• benchmarking
• bt
• energy consumption
• engineering
• gap analysis
• index
• lost opportunity
• metrics
• operator
• performance
• target

The objective of this thesis is to create a monitoring and reporting system of the energy performances of an oil refinery.
This has to be compact, effective and easy to understand.
The purpose is to show this model to KBC customers as a demonstration of the potential benefits arising from the resolutions of some of the most typical problems related to the high energy consumption of a plant.
The report will be used by KBC to show clients the benefits of implementing KBC best practices on an hypothetical refinery running with non optimised operations.
A static model was created on ppt, and this was later developed into a dynamic demo.

The model is organised in a form of a hierarchical structure: at the top there is the “site” level, then there is the “units” level and at the base the “equipments” level.
Each item has a screenshot in the demo.
Each screenshot will contain energy performance parameters of interest in a management, engineering and operational levels. These parameters will be hereafter referred to as “metrics”.
The model that will result on the excel interface contains one months’ hourly data. All the items, from graphs to the values, are up to date dynamic in function of the selected date.
The developed model will be used from KBC to tell a “story” of the plant. The goal of that model is to simulate the actual operation of a refinery, modifying some data in order to show the customer the potential benefits arising from changes to the most energy consuming metrics of the process.
These benefits are displayed for the particular equipment. Rising in the hierarchy the impact on individual units can be seen and then further the effect on the entire site performance.
The data have been put together from real plant data, suitably modified so as to be anonymized and have been further manipulated to illustrate the benefits of operating under improved conditions.
As specified above, the correlations at the base of the model have been obtained through the use of various tools provided by KBC, including PetroSim™, HTRI™ and ProSteam™.
The metrics are substantially different in two basic categories: EIV and KPIs, the first (Energy Influencing Variables) are the variables directly influencing from the operators (eg: the level of O2 in the exhaust from a furnace), while the KPI (Key Performance Indicator) are still performance monitoring parameters but not directly influenced by the operators (eg: furnace efficiency).
The hierarchical structure of the energy performance display system comprises three levels:
• Site level
• Unit level (CDU, VDU, FCC, HDT. HDS, etc)
• Equipment level
Given the huge extension of the system, it has been chosen to focus only on two units: the CDU and the FCC, which are commonly considered the largest energy consumers in the system.
In addition of these, dominant importance for the energy consumption has the utility system.
In a similar way have been taken into account only some of the many metrics analyzed for the various units.