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RISS 인기검색어
- 검색키워드
- 저자 : Payam Ghorbannezhad (검색결과 2 건)
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Iman Janghorban,Hongbin Liu,Payam Ghorbannezhad,ChangKyoo Yoo 제어로봇시스템학회 2011 제어로봇시스템학회 국제학술대회 논문집 Vol.2011 No.10
Fresh water and cold which are produced by desalination and cooling processes are simultaneously utilized in many factories and industries. Energy saving can be possible by integration of desalination and cooling systems. This paper contributes to a new integration scheme of the reverse osmosis (RO) and refrigeration systems. Compressor intercooler and condenser waste heat are recovered to increase the intake seawater temperature, which causes decrease in RO pump usage and compressor power consumption. The RO system and refrigeration cycle is modeled. Experimental design of central composite design (CCD) is applied to determine the input decision variables, which are consist of intercooler pressure and heat source temperature (TH) responses variables are coefficient of performance (COP) and power consumption of reverse osmosis (PRO). Multi objective optimization to minimize PRO and maximize COP is performed using genetic algorithm (GA) over the ANN model. The input decision variables corresponding to Pareto optimal sets are presented minimum as the optimal design parameters.
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Hongbin Liu,Mingzhi Huang,Iman Janghorban,Payam Ghorbannezhad,ChangKyoo Yoo 제어로봇시스템학회 2011 제어로봇시스템학회 국제학술대회 논문집 Vol.2011 No.10
Indoor air quality (IAQ) is important in subway stations because it can influence the health and comfort of passengers significantly. To effectively monitor and control the IAQ in subway stations, several key air pollutants data were collected by the air sampler and tele-monitoring system. In this study, an air pollutant prediction model based an adaptive network-based fuzzy inference system (ANFIS) was used to detect sensor fault, and a structured residual approach with maximum sensitivity (SRAMS) method was used to identify and reconstruct sensor faults existing in subway system. When a sensor failure was detected, the faulty sensor was identified using the exponential weighted moving average filtered squared residual (FSR). Four identification indices, including the identification index based on FSR (IFSR), the identification index based on generalized likelihood ratio (IGLR), the identification index based on cumulative sum of residuals (IQsum), and the identification index based on cumulative variances index (IVsum) were used to assist in identifying sensor faults. The best reconstructed sensor value can be estimated based on a given sensor fault direction. The drifting sensor failure was tested and the effectiveness of the proposed sensor validation procedure was verified.
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