IIoT in energy optimisation
Leveraging IIOT concepts leads to a reduction in energy consumption and improved refining margin
Engineers India Limited
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To some, the Industrial Internet of Things (IIoT) is just a new buzzword – but to the process industries IIoT is becoming a necessity to maintain competitiveness. Refineries are trying to cope with various market forces, many of which require improved plant performance. Over the years, refinery operation has undergone tremendous change. Due to stiff competition, there is a need to operate at higher gross refinery margin (GRM). Moreover, environmental regulations have become more stringent. Both these requirements have pushed refineries to look for available methods to reduce energy consumption. Therefore, in the past few years, the need for effective cost reduction in addition to ever more carbon-conscious operation has driven refiners to look for ways to improve the energy efficiency of their units, in particular the crude and vacuum distillation unit, as one of the few available avenues for sustainability. This has led designers as well as operators to undertake optimisation studies which increase energy efficiency.
A multiplicity of variables and parameters for this optimisation, as well as time being an important factor in implementation, makes it imperative to use IIoT to keep pace with the technological advances the world is seeing today.
The discussion in this article pertains to the benefits of using IIoT applications in energy optimisation during refinery operation. Crude and vacuum distillation unit operation is used for this discussion. The basic methodology involves a digital twin updated for crude processing using a knowledge base with the help of an optimisation tool to prepare a set of optimum operating conditions for the refinery operations team. The difference between key plant operating parameters and optimum performance is highlighted with suggestions for changes. Energy consumption is mapped with optimum values to highlight achievable benefits in terms of net refinery operating margins expressed as dollars per barrel.
Refineries typically run on tight profit margins. Because of changing market scenarios and global competition, refineries are faced with the task of minimising costs and ensuring high profitability. Any opportunity to reduce operating costs is of interest to the refiner. As refineries have little control over crude and product prices, they need to rely on operational efficiency for their competitive edge. This is achieved by increasing yields and reducing energy consumption, thereby reducing operating costs and improving gross refinery margin.
Refinery processes require high energy consumption during operation in the form of fuel, steam, power, and cooling water. Therefore, minimising consumption of utilities is a primary goal. Apart from the cost of feed, energy consumption represents the major share of the operating costs.
There are many factors influencing refinery operations, including changes in the crude oil processed, economic factors related to market demand, and the processing feasibility of each unit. Even a single factor such as a change in crude oil composition can have a significant impact on product yields. As Figure 1 shows, although both Crude A and Crude B have the same API of 44.1 and sulphur content of 0.04 wt%, there are major differences between the yields of various cuts. The yield for the 15-70°C cut is 65% higher for Crude B, whereas 370°C+ yields are 80% higher for Crude A. Therefore, optimised operation with Crude A processing would be different from optimised operation with Crude B.
Furthermore, there are many variables which can be used to optimise energy consumption, namely operating pressure, operating temperature, flow rate of streams, and so on. Because there are multiple factors influencing refinery (unit) operation, and multiple optimisation variables, IIoT can be leveraged to optimise unit and refinery operation.
The crude and vacuum distillation unit (CDU/VDU) has the highest processing capacity in a refinery. It is a highly energy intensive process representing around 20% of a refinery’s total energy consumption. In terms of fuel oil equivalent, it consumes around 2% of the total crude processed to meet its energy requirement. In the present market scenario, where refinery margins are shrinking due to crude price volatility, it becomes imperative to target the crude and vacuum distillation unit for energy optimisation because its energy footprint is among the largest in the refinery.
Energy in the CDU/VDU is consumed in the following ways:
• As direct fuel in process heaters
• As steam for stripping or motive fluid
• As power for drivers
Application of IIoT helps to build an analysis solution for plant processes which uses operational data coupled with design and engineering models to suggest improvements for enhancing yield, based on a crude assay to match the refinery’s configuration and market demand, and achieving energy optimisation in the CDU/VDU.
Industrial Internet of Things
IIoT is based on gathering information and automating physical processes to provide remote monitoring and operational support for plant or equipment.
IIOT targets optimisation at three levels: asset, process, and business. The asset level pertains to individual equipment such as heat exchangers, pumps, columns, and heaters. The process level is the unit level, including CDU/VDU, FCC, DCU, and so on. The business level involves optimisation at the refinery complex level. The benefits of implementing an IIoT model include:
• Improved operational support
• Improved operating efficiency
• Optimum utilisation of assets
• Reduced unplanned downtime
• Improved operational safety
• Reduced maintenance costs
Core IIoT technologies comprise smart sensors and actuators, connectivity via the internet, and a computer system or cloud with an IIoT model which includes a digital twin combined with a data historian, database, optimisation tool, and knowledge base. The digital twin is a virtual replica of a plant and its equipment.
The major constituent of a digital twin is the simulation model, which refers to using a computer program to quantitatively model the characteristic equations of a chemical process using equilibrium relationships, and mass and energy balance to predict stream flow rate, composition and properties, and the operating condition of equipment.
The knowledge base coupled with an optimisation tool can use the digital twin to gain an accurate representation of the plant over its full range of operation.
The architecture of the model is shown in Figure 2. The historian data and lab analysis flows from the refinery via a secure line and is captured. Data plays a key role in any plant and is a critical part of preparing an IIoT model. Accuracy of measurement and reliable instrumentation are key to process flexibility and improved process control. Confidence in the quality and the accuracy of sensor data are paramount to the success of the model. Therefore data cleaning or validation must be carried out. After reconciliation, the data is fed to a model consisting of a simulation model, equipment sizing tools, optimisation tools, and a knowledge base. Based on its analysis, a report to optimise plant operation is then sent via a secure line to the refinery.
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