Steady-state Identification Research Articles

Steadystate: A MATLAB-based routine for determining steady-state friction conditions in the framework of rate-and-state friction analysis

Reliable determination of rate-and-state friction (RSF) parameters depends on achieving steady-state (SS) friction conditions before and after experimental velocity-stepping friction tests. This operation, through nonlinear least squares fitting, is commonly preceded by the removal of any overall slip weakening/hardening after friction velocity steps (VSs) through a sufficiently large window of slip displacement at SS ( = linear detrend). However, to date, the identification of SS and thus the correct linear detrend is dependent on the user, which potentially results in differing RSF outputs from the same data set. Here, we demonstrate that large errors in the determination of the fitted RSF parameters can result if SS conditions are not reached before and after VSs. Such errors can be particularly relevant for materials characterized by long evolution of frictional resistance with slip, such as clay-rich gouge layers, in which identifying SS after VSs is not always obvious. To this end, we propose a methodology to accurately and consistently identify where SS is achieved after VSs. This methodology is coded into a new MATLAB-based routine, steadystate. We show the key features of the methodology, as well as how to use steadystate and read its output. We also illustrate the broad applicability of the approach to friction data with different noise levels and sampling frequencies referenced to slip velocity, by reviewing observations from synthetic data sets and specific examples of experiments from different laboratories involving various sheared materials.

NOx formation model for utility boilers using robust two-step steady-state detection and multimodal residual convolutional auto-encoder

BackgroundTo achieve carbon neutrality, thermal power plants will take on greater peak-shaving responsibilities, resulting in utility boilers running at more frequently-changing states. This variation inevitably contributes to dirtier measurement data, and the data will follow new distributions that represent asynchronous operational characteristics. MethodTo predict the NOx formation concentration in this new context, a novel modelling framework is proposed. First, a two-step steady-state detection algorithm, including a basic and a precise identification procedure, is proposed to select steady-state samples from operational data. This algorithm also combines with a bayesian optimization–variational mode decomposition (BO–VMD) approach to enhance its robustness by eliminating high-frequency noise, and with an empirical cumulative distribution based outlier detection (ECOD) to remove outliers. Therefore, a high-quality steady-state dataset is acquired. Based on these data, a multimodal residual convolutional auto-encoder is developed to extract the latent variables that vary with the operation modes. These variables are then used to construct the multimodal NOx prediction model. Significant findingsThe findings on a 660 MW utility boiler reveal that, the proposed modelling framework has a lower misdiagnosis rate in steady-state identification compared to the one-step detection technique, and achieves higher fitting and generalization accuracy than the global modelling method.

Model-Based Improved Advanced Adaptive Performance Recovery Control Method for a Commercial Turbofan Engine

In-service commercial aero-engine controls its thrust by controlling parameters such as speed/pressure ratio (Nf/EPR) characterized as measurable and indirectly related to the thrust. However, the mapping relationship between the Nf/EPR and the thrust would have unknown and nonlinear changes with the engine performance degrades, naturally causing it difficult to obtain the desired thrust by primarily designed Nf/EPR control and resulting in task degradation. Therefore, this paper proposes an improved double closed-loop self-adaptive performance recovery control method to yield the engine to maintain almost consistent performance in the whole life cycle. Firstly, the inner control loop of speed is designed based on the MPC algorithm to obtain the optimal fuel output through receding horizon optimization to improve the control performance. Secondly, the speed command from the inner control loop of the degraded engine is compensated by adding an outer control loop of thrust to maintain the desired thrust. In this control method, an improved high-precision parameter estimation method is present to estimate unmeasurable engine performance parameters. A steady-state identification algorithm therein is designed to judge the engine state to deal with the problem that the estimator might diverge due to the engine's strongly nonlinear performance changes under the circumstances of high dynamic operation. Finally, the simulation results show that, compared with the traditional method, the proposed method achieves the estimator's stable work, improves the control performance by 25.8%, and maintains the desired thrust in case of engine degradation.

Transmission line modeling co-simulation with distributed delay-size control using steady-state identification

Combining performance and numerical stability is a key issue in co-simulation. The Transmission Line Modeling method uses physically motivated communication delays to ensure numerical stability for stiff connections. However, using a fixed communication delay may limit performance for some models. This paper proposes Steady-State Identification for enabling variable communication delays. Three algorithms for online Steady-State Identification are evaluated in three different co-simulation models. All algorithms are able to identify steady state and can thereby determine when communication delays can be allowed to increase without compromising accuracy and stability. The results show a reduction in number of the solver derivative evaluations by roughly 40–60% depending on the model. The proposed method additionally supports connections with asymmetric communication delays, which allows each sub-model to independently control the delay of its input variables. Models supporting delay-size control can thereby be connected to those that do not, so that the step length of each individual sub-model is maximized. Controlling the delay-size in sub-models also makes the method independent of the master co-simulation algorithm.

Development of Classification Model on SAC Refrigerant Charge Level Using Clustering-based Steady-state Identification

Development of Classification Model on SAC Refrigerant Charge Level Using Clustering-based Steady-state Identification

识别稳态与瞬态

识别稳态与瞬态

Evaporative flux method of leaf hydraulic conductance estimation: sources of uncertainty and reporting format recommendation

BackgroundThe accurate estimation of leaf hydraulic conductance (Kleaf) is important for revealing leaf physiological characteristics and function. However, the Kleaf values are largely incomparable in previous studies for a given species indicating some uncertain influencing factors in Kleaf measurement.ResultWe investigated the potential impacts of plant sampling method, measurement setup, environmental factors, and transpiration steady state identification on Kleaf estimation in Oryza sativa and Cinnamomum camphora using evaporation flux method (EFM). The effects of sampling and rehydration time, the small gravity pressure gradients between water sources and leaves, and water degassing on Kleaf estimation were negligible. As expected, the estimated steady flow rate (E) was significantly affected by multiple environmental factors including airflow around leaf, photosynthetically active radiation (PARa) on leaf surfaces and air temperature. Kleaf decreased by 40% when PARa declined from 1000 to 500 µmol m−2 s−1 and decreased by 15.1% when air temperature increased from 27 to 37 °C. In addition, accurate steady-state flow rate identification and leaf water potential measurement were important for Kleaf estimation.ConclusionsBased on the analysis of influencing factors, we provided a format for reporting the metadata of EFM-based Kleaf to achieve greater comparability among studies and interpretation of differences.

Slow Time-Varying Batch Process Quality Prediction Based on Batch Augmentation Analysis.

In this paper, focusing on the slow time-varying characteristics, a series of works have been conducted to implement an accurate quality prediction for batch processes. To deal with the time-varying characteristics along the batch direction, sliding windows can be constructed. Then, the start-up process is identified and the whole process is divided into two modes according to the steady-state identification. In the most important mode, the process data matrix, used to establish the regression model of the current batch, is expanded to involve the process data of previous batches, which is called batch augmentation. Thus, the process data of previous batches, which have an important influence on the quality of the current batch, will be identified and form a new batch augmentation matrix for modeling using the partial least squares (PLS) method. Moreover, considering the multiphase characteristic, batch augmentation analysis and modeling is conducted within each phase. Finally, the proposed method is applied to a typical batch process, the injection molding process. The quality prediction results are compared with those of the traditional quality prediction method based on PLS and the ridge regression method under the proposed batch augmentation analysis framework. The conclusion is obtained that the proposed method based on the batch augmentation analysis is superior.

Identification of maximal lactate steady state by a short lactate minimum test in walking

The purpose of this study was to analyze the validity of a short Lactate Minimum test (LM) to assess the maximum lactate steady state (MLSS) during walking tests. Twelve healthy young participants performed LM on a treadmill at 5.5 km·h −1 , with the intensity being incremented by inclination (%). Participants first performed a walking trial at 5.5 km·h −1 with 20% or 22% inclination until voluntary exhaustion. After 7 min of recovery an incremental test was initiated by using only 3 incremental stages that were selected based on the rate of perceived exertion (RPE). The LM intensity was identified as the lowest [bLac] during the incremental test, by using a visual inspection (LM3v) and applying a polynomial function of second order (LM3p). The MLSS intensity was also measured. ANOVA showed no difference between the intensities (i.e., treadmill inclination) of MLSS (13.3 ± 2.2%), LM3 v (12.6 ± 1.6%), and LM3p (13.4 ± 1.3%). It was possible to identify the LM during walking by using 3 stages in most of the participants expressing the MLSS intensity with good agreement and correlation between them. Le but de cette étude était d’analyser la validité d’un test lactate Minimum (LM) pour évaluer l’état stable de lactate (MLSS) pendant les tests de marche. Douze jeunes participants en santé ont effectué LM sur un tapis roulant à 5,5 km0h-1, l’intensité étant augmentée par l’inclinaison (%). Les participants ont d’abord effectué un essai ambulant à 5,5 km0h-1 avec une inclinaison de 20 % ou 22 % jusqu’à l’épuisement volontaire. Après 7 min de récupération, un test incrémental a été initié en utilisant seulement 3 étapes incrémentales sélectionnées en fonction du taux d’effort perçu (RPE). L’intensité LM a été identifiée comme la plus faible [bLac] pendant le test incrémental, en utilisant une inspection visuelle (LM3v) et en appliquant une fonction polynomiale de second ordre (LM3p). L’intensité MLSS a également été mesurée. L’Anova n’a montré aucune différence entre les intensités (c’est-à-dire l’inclinaison du tapis roulant) de MLSS (13,3 ± 2,2 %), LM3 v (12,6 ± 1,6 %) et LM3p (13,4 ± 1,3 %). Il a été possible d’identifier le LM pendant la marche en utilisant 3 étapes dans la plupart des participants exprimant l’intensité MLSS avec un bon accord et une corrélation entre eux.

Data-Driven Fault Diagnosis for Automotive PEMFC Systems Based on the Steady-State Identification

Data-driven diagnosis methods for faults of proton exchange membrane fuel cell (PEMFC) systems can diagnose faults through the state variable data collected during the operation of the PEMFC system. However, the state variable data collected from the PEMFC system during the stack switching between different operating points can easily cause false alarms, such that the practical value of the diagnosis system is reduced. To overcome this problem, a fault diagnosis method for PEMFC systems based on steady-state identification is proposed in this paper. The support vector data description (SVDD) and relevance vector machine (RVM) optimized by the artificial bee colony (ABC) are used for the steady-state identification and fault diagnosis. The density-based spatial clustering of applications with noise (DBSCAN) and linear least squares fitting (LLSF) are used to identify the abnormal data in datasets and estimate change rates of the system state variables respectively. The proposed method can automatically identify the state variable data collected from the PEMFC system during the stack switching between different operating points, so that the diagnosis accuracy can be improved and false alarms can be reduced. The proposed method has a certain practical value and can provide a reference for further study.

Impact of Afforestation on Atmospheric Recharge to Groundwater in a Semiarid Area

AbstractWhile the global forest area is generally decreasing, various afforestation projects have been implemented, including the large‐scale Three‐North Afforestation Program (TNAP) and the Conversion of Cropland to Forest Program in China, under which 0.63 million square kilometers of trees have been planted. The large‐scale land use and cover change (LUCC) would affect the redistribution of precipitation and change the water cycle, especially groundwater recharge. The chloride mass balance (CMB) between precipitation and soil water/groundwater is the most widely used technique to estimate the recharge in arid and semiarid areas. However, in the case of decreased recharge following LUCC, a new equilibrium of water and chloride flux is not easily reached and the identification of steady state is the premise to use CMB. This study provided a method to assess the steady state by comparing the history of LUCC and chloride cumulative age at sampling depth and by checking the breaks in the slope of the line for cumulative chloride and soil water. The case study in the Mu Us Sandy Land, affected by the TNAP, shows that soil profiles beneath sparse grassland, shrubland, and woodland have reached steady state. However, new equilibrium has not been reached in the soil profiles beneath the dense shrubland. The estimated recharge rates beneath the plantations represent reductions from 33% to >90% relative to the surrounding bare sandy land (50–54 mm/year). The results highlight the unfavorable effects of some afforestation and ecological rehabilitation approaches in arid and semiarid areas on regional groundwater resources.

In situ process monitoring of nuclear waste glass melts using non-contact microwave sensor

• Non-contact microwave sensor for in situ high temperature measurements. • Algorithms to extract process parameters from SFCW radar measurements. • Demonstration of continuous monitoring of absolute level of glass melt. • In situ identification of thermal steady state of glass melt. • Remote monitoring of all stages in a typical vitrification batch process. A non-contact microwave sensor is presented for in situ process monitoring of nuclear waste glass melts inside a cold crucible induction melting (CCIM) furnace. The level and thermal steady state of the molten glass inside a 1400 °C CCIM furnace were measured during 6-hour long vitrification process using a corrugated horn antenna operating over 20–24 GHz designed for high temperature measurement. The non-contact in situ microwave measurements indicate the ability to measure absolute level, identify thermal steady state of the glass melt, and remotely monitor the vitrification process for safe immobilization of the liquid radioactive waste.

A computationally efficient method for identification of steady state in time series data from ship monitoring

Most applied time series are non-stationary, or exhibit some kind of non-stationarity for at least parts of the time series. For time series analyses or mathematical modeling purposes, the non-stationarities can be difficult to handle. Therefore, identification of stationary and non-stationary behavior is of great practical interest in time series analysis. In this study a robust and computationally efficient method to identify steady state parts of time series data is presented. The method is based on the class of deterministic trend models using a sliding window, and is focused towards being easy to implement, efficient and practical in use and to preserve data completeness. To demonstrate the performance of the steady state identifier, the method is applied on different sets of time series data from two ships equipped with systems for in-service monitoring. The method is shown to be reliable and practical for identifying steady state parts of time series data, and can serve as a practical preprocessing tool for time series data analysis.

Integration of continuous restricted Boltzmann machine and SVR in NOx emissions prediction of a tangential firing boiler

The NOx emissions prediction modeling of a coal-fired boiler is a complicated problem because of its highly nonlinear and strongly correlated multi-variables. To address this problem, this paper presents a novel deep structure using continuous restricted Boltzmann machine (CRBM) with support vector regression (SVR). A new steady-state identification method is first established by applying kernel principal component analysis (KPCA). Subsequently the stationary samples are subjected to the stacked CRBM network for extracting implied features. The combination of the optimal features and target NOx values are further utilized for training SVR to establish the regression pzmart of the CRBM-SVR prediction model. Additionally, particle swarm optimization (PSO) is applied for optimizing hyper-parameters of SVR, and weights of CRBMs are fine-tuned with gradient descent on account of prediction errors. Compared with the existing state of the art, the proposed structure achieves a good performance of 4.83 mg/m3, 3.55 mg/m3 and 0.963 of RMSE, MAE and R2.

Prediction of SCR Catalysts Performance in Coal-Fired Power Plants Based on Fusion of Multi-source Information

In this paper, an integrated algorithm was proposed to evaluate the DeNOx ability of SCR reactor using the operational data of power plant, including the identification of steady state based on R-statistic, the detection of the wrong points and the classification of working conditions by CNN-clustering algorithm. Then the performance profiles of the SCR reactor were extracted from the mass and mixed raw data. Furthermore, the catalyst samples after 0 days, 540 days and 1246 days of operation were collected from the first catalyst layer, and the degradation of the catalyst activity and the micro geometrical characteristic were obtained by the laboratory test. Finally, the FCM fuzzy clustering algorithm was used to fuse the health characteristics of the SCR catalyst layer from multi-source information, such as DeNOx ability of the SCR reactor, catalyst activity, the service of catalyst, and the health evaluation system of the catalyst layer in a full lifecycle was obtained.

Experimental results of a small-scale organic Rankine cycle: Steady state identification and application to off-design model validation

This paper presents experimental results from a prototype of an 11 kWe organic Rankine cycle (ORC). Special attention has been paid to detecting reliable steady state data points. This has been achieved by application of a novel steady state detection algorithm. This method also allows evaluating the performance of the sensors in relation to remaining steady state fluctuations. The reliability of the experimental data is evaluated by investigating the heat balances over the heat exchangers and through error propagation of the measurement uncertainties. In addition, the experimental data from the setup is used for validating an off-design ORC model. The proposed simulation model solely requires the pump and expander speed as inputs besides the given heat source and sink conditions while subcooling is assumed to be constant. The important dependent model outputs are the evaporation pressure, the condensation pressure and the working fluid mass flow rate. All three predicted outputs show a maximum deviation of less than ±1% from the measured values. The modelled net power output deviates less than ±2% from the measured values. In general, this is a satisfactory result that gives confidence in using these models in future off-design simulations.

Process Monitoring and Steady State Identification for Start-up Process of Batch Processes

Inter-batch evolution widely exists in the start-up process of batch processes, and different operation phases exhibit significantly different behaviors, so multiple models are necessary both along the batch direction and the time direction. In this work, reference windows and sliding windows are constructed within each phase for analysis of inter-batch evolution. Through analyzing two indexes, Ratio and , the process is divided into different operating states. Different models are constructed for these states. In addition, a PCA similarity factor and the Mahalanobis distances between batch trajectories are used for online steady-state identification (SSID). The application to a typical multiphase batch process with inter-batch evolution, injection molding start-up process, illustrates the feasibility and performance of the proposed algorithm.

Reliable Iterative RTO of a Continuously Operated Hydroformylation Process

In this work, the application of a reliable iterative real-time optimization (RTO) scheme to a continuously operated transition metal complex catalyzed process for the hy-droformylation of 1-dodecene is presented. The aim of the proposed scheme is to ensure optimal operation despite the presence of model uncertainties and measurement errors. Iterative optimization using Modifier Adaptation with Quadratic Approximation (MAWQA) is applied. Furthermore, additional modules for steady-state identification (SSI) and robust data reconciliation (DR) were designed and implemented. The proposed scheme was commissioned in a real miniplant, and a significantly improved performance in comparison to the optimal operating point that was obtained from the nominal process model was achieved.

Online Inductance and Capacitance Identification Based on Variable Forgetting Factor Recursive Least-Squares Algorithm for Boost Converter

The control performance of boost converter suffers from the variations of important component parameters, such as inductance and capacitance. In this paper, an online inductance and capacitance identification based on variable forgetting factor recursive least-squares (VFF-RLS) algorithm for boost converter is proposed. First, accurate inductance and capacitance identification models and the RLS algorithm are introduced. In order to balance the steady-state identification accuracy and parameter tracking ability, a forgetting factor control technique is investigated. By recovering system noise in the error signal of the algorithm, the value of forgetting factor is dynamically calculated. In addition, since the sampling rate is much lower than the existing identification methods, the proposed algorithm is practical for low-cost applications. Finally, the effectiveness of the proposed algorithm is verified by experiment. The experiment results show that the algorithm has good performance in tracking inductance and capacitance variations.

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

In molecular beam epitaxy, it is known that a planar surface may suffer from a morphological instability in favour to different front pattern formations. In this context, many studies turned their focus to the theoretical and numerical analysis of highly nonlinear partial differential equations which exhibit different scenarios ranging from spatio-temporal chaos to coarsening processes (i.e., an emerging pattern whose typical length scale with time). In this work our attention is addressed toward the study of a highly nonlinear front evolution equation proposed by Csahok et al. (1999) where the unknowns are the periodic steady states which play a major role in investigating the coarsening dynamics. Therefore the classical methods of Newton or a fixed point type are not suitable in this situation. To overcome this problem, we develop in this paper a new approach based on heuristic methods such as genetic algorithms in order to compute the unknowns.