Honeywell International Inc.

Honeywell G.m.b.h.

Other

Tariq Samad

Michael L Rhodes

Anoop Kumar Mathur

Syed M Shahed

Gregory Edward Stewart

Ahmet F Konar

Soumitri N Kolavennu

Wei Yang

Gregory James Hampson

Francesco Borrelli

Vladimir Havlena

Joseph Z Lu

Alex Gu

Steven Alex Harp

Lane Desborough

Paul Francis McLaughlin

Jan Jelinek

Alexander Chernoguzov

Adam Dewey McBrady

Brian Parsonnet

Aloke Guha

Vipin Gopal

Wendy K Graber

Vladimir Ravlena

Lubmir Baramov

Paul F Mclaughlin

Roger N c/o Honeywell International Inc Schmidt

Soumitri N c/o Honeywell Int Inc Kolavennu

Air scrubbers and approaches for removing CO, SO, and other gaseous contaminates from gas emissions. An approach for removing a gaseous contaminant from a gas emission may include providing a supply of an ionic liquid. The gaseous contaminant may be absorbable in the ionic liquid. The approach may also include spraying the ionic liquid into the gas emission. The gaseous contaminant in the gas emission may be absorbed in the ionic liquid. The ionic liquid having the gaseous contaminant absorbed therein may be collected on a counter electrode and separated from the gas emission. Spraying the ionic liquid into a gas emission may include ultrasound agitation of the ionic liquid. The approach may also include venting the gas emission having the gaseous contaminant separated therefrom.

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A system includes an ultrasound wave generator, a fluid cavity coupled to the ultrasound wave generator, an array of micro-machined ultrasonic-assisted electrospray nozzles coupled to the fluid cavity, an electrode configured to impart a charge onto the fluid output of the electrospray nozzles, and a carbon dioxide release unit configured to receive the ionic liquid after absorption of carbon dioxide by the ionic liquid and to remove carbon dioxide from the ionic liquid such that the regenerated ionic liquid can be recycled for additional carbon dioxide capture.

Ultrasound-assisted electrospray ionic liquid for carbon dioxide capture

A method includes determining first and second communication paths from a leaf node of a plurality of leaf nodes to first and second gateway nodes, respectively, of a plurality of gateway nodes. The first and second communication paths may include first and second intermediate nodes, respectively, of a plurality of intermediate nodes. A wireless message is sent from the leaf node to the first and second gateway nodes via the first and second communication paths, respectively. Information in the wireless message is communicated from at least one of the first and second gateway nodes to a component of a process control system.

Method and system for integration of wireless devices with a distributed control system

A catalyst system that may regenerate while removing pollutants from an exhaust gas of an engine. The system may have a converter with multiple segments of chambers. At least one of the chambers may be regenerated while the remaining chambers are removing pollutants from the exhaust. The chambers may be rotated in turn for one-at-a-time regeneration. More than one chamber may be regenerated at a time to remove collected pollutants. The system may have plumbing and valves, and possibly mechanical movement of the chambers, within the system to effect the changing of a chamber for regeneration. The chambers connected to the exhaust may be in series or parallel. A particulate matter filter may be connected to the system, and it also may be regenerated to remove collected matter.

Exhaust catalyst system

Systems and methods for controlling automotive powertrains using a distributed control architecture are disclosed. A distributed control system may include a supervisory control unit for controlling one or more powertrain subsystems, and one or more subsystem control units in communication with the supervisory control unit. The supervisory control unit can be configured to execute a central optimization algorithm that computes variables from across multiple powertrain subsystems, and then outputs a number of globally approximated command values to each associated subsystem control unit. In some embodiments, the central optimization algorithm can be configured to solve a global cost function or optimization routine. One or more of the subsystem control units can be configured to execute a lower-level algorithm or routine, which can comprise a higher-fidelity model than that used by the central optimization algorithm.

Distributed control architecture for powertrains

Method and system for controlling cylinder to cylinder variations of undesirable emissions in exhaust gas of engines with exhaust gas recirculation. An engine model is used to predict the intake composition of the next cylinder firing based on the composition of the exhaust, as a function of time and applicable engine conditions. At least one engine parameter is adjusted to compensate for the predicted intake composition in order to minimize cylinder to cylinder variations.

Cylinder to cylinder variation control

A multivariable controller that models the interaction between groups of sensors and groups of actuators during the operation of an engine. By accounting for the interactive effects that a group of actuators has on a sensor or group of sensors, improved system performance may be achieved. When emission sensors are used, such as NOX and/or PM sensors, the multivariable controller may help control the various engine actuators to reduce emissions of the engine.

Multivariable control for an engine

A network between customers generating service requests and vendors capable of electronically fulfilling the service requests includes a system for monitoring, controlling, and administering work flows associated with the service requests. A main controller creates and controls a first work flow record associated with a first service request for storage in a storage device. The main controller also receives work flow elements including messages, data files, software applications, and documents from a first customer and a first vendor associated with the first work flow, stores the work flow elements in the storage device, and transfers the work flow elements to the first customer and the first vendor. An accounting controller associated with the main controller identifies fees associated with the first work flow and stores fee data associated with the fees in the first work flow record.

Systems and methods for remote role-based collaborative work environment

Systems and methods for controlling an engine using feedback from one or more sensors are disclosed. An illustrative control system for controlling a diesel engine may include one or more post-combustion sensors adapted to directly sense at least one constituent of exhaust gasses emitted from the exhaust manifold of the engine, and a state observer for estimating the internal state of the diesel engine based on feedback signals received from the post-combustion sensors and from subsequent use of the estimated state in a controller that sends the actuator setpoints. The post-combustion sensors can be configured to directly measure emissions such as oxides of nitrogen (NO) and/or particulate matter (PM) within the exhaust stream, and provide such information to a state observer that, in turn, updates an internal dynamical state based on these measurements. In some cases, other sensors such as a torque load sensor, an in-cylinder pressure sensor, and/or a fuel composition sensor can be further used to update the internal state of the state space model, as needed. Using an estimated state from the state observer, a state feedback controller can compute and adjust various actuator setpoints from values that more accurately represent the true state of the system.

Use of sensors in a state observer for a diesel engine

An novel recipe that embodies a higher level description than currently used in the industry is described. The novel recipe is referred to herein as an 'advanced recipe.' A traditional recipe consists of a general set of instructions called a 'recipe' which is used to make a product. An advanced recipe integrates the intent behind these set of instructions. A novel knowledge management system for capturing, integrating, and using broad knowledge is also described below. Some embodiments of the knowledge management system enable modification of recipes for maximizing the overall processing objectives. An example embodiment of the knowledge management system comprises a Knowledge Builder, a Structured Knowledge Repository, and a Decision Maker.

Advanced recipe&#x2014;a knowledge based information system for production processes

A tool, and the method of making the tool, for process system identification that is based on the general purpose learning capabilities of neural networks. The tool and method can be used for a wide variety of system identification problems with little or no analytic effort. A neural network is trained using a process model to approximate a function which relates process input and output data to process parameter values. Once trained, the network can be used as a system identification tool. In principle, this approach can be used for linear or nonlinear processes, for open or closed loop identification, and for identifying any or all process parameters.

Method for developing a neural network tool for process identification

An automatic tuner for control systems that produces, as output values, parameters of an arbitrary controller. The controller is in a control loop so that its output effects changes in actuators and regulates a physical process. The controller has either linear or nonlinear controller components, or a combination of both. The tuner has a nonlinear approximator that has been optimized off-line. The off-line optimization is done without supervised learning so that desired outputs of the nonlinear approximator do not need to be available, and separate optimization to generate the desired outputs is not necessary. The off-line optimization can also rely on arbitrary criteria. Such optimization ensures robustness of generated controller parameters so that the input process characteristics do not need to be highly accurate. The inputs to the nonlinear approximator consist of two sets of input parameters, either of which may be empty. A first set of input parameters can relate to process characteristics. A second set of input parameters can relate to desired closed-loop system behavior. The output values may be proportional and/or integral and/or derivative gains for PID-like controllers, or otherwise be parameters for delay-compensation controllers, controllers that consist of lead-lag terms in combination with PID controllers, higher-order linear controllers, or nonlinear controllers of predetermined structure. The nonlinear approximator may be implemented as a compositional sigmoidal mapping, a multilayer perception structure, a fuzzy logic model, a radial basis function network, a polynomial expansion, or other parametrized nonlinear structure.

Nonlinear-approximator-based automatic tuner

An apparatus for identifying the structure and estimating the parameters in the structure of a controlled system is described. The apparatus uses a structure identifier, in a preferred embodiment, employing a neural network, to match the relationship of the system inputs and outputs to a mathematical relation. This identified structure is communicated to a parameter estimator. The parameter estimator may employ a neural network to give an initial parameter vector which can be used to generate, iteratively, better parameter estimates. This increases the efficacy of the parameter estimator. The apparatus can be inserted into controllers for various systems to improve performance. Using a neural network for parameter estimation as shown can be generalized to any nonlinear optimization problem.

Two-level system identifier apparatus with optimization

The method of making the tool, for process system identification that is based on the general purpose learning capabilities of neural networks. The method can be used for a wide variety of system identification problems with little or no analytic effort. A neural network is trained using a process model to approximate a function which relates process input and output data to process parameter values. Once trained, the network can be used as a system identification tool. In principle, this approach can be used for linear or nonlinear processes, for open or closed loop identification, and for identifying any or all process parameters.

Method for process system identification using neural network

A closed loop neural network based autotuner develops optimized proportional, integral and/or derivative parameters based on the outputs of other elements in the loop. Adjustments are initiated by making a step change in the setpoint which may be done by a user or automatically. A Smith predictor may also be employed.

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Minneapolis, MN, United States of America

Tariq Samad