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At this critical junction of industrialization, China is facing an increasingly prominent imbalance between energy supply and demand. It is of great social significance to study the energysaving of buildings that consume a huge amount of energy. For most heating systems, energy waste mainly arises from inefficient operations of boilers, low efficiency of pipe transmission, and poor quality of heat supply. This paper researches and evaluates the energysaving reconstruction of intelligent community heating (ICH) system based on the Internet of things (IoT). Firstly, the energysaving reconstruction of the ICH system was analyzed, and a hydraulic regime model was derived from the simplified model of the water supply and return pipe networks. Then, the system pipe network was investigated in three aspects, namely, node flow balance, pipeline pressure drop balance, and loop pressure drop balance. On this basis, the authors presented the balance equation and finalized the hydraulic regime model of the system. After that, the energy consumption of the system was evaluated before and after the reconstruction, and the energysaving benefit was predicted. The effectiveness of the proposed method was proved through experiments.
internet of things (IoT), intelligent community heating (ICH), energysaving reconstruction, energysaving evaluation
At this critical junction of industrialization, China is facing an increasingly prominent imbalance between energy supply and demand. It is urgent to innovate the institutions and technologies in the energy sector. Buildings that consume a huge amount of energy boast a great potential of energysaving [15]. According to Energy Conservation Design Standard for New Heating Residential Buildings (JGJ 261995), the centralized community heating system, which consists of a heat source like boiler room/thermal power plant, an outdoor heating pipe network, and indoor heating terminals, should reach an energysaving rate of 20% [68]. However, most of the existing heating systems cannot live up to this standard. More advanced means is needed to effectively enhance the heating efficiency of heating systems [913].
Kobzar et al. [14] analyzed the energy efficiency of a community heating system after energysaving reconstruction, and evaluated the energy efficiency in terms of heating quality, heating cost, and environmental benefit, according to the realtime energy consumption in the heating season. Abdelaziz et al. [15] examined the distribution features of heating buildings in regional economic development zones, developed a regional intelligent heating monitoring system based on the Internet of things (IoT), and applied the system to realtime remote monitoring of the planned heating pipe network. Hwang and Jeong [16] constructed an IoTbased college heating system, divided the network nodes in the system (e.g., decentralized heating and room temperature compensation controllers), and realized the remote monitoring based on remote control center and online interactions.
To improve the scientific level of system design data, improve the energysaving analysis in operation and management, and create a comfortable indoor heating environment, it is necessary to integrate intelligent control technologies that can elevate the automation of heating system with building information modeling (BIM) [1721]. Long et al. [22] developed a heat source for college heating system through intelligent frequency conversion, realized the dynamic intelligent heating control of heat source, water supply pipe network, water return pipe network, and buildings by adjusting and transforming valve layout, and introduced the BIM technology that analyzes the mean indoor thermal sensation index and computes realtime dynamic heat load, thereby achieving the data upload to operation and maintenance platform and realtime control of the center of the intelligent control system. Sun et al. [23] designed the proportionalintegrationderivative (PID) control algorithm for backpropagation neural network (BPNN), and effectively controlled the realtime opening of intelligent balance valves in community heating system.
For most heating systems, energy waste mainly arises from inefficient operations of boilers, low efficiency of pipe transmission, and poor quality of heat supply. Therefore, it is of high necessity to analyze the energy consumption of the heating system, and reconstruct the system for better energy efficiency [2428]. This paper researches and evaluates the energysaving reconstruction of intelligent community heating (ICH) system based on IoT. Section 2 analyzes the energysaving reconstruction of the ICH system, simplifies the water supply and return pipe networks, and constructs the hydraulic regime model of the simplified model, laying the basis for IoTbased analysis of energysaving reconstruction. Section 3 explores the system pipe network in three aspects, namely, node flow balance, pipeline pressure drop balance, and loop pressure drop balance, and presents the balance equation and finalizes the hydraulic regime model of the system. Section 4 analyzes the energy consumption of the system before and after the reconstruction, and predicts the energysaving benefit of the system. The effectiveness of the proposed method was proved through experiments.
In an intelligent community, the heating demand of buildings increases with the occupancy rate. The IoTbased energysaving reconstruction of ICH system requires the verification of hydraulic regime and redesign of heating meter distribution in the current water supply and return pipe networks. This is to ensure the realtime collection and processing of heat supply data, and automate the control of heat supply and distribution, without sacrificing the heating demand and safety of community users.
2.1 Pipe network simplification
Figure 1. Simplified structure of community heating system
The water supply and return network of ICH system should be simplified under the principle of maintaining the correlations between network nodes and pipes, and minimizing the error relative to the actual system. Figure 1 provides the simplified structure of community heating system. The direction of hot water flowing in the pipeline could be simplified as the direction of an edge in the directed graph D_{BS} of the water supply and return network, where u_{i} is heating nodes in the graph, r_{i} is the heating pipeline connected to heating node u, and g is the relationship between u_{i} and r_{i}. Then, the directed graph can be defined as:
$D_{B S}=g\left(u_{i}, r_{i}\right)$ (1)
Suppose the directed graph D_{BS} of the simplified water supply and return network has a total of N_{P} heating pipelines, N_{NO} heating nodes, Q heating branches, and V heating loops. Let R and U be the set of heating pipelines, and the set of heating nodes, respectively. Then, the following can be derived from the Euler formula:
$V+M_{N O}=N_{P}+Q$ (2)
This paper describes the correlations between all the heating pipelines and heating nodes as an incidence matrix of pipeline correlations. Then, the augmented incidence matrix F of water supply and return network can be described by:
$F\left(D_{B S}\right)=\left\{f_{i j}\right\}$ (3)
The elements in matrix F can be described by a ternary function:
${{f}_{ij}}=\left\{ \begin{align} & 1\text{ }{{u}_{i}}\text{ }is\text{ }the\text{ }starting\text{ }point\text{ }of\text{ }{{r}_{i}} \\ & 0\text{ }{{u}_{i}}\text{ }is\text{ }not\text{ }correlated\text{ }with\text{ }{{r}_{i}} \\ & 1\text{ }{{u}_{i}}\text{ }is\text{ }the\text{ }ending\text{ }point\text{ }of\text{ }{{r}_{i}} \\\end{align} \right.$ (4)
In the water supply and return network, the correlations between the basic heating loop and the heating pipelines could be described by a loop incidence matrix. The number of heating loops V equals QN_{NO}+1. The loop incidence matrix can be described by H(D_{BS})={h_{ij}}. The elements of matrix H can be described by a ternary function:
$h_{i j}=\left\{\begin{array}{cc}1 & r_{i} \text { flows in the same } \\ & \text { direction as the hot water } \\ 0 & r_{i} \text { is not correlated } \\ & \text { with the heating loop } \\ 1 & r_{i} \text { flows oppositely to the hot water }\end{array}\right.$ (5)
Based on the loop incidence matrix and pipeline incidence matrix of D_{BS}, it is possible to derive the equation set for the mathematical model of the working conditions of water supply and return network.
2.2 Modeling of hydraulic regime
Figure 2. Mathematical model of the hydraulic regime of the simplified system
Figure 2 presents the mathematical model of the hydraulic regime of the simplified system. Suppose the incoming and outgoing flows at any heating node in the water supply and return network of the ICH system are negative and positive, respectively. According to the law of conservation of mass, the sum of positive and negative flows is zero. Let N_{I} be the number of independent heating pipelines in the water supply and return network; N_{A} be the number of associated heating nodes; F be the (N_{A}1)×N_{I}dimensional incidence matrix of the heating pipelines; LF=(LF_{1}, LF_{2}, …, LF_{NI})^{T} be the column vectors of the flows in heating pipelines; PF=(PF_{1}, PF_{2}, …, PF_{NA})^{T} be the column vectors of the flows at heating nodes. Then, the flow matrix of each heating node in the water supply and return network can be described by:
$F \cdot L F=P F$ (6)
When there is not fault pipeline in the water supply and return network, PF is a zero vector, i.e., PF=(0, 0, …, 0)^{T}.
In the concentrated heating pipe network, energy loss may occur as the fluid rubs the pipe walls, and passes through pipeline accessories (where the flow state suddenly changes). Let DL_{b} and ∑DL_{i} be the frictional losses along the heating pipeline and in local areas, respectively; S be the loss per unit distance. Then, the frictional loss DL of a pipeline of the length C can be expressed as:
$D L=D L_{b}+\Sigma D L_{i}=S \cdot C+\Sigma D L_{i}$ (7)
where, DL_{b} can be calculated by:
$D L_{b}=S \cdot C$ (8)
Let μ be the frictional coefficient between pipe walls and hot water; ε be the inner diameter of pipeline; σ and υ be the density and flow velocity of hot water, respectively. Then, the specific frictional resistance of heating pipeline can be calculated based on the Darcy–Weisbach equation:
$S=\frac{\mu}{\varepsilon} \cdot \frac{\sigma v^{2}}{2}$ (9)
The value of μ depends on the flow state of the hot water in the heating pipeline, and the smoothness of the pipe walls. Suppose the hot water is in a turbulent flow state. The μ value of a heating pipeline with an inner diameter <40mm can be derived by the Nikuradse empirical formula:
$\mu=\frac{1}{\left(1.14+2.1 g \frac{\varepsilon}{\Phi}\right)^{2}}$ (10)
If the inner diameter is greater than 40mm, the μ value can be computed by the Щuфрuнсон formula:
$\mu=0.11 \cdot\left(\frac{\Phi}{\varepsilon}+\frac{68}{R e}\right)^{0.25}$ (11)
where, Re is the Reynolds number of hot water flowing in the pipeline. The value of Φ differs from indoor to outdoor cases. In the outdoor case, the Φ value equals 5*10^{4}. In the indoor case, the Φ value equals 2*10^{4}. Let ∑δ be the sum of local resistance coefficients of a heating pipeline. Then, DL_{i} can be calculated by:
$\Sigma D L_{i}=\sum \delta \cdot \frac{\sigma v^{2}}{2}$ (12)
During the calculation, ∑DL_{i} can be substituted by frictional loss along the pipeline based on the equivalent length method. Then, the corresponding pipeline length can be calculated by:
$C_{\varepsilon}=\sum \delta \cdot \frac{\varepsilon}{\mu}=9 \cdot \varepsilon^{\frac{5}{4}} \cdot \sum \delta / \Phi^{\frac{1}{4}}$ (13)
Then, DL can be described by:
$D L=S \cdot\left(C+C_{\varepsilon}\right)$ (14)
When the hot water in the outdoor heating pipe network is a turbulent flow, DL is positively proportional to the square of hot water flow w. The proportionality coefficient can be denoted as φ. Then, DL can be calculated by:
$D L=S \cdot\left(C+C_{\varepsilon}\right)=\phi \cdot w^{2}=R C \cdot\left(C+C_{\varepsilon}\right) \cdot w^{2}$ (15)
The resistance characteristic coefficient φ of the heating pipe network can be calculated by:
$\phi=\frac{6.9 \cdot 10^{9} \cdot \Phi^{\frac{1}{4}}}{\sigma \cdot \varepsilon^{5 \frac{1}{4}}}$ (16)
The resistance characteristic coefficient per unit length of the heating pipeline can be calculated by:
$R C=\frac{6.9 \cdot 10^{9} \cdot \Phi^{\frac{1}{4}}\left(C+C_{\varepsilon}\right)}{\sigma \cdot \varepsilon^{5 \frac{1}{4}}}$ (17)
To drive the continuous flow of hot water in the pipeline, circulating water pumps should be arranged in the community heating system. Apart from the pressure of the circulating water pumps, the elevation difference could affect the pressure difference between two heating nodes. Let US_{Oi} and US_{Fi} be the start and end points of the ith pipe segment in the hot water flow direction, respectively; ED_{i} and e_{ci} be the elevation difference between the two points, and lift of circulation water pumps, respectively. Suppose the e_{c} in the heating pipeline without circulating water pumps equals zero. Then, the pressure difference between two heating nodes can be calculated by:
$U S_{F \bar{i}}U S_{O i}=e_{i}+E D_{i}e_{c i}$ (18)
In the community heating system, the pressure drop equation of the heating loop, i.e., the energy balance equation of the heating loop, can be characterized by:
$\sum e_{i}\Sigma e_{c i}=0$ (19)
From formula (19), it can be seen that the sum of the lifts of circulating water pumps in the closed heating loop equals the sum of the pressure losses in each pipe segments. That is, the algebraic sum equals zero.
During the IoTbased energysaving reconstruction of community heating system, the water supply pipeline and water return pipeline might be out of sync in terms of hot water temperature, and water flow velocity, owing to the disparity of pipe materials and construction techniques. Suppose the incidence matrix F about the topological relationship between heating nodes and heating pipeline has a rank of (N_{A}1), and a size of N_{A}1)×N_{I}. Then, the basic loop matrix with a rank of (HN_{A}+1), and a size of (HN_{A}+1)×S can be denoted as H, which equals the number of pressure drop equations in the heating loop. Let φ be the diagonal matrix of the frictional characteristic coefficients of heating pipelines; D be the combined vector of heating node flows; LV be the lift vectors of circulating water pumps. Then, a more realistic model can be constructed for the hydraulic regime in water supply pipeline and water return pipeline, respectively:
$\left\{\begin{array}{l}A \cdot G=W \\ H_{f} \cdot \Delta E=0 \\ \Delta E=\phi \cdot\phi \cdot D+CL V\end{array}\right.$ (20)
Comparing the dimensionalities and ranks of different matrices, the above model has a unique solution.
2.3 Analysis of heating pipe network reconstruction
Figure 3. IoTbased monitoring of community heating pipe network
Figure 4. Control variables of heating system
Figure 3 illustrates the monitoring requirements of the IoTbased community heating system. For energysaving reconstruction of community heating system, it is necessary to reevaluate the hydraulic balance state of the heating pipe network in the community, replace the pipelines whose sizes are unsuitable, and add circulating water pumps to the pipelines with insufficient circulation power. In addition, indoor temperature sensors, pipeline temperature sensors, pipeline pressure sensors, and water flow velocity testers should be added to collect the realtime heating data. Meanwhile, a hydraulic balance master control valve should be deployed at the hot water inlet of each building in the community; a hydraulic balance branch control valve should be added to the hot water inlet on each floor in each building. The data should be collected in real time, and remotely transmitted to the remote monitoring center of community heating. Then, the hydraulic balance solenoid values would be regulated automatically, such that the indoor heating effect reaches the local standards. The above strategy of energysaving reconstruction can counter the hydraulic and thermodynamic fluctuations at different periods in the community, and satisfy the stable operations of the system. The IoTbased technology helps the system to automatically adjust to the changes of external temperature.
After energysaving reconstruction, the hydraulic situation of the community heating system was checked again. The results show that the hydraulic unbalance rate of the heating nodes was effectively reduced, and the change range of [5.6, 8.5] meets the requirement on hydraulic unbalance rate of community hot water supply. As a result, the heat demands between different layers of each building are well balanced.
The wireless data transmission terminals, which are responsible for collecting realtime data, were configured as follows: devices were added to and communication protocols were defined for the monitoring platform server; the serial ports were configured to read and write the field data of programmable logic controller (PLC). Figure 4 presents the control variables of heating system, and displays the structural relationship between modules.
After energysaving reconstruction, the community heating system was changed in operating model and heating control. The indoor temperature control was replaced with dual control of temperature and flow. When the outdoor temperature drops, the water supply temperature increases, and the flow of circulating hot water rises in the heating system. Under the dual control model, thermal misadjustment will not occur in the community under the optimal working condition, the thermal working state will remain stable, and the flow of circulating hot water will change adaptively to outdoor temperature. In this way, the electricity consumption will be reduced effectively. Figure 5 provides the evaluation indices of energysaving reconstruction for IoTbased heating system. Some important quantifiable indices are simulated below.
Figure 5. Evaluation indices of energysaving reconstruction for IoTbased heating system
Let τ_{m} be the goal of indoor temperature control; τ_{H} be the temperature in a place outdoor during heating; RF^{*} be the ratio of actual flow RF to preset flow RF^{*} of circulating water pump; HLR^{*} be the relative heat load ratio of heating; θ be the flow optimization adjustment coefficient. Then, the preset flow can be calculated by:
$R F^{*}=\left(H L R^{*}\right)^{\theta}=\frac{R F}{R F^{\prime}}=\left(\frac{\tau_{m}\tau_{H}}{\tau_{m}\tau_{H}^{\prime}}\right)^{\theta}$ (21)
Let τ_{O} and τ_{W} be the water temperatures of water supply pipeline and water return pipeline, respectively, without energysaving control; τ'_{O} and τ'_{W} be the water temperatures of water supply pipeline and water return pipeline, respectively, under energysaving control; ω be the heating attribute coefficient of the radiator. Then, the optimal heating control method for community heating system after energysaving reconstruction can be described by:
$\tau_{O}=\tau_{m}+\frac{1}{2}\left(\tau_{0}^{\prime}+\tau_{m}^{\prime}2 \tau_{m}^{\prime}\right) \times\left(\frac{\tau_{m}\tau_{H}}{\tau_{m}\tau_{H}^{\prime}}\right)^{1 /(1+\omega)}$$+\frac{1}{2}\left(\tau_{o}^{\prime}\tau_{m}^{\prime}\right) \times\left(\frac{\tau_{m}\tau_{H}}{\tau_{m}\tau_{H}^{\prime}}\right)^{1\theta}$ (22)
$\tau_{O}=\tau_{m}+\frac{1}{2}\left(\tau_{o}^{\prime}+\tau_{m}^{\prime}2 \tau_{m}^{\prime}\right) \times\left(\frac{\tau_{m}\tau_{H}}{\tau_{m}\tau_{H}^{\prime}}\right)^{1 /(1+\omega)}$$\frac{1}{2}\left(\tau_{o}^{\prime}\tau_{m}^{\prime}\right) \times\left(\frac{\tau_{m}\tau_{H}}{\tau_{m}\tau_{H}^{\prime}}\right)^{1\theta}$ (23)
Formulas (22) and (23) show that, when the energysaving reconstruction conditions are constant for community heating system, it is important to ensure the uniqueness of the temperature and flow adjustment values during the vertical disorder of the thermal regime. Let HLR^{*} be the relative heat load of heating supply. Then, θ can be calculated by:
$\theta=\frac{\lg R F^{*}}{\lg H L R^{*}}$ (24)
In general, the θ value of the primary heating pipe network of the community equals 1; the θ values of single pipeline and dual pipelines in the secondary pipe network equal ω/(1+ω), and 0.33, respectively. Under energysaving control, the operating efficiency and lift of circulating water pumps could be denoted as ξ' and E', respectively; the number of continuous heating hours in the heating season could be denoted as p. Then, the power consumption PC of circulating water pumps under energysaving control can be calculated by:
$P C=\frac{R F^{\prime} E^{\prime} p}{367 \xi^{\prime}}$ (25)
If τ_{H}<τ_{H}', the hot water flows at the preset flow RF' of energysaving control, i.e., RF=RF'; If τ_{H}>τ_{H}' or τ_{H}<τ_{OR}, the hot water flows beneath the preset flow RF' of energysaving control, i.e., RF<RF'. The sum of power consumptions under the two scenarios is the power consumption PC in the heating season.
Let PC_{1} and PC_{2} be the sums of power consumption under τ_{H}. w≥τ_{H}≥τ_{H} and τ_{H}<τ_{H}', respectively; RF_{i} be the flow of hot water circulating in an outdoor temperature interval; τ_{OR} and ∆p_{i} be the minimum temperature and duration of the temperature interval, respectively; ξ_{i} be the operating efficiency of the circulating water pump; E be the control pressure difference of the pipeline; p_{0} be the duration of the preset outdoor temperature; p_{S} be the duration of heating; p be the duration of an outdoor temperature. Then, PC can be calculated by:
$P C=P C_{1}+P C_{2}$$=\sum_{i=1}^{N_{I}} \frac{R F_{i} E \Delta p_{i}}{90 \xi_{i}}+\frac{R F^{\prime} E^{\prime} p_{0}}{90 \xi^{\prime}}$$=\frac{R F^{\prime}}{90 \xi^{\prime}} \sum_{i=1}^{N_{I}} \frac{R F_{i}\left(E_{G}+\frac{E^{\prime}}{R F^{\prime 2}} \times R F_{i}^{2}\right) \Delta p_{i}}{R F^{\prime}}$
$+\frac{R F^{\prime} E^{\prime} p_{0}}{90 \xi^{\prime}}$$=\frac{R F^{\prime}}{90 \xi^{\prime}} \sum_{i=1}^{N_{I}}\left(R F^{*} E+\left(R F^{*}\right)^{3} E^{\prime}\right) \Delta p_{i}+\frac{R F^{\prime} E^{\prime} p_{0}}{90 \xi^{\prime}}$$=\frac{R F^{\prime}}{90 \xi^{\prime}} \int_{p_{0}}^{p_{s}}\left(R F^{*} E+\left(R F^{*}\right)^{3} E^{\prime}\right) d p_{i}+\frac{R F^{\prime} E^{\prime} p_{0}}{90 \xi^{\prime}}$ (26)
Let τ_{c} be the mean outdoor temperature in the heating season; p_{C} be the duration of outdoor temperature equal to or below τ_{H}; DG_{m} be the set of dimensionless durations; DG_{τ} be the set of dimensionless outdoor temperatures. Then, the temperature distribution law in the heating season can be described by DG_{m} and DG_{τ}:
$D G_{\tau}=\left\{\begin{array}{l}0 \quad p_{C} \leq 7 \\ D G_{m}^{\omega} \quad 7<p_{C} \leq p_{S}\end{array}\right.$ (27)
The law can also be described by:
$\tau_{H}=\left\{\begin{array}{lc}\tau_{H}^{\prime} & p_{C} \leq 7 \\ \tau_{H}^{\prime}+\left(7\tau_{H}^{\prime}\right) D G_{m}^{\omega} & 7<p_{C} \leq p_{S}\end{array}\right.$ (28)
DG_{m}, DG_{τ}, ω and λ can be calculated by:
$D G_{m}=\frac{p_{C}7}{p_{S}7}, D G_{\tau}=\frac{\tau_{H}\tau_{H}^{\prime}}{7\tau_{H}^{\prime}}$ $\omega=7 \frac{5\lambda \times \tau_{c}}{\lambda \times \tau_{c}\tau_{H}^{\prime}}, \lambda=\frac{p_{S}}{p_{S}7}$ (29)
Combining formulas (29) and (21):
$H L R^{*}=\left\{\begin{array}{lc}1 & p_{C} \leq 7 \\ 1\alpha_{0} D G_{m}^{\omega} & 5<p_{C} \leq p_{S}\end{array}\right.$ (30)
where, α_{0} can be expressed by:
$\alpha_{0}=\frac{7\tau_{H}^{\prime}}{\tau_{m}\tau_{H}^{\prime}}$ (31)
For primary and secondary pipe networks, we have:
$R F^{*}=H L R^{*},\left(R F^{*}\right)^{3}=H L R^{*}$ (32)
After sorting, the power consumption of circulating water pumps in the primary pipe network can be described as:
$P C=P C_{1}+P C_{2}=$$\frac{R F^{\prime}}{90 \xi^{\prime}} \int_{p_{0}}^{p_{s}}\left\{\begin{array}{l}E\left[1\frac{7\tau_{H}^{\prime}}{\tau_{m}\tau_{H}^{\prime}} \cdot\left(\frac{p_{C}7}{p_{S}7}\right)^{\omega}\right] \\ \left.+\left[1\frac{7\tau_{H}^{\prime}}{\tau_{m}\tau_{H}^{\prime}} \cdot\left(\frac{p_{C}7}{p_{S}7}\right)^{\omega}\right]^{3}\right\}\end{array}\right\} d p$$+\frac{R F^{\prime} E^{\prime} p_{0}}{90 \xi^{\prime}}$ (33)
Similarly, the power consumption of circulating water pumps in the secondary pipe network can be sorted out as:
$P C=\frac{R F^{\prime}}{90 \xi^{\prime}} \int_{p_{0}}^{p_{s}}\left\{\begin{array}{l}E\left[1\frac{7\tau_{H}^{\prime}}{\tau_{m}\tau_{H}^{\prime}} \cdot\left(\frac{p_{C}7}{p_{S}7}\right)^{6}\right]^{\frac{1}{3}} \\ +\left[1\frac{7\tau_{H}^{\prime}}{\tau_{m}\tau_{H}^{\prime}} \cdot\left(\frac{p_{C}7}{p_{S}7}\right)^{6}\right]\end{array}\right\} d p$$+\frac{R F^{\prime} E^{\prime} p_{0}}{90 \xi^{\prime}}$ (34)
Let g_{BU} be the thermal consumption of buildings in the community; g_{HN} be amount of standard coal consumed for heating; ST_{HN} be the calorific value of standard coal; γ_{1} be the transmission efficiency of heat supply pipe network of the community; γ_{2} be the efficiency of the heat source. Then, the coal consumption of the community for heating after energysaving reconstruction can be calculated by:Formula 34 shows that the power consumption of circulating water pumps is a univalent function of p_{C}. The total power consumption of circulating water pumps in the heating period after energysaving reconstruction can be calculated by substituting the realtime data collected by IoT monitoring system (e.g., thermal exchanger parameters and circulating water pump efficiencies of primary and secondary pipe networks) into formula 34.
$g_{H N}=\frac{24 \cdot p_{S} \cdot g_{B U}}{S T_{H N} \cdot \gamma_{1} \cdot \gamma_{2}}$ (35)
Table 1 lists the hydraulic balance results of community heating system during the IoTbased energysaving reconstruction.
Table 1. Hydraulic conditions of system loops before energysaving reconstruction
Serial number 
Load 
Flow 
Pipe diameter 
Pipe length 
υ 
S 
Dynamic pressure 
Pipeline frictional loss 
1 
445 
15352.8 
DN100 
120 
0.495 
27.963 
118.775 
3215.367 
2 
445 
15352.8 
DN100 
15 
0.491 
27.963 
118.775 
375.752 
3 
882 
31712.1 
DN130 
261 
0.643 
35.251 
207.021 
9572.341 
4 
385 
13525 
DN90 
75 
0.742 
87.128 
273.672 
7176.213 
5 
385 
13532 
DN90 
32 
0.742 
87.128 
273.672 
2571.318 
6 
760 
26718 
DN110 
127 
0.854 
82.351 
375.128 
13172.051 
7 
479 
16351.9 
DN110 
85 
0.526 
31.972 
134.875 
2671.389 
8 
479 
16351.9 
DN110 
49 
0.526 
31.972 
135.729 
1767.428 
9 
973 
32547.3 
DN130 
15 
0.679 
40.075 
275.671 
3276.84 
10 
1425 
16584.2 
DN110 
103 
0.520 
30.784 
523.487 
473.592 
Total 
7607 
262993.5 

1017 



54105.337 
Table 2. Indoor temperatures before and after energysaving reconstruction
Building number 
Prereconstruction 
Postreconstruction 
1# 
15 
18 
2# 
13 
19 
3# 
22 
22 
4# 
19 
20 
5# 
17 
18 
6# 
16 
18 
7# 
19 
25 
8# 
12 
19 
9# 
14 
19 
10# 
17 
24 
Figure 6. Temperature and flow changes of primary pipe network
Before energysaving reconstruction, the buildings, except for buildings 3#, 4#, and 7#, did not reach the standard indoor temperature of 18℃ during the heating season. After the IoTbased energysaving reconstruction, the indoor temperature of all buildings in the community increased greatly, surpassing 18℃. The indoor thermal comfort, and heat supply quality both improved (Table 2).
Figure 6 presents the temperature and flow changes of primary pipe network after energysaving reconstruction. When the outdoor temperature remained the same, the temperature of the water supplied by the heat source changed in the range of [6, 12], and that of water return pipeline changed within [5, 10]. Therefore, the water supply and return of the primary pipe network are controlled with a high precision. In addition, the hot water flow, which changed in [5, 8], also fell in the ideal control range.
Figure 7. Variation of system energy consumption with outdoor temperatures
Figure 7 shows how the energy consumption of community heating system changed with outdoor temperature. It can be seen that the energy saved by the heat source of the system fluctuated very slightly, but the water consumption, which is loosely coupled with outdoor temperature, oscillated greatly. The coal consumption for heating, which is strongly associated with outdoor temperature, was relatively loosely controlled. Under the same outdoor temperature, there was a 16.5% difference of coal consumption. Therefore, the control of coal consumption should be further improved.
Figure 8. Variation in water supply temperature of heat exchangers in the secondary pipe network
Figure 8 shows the variation in water supply temperature of heat exchangers in the secondary pipe network of the community heating system during the heating season of 2019. Almost all heat exchangers operated with a large flow and a small temperature difference. The temperature difference fluctuated in [2, 5]. The actual flow of circulating hot water was about 1.14 times of the preset flow. Figure 9 records the utilizable pressure head of heat exchangers in the secondary pipe network. To a certain extent, the utilizable pressure head reflects the flow and power of hot water circulation in the system. As shown in Figure 9, the utilizable pressure head of heat exchangers changed within [0.05, 0.14], which is within the ideal controllable range. Table 3 provides the specific operating data of heat exchangers.
Figure 9. Variation in utilizable pressure head of heat exchangers in secondary pipe network
The total heating energy reduction of the community consists of three parts: coal consumption reduction, power consumption reduction of the heat source, primary pipe network, and secondary pipe network, and water consumption reduction of primary and secondary pipe networks. Tables 4 and 5 display the coal consumption reduction and power consumption reduction in different heating seasons, which were calculated based on the measured data in these seasons before and after energysaving reconstruction.
As shown in Tables 4 and 5, the energysaving reconstruction effectively suppressed the actual coal consumption of the heat source in the community heating system. The coal consumption indices dropped from 30.35 and 31.47 before the reconstruction to 30.72 and 29.63 after the reconstruction. Without considering the power consumed by the environmentallyfriendly measure of desulfurization, the heat source, heat exchangers, and the system saved 33.98%, 17.93%, and 7.52% of power, respectively. The primary and secondary pipe networks consumed a total of 2.54kWh/m^{2} of power, about 24.13% lower than the prereconstruction power consumption.
Table 3. Operating data of heat exchangers
Serial number of heat exchangers 
Primary water supply pipe network 
Primary water return pipe network 
Secondary water supply pipe network 
Secondary water return pipe network 

Pressure 
Temperature 
Pressure 
Temperature 
Pressure 
Temperature 
Pressure 
Temperature 

1# 
0.57 
65 
0.57 
38 
0.27 
35 
0.21 
35 
2# 
0.63 
72 
0.62 
36 
0.35 
43 
0.23 
37 
3# 
0.72 
69 
0.69 
37 
0.36 
41 
0.25 
39 
4# 
0.61 
63 
0.64 
41 
0.27 
45 
0.27 
32 
5# 
0.43 
65 
0.38 
42 
0.42 
40 
0.26 
32 
6# 
0.89 
62 
0.76 
41 
0.35 
43 
0.35 
36 
7# 
0.68 
68 
0.65 
35 
0.34 
41 
0.31 
36 
8# 
0.72 
53 
0.62 
36 
0.25 
36 
0.27 
31 
9# 
0.23 
67 
0.25 
35 
0.29 
38 
0.26 
35 
Table 4. Coal consumption reduction in different heating seasons
Year 
Actual heating area 
Mean outdoor temperature 
Mean indoor temperature 
Coal consumption of heat source 
Coal consumption index 
Remark 
20152016 
1823100 
6.5 
17.84 
57120 
30.35 
Preconsumption 
20162017 
1804900 
6.32 
17.23 
54360 
31.47 
Preconsumption 
20172018 
1807520 
5.21 
19.95 
53375 
30.72 
Postconsumption 
20182019 
1879160 
5.67 
18.27 
53621 
29.63 
Postconsumption 
Table 5. Power consumption reduction in different heating seasons
Year 
Heat source 
Heat exchangers 
System 

Power consumption 
Power consumption index 
Power consumption 
Power consumption index 
Power consumption 
Power consumption index 

20152016 
112350 
1.35 
2971252 
1.55 
4712012 
2.65 
20162017 
935452 
1.89 
2871423 
1.58 
4516276 
2.55 
20172018 
452125 
0.95 
2854987 
1.52 
4372318 
2.45 
20182019 
632510 
1.23 
2781565 
1.44 
4395293 
2.49 
Node proportion % 
725.12 
33.98 
16.47 
17.93 
6.35 
7.52 
Table 6. Emission reduction of different pollutants
Item 
Waste gas emissions 
Wastewater emissions 
Solid waste emissions 
Prereconstruction 
27.41 
20.35 
14.71 
Postreconstruction 
25.13 
19.23 
13.52 
Emission reduction 
2.28 
1.12 
1.19 
Before and after the reconstruction, the community heating system always adopts gasfired boilers. Table 6 presents the changes in the emission reduction of main pollutants. The emissions of waste gas declined by 2.28kg from 27.41kg before the reconstruction to 25.13kg after the reconstruction. The emissions of wastewater dropped by 1.12 tons from 20.35kg before the reconstruction to 19.23 tons after the reconstruction. The emissions of solid waste decreased by 1.19 tons from 14.71 tons before the reconstruction to 13.52 tons after the reconstruction.
This paper researches and evaluates the energysaving reconstruction of IoTbased ICH system. Firstly, the water supply and return networks were simplified, and a mathematical model was built up for the hydraulic regime of the simplified system. Next, the authors constructed the equations of node flow balance, pipeline pressure drop balance, and loop pressure drop balance, and finalized the mathematical model of the hydraulic regime of the system. After that, the energy consumptions of the system before and after energysaving reconstruction were detailed, and the energysaving benefit of the system was predicted accurately. Finally, multiple parameters of the system, i.e., indoor temperature, water supply temperature, water supply flow, utilizable pressure head, and operating data of heat exchangers, were compared through experiments. The comparison verifies the effectiveness of our energysaving reconstruction plan. In addition, the energy consumption reduction in multiple heating seasons was recorded before and after the reconstruction.
This paper was supported by the Fundamental Research Funds for the Central Universities (Grant No.: ZY20210301); Langfang science and technology research selffunded project (Grant No.: 2021011020).
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