Correlation Integral Combined With Predictive Control To Solve Real Time Optimization Problem And Its Application

With the increasing competition in the market and the increasing requirements for environmental protection,the production philosophy of chemical companies is also undergoing great changes.In the past,the smooth operation of the plant may be the first concern of the enterprise,thus focusing on the production at the expense of energy consumption and environment.Nowadays,enterprises are constantly improving their comprehensive competitiveness from all aspects,and have a new understanding of energy saving,low carbon emission reduction,etc.Operational optimization of production processes is an effective means to improve the competitiveness of enterprises,and the implementation of operational optimization can keep the system operating close to the optimal operating target in the face of disturbances and other time-varying characteristics.Traditional process optimization often assumes a single static relationship between the tuning variables and the objective function,and does not take into account other disturbances in the process,making it very sensitive to disturbances and difficult to handle real-time optimization of dynamic processes.The correlation integral optimization method,which reconstructs the formulation of steady-state optimization of production processes,takes into account the time and disturbance terms,making the algorithm highly resistant to disturbances.At the same time,it is data-driven and works with real-time data of the optimization variables as well as the objective function variables,without the need to build an exact mechanistic model.However,the traditional correlation integral optimization method has limitations such as poor adaptability to different working conditions and not considering the real-time constraints existing in the process.This paper addresses the limitations of the traditional correlation integral optimization method by combining the correlation integral with predictive control and using a control method to solve the real-time optimization problem.To address the limitations of the traditional algorithm that does not consider real-time constraints and poor adaptability to different operating conditions,robust predictive control is used to control the gradient of intermediate variables that characterize whether the system still has optimization margin.To address the drawback that linear predictive control becomes less effective in strongly nonlinear systems,Volterra nonlinear model predictive control is used to control the gradient.Finally,the improved correlated integral optimization algorithm is validated by simulation,and the related software is developed and tested for industrial application in the process of thermal efficiency tuning of a xylene heater in a Sinopec refinery to verify the feasibility and effectiveness of the improved algorithm.