scholarly journals Heating, Ventilation, and Air Conditioning System Optimization Control Strategy Involving Fan Coil Unit Temperature Control

2019 ◽  
Vol 9 (11) ◽  
pp. 2391 ◽  
Author(s):  
Chang-Ming Lin ◽  
Hsin-Yu Liu ◽  
Ko-Ying Tseng ◽  
Sheng-Fuu Lin
Keyword(s):  
Genetic Algorithm ◽  
Energy Conservation ◽  
Temperature Control ◽  
Control Strategy ◽  
Air Conditioning ◽  
Minimum Energy ◽  
System Optimization ◽  
Hvac System ◽  
Minimum Energy Consumption ◽  
Optimization Control

The objective of this study was to develop a heating, ventilation, and air conditioning (HVAC) system optimization control strategy involving fan coil unit (FCU) temperature control for energy conservation in chilled water systems to enhance the operating efficiency of HVAC systems. The proposed control strategy involves three techniques, which are described as follows. The first technique is an algorithm for dynamic FCU temperature setting, which enables the FCU temperature to be set in accordance with changes in the outdoor temperature to satisfy the indoor thermal comfort for occupants. The second technique is an approach for determining the indoor cold air demand, which collects the set FCU temperature and converts it to the refrigeration ton required for the chilled water system; this serves as the control target for ensuring optimal HVAC operation. The third technique is a genetic algorithm for calculating the minimum energy consumption for an HVAC system. The genetic algorithm determines the pump operating frequency associated with minimum energy consumption per refrigeration ton to control energy conservation. To demonstrate the effectiveness of the proposed HVAC system optimization control strategy combining FCU temperature control, this study conducted a field experiment. The results revealed that the proposed strategy enabled an HVAC system to achieve 39.71% energy conservation compared with an HVAC system operating at full load.

scholarly journals Optimal Control Strategy for Variable Air Volume Air-Conditioning Systems Using Genetic Algorithms

2019 ◽  
Vol 11 (18) ◽  
pp. 5122 ◽  
Author(s):  
Nam-Chul Seong ◽  
Jee-Heon Kim ◽  
Wonchang Choi
Keyword(s):  
Optimal Control ◽  
Genetic Algorithms ◽  
Control Strategy ◽  
Air Conditioning ◽  
Static Pressure ◽  
Hvac System ◽  
Total Energy Consumption ◽  
Optimal Control Strategy ◽  
Variable Air Volume ◽  
Air Volume

This study is aimed at developing a real-time optimal control strategy for variable air volume (VAV) air-conditioning in a heating, ventilation, and air-conditioning (HVAC) system using genetic algorithms and a simulated large-scale office building. The two selected control variables are the settings for the supply air temperature and the duct static pressure to provide optimal control for the VAV air-conditioning system. Genetic algorithms were employed to calculate the optimal control settings for each control variable. The proposed optimal control conditions were evaluated according to the total energy consumption of the HVAC system based on its component parts (fan, chiller, and cold-water pump). The results confirm that the supply air temperature and duct static pressure change according to the cooling load of the simulated building. Using the proposed optimal control variables, the total energy consumption of the building was reduced up to 5.72% compared to under ‘normal’ settings and conditions.

Optimization Control of Ice Storage Air-Conditioning System

2013 ◽  
Vol 655-657 ◽  
pp. 1492-1495
Author(s):  
Ting Wu ◽  
Gang Wu ◽  
Zhe Jing Bao ◽  
Wen Jun Yan
Keyword(s):  
Control Strategy ◽  
Air Conditioning ◽  
Particle Swarm Optimization Algorithm ◽  
Ice Storage ◽  
Optimal Control Strategy ◽  
Air Conditioning System ◽  
Control Scheme ◽  
Optimization Control ◽  
Building Cooling ◽  
Operation Cost

Ice storage air-conditioning system can bring benefits to power supplier and consumers for its advantage of shifting power consumption at peak hours during day to the off-peak hours at night. In this paper, we adopted an improved particle swarm optimization algorithm to develop an optimal control strategy for ice storage air-conditioning system with the aim of minimizing operation cost subject to various operational constrains. Comparing with the usual chiller-priority and ice-storage-priority control strategy, the proposed control scheme can not only meet the building cooling load but also achieve the minimum operation cost.

Energy Conservation Analysis for Air Conditioning System in Rail Transit Stations

2014 ◽  
Vol 494-495 ◽  
pp. 1674-1677
Author(s):  
Bing Xu ◽  
Fang Hong Yuan ◽  
Bao Guo Zheng ◽  
Zhong Jin Shi ◽  
Yi Huan Hu
Keyword(s):  
Energy Consumption ◽  
Energy Conservation ◽  
Energy Saving ◽  
Control Strategy ◽  
Air Conditioning ◽  
Rail Transit ◽  
Air Conditioning System ◽  
Conservation Analysis ◽  
Air Conditioning Systems ◽  
Reducing Energy

This article discusses energy conservation for air conditioning systems in rail transit stations. At first, the paper analyzes the energy consumption condition in the air conditioning systems in rail transit stations. Then, it discusses application of appropriate control strategy for reducing energy consumption. In the end, the paper calculates effiency and amount of the energy saving based on the control strategy.

Fuzzy Neural Network Based on Genetic Algorithm for Temperature Control of Variable Air Volume Air Conditioning

2014 ◽  
Vol 599-601 ◽  
pp. 952-955
Author(s):  
Jie Jia Li ◽  
Yong Qiang Chen ◽  
Xiao Yan Han
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fuzzy Control ◽  
Temperature Control ◽  
Air Conditioning ◽  
Fuzzy Neural Network ◽  
Learning Ability ◽  
Variable Air Volume ◽  
Air Volume ◽  
Fuzzy Neural

In this paper, the theory of the fuzzy control and self-learning ability of neural network is combined, joining the genetic algorithm to optimize the fuzzy control rules, so in the light of temperature control system of variable air volume air conditioning puts forward a fuzzy neural network control method based on genetic algorithm,and this paper introduces in detail the structure, algorithm of fuzzy control and neural network. In addition,this paper verifies the superiority of the fuzzy neural network based on genetic algorithm and ordinary fuzzy neural control.

scholarly journals Optimisation of Control Input Allocation Maps for Electric Vehicle Heat Pump-based Cabin Heating Systems

Energies ◽  
2020 ◽  
Vol 13 (19) ◽  
pp. 5131
Author(s):  
Ivan Cvok ◽  
Igor Ratković ◽  
Joško Deur
Keyword(s):  
Electric Vehicle ◽  
Heat Pump ◽  
Air Temperature ◽  
Control Strategy ◽  
Air Conditioning ◽  
Control Input ◽  
Hvac System ◽  
System Efficiency ◽  
Pump Mode ◽  
Superheat Temperature

The heating, ventilation and air conditioning (HVAC) system negatively affects the electric vehicle (EV) driving range, especially under cold ambient conditions. Modern HVAC systems based on the vapour-compression cycle can be rearranged to operate in the heat pump mode to improve the overall system efficiency compared to conventional electrical/resistive heaters. Since such an HVAC system is typically equipped with multiple actuators (compressor, pumps, fans, valves), with the majority of them being controlled in open loop, an optimisation-based control input allocation is necessary to achieve the highest efficiency. This paper presents a genetic algorithm optimisation-based HVAC control input allocation method, which utilises a multi-physical HVAC system model implemented in Dymola/Modelica. The considered control inputs include the cabin inlet air temperature reference, blower and radiator fan air mass flows and secondary coolant loop pumps’ speeds. The optimal allocation is subject to specified, target cabin air temperatures and heating power. Additional constraints include actuator hardware limits and safety functions, such as maintaining the superheat temperature at its reference level. The optimisation objective is to maximise the system efficiency defined by the coefficient of performance (COP). The optimised allocation maps are fitted by proper mathematical functions to facilitate the control strategy implementation and calibration. The overall control strategy consists of superimposed cabin air temperature controller that commands heating power, control input allocation functions, and low-level controllers that ensure cabin inlet air and superheat temperature regulation. The control system performance is verified through Dymola simulations for the heat pump mode in a heat-up scenario. Control input allocation map optimisation results are presented for air-conditioning (A/C) mode, as well.

Research and Optimization for Transverse Thickness Difference Control Strategy of UCMW Cold Tandem Mill

2013 ◽  
Vol 655-657 ◽  
pp. 1520-1524 ◽  
Author(s):  
Lu Bing Wang ◽  
Lin Jing Yan ◽  
An Rui He ◽  
Guang Lin Li
Keyword(s):  
Control System ◽  
Control Strategy ◽  
System Optimization ◽  
Gauge Length ◽  
Quality Level ◽  
Flexible Control ◽  
Optimization Control ◽  
Plate Shape ◽  
Rolling Stability ◽  
Edge Drop

Transverse thickness difference is a key index of plate shape for electrical steel product. This paper analyzed and optimized original automatic control system for transverse thickness difference of MH UCMW rolling mill. By adding Multi-points assessment in edge drop region, steep drop width is reduced, and system yield is improved; By optimizing rigid control strategy into flexible control strategy, using three stands control instead of two stands, and rationally allocating each stand load for edge drop control, transverse thickness difference quality level is improved, and rolling stability is promoted. Based on control system optimization, control ability for transverse thickness difference is significantly improved, head-tail ultra gauge length is shortened from 110 meter to 50 meter, and transverse thickness difference high-quality ratio is improved from 35% to 87%.

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