Process model identification

2011 ◽  
pp. 1-22
Author(s):  
J. Pico ◽  
P. Albertos ◽  
M. Martinez
Keyword(s):  
Process Model ◽  
Model Identification

A New Signal Design Tool for Process Model Identification

2004 ◽  
Vol 37 (9) ◽  
pp. 23-28
Author(s):  
V.C. Machado ◽  
J.O. Trierweiler ◽  
A.R. Secchi
Keyword(s):  
Process Model ◽  
Model Identification ◽  
Design Tool ◽  
Signal Design

A SYSTEMATIC APPROACH TO SMB PROCESS MODEL IDENTIFICATION FROM SMB PROCESS DATA

2005 ◽  
Vol 38 (1) ◽  
pp. 189-194
Author(s):  
V. Grosfils ◽  
C. Levrie ◽  
M. Kinnaert ◽  
A. Vande Wouwer
Keyword(s):  
Process Model ◽  
Systematic Approach ◽  
Model Identification ◽  
Process Data

scholarly journals Data-driven Modelling of Thermal Solid Sorption Storage Systems

2021 ◽  
Vol 6 ◽  
pp. 27
Author(s):  
Harald Kirchsteiger ◽  
Nayrana Daborer-Prado
Keyword(s):  
Simulation Model ◽  
Process Model ◽  
Storage Systems ◽  
Model Identification ◽  
Systems Design ◽  
Simulated Data ◽  
Absolute Humidity ◽  
Data Driven ◽  
Dynamic State ◽  
Air Stream

An approach fofsr data-driven modelling of open sorption storage systems using zeolite as storage material is presented. The overall dynamic simulation model has the inflow air stream mass flow and absolute humidity as inputs and computes the outflow air temperature. The model is sub-divided into several components, where dynamic state space and process model identification techniques are applied. A comparison of the proposed modelling technique with simulated data from a validated model based on first principles shows that a reasonable accuracy − for a model application in temperature control systems design − can be obtained. It was found that using the proposed strategy, only a limited number of experiments are required, thus saving experimental time. Moreover, the computational requirements for a simulation using the proposed model are greatly reduced compared to a simulation model where differential equations discretised in time and space must be solved.

Scarce Data Operating Conditions: Process Model Identification

1997 ◽  
Vol 30 (11) ◽  
pp. 453-458 ◽  
Author(s):  
R. Sanchis ◽  
A. Sala ◽  
P. Albertos
Keyword(s):  
Process Model ◽  
Model Identification ◽  
Operating Conditions ◽  
Scarce Data

Reliability Evaluation for Complex Machinery Products

2011 ◽  
Vol 480-481 ◽  
pp. 763-768
Author(s):  
Feng Du ◽  
Zhi Wei Guan ◽  
Yi Zhou
Keyword(s):  
Random Process ◽  
Process Model ◽  
Large Scale ◽  
Model Identification ◽  
Reliability Evaluation ◽  
Reliability Theory ◽  
Reliability Test ◽  
Treatment Procedure ◽  
Evaluation Mode ◽  
Mechanical Products

For those large-scale and complex mechanical products, considering economy and applicability, these products are impossible to be discarded as soon as they take place malfunction. Generally, they can be reused again and again after be repaired repeatedly, that is, they usually have multi-lifespan. Accordingly, it is very necessary to establish a kind of proper reliability evaluation mode for these complex repairable products. In this paper, the treatment procedure of the reliability test data for these repairable products is researched based on the repairable reliability theory, and the method of model identification about failure distributions law is proposed. In addition, how to reasonably determine or predict product lives by using random process model is also introduced. Finally, an analysis case for putting this mean into practice is expounded. This method for product reliability analysis is suitable to most large-scale and complex mechanical products.

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