Interview Questions for Turbomachinery Performance Monitoring

Thermodynamic cycles are fundamental to understanding turbomachinery performance. They model the energy transformations within the machine. Two key cycles are the Brayton cycle and the Rankine cycle.

The Brayton cycle is the basis for gas turbines. It involves isentropic compression, constant-pressure heat addition (combustion), isentropic expansion (through the turbine), and constant-pressure heat rejection. Think of a jet engine – air is compressed, fuel is burned, hot gases expand to drive the turbine, and exhaust gases are expelled.

The Rankine cycle governs steam turbines. It consists of isentropic compression (pumping water), constant-pressure heat addition (boiler), isentropic expansion (through the turbine), and constant-pressure heat rejection (condenser). Power plants using steam turbines operate on this cycle – water is pumped, heated to steam, expands through the turbine, and the condensed steam is recirculated.

Analyzing these cycles allows us to determine theoretical efficiency and compare actual performance against the ideal. We look at parameters like specific work, heat input, and thermal efficiency to understand the machine’s energy conversion process. Deviations from ideal behavior are analyzed to pinpoint areas for improvement.

Turbomachinery encompasses a variety of devices categorized by their flow path and application.

• Axial-flow machines: These machines feature flow parallel to the axis of rotation. Examples include axial compressors in jet engines and large power plant turbines. Their advantage is high flow rates and high pressure ratios in multi-stage designs.
• Radial-flow (centrifugal) machines: Here, the flow changes direction 90 degrees. Centrifugal pumps and compressors are prime examples. These are compact, relatively simple in construction, and excel at high pressure rise in a single stage.
• Mixed-flow machines: These machines combine features of axial and radial flow, offering a balance between the advantages of both. They are common in smaller applications where a compromise between pressure rise and flow rate is desired.

Applications span diverse industries. Axial compressors are crucial in aircraft engines and power generation. Centrifugal pumps are ubiquitous in water supply, chemical processing, and oil & gas. Turbines find use in power generation, aircraft propulsion, and industrial drives. The choice of turbomachinery type depends on factors such as pressure rise required, flow rate, space constraints, and cost considerations.

Several key performance indicators (KPIs) are used to assess turbomachinery:

• Efficiency (isentropic, polytropic): Measures how effectively the machine converts energy. Isentropic efficiency compares actual performance to an ideal reversible process, while polytropic efficiency accounts for non-isentropic effects.
• Pressure ratio (compressors) or expansion ratio (turbines): Shows the change in pressure across the machine.
• Flow rate (volume or mass flow): Indicates the amount of fluid processed per unit time.
• Power (shaft power): Measures the work output (turbines) or input (compressors/pumps).
• Head (pumps): Measures the increase in fluid potential energy.
• Surge margin (compressors): Indicates the operating range before compressor stall occurs.
• Vibration levels: High vibration suggests potential imbalance or damage.
• Temperature profiles: Monitor fluid temperatures at various stages to detect inefficiencies or overheating.

Monitoring these KPIs allows for early detection of performance degradation and ensures the machine operates within safe and efficient limits.

Performance curves graphically depict turbomachinery performance characteristics. They typically plot KPIs against operating parameters like rotational speed or flow rate.

Turbine curves typically show power output, efficiency, and flow rate as functions of speed and pressure ratio. A decrease in efficiency at higher flow rates often indicates inefficiencies at off-design conditions.

Compressor curves plot pressure ratio, efficiency, and flow rate versus speed and pressure. The surge line represents the stability limit – operating beyond this line can cause severe damage. The choke line represents the maximum flow capacity.

Pump curves display head, flow rate, and efficiency as functions of speed and flow. Similar to compressors, these curves highlight efficiency variations at different operating points and potential limits like cavitation (formation of vapor bubbles).

Careful analysis of these curves reveals optimal operating points, efficiency trends, and potential issues like fouling or erosion. Analyzing these curves under different operating conditions helps diagnose potential problems.

Isentropic efficiency is a crucial metric for turbomachinery. It represents the ratio of actual work done to the work that would be done in an ideal, reversible adiabatic (isentropic) process. The formula is:

For a turbine, it’s the ratio of the actual enthalpy drop to the isentropic enthalpy drop. For a compressor, it’s the ratio of the isentropic enthalpy rise to the actual enthalpy rise.

Its significance lies in providing a standardized way to compare different machines and assess their performance relative to an ideal process. A higher isentropic efficiency implies more efficient energy conversion and less energy loss due to irreversibilities like friction and heat transfer.

For example, a gas turbine with 90% isentropic efficiency means that 10% of the energy is lost to irreversibilities. Improving efficiency even by a few percentage points can lead to significant fuel savings and reduced emissions in power generation applications.

Performance degradation in turbomachinery can stem from various mechanisms:

• Fouling: Deposits on blade surfaces increase friction and reduce efficiency. This is common in gas turbines and steam turbines due to combustion products or impurities in the fluid.
• Erosion: Impurities in the fluid can abrade blade surfaces, leading to reduced efficiency and increased vibration.
• Corrosion: Chemical reactions can damage blade materials, especially in harsh environments.
• Blade damage: Foreign object damage, fatigue cracks, or material degradation can affect blade performance.
• Seal leakage: Leaks in seals between stages reduce the pressure differential and overall efficiency.
• Bearing wear: Worn bearings increase friction and vibration.
• Imbalance: Uneven mass distribution leads to increased vibration and stress.

These mechanisms often interact, leading to compounded performance losses. Regular inspection and maintenance are essential to mitigate these issues.

Troubleshooting involves a systematic approach combining data analysis and physical inspection.

1. Data acquisition: Collect performance data, including KPIs and operating parameters. Analyze trends over time to identify deviations from normal operation. Note any unusual vibration levels or temperature changes.
2. Performance curve analysis: Compare measured performance against expected performance curves. Deviations highlight potential areas of concern.
3. Visual inspection: Perform a thorough visual inspection of the machine, looking for signs of fouling, erosion, corrosion, or damage. Pay attention to seal conditions and bearing wear.
4. Vibration analysis: Measure vibration levels at various locations to identify potential imbalances or mechanical issues.
5. Leak detection: Inspect seals and connections for leaks.
6. Non-destructive testing: Utilize techniques like ultrasonic inspection or dye penetrant testing to identify internal flaws or cracks in blades or shafts.
7. Computational Fluid Dynamics (CFD): For complex situations, use CFD simulations to model flow patterns and identify areas for improvement.

The specific troubleshooting steps will depend on the nature of the problem and the type of turbomachinery. A methodical approach and thorough data analysis are crucial for effective troubleshooting and minimizing downtime.

Instrumentation and data acquisition are the cornerstones of effective turbomachinery performance monitoring. Think of them as the eyes and ears of the machine, providing crucial data about its operational health and efficiency. Instrumentation involves strategically placing sensors throughout the turbomachinery to measure key parameters. This includes pressure, temperature, flow rate, vibration, speed, and acoustic emissions. Data acquisition systems then collect, process, and store this raw data, typically using sophisticated software and hardware. Without proper instrumentation, we’re essentially flying blind, unable to assess performance or identify potential problems. For example, pressure taps at various stages of a compressor allow us to track pressure rise and identify inefficiencies. Similarly, vibration sensors can detect early signs of bearing wear or blade imbalance, preventing catastrophic failures.

Various performance monitoring techniques exist, each with its own strengths and weaknesses. Let’s consider two common methods: online monitoring and offline performance testing.

• Online Monitoring: This involves continuous, real-time data acquisition using permanently installed sensors. Advantages include immediate detection of anomalies, allowing for proactive intervention. Disadvantages include potential for sensor drift or failure, requiring calibration and maintenance. The data volume can also be substantial, necessitating robust data handling systems.
• Offline Performance Testing: This is a more comprehensive, but less frequent, evaluation conducted under controlled conditions. Advantages include high accuracy and detailed analysis. Disadvantages are that it requires significant downtime and is more expensive to conduct. It also only provides a snapshot of performance at that specific time.

The choice depends on factors like the criticality of the equipment, budget constraints, and the desired level of detail.

My experience in data analysis and interpretation of turbomachinery performance is extensive. I’ve worked on numerous projects, from analyzing compressor maps to diagnosing turbine blade failures. A typical analysis begins with data cleaning and validation, ensuring the accuracy and reliability of the data. Then, we use various techniques, including statistical analysis, trend analysis, and advanced modelling to identify patterns and anomalies. For instance, I once investigated a sudden drop in efficiency in a gas turbine. By analyzing the pressure and temperature profiles across the turbine stages, combined with vibration data, we pinpointed the problem to a loose turbine blade. This led to a timely repair, avoiding a potentially catastrophic failure. Data visualization tools are crucial here – plots, charts, and 3D models help to reveal hidden patterns and anomalies within the data. We use specific software packages like MATLAB or specialized turbomachinery analysis software for efficient analysis.

Predictive maintenance relies heavily on performance monitoring data to anticipate potential failures before they occur. By analyzing trends in key performance indicators (KPIs), we can identify developing issues. For example, a gradual increase in vibration amplitude could indicate impending bearing failure. Similarly, a decreasing pressure ratio in a compressor might signal fouling or erosion. We develop statistical models and machine learning algorithms to predict the remaining useful life (RUL) of components. This allows for scheduled maintenance to be performed at the optimal time, minimizing downtime and maximizing operational efficiency. Think of it like regularly servicing your car—you don’t wait for it to completely break down, but instead perform preventative maintenance based on mileage and observed trends.

Surge and stall are two critical instability phenomena in compressors. Imagine a compressor as a pump pushing air.

• Surge: This is a violent, oscillatory flow reversal that can damage the compressor. Think of it like a wave crashing back up the pipe. It’s characterized by large pressure fluctuations and can lead to severe vibrations and potentially catastrophic failure. It’s often triggered by operating outside the compressor’s stable operating range, particularly at low flow rates.
• Stall: This is a localized flow separation on the compressor blades, where the airflow becomes detached from the blade surface. It’s a less dramatic event than surge but can significantly reduce efficiency and increase noise. It’s usually caused by exceeding the angle of attack of the blades –think of an airplane wing stalling.

Both phenomena can be avoided by proper compressor design and operational control. Monitoring pressure and flow characteristics is crucial to detect and mitigate these instabilities.

Calculating compressor pressure ratio and efficiency is fundamental to evaluating its performance.

• Pressure Ratio: This is the ratio of the compressor’s discharge pressure (P_d) to its inlet pressure (P_i). Pressure Ratio = Pd / Pi. A higher pressure ratio indicates a more effective compressor.
• Efficiency: There are various efficiency measures; adiabatic efficiency is commonly used. It’s the ratio of the ideal isentropic work to the actual work required to compress the gas. It’s calculated using the pressure ratio and the specific heat ratio (γ) of the gas, along with inlet and outlet temperatures. The formula involves logarithmic terms related to the isentropic process. Specialized software typically handles these calculations. For example, a compressor with an adiabatic efficiency of 85% means that 15% of the input work is lost as heat.

These calculations are crucial in evaluating compressor performance and comparing different designs or operating conditions.

Turbine blade failures can be categorized in several ways. Some common types include:

• Fatigue Failures: These are caused by repeated cyclical stresses, eventually leading to crack propagation and blade failure. This can be due to resonance, thermal stresses, or high-cycle fatigue. Think of repeatedly bending a paper clip until it breaks.
• Creep Failures: At high temperatures, materials deform permanently over time, leading to creep failures. This is particularly relevant for high-temperature turbines in gas turbines. The blades slowly elongate and eventually fail.
• Oxidation and Corrosion: Exposure to high temperatures and corrosive gases can degrade the blades, making them brittle and susceptible to failure. This is particularly relevant in combustion turbines.
• Foreign Object Damage (FOD): Impact with foreign objects, such as debris, can cause immediate damage. This can lead to sudden and catastrophic failure.

Understanding the root cause is vital for effective preventative measures. Careful material selection, improved design, and efficient operation are key in preventing these failures.

Optimizing turbomachinery operation using performance monitoring data involves a systematic approach. We start by collecting data from various sensors, including pressure, temperature, flow rate, speed, and vibration. This data provides a real-time snapshot of the machine’s health and efficiency. By analyzing this data, we can identify deviations from optimal performance, such as reduced efficiency, increased vibration, or abnormal temperatures. For example, if we observe a consistent drop in efficiency at a specific operating point, we can investigate the cause – perhaps blade fouling, a leak, or control system issues. Once the cause is identified, we can implement corrective actions, such as cleaning the blades, repairing the leak, or adjusting the control parameters. This iterative process, of data collection, analysis, corrective action, and monitoring, leads to improved overall operational efficiency and longevity.

Imagine a gas turbine in a power plant. By monitoring its exhaust temperature, we can detect if combustion is incomplete. A high exhaust temperature suggests inefficient combustion, potentially leading to increased fuel consumption and reduced power output. We can then investigate the fuel-air mixture ratio, check for issues in the combustion chamber, and adjust parameters to optimize combustion efficiency. This might involve adjusting the fuel injection system or implementing preventative maintenance on related components.

Vibration analysis is crucial for turbomachinery health monitoring because it provides early warning signs of developing problems. Excessive vibration can be a symptom of various issues, from imbalance and misalignment to bearing wear, blade damage, or even looseness in the foundation. We use various techniques, including spectral analysis and time-domain analysis, to analyze vibration data collected by accelerometers mounted on the machine. Identifying specific frequencies and amplitudes in the vibration signature helps diagnose the root cause of the problem. For instance, a specific frequency might correspond to a certain blade resonance frequency, indicating potential blade damage. By detecting abnormal vibrations early on, we can prevent catastrophic failures and costly downtime. Think of it as listening to the machine’s heartbeat; any irregularities in the rhythm indicate potential problems.

For example, a high amplitude at a specific rotational frequency might suggest an imbalance in a rotor. If the amplitude gradually increases over time, it indicates a worsening problem that needs immediate attention. Early detection allows for scheduled maintenance, preventing a sudden and catastrophic failure.

Oil analysis is a powerful predictive maintenance technique for turbomachinery. It involves regularly sampling the lubricating oil and analyzing its properties, such as viscosity, acidity, particulate contamination, and the presence of wear metals. Changes in these properties indicate potential problems within the machine’s internal components. For example, an increase in acidity suggests oxidation of the oil, possibly due to overheating. The presence of specific wear metals, such as iron or copper, can indicate wear in bearings or seals. By regularly monitoring oil condition, we can schedule maintenance before problems escalate and prevent unexpected failures.

Imagine a scenario where the oil analysis reveals an increase in iron particles. This could indicate wear in the bearings. We can then schedule a bearing inspection or replacement, preventing a potentially costly breakdown. Regular oil analysis allows us to adopt a proactive maintenance strategy instead of a reactive one, saving both time and money.

Throughout my career, I’ve extensively used various turbomachinery software and tools for data acquisition, analysis, and modeling. These include data acquisition systems (DAS) like those from National Instruments and Yokogawa, which allow us to collect real-time data from multiple sensors. For analysis, I frequently use specialized software packages like Siemens NX, ANSYS, and specialized turbomachinery performance prediction software packages. These tools provide comprehensive capabilities for data visualization, signal processing, fault detection, and performance prediction. I’m also proficient in using MATLAB and Python for custom data analysis and algorithm development. These scripting languages allow for sophisticated data manipulation, statistical analysis, and the development of customized diagnostic algorithms. Further, I have experience with various thermodynamic modeling software to simulate turbomachinery performance and to predict potential issues under different operating conditions.

Validating and calibrating performance monitoring instruments is crucial to ensure data accuracy and reliability. We use a multi-step process. First, we perform a pre-installation check to verify the instrument’s functionality and accuracy based on manufacturer specifications. This might involve checking sensor linearity, range, and resolution. Next, we install the instruments and perform an in-situ calibration against traceable standards. This often involves comparing the instrument’s readings against a known, accurate reference. For example, we might calibrate a pressure transducer using a high-accuracy pressure gauge. Regular calibration checks and comparisons against historical data help verify ongoing accuracy.

For example, we might use a calibrated flow meter to verify the accuracy of a flow measurement system. If significant deviations are detected, the system needs to be recalibrated or repaired to ensure the reliability of the collected data for performance analysis and decision making.

Safety is paramount during turbomachinery performance testing. We adhere to strict safety procedures, including lockout/tagout procedures to prevent accidental start-up during testing and maintenance. Personnel working near the machine must wear appropriate personal protective equipment (PPE), including safety glasses, hearing protection, and safety shoes. Furthermore, we conduct thorough risk assessments to identify potential hazards and implement appropriate control measures. Emergency response plans are in place to handle unexpected events, including the presence of trained personnel familiar with emergency shut-down procedures and first aid. Detailed operating procedures are followed to ensure that testing is carried out under safe and controlled conditions.

A pre-test safety review ensures everyone understands the risks and safety precautions. A clear communication protocol is essential during testing, allowing personnel to report any unsafe condition immediately.

Root cause analysis of turbomachinery failures involves a systematic investigation to determine the underlying causes of a failure. We use a combination of techniques, including failure mode and effects analysis (FMEA), fault tree analysis (FTA), and 5 Whys analysis. We start by gathering all available data, including historical maintenance records, operating logs, and sensor data. We then examine physical evidence, such as damaged components, to identify the immediate cause of the failure. We then apply systematic analysis techniques to trace the failure back to its root cause. For example, a failed bearing might be the immediate cause of a turbine trip, but the root cause might be improper lubrication, inadequate maintenance, or even a design flaw.

For instance, a compressor surge event might be due to an unexpected valve closure. However, the root cause may be a failure in the control system or inadequate instrumentation, which failed to respond adequately to the changing operating conditions. Once the root cause is identified, we implement corrective actions to prevent future failures.

Discrepancies between predicted and measured performance data in turbomachinery are common and often indicate underlying issues. The first step is a systematic investigation, ruling out simple measurement errors. This involves verifying the accuracy of instrumentation (pressure, temperature, flow sensors) and checking for calibration drifts. We then carefully review the underlying performance model used for prediction. Are the assumptions valid? Are the model parameters properly tuned to the specific machine and operating conditions?

Once measurement and model accuracy are confirmed, we look at potential causes within the machine itself. This could include:

• Fouling: Deposits on blades reducing efficiency.
• Erosion: Wear and tear affecting aerodynamic performance.
• Blade damage: Cracks, foreign object damage (FOD).
• Seal leakage: Reducing the overall system efficiency.
• Changes in operating conditions: Inlet air conditions or backpressure variations can lead to performance deviations.

We use diagnostic tools like performance maps, trend analysis, and root-cause analysis techniques to pinpoint the problem. For instance, a significant drop in efficiency at a particular operating point might suggest a problem with the nozzle guide vanes or rotor blades in a turbine. A systematic approach, combining careful data analysis with a good understanding of the turbomachinery’s operational characteristics, is crucial for effectively addressing these discrepancies.

Improving turbomachinery efficiency and reliability involves a multi-pronged approach focusing on both operational strategies and proactive maintenance.

• Optimization of operating parameters: Fine-tuning operating points based on real-time performance data can significantly improve efficiency. This often involves advanced control strategies using sophisticated algorithms.
• Predictive maintenance: Utilizing performance monitoring data to predict potential failures allows for scheduled maintenance, reducing downtime and preventing catastrophic events. This might involve vibration analysis, oil condition monitoring, or advanced diagnostic techniques.
• Blade design improvements: Advances in computational fluid dynamics (CFD) and airfoil design lead to improved aerodynamic efficiency. Regular inspections for erosion and fouling are also important.
• Seal upgrades: Replacing worn-out or inefficient seals minimizes leakage and improves efficiency.
• Regular cleaning and inspection: Periodic cleaning removes fouling deposits, restoring aerodynamic performance and efficiency. Regular visual inspections help identify any signs of damage or wear.
• Operator training: Well-trained operators can significantly improve efficiency through optimized control strategies and early detection of anomalies.

For instance, implementing a condition-based maintenance (CBM) program by analyzing vibration data to anticipate bearing failures can drastically reduce the risk of unexpected outages and maintenance costs.

My experience encompasses various turbomachinery control systems, ranging from simple PID (Proportional-Integral-Derivative) controllers to sophisticated model-based control systems. I’ve worked with both analog and digital control systems, including distributed control systems (DCS) and programmable logic controllers (PLCs).

• PID controllers: These are widely used for basic speed and pressure control. They are relatively simple to implement and maintain but might not offer the same level of precision and adaptability as more advanced systems.
• Model-based control: This involves developing a mathematical model of the turbomachinery to predict its behavior and optimize its performance. This technique allows for advanced control strategies like gain scheduling and predictive control, which are particularly useful in handling variable operating conditions.
• Adaptive control: This type of control system adapts to changing conditions automatically, maintaining optimal performance despite variations in operating parameters or environmental factors. This is often crucial in applications with significant process variability.
• Distributed control systems (DCS): These systems are commonly used in larger industrial applications, providing centralized control and monitoring of multiple turbomachines and related equipment. They offer advanced features like data logging, alarm management, and remote access.

In my previous role, we implemented a model predictive control (MPC) system on a large gas turbine, resulting in a 3% increase in efficiency and a significant reduction in emissions. The selection of the appropriate control system depends heavily on the specific application, cost constraints, and desired level of performance and reliability.

Environmental factors significantly impact turbomachinery performance. Ambient temperature, pressure, and humidity directly affect the density of the working fluid (air, gas, steam), influencing mass flow rate and power output. High ambient temperatures, for example, decrease air density, resulting in reduced power output for gas turbines.

Other environmental factors include:

• Altitude: Higher altitudes lead to lower air density, impacting gas turbine performance.
• Dust and particulate matter: These can cause erosion of blades and fouling, reducing efficiency and lifespan.
• Extreme weather conditions: Severe storms, icing, and extreme temperatures can affect operation and lead to unexpected shutdowns.
• Ingress of foreign objects: Wind-blown debris can damage compressor blades, impacting efficiency and reliability.

Performance monitoring systems must take these environmental factors into account to accurately assess machine performance and identify potential issues. For example, a gas turbine performance monitoring system would typically use a correction factor to account for the impact of ambient temperature and pressure on power output. This allows for meaningful comparisons of performance across different operating conditions.

Fouling, the accumulation of deposits on blade surfaces, significantly impacts turbomachinery performance. These deposits, which can consist of various substances depending on the application (e.g., salts, ash, or oil), disrupt the smooth airflow over the blades, causing increased drag and reduced efficiency.

The impact of fouling varies depending on several factors, including the type and amount of deposits, their location, and the machine’s operating conditions. In general, fouling leads to:

• Reduced efficiency: Increased drag reduces the energy conversion efficiency of the turbomachinery.
• Increased pressure drop: Fouling restricts airflow, leading to higher pressure losses within the machine.
• Increased vibration: Uneven deposits can cause imbalance, resulting in increased vibration and potential damage to the machine.
• Overheating: Reduced airflow can lead to increased blade temperatures, potentially causing damage.
• Surge or stall: In compressors, severe fouling can lead to surge or stall, causing operational instability and potential damage.

Managing fouling involves a combination of preventative measures (e.g., improved filtration, surface treatments) and mitigation strategies (e.g., regular cleaning, chemical washing). The economic impact of fouling can be substantial, so proactive measures are essential for maintaining optimal performance and reducing downtime.

Performance monitoring data is crucial for making informed decisions regarding maintenance schedules. Instead of relying solely on time-based maintenance, we use condition-based maintenance (CBM) strategies that leverage real-time data to predict potential failures and optimize maintenance schedules.

This approach involves:

• Trend analysis: Monitoring key performance indicators (KPIs) like efficiency, vibration levels, temperature, and pressure to identify gradual degradation.
• Alert thresholds: Setting thresholds for KPIs that trigger alerts when values deviate significantly from normal operating ranges. This allows for early detection of potential problems.
• Predictive modeling: Using historical performance data and advanced analytics to predict the remaining useful life (RUL) of components. This allows for proactive maintenance before catastrophic failures.
• Root cause analysis: Investigating the root cause of performance deviations to prevent recurrence and improve future maintenance planning.

For instance, by monitoring vibration levels, we might detect a bearing nearing failure weeks in advance, enabling scheduled replacement before it causes a major disruption. This proactive approach reduces unexpected downtime, improves safety, and optimizes maintenance costs.

One particularly challenging issue involved a significant drop in efficiency in a large industrial gas turbine. Initial investigations revealed no obvious mechanical damage. The performance degradation was gradual, making diagnosis difficult. We meticulously analyzed performance data, comparing it to historical trends and baseline performance maps. We found a slight, but consistent, increase in exhaust gas temperature, suggesting an issue with combustion efficiency.

Our investigation then focused on the combustion system. Through detailed inspection and analysis of the fuel system, we identified subtle variations in fuel delivery that were not readily apparent through routine monitoring. These small variations, over time, had led to incomplete combustion and reduced overall efficiency. We implemented modifications to the fuel control system to ensure more precise and consistent fuel delivery. The problem was solved through a systematic investigation using data analysis, a deep understanding of the system’s operation, and a combination of sophisticated data analytics tools alongside our deep understanding of the physical processes involved. The subsequent improvements were verified with post-modification performance tests.