August 1, 2019
By Evan Almberg
Many ethanol plants are up to 15 years old, or older, and the subject of increasing production, efficiency and profitability is more prevalent than ever. With new, more efficient technologies coming to market for various processes within a biorefinery, the desire to debottleneck older equipment can drive maintenance and capital project decisions.
Producers must decide if they want to budget and plan to maintain, upgrade or scrap and replace with a new unit. When it comes to boilers and heat recovery steam generators (HRSGs), maintenance cost and upgrade opportunities often dictate the conversation.
Tools for Making Data-Driven Decisions
Fully assessing the operation of a boiler or HRSG requires accounting for multiple factors including how a process change will affect related equipment, not just the direct system involved. Process operators and plant engineers can utilize the distributed control system (DCS) to perform on-the-fly performance checks confidently. Combining this with information located in the original equipment manufacturer (OEM) predicted performance data sheets, producers can estimate as-is performance while operating. Additionally, the data historian system provides the ability to trend data over time, providing long-term review capabilities of the boiler.
Where Modeling and Data are Key
While a control room data assessment can give a brief insight of as-is performance, it doesn’t tell the whole story. Boilers are often run differently than originally designed, making a direct comparison to the OEM predicted performance difficult. Additionally, a process change—such as dryer throughput or feedwater temperature—can greatly affect multiple aspects of the system that are not as easily quantified.
A thermal model can provide more accurate insight into the boiler performance and help fully assess the system. Modeling provides a digital representation of the boiler system, taking into consideration the physical geometries of the water and gas-side flow paths, along with OEM predicted performance values used to validate the model.
Taking it one step further, DCS and process information (PI) historian data can be reviewed and matched to the thermal model to provide an as-is representation of operation, ideal for plants that have been operating for multiple years or have changed components. Accurate data is important, so performing mass and energy balances about various sections of the boiler is needed to identify good and bad data, which leads to investigating and correcting miscalibrated instruments or DCS conversion algorithms.
Once developed, the model can be used to track degradation, identify under or overperforming sections of the boiler and simulate upgrade and process changes, as well as provide key insights into pressures, temperatures, flows and how they affect ancillary equipment.
Case Study: Process Upgrade Effects
A facility comprised of two identical firetube waste heat boilers, operating in parallel and sharing a common final steam separator, was investigated for a potential upstream process upgrade being performed elsewhere in the plant. Using HRST’s PerformancePro thermal modeling software, a thermal model was developed based on the OEM thermal and mechanical data sheets. Typical process conditions gathered from the PI data historian were used to replicate the existing boiler operation, creating an as-is representation of the system performance.
The process upgrade parameters were provided by the plant, including values for increased boiler feedwater (BFW) temperature, as well as flue gas temperatures and mass flow rates. Primary areas of concern were the American Society of Mechanical Engineers boiler design pressures and temperatures, stamped boiler capacity, safety relief valve pressure set points and relieving capacity, as well as the final steam separator capacity.
Upon modeling and subsequent analysis, it was determined that multiple shortfalls existed in the system when simulated under the process upgrade conditions, including:
• Metal temperatures exceeding the ASME design temperatures for the upstream tube sheet, tube inlets and, in some cases, the bulk tube temperature.
• Steam generation exceeding the boiler rated capacity and the safety valve relieving capacity.
• Combined (boiler one and two) steam flow exceeding the final steam separator capacity.
Additionally, it was determined that the pressure drop from the boiler to the main steam header would increase on the order of 2.25 times over existing, and the increased flue gas mass flow and temperature would increase the stack temperatures by 40 to 70 degrees Fahrenheit over the current baseline.
Solutions for meeting ASME code would require a capacity and design temperature rerate, whereas the final steam separator would only require upgraded internal equipment to meet capacity requirements. To rerate for higher temperatures, a condition assessment of all tubes and welds would be required to validate the component thickness for ASME code calculations. Plant personnel said abrasive particulates in the flow eroded tube inlets, causing plugging.
While a steam capacity rerate would be advantageous, discussion with plant personnel determined that the scope required to validate all tube and pressure part thicknesses would be costly and, because of the failure history, may only indicate that a temperature rerate would not be possible.
Because of those factors and the age of the boilers, the plant opted to continue use under existing conditions for the short term. Ultimately, the decision was made to replace each of the boilers with newer, higher-capacity units specifically designed for the upgrade parameters, with results of the thermal modeling assessment used as justification.
When it comes to making long-term decisions about boiler operation or replacement, historical operating data trends and future operating conditions can provide helpful insight and, when combined with system modeling, create a useful tool to analyze the boiler as well as ancillary equipment.
Approaches such as this can help operators and plant management make data-driven decisions when facing the choices to maintain, upgrade or replace.
Author: Evan Almberg
Read the original article: Performance Matters