CN-115455621-B - Heating system topological structure optimization method based on bionic principle

CN115455621BCN 115455621 BCN115455621 BCN 115455621BCN-115455621-B

Abstract

The invention discloses a heating system topological structure optimization method based on a bionic principle. The method comprises the steps of S1, obtaining position and quantity information of heat users, carrying out clustering analysis according to distribution conditions of the heat users, S2, taking heat sources, heating stations, heat user buildings at all levels and pipeline valve groups at all levels of a heat supply system as system pipeline network nodes based on a bionic principle, constructing a topological structure model of the heat supply pipeline network by utilizing ArcGIS according to the system pipeline network nodes and the existing pipeline information, S3, constructing a dissipation thermal resistance model of the heat supply system according to an impedance model of a blood system, and S4, solving the dissipation thermal resistance minimum of the heat supply system as an objective function to obtain parameter optimization values of all nodes and pipeline structures of the pipeline network. The invention realizes the integral optimization of the topological structure of the heating system, provides a new thought for the analysis and evaluation of the topological structure of the urban heating system, and has reference value for the engineering design and transformation of the actual heating system.

Inventors

ZHANG JUNWEI
Zhong Wai
LIN XIAOJIE

Assignees

浙江大学

Dates

Publication Date: 20260505
Application Date: 20220913

Claims (4)

1. The heating system topological structure optimization method based on the bionic principle is characterized by comprising the following steps of: step S1, acquiring position and quantity information of heat users, and performing cluster analysis according to the distribution situation of the heat users; Step S2, based on a bionic principle, using heat sources, heat stations, heat user buildings at all levels and pipeline valve groups at all levels of a heat supply system as system pipe network nodes, and constructing a topological structure model of a heat supply pipe network in the heat supply system by utilizing ArcGIS according to the system pipe network nodes and the existing pipeline information; S3, constructing a dissipation thermal resistance model of the heating system according to the impedance model of the blood system; S4, solving an objective function with the minimum dissipation thermal resistance of the heating system as the objective function to obtain parameter optimization values of all nodes and pipeline structures of the pipe network, thereby obtaining an overall optimization result of the topological structure of the heating system; The step S3 specifically comprises the following steps: Step S31, based on the bionic principle of the blood system, firstly obtaining a flow impedance model of a blood network, wherein the flow impedance model is represented by the following formula: Wherein R is blood flow resistance, Q is blood flow, P 1 and P 2 are pressures of the head end and the tail end of a node in a blood pipe network respectively, and the unit is Pa; Step S32, for a central heating system, firstly obtaining flow and temperature data of each node of a heating power station, a heating pipe network and a radiator of a terminal heat user, and respectively constructing corresponding dissipation thermal resistance models; the thermal dissipation resistance of a heat exchanger in a thermal station can be expressed as In the formula, ; And The mass flow of hot water in the primary and secondary heating networks is respectively; for a specific constant pressure heat capacity of the hot water, Is the heat conductance of the heat exchanger in the heating power station; M i is the mass flow of hot water in the i-th building, i=1, 2,..n; the dissipation thermal resistance of each node of the pipeline in the heat supply network can be expressed as In the formula, The subscript ab denotes the a-th pipe b-th node, The mass flow of hot water in a pipeline between ab nodes; indicating the thermal conductance of the pipe; The thermal dissipation resistance of an end-heat user heat sink can be expressed as Wherein, the subscript rad is a radiator, the subscript ij is a radiator of a j-th end heat user of the i-th building, Thermally conductive to the heat consumer heat sink.
2. The method for optimizing the topological structure of the heating system based on the bionic principle according to claim 1, wherein in the step S1, cluster analysis is performed according to the distribution condition of heat users, and the specific method comprises the following steps: S11, firstly, the scattered heat users in the heat supply system are formed into a low-density data point set The method comprises the steps of selecting a neighborhood parameter (epsilon, minPts) according to actual conditions, wherein X 1 ~X n corresponds to one hot user, selecting a specific value of the neighborhood parameter, and determining epsilon-core neighborhood of the hot user and selecting a hot user core object, regarding X j epsilon D, wherein epsilon-neighborhood is a sample with epsilon-neighborhood containing a distance from X j to be not more than epsilon in a dataset D, and X j is a core object if epsilon-core neighborhood of a hot user X j contains at least MinPts samples, and finding epsilon-neighborhood of each data in D and determining a core object set omega by using a DBSCAN algorithm; S12, randomly selecting a core object in omega, if X j is located in epsilon-neighborhood of X i and X i is the core object, then X j is directly reached by X i density, finding out all samples directly reached by the core object density to form a new cluster C 1 , and then removing the core object contained in C 1 from omega to obtain an updated set omega; And S13, repeating the step S12 until the set omega is empty, and ending to obtain a clustering analysis result of the hot user.
3. The method for optimizing the topology of the heating system based on the bionic principle according to claim 2, wherein the step S2 is specifically: Step S21, firstly, acquiring specific position coordinates of heat sources, heating stations at all levels, heat user buildings at all levels and valve groups at all levels in a pipe network in a heat supply system, and specific position coordinates of pipeline length of the heat supply pipe network, starting points and ending points of pipelines and pipeline fluid flow direction data; S22, simulating a heat source in a heat supply system as a heart of a human body, simulating a heat exchange station and valve groups of each level as regulating organs of the human body, simulating the pushing action of a pressurizing pump in the heat source and the heat exchange stations of each level on a heating medium as the pushing action of the heart of the human body on blood, wherein the valve groups of each level comprise a stop valve, an exhaust valve and a check valve, and the heat user building of each level comprises a tail end water tank, a pressure regulating tower and a radiator; step S23, using heat exchangers and booster pump equipment of heat sources and all levels of regional heat exchange stations as pushing nodes of a heating system pipe network model, using district heat exchange stations of all levels of heat user buildings as primary adjusting nodes, using radiators and valves of heat users as secondary adjusting nodes, and using all adjusting nodes and heating pipe network pipelines as constituent units of a heating system topological structure; And step S24, constructing a topological structure model of the heating network by utilizing ArcGIS based on the component unit information of the topological structure in the step S23, thereby obtaining a three-dimensional visual model of the topological structure of the heating system, wherein the three-dimensional visual model comprises a three-dimensional model of a main branch line of the heating network and a terrain model of an integral heating area.
4. The method for optimizing the topology of the heating system based on the bionic principle according to claim 1, wherein the step S4 is specifically: step S41, constructing an optimized objective function with the minimum dissipation thermal resistance of the heating system, may be expressed as: Wherein N is the total number of buildings, and N i is the total number of end heat user radiators in the ith building; step S42, determining constraint conditions for solving the optimization objective function; in the heating system, the total heat capacity of the water supplied by the secondary heating network is fixed, namely ; In the central heating network, the heat user buildings of the same branch are in parallel connection, namely, the temperatures of hot water conveyed to the 1 st building heat user and the i th building heat user are equal, and the parallel constraint condition of the central heating network is expressed as follows: Wherein T 1 is the water temperature at the inlet of the radiator of the 1 st user of each building, Q 11 is the heat load of the 1 st heat user in the 1 st building, R 11,rad is the heat dissipation resistance of the radiator of the 1 st end heat user in the 1 st building, T i is the water temperature at the inlet of the radiator of the i th user of each building, Q i1 is the heat load of the 1 st heat user in the i th building, R i1,rad is the heat dissipation resistance of the radiator of the 1 st end heat user in the i th building; Because the radiators of all heat users in the same building are in a series connection relationship, and the series constraint condition of the central heating network system is obtained by combining an energy conservation equation, the series constraint condition is expressed as follows: Wherein, T ' and T '' are respectively the water temperatures before and after the heat exchange of the primary heat supply network hot water in the heat exchange station, K and Q t are the sum of the heat loads of all heat users of the secondary heat supply network, Q ij is the radiator heat load of the jth end heat user of the ith building, k is the kth end heat user radiator, and Q ik is the radiator heat load of the kth end heat user of the ith building; Step S43, constructing a Lagrange function by means of a Lagrange multiplier method: In the formula, Are Lagrangian multipliers, T k is the water temperature at the inlet of a radiator of a kth user of each building, Q k1 is the heat load of a1 st heat user in the kth building, R k1,rad is the heat dissipation resistance of a1 st tail heat user radiator in the kth building, and m k is the mass flow of hot water in the kth building; Order the Regarding the partial derivatives of all variables equal zero, a system of optimization equations is obtained: Solving the optimized equation set to obtain an optimal value of the dissipation thermal resistance of each node of the heating system, and further calculating to obtain an optimal value of the topological structure of the heating system, the pipe diameter of the pipe network and the opening of the pump valve; And S44, comparing the structural parameters of the existing heating system, analyzing and evaluating the topological structure of the heating system, and for the situation that the flow threshold and the safety requirement of the pipe network are difficult to be realized by changing the pipe diameter of the pipe or adjusting valve group equipment in the area with larger difference of results, adding the pipe in the pipe network, changing the integral topological structure of the heating pipe network, and then adopting the steps S41-43 to construct a new optimized equation set for solving to obtain the optimal values of the topological structure of the heating system, the pipe diameter of the pipe network and the opening of the pump valve.

Description

Heating system topological structure optimization method based on bionic principle Technical Field The invention belongs to the field of heating systems, and particularly relates to a heating system topological structure optimization method based on a bionic principle. Background The heat supply system is used for producing high-temperature hot water in a heat source factory and driving the hot water to circularly flow in a primary side pipe network to convey heat energy to each heating power station, and in the heating power station, the primary side and the secondary side exchange heat to exchange heat from the primary side to the secondary side, and the secondary side supplies heat to each heat user in the secondary side pipe network. Along with the increasing complexity of the heating network and the increasing energy-saving requirement of the heating system, the suitability of the original heating network design method is gradually reduced. At present, the structural design of a heat supply pipe network system lacks an integral planning method, and the problems of energy waste, low utilization efficiency, difficult adjustment of pipe network hydraulic balance and the like are caused by unreasonable pipe network topological structure. Disclosure of Invention Aiming at the current situation that the conventional heating system pipe network structure lacks systematic evaluation method, the invention provides an optimization method of the heating system topological structure based on the bionic principle. The invention is realized by adopting the following technical scheme: The invention discloses a heating system topological structure optimization method based on a blood system bionic principle, which comprises the following steps of: step S1, acquiring position and quantity information of heat users, and performing cluster analysis according to the distribution situation of the heat users; Step S2, based on a bionic principle, using heat sources, heat stations, heat user buildings at all levels and pipeline valve groups at all levels of a heat supply system as system pipe network nodes, and constructing a topological structure model of a heat supply pipe network in the heat supply system by utilizing ArcGIS according to the system pipe network nodes and the existing pipeline information; S3, constructing a dissipation thermal resistance model of the heating system according to the impedance model of the blood system; And S4, solving the objective function by taking the minimum dissipation thermal resistance of the heating system as the objective function to obtain the parameter optimization values of all nodes and pipeline structures of the pipe network, thereby obtaining the overall optimization result of the topological structure of the heating system. In the above technical solution, further, in step S1, cluster analysis is performed according to the distribution situation of the heat user, and the specific method is as follows: S11, firstly, forming a low-density data point set D= { X 1,X2,X3,…,Xn } by scattered heat users in a heat supply system, wherein X 1～Xn corresponds to one heat user respectively, supposing neighborhood parameters (epsilon, minPts), wherein specific numerical values of the neighborhood parameters are selected according to actual conditions and are used for determining epsilon-core neighborhood of the heat user and selecting heat user core objects, for X j epsilon D, the epsilon-neighborhood is a sample with the epsilon-neighborhood being not more than epsilon from X j in the data set D, and if the epsilon-core neighborhood of the heat user X j at least comprises MinPts samples, X j is a core object, and finding epsilon-neighborhood of each data in D by using a DBSCAN algorithm and determining a core object set omega; S12, randomly selecting a core object in omega, if X j is located in epsilon-neighborhood of X i and X i is the core object, then X j is directly reached by X i density, finding out all samples directly reached by the core object density to form a new cluster C 1, and then removing the core object contained in C 1 from omega to obtain an updated set omega; And S13, repeating the step S12 until the set omega is empty, and ending to obtain a clustering analysis result of the hot user. Further, the step S2 specifically includes: Step S21, firstly, acquiring specific position coordinates of heat sources, heating stations at all levels, heat user buildings at all levels and valve groups at all levels in a pipe network in a heat supply system, and specific position coordinates of pipeline length of the heat supply pipe network, starting points and ending points of pipelines and pipeline fluid flow direction data; S22, simulating a heat source in a heat supply system as a heart of a human body, simulating a heat exchange station and valve groups of each level as regulating organs of the human body, simulating the pushing action of a pressurizing pump in the heat source a