Advanced Control and Optimisation in Petrochemical Industry - An Introduction

The petrochemical industry produces a wide range of products that are supporting our modern societies. The fuels produced by petrochemical plants meet the needs of energy for our living, production and transportation. It is hard to imagine how the societies sustain a high standard of living without these fuels. During the past decades, the petrochemical industry has faced ‘major challenges due to global competition and rapid changes in economic conditions’. ¹ Advanced control and optimisation have played key roles in improving plant performance.

Process control refers to the technologies required to maintain the operating conditions of a plant at desired or safe values. Every petrochemical plant relies on process control systems for their operation. It is not feasible to operate these plants without process control. Among these process control systems, ‘the vast majority (85-95%) of the process control systems in petrochemical plants are based on the proportional-integral (PI) and proportional-integral-derivative (PID) feedback control technologies that originated in 1930s’. ¹ PI or PID controllers dominate most process control applications because they work well in petrochemical plants. They are easy to use, do not rely on a process model and are relatively easy to tune according to well-established tuning rules. However, there are some difficult petrochemical plants with strongly nonlinear behaviour, long time delay and/or frequent and unanticipated disturbances, to which the traditional PID feedback control is not capable and has a poor control performance. Advanced control, by name, is a control technology which can perform much better than PID feedback control. Advanced control is usually based on a process mathematical model, and called model-based control, in which computation activities are involved. Advanced control and traditional PID control are usually cascaded together and used in a series. The advanced control produces desired set-point values for the PID controllers, and then the PID controllers keep the controlled variables at or close to their desired set-points under unanticipated disturbances. Optimisation in petrochemical industry refers to the calculation of the new operating condition in which the performance of the plant is optimal in terms of production rate, energy consumption or other criteria. Even though ‘process control is in many aspects a mature technology serving mature industries’, ² many petrochemical processes are still facing critical technical challenges such as enormous fluctuations in raw material and increased customer demand for high-quality products and are in need of advanced control and optimisation in order to achieve the better economic performance.

The main objective of this theme is to show the latest real application cases of advanced control and optimisation in petrochemical plants. This theme was produced by Liaoning Shihua University. Shihua in Chinese means Petroleum and Chemical Engineering. The theme aims to help us understand the state-of-art in the industrial applications of advanced control and optimisation in petrochemical industry.

The first article ‘Advanced process control of an ethylene cracking furnace’ reports the application of advanced control for an ethylene cracking furnace. The ethylene cracking furnace is a key unit in the ethylene production plant. Ethylene, the simplest of olefins, is used as a base product for many syntheses in the petrochemical industry. The demand for ethylene is over 125 million ton per year worldwide with a continuous growth rate. This article addresses the difficulty of multivariable, strong coupling and nonlinear features to which the traditional PID control is not capable in achieving a satisfactory performance. A cascade control structure is adopted with a PID controller as the inner loop control and the cracking outlet temperature (COT) advanced control as the outer loop control. A feed-forward channel is added to compensate the unanticipated disturbance from feed materials. The feature of this application is that eight independent flow controls are cooperating with one another and trying to achieve temperature balance. The advanced control is implemented in the upper computer connecting with a distributed control system (DCS) through an Object Linking and Embedding (OLE) for Process Control (OPC) connection. Over 2-year operation of the advanced control for the ethylene cracking furnace shows that the ethylene production rate is increased by 0.3% due to the reduced fluctuation in the total feed volume. Also, the interval cycle of decoking of the cracking furnace is prolonged for the same reason.

The challenges in controlling a delayed coking furnace are addressed in the second article ‘Advanced control in a delayed coking furnace’. Delayed coking unit is a petrochemical plant used to process residual oil. Delayed coking furnace is the heart of the delayed coking unit. Due to the semi-batch and semi-continuous nature of the unit, the traditional PID control has been proven not sufficient and capable for this plant. A predictive functional control (PFC) is designed as the advanced control for the plant. A cascade structure is also adopted with a PID controller as the inner loop control and the PFC as the outer loop control. A feed-forward disturbance compensator is added to the cascade structure to reduce the impact of the disturbance caused by the feed flow. The PFC is based on transfer function models of the delayed coking furnace. A sample of implementation in Yokogawa CENTTUM CS3000 integrated production control system is given to show how the advanced control is realised by configuring various blocks in the CS3000. Smoothly switching between advanced control and traditional control and the safe control ranges for both the outlet temperature and oxygen concentration are introduced as the generic suggestions and the experience obtained from the implementation. The fluctuation of the outlet temperature decreases by more than 20% and the standard deviation of the oxygen concentration decreases by 40% after the advanced control have been in use.

As we know that traditional PID control does not work well with a strongly nonlinear, large lag, and time-varying process, the third article ‘Resistance furnace temperature control system based on OPC and MatLab’ presents the application results of a fuzzy PID controller for a resistance furnace. The PID parameters are tuned by fuzzy rules implemented in the MatLab toolbox and transmitted to the PID controller implemented in a DCS system. The fuzzy reasoning functions are implemented in an upper computer equipped as an OPC client. The data exchange between DCS and MATLAB is realised by the OPC standard. This application shows the convenient way of implementing an advanced control in an existing DCS system by deploying an upper computer connecting with the DCS through the OPC communication.

The fourth article is on optimisation of petrochemical processes. As presented in the article, there are multiple optimal objectives for gas fractionation unit operation. They are minimising energy consumption for three columns in series, maximising both yield of depropaniser and yield of propylene and minimising propylene emission. These objectives are identified from the historical data gathered from the operations over a period of time. The operation ranges of six optimal variables are included in the optimisation model as the constraints. The optimisation problem is solved by applying an evolutionary algorithm in the article. The result of the optimisation is a set of optimal operating conditions, called optimal operation scheme in the article. The application results show that the yield of product is increased and the energy consumption is reduced in the gas fractionation unit.