Zhou et al 2023a - Revision history

Rimni at 08:38, 22 April 2024

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Rimni at 08:14, 22 April 2024

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Rimni: /* References */

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‎References

Rimni at 12:38, 19 April 2024

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Rimni at 12:36, 19 April 2024

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Rimni at 12:35, 19 April 2024

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Rimni: /* References */

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‎References

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Rimni at 12:09, 19 April 2024

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 {| class="wikitable" style="margin: 1em auto 0.1em auto;border-collapse: collapse;font-size:85%;width:auto;"
 |-style="text-align:center"
-! Norm !! Arithmetic !! Working condition 1 !! Working condtion 2 !! Working condition 3 !! Working condition 4
+! Norm !! Arithmetic !! Working condition 1 !! Working condition 2 !! Working condition 3 !! Working condition 4
 |-
 |  rowspan='2' style="text-align: center;"|GD

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 {| style="text-align: center; margin:auto;width: 100%;"
 |-
-| style="text-align: center;" | <math>s_{n,n}=\frac{8(\lambda \frac{l_n}{d_n}+\sum {\xi }_n){\rho }_n}{{\pi }^2d_n^4}</math>
+| style="text-align: center;" | <math>s_{n,n}=\frac{8(\lambda \frac{l_n}{d_n}+\displaystyle\sum {\xi }_n){\rho }_n}{{\pi }^2d_n^4}</math>
 |}
 | style="width: 5px;text-align: right;white-space: nowrap;" |(6)

← Older revision		Revision as of 08:14, 22 April 2024
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−	In Eq. (2), <math display="inline">GM_m</math> is the calculated flow rate value of the <math display="inline">m</math>th measurement point, m<math>^3</math>/h; <math display="inline">GC_m</math> is the measured flow rate value of the <math display="inline">m</math>th measurement point, m<math>^3</math>/h. In Eq. (3), <math display="inline">s</math> is the resistance coefficient of the recognized pipe section, Pa/(m<math>^3</math>/h)<math>^2</math>; <math display="inline">S</math> is the n-dimensional decision space. In Eq. (4), <math display="inline">>s_{n,\min}</math> is the lower limit of the resistance coefficient of the pipe section, Pa/(m<math>^3</math>/h)<math>^2</math>; <math display="inline">>s_{n,\max}</math> is the upper limit of the resistance coefficient of the pipe section, Pa/(m<math>^3</math>/h)<math>^2</math>. In Eq. (5), <math display="inline">y</math> is called the objective function, and <math display="inline">Y</math> is the m-dimensional objective space <span id='cite-_Ref153110550'></span>[[#_Ref153110550\|[26]]].	+	In Eq. (2), <math display="inline">GM_m</math> is the calculated flow rate value of the <math display="inline">m</math>th measurement point, m<math>^3</math>/h; <math display="inline">GC_m</math> is the measured flow rate value of the <math display="inline">m</math>th measurement point, m<math>^3</math>/h. In Eq. (3), <math display="inline">s</math> is the resistance coefficient of the recognized pipe section, Pa/(m<math>^3</math>/h)<math>^2</math>; <math display="inline">S</math> is the n-dimensional decision space. In Eq. (4), <math display="inline">s_{n,\min}</math> is the lower limit of the resistance coefficient of the pipe section, Pa/(m<math>^3</math>/h)<math>^2</math>; <math display="inline">s_{n,\max}</math> is the upper limit of the resistance coefficient of the pipe section, Pa/(m<math>^3</math>/h)<math>^2</math>. In Eq. (5), <math display="inline">y</math> is called the objective function, and <math display="inline">Y</math> is the m-dimensional objective space <span id='cite-_Ref153110550'></span>[[#_Ref153110550\|[26]]].

	Affected by the system running time, the thermal user's self-regulation and other factors, the actual resistance coefficient of the pipe section will deviate from the design resistance coefficient to a certain extent, so there are two situations in determining the search range of the resistance coefficient of each pipe section.		Affected by the system running time, the thermal user's self-regulation and other factors, the actual resistance coefficient of the pipe section will deviate from the design resistance coefficient to a certain extent, so there are two situations in determining the search range of the resistance coefficient of each pipe section.

@@ Line 817: / Line 817: @@
 <span id='_Ref153110397'>[[#cite-_Ref153110397|[21]]] Wang H., Wang H.Y., Zhou W.G. Identification method of resistance coefficients of pipe segments in heat supply pipe networks. Computational Physics, 30(3):422-432, 2013.
-<span id='_Ref143695653'>[[#cite-_Ref143695653|[22]]] XU G., Zhang T.Q., Lv Mou., et al. Genetic algorithm for correcting pipe resistance coefficients under multiple working conditions. China Water Supply and Drainage, (08):50-53, 2004.
+<span id='_Ref143695653'>[[#cite-_Ref143695653|[22]]] Xu G., Zhang T.Q., Lv Mou., et al. Genetic algorithm for correcting pipe resistance coefficients under multiple working conditions. China Water Supply and Drainage, (08):50-53, 2004.
 <span id='_Ref143695665'>[[#cite-_Ref143695665|[23]]] Liu Q.Q. Multi-objective problem optimization based on artificial bee colony algorithm. Shenzhen University, 2016.

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-<span id='_Ref153110397'>[[#cite-_Ref153110397|[21]]] Wang H., Wang H. Y., Zhou W. G. Identification method of resistance coefficients of pipe segments in heat supply pipe networks. Computational Physics,30(3):422-432, 2013.
+<span id='_Ref153110397'>[[#cite-_Ref153110397|[21]]] Wang H., Wang H.Y., Zhou W.G. Identification method of resistance coefficients of pipe segments in heat supply pipe networks. Computational Physics, 30(3):422-432, 2013.
-<span id='_Ref143695653'>[[#cite-_Ref143695653|[22]]] XU G., Zhang T. Q., Lv Mou., et al. Genetic Algorithm for Correcting Pipe Resistance Coefficients under Multiple Working Conditions. China Water Supply and Drainage, (08):50-53, 2004.
+<span id='_Ref143695653'>[[#cite-_Ref143695653|[22]]] XU G., Zhang T.Q., Lv Mou., et al. Genetic algorithm for correcting pipe resistance coefficients under multiple working conditions. China Water Supply and Drainage, (08):50-53, 2004.
-<span id='_Ref143695665'>[[#cite-_Ref143695665|[23]]] Liu Q. Q. Multi-objective problem optimization based on artificial bee colony algorithm. Shenzhen University, 2016.
+<span id='_Ref143695665'>[[#cite-_Ref143695665|[23]]] Liu Q.Q. Multi-objective problem optimization based on artificial bee colony algorithm. Shenzhen University, 2016.
-<span id='_Ref143695668'>[[#cite-_Ref143695668|[24]]] Xie C. W., Pan J. M., Guo H., et al. A large-scale multi-objective evolutionary algorithm using hybrid strategies. Journal of Computing:1-21, 2023.
+<span id='_Ref143695668'>[[#cite-_Ref143695668|[24]]] Xie C.W., Pan J.M., Guo H., et al. A large-scale multi-objective evolutionary algorithm using hybrid strategies. Journal of Computing, 1-21, 2023.
-<span id='_Ref153110461'><span id='_Ref143695676'>[[#cite-_Ref143695676|[25]]] Fu X. Z., Xiao Y. M. Fluid Transmission and Distribution Pipe Network (Fourth Edition). China Construction Industry Press, 2018.
+<span id='_Ref153110461'><span id='_Ref143695676'>[[#cite-_Ref143695676|[25]]] Fu X.Z., Xiao Y.M. Fluid transmission and distribution pipe network (Fourth Edition). China Construction Industry Press, 2018.
-<span id='_Ref143695689'><span id='_Ref153110550'>[[#cite-_Ref153110550|[26]]] Xiao J., Xu X. K., Zhang Y. J., et al. Differential evolutionary algorithm and its application to high-dimensional multi-objective optimization. People's Posts and Telecommunications Press: Academic Monographs on Information and Communication Innovation, 2018.
+<span id='_Ref143695689'><span id='_Ref153110550'>[[#cite-_Ref153110550|[26]]] Xiao J., Xu X.K., Zhang Y.J., et al. Differential evolutionary algorithm and its application to high-dimensional multi-objective optimization. People's Posts and Telecommunications Press: Academic Monographs on Information and Communication Innovation, 2018.
-<span id='_Ref143695722'><span id='_Ref153110613'>[[#cite-_Ref153110613|[27]]] Xiao J., Bi X. J., Wang K. J. Research on high-dimensional multi-objective optimization based on global ranking. Journal of Software, 26(07):1574-1583, 2015.
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 <span id='_Ref143695727'>[[#cite-_Ref143695727|[28]]] Ding Y., Yang J. Comparison of Euclidean distance and normalized Euclidean distance in k-nearest neighbor algorithm. Software, 41(10):135-136+140, 2020.
-<span id='_Ref143695756'><span id='_Ref153110649'><span id='_Ref149310369'>[[#cite-_Ref149310369|[29]]] Montano A. A., Coello A. C., Mezura-Montes E. A novel differential evolution algorithm incorporating local dominance and scalar selection mechanisms for multi-objective optimization[C]//IEEE Congress on Evolutionary Computation. ieee, 1-8, 2010.
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-<span id='_Ref153110660'>[[#cite-_Ref153110660|[30]]] Wu X. F. Research on impedance identification and variable differential pressure optimal control method of heating pipe network based on differential evolution algorithm. Qingdao University of Technology. 2023.
+<span id='_Ref153110660'>[[#cite-_Ref153110660|[30]]] Wu X.F. Research on impedance identification and variable differential pressure optimal control method of heating pipe network based on differential evolution algorithm. Qingdao University of Technology, 2023.

@@ Line 18: / Line 18: @@
 == 1. Introduction ==
-Heating systems are critical infrastructure for cities in northern China. With the rapid development of Internet of Things (IoT) technology, the urban centralized heat supply system is transforming into a new type of heat supply system that is jointly composed of a heat supply physical equipment network, heat supply IoT, and heat supply information management platform. In the new heating system, intelligent regulation devices with communication functions and heat metering instruments were installed at the heat inlets of heat consumers. The application of intelligent regulating devices allows heat users to actively adjust hot water flow according to their room temperature demand, realizing accurate heat supply. However, the independent adjustment of many heat users will lead to real-time changes in the resistance distribution of the pipeline network, which will also make the flow regulation of the heat station face greater difficulties. If the flow rate adjustment of the heat station can not adapt to the heating needs of the heat users, it will lead to system hydraulic imbalance, resulting in energy waste [[#_Ref143695261|[1]]]. To realize the accurate flow regulation of the heat station, it is necessary to grasp the changes in the resistance distribution of the heat network in real time.
+Heating systems are critical infrastructure for cities in northern China. With the rapid development of Internet of Things (IoT) technology, the urban centralized heat supply system is transforming into a new type of heat supply system that is jointly composed of a heat supply physical equipment network, heat supply IoT, and heat supply information management platform. In the new heating system, intelligent regulation devices with communication functions and heat metering instruments were installed at the heat inlets of heat consumers. The application of intelligent regulating devices allows heat users to actively adjust hot water flow according to their room temperature demand, realizing accurate heat supply. However, the independent adjustment of many heat users will lead to real-time changes in the resistance distribution of the pipeline network, which will also make the flow regulation of the heat station face greater difficulties. If the flow rate adjustment of the heat station can not adapt to the heating needs of the heat users, it will lead to system hydraulic imbalance, resulting in energy waste <span id='cite-_Ref143695261'></span>[[#_Ref143695261|[1]]]. To realize the accurate flow regulation of the heat station, it is necessary to grasp the changes in the resistance distribution of the heat network in real time.
 The continuous development of the heat metering system and the application of a large number of intelligent heat metering instruments make it possible to obtain real-time changes in the flow rate of each user through the heat supply information management platform, which provides a data basis for the real-time identification of the resistance distribution of the heat network. Lin <span id='cite-_Ref153109712'></span>[[#_Ref153109712|[2]]], Wang et al. <span id='cite-_Ref153109724'></span>[[#_Ref153109724|[3]]] calculated the resistance coefficients of the pipe network based on the generalized inverse matrix theory by using multiple sets of hydraulic conditions. Bekibayev et al. <span id='cite-_Ref153109752'></span>[[#_Ref153109752|[4]]] determined the resistance coefficients of the pipes by comparing the results of hydraulic calculations with the actual data from the SCADA system. Kaltenbacher et al. <span id='cite-_Ref153109760'></span>[[#_Ref153109760|[5]]] proposed a method for identifying the resistance coefficients of individual pipes in a water supply network using the inverse of the steady-state hydraulic equations of the network.  Zecchin et al. <span id='cite-_Ref153109768'></span>[[#_Ref153109768|[6]]] used a particle swarm algorithm to identify the resistance of pipe networks. Dini and Tabesh <span id='cite-_Ref153109776'></span>[[#_Ref153109776|[7]]] completed the identification of resistance coefficients of pipe networks using an ant colony optimization algorithm. Savic and Walters <span id='cite-_Ref143695480'></span>[[#_Ref143695480|[8]]], Lingireddy and Ormsbee <span id='cite-_Ref153109792'></span>[[#_Ref153109792|[9]]], Fan et al. <span id='cite-_Ref153294601'></span>[[#_Ref153294601|[10]]], Liang <span id='cite-_Ref153294613'></span>[[#_Ref153294613|[11]]] and Liu et al. <span id='cite-_Ref153294623'></span>[[#_Ref153294623|[12]]] successfully applied genetic algorithms to the resistance identification of heating pipe network. Sherri et al. <span id='cite-_Ref153109819'></span>[[#_Ref153109819|[13]]], Lv <span id='cite-_Ref143695587'></span>[[#_Ref143695587|[14]]], Zhou et al. <span id='cite-_Ref143695592'></span>[[#_Ref143695592|[15]]] used the improved genetic algorithm to improve the efficiency of resistance coefficient optimization identification of heat supply network.
@@ Line 775: / Line 775: @@
 <div class="auto" style="text-align: left;width: auto; margin-left: auto; margin-right: auto;font-size: 85%;">
-[[#cite-_Ref143695261|[1]]] Wang Z.J., Dong L.H., Jiang Y.C., Fang X.-M. Dynamic hydraulic misalignment and control of outdoor pipe network for heat metering variable flow heating system. Journal of Harbin Institute of Technology, 42(02):218-222+258, 2010.
+<span id='_Ref143695261'>[[#cite-_Ref143695261|[1]]] Wang Z.J., Dong L.H., Jiang Y.C., Fang X.-M. Dynamic hydraulic misalignment and control of outdoor pipe network for heat metering variable flow heating system. Journal of Harbin Institute of Technology, 42(02):218-222+258, 2010.
-[[#cite-_Ref153109712|[2]]] Lin R.Q. Research on non-similar hydraulic working condition construction and pipe resistance coefficient identification of branch heating pipe network. Harbin Institute of Technology, 2021.
+<span id='_Ref143695271'><span id='_Ref153109712'>[[#cite-_Ref153109712|[2]]] Lin R.Q. Research on non-similar hydraulic working condition construction and pipe resistance coefficient identification of branch heating pipe network. Harbin Institute of Technology, 2021.
-[[#cite-_Ref153109724|[3]]] Wang N., You S.J., Wang Y., et al. Hydraulic resistance identification and optimal pressure control of district heating network. Energy and Buildings, 170:83-94, 2018.
+<span id='_Ref143695278'><span id='_Ref153109724'>[[#cite-_Ref153109724|[3]]] Wang N., You S.J., Wang Y., et al. Hydraulic resistance identification and optimal pressure control of district heating network. Energy and Buildings, 170:83-94, 2018.
-[[#cite-_Ref153109752|[4]]] Bekibayev T., Zhapbasbayev U., Ramazanova G., Bossinov D., Pham D.T.  Oil pipeline hydraulic resistance coefficient identification. Cogent Engineering, 8(1):1950303, 2021.
+<span id='_Ref153109752'>[[#cite-_Ref153109752|[4]]] Bekibayev T., Zhapbasbayev U., Ramazanova G., Bossinov D., Pham D.T.  Oil pipeline hydraulic resistance coefficient identification. Cogent Engineering, 8(1):1950303, 2021.
-[[#cite-_Ref143695462|[5]]] Kaltenbacher S., Steinberger M., Horn M. Pipe roughness identification of water distribution networks: The full turbulent case. Applied Mathematical Modelling, 80:879-894, 2020.
+<span id='_Ref143695300'><span id='_Ref153109760'><span id='_Ref143695462'>[[#cite-_Ref143695462|[5]]] Kaltenbacher S., Steinberger M., Horn M. Pipe roughness identification of water distribution networks: The full turbulent case. Applied Mathematical Modelling, 80:879-894, 2020.
 <span id='_Ref153109768'>[[#cite-_Ref153109768|[6]]] Zecchin A.C., Lambert M.F., Simpson A.R., White L.B. Parameter identification in pipeline networks: transient-based expectation-maximization approach for systems containing unknown boundary conditions. Journal of Hydraulic Engineering, 140(6):04014020, 2014.