Study Of Advanced Process Control And Its Application On Petrochemical Devices

Advanced Process Control (APC) can bring significant economic benefits for manufacturers, which has become a world-wide consensus. But domestic APC can not run for a long time and conventional control can not obtain the desired effect. In order to overcome such shortcomings, this researches APC technology based on DCS control system. The study uses of modern control theory combined with practical on-site and based on the existing industrial control system. The results of the study ensure the smooth operation of devices and access to the most cost-effective, at the same time, it will also lay the foundation for long-term stable operation of APC and Optimal Process Control based on DCS control.The purpose of this research is how to enhance the Stability and the proportion of automatic control loop of devices using APC and under the condition of existing devices and technology, how to reduce energy consumption, how to reduce labor intensity of the operator and get more cost-effective. For these purposes, the article using IMC technology to enhance the Stability and the proportion of automatic control loop of devices, using fuzzy control with a combination of conventional control to decrease the load fluctuations between processes, using improved RBF neural network modeling algorithm to improve the soft-meter’s forecast accuracy.IMC is an advanced process control technology based on the object model. The accuracy of the model identification directs impact on the effectiveness of its control. In order to enhance the accuracy of object recognition, a new method is given to identify the object under the non-steady-state conditions. The method can identify Object parameter and the initial value of state equation at the same time, it provides a new solution for on-line object identification and PID tuning of non-steady-state conditions. Industrial process objects can be expressed by the first-order or second-order. with time delay, about. Most of the literatures solve above-mentioned objects’ computing problems using Pade approximation. But sometimes it will bring a big error when using this method to identify object with big time delay. In order to improve the accuracy of identified objects, a new method named Shift-Interpolation is presented. The method can get more calculation accuracy and it does not increase computational complexity.At present, industrial production control systems provided by different vendors, and the control algorithms is various. In the process of closed-loop object identification and controller design, it will cause great difficulties on computing and programming, if each closed-loop transfer function calculated for every type of object or controller. Matrix-module approach is a good method to achieve the closed-loop identified object and parameters of controller. Using matrix-module method to identify object, when the modules of closed-loop system changed, the only thing need to do is simply change the corresponding parameters. The structure of the entire system is not changed. And duplication of work is avoided.Shift-interpolation method is used for object with time delay, and it can avoid errors by Pade approximation. Since the identification taking into account the initial values of state-space equations, which can be convenient to identify object by filed data without considering the status of test data. It is easy to identify the industrial object, and it can get more accurate results.To reduce the load fluctuations between processes which can cause installation smooth issues, Based on the characteristics of the liquid level to allow a certain scope of the changes, using fuzzy control with a combination of conventional control, a new level control named non-linear adaptive region level control is brought up.The method based on the thinking of operator, the controller will do nothing when the level is in the scope of allowed range, and thus it can ensure the stability of the system.The controller has just one parameter-the ratio coefficient can be adjusted. The operator does not have the knowledge of fuzzy control, and he can easily set it up by referring to the results of the previous regulation.Level non-linear adaptive region control can enhance the stability of previous process and can reduce operation Frequency of the follow-up procedure. Labor intensity of the operator is reduced furthermore.Soft instrument based on neural network, it sometimes can cause the large forecasts error in the practical application, in order to improve the forecast accuracy of the instrument, the original algorithm shortcomings are found. A new RBF neural network modeling method is proposed to improve the soft-sensing data accuracy.At last, PID controller is transformed into IMC-PID controller by using IMC technology. The practical application problem of IMC is resolved.