PE0100 : Process Plant Optimization Technology & Continuous Improvement
COURSE TITLE : PE0100 : Process Plant Optimization Technology & Continuous Improvement
COURSE DATE : Aug 12 - Aug 15 2024
INSTRUCTOR : Mr. Dimitry Rovas
VENUE : Istanbul, Turkey
COURSE FEE : $ 5000
Register For Course Outline
Date : May 27 - May 30 (4 Days) Location : Abu Dhabi, UAE Classroom Fee (US$) : $ 4500 Course Info
Date : Nov 11 - Nov 14 (4 Days) Location : Dubai, UAE Classroom Fee (US$) : $ 4500 Course Info

Course Description

This practical and highly-interactive course includes various practical sessions and exercises. Theory learnt will be applied using our state-of-the-art simulators. In a typical processing plant, such as a petrochemical plant or oil refinery, there are hundreds and even thousands of control loops. Each control loop is responsible for controlling one part of the process, such as maintaining a temperature, level or flow. If the control loop is not properly designed and tuned, the process runs below its optimum. The process will be more expensive to operate, and equipment will wear out prematurely. For each control loop to run optimally, identification of sensor, valve, and tuning problems is important. It has been well documented that over 35% of control loops typically have problems. Process plant optimization is the set of adjustments of the various processes in order to optimize some specified set of parameters without violating some constraints. The most common goals are minimizing cost and maximizing throughput and efficiency. When optimizing a process, the goal is to maximize one or more of the process specifications, while keeping all others within their constraints. This can be done by using a process mining tool, discovering the critical activities and bottlenecks, and acting only on them.Process plant optimization involves evaluating every process and interaction in order to determine the best possible outcome. It includes the optimization of process equipment, operating procedure and control systems. This can result in improved flexibility, modernization and the best use of equipment, improved automation, decreased production time, and increased innovation. The aim of this course is to provide participants with a complete and up-to-date overview of process plant optimization. Upon the successful completion of this course, participant will gain a satisfactory understanding of the concepts of optimization fundamentals, process plant design optimization, process plant planning optimization, process plant operations optimization, process controls, optimizing reliability, optimizing offsite operations, continuous improvement and integrated supply chain optimization. Actual case studies from around the world will be demonstrated to highlight the topics discussed. link to course overview PDF


This interactive training course includes the following training methodologies as a percentage of the total tuition hours

Workshops & Work Presentations
Case Studies & Practical Exercises
Videos, Software & Simulators

In an unlikely event, the course instructor may modify the above training methodology before or during the course for technical reasons.


If this course is delivered online as a Virtual Training, the following limitations will be applicable

Certificates: Only soft copy certificates will be issued to participants through Haward’s Portal. This includes Wallet Card Certificates if applicable
Training Materials: Only soft copy Training Materials (PDF format) will be issued to participant through the Virtual Training Platform
Training Methodology: 80% of the program will be theory and 20% will be practical sessions, exercises, case studies, simulators or videos
Training Program: The training will be for 4 hours per day starting at 09:30 and ending at 13:30
H-STK Smart Training Kit: Not Applicable
Hands-on Practical Workshops: Not Applicable
Site Visit: Not Applicable
Simulators: Only software simulators will be used in the virtual courses. Hardware simulators are not applicable and will not be used in Virtual Training


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