refining india 2024
shows a temperature higher than 900°C. Proprietary technology for real-time ther- mal imaging of catalyst tubes is available in the industry; however, it might not meet the cost economics of Capex and plant outage for installation for all refiners. Intelligent use of Tube Skin Temperature Data and Advanced Analytics Background temperature correction model IOCL MR has developed a furnace-spe- cific model for background correction and obtaining true temperatures. The back- ground correction model is specific to the constructional geometry of the furnace, has built-in formulae, algorithms, and procedures to generate corrected tube temperatures. Field trials at varying plant loads were carried out to verify and improve the accu- racy of the model. It was discovered that the actual tube temperatures, after back- ground correction, were 25-40° lower than the uncorrected temperatures. Additionally, to validate the accuracy of the model, gold cup pyrometry was used as a benchmark. The results of the gold cup pyrometry confirmed an error in the uncorrected TST of 30-40°C, thereby affirming the accuracy of the background correction model. With a TST margin of 25-40°C estab- lished, the plant capacity was safely increased, and TST was no longer limiting up to 84% plant capacity. v Data visualisation (heat map, burner management, and optimisation) In the subsequent development, TST data was used to develop an advanced data visualisation tool. The thermal map of the reformer furnace was generated using the TST readings captured by the pyrometer. With the thermal map, operators were able to see the exact visual profile of the furnace. They could identify areas of local- ised overfiring, tubes that were exceeding their design limits, burners causing flame impingement, and other crucial informa- tion that was previously not visible. Based upon the thermal map, the operators made adjustments to the burners and carried out optimisation. With regular furnace optimisation, local- ised overfiring was eliminated, and we could achieve up to 90% production capacity. w Advanced reliability monitoring (uti- lisation of TST for near real-time creep assessment and fitness for service evaluation) Utilising the tube skin temperature, stress-strain values from the Larson-Miller Parameter (LMP) curve provided by the tube manufacturer and following the pro- cedures laid down in API 579, which is the API code for Fitness for Service (FFS), the creep damage and fitness for service evaluations for each tube are performed in near real time. The entire evaluation is automated within the tool. So, the temperature of each tube is uti- lised to assess the daily health and reliabil- ity condition of each tube of the reformer, enhancing the reliability monitoring. All three functions of background tem- perature correction, data visualisation, and reliability monitoring have been encapsu- lated in an in-house developed tool known as REFORM (Reformer Optimisation and Reliability Monitoring) Tool.
Thermal map of Reformer
Thermal map of Reformer
Date: 04.10.2023, H gen (%): 74%, Max tube skin temp: 941 ˚C Before Optimisation
Date: 06.10.2023, H gen (%): 74%, Max tube skin temp: 920 ˚C After Optimisation
Figure 2 Optimisation of burner firing with aid of data visualisation
FFS Level 1 Assessment
Accumulated Creep Damage
Figure 3 Creep assessment and fitness for service evaluation using daily TST data
TST values are recorded directly using the REFORM Tool loaded on an Intrinsically Safe Tablet. Background correction takes place automatically to provide accurate tube temperatures. A thermal map is gen- erated instantaneously, and the optimisa- tion process is carried out if required. The data is transferred to server, and reports are available on a dashboard hosted on the local intranet for operators of the next shift or anyone to view. Benefits The intelligent use of TST data and data analytics has resulted in the removal of bottlenecks in throughput (achieving 90% plant capacity utilisation without exceed- ing tube skin limits) with respect to tube skin temperature, more effective reformer furnace management (control of flame impingement and more optimised and balanced thermal profile of the furnace), and enhanced reliability monitoring (bet- ter understanding of the impact of every- day TST on tube reliability and enhanced tube life).
Background correction
Burner management
Heat map
TST DATA
Reliability monitoring
Optimisation
Data analytics
Creep assessment
Fitness for service
Figure 4 Comprehensive use of TST data
Thermal map of reformer
Optimisation map
Accumulated creep damage
API 579-1ASME FFS-1 Fitness for service evaluation
FFS Level 1 assessment
DATA MODULE
Tube skin temperature
Background correction
Optimisation Thermal map
Reliability monitoring
Creep assessment
Fitness for service
Burner management
Data analytics
Figure 5 Reformer optimisation and reliability monitoring tool in use at IOCL
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