http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Performance Assessment of Cascade Control Strategy in Wastewater Treatment Process
Hongbin Liu,MinJung Kim,JungJin Lim,ChangKyoo Yoo 제어로봇시스템학회 2010 제어로봇시스템학회 국제학술대회 논문집 Vol.2010 No.10
As the public awareness of environmental protection increases and the environmental regulations become more stringent, effective control of the wastewater treatment process (WWTP) has become a research hotspot. In this paper, a cascade MPC and PID control strategy is introduced to the Benchmark Simulation Model 1 (BSM1). The proposed cascade control structure is composed of a primary MPC controller to control the nitrate concentration in the effluent and a secondary PID controller to control the nitrate concentration in the final anoxic compartment. The suggested method controls the nitrate concentrations in the effluent as well as in the final anoxic reactor simultaneously to strictly satisfy the quality of the effluent as well as to remove the effects of disturbances more quickly by manipulating the external carbon dosage. Because the control performance assessment (CPA) technique has the features of determining the capability of the current controller and locating the best achievable performance, the other novelty of this paper is to take the CPA technology into the wastewater treatment process. The CPA results indicate that the primary MPC controller has more potential to improve compared with the secondary PID controller.
A robust localized soft sensor for particulate matter modeling in Seoul metro systems
Liu, Hongbin,Yoo, ChangKyoo Elsevier 2016 Journal of hazardous materials Vol.305 No.-
<P><B>Abstract</B></P> <P>Developing accurate soft sensors to predict and monitor the indoor air quality (IAQ) of hazardous pollutants that accumulate in underground metro systems is of key importance. The just-in-time (JIT) learning technique possesses a local feature that can track the variations in the dynamic process more effectively, which is different from the traditional soft sensor modeling methods, such as partial least squares (PLS), which models the process in an offline and global way. In this study, a robust soft sensor that combined the JIT learning technique with a least squares support vector regression (LSSVR) method, named JIT–LSSVR, was derived in order to improve the prediction performance of a PM<SUB>2.5</SUB> soft sensor in a subway station. Additionally, in order to eliminate the adverse effects caused by the outliers in the process variables, an outlier detection step was integrated into the JIT–LSSVR modeling procedure. The performance evaluation results demonstrated that the proposed robust JIT–LSSVR soft sensor has the capability to model nonlinear and dynamic subway systems. The root mean square error of the JIT–LSSVR soft sensor was improved by 55% in comparison with that of the LSSVR soft sensor.</P> <P><B>Highlights</B></P> <P> <UL> <LI> A robust localized soft sensor was proposed to predict particulate matter (PM<SUB>2.5</SUB>) in a subway. </LI> <LI> The just-in-time (JIT) learning-based methods outperformed global methods in predicting PM<SUB>2.5</SUB>. </LI> <LI> An outlier detection step was integrated into the local soft sensor to improve its robustness. </LI> <LI> The prediction performance of JIT-LSSVR was greatly improved in comparison to that of global LSSVR. </LI> </UL> </P>
Liu, Hongbin,Lee, SeungChul,Kim, MinJeong,Shi, Honglan,Kim, Jeong Tai,Yoo, ChangKyoo Sage Publications 2014 Indoor and Built Environment Vol.23 No.1
<P>The aim of this study was to propose a new multi-objective optimization (MOO) of a ventilation controller which finds the optimal set points for simultaneously improving the indoor air quality (IAQ) and increasing the energy efficiency in buildings under changes in outdoor air quality and climate change. The outdoor weather information, such as ambient temperature, humidity, solar radiation and wind speed, was applied and treated as external disturbances in the building system. Two control strategies including proportional-integral (PI) control and multivariate model predictive control (MPC) were implemented and compared while controlling the indoor air temperature and CO<SUB>2</SUB> concentration in the targeted building system. A control performance assessment (CPA) technique was proposed and implemented for monitoring the MPC controller performance. With the goal of determining the optimal set points for the MPC controller, the multi-objective genetic algorithm was developed to enhance the energy consumption as well as to keep the IAQ within an acceptable range. The results indicate that the performance of the MPC controller with the optimized set points is superior to that of the PI controller in indoor building systems. More specifically, the MPC controller with the optimal set points for the indoor temperature and CO<SUB>2</SUB> control could reduce energy consumption by 5.22% and CO<SUB>2</SUB> content by 13.39% in comparison to the PI controller. In addition, the MPC controller equipped with MOO could be useful for building climate control depending on the variation of the outdoor air pollutants.</P>
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.
Modeling of subway indoor air quality using Gaussian process regression
Liu, Hongbin,Yang, Chong,Huang, Mingzhi,Wang, Dongsheng,Yoo, ChangKyoo Elsevier 2018 Journal of hazardous materials Vol.359 No.-
<P><B>Abstract</B></P> <P>Soft sensor modeling of indoor air quality (IAQ) in subway stations is essential for public health. Gaussian process regression (GPR), as an efficient nonlinear modeling method, can effectively interpret the complicated features of industrial data by using composite covariance functions derived from base kernels. In this work, an accurate GPR soft sensor with the sum of squared-exponential covariance function and periodic covariance function is proposed to capture the time varying and periodic characteristics in the subway IAQ data. The results demonstrate that the prediction performance of the proposed GPR model is superior to that of the traditional soft sensors consisting of partial least squares, back propagation artificial neural networks, and least squares support vector regression (LSSVR). More specifically, the values of root mean square error, mean absolute percentage error, and coefficient of determination are improved by 12.35%, 9.53%, and 40.05%, respectively, in comparison with LSSVR.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Gaussian process regression (GPR) is proposed to predict particulate matter (PM<SUB>2.5</SUB>) in a subway. </LI> <LI> Compositional structure of different base kernels of GPR is proposed to model PM<SUB>2.5</SUB>. </LI> <LI> GPR with sum of squared-exponential and periodic kernels provides the best modeling performance. </LI> <LI> The proposed GPR was compared with other soft sensors including PLS, ANN, and SVM. </LI> </UL> </P>