Browsing by Author "Lin, Chang-Ming"

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Applying Two-Stage Differential Evolution for Energy Saving in Optimal Chiller Loading
(2019-02-02)
Lin, Chang-Ming
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Wu, Chun-Yin
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Tseng, Ko-Ying
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Ku, Chih-Chiang
;
Lin, Sheng-Fuu
In Taiwan, over 45% of the energy in common buildings is used for the air-conditioning system. In particular, the chiller plant consumes about 70% of the energy in air-conditioning system. The electric energy consumption of air-condition system in a clean room of semiconductor factory is about 5-10 times of that in a common building. Consequently, the optimal chiller loading in energy saving of building is a vital issue. This paper develops a new algorithm to solve optimal chiller loading (OCL) problems. The proposed two-stage differential evolution algorithm integrated the advantages of exploration (global search) in the modified binary differential evolution (MBDE) algorithm and exploitation (local search) in the real-valued differential evolution (DE) algorithm for finding the optimal solution of OCL problems. In order to show the performance of the proposed algorithm, comparison with other optimization methods has been done and analyzed. The result shows that the proposed algorithm can obtain similar or better solution in comparison to previous studies. It is a promising approach for the OCL problem.
Heating, Ventilation, and Air Conditioning System Optimization Control Strategy Involving Fan Coil Unit Temperature Control
(2019-06-01)
Lin, Chang-Ming
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Liu, Hsin-Yu
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Tseng, Ko-Ying
;
Lin, Sheng-Fuu
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電控工程研究所
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.
Optimal Chiller Loading Using Modified Artificial Bee Colony Algorithm
(2020-01-01)
Lin, Chang-Ming
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Tseng, Ko-Ying
;
Lin, Sheng-Fuu
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電控工程研究所
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Institute of Electrical and Control Engineering
In Taipei, over 45% of the energy used in buildings is for air-conditioning systems. In particular, multiple chiller systems consume about 70% of the energy in an air-conditioning system. Consequently, optimal chiller loading (OCL) or energy saving of a building is a vital issue. In this paper, we report a newly developed heuristic algorithm to solve OCL problems. A digital flow meter and a digital meter are installed to calculate the energy efficiency of a chiller. The exploration and exploitation of chiller loading can be efficiently improved without increasing the number of iterations by adopting the proposed modified artificial bee colony (MABC) algorithm. To demonstrate the performance of the proposed algorithm, it has been analyzed in comparison with other optimization methods. The result shows that the proposed algorithm can obtain a similar or better solution than previous algorithms. Therefore, it is a promising approach for solving the OCL problem.
Statistical Features and Expert Knowledge for Monitoring Operating Efficiency and Conditions of Wastewater Treatment Pumps
(2019-12-02)
Lin, Chang-Ming
;
Lin, Sheng-Fuu
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電控工程研究所
;
Institute of Electrical and Control Engineering
Wastewater treatment requires energy-intensive technology. The demand for industrial and residential wastewater treatment is increasing, and it has been widely accepted that low energy consumption and high operating efficiencies are essential to achieve high operating benefits in wastewater treatment plants (WWTPs). In this study, characteristic parameters of equipment operation were collected and subject to statistical analysis for trend observations in combination with expert knowledge to monitor equipment operating conditions. A methodology was developed to monitor and assess operating efficiencies of industrial equipment while not interfering with the existing operation. The proposed methodology was applied to monitor the pump efficiency in a WWTP. The results showed that the new methodology resulted in real-time acquisition of statistical operating data and enabled online detection of abnormal pump operation. The energy loss to low operating efficiencies of a malfunctioning pump was prevented, thereby allowing the pump to operate at high efficiency with an extended equipment life.