Spatiotemporal Parameter Estimation of Thermal Treatment Process via Initial Condition Reconstruction Using Neural Networks

Hadjiski, Mincho; Deliiski, Nencho; Grancharova, Alexandra

doi:10.1007/978-3-319-78931-6_4

Cited by 9 publications

(8 citation statements)

References 29 publications

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“…Good adequacy and precision of the model toward the results from wide own experimental studies allow the carrying out of various calculations with the model, which are connected to the nonstationary temperature distribution and energy characteristics of logs from different wood species during their refrigeration. The mathematical model, after its connection with other our model of the logs' defrosting process [9,10], could be input into the software of programmable controllers for optimized model-based automatic control [8,20,21,35] of thermal treatment of frozen logs in the production of veneer.…”

Section: Discussionmentioning

confidence: 99%

Modeling of the Energy for Bound Water Freezing in Logs Subjected to Refrigeration

Deliiski¹,

Tumbarkova²

2020

Low-Temperature Technologies

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This study suggests an approach for modeling of the total thermal energy needed for freezing the bound water in logs subjected to refrigeration. The approach maximally considers the physics of the freezing process of the bound water in wood. It considers the nonstationary change in the icing degree of logs formed by the crystallization of the bound water in them, as well as the influence of the fiber saturation point of each wood species on its current amount of nonfrozen bound water depending on temperatures below À1°C. Mathematical descriptions of the thermal energy of the phase transition of bound water in logs and also of the latent thermal energy of the bound water released in logs during their freezing have been executed. These descriptions were introduced in our own 2D nonlinear mathematical model of the freezing process of logs. The model was transformed in a form suitable for programming with the help of explicit schemes of the finite difference method. For the solution of the model and energy simulations with it, a software program was prepared, which was input into the calculation environment of Visual Fortran Professional.

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Section: Discussionmentioning

confidence: 99%

Modeling of the Energy for Bound Water Freezing in Logs Subjected to Refrigeration

Deliiski¹,

Tumbarkova²

2020

Low-Temperature Technologies

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“…UVOD It is known that the duration and energy consumption of the thermal treatment of frozen logs in the winter, aimed at their plasticizing for the production of veneer, depend on the degree of the logs' icing (Chudinov, 1966(Chudinov, , 1968(Chudinov, , 1984Kollmann and Côté, 1984;Shubin, 1990; Požgaj et al, 1997;Trebula and Klement, 2002;Videlov, 2003;Pervan, 2009; Deliiski and Dzurenda, 2010;Deliiski, 2011Deliiski, , 2013b). In the available specialized literature, there are limited reports about the temperature distribution in frozen logs subjected to defrosting (Steinhagen, 1986(Steinhagen, , 1991; Steinhagen and Lee, 1988; Khattabi and Steinhagen, 1992, 1993, 1995Deliiski, 2004Deliiski, , 2009Deliiski, , 2011Deliiski and Dzurenda, 2010;Deliiski et al, 2015;Deliiski, 2015, 2016) and there is very little information about research of the temperature distribution in logs during their freezing (Deliiski and Tumbarkova, 2016, 2017, 2018. That is why the modeling and the multi-parameter study of the freezing process of logs are of considerable scientifi c and practical interest.…”

Section: Sažetak • Predložena Je Metodologija Za Matematičko Modeliramentioning

confidence: 99%

Modeling Latent Heat Fluxes of Water in Logs during their Freezing

Deliiski

Tumbarkova

2019

Drv. ind. (Online)

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This paper suggests a methodology for mathematical modelling and research of two interconnected problems: 2D non-stationary temperature distribution in logs subjected to freezing and change in the latent heat fluxes of the free and bound water in logs during the freezing process. For the purpose of this methodology, a 2-dimensional mathematical model has been created, solved, and verified for the transient non-linear heat conduction in logs during their freezing at convective boundary conditions. The model includes a mathematical description of the specific latent heat fluxes, qLHv-fw and qLHv-bw, formed by the freezing of the free and bound water in the logs, respectively. The paper presents solutions of the model with explicit form of the finite-difference method in the calculation environment of Visual Fortran Professional and its verification in accordance with our own experimental studies. The paper presents the results of simulation analysis of 2D non-stationary temperature distribution in the longitudinal section of pine log with a diameter of 0.24 m, length of 0.48 m, and moisture content above the hygroscopic range during its 30-hour freezing in a freezer at the temperature of the processing air medium of approximately –30 °C. The change in the latent heat fluxes qLHv-fw and qLHv-bw during the log freezing is presented, visualized, and analyzed.

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“…As the specific value of π and the power u 0 are prescribed, the only vector p j represents the unknown input in the current autoclave j-run necessary to define the optimal heating time T 1 * (p j ). Following the proposed in [14] system for p j identification, one of the values p(0) or p(l) will be available as the input to the neural network NN s after finishing the estimation procedure. As the duration T m of the first TTP stage is many times shorter than the expected whole heating time T, the derived from the neural network NN s optimal value T 1 * (p) will be accessible for the control system.…”

Section: Optimization Of the Ttp Process By Means Of Neural Networkmentioning

confidence: 99%

Application of Neural Networks to the Optimization of the Thermal Treatment Process of Wood Materials

Hadjiski¹,

Deliiski²,

Grancharova³

2017

Information Technologies and Control

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In this paper, a software system for 2-dimensional and 3-dimensional modeling of frozen and not frozen logs and prismatic wood materials is used as a basis for optimization of the thermal treatment process of such materials. Then, the dependence of the optimal heating time on various parameters is approximated by means of neural networks. This would allow a fast real-time computation of the optimal heating time instead of performing extensive numerical simulations of the model comprised of partial differential equations.

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Spatiotemporal Parameter Estimation of Thermal Treatment Process via Initial Condition Reconstruction Using Neural Networks

Cited by 9 publications

References 29 publications

Modeling of the Energy for Bound Water Freezing in Logs Subjected to Refrigeration

Modeling of the Energy for Bound Water Freezing in Logs Subjected to Refrigeration

Modeling Latent Heat Fluxes of Water in Logs during their Freezing

Application of Neural Networks to the Optimization of the Thermal Treatment Process of Wood Materials

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