and the actual values of the inputs and outputs are all within the constraint ranges and the actual values settle to the optimal setpoints, and demonstrate that the method proposed in this paper can effectively solve the steady-state optimization problem for integrating processes when economical optimization of the inputs and outputs is considered.