Operator Training Simulator for Power Plant Combustion Optimization System Design

Don Labbe (Donald.Labbe@Invensys.com)Marlina Lukman (Marlina.Lukman@Invensys.com)Robert McHugh (Robert.Mchugh@Invensys.com)Invensys Operations Management

Keywords: Operator Training Simulator, OTS, Optimization, Combustion Optimization System, Once Through Boiler

ABSTRACT

An Operator Training Simulator (OTS) provides many valuable services in support of power plant operation and design. The primary objective is operator training for start-up, shut downs, load management, equipment malfunctions and special case scenarios. Such training provides substantial operating cost savings by lowering unforced outage rates due to operator error and speeding startup and shutdown rates for additional revenue and fuel savings. OTS systems have supported control system upgrades and plant design enhancements by confirming design changes prior to installation in the actual plant. Such capability has become increasingly important for cyber security compliance.

In this project a Combustion Optimization System (COS) was integrated with an OTS, comprised of both a process model and a complete emulation of the DCS control design. The OTS process model includes models of the air and fuel within the furnace and heat transfer models, allowing it to provide detailed predictions of emissions values, steam temperatures, spray flows and other parameters impacted by adjustments to fuel and air distribution. The complete emulation of the DCS control design allowed the full integration of the COS interface logic and graphic displays. The integrated system was applied to conduct parametric testing for COS model development. The faster than real time feature coupled with the full time availability provided for rapid generation of modeling data. The design of the COS was confirmed and potential benefits were quantified. Operator graphics and operating scenarios were available to train operators in the details of the impact of COS on operations. By applying the OTS, the interface logic and graphics were fully designed and checked out with preliminary models prior to touching the actual plant. This greatly reduced any operational concerns to the plant for the implementation of the COS and is yet another illustration of the value of an OTS.

Distributed with permission of authors by ISA 2013Presented at ISA POWID 2013; http://www.isa.org

INTRODUCTION

The traditional role of an operator training simulator (OTS) has been to provide operator training for start-up, shut downs, load management, equipment malfunctions and special case scenarios. High fidelity OTS provides operator training with a close approximation of the specific operating characteristics of the unit. Such training has demonstrated significant reduction in operator error, speeds the operator response to undertake corrective action, and enhances startup and shutdown rates. The net impact is substantial operating cost savings by lowering unforced outage rates due to operator error, extending equipment life through stress mitigation, and fuel savings through faster start-ups and shut-downs.

The role of high fidelity OTS has expanded beyond operator training to support a wide range of unit related activities such as control logic modifications, control system upgrades and plant design enhancements. The OTS can confirm design changes prior to installation in the actual plant. DCS logic confirmation has become increasingly important for cyber security compliance.

However, applying OTS for unit heat rate and emission optimization lies beyond the typical application domain. The required modeling demands a rigor beyond the typical high fidelity OTS framework. A project was undertaken to explore the integration of a Combustion Optimization System (COS) with an OTS and this paper describes the methods and results. The OTS featured sophisticated air and fuel modeling within the furnace along with water/steam heat transfer models that provided the framework for detailed predictions of emissions values, steam temperatures, spray flows and other performance parameters. Through the OTS, adjustments in fuel and air distribution undertaken by COS simulate the impact on emissions and thermal performance.

Other components of the OTS included a complete emulation of the DCS control design including graphics. This provided the means to undertake a full integration of the COS interface logic and the associated graphic displays.

The simulated unit is a pulverized coal fired supercritical once thru unit rated at approximately 800 MW gross featuring a split furnace design with 7 coal mills and 8 corners for fuel and air injection with three levels of newly installed over-fire air. The simulator has been verified against an actual power plant.

Distributed with permission of authors by ISA 2013Presented at ISA POWID 2013; http://www.isa.org

OPERATOR TRAINING SIMULATOR (OTS) BACKGROUND

Figure 1: Overview of Operator Training Simulator

The figure above illustrates that there are two major software modules to an OTS: a dynamic simulation module, that replicates the actual plant, and a control system module, that emulates the control system. A simulation executive module coordinates the two modules, synchronizing time control of the two modules. Time can be allowed to advance slower or faster than real time. The state of the dynamic simulation module and the control system module can be saved as an initial condition, which can be re-loaded to allow simulator operation to begin at a fixed condition.

DYNAMIC SIMULATION MODULE

This module undertakes the mathematical modeling of the plant using comprehensive, rigorous, field proven dynamic process simulation software. The model of this power plant features the primary components of the plant, including the water and steam systems, the fuel, air and flue gas systems, and the auxiliary systems. The water and steam systems include detailed models of the steam turbine, condenser, deaerator, feedwater heaters, pumps, steam and water piping systems, and the heat transfer surfaces comprising the boiler. The fuel, air and flue gas systems include detailed models of the pulverizers, fans, ducting, dampers, furnace enclosure, and air heater. The auxiliary systems include models of the cooling water, lubrication, and electrohydraulic system. The models are dynamic,

Distributed with permission of authors by ISA 2013Presented at ISA POWID 2013; http://www.isa.org

predicting transient behavior by using equations based on conservation of mass and energy and appropriate rate equations.

The furnace model divides the furnace into several discrete volumes, including discrete volumes for each elevation where fuel and air are introduced into the furnace and accounting for the side to side variations in fuel and air flows. Calculations for each volume account for the mass transfer, heat transfer, and chemical reactions taking place in the volume. Mass transfer accounts for the vertical and horizontal movement of the gas. Heat transfer accounts for various modes of heat transfer from the volume to the boiler heat transfer surfaces, including flame and gas radiation and convection. The chemical reaction models produce a time-varying heat release, gas composition, and temperature profile throughout the furnace.

The main steam temperature leaving the boiler is measured along the four paths providing steam to the main steam header and the heat transfer surfaces are modeled to account for the possibility of different heat absorptions along each path. The reheat steam temperature leaving the boiler is measured along the two paths providing steam to the hot reheat header and the reheat heat transfer surfaces are modeled to account for the possibility of different heat absorptions along each path.

This level of detail allows the simulator to describe the behavior of the plant over a wide range of conditions, For example, the model predicts the changes in heat absorption by the various in-furnace heat exchangers due to changing the distribution of air between primary and secondary levels or among the secondary air dampers.

Models of CO and NOx production are also included in the OTS model. These models are empirical, rather than rigorously derived from first principles. The emissions models predict the CO level based on oxygen concentrations at different furnace elevations and secondary over fire air damper positions. The emissions models predict the NOx level based on oxygen concentrations at different furnace elevations and burner tilt positions (Reference 1).

CONTROL SYSTEM MODULE

This module is where the actual DCS is being emulated. In this case the plant control algorithms are matched one to one using emulation software. The emulation is, in essence, identical to the DCS software; providing a virtual controller instead of actual controller hardware by executing the controller software on the simulation workstation rather than a real-time control processor. The control emulation was based on exactly the same computer software (source code) as the actual DCS. This enables the load of actual configuration files from a project directly into the simulator without any need for translation. In addition, since it behaves just like a normal DCS system, any peer-to-peer stations on the control network will connect to the control emulation without any need for re-configuration. This includes graphic workstations, engineering stations, historian packages, off-platform network connections, etc. And it guarantees that the behavior of the control system and connected graphics screens function on the OTS just like they do on the main control system.

Distributed with permission of authors by ISA 2013Presented at ISA POWID 2013; http://www.isa.org

For this simulator, the control configuration and graphics are identical to the actual DCS as the OTS is configured from the same files as the actual DCS and uses actual DCS operator console hardware. This integrated simulator then provides an environment for operators to practice a wide range of activities, including start-up, shut-down, raising or lowering load, and responding to emergency situations such as equipment failures.

In this project the simulator also provides an environment for operators to observe and understand the consequences of various actions on heat-rate and emissions, for example, how increasing excess air may decrease heat-rate but also decrease the amount of CO production and increase NOx production.

PROJECT OBJECTIVE

The intent of this project was to demonstrate that COS could be integrated with the OTS plant controls and that COS could drive the unit to an optimum condition of reduced emissions and improved thermal performance. The selected optimization control variables were furnace NOx, CO, O2 and steam temperatures, since these are the dominant emissions and thermal performance variables in once through coal fired application.

COS – OTS INTEGRATION METHODOLOGY

The COS interface to a DCS involves a degree of complexity and security, since COS exports control signals to an active control system. These exported signals automatically modulate key manipulated variables and drive the plant to optimum conditions for emissions and thermal performance. By first implementing within the OTS environment, the control interface design is confirmed prior to commissioning on the DCS.

A staged approach was undertaken to establish the COS – OTS Integration: Link the COS computer and software to the OTS control system module and demonstrate COS

data collection Apply COS software to conduct Pseudo Random Binary Sequencing (PRBS) testing of the

dynamic simulation module in support of the generation of COS controller models Configure the COS controller models and optimization strategy Configure the COS control interface to the OTS for closed loop optimizing control Operate the OTS and enable COS to simulate emissions and thermal performance changes Assess the optimization performance

The OTS control system module software was compliant with current NERC Critical Infrastructure Protection (CIP) requirements. The COS computer and software were linked to the OTS consistent with these requirements. Software communications were established applying a proprietary protocol and COS data collection was established. The integrated system was applied to conduct parametric testing using PRBS testing for model development. The faster than real time feature coupled with the full time availability of the simulator provided for rapid generation of modeling data.

Distributed with permission of authors by ISA 2013Presented at ISA POWID 2013; http://www.isa.org

The control variables (CVs) are the process variables that the COS will optimize. For this work the selected CVs included: NOx, CO, O2, superheat temperature, reheat steam temperatures, and other select variables. These are representative of the crucial control variables necessary to achieve emissions and performance benefits.

The manipulated variables (MVs) are the input signals generated by the COS and sent to the DCS. For this work the selection of MVs was based on prior experience with similar boilers and the desire to simplify the demonstration yet retain the dominant variables. The following MVs are included: O2 controller setpoint bias, windbox controller setpoint bias, and a number of damper bias signals.

The COS interface to the DCS includes a secure supervisory control system which employs watch dogs and other techniques within both the DCS domain and the COS domain to ensure continuous and secure communication. Should a loss of communication be detected, each manipulated variable falls back automatically according to the specific individual configuration. All transfers to and from normal control and COS optimization are bumpless.

The emissions models for NOx, CO and O2 distribution within the OTS are empirical and not intended to be rigorous. When COS is applied to the actual unit, the emissions models within COS would be finalized based on the emissions data from the actual unit. An on-line model adaptation technique can be applied to finalize the COS emissions models on an operating unit (Reference 2).

RESULTS

The Delta Heat Rate Methodology (Reference 3) was applied to quantify the performance benefits.

Figure 2 presents a trend chart of 16 pens in groups of four and illustrates: The delta efficiency, delta NOx, furnace stoichiometry and furnace O2

#1, 2, 3, and 4 finish superheat steam temperatures NOx, CO, A & B side reheat steam temperatures A, C, E and G level furnace stoichiometry

This trend graphic illustrates the results for the 1st hour after enabling the optimizer. The key performance variables impacting efficiency (heat rate) are furnace excess air (O2) and superheat and reheat steam temperatures. A significant reduction in furnace excess air is achieved without a significant increase in CO, providing an efficiency improvement of 0.31% within the hour. The furnace stoichiometry is adjusted to a more favorable range and achieves a NOx reduction of 9.7%. Superheat and reheat steam temperatures are maintained near setpoint throughout.

Distributed with permission of authors by ISA 2013Presented at ISA POWID 2013; http://www.isa.org

Figure 2: COS Performance Trends

Figure 3: COS Trend: NOx, CO, O2, Reheat, Air Heater and Superheat Temperatures

Distributed with permission of authors by ISA 2013Presented at ISA POWID 2013; http://www.isa.org

Optimizer Enabled

Baseline CO increased, prior to enabling

Figure 3 presents the emissions and key thermal performance parameters following the enabling of the optimizer. This COS trend charts below present more detail of the unit response and optimizer action and displays the following variables:

NOx and O2 corrected NOx in ppm CO in ppm Furnace O2 controller measurement and setpoint Reheat A/B side steam temperatures The air heater A/B side gas outlet temperatures The four superheat outlet temperatures

Just prior to enabling the optimizer, the baseline CO level used in the simulator was adjusted to a higher and more typical value. Since CO is a key feedback to combustion optimization, a higher baseline would increase the CO sensitivity and challenge the optimizer to achieve improved air distribution and lower the O2.

After enabling the optimizer the O2 setpoint bias and other MVs transitioned towards their optimum targets, a solution representing optimum performance. The steam and gas temperatures responded to the MV moves, but settled soon after at near optimum values. The calculated benefits are displayed in the following trend.

Figure 4: COS Trend: Δ NOx and ΔEfficiency including gross MW, Fuel and Btu/kw-hr

Distributed with permission of authors by ISA 2013Presented at ISA POWID 2013; http://www.isa.org

Gross Efficiency

Figure 4 presents the delta efficiency values (based on Delta Heat Rate Methodology) and the calculated gross unit heat rate displaying the following variables:

Percentage NOx reduction Percentage efficiency improvement total and by variable: superheat, reheat and air heater exit

gas temperatures and O2 Gross unit efficiency, Btu/kw-hr Gross MW Total fuel, tons/hr

The Delta Heat Rate Methodology approximates a total heat rate improvement of 0.332%, which compares favorably to the 0.255% heat rate improvement based on a comparison of the before and after ratio of total fuel consumption to gross MW generation, shown in Figure 4. Assuming 70% capacity factor and $50/ton the projected annual fuel savings for 0.332% is ~$380,000.

To further investigate the performance of the optimizer an additional constraint was placed on the optimizer to balance the four superheat steam temperatures, thereby increasing the average steam temperature. The trends below illustrate the results of a change to the superheat temperature constraint.

Figure 5: Sensitivity results of Superheat Steam Temperature Constraint

Distributed with permission of authors by ISA 2013Presented at ISA POWID 2013; http://www.isa.org

The initial conditions show differences between the four finish superheater outlets, which is quite typical for large boilers. The #2 finish superheat steam temperature was operating approximately 10°F cooler than setpoint. Since balanced steam temperatures can improve turbine cycle performance, the side to side superheat temperature difference constraint was tightened from 5°F to 0.5°F. The response illustrates an increase in the #2 superheat steam temperature and impacts emissions performance.

The response is not representative of final tuning, but this example demonstrates the “give and take” characteristics of a multivariable optimizer. Action is undertaken by the MVs to address CV constraints with the ultimate response dependent on assigned tuning weights and optimizer importance factors. For example, the CO increases slightly due to the action undertaken to balance superheat steam temperature which increases the O2 setpoint.

CONCLUSIONS

The COS was successfully integrated to an OTS featuring current NERC CIP compliance. The OTS served as an effective platform to conduct parametric testing, design the COS interface control logic, build operator graphics, commission the COS and evaluate COS performance.

The integrated optimizer/simulator demonstrates key principles of the COS operation potentially providing operators and engineers with insight to the incremental performance improvements available to the unit. The system illustrates how both heat rate and emissions objectives can be addressed through multivariable optimization. With such a tool the operators and engineers can widen their optimization experience and further the achieved benefits.

The design of the COS was confirmed and potential annual fuel saving benefits of $380,000 were quantified. Operator graphics and operating scenarios were available to train operators in the details of the impact of COS on operations. By applying the OTS, the interface logic and graphics were fully designed and checked out with preliminary models prior to touching the actual plant. This not only greatly reduces any operational concerns to the plant for the implementation of the COS, but addresses NERC CIP requirements for control validation prior to installation. The COS-OTS integration is yet another illustration of the value of an OTS.

REFERENCES

1. Influence of Process Parameters on Nitrogen Oxide Formation in Pulverized Coal Burners, R. P. van der Lans, P. Glarborg, K. Dam-Johansen, Prog. Energy Combust. Sci. Vol. 23, pp. 349, 1997.

2. Smart Firing Control System, by C. Houn, B. Begley of Wisconsin Public Service and D. Labbe, T. Kinney, A. Morrow, and A. Speziale of Invensys Operations Management, presented at the 55th

ISA POWID Controls & Instrumentation Symposium, June 3-8, 2012, Austin, TX.3. Entergy Independence NOx/Heat Rate Optimization and Steam Temperature Control with

Neural Net/Model Predictive Control Combo, by D. Labbe, A. Speziale and S. Coker, presented at the 15th Annual Joint ISA POWID/EPRI Controls and Instrumentation Conference, 48th Annual ISA POWID Symposium, 5-10 June 2005, Nashville, TN.

Distributed with permission of authors by ISA 2013Presented at ISA POWID 2013; http://www.isa.org