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The Next Generation of Steam Plant ControlsUnderstanding how advanced distributed control system capabilities drive optimized production

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Introduction

The increase in energy-only resources is placing additional demands on traditional methods for energy production such as steam cycle generation. These new demands include meeting profitability goals, environmental constraints, and different types of production schedules that may not have been envisioned by the original designers of the steam plant.

Optimizing the plant still implies running the plant at maximum efficiency with no wasted fuel, but adds new emphasis on oper-ating consistently without unplanned outages. It also requires maximizing profitability by running the plant according to changing production schedules or derates, which are based on external conditions such as the attempt to integrate more energy-only renewable resources. Given the tools that are deployed at most steam plants today, there is a clear need for increased operational insight and advanced control to meet these modern challenges.

As many plants seek to replace their older, existing distributed control systems (DCS), the selection of the next DCS is a critical decision that will significantly affect their current and future ability to achieve true process optimization. Forward-looking plants that understand the technological capabilities required to address the new constraints and modern demands—while providing flexibility and scalability for future growth—need to look to a next-generation DCS solution that enables increased responsiveness, flexibility, and reliability.

This paper discusses some key DCS specifications to consider that enable plants to leverage critical insight and uncover additional opportunities for optimized performance. Readers will understand some of the factors driving the need for advanced capabilities, including an external market and increasing environmental regulations, and the central role a DCS plays in taking process optimization to a new level and enabling maximized profitability today and into the future.

Enhancing Classic Boiler OptimizationTo begin any type of process optimization, the foundation lies in the quality and comprehensiveness of the operational data at hand. Plants must have the ability to collect data from all dispa-rate sources across their operations for a consolidated view, enabling further analysis and contextualization, and to use that information to drive continuous improvements. The visibility and operational insight that comes from data aggregation supports intelligent decision making needed for process optimization.

Continuous analysisBased on the premise of classic boiler optimization—highest output for minimal input—a plant engineer today may believe that the output of the boiler is optimal given the amount of fuel used over a specific period of time or run cycle. However, the shortcoming with this static approach is that it is an after-the-fact analysis done under “steady-state” conditions, which may not uncover all the optimization opportunities.

To truly understand a plant’s operation, data is needed over shorter periods of time, and the analysis needs to be performed continually through the time period of the run cycle. When itera-tive analysis takes place at different times during the process cycle as opposed to only steady-state conditions, it can reveal findings that may otherwise go unnoticed.

For example, does the plant operation vary over time? In what areas do the variations occur? What are the causes? Without performing this iterative analysis, it is difficult to understand and address the reasons behind sub-optimal performance.

As a starting point, a steam generator needs to consider what data is available for analysis and whether that data provides the richness required to perform this iterative analysis. If the plant is storing data for temperature, fuel consumption, O2, water consumption, water level, pressure, MWh output, etc. at a frequency rate of two seconds or less, trending and other analysis can help plants gain the insight needed to balance the various parameters for optimization.

The Next Generation of Steam Plant Controls

Figure 1 Continuous analysis helps increase visibility and can reveal find-ings that may otherwise go undetected—providing the critical insight needed to balance key parameters over time.

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Figure 2 Intuitive Operator Interface enables optimized performance and output.

Real-time control For most steam plants today, variations are caused by minor adjustments that are made manually by operators, either too soon or too late to keep the plant operating optimally. They result in problems such as excess air, insufficient water flow, too much fuel, pressure levels that are too high/low, and incorrect temperature.

To ensure boiler optimization, a next-generation DCS solution can preempt the need for human intervention with automated real-time control that eliminates manual tweaks to the system that result in sub-optimal processes. The real-time capability enables minor adjustments to be made to the system immediately to continually keep the process at optimal levels, regardless of air and water temperature, fuel consistency, fuel load imbalance, blocked grates, reduced air flow, or any number of other variables that affect the operation.

Inherent in a next-generation DCS solution is a closed-loop system, which runs with advanced controllers connected to all control points and gages, with high-speed data storage and opti-mization from a centralized system around the classic parameters of minimal input to maximum output. The DCS solution controls the fuel, air, water level and other turbine auxiliaries with auto-mated tools and algorithms, which are proven to enable enhanced precision and better performance over manual plant operations.

Consistent performanceAn integral part of boiler optimization is consistency, which a closed-loop DCS solution can deliver by continually monitoring and controlling the various parameters that impact production. Anytime a steam plant is operating in a steady-state mode, the system will make all the necessary minor corrections to sustain the optimized state—enabling the boiler to run better so the plant runs better.

The control strategies that enable this consistency are usually programmed into the controller in the master controls station by the plant engineers or consultants to automatically respond to varying steam loads, control flow, and pressure levels. Furthermore, the controls need to provide the flexibility to quickly compensate for fuel variability changes with minimal upset to the process.

Moving Beyond Classic OptimizationThe advanced capabilities that a next-generation DCS delivers can significantly enhance the ability for steam plants to realize true process optimization on a consistent real-time basis, a change from the way classic optimization has traditionally been approached. In addition, there are other critical technology

considerations as discussed in the following sections that provide strategic operational advantages—potentially paving the way for steam cycle plants to meet the demands and new constraints of today’s power generation landscape.

Integrating Intelligence to Maintain Optimum OutputData-driven decision makingThe optimization of a plant can actually mean several things. In the classical case above, it is about using the lowest amount of fuel for the maximum amount of output on a consistent basis, which is the steady state goal of an automated DCS. But the tuning of the system can be enhanced through the closed-loop expert system of a next-generation DCS to include additional parameters and constraints—redefining optimization based on non-classic parameters.

For example, permitting regulations may require that the firebox not produce more than a specific amount of SOX and NOX with financial penalties imposed for exceeding the permitted values; optimization must then be redefined based on these byprod-ucts rather than the classic definition. However, minimizing SOX and NOX may or may not correspond to the classic optimization definition, depending on the plant production schedule and the limitations of a particular regulation.

What if the plant design dictates that if the plant runs at more than 80% capacity, the regulatory limits cannot be met? This does not necessarily mean that the plant can directly scale its operating parameters down 20% and still get the lowest fuel to

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highest output ratio; rather, these additional constraints must be evaluated with all data available from the plant as a system, and a next-generation DCS solution needs to integrate analytical intelligence to contextualize the data into actionable information.

Thus, as optimization is continually redefined with increasing regulations and other constraints, the data collected from the DCS and the knowledge extracted from it becomes vitally impor-tant in enhancing plant performance. Going back to the example mentioned above, the SOX and NOX limits must be monitored along with the fuel, combustion control, and feed water, which must all be regulated in unison to achieve this goal.

Having the standard parameter settings for classic optimiza-tion is the starting point, but the system must learn the correct setting, taking into account each variable, to meet the SOX

and NOX constraints.

Protecting Against Failure and Maximizing UptimePredictive analysisOnce the operating data from classic or constrained optimiza-tion is stored in the database, other valuable analysis can be undertaken to review operations of individual systems and pieces of equipment. If the standard heat, energy, or output data from the system or equipment begins to change over time or loading conditions, the central control system should have the ability to predict operational problems and alert the operator to review the data and take corrective action.

For example, in the feed water control system, if the intake water flow starts to decrease and the amperage or temperature of the main pump increases, the pump could be having problems or a valve could be stuck. The central control system should be able to provide early and actionable insight by identifying a looming problem such as a pump failure well before it happens—resulting in increased reliability and availability, and in turn, greater productivity at lower costs. A planned derate of the plant is less expensive than an emergency shutdown.

Predictive analysis may involve additional monitoring capability such as ammeters or vibration analyzers. These may not have been considered in the past because without a central system to track them in real-time, their value may have been question-

The Next Generation of Steam Plant Controls

A next-generation DCS solution that helps plants

transform data into actionable information with an

understanding into the interrelationships between

variables enables intelligent decision making — a key

advantage for driving process optimization.

Figure 3 Data-driven decision making can help plants evaluate additional parameters and constraints with all data available from the plant as a system to achieve optimum efficiency.

FUEL

LO

SS

Optimumefficiency

Fuel Losson Gas

EXCESS O2

35

30

25

20

15

10

5

00 1 1.5 2 4 6 8 10 12

CO LossLoss at 600FLoss at 400F

Advanced Control to Achieve Optimum Efficiency

System Monitors and Controls Excess Air and Combustibles

• Optimize Fuel Usage• Maximize Safety• Reduce Stack Emissions

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able. But monitoring and real-time analysis can now take the plant operations to the next level of minimizing planned outages, which is evermore critical with the increasing risks associated with missing operating schedules.

Critical alarm managementThe essence of alarms is to notify operators of an issue or upset event such as an out-of-spec condition, but too many alarms can inundate operators and deter productivity. The risk of not addressing alarms correctly or at all can lead to unsafe condi-tions and operational failures. Operators need the ability to efficiently prioritize and respond to the most critical alarms with the proper corrective action while keeping secondary alarms silenced for follow up at a later point.

A DCS solution that encompasses advanced critical alarm management can provide a logical hierarchy and guide opera-tors through proper procedures—helping plants take automatic action on the most pressing needs in priority order, responding with the right action every time. It can also detect multiple alarms related to the same cause.

For example, if plant electric power is lost, it is not desirable to have every pump in the plant alarming, swamping the operator with an indication that water flows are decreasing. The logic function of the DCS solution should be capable of performing root cause analysis and presenting the operator with the source of the problem and triggering the workflow to help troubleshoot the critical alarms, enabling quick resolution—or in some cases, preventing problems before they occur—to drive maximum uptime.

Figure 4 With the ability to compare actual measurements with ideal models, operators can detect deviations and leverage causal analysis to identify contributing parameters and predict operational problems before they occur.

Figure 5 Logic like this can be used to build alarm responses by linking multiple alarms, providing information about common causes.

Ensuring Flexibility and ResponsivenessOperating a capacity resource in a central marketToday, hydro generation and spinning reserves are used to balance loads and resources in many control areas. As more wind and solar are added to the grid and loads increase, there may not be enough natural gas spinning reserves to provide these ancillary services necessary to balance the control area. Additionally, hydro plants are experiencing more constraints due to environmental issues such as water levels and temperatures to meet fish habitat requirements—limiting the ability to make sudden increases or decreases to flow.

With these oncoming trends, how can steam boiler plants posi-tion their production to flexibly respond to increasing market prices for reserves?

When the price is high enough, for example, a plant owner may consider committing 80% of the capacity into the market and keeping 20% as a spinning reserve for control area balancing. To meet this challenge, the DCS solution has to optimize the plant based on a constant state of change—requiring the ability to automatically adjust to fluctuating production schedules and spinning reserve calls. A DCS that delivers this type of flexibility can open new options for bidding strategies to the owner, who can take advantage of financial rewards for flexible operations.

Managing Environmental ComplianceCentralized data platformA final consideration on the future of steam production is that environmental regulations are not going away. A centralized

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The critical need to optimally balance various parameters is inevitably changing the way steam cycle plants must operate to remain competitive and profitable, and in turn, requiring the capabilities of a next-generation DCS solution to support them. The type of DCS implemented cannot be overstated as it can profoundly affect a plant’s ability to succeed today and into the future.

As plants look to replace their older control systems, specifica-tions in the new solution should be evaluated in the context of whether they enable true process optimization consistently, taking into account evolving constraints that affect profitability goals, production schedules, and operational processes.

A DCS solution is a significant investment that sets the direc-tion of how successfully plants can operate, and those that take a forward-looking approach can be better positioned to optimize their output—regardless of fluctuating demands and constraints—increasing productivity and profitability to gain a sustainable competitive advantage.

data platform that consolidates and contextualizes information from multiple systems can provide real-time insight that helps plants understand and affect the true drivers of consumption and emissions. It is key to providing a single viewpoint needed to effectively balance environmental and operational performance.

As steam plants install or replace their automation systems such as DCS, ensuring a technology architecture that enables consolidation of all plant data in one central repository will ease access to environmental compliance data and reporting as well as provide the critical foundation for deeper analytics—driving continuous improvements. Integrating these capabilities as part of the feedback loop upfront can ensure operational optimiza-tion, regardless of evolving environmental regulations.

Conclusion

With changing demands in today’s energy environment, the need for steam plants to maximize production with minimal input is still imperative, but now they must also look ahead and beyond their own operations—factoring in external constraints and the increasing integration of renewable resources, along with the financial opportunities of participating in a central market.

The Next Generation of Steam Plant Controls