Abstract: A system and computer software mechanism is disclosed that can automatically detect smoking conditions for industrial flares. In an online mode, the software mechanism can analyze live video images to identify smoking conditions and generate a “Smoke Level” value to represent the seriousness of the smoking events. In an off-line mode, the software mechanism can load saved video files and analyze video images to identify smoking conditions and generate a “Smoke Level” value to represent the seriousness of the smoking events. In the online mode, the Smoke Level data can be sent to the plant distributed control system (DCS) or other control devices so alarms can be generated and automatic or manual control actions can be taken quickly to stop the flare from smoking. In both online and off-line modes, Smoke Level data can also be saved in historical files for reporting and smoke event tracking purposes.

Abstract: A method and system is disclosed that can automatically detect smoking conditions for industrial flares. It can analyze live or historical video images to identify smoking conditions and generate a “Smoke Level” value in the range of 0 to 10 to represent the seriousness of the smoking events. The Smoke Level data can be sent to the plant distributed control system (DCS) or other control devices so alarms can be generated and automatic or manual control actions can be taken to stop the flare from smoking quickly. The Smoke Level data can also be saved in historical files for reporting and smoke event tracking purposes. Using the described method and system, industrial plants can comply with EPA regulations at all times, improve flare control and monitoring, be better prepared for EPA reporting and auditing, and save energy and manpower.

Abstract: A novel 3-Input-3-Output (3×3) Model-Free Adaptive (MFA) controller with a set of artificial neural networks as part of the controller is introduced. A 3×3 MFA control system using the inventive 3×3 MFA controller is described to control key process variables including Power, Steam Throttle Pressure, and Steam Temperature of boiler-turbine-generator (BTG) units in conventional and advanced power plants. Those advanced power plants may comprise Once-Through Supercritical (OTSC) Boilers, Circulating Fluidized-Bed (CFB) Boilers, and Once-Through Supercritical Circulating Fluidized-Bed (OTSC CFB) Boilers.

Abstract: A novel 3-Input-3-Output (3×3) Fuel-Air Ratio Model-Free Adaptive (MFA) controller is introduced, which can effectively control key process variables including Bed Temperature, Excess O2, and Furnace Negative Pressure of combustion processes of advanced boilers. A novel 7-input-7-output (7×7) MFA control system is also described for controlling a combined 3-Input-3-Output (3×3) process of Boiler-Turbine-Generator (BTG) units and a 5×5 CFB combustion process of advanced boilers. Those boilers include Circulating Fluidized-Bed (CFB) Boilers and Once-Through Supercritical Circulating Fluidized-Bed (OTSC CFB) Boilers.

Abstract: A Self-Organizing Process Control Architecture is introduced with a Sensing Layer, Control Layer, Actuation Layer, Process Layer, as well as Self-Organizing Sensors (SOS), Self-Organizing Actuators (SOA), and Self-Organizing Actuation and Control Units (SOACU). The method and apparatus of SOA and SOACU for process control are presented. A control system as a case example for a gas mixing process is described using the unique SOA and SOACU approaches. A 2x1 Robust MFA (Model-Free Adaptive) controller as a key component of the SOACU is also disclosed.

Abstract: A Self-Organizing Multi-Stream Flow Delivery Process and Enabling Actuation and Control are introduced. The method and apparatus of building a general-purpose self-organizing multi-stream flow delivery process are presented. As a case example, an actuation and control system to control a multi-stream liquid flow delivery process using Self-Organizing Actuation and Control Units (SOACU) is described.

Abstract: A Self-Organizing Process Control Architecture is introduced with a Sensing Layer, Control Layer, Actuation Layer, Process Layer, as well as Self-Organizing Sensors (SOS) and Self-Organizing Actuators (SOA). A Self-Organizing Sensor for a process variable with one or multiple input variables is disclosed. An artificial neural network (ANN) based dynamic modeling mechanism as part of the Self-Organizing Sensor is described. As a case example, a Self-Organizing Soft-Sensor for CFB Boiler Bed Height is presented. Also provided is a method to develop a Self-Organizing Sensor.

Abstract: A method and apparatus for intelligently controlling continuous process variables. An automatic controller comprises an Intelligent Engine mechanism and a number of Model-Free Adaptive (MFA) controllers, each of which is suitable to control a process with specific behaviors. The Intelligent Engine can automatically select the appropriate MFA controller and its parameters so that the automatic controller can be easily used by people with limited control experience and those who do not have the time to commission, tune, and maintain automatic controllers.

Abstract: A method and apparatus for intelligently controlling continuous process variables. A Dream Controller comprises an Intelligent Engine mechanism and a number of Model-Free Adaptive (MFA) controllers, each of which is suitable to control a process with specific behaviors. The Intelligent Engine can automatically select the appropriate MFA controller and its parameters so that the Dream Controller can be easily used by people with limited control experience and those who do not have the time to commission, tune, and maintain automatic controllers.

Abstract: A novel 3-Input-3-Output (3×3) Fuel-Air Ratio Model-Free Adaptive (MFA) controller is introduced, which can effectively control key process variables including Bed Temperature, Excess O2, and Furnace Negative Pressure of combustion processes of advanced boilers. A novel 7-input-7-output (7×7) MFA control system is also described for controlling a combined 3-Input-3-Output (3×3) process of Boiler-Turbine-Generator (BTG) units and a 5×5 CFB combustion process of advanced boilers. Those boilers include Circulating Fluidized-Bed (CFB) Boilers and Once-Through Supercritical Circulating Fluidized-Bed (OTSC CFB) Boilers.

Abstract: An arrangement for use in connection with an air handling unit includes a controller and a processing circuit. The controller is configured to control a device within an air handler unit, which includes at least a first coil. The processing circuit is configured to receive first information representative of a maximum liquid flow rate through the first coil and second information representative of a maximum air flow rate of the first coil. The processing circuit is further configured to generate a time constant estimate, based on the first information and second information, for use in the controller in performing control of the device.

Abstract: A novel 3-Input-3 -Output (3×3) Model-Free Adaptive (MFA) controller with a set of artificial neural networks as part of the controller is introduced. A 3×3 MFA control system using the inventive 3×3 MFA controller is described to control key process variables including Power, Steam Throttle Pressure, and Steam Temperature of boiler-turbine-generator (BTG) units in conventional and advanced power plants. Those advanced power plants may comprise Once-Through Supercritical (OTSC) Boilers, Circulating Fluidized-Bed (CFB) Boilers, and Once-Through Supercritical Circulating Fluidized-Bed (OTSC CFB) Boilers.

Abstract: A method and apparatus for intelligently controlling continuous process variables. A Dream Controller comprises an intelligent Engine mechanism and a number of Model-Free Adaptive (MFA) controllers, each of which is suitable to control a process with specific behaviors. The Intelligent Engine can automatically select the appropriate MFA controller and its parameters so that the Dream Controller can be easily used by people with limited control experience and those who do not have the time to commission, tune, and maintain automatic controllers.

Abstract: A method and apparatus is disclosed for intelligently inverting DC power from DC sources such as photovoltaic (PV) solar modules to single-phase or three-phase AC power to feed the power grid for electricity generation. A number of smart single-input, dual-input, triple-input, quad-input, and multiple-input power inverters in a mixed variety can easily connect to single, dual, triple, quad, and multiple DC power sources, invert the DC power to AC power, and daisy chain together to generate a total power, which is equal to the summation of the AC power supplied by each smart and scalable power inverter of this invention.

Abstract: An apparatus and method is disclosed for solving optimization problems without the need to build mathematical models or rules. The inventive method combines the structure of a single-loop feedback control system and optimization search engine mechanisms. Running in real-time, a Model-Free Adaptive (MFA) optimizer can automatically search for the optimal operating point for a dynamic system when a parabolic relationship exists between the input and output. The MFA optimizer comprises a user-selected Min/Max setter to define the searching objective, a process acting-mode search engine to determine if the process is running in direct-acting or reverse-acting mode, a maximum search engine and a minimum search engine to find the maximum or minimum. This apparatus and method is useful in fuel-and-air ratio optimization for combustion processes, yield optimization for chemical or biological reactors, and operating efficiency optimization for coal or ore ball mills.

Abstract: A Model-Free Adaptive Quality Variable control system is disclosed for effectively controlling quality variables on-line in closed-loop fashion. It is able to automatically control quality variables under the conditions where there are significant varying time delays and disturbances in the process. Because of its unique capability, the control system is useful for building flexible and adaptive production systems, achieving Six Sigma quality control goals, and fulfilling the on demand manufacturing needs in the new e-commerce environment.

Abstract: A model-free adaptive controller is disclosed, which uses a dynamic artificial neural network with a learning algorithm to control any single-variable or multivariable open-loop stable, controllable, and consistently direct-acting or reverse-acting industrial process without requiring any manual tuning, quantitative knowledge of the process, or process identifiers. The need for process knowledge is avoided by substituting 1 for the actual sensitivity function .differential.y(t)/.differential.u(t) of the process.