# American Institute of Mathematical Sciences

## PID parameter optimization algorithm for pressure control of heating system of ground source heat pump

 1 Scientific Research and Industry Department, Hebei University of Architecture, Zhangjiakou 075000, China 2 Technology Center, North Navigation Control Technology Co., Ltd., Beijing 100000, China 3 The Library, Hebei North University, Zhangjiakou 075000, China

* Corresponding author: Yugui Jia

Received  April 2019 Revised  May 2019 Published  January 2020

In order to improve the pressure control effect of the ground source heat pump heating system, a PID parameter optimization algorithm for the pressure control of the ground source heat pump heating system was designed. The ground source heat pump heating control system and pressure PID control model with pressure value control object are established. BP neural network and gradient method are combined to optimize the PID control parameters. The descending method is used to correct the weighting coefficient of BP neural network. The global minimum inertia term for fast convergence. The BP neural network can modify the weight of the output layer and the hidden layer to make the control error of the geothermal heat pump heating system pressure PID control model meet the standard requirements, so as to optimize the PID parameters of the ground source heat pump heating system pressure control and obtain the optimal. Pressure control. The experimental results show that the algorithm has a good control effect on the pressure of the ground source heat pump heating system, no overshoot and fast response. When adding 50dB of noise, it takes only 5 seconds to use the algorithm to achieve the specified pressure value and good anti-noise performance. In addition, the algorithm also solves the thermal imbalance problem of the heating system, effectively reducing the energy consumption of the ground source heat pump heating system.

Citation: Jing Qin, Mengmeng Cui, Yiping Lu, Chao Yang, Yanhua Huo, Yugui Jia. PID parameter optimization algorithm for pressure control of heating system of ground source heat pump. Discrete & Continuous Dynamical Systems - S, doi: 10.3934/dcdss.2020257
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Pressure differential flow control frame diagram
Box diagram of BP neural network PID differential control system
Principle diagram of neural network PID controller
BP neural network structure
Optimal algorithms of PID control parameters for BP neural networks
PID comparison of three algorithms
Comparison of 25 s sudden disturbance response curve
Comparison of 40 s sudden disturbance response curve
System change curve when pressure becomes 0.85 MPa
System change curve when pressure becomes 1.65 MPa
Add 30 dB white noise pressure change curve
Add 50 dB white noise pressure change curve
Comparison of energy consumption of PID heating system with different algorithms
Parameter values before and after adjustment
 Building No. unit Area ($m^{2}$) Design Flow (Kg/h) Actual flow (Kg/h) Front and rear pipe network pressure difference (mm) Move head before adjustment (mm) Move the head after adjustment (mm) Prior to adjustment (Kg/h) Adjusted traffic (Kg/h) 1$\sharp$ 1 983 2.11 5.51 161.92 24.17 7.85 4.86 2.65 2 983 2.11 4.85 133.16 21.82 8.35 4.61 2.68 3 821 1.85 3.55 81.62 15.86 6.75 3.91 2.01 4 821 1.85 3.71 64.77 13.274 5.84 3.57 2.05 5 821 1.85 3.31 43.55 11.52 6.26 3.33 2.08 2$\sharp$ 1 1215 2.72 3.76 34.93 13.48 8.81 3.56 2.98 2 1131 2.52 4.15 68.71 16.27 8.53 3.96 2.98 3 1131 2.52 5.35 182.32 26.77 8.48 5.12 2.75 4 1131 2.52 5.26 77.12 17.45 8.71 4.11 2.85 4# 2 1131 2.52 1.12 34.15 1.15 5.62 1.12 2.94 5$\sharp$ 1 953 2.14 2.38 43.25 14.35 9.04 3.71 2.47 3 979 2.19 3.16 31.24 10.24 7.25 3.11 2.35 10$\sharp$ 1 947 2.12 5.51 63.58 17.48 10.25 4.11 2.65 15$\sharp$ 3 981 2.11 3.71 101.25 19.57 9.65 4.36 2.33
 Building No. unit Area ($m^{2}$) Design Flow (Kg/h) Actual flow (Kg/h) Front and rear pipe network pressure difference (mm) Move head before adjustment (mm) Move the head after adjustment (mm) Prior to adjustment (Kg/h) Adjusted traffic (Kg/h) 1$\sharp$ 1 983 2.11 5.51 161.92 24.17 7.85 4.86 2.65 2 983 2.11 4.85 133.16 21.82 8.35 4.61 2.68 3 821 1.85 3.55 81.62 15.86 6.75 3.91 2.01 4 821 1.85 3.71 64.77 13.274 5.84 3.57 2.05 5 821 1.85 3.31 43.55 11.52 6.26 3.33 2.08 2$\sharp$ 1 1215 2.72 3.76 34.93 13.48 8.81 3.56 2.98 2 1131 2.52 4.15 68.71 16.27 8.53 3.96 2.98 3 1131 2.52 5.35 182.32 26.77 8.48 5.12 2.75 4 1131 2.52 5.26 77.12 17.45 8.71 4.11 2.85 4# 2 1131 2.52 1.12 34.15 1.15 5.62 1.12 2.94 5$\sharp$ 1 953 2.14 2.38 43.25 14.35 9.04 3.71 2.47 3 979 2.19 3.16 31.24 10.24 7.25 3.11 2.35 10$\sharp$ 1 947 2.12 5.51 63.58 17.48 10.25 4.11 2.65 15$\sharp$ 3 981 2.11 3.71 101.25 19.57 9.65 4.36 2.33
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