PARAMETRIC IDENTIFICATION OF THE DIFFERENTIAL MODEL OF HEAT EXCHANGE IN A GASIFIER

На основе одномерной дифференциальной модели теплообмена в газификаторе закрытого типа сформулирована задача параметрической идентификации модели на основе измерений на штатном оборудовании промышленной газификационной установки. Модель включает в себя дополнительное интегральное условие и самосогласованно определяемую подвижную границу, отделяющую зону обледенения трубки испарителя. С применением метода сглаживания особенности разработан алгоритм итерационного решения уравнений модели, использующий метод сквозного счета для решения уравнения переноса на одной итерации. Для параметрической идентификации модели использована смешанная стратегия. Часть идентифицируемых параметров (теплоемкость испарителя, мощность нагревателя, массовая производительность насоса, коэффициент теплоотдачи в окружающую среду) определялась на основе специально организованных измерений: нагрева испарителя без прокачки сверхкритического флюида, газификации в условиях теплоизолированности корпуса испарителя, газификации в стационарном режиме работы. Остальные параметры (коэффициенты теплоотдачи в теплоноситель и сверхкритический флюид) идентифицировались в пассивных измерениях с различными производительностями насоса. Отмечено, что ввиду плохой обусловленности задачи и ограниченности вариаций коэффициентов применение регрессионных методов в данной модели неэффективно. На основе метода стрельбы разработан способ идентификации, заключающийся в определении параметров по измерениям с предельными производительностями с построением функциональной связи между идентифицируемыми параметрами, с последующей верификацией на промежуточных измерениях. Метод апробирован на примере штатной газификационной установки СГУ-7КМ-У We formulated the problem of parametric identification of the model based on measurements on the standard equipment of an industrial gasification plant on the basis of a one-dimensional differential model of heat transfer in a closed-type gasifier. The model includes an additional integral condition and a self-consistently defined movable boundary separating the icing zone of the evaporator tube. Using the method of smoothing the singularity, we developed an algorithm for iterative solution of the model equations, using the end-to-end counting method to solve the transfer equation in one iteration. We used a mixed strategy for parametric identification of the model. We determined some of the identified parameters (evaporator heat capacity, heater power, mass pump capacity, heat transfer coefficient to the environment) on the basis of specially organized measurements: heating of the evaporator without pumping supercritical fluid, gasification under conditions of thermal insulation of the evaporator body, gasification in stationary operation. We identified the remaining parameters (heat transfer coefficients to the coolant and supercritical fluid) in passive measurements with different pump capacities. We noted that due to the poor conditionality of the problem and the limited variation of coefficients, the use of regression methods in this model is ineffective. Based on the ballistic method, we developed an identification method, which consists in determining parameters by measurements with marginal performance with the construction of a functional relationship between the identified parameters, followed by verification on intermediate measurements. We tested the method on the example of a standard gasification plant SGU-7KM-U

Simulation of the electroerosion treatment process using neural networks

The paper provides formalization and construction of a model of the process of electrical discharge machining. When describing the process, a T-shaped equivalent circuit containing an RLC circuit was used. Determine the transfer function of the proposed substitution scheme. Also, a task is formulated and an algorithm for neural network parametric identification of a T-shaped equivalent circuit is proposed. The problem is posed and an algorithm is developed for neural network parametric identification of the equivalent circuit with a computational experiment, the formation of training samples on its basis, and the subsequent training of dynamic and static neural networks used in the identification problem. The process was simulated in Simulink, Matlab package. Acceptable coincidence of the calculated data with the experimental ones showed that the proposed model of electrical discharge machining reflects real electromagnetic processes occurring in the interelectrode gap.

Developed Transfer Function Allows Hydro Generators to Enter the Full Range of Ancillary Services Market

As the number of available renewable energy sources has increased annually, there has been a corresponding rise in the levels of pollution created by traditional electricity generation, ultimately contributing to breaking down the stability of the electrical system at large. Therefore, there is an increasing need to integrate the use of nonpolluting electricity sources, such as pumped storage hydropower plants (PSHP), to ensure the stability of the power system and to maintain the frequency of the system from year-to-year. This paper addresses the issue of PSHP being unsuitable for providing Frequency Containment Reserve (FCR) services and proposes real measurements of the aggregation approach to obtain different data arrays. Based on this, the proposed methodology is orientated toward obtaining transfer functions that were developed using the parametric identification models, and the efficiency of these functions was thoroughly investigated. The proposed transfer function in this paper, in combination with battery energy storage system (BESS) technologies, would allow PSHP technologies to occupy a space in the ancillary services market by providing FCR, Frequency Restoration Reserve (FRR), and Replacement Reserve (RR) services. The performance of the function activated in the BESS is positively validated using the Simulink modeling environment.

Method for tuning feedforward in electric feed drive control systems

The electric feed drive used in metal-cutting machines like any high-precision electric drive requires high accuracy of reference processing and robustness against perturbations. For this purpose, feedforwards are added to the position controller to improve set point processing time and to compensate for disturbances. Feedforwards are usually tuned manually when the machine is setup, either by applying a series of tests on the motor or by calculation. The calculation requires some information about the magnitudes of disturbances that can be compensated by appropriate feedforwards, but this information is not always available a priori. In this paper, we propose tuning the feedforward coefficients based on the results of the parametric identification of the values of the torques acting on the electric drive, as well as the apparent moment of inertia. For parametric identification the methods of electric drive theory, method of least squares, and digital signal processing method are used; mathematical modeling method is applied to assess the compensation quality. The authors propose the method of tuning the parameters of the control system of electric feed drive based on parametric identification of the values of torques acting on the motor and/or the operating device. The results of control system simulation show both high identification accuracy and significant reduction of dynamic control error when feedforwards are activated. The considered structure of the control system and the proposed algorithm of identification and adjustment of its parameters can be used in electric drives of metal-cutting machine tools. The simulation results have shown that the use of feedforwards, tuned in accordance with the algorithm, enable to reduce the dynamic position tracking error by more than 50 times, which can be critical in contour machining.

Structural and Parametric Identification of Knowm Memristors

This paper proposes a novel identification method for memristive devices using Knowm memristors as an example. The suggested identification method is presented as a generalized process for a wide range of memristive elements. An experimental setup was created to obtain a set of intrinsic I–V curves for Knowm memristors. Using the acquired measurements data and proposed identification technique, we developed a new mathematical model that considers low-current effects and cycle-to-cycle variability. The process of parametric identification for the proposed model is described. The obtained memristor model represents the switching threshold as a function of the state variables vector, making it possible to account for snapforward or snapback effects, frequency properties, and switching variability. Several tools for the visual presentation of the identification results are considered, and some limitations of the proposed model are discussed.

AGE-SPECIFIC DYNAMICS OF ESSENTIAL OIL CONTENT IN ABIES SIBIRICA BARK. THEORETICAL ASPECTS OF EVALUATION

The variability of the essential oil content in Abies Sibirica bark in the Eastern and Western parts of the Krasnoyarsk forest-steppe was evaluated in eight age groups. The model of the age dynamics of changes in the essential oil content in the bark of Abies Sibirica, which is adequate to the experimental data, was obtained. With increasing age of fir, the content of fir oil in the bark increases from 1.4±0.1% of a. s. s. in 20–30 years of age to 2.8±0.2% of a.s.s. at the age of 70–80 years, with further decreases to 0.4±0.1% of a. s. s. in overmature trees. The results of parametric identification of the model for Abies Sibirica growing in the Krasnoyarsk forest-steppe are presented. Based on the results of 50.000 computational experiments, the simulation method estimates the statistics of changes in the model coefficients and 95% of the boundary of the forecast values of the content of fir oil in the bark. A compartment model is proposed that explains the age-specific dynamics of essential oil content in tree greens and Siberian fir bark. The obtained solution compartment model allows us to associate its parameters with the coefficients of models of changes in the content of fir oil in tree greens and bark obtained by processing experimental data.

Response-Only Parametric Estimation of Structural Systems Using a Modified Stochastic Subspace Identification Technique

The present paper is a study of output-only modal estimation based on the stochastic subspace identification technique (SSI) to avoid the restrictions of well-controlled laboratory conditions when performing experimental modal analysis and aims to develop the appropriate algorithms for ambient modal estimation. The conventional SSI technique, including two types of covariance-driven and data-driven algorithms, is employed for parametric identification of a system subjected to stationary white excitation. By introducing the procedure of solving the system matrix in SSI-COV in conjunction with SSI-DATA, the SSI technique can be efficiently performed without using the original large-dimension data matrix, through the singular value decomposition of the improved projection matrix. In addition, the computational efficiency of the SSI technique is also improved by extracting two predictive-state matrixes with recursive relationship from the same original predictive-state matrix, and then omitting the step of reevaluating the predictive-state matrix at the next-time moment. Numerical simulations and experimental verification illustrate and confirm that the present method can accurately implement modal estimation from stationary response data only.

Inverse problems of the dynamics of observation interpretation systems

Based on the analysis and systematization of the inverse problems of dynamics, the study of the properties and features of the types of dynamic models under consideration, an approach is proposed for the development of appropriate methods of mathematical modeling based on the use and implementation of integral models in the form of Volterra equations of the I and II kind, their functional capabilities are determined in the study of various classes of problems, and also formulated the features that affect the choice of methods for their numerical solution. Methods for obtaining integral models are proposed, which are the basis for constructing algorithms for solving inverse problems of dynamics for a fairly wide class of dynamic objects. Integral methods for the identification of dynamic objects have been developed, which make it possible to obtain stable non-optimization algorithms for calculating the parameters of mathematical models. Recurrent methods of parametric identification of transfer functions of dynamic objects with an arbitrary input action are proposed (the obtained parameters of the transfer functions are also coefficients of the corresponding differential equations, which makes it possible to obtain equivalent mathematical models in the form of integral equations). The study of algorithms that implement the proposed identification methods allows us to conclude about their efficiency in terms of the amount of computation and ease of implementation, as well as the high accuracy of calculating the model parameters.