A Grey-Box Dynamic Model of Plate Heat Exchangers Used in an Urban Heating System

Miao, Qing; You, Shijun; Zheng, Wandong; Zheng, Xuejing; Zhang, Huan; Wang, Yaran

doi:10.3390/en10091398

Cited by 19 publications

(6 citation statements)

References 17 publications

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“…Further more modelling of the heat exchangers in the system and elements are the system are described in Refs. [40][41][42][43][44].…”

Section: Literature Overviewmentioning

confidence: 99%

Digital Twin of Heat Exchanger

Bobič¹

2023

Heat Transfer - Fundamentals, Enhancement and Applications

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How to create proper digital twin of plate heat exchange. The chapter will investigate alternatives for modeling and propose the best choices in modeling to trade-off between accuracy of the calculation vs. speed. Lump sum vs. distributed model, how to make plate model discrete, temperature distribution models, and details in heat exchange wall modeling will be considered. Last part would focus on experimental evaluation of the simulation using IR temperature vision measurements. Conclusions would take concrete examples of dynamic responses of heat exchange units, comparing numeric simulation with experimental results.

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“…Further more modelling of the heat exchangers in the system and elements are the system are described in Refs. [40][41][42][43][44].…”

Section: Literature Overviewmentioning

confidence: 99%

Digital Twin of Heat Exchanger

Bobič¹

2023

Heat Transfer - Fundamentals, Enhancement and Applications

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“…The recursive least-squares identification method was used as BB model. Miao et al [105] developed a serial GB model for the prediction of outlet temperatures of plate heat exchangers. A parameter identification method was established through the Taylor series.…”

Section: Heat Treatment Processesmentioning

confidence: 99%

“…Application Category Process GB Type Target BB Type [65][66][67] Industrial robot estimation and control process automation serial "motor angular speed", "deflection of a piezoelectric micromanipulator", "machine position" "weighted logarithmic least squares", "ANN", "weighted nonlinear least squares and weighted logarithmic least squares" [95][96][97][98][99][100][101][102] Miscellaneous estimation and control "reactive systems" "serial", "paralel" "reacytion rate", "product composition", "heat released", "design of CSTR", "concentration distributions in the context of modelling a reaction-advection-diffusion system" "LS-SVM", "ANN and EKF", "ARXIs", [103][104][105][106][107][108][109][110][111] Miscellaneous estmation and control heat treatment process serial "heat release inside the reactor", "settled material breaking away from the heat transfer surface", "outlet temperatures of plate heat exchangers", "temperature of combustion chamber", "boiler temperature", "electricity consumption of a refrigeration system", "spray dryer performance", "temperature within an imperfectly mixed fluid" "LS", "recursive least squares identification", "taylor series", "ANN", "nonlinear least squares optimization", "linear interpolation", "MLE"…”

Section: Paper Industrymentioning

confidence: 99%

Gray-box Soft Sensors in Process Industry: Current Practice, and Future Prospects in Era of Big Data

et al. 2020

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Virtual sensors, or soft sensors, have greatly contributed to the evolution of the sensing systems in industry. The soft sensors are process models having three fundamental categories, namely white-box (WB), black-box (BB) and gray-box (GB) models. WB models are based on process knowledge while the BB models are developed using data collected from the process. The GB models integrate the WB and BB models for addressing the concerns, i.e., accuracy and intuitiveness, of industrial operators. In this work, various design aspects of the GB models are discussed followed by their application in the process industry. In addition, the changes in the data-driven part of the GB models in the context of enormous amount of process data collected in Industry 4.0 are elaborated.

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“…The other approach consists of using data-based techniques, also known as black-box modeling or process identification [16][17][18], or even in using artificial neural networks [19][20][21]. The so-called gray-box modeling approach, which combines qualitative prior knowledge with quantitative data, can also be used here [22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Rational Transfer Function Model for a Double-Pipe Parallel-Flow Heat Exchanger

Bartecki

2020

Symmetry

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Transfer functions of typical heat exchangers, resulting from their partial differential equations, usually contain irrational functions which quite accurately describe the spatio-temporal nature of the processes occurring therein. However, such an accurate but complex mathematical representation is often not convenient from the practical point of view, and some approximation of the original model would be more useful. This paper discusses approximate rational transfer functions for a typical thick-walled double-pipe heat exchanger working in the parallel-flow configuration. Using the method of lines with the backward difference scheme, the original symmetric hyperbolic partial differential equations describing the heat transfer phenomena are transformed into a set of ordinary differential equations and expressed in the form of N subsystems representing spatial sections of the exchanger. Each section is described by a rational transfer function matrix and their cascade interconnection results in the overall approximation model expressed by a matrix of rational transfer functions of high order. Based on the rational transfer function representation, the frequency and steady-state responses of the approximate model are evaluated and compared with those resulting from its original irrational transfer function model. The presented results show better approximation quality for the “crossover” input–output channels where the in-domain heat conduction effects prevail as compared to the “straightforward” channels, where the transport delay associated with the heat convection dominates.

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A Grey-Box Dynamic Model of Plate Heat Exchangers Used in an Urban Heating System

Cited by 19 publications

References 17 publications

Digital Twin of Heat Exchanger

Digital Twin of Heat Exchanger

Gray-box Soft Sensors in Process Industry: Current Practice, and Future Prospects in Era of Big Data

Rational Transfer Function Model for a Double-Pipe Parallel-Flow Heat Exchanger

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