Abstract: A pressure and flow calculation technique can be used in a distributed process network simulation system that uses the sequential solving method to perform better or faster simulations of a process flow, especially with respect to process junction nodes at which flow either converges or diverges. The pressure and flow variable determination technique uses a grouped node identification technique that identifies a local set of nodes for each junction node of the process network to use when solving for the pressure at the junction node, a grouped node iteration technique that uses the grouped set of nodes at each junction node to perform iterative pressure calculations at the junction node, and a flow-based pressure calibration technique at each junction node to enable the system to perform highly accurate pressure and flow variable determination at each junction node in real-time.

Abstract: A system and method for operating a remote plant simulation system is disclosed. The system and method uses a light application at the plant to collect relevant data and communicate it to a remote plant simulation. The remote plant simulation uses the relevant data, including data from the actual process, to create a process simulation and communicate the display data to the light application operating at the plant where it is displayed to a user. The remote system offers the advantage of offering decreased cost and improved simulation as the equipment cost, operator cost and set up cost is shared by a plurality of users. Further, the data may be stored remotely and subject to data analytics which may identify additional areas for efficiency in the plant.

Abstract: Embodiments of methods and systems for controlling a load generated by a power generating system may include controlling at least a portion of the system using model-based control techniques. The model-based control techniques may include a dynamic matrix controller (DMC) that receives a load demand and a process variable as inputs and generates a control signal based on the inputs and a stored model. The model may be configured based on parametric testing, and may be modifiable. Other inputs may also be used to determine the control signal. In an embodiment, a turbine is controlled by a first DMC and a boiler is controlled by a second DMC, and the control signals generated by the first and the second DMCs are used in conjunction to control the generated load. Techniques to move the power generating system from Proportional-Integral-Derivative based control to model-based control are also disclosed.

Abstract: An optimization and control system for a utility plant that uses fan based air cooled condensers controls the operation of the power generation system at the plant in conjunction with the operation of the air cooled condensers so as to run the power plant at an optimum operating point associated with minimizing or reducing the cost energy produced by the plant. The optimization and control system includes an optimizer having a numerical solver that determines values for a set of control variables associated with an optimal operating point of the plant and an expert system that oversees and modifies the control variable settings prior to providing these settings to a plant controller. The numerical solver uses an objective function and one or more models of plant equipment to determine the operating point of the plant that minimizes the cost per unit of useful energy generated by the plant.

Abstract: The present invention relates generally to process control systems and devices and, more particularly, to an apparatus for and a method of implementing redundant controller synchronization for bump-less failover during normal and mismatch conditions at the redundant controllers. The redundant controllers are configured to transmit state information of the process control areas of the primary controller to the backup controller that is necessary for synchronizing the redundant controllers but is not typically transmitted to other devices during the performance of process control functions. Synchronization messages are transmitted from the primary controller to the backup controller each time one of the control areas executes to perform process control functions.

Abstract: Example methods and apparatus to arbitrate valve position sensor redundancy are disclosed. A disclosed example method comprises measuring a first value representative of a position of a valve, measuring a second value representative of the position of the valve, computing a first estimated position of the valve, selecting one of the first and second values based on the first estimated position, and generating a first valve control signal for the valve based on the selected one of the first and second values.

Abstract: A technique of implementing performance monitoring in a power plant is appropriate to control operating parameters and factors connected with the efficiency of the energy production process in an energy marketplace that is more complex than in the past, and that takes variable costs besides the cost of fuel into account, e.g., environmental credits, equipment degradation and repair costs, electrical energy trade market factors like ramp rate, LMP (Locational Marginal Pricing) factors, the ability to deliver contracted power levels and spot transactions, etc. The technique applies a statistical analysis to collected power plant data to determine the factors that are best controlled or changed to affect (increase) the efficiency or other primary performance indication of the plant, as well as to establish baseline or best-possible operational constraints to be used to control the plant in the future.

Abstract: A statistical performance evaluation system for a thermodynamic device and process uses the achievable performance derived from statistics and real-time data for the device or process to evaluate the current performance of the device or process, and to adjust the operations of the device or process accordingly, or provide feedback to an operator or other monitoring system for taking corrective actions to obtain performance approaching the optimum achievable performance. The achievable performance of the device or process is derived from data collected during operational periods when the best achievable performance is anticipated, such as after maintenance is performed, and supersedes the ideal or design performance specified by the manufacturer, which typically does not represent the actual operating conditions in the field, as the basis for evaluating the real-time performance of the device.

Abstract: A technique of controlling a steam generating boiler system using dynamic matrix control includes preventing saturated steam from entering a superheater section. A dynamic matrix control block uses a rate of change of a disturbance variable, a current output steam temperature, and an output steam setpoint as inputs to generate a control signal. A prevention block modifies the control signal based on a saturatec temperature and an intermediate steam temperature. In some embodiments, the control signal is modified based on a threshold and/or an adjustable function g(x). The modified control signal is used to control a field device that, at least in part, affects the intermediate steam and output steam of the boiler system. In some embodiments, the prevention block is included in the dynamic matrix control block.

Abstract: A technique of controlling a steam generating boiler system includes using a rate of change of disturbance variables to control operation of a portion of the boiler system, and in particular, to control a temperature of output steam to a turbine. The technique uses a primary dynamic matrix control (DMC) block to control a field device that, at least in part, affects the output steam temperature. The primary DMC block uses the rate of change of a disturbance variable, a current output steam temperature, and an output steam temperature setpoint as inputs to generate a control signal. A derivative DMC block may be included to provide a boost signal based on the rate of change of the disturbance variable and/or other desired weighting. The boost signal is combined the control output of the primary DMC block to more quickly control the output steam temperature towards its desired level.

Abstract: A technique of controlling a steam generating boiler system includes dynamically tuning a rate of change of a disturbance variable (DV) to control operation of a portion of the boiler system, and in particular, to control a temperature of output steam to a turbine. The rate of change of the DV is dynamically tuned based on a magnitude of an error or difference between an actual and a desired level of an output parameter, e.g., output steam temperature. In an embodiment, as the magnitude of the error increases, the rate of change of the DV is increased according to a function f(x). A dynamic matrix control block uses the dynamically-tuned rate of change of the DV, a current output parameter level, and an output parameter setpoint as inputs to generate a control signal to control a field device that, at least in part, affects the output parameter level.

Abstract: A technique of controlling a boiler system such as that used in a power generation plant includes using manipulated variables associated with or control inputs to a reheater section of the boiler system to control the operation of the furnace, and in particular to control the fuel/air mixture provided to the furnace or the fuel to feedwater ratio used in the furnace or boiler. In the case of a once-through boiler type of boiler system, using the burner tilt position, damper position or reheater spray amount to control the fuel/air mixture or the fuel to feedwater flow ratio of the system provides better unit operational efficiency.

Abstract: An energy management system uses an expert engine and a numerical solver to determine an optimal manner of using and controlling the various energy consumption, producing and storage equipment in a plant/communities in order to for example reduce energy costs within the plant, and is especially applicable to plants that require or that are capable of using and/or producing different types of energy at different times. The energy management system operates the various energy manufacturing and energy usage components of the plant to minimize the cost of energy over time, or at various different times, while still meeting certain constraints or requirements within the operational system, such as producing a certain amount of heat or cooling, a certain power level, a certain level of production, etc.

Abstract: An integrated optimization and control technique performs process control and optimization using stochastic optimization similar to the manner in which biological immune systems work, and thus without the use of historical process models that must be created prior to placing the control and optimization routine in operation within a plant. An integrated optimization and control technique collects various indications of process control states during the on-line operation of the process, and attempts to optimize the process operation by developing a series of sets of process control inputs to be provided to the process, wherein the control inputs may be developed from the stored process control states using an objective function that defines a particular optimality criteria to be used in optimizing the operation of the process.

Abstract: A high fidelity distributed plant simulation technique includes a plurality of separate simulation modules that may be stored and executed separately in different drops or computing devices. The simulation modules communicate directly with one another to perform accurate simulation of a plant, without requiring a centralized coordinator to coordinate the operation of the simulation system. In particular, numerous simulation modules are created, with each simulation module including a model of an associated plant element and these simulation modules are stored in different drops of a computer network to perform distributed simulation of a plant or a portion of a plant. At least some of the simulation modules, when executing, perform mass flow balances taking into account process variables associated with adjacent simulation modules to thereby assure pressure, temperature and flow balancing (i.e., conservation of mass flow) through the entire simulation system.

Abstract: A two-stage model predictive control (MPC) controller uses a process model and two separate MPC control modules, including a feedforward MPC control module and a feedback MPC control module, to determine a set of control signals for use in controlling a process. The feedforward MPC control module uses the process model to determine a feedforward control component for each of a set of control signals and the feedback MPC control module uses the process model and one or more measured process outputs to determine a feedback control component for each of the set of control signals. The two-stage MPC controller combines the feedforward control components with the feedback control components to form the final control signals used to control the process.

Abstract: An isolating manifold fluidly connected between a hydraulic valve and a hydraulic manifold operates to automatically isolate the valve from the hydraulic manifold as the valve is removed from the manifold, without requiring any particular blocking and bleeding procedures to be implemented on the valve or the manifold prior to the removal process. The isolating manifold includes an adaptor removably mounted onto a base with various pressure, control and tank fluid channels disposed in alignment through the adaptor and base portions to allow fluid to flow through each of the channels between the hydraulic manifold and the valve. During operation, the valve is mounted onto the adaptor while the base is mounted onto the hydraulic manifold.

Abstract: An optimization and control system for a utility plant that uses fan based air cooled condensers controls the operation of the power generation system at the plant in conjunction with the operation of the air cooled condensers so as to run the power plant at an optimum operating point associated with minimizing or reducing the cost of each kilowatt-hour of energy or other useful energy produced by the plant. The optimization and control system includes an optimizer having a numerical solver that determines values for a set of control variables associated with an optimal operating point of the plant and an expert system that oversees and modifies the control variable settings prior to providing these settings to a plant controller. The numerical solver uses an objective function and one or more models of plant equipment to determine the operating point of the plant that minimizes the cost per unit of useful energy generated by the plant.

Abstract: A statistical process control system employs a consistent soot blowing operation for a heat exchange section of, for example, a fuel burning boiler, collects heat absorption data for the heat exchange section and analyzes the distribution of the heat absorption data as well as various parameters of the heat absorption distribution to readjust the soot blowing operation. The statistical process control system may set a desired lower heat absorption limit and a desired upper heat absorption limit and compare them, respectively, with an actual lower heat absorption limit and an actual upper heat absorption limit to determine the readjustment to be made to the soot blowing operation. Alternatively, the statistical process control system may be used to determine permanent slagging of the heat exchange section.

Abstract: A method of controlling soot blowers near a heat exchange section includes generating models of both the ideal clean operating condition of the section and the dirty operating condition. The current operating condition of the section is used to calculate a reliability parameter that provides an indication of the reliability of the ideal and dirty models. If the reliability parameter indicates that the models are reliable, the models are used to help evaluate the cleanliness status of a particular heat exchange section and assist in making decisions on whether to blow the section or not, and whether to make any necessary adjustments to the operating sequence of the soot blowers. If the reliability parameter indicates that the models are unreliable, the models are regenerated using additional process data.