Honeywell G.m.b.h.

Honeywell Asca Inc.

Honeywell International Inc.

Honeywell Acsa Inc.

Johan Backstrom

Michael Forbes

Gregory Edward Stewart

Tongwen Chen

Danlei Chu

Ning He

Guy A Dumont

Philip D Loewen

Qiugang Lu

Cristian Gheorghe

R Bhushan Gopaluni

Sebastien Tixier

Frank Martin Haran

Junqiang Fan

Iouri Markevitch

Xiaotao Liu

Mattias Bjorklund

Pengling He

Dawei Shi

Michael Kon Yew Hughes

Adrian Matias Fuxman

Stephane Savard

Sarabjit Singh

Ian Charles Baron

Joyce Choi

Bhusban Gopaluni

Pezhman Nafissi

Ghulam Mustafa

Jiadong Wang

Kelsey Gerbrandt

Cheul Chung

Lee D Rippon

A method includes obtaining operating data associated with operation of a cross-directional industrial process controlled by at least one model-based process controller. The method also includes, during a training period, performing closed-loop model identification with a first portion of the operating data to identify multiple sets of first spatial and temporal models. The method further includes identifying clusters associated with parameter values of the first spatial and temporal models. The method also includes, during a testing period, performing closed-loop model identification with a second portion of the operating data to identify second spatial and temporal models. The method further includes determining whether at least one parameter value of at least one of the second spatial and temporal models falls outside at least one of the clusters. In addition, the method includes, in response to such a determination, detecting that a mismatch exists between actual and modeled behaviors of the industrial process.

Model-plant mismatch detection with support vector machine for cross-directional process behavior monitoring

A method includes acquiring process data collected in an industrial process control and automation system. The method also includes reducing a dimension space of the process data by combining two or more parameters of the process data or examining a frequency response of the process data. The method further includes determining a change in a process based on a change in the process data in the reduced dimension space. The method also includes outputting a result based on the determined change in the process.

System and method for monitoring changes in process dynamic behavior by mapping parameters to a lower dimensional space

A controller reference trajectory design technique to enable high-performing automatic grade change performed by a model predictive control (MPC). Techniques include: (1) automatic determination of appropriate process output reference trajectory delays to enable optimum coordination of process input movements; (2) providing the entire planning process output reference trajectory ramp at the start of the grade change instead of just incrementally as the grade change progresses, again enabling movement of the process inputs to drive process outputs along the planned future path instead of just towards the current target; and (3) use of the process input forced ramping to allow linear ramping of process inputs with optimal coordination of other process input movements to keep all process outputs following the desired trajectories. The technical benefits are faster and higher performing grade changes. In addition, the use of this technology allows easier setup and maintenance of the automatic grade change package.

Application of model predictive control (MPC)-based forced ramping of process input variables and process output reference trajectory design over a prediction horizon for MPC-based paper machine grade change control

A method includes obtaining one or more models associated with a model-based controller in an industrial process having multiple actuator arrays and performing spatial tuning of the controller. The spatial tuning includes identifying weighting matrices that suppress one or more frequency components in actuator profiles of the actuator arrays. The spatial tuning could also include finding a worst-case cutoff frequency over all output channels for each process input, designing the weighting matrices to penalize higher-frequency actuator variability based on the model(s) and the cutoff frequencies, and finding a multiplier for a spatial frequency weighted actuator variability term in a function that guarantees robust spatial stability. The controller could be configured to use a function during control of the industrial process, where a change to one or more terms of the function alters operation of the controller and the industrial process and at least one term is based on the weighting matrices.

Method and apparatus for designing model-based control having spatial robustness for multiple array cross-direction (CD) web manufacturing or processing systems or other systems

A method includes repeatedly identifying one or more values for one or more model parameters of at least one model associated with a process. The one or more values for the one or more model parameters are identified using data associated with the process. The method also includes clustering the values of the one or more model parameters into one or more clusters. The method further includes identifying one or more additional values for the one or more model parameters using additional data associated with the process. In addition, the method includes detecting a mismatch between the at least one model and the process in response to determining that at least some of the one or more additional values fall outside of the one or more clusters. The values could be clustered using a support vector machine.

Model-plant mismatch detection using model parameter data clustering for paper machines or other systems

A method includes obtaining closed-loop data associated with operation of an industrial process controller, where the industrial process controller is configured to control at least part of an industrial process using at least one model. The method also includes generating at least one noise model associated with the industrial process controller using at least some of the closed-loop data. The method further includes filtering the closed-loop data based on the at least one noise model. In addition, the method includes generating one or more model parameters for the industrial process controller using the filtered closed-loop data.

Closed-loop model parameter identification techniques for industrial model-based process controllers

A method includes obtaining a model associated with a model-based controller in an industrial process having multiple actuator arrays and performing temporal tuning of the controller. The temporal tuning includes adjusting one or more parameters of a multivariable filter used to smooth reference trajectories of actuator profiles of the actuator arrays. The temporal tuning could also include obtaining one or more uncertainty specifications for one or more temporal parameters of the model, obtaining one or more overshoot limits for the actuator profiles, identifying a minimum bound for profile trajectory tuning parameters, and identifying one or more of the profile trajectory tuning parameters that minimize one or more measurement settling times without exceeding the one or more overshoot limits. The controller could be configured to use the adjusted parameter(s) during control of the industrial process such that the adjusting of the parameter(s) alters operation of the controller and the industrial process.

Method and apparatus for designing model-based control having temporally robust stability and performance for multiple-array cross-direction (CD) web manufacturing or processing systems or other systems

Sheetmaking cross-directional (CD) control requires a sophisticated model-based controller whose operation requires an accurate model of process behavior, but due to the complexity of the process, identifying these process models is challenging. Current techniques rely on open-loop process experimentation. Using non-causal scalar transfer functions for the steady-state CD process model and controller model avoid the problem of large dimensions associated with the CD process. These non-causal transfer functions can be represented by causal transfer functions that are equivalent to the non-causal ones in the sense of the output spectrum. A closed-loop optimal input design framework is proposed based on these causal equivalent models. CD actuators have responses in both sides along the cross direction which can be viewed as a non-causal behavior. Techniques to perform the non-causal modeling are demonstrated and developed in a closed-loop optimal input design framework based on non-causal modeling of the closed-loop CD process.

Optimal closed-loop input design for identification of flat-sheet process models

An apparatus includes a transmitter configured to transmit a signal including an electromagnetic pulse towards material in a tank. The apparatus also includes a receiver configured to receive a signal including multiple reflections of the pulse. The apparatus further includes at least one processing device configured to process the received signal and determine a measurement associated with the material in the tank. To process the received signal, the at least one processing device is configured to fit multiple models onto the received signal, select one of the models, identify at least one of the multiple reflections in the received signal using the selected model, and determine the measurement using the at least one identified reflection in the received signal. Each model is constructed from a superposition of multiple types of pulse reflections.

Apparatus and method for measuring thin material thicknesses in inventory management applications

Automated parameter tuning techniques for cross-directional model predictive control for paper-making under user-specified parametric uncertainties to reduce variability of the actuator and measurement profiles in the spatial domain is proposed. Decoupling properties of the spatial and temporal frequency components permit separate controller design and parameter tuning. CD-MPC design that explicitly accounts for parametric model uncertainty while finding MPC cost function weighing matrices that prevent actuator picketing and guarantee robust stability of the spatial CD profile. Picketing refers to periodic variation patterns in the actuator array. The inventive technique includes: (i) determining the worst case cutoff frequency of all process models, given parametric uncertainty, (ii) designing a weighing matrix to penalize high frequency actuator variability based on the process model and worst case cutoff frequency, and (iii) finding a multiplier for the spatial frequency weighted actuator variability term in the MPC cost function that assures robust spatial stability.

Method of designing model predictive control for cross directional flat sheet manufacturing processes to guarantee spatial robustness and to prevent actuator picketing

Automated parameter tuning techniques for cross-directional model predictive control for paper-making under user-specified parametric uncertainties are developed. The CD-MPC design explicitly accounts for parametric model uncertainty while finding a value for the CD profile trajectory generation that minimizes the CD settling time of the measurement 2 sigma spread and does not exceed an overshoot limit for actuator 2 sigma spread. The inventive technique includes: (i) providing uncertainty specifications for the temporal parameters of the process model, (ii) specifying 2 sigma overshoot limits for the CD actuator profile, (iii) using robust stability theory to find a minimum bound for the profile trajectory tuning parameter, (iv) using a frequency domain technique to reduce the search range for the profile trajectory tuning parameter, and (v) performing an intelligent search for the tuning parameter that minimizes measurement 2 sigma settling time without exceeding the actuator 2 sigma overshoot limit.

Method of designing model predictive control for cross directional flat sheet manufacturing processes to guarantee temporal robust stability and performance

A method of level finding includes providing characteristics of a shape of a transmitted pulse in time domain launched onto a waveguide into a tank having at least one material therein, physical properties of the waveguide and real and imaginary dielectric characteristics of the material at a frequency of the pulse. A level finding algorithm having a coarse search and a fine search is implemented, where the coarse search minimizes a prediction error between an echo signal (echo curve y(k)) and a sampled pulse model echo p(k) to obtain an objective function J(k) in a vicinity of a minimum prediction error (k*). The fine search calculates at least one minimum or maximum using J(k) in the vicinity of k*. The minimum or the maximum corresponds to a level of the material or an interface involving the material.

Level finding using multiple search steps

Controlling a multiple-array, sheetmaking cross-directional process with a multivariable model predictive controller (MPC) employs a cost function incorporating a prediction horizon. The MPC provides a measurement profile target reference trajectory over the prediction horizon of the MPC cost function. Improved CD-MPC performance is achieved by employing a measurement profile target reference trajectory over the prediction horizon in the MPC cost function. A series of target profiles creates a reference trajectory to bring the cross-direction measurements smoothly from their current profile to the final target. By carefully designing the reference trajectory, the CD-MPC exhibits a good measurement response without aggressive control action. The current measurement target profile can be filtered through a first order plus deadtime process at each controller update and repeating the filter operation once for each step of the MPC prediction horizon generates a full reference trajectory for the profile.

Technique to improve paper machine cross-directional model predictive control performance by creating a measurement profile reference trajectory

A method of simulating a pulsed radar gauge (PRG) on a tank. Provided are (i) a parametrized model of an echo reflection responsive to a transmitted radar pulse defined by an initial spatial model function, a first signal model modeling the pulse traveling through the media, and a second signal model modeling reflection and transmission of the pulse striking boundaries involving the media, and an (ii) echo curve calculation (ECC) program. The ECC program divides the signal path into path components for the pulse using the first and second signal model together as a recursive program with stop conditions to indicate an end result for the path components when taken together for reflected signals reaching the transceiver include locations of reflections and respective amplitudes. A shape of the initial model is placed at reflection locations using their respective amplitudes applied to size their amplitude to generate a simulated echo curve.

Recursive multi-model echo curve simulation

A method includes generating a model associated with cross-directional fiber orientation of a web, which includes identifying spatial frequency characteristics of a fiber orientation (FO) process. The method also includes providing the model for control of the FO process. Generating the model could include performing a spatial impulse test of the FO process, and long wavelength responses of the FO process can be identified by performing a spatial long wavelength test of the FO process or by retrieving information from a historical database. Actuator edge padding can be applied to the model in order to generate a controller model. A controller can be used to control the process based on the controller model. At least one parameter of the controller model can be dynamically adjusted during operation of the controller. The controller can change average fiber orientation angle profiles and twist profiles by only adjusting slice lip actuators in a headbox.

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Vancouver, Canada

Johan Backstrom