Steady-state Identification Research Articles

Period steady-state identification for a nonlinear front evolution equation using genetic algorithms

Period steady-state identification for a nonlinear front evolution equation using genetic algorithms

On identifying steady-state parameters of an experimental mechanical-compression refrigeration plant

This paper describes the thermodynamic model of a vapour compression refrigeration system, focusing on the performance of the heat exchangers. A novel steady-state identification method is proposed, which is based on inaccessible refrigerant phase-change zones assuming that a constant overall heat transfer coefficient can be identified for each zone. The proposed method has been applied to a configurable two-stage, two-load-demand vapour compression refrigeration plant. Considering steady-state experimental data and taking into account orders of magnitude of the global heat transfer coefficients found in the literature, some relationships between them and the heat exchanger zone lengths are suggested. Then coherent values are obtained for all heat transfer coefficients, which confirms the validity of the hypothesis. Additionally the remaining elements of the refrigeration system have been identified using well-known models. Experiments have been carried out in order to validate this method, using a one-stage, two-load-demand configuration. Although all components are separately identified, a global validation method is implemented, of which results show good agreement with experimental data, with relative estimation errors around 10% in the key variables.

Control and Optimization of a Variable-Pitch Quadrotor with Minimum Power Consumption

Recently, there has been a rapid growth of interest in quadrotors with electric variable-pitch propellers. The control and optimization of such propellers are important factors for improving the flight performance of the vehicles. Therefore, the steady-state identification method to estimate the parameters of the mathematical model of the electric variable-pitch propeller is developed. The steady-state control and optimization scheme with minimum power consumption and the adaptive compensation scheme for the variable-pitch propeller are then proposed, based on which the response performance of the lift force produced by the variable-pitch propeller can be greatly improved by using a cascade compensation scheme. Furthermore, the direct lift-based flight control strategy is presented, which can significantly contribute to the improvement of the flight performance, precisely because the roll, pitch, yaw and vertical channels of the variable-pitch quadrotor are approximately linearized and completely decoupled from each other in this case. The experimental results demonstrate that both the endurance performance and the positioning accuracy of the variable-pitch quadrotor are improved simultaneously by using the proposed method with minimum power consumption.

Robust methodology for steady state measurements estimation based framework for a reliable long term thermal power plant operation performance monitoring

Steady state identification is a process control research approximating the successive values of samples in steady state into its average values. According to the plant-wide control hierarchical model, these results implement monitoring and optimizing functions. Thermal power plant operates into a wide range of mean value active power. Systematic plant-wide slow developing disturbances affect the power plant operation performance through deviations of each process variable between its current true process value and the expected good performance relative value. Supervised records are realizations contaminated with stationary correlated noise carrying successive steady state deviations. Long term thermal power plant operation performance monitoring depending on (i) accuracy and precision of steady state identification method and (ii) fitness approximation per process variable versus mean value active power. This paper bases: (i) a computational experiment design to calibrate a steady state identification before to embed into a real system, and (ii) a solution for curve structure to capture good performance relative value per process variable with few knots availability right after the start-up of the plant at base load regime. A case study tracking the cumulative effects of degradation due to fouling on a heat exchanger was performed.

Robust adaptive multiple models based fuzzy control of nonlinear systems

A new robust adaptive multiple models based fuzzy control scheme for a class of unknown nonlinear systems is proposed in this paper. The nonlinear system is expressed by using the Takagi–Sugeno (T–S) method, and some identification adaptive T–S models along with their corresponding controllers, are used in order to control efficiently the unknown system. The modeling error that is produced due to the use of the T–S plant model can cause instability problems if it is not taken into account in the adaptation rules. In this paper, in order to solve this problem, we design a control scheme that is based on updating rules that utilize the σ-modification method. Every T–S controller is updated indirectly by using the robust updating rules and the final control signal is determined by using a performance index and a switching rule. By using the Lyapunov stability theory it is shown that σ-modification based rules can ensure the robustness of the system and define a bound for the steady state identification error. The main objectives of the robust controller are: (i) to ensure that the real plant system will remain stable despite the existence of modeling errors and (ii) to ensure that the real plant will track with a high accuracy the state trajectory of a given reference model. The effectiveness of the proposed method is demonstrated by computer simulations on a well known benchmark problem.

A Novel ARX-Based Approach for the Steady-State Identification Analysis of Industrial Depropanizer Column Datasets

This paper introduces a novel steady-state identification (SSI) method based on the auto-regressive model with exogenous inputs (ARX). This method allows the SSI with reduced tuning by analyzing the identifiability properties of the system. In particular, the singularity of the model matrices is used as an index for steady-state determination. In this contribution, the novel SSI method is compared to other available techniques, namely the F-like test, wavelet transform and a polynomial-based approach. These methods are implemented for SSI of three different case studies. In the first case, a simulated dataset is used for calibrating the output-based SSI methods. The second case corresponds to a literature nonlinear continuous stirred-tank reactor (CSTR) example running at different steady states in which the ARX-based approach is tuned with the available input-output data. Finally, an industrial case with real data of a depropanizer column from PETROBRAS S.A. considering different pieces of equipment is analyzed. The results for a reflux drum case indicate that the wavelet and the F-like test can satisfactorily detect the steady-state periods after careful tuning and when respecting their hypothesis, i.e., smooth data for the wavelet method and the presence of variance in the data for the F-like test. Through a heat exchanger case with different measurement frequencies, we demonstrate the advantages of using the ARX-based method over the other techniques, which include the aspect of online implementation.

Steady-State Gain Identification and Control of Multivariable Unstable Systems

A method is presented to identify the steady-state gain matrix of a multivariable (SSGM) unstable system under closed-loop control. Effects of disturbances and measurement noise on the identification of SSGM were also studied. Davison's method (Davison, 1976) was modified to design single-stage multivariable PI controllers using only the steady-state gain matrix of the system. Since the overshoots in the responses are larger, a two-stage P-PI control system is proposed. Based on the SSGM, a simple proportional controller matrix was designed by the modified Davison's method (1976) to stabilize the system. Based on the gain matrix of the stabilized system, diagonal PI controllers were designed. The performance of the two-stage control system was evaluated and compared with that of single-stage multivariable controllers. Simulation results on two examples show the effectiveness of the proposed methods for both servo and regulatory problems.

Automatic steady state identification for batch processes by nonparametric signal decomposition and statistical hypothesis test

In chemical batch processes, online identification of the batch-to-batch steady state is important for ensuring consistency of final product quality and satisfactory process control. In this paper, an automatic steady state identification (SSID) method is developed for batch processes, which utilizes a nonparametric signal decomposition technique named ensemble empirical mode decomposition (EEMD) to extract related information contained in variable trajectories and then conducts a statistical hypothesis test. In the proposed method, EEMD is combined with a moving window procedure to decompose the signal of each variable trajectory into a finite number of intrinsic mode functions (IMFs) in real-time. Then, the inter-batch trend information is extracted by computing the instantaneous frequencies of each IMF. Using the variance ratio test, batch-to-batch steady state can be identified from the inter-batch trend of each process variable. Since most of the disturbance and noise information have been removed through EEMD, robust SSID result can be expected. To deal with the multiple process variables, a multivariate SSID algorithm is proposed based on the statistical test for the equality of covariance matrices. The effectiveness of the proposed method is demonstrated with an injection molding process.

Steady state identification for on-line data reconciliation based on wavelet transform and filtering

In order to derive higher value operational knowledge from raw process measurements, advanced techniques and methodologies need to be exploited. In this paper a methodology for online steady-state detection in continuous processes is presented. It is based on a wavelet multiscale decomposition of the temporal signal of a measured process variable, which simultaneously allows for two important pre-processing tasks: filtering-out the high frequency noise via soft-thresholding and correcting abnormalities by analyzing the maximums of wavelet transform modulus. Wavelet features involved in the pre-processing task are simultaneously exploited in analyzing a process trend of measured variable. The near steady state starting and ending points are identified by using the first and the second order of wavelet transform. Simultaneously a low filter with a probability density function is employed to approximate the duration of a near stationary condition. The method provides an improvement in the quality of steady-state data sets, which will directly improve the outcomes of data reconciliation and manufacturing costs. A comparison with other steady-state detection methods on an example of case study indicates that the proposed methodology is efficient in detecting steady-state and suitable for online implementation.

Thermal Modeling of a Claw-Pole Electrical Generator: Steady-State Computation and Identification of Free and Forced Convection Coefficients

The production of heat is a key characteristic of a machine's behavior. Heat sources come mainly from energy losses (electrical in copper, magnetic in iron, aerolic in fans, and mechanical in bearings). Temperature changes can be described owing to thermal conduction, convection, and radiation equations. To understand and design a machine, it is important to construct a dedicated model and to know every physical loss. The aim is therefore to build a static thermal model adapted to the simulation and sizing of claw-pole car alternators. Currently, in cars with internal combustion engines, generators rotors are wire wound, doubly excited with a dc concentrated winding and small permanent magnets, and self-air-cooled with fans. This paper proposes a nodal model with 16 nodes, taking all the heat transfers and space directions into account. The thermal characterization of the machine is permitted by only four physical parameters, varying with the temperature T and the rotating velocity Ω, shown to be sufficient to deduce the model conductances. These properties are as follows: the thermal exchange coefficients due to free and forced convection into the air gap and the cavity, the thermal exchange coefficient due to free superficial convection at the external surface, and the thermal equivalent conductivity within the bearings. The consistency and usefulness of both the model and identifications are checked against numerical studies and measurements.

Nonlinear Audio Systems Identification Through Audio Input Gaussianization

Nonlinear audio system identification generally relies on Gaussianity, whiteness and stationarity hypothesis on the input signal, although audio signals are non-Gaussian, highly correlated and non-stationary. However, since the physical behavior of nonlinear audio systems is input-dependent, they should be identified using natural audio signals (speech or music) as input, instead of artificial signals (sweeps or noise) as usually done. We propose an identification scheme that conditions audio signals to fit the desired properties for an efficient identification. The identification system consists in (1) a Gaussianization step that makes the signal near-Gaussian under a perceptual constraint; (2) a predictor filterbank that whitens the signal; (3) an orthonormalization step that enhances the statistical properties of the input vector of the last step, under a Gaussianity hypothesis; (4) an adaptive nonlinear model. The proposed scheme enhances the convergence rate of the identification and reduces the steady state identification error, compared to other schemes, for example the classical adaptive nonlinear identification.

Batch-to-Batch Steady State Identification Based on Variable Correlation and Mahalanobis Distance

Online steady state identification (SSID) is an important task to ensure the quality consistence of final products in batch processes. Additionally, it is also critical for satisfactory control of ...

Capacidade aeróbia de ratos alimentados com dieta rica em frutose

INTRODUÇÃO: Evidências apontam que a ingestão exacerbada de frutose pode desencadear distúrbios característicos da síndrome metabólica. OBJETIVOS: Analisar os efeitos da ingestão de dieta rica em frutose sobre aspectos metabólicos de ratos da linhagem Wistar. Adicionalmente, verificar a capacidade aeróbia através da identificação da máxima fase estável de lactato (MFEL). MÉTODOS: Dezesseis ratos foram separados em dois grupos de oito animais: a) controle, alimentados com dieta balanceada, e b) frutose, alimentados com dieta rica em frutose. Foram analisadas a tolerância à glicose (área sob a curva de glicose durante teste de tolerância à glicose), sensibilidade à insulina (taxa de remoção da glicose sérica após sobrecarga exógena de insulina), perfil lipídico sérico e concentração de lactato sanguíneo [lac]s durante exercício na intensidade da MFEL. RESULTADOS: Teste t não pareado (p < 0,05) revelou diferença para a tolerância à glicose e triglicérides, porém não houve diferença na sensibilidade à insulina e na [lac]s. Anova one way com post hoc de Newman-Keuls (p < 0,05) revelou diferença para a cinética da glicose durante o teste de tolerância, mas não para a cinética do lactato durante exercício na MSSL. CONCLUSÃO: As Alterações fisiológicas provocadas pela dieta rica em frutose e inerentes à síndrome metabólica não prejudicam a capacidade aeróbia de ratos.

Synchronous versus asynchronous modeling of gene regulatory networks

Motivation: In silico modeling of gene regulatory networks has gained some momentum recently due to increased interest in analyzing the dynamics of biological systems. This has been further facilitated by the increasing availability of experimental data on gene–gene, protein–protein and gene–protein interactions. The two dynamical properties that are often experimentally testable are perturbations and stable steady states. Although a lot of work has been done on the identification of steady states, not much work has been reported on in silico modeling of cellular differentiation processes.Results: In this manuscript, we provide algorithms based on reduced ordered binary decision diagrams (ROBDDs) for Boolean modeling of gene regulatory networks. Algorithms for synchronous and asynchronous transition models have been proposed and their corresponding computational properties have been analyzed. These algorithms allow users to compute cyclic attractors of large networks that are currently not feasible using existing software.Hereby we provide a framework to analyze the effect of multiple gene perturbation protocols, and their effect on cell differentiation processes. These algorithms were validated on the T-helper model showing the correct steady state identification and Th1–Th2 cellular differentiation process.Availability: The software binaries for Windows and Linux platforms can be downloaded from http://si2.epfl.ch/~garg/genysis.html.Contact: abhishek.garg@epfl.ch

Using Unfold‐PCA for batch‐to‐batch start‐up process understanding and steady‐state identification in a sequencing batch reactor

AbstractIn chemical and biochemical processes, steady‐state models are widely used for process assessment, control and optimisation. In these models, parameter adjustment requires data collected under nearly steady‐state conditions. Several approaches have been developed for steady‐state identification (SSID) in continuous processes, but no attempt has been made to adapt them to the singularities of batch processes. The main aim of this paper is to propose an automated method based on batch‐wise unfolding of the three‐way batch process data followed by a principal component analysis (Unfold‐PCA) in combination with the methodology of Brown and Rhinehart 2 for SSID. A second goal of this paper is to illustrate how by using Unfold‐PCA, process understanding can be gained from the batch‐to‐batch start‐ups and transitions data analysis. The potential of the proposed methodology is illustrated using historical data from a laboratory‐scale sequencing batch reactor (SBR) operated for enhanced biological phosphorus removal (EBPR). The results demonstrate that the proposed approach can be efficiently used to detect when the batches reach the steady‐state condition, to interpret the overall batch‐to‐batch process evolution and also to isolate the causes of changes between batches using contribution plots. Copyright © 2007 John Wiley & Sons, Ltd.

Design of a steady-state detector for fault detection and diagnosis of a residential air conditioner

This paper presents a general methodology for developing a steady-state detector for a vapor compression system based on a moving window and using standard deviations of seven measurements selected as features. The feature thresholds and optimized moving window size were based upon steady-state no-fault tests and startup transient tests. The study showed that evaporator superheat and condenser subcooling were sufficient for determining the onset of steady-state during the startup transient. However, they misidentified steady-state during indoor temperature change tests where evaporator saturation temperature and air temperature change across the evaporator were needed for proper steady-state identification. Hence, the paper recommends including all fault detection and diagnosis (FDD) features in the steady-state detector to ensure the robustness of the detector because different features may play key roles with different transients.

Steady-State Identification, Gross Error Detection, and Data Reconciliation for Industrial Process Units

Processing of online data for use with steady-state models requires identification of the existence of a steady state in a process, detection of the presence of gross errors, if any, and data reconciliation to eliminate random measurement noise. The method of Cao and Rhinehart (J. Process Control 1995, 5 (6), 363−374) was modified for steady-state identification by optimizing the filter constants so as to minimize type I and II errors and simultaneously reduce the delay in state detection. A simple algorithm has been presented that makes use of past measurements and the Kalman filter to detect the presence of gross error and estimate its magnitude. This algorithm simultaneously reconciles data for random measurement errors. Another algorithm that makes use of the exponential filter along with the least-squares minimization strategy is also applied for data reconciliation and found to perform equally well. All of these algorithms have been applied to data from an industrial crude distillation unit. The pre...

A novel fuzzy index for steady state voltage stability analysis and identification of critical busbars

Abstract In this paper, a novel fuzzy index is proposed for the prediction of steady state voltage stability conditions in transmission networks. The uncertainties in the input parameters are efficiently modeled in terms of fuzzy sets by assigning trapezoidal and triangular membership functions. The results include fuzzy load flow solutions for the base case and critical conditions with and without contingencies. The proposed fuzzy voltage stability index clearly indicates the location and status of critical busbars. Case studies have been conducted on standard test systems (IEEE 14-bus, 30-bus, and 57-bus) with proper validation of the results.

A 1-day maximal lactate steady-state assessment protocol for trained runners.

Identification of the maximal lactate steady state (MLSS) involves multiple days of testing. Heart rate (HR), rating of perceived exertion (RPE), breathing frequency (bf), and race pace may be useful in estimating the MLSS, thus allowing for testing to occur in a single day. The purpose of this investigation was to design a single-session protocol for determining MLSS using HR, RPE, bf, and race pace as predictors. Twelve endurance athletes (mean +/- SD, VO2max 64.6 +/- 7.8 mL x kg(-1) x min(-1)) performed the MLSS protocol run and two 27-min validation runs on a treadmill. Running velocity at 87% HRmax RPE of 12, bf of 32 breaths x min(-1), and race pace were used as a starting point for testing. Blood was collected every 3 min of each 9-min stage of the protocol run and analyzed for lactate (La) concentration. The velocity associated with the MLSS was determined as the average of the stage of La steady state and the stage of La accumulation. Validation runs were performed at a velocity 7.5 m x min(-1) below and 7.5 m x min(-1) above the protocol-determined MLSS. If the slower run exhibited a La steady state and the faster run an accumulation of La, then the protocol-determined MLSS value was considered valid. The protocol run was successful in predicting the MLSS in 9 out of 12 subjects (P < or = 0.05). The proposed protocol employing HR, RPE, bf, and race pace as a starting point for testing can be used to identify the MLSS in one testing session.

Critical values for a steady-state identifier

Abstract A table of critical values for a recently proposed R -statistic for steady-state identification is presented. The power of the R -test is also discussed.