CN115455621A - Heat supply system topological structure optimization method based on bionic principle - Google Patents
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Abstract
The invention discloses a heat supply system topological structure optimization method based on a bionic principle. The method comprises the following steps: s1, acquiring position and quantity information of hot users, and performing cluster analysis according to the distribution condition of the hot users; s2, based on a bionic principle, taking a heat source, a heat station, heat user buildings and pipeline valve banks of each level of a heat supply system as system pipe network nodes, and constructing a topological structure model of the heat supply pipe network by utilizing ArcGIS according to the system pipe network nodes and the existing pipeline information; s3, constructing a dissipation thermal resistance model of the heat supply system according to the impedance model of the blood system; and S4, solving by taking the minimum dissipation thermal resistance of the heat supply system as an objective function to obtain the parameter optimization values of each node and the pipeline structure of the pipe network. The invention realizes the integral optimization of the topological structure of the heating system, provides a new idea for the analysis and evaluation of the topological structure of the urban heating system, and has reference value for the engineering design and the reconstruction of the actual heating system.
Description
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 plant and driving the hot water to circularly flow in a primary side pipe network to convey heat energy to each heat station, the primary side and the secondary side exchange heat in the heat station to transfer the 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 continuous increase of the complexity of the heat supply pipe network and the increasingly prominent energy-saving requirement of the heat supply system, the suitability of the original heat supply pipe network design method is gradually reduced. The structural design of the existing heat supply pipeline network system lacks an integral planning method, and the problems of energy waste, low utilization efficiency, difficult adjustment of hydraulic balance of a pipeline network and the like are caused by unreasonable topological structure of the pipeline network.
Disclosure of Invention
Aiming at the current situation that the existing heat supply system pipe network structure lacks a systematic assessment method, the invention provides a heat supply system topological structure optimization method based on a bionic principle.
The invention is realized by adopting the following technical scheme:
the invention discloses a heat supply system topological structure optimization method based on a blood system bionic principle, which comprises the following steps of:
s1, acquiring position and quantity information of hot users, and performing cluster analysis according to distribution conditions of the hot users;
s2, based on a bionic principle, taking a heat source, a heating power station, each level of heat user buildings and each level of pipeline valve banks of the heat supply system as system pipe network nodes, and constructing a topological structure model of the 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 heat supply 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 heat supply system as the objective function to obtain the parameter optimization values of each node of the pipe network and the pipeline structure, thereby obtaining the overall optimization result of the topological structure of the heat supply system.
In the above technical solution, further, in step S1, cluster analysis is performed according to the distribution of the hot users, and the specific method includes:
s11, firstly, forming dispersed heat users in a heat supply system into a low-density data point set D = { X = { (X) } 1 ,X 2 ,X 3 ,…,X n In which X is 1 ～X n Respectively corresponding to one of the hot users; assuming neighborhood parameters (epsilon, minPts), wherein specific numerical values of the neighborhood parameters are selected according to actual conditions and are used for determining an epsilon-core neighborhood of a hot user and selecting a hot user core object; for X j E.g. D, the epsilon-neighborhood is: the epsilon-neighborhood contains the sum X in the data set D j Samples having a distance of not more than epsilon; if the user is hot X j The epsilon-kernel neighborhood of (a) contains at least MinPts samples, then X j Is a core object; finding out an epsilon-neighborhood of each data in D by using a DBSCAN algorithm and determining a core object set omega;
s12, randomly selecting the omegaA core object, if X j Is located at X i E-neighborhood of (a), and X i Is a core object, then X j From X i The density is direct; finding all samples directly from the density of the core object to form a new cluster C 1 (ii) a Then C is mixed 1 Removing the core object contained in the set omega 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 cluster analysis result of the hot user.
Further, the step S2 specifically includes:
step S21, firstly, acquiring specific position coordinates of heat sources, all levels of heating power stations, all levels of heat user buildings and all levels of valve banks in a pipe network in a heating system, the specific position coordinates of the pipeline length of the heating pipe network and the starting point and the ending point of a pipeline and pipeline fluid flowing direction data;
s22, simulating a heat source in the heat supply system as a heart of a human body, and simulating a heat exchange station and each valve group as a regulating organ of the human body; the pushing action of a heat source and a pressurizing pump in each stage of heat exchange station on a heat medium fluid medium is compared with the pushing action of a human heart on blood, wherein each stage of valve bank comprises a stop valve, an exhaust valve and a check valve, and each stage of heat user building comprises a tail end water tank, a pressure regulating tower and a radiator;
step S23, using heat sources and heat exchangers and pressure pump equipment of all levels of regional heat exchange stations as pushing nodes of a heat supply system pipe network model, using a district heat exchange station where all levels of heat user buildings are located as a first-level adjusting node, using radiators and valves of heat users as second-level adjusting nodes, and using all adjusting nodes and heat supply pipe network pipelines as a component unit of a heat supply system topological structure;
step S24, constructing a topological structure model of the heat supply pipe network by utilizing ArcGIS based on the information of the composition units of the topological structure in the step S23; and obtaining a three-dimensional visual model of the topological structure of the heat supply system, wherein the three-dimensional visual model comprises a branch and trunk line three-dimensional model of the heat supply pipe network and a terrain model of the whole heat supply area.
Further, the step S3 specifically includes:
step S31, based on the blood system bionic principle, firstly obtaining a flow impedance model of the blood system, which is expressed by the following formula:
wherein R is the blood flow resistance; q is the blood flow; p 1 And P 2 Respectively the pressure, pa, at the head end and the tail end of the node in the blood pipe network.
Step S32, for a centralized heating system, firstly obtaining flow and temperature data of each node of a heating power station, a heating pipe network and a tail end heat user radiator, and respectively constructing corresponding dissipative thermal resistance models;
the dissipation thermal resistance of a heat exchanger in a thermal power station can be expressed as
In the formula (I), the compound is shown in the specification,
m 0 and m t Mass flow of hot water in the primary and secondary heat supply networks respectively; c. C p Specific constant pressure heat capacity of hot water (KA) sub Thermal conductance of a heat exchanger in a heating station;
m i i =1,2, \ 8230;, n for the mass flow rate of hot water in the ith building.
The dissipative thermal resistance of each node of the pipeline in the heat supply network can be expressed as
In the formula (I), the compound is shown in the specification,
subscript ab denotes the b-th node of the a-th conduit, m ab The mass flow of the hot water in the pipeline between the ab nodes; (KA) ab Representing the thermal conductance of the pipe;
the dissipative thermal resistance of the end heat consumer heat sink can be expressed as
Where subscript rad is the heat sink, subscript ij (heat sink for jth end hot user of ith building, i =1,2,3 \8230;, n, (KA) ij To provide thermal conduction to the thermal user heat sink.
Further, the step S4 is specifically:
step S41, constructing an optimization objective function with the minimum dissipation thermal resistance of the heat supply system, which can be expressed as:
wherein N is the total number of buildings, N i The total number of end hot user radiators in the ith building.
And step S42, determining constraint conditions for solving the optimization objective function.
In heating systems, the total heat capacity of the water supplied by the secondary heating network is constant, i.e.
In the central heating network, the hot water users of the same branch are in parallel connection, that is, the hot water temperature delivered to the 1 st building hot user is equal to that delivered to the i th building hot user, the parallel connection constraint condition of the central heating network is expressed as follows:
in the formula, T 1 For the water temperature at the inlet of the radiator of the 1 st user of each building, Q 11 Heat load, R, for the 1 st thermal consumer in the 1 st building 11，rad Dissipative thermal resistance for the 1 st end heat consumer radiator in the 1 st building; t is a unit of i For the water temperature at the inlet of the radiator of the ith user of each building, Q i1 Heat load, R, for the 1 st heat consumer in the ith building i1，rad Dissipation thermal resistance for the 1 st end thermal user radiator in the ith building;
because the radiators of all the heat users in the same building are in a series connection relationship, the series connection constraint condition of the centralized heating network system is obtained by combining an energy conservation equation, and can be expressed as follows:
in the formula, T 'and T' are the water temperatures before and after heat exchange of the hot water of the primary heat supply network in the heat exchange station respectively, and K; q t Is the sum of the heat loads of all the heat users of the secondary heat supply network,
Q ij a heat sink heat load for the jth end heat consumer for the ith building; k is the kth end heat user heat sink, Q ik Heat sink thermal load for the kth end thermal user of the ith building.
Step S43, constructing a Lagrange function by means of a Lagrange multiplier method:
in the formula, alpha, lambda k 、λ ij Are all lagrange multipliers, T k For the water temperature at the inlet of the k-th user radiator of each building, Q k1 Heat load, R, for the 1 st heat consumer in the kth building k1，rad Dissipative thermal resistance for the 1 st end heat consumer radiator in the kth building; m is a unit of k The mass flow of the hot water in the kth building;
let II 1 Partial derivatives with respect to all variables, etcAt zero, an optimization equation set is obtained:
solving the optimization equation set to obtain the optimal value of the dissipation thermal resistance of each node of the heating system, and further calculating 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;
s44, comparing the structural parameters of the existing heating system, and analyzing and evaluating the topological structure of the heating system; for areas with larger difference in result, the conditions of pipe network flow threshold and safety requirements are difficult to realize by changing the pipe diameter of the pipeline or adjusting valve group equipment, the pipeline is added in the pipe network, the overall topological structure of the heat supply pipe network is changed, and then a new optimization equation set is constructed by adopting the steps S41-43 to solve, so that the optimal values of the topological structure of the heat supply system, the pipe diameter of the pipe network and the opening degree of the pump valve are obtained.
The invention idea is as follows:
the applicant discovers through bionics that a blood system of a human body is similar to a heat supply system, a heart is similar to a heat source, an organ is similar to a heat exchange station, and a moving and static vascular network is similar to a heat supply pipe network, and the blood pipe network of the human body has a topological structure with excellent performance through biological evolution so as to guarantee the transportation of blood and the overall function of the system. Therefore, the topological structure of the blood system is deeply researched, and the topological structure of the heat supply system is optimized based on the topological structure.
The invention has the beneficial effects that:
the invention provides an optimization method of the topological structure of the existing heat supply system, which provides a new idea for the overall analysis and optimization of the topological structure of the heat supply system based on the bionic principle on the basis of a blood system, realizes the overall optimization of the structure of a heat supply pipe network, improves the hydraulic and thermal imbalance problem and the uneven heat supply phenomenon of the system, and further improves the energy-saving effect of the system.
Drawings
The invention is further explained below with reference to the drawings and the embodiments.
FIG. 1 is a flow chart of the method of the present invention.
FIG. 2 is a schematic diagram of the cluster analysis process in step 1 of the method of the present invention.
FIG. 3 is a schematic diagram of a topological structure optimization model of a heating system in the method.
Detailed Description
The invention relates to a heat supply system topological structure optimization model and method based on a blood system bionic principle, which comprises the following steps of:
s1, acquiring position and quantity information of hot users, and performing cluster analysis according to distribution conditions of the hot users; the method comprises the following specific steps:
1) Firstly, specific position and quantity information data of a hot user building in a heating system are obtained.
2) And (4) carrying out clustering analysis by adopting a dynamic clustering method according to the distribution condition of the hot users. The method comprises the following specific steps:
21 First, the scattered heat users in the heating system are combined into a low-density data point set D = { X = { (X) } 1 ,X 2 ,X 3 ,…,X n In which X is 1 ～X n Respectively corresponding to one of the hot users; supposing neighborhood parameters (epsilon, minPts), wherein specific numerical values of the neighborhood parameters are selected according to actual conditions and are used for determining an epsilon-core neighborhood of a hot user and selecting a hot user core object; for X j E.g. D, the epsilon-neighborhood is: the epsilon-neighborhood contains the sum X in the data set D j Samples having a distance of not more than epsilon; if the user is hot X j The epsilon-kernel neighborhood of (a) contains at least MinPts samples, then X j Is a core object; finding out epsilon-neighborhoods of all data in D by using a DBSCAN algorithm and determining a core object set omega;
22 Randomly selecting a core object in omega if X j At X i In the epsilon-neighborhood of (c), and X i Is a core object, then X j From X i The density is direct; finding all samples directly from the density of the core object to form a new cluster C 1 (ii) a Then C is mixed 1 The core object contained in the table is removed from omega and updatedThe latter set Ω;
23 And 4) repeating the step 22) until the set omega is empty, and obtaining the cluster analysis result of the hot user.
S2, based on a bionic principle, taking a heat source, a heating power station, each level of heat user buildings and each level of pipeline valve banks of the heat supply system as system pipeline network nodes, and constructing a topological structure model of a heat supply pipeline in the heat supply system by utilizing ArcGIS according to the system pipeline network nodes and the existing pipeline information; the method comprises the following specific steps:
1) Firstly, acquiring specific position coordinates of a heat source, each level of heating power station, each level of heat user building and each level of valve group in a pipe network in a heating system, the length of a pipeline of the heating pipe network, the specific position coordinates of a starting point and an end point of the pipeline and pipeline fluid flowing direction data;
2) The heat source in the heat supply system is similar to the heart of a human body, and the heat exchange station and the valve sets of each stage are similar to the regulating organs of the human body; the pushing action of a heat source and a pressurizing pump in each stage of heat exchange station on a heat medium fluid medium is similar to the pushing action of human heart on blood, wherein each stage of valve bank comprises a stop valve, an exhaust valve and a check valve, and each stage of heat user building comprises a tail end water tank, a pressure regulating tower and a radiator;
3) The heat source and the heat exchangers and the pressure pump equipment of the regional heat exchange stations at all levels are used as pushing nodes of a heat supply system pipe network model, the district heat exchange station where the buildings of heat users at all levels are located is used as a first-level adjusting node, the radiators and valves of the heat users are used as a second-level adjusting node, and all the adjusting nodes and the heat supply pipe network pipeline are all constituent units of a heat supply system topological structure;
4) Constructing a topological structure model of the heat supply pipe network by utilizing ArcGIS based on the information of the topological structure forming units in the step 3); and obtaining a three-dimensional visual model of the topological structure of the heat supply system, wherein the model comprises a three-dimensional model of a main branch line of the heat supply pipe network and a terrain model of the whole heat supply area.
S3, constructing a dissipative thermal resistance model of the heating system according to the impedance model of the blood system; the method comprises the following specific steps:
1) Based on the blood system bionic principle, firstly, a flow impedance model of the blood system is obtained and is represented by the following formula:
wherein R is the blood flow resistance; q is the blood flow; p is 1 And P 2 Respectively the pressure, pa, at the head end and the tail end of the node in the blood pipe network.
2) For a centralized heating system, firstly, obtaining flow and temperature data of each node of a heating power station, a heating pipe network and a tail end heat user radiator, and respectively constructing corresponding dissipative thermal resistance models;
the dissipation thermal resistance of a heat exchanger in a thermal power station can be expressed as
In the formula (I), the compound is shown in the specification,
m 0 and m t Mass flow of hot water in the primary and secondary heat supply networks respectively; c. C p Specific constant pressure heat capacity for hot water (KA) sub Thermal conductance of a heat exchanger in a heating station;
m i i =1,2, \ 8230;, n for the mass flow rate of hot water in the ith building.
The dissipative thermal resistance of each node of the pipeline in the heat supply network can be expressed as
In the formula (I), the compound is shown in the specification,
subscript ab denotes the b-th node of the a-th conduit, m ab For hot water in ab-node conduitsMass flow rate; (KA) ab Representing the thermal conductance of the pipe;
the dissipative thermal resistance of the end heat consumer heat sink can be expressed as
Where subscript rad is radiator and subscript ij is radiator of jth end heat user of ith building, (i =1,2,3 \ 8230;, n), (KA) ij To provide thermal conduction to the thermal user heat sink.
S4, solving the objective function by taking the minimum dissipation thermal resistance of the heat supply system as the objective function to obtain parameter optimization values of each node and pipeline structure of the pipe network, so as to obtain an overall optimization result of the topological structure of the heat supply system; the method comprises the following specific steps:
1) An optimization objective function is constructed by using the minimum dissipation thermal resistance of the heating system, and can be expressed as follows:
wherein N is the total number of buildings, N i The total number of end hot user radiators in the ith building.
2) And determining constraint conditions for solving the optimization objective function.
In heating systems, the total heat capacity of the water supplied by the secondary heating network is constant, i.e.
In the central heating network, the hot water users of the same branch are in parallel connection, that is, the hot water temperature delivered to the 1 st building hot user is equal to that delivered to the i th building hot user, the parallel connection constraint condition of the central heating network is expressed as follows:
in the formula (I), the compound is shown in the specification,T 1 for the water temperature at the inlet of the radiator of the 1 st user of each building, Q 11 Heat load, R, for the 1 st thermal consumer in the 1 st building 11，rad Dissipative thermal resistance for the 1 st end heat consumer radiator in the 1 st building; t is i For the water temperature at the inlet of the radiator of the ith user of each building, Q i1 Heat load, R, for the 1 st thermal user in the ith building i1，rad Dissipation thermal resistance for the 1 st end thermal user radiator in the ith building;
because the radiators of all the heat users in the same building are in a series connection relationship, the series connection constraint condition of the centralized heating network system is obtained by combining an energy conservation equation, and can be expressed as follows:
in the formula, T 'and T' are the water temperatures before and after heat exchange of the hot water of the primary heat supply network in the heat exchange station respectively, and K; q t Is the sum of the heat loads of all the heat users of the secondary heat supply network,
Q ij a heat sink thermal load for the jth end thermal user of the ith building; k is the kth end heat user heat sink, Q ik The heat sink heat load for the kth end heat consumer of the ith building.
3) Constructing a Lagrangian function by means of a Lagrangian multiplier method:
in the formula, alpha, lambda k 、λ ij Are all Lagrange multipliers, T k For the water temperature at the inlet of the k-th user radiator of each building, Q k1 Heat load, R, for the 1 st thermal user in the kth building k1，rad Dissipative thermal resistance for the 1 st end heat consumer radiator in the kth building; m is k Mass flow of hot water in the kth building;
let II 1 The partial derivatives for all variables are equal to zero, resulting in an optimized system of equations:
solving the optimization equation set to obtain the optimal value of the dissipation thermal resistance of each node of the heat supply system, and further calculating to obtain the optimal values of the topological structure, the pipe network pipe diameter and the pump valve opening degree of the heat supply system;
4) Comparing the structural parameters of the existing heating system, and analyzing and evaluating the topological structure of the heating system; for the areas with larger difference in result, the conditions that the flow threshold value and the safety requirement of the pipe network are difficult to realize by changing the pipe diameter of the pipeline or adjusting valve group equipment are added in the pipe network, the overall topological structure of the heat supply pipe network is changed, and then a new optimization equation set is constructed in the steps S41-43 to solve, so that the optimal values of the topological structure of the heat supply system, the pipe diameter of the pipe network and the opening degree of the pump valve are obtained.
As shown in fig. 3, on the basis of the original heat supply system, a pipeline is added, so that the water power and the heat power of the whole system are balanced, the safety of the heat supply system is improved, and meanwhile, the solution result of the subsequent optimization equation set is more accurate.
Claims (5)
1. A heat supply system topological structure optimization method based on a bionic principle is characterized by comprising the following steps:
s1, acquiring position and quantity information of hot users, and performing cluster analysis according to distribution conditions of the hot users;
s2, based on a bionic principle, taking a heat source, a heating power station, each level of heat user buildings and each level of pipeline valve banks of the heat supply system as system pipeline network nodes, and constructing a topological structure model of a heat supply pipeline in the heat supply system by utilizing ArcGIS according to the system pipeline network nodes and the existing pipeline information;
s3, constructing a dissipative 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 heat supply system as the objective function to obtain the parameter optimization values of each node of the pipe network and the pipeline structure, thereby obtaining the overall optimization result of the topological structure of the heat supply system.
2. A heating system topology optimization method based on bionic principle as claimed in claim 1, wherein in step S1, according to distribution of thermal users, clustering analysis is performed, and the specific method is:
the method comprises the following steps of S11, firstly, scattered heat users in a heating system are formed into a low-density data point set D = { X = { (X) } 1 ,X 2 ,X 3 ,…,X n In which X is 1 ～X n Respectively corresponding to one of the hot users; assuming neighborhood parameters (epsilon, minPts), wherein specific numerical values of the neighborhood parameters are selected according to actual conditions and are used for determining an epsilon-core neighborhood of a hot user and selecting a hot user core object; for X j Epsilon, D, and epsilon-neighborhood is as follows: the epsilon-neighborhood contains the sum X in the data set D j Samples having a distance of not more than epsilon; if hot user X j The epsilon-kernel neighborhood of (a) contains at least MinPts samples, then X j Is a core object; finding out epsilon-neighborhoods of all 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 At X i E-neighborhood of (a), and X i Is a core object, then X j From X i The density is direct; finding all samples directly reached by the density of the core object to form a new cluster C 1 (ii) a Then C is mixed 1 Removing the core object contained in the set omega 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 cluster analysis result of the hot user.
3. A heating system topology optimization method based on bionic principles according to claim 2, characterized in that the step S2 specifically comprises:
step S21, firstly, acquiring specific position coordinates of heat sources, all levels of heating power stations, all levels of heat user buildings and all levels of valve banks in a pipe network in a heating system, the specific position coordinates of the pipeline length of the heating pipe network and the starting point and the ending point of a pipeline and pipeline fluid flowing direction data;
s22, simulating a heat source in the heat supply system as a heart of a human body, and simulating a heat exchange station and each valve group as a regulating organ of the human body; the pushing action of a heat source and a pressurizing pump in each stage of heat exchange station on a heat medium fluid medium is compared with the pushing action of a human heart on blood, wherein each stage of valve bank comprises a stop valve, an exhaust valve and a check valve, and each stage of heat user building comprises a tail end water tank, a pressure regulating tower and a radiator;
step S23, using heat sources and heat exchangers and pressure pump equipment of all levels of regional heat exchange stations as pushing nodes of a heat supply system pipe network model, using community heat exchange stations where all levels of heat user buildings are located as primary adjusting nodes, using radiators and valves of heat users as secondary adjusting nodes, and using all adjusting nodes and heat supply pipe network pipelines as constituent units of a heat supply system topological structure;
step S24, constructing a topological structure model of the heat supply pipe network by utilizing ArcGIS based on the information of the composition units of the topological structure in the step S23; and obtaining a three-dimensional visual model of the topological structure of the heat supply system, wherein the three-dimensional visual model comprises a branch and trunk line three-dimensional model of the heat supply pipe network and a terrain model of the whole heat supply area.
4. The heating system topological structure optimization method based on the bionic principle according to claim 3, wherein the step S3 specifically comprises:
step S31, based on the blood system bionic principle, firstly obtaining a flow impedance model of the blood pipe network, and expressing the model by the following formula:
wherein R is the blood flow resistance; q is the blood flow; p 1 And P 2 Respectively the pressure, pa, of the head end and the tail end of a node in the blood pipe network;
step S32, for a centralized heating system, firstly obtaining flow and temperature data of each node of a heating power station, a heating pipe network and a tail end heat user radiator, and respectively constructing corresponding dissipative thermal resistance models;
the dissipative thermal resistance of a heat exchanger in a thermal power station can be expressed as
In the formula (I), the compound is shown in the specification,
m 0 and m t Mass flow of hot water in the primary and secondary heat supply networks respectively; c. C p Specific constant pressure heat capacity for hot water (KA) sub Thermal conductance of a heat exchanger in a heating station;
m i i =1,2, \ 8230;, n;
the dissipative thermal resistance of each node of the pipeline in the heat supply network can be expressed as
In the formula (I), the compound is shown in the specification,
subscript ab denotes the b-th node of the a-th conduit, m ab The mass flow of the hot water in the pipeline between the ab nodes; (KA) ab Representing the thermal conductance of the pipe;
the dissipative thermal resistance of the end heat consumer heat sink can be expressed as
In the formula, subscript rad is the heat radiator, subscript ij is the heat radiator of the jth end hot user of the ith building, (KA) ij To provide thermal conduction to the thermal user heat sink.
5. The heating system topological structure optimization method based on the bionic principle according to claim 4, wherein the step S4 specifically comprises:
step S41, constructing an optimization objective function with the minimum dissipative thermal resistance of the heating system, which can be expressed as:
wherein N is the total number of buildings, N i The total number of end hot user radiators in the ith building;
step S42, determining constraint conditions for solving the optimization objective function;
in heating systems, the total heat capacity of the water supplied by the secondary heating network is constant, i.e.
In the central heating network, the hot water users of the same branch are in parallel connection, that is, the hot water temperature delivered to the 1 st building hot user is equal to that delivered to the i th building hot user, the parallel connection constraint condition of the central heating network is expressed as follows:
in the formula, T 1 For the water temperature at the inlet of the radiator of the 1 st user of each building, Q 11 Heat load, R, for the 1 st heat consumer in the 1 st building 11，rad Dissipative thermal resistance for the 1 st end heat consumer radiator in the 1 st building; t is a unit of i For each building ith user radiator inletWater temperature of (C), Q i1 Heat load, R, for the 1 st heat consumer in the ith building i1，rad Dissipation thermal resistance for the 1 st end thermal user radiator in the ith building;
because all the heat user radiators in the same building are in a series connection relationship, the series connection constraint condition of the central heating network system is obtained by combining an energy conservation equation, and the expression is as follows:
in the formula, T 'and T' are the water temperatures before and after heat exchange of the hot water of the primary heat supply network in the heat exchange station respectively, and K; q t Is the sum of the heat loads of all the heat users of the secondary heat supply network,
Q ij a heat sink thermal load for the jth end thermal user of the ith building; k is the kth end heat user heat sink, Q ik A heat sink heat load for the kth end heat consumer of the ith building;
step S43, constructing a Lagrangian function by means of a Lagrangian multiplier method:
in the formula, alpha, lambda k 、λ ij Are all lagrange multipliers, T k For the water temperature at the inlet of the k-th user radiator of each building, Q k1 Heat load, R, for the 1 st heat consumer in the kth building k1，rad Dissipative thermal resistance for the 1 st end heat consumer radiator in the kth building; m is k Mass flow of hot water in the kth building;
let II 1 The partial derivatives for all variables are equal to zero, resulting in an optimized system of equations:
solving the optimization equation set to obtain the optimal value of the dissipation thermal resistance of each node of the heat supply system, and further calculating to obtain the optimal values of the topological structure, the pipe network pipe diameter and the pump valve opening degree of the heat supply system;
s44, comparing the structural parameters of the existing heating system, and analyzing and evaluating the topological structure of the heating system; for the areas with larger difference in result, the conditions that the flow threshold value and the safety requirement of the pipe network are difficult to realize by changing the pipe diameter of the pipeline or adjusting valve group equipment are added in the pipe network, the overall topological structure of the heat supply pipe network is changed, and then a new optimization equation set is constructed in the steps S41-43 to solve, so that the optimal values of the topological structure of the heat supply system, the pipe diameter of the pipe network and the opening degree of the pump valve are obtained.
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| CN117784243A (en) * | 2024-02-27 | 2024-03-29 | 中国石油大学(华东) | Connectivity evaluation method based on sub-seismic fracture system topological structure |
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| CN116447643A (en) * | 2023-03-10 | 2023-07-18 | 北京热力智能控制技术有限责任公司 | Energy evaluation method and system for heat supply network adjustment optimization |
| CN116447643B (en) * | 2023-03-10 | 2023-12-05 | 北京热力智能控制技术有限责任公司 | Energy evaluation method and system for heat supply network adjustment optimization |
| CN117784243A (en) * | 2024-02-27 | 2024-03-29 | 中国石油大学(华东) | Connectivity evaluation method based on sub-seismic fracture system topological structure |
| CN117784243B (en) * | 2024-02-27 | 2024-04-30 | 中国石油大学(华东) | Connectivity evaluation method based on sub-seismic fracture system topological structure |
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