scholarly journals CONTROL OF WATER CONSUMPTION OF ENTERPRISES OF ROSTOV REGION

2021 ◽  
Vol 1 (6) ◽  
pp. 57-62
Author(s):  
M.A. Reshitko ◽  
◽  
A.B. Usov
Keyword(s):  
Maximum Principle ◽  
Least Squares ◽  
Dynamic Model ◽  
Water Consumption ◽  
Economic Activity ◽  
Least Squares Method ◽  
Model Identification ◽  
Goal Function ◽  
Rostov Region ◽  
Control Water

. In this paper we consider the hierarchical two-level dynamic model of water consumption of enterprises of Rostov region. On the top level there is region’s government (supervisor) and on the lower level there are enterprises grouped by the types of economic activity. Such grouping allows one to avoid modelling all region’s enterprises which consumes water (there were about 500 enterprises in 2019) while still considering the uniqueness of the enterprises. We consider three groups of enterprises which, according to statistics, are the main water consumers. The enterprises aim to maximize their production while supervisor seeks to minimize water consumption and to maximize production. We introduce generalized goal function for supervisor, which it seeks to maximize depending on what it values the most – production or water consumption. Using the model, we study how supervisor is able to control water consumption of the enterprises with extra fees and how it may affect production level. We provide algorithms for model identification and study. For model identification we use least squares method and the data from publicly available sources. To study model we use Pontryagin’s maximum principle and quality representative scenarios method. We provide various calculations for the model with different supervisor’s preferences. We show that it is possible to reduce water consumption by more than 50 % with production decrease being less than 25%

scholarly journals The model identification of buildings horizontal displacements with the use of a free geodetic network

2021 ◽  
Vol 906 (1) ◽  
pp. 012056
Author(s):  
Maria Mrówczyńska ◽  
Jacek Sztubecki ◽  
Zofia Ziçba ◽  
Izabela Wilczyńska
Keyword(s):  
Least Squares ◽  
Least Squares Method ◽  
Geometric Model ◽  
Model Identification ◽  
Geodetic Network ◽  
Differential Method ◽  
Wind Pressure ◽  
Engineering Structures ◽  
Measurement Results ◽  
Horizontal Displacements

Abstract The geodetic monitoring of engineering structures, their displacements, and deformations, carried out permanently or periodically, allows obtaining information on the technical condition of facilities. The achieved information enables determining the necessary changes in using objects and minimizing future errors in the similar object’s design. The measurement results are subject to geometric interpretation based on the determined displacement parameters of the object’s shape and the approximation of the vector displacement field. Due to the influence of random factors characterized by a change in time and varying intensity, the deformation measurements performed during the operation of the facilities are of great importance for the safety of structures and engineering structures. In actual tasks of determining the object’s deformation and building a geometric model of displacements, the dominant method is the differential method, the advantage of eliminating systematic errors in measurement results while maintaining the geometric structure of the measurement and control network. The displacement’s geometric model, built based on measurements and calculations, can build a dynamic model of a building object, additionally considering such causes of deformation as, for example, own and usable weight, wind pressure, changes in ambient temperature, or ground vibrations. The article proposes approaches using the free alignment of linear and angular observations made in a geodetic network to determine horizontal displacements of an engineering object. This method may be necessary to study displacements of various parts of the object, thus analyzing its deformation. Free alignment allows for an optimal fit of the equalized network into the approximate network by imposing additional conditions (compared to the classic least squares method) on the vector of estimates of increments to approximate coordinates and the value of the covariance matrix. As an example of applying the proposed approach, the actual data received from the geodetic monitoring of the building structure was used. The structure was a road viaduct located along Wojska Polskiego Street in Bydgoszcz. The object of measurements and analyses was represented by finite sets of fixed points, subject to periodic observations over two years. The authors tested the effectiveness of the proposed algorithm and compared the obtained results with the values of horizontal displacements, which were calculated based on the classic study of geodetic monitoring results using the least-squares method. The accuracy analysis of the obtained values of the geodetic network horizontal displacements using free alignment and the least-squares method was also performed. The results indicate the possibility of using the presented approach to identify the geometric model of horizontal displacements without losing the accuracy of their determination.

scholarly journals Subspace Based Model Identification for an Industrial Bioreactor: Handling Infrequent Sampling Using Missing Data Algorithms

Processes ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 1686
Author(s):  
Nikesh Patel ◽  
Brandon Corbett ◽  
Johan Trygg ◽  
Chris McCready ◽  
Prashant Mhaskar
Keyword(s):  
Missing Data ◽  
Least Squares ◽  
Dynamic Model ◽  
Model Identification ◽  
The Other ◽  
Model Data ◽  
Process Data ◽  
Identification Technique ◽  
Identification Approach ◽  
Insight Into

This manuscript addresses the problem of modeling an industrial (Sartorius) bioreactor using process data. In the context of the Sartorius Bioreactor, it is important to appropriately address the problem of dealing with a large number of variables, which are not always measured or are measured at different sampling rates, without taking recourse to simpler interpolation- or imputation-based approaches. To this end, a dynamic model for the Sartorius Bioreactor is developed via appropriately adapting a recently presented subspace model identification technique, which in turn uses nonlinear iterative partial least squares (NIPALS) algorithms to gracefully handle the missing data. The other key contribution is evaluating the ability of the identification approach to provide insight into the process by computing interpretable variables such as metabolite rates. The results demonstrate the ability of the proposed approach to model data from the Sartorius Bioreactor.

Identification of Fractional Model by Least-Squares Method and Instrumental Variable

2015 ◽  
Vol 10 (5) ◽  
Author(s):  
Abir Khadhraoui ◽  
Khaled Jelassi ◽  
Jean-Claude Trigeassou ◽  
Pierre Melchior
Keyword(s):  
Monte Carlo Simulation ◽  
Least Squares ◽  
Instrumental Variable ◽  
Least Squares Method ◽  
Model Identification ◽  
Order System ◽  
Fractional Model ◽  
Identification Method ◽  
Fractional System ◽  
Noisy Output

This paper deals with fractional model identification using least-squares (LS) method and instrumental variable (IV) in a noisy output context. A new identification method, which extends LS techniques to fractional system to identify not only the parameters but also the unknown order, is presented. In order to eliminate the bias of identification results, IV method is chosen which permits unbiased parameter estimation. Monte Carlo simulation analyses are used to demonstrate the validity and the performance of the proposed fractional order system identification method.

MODEL IDENTIFICATION OF A SERVO-TRACKING SYSTEM USING FUZZY CLUSTERING

1999 ◽  
Vol 07 (04) ◽  
pp. 337-346
Author(s):  
ERIC M. NGUYEN ◽  
NADIPURAM R. PRASAD
Keyword(s):  
Least Squares ◽  
Observational Data ◽  
Fuzzy Clustering ◽  
Least Squares Method ◽  
Tracking System ◽  
Model Identification ◽  
Automatic Generation ◽  
Non Linear ◽  
Servo Tracking ◽  
Takagi Sugeno

This paper investigates the use of Fuzzy Clustering as a means for model identification of a complex and highly non-linear servo-tracking system when only observational data is available. The use of Fuzzy Clustering facilities automatic generation of rules and its antecedent parameters. The consequent of the model is then formulated in the form of Takagi, Sugeno and Kang (TSK), and its parameters determined by the Least Squares Method (LSM).

‘Least squares’ method—theorem or law?

1980 ◽  
Vol 59 (9) ◽  
pp. 8
Author(s):  
D.E. Turnbull
Keyword(s):  
Least Squares ◽  
Least Squares Method
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