An Efficient Method for Conducting Step Testing ofCrude Distillation Unit

• Main Author: Zulkifli, Mohd Zarul Ikmal
• Format: Final Year Project
• Language: English
• Published: Universiti Teknologi PETRONAS 2012
• Subjects: TP Chemical technology
• Online Access: http://utpedia.utp.edu.my/9644/1/2012%20-%20An%20Efficient%20Method%20for%20Conducting%20Step%20Testing%20of%20Crude%20Distillation%20Unit.pdf; http://utpedia.utp.edu.my/9644/

The objective in implementing the process control strategy is to maintain the process at desired condition, safely and efficiently, while satisfying environmental and product quality requirement. There are seven layers of a process control hierarchy which are instrument, safety, regulator, multivariable, real time optimization and planning and scheduling. Some of the strategies, as for example model predictive controller which located at multivariable stage requires a model to enable it to be implement. There are four phase in developing a model-required process control strategy such as MPC. The phases are; 1) pretest and preliminary MPC design 2) plant testing 3) model and controller development and 4) commissioning and training. The accuracy and reliability of the deliverables from each phases is extremely crucial in determining the success of the developed process control strategy. According to literature, plant testing took the longest period of among all the stages. The plant testing could consume up to 50% of the time used in order to develop the model. In orderto run the plant testing, taking steptesting as for an example, there is a literature suggested that the step testing shall be made between eight and twelve step, where for each step, the developer have to let the process to reach steady state before implementinganother step. This study has the objective to reduced the effort, generally, and time particularly, while conducting a plant testing. In this report, the Case Study of Crude Distillation Unit by Aspen HYSYS was used to generating the experimental data. From the data generated, the MATLAB System Identification Toolbox was later used to generate the model. The model generated later was analyzed to investigate the project objective. All the necessary steps that are required will be explained through this report.