Improving nitrogen removal using a fuzzy neural network-based control system in the anoxic/oxic process.

Huang, Mingzhi,Ma, Yongwen,Wan, Jinquan,Wang, Yan,Chen, Yangmei,Yoo, Changkyoo Ecomed 2014 Environmental Science and Pollution Research Vol.21 No.20

<P>Due to the inherent complexity, uncertainty, and posterity in operating a biological wastewater treatment process, it is difficult to control nitrogen removal in the biological wastewater treatment process. In order to cope with this problem and perform a cost-effective operation, an integrated neural-fuzzy control system including a fuzzy neural network (FNN) predicted model for forecasting the nitrate concentration of the last anoxic zone and a FNN controller were developed to control the nitrate recirculation flow and realize nitrogen removal in an anoxic/oxic (A/O) process. In order to improve the network performance, a self-learning ability embedded in the FNN model was emphasized for improving the rule extraction performance. The results indicate that reasonable forecasting and control performances had been achieved through the developed control system. The effluent COD, TN, and the operation cost were reduced by about 14, 10.5, and 17 %, respectively.</P>

Modeling of a paper-making wastewater treatment process using a fuzzy neural network

Mingzhi Huang,유창규,Jinquan Wan,Yan Wang,Yongwen Ma,Huiping Zhang,류홍빈,Zhanzhan Hu 한국화학공학회 2012 Korean Journal of Chemical Engineering Vol.29 No.5

An intelligent system that includes a predictive model and a control was developed to predict and control the performance of a wastewater treatment plant. The predictive model was based on fuzzy C-means clustering, fuzzy inference and neural networks. Fuzzy C-means clustering was used to identify model’s architecture, extract and optimize fuzzy rule. When predicting, MAPE was 4.7582% and R was 0.8535. The simulative results indicate that the learning ability and generalization of the model was good, and it can achieve a good predication of effluent COD. The control model was based on a fuzzy neural network model, taking into account the difference between the predicted value of COD and the setpoint. When simulating, R was 0.9164, MAPE was 5.273%, and RMSE was 0.0808, which showed that the FNN control model can effectively change the additive dosages. The control of a paper-making wastewater treatment process in the laboratory using the developed predictive control model and MCGS (monitor and control generated system) software shows the dosage was computed accurately to make the effluent COD remained at the setpoint,when the influent COD value or inflow flowrate was changed. The results indicate that reasonable forecasting and control performances were achieved through the developed system; the maximum error was only 3.67%, and the average relative error was 2%.

A GA-Based Neural Fuzzy System for Modeling a Paper Mill Wastewater Treatment Process

Huang, Mingzhi,Wan, Jinquan,Ma, Yongwen,Zhang, Huiping,Wang, Yan,Wei, Chaohai,Liu, Hongbin,Yoo, ChangKyoo American Chemical Society 2011 INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH - Vol.50 No.23

<P>A genetic algorithm-based neural fuzzy system (GA-NFS) was presented for studying the coagulation process of wastewater treatment in a paper mill. In order to adapt the system to a variety of operating conditions and acquire a more flexible learning ability, the GA-NFS was employed to model the nonlinear relationships between the effluent concentration of pollutants and the chemical dosages, and a hybrid learning algorithm divided into two stages was proposed for parameters learning. During the first learning stage, a genetic algorithm was used to optimize the structure of GA-NFS and the membership function of each fuzzy term due to its capability of parallel and global search. On the basis of an optimized training stage, the back-propagation algorithm (BP algorithm) was chosen to update the parameters of GA-NFS to improve the system precision. The GA-NFS proves to be very effective in modeling coagulation perform and performs better than adaptive-network-based fuzzy inference system (ANFIS). RMSE, MAPE, and <I>R</I> between the predicted and observed values for GA-NFS were only 0.01099, 2.3337, and 0.9375, respectively.</P>

Suppressive Effect of 4-Hydroxy-2-(4-Hydroxyphenethyl) Isoindoline-1,3-Dione on Ovalbumin-Induced Allergic Asthma

Huang, Jin,Su, Mingzhi,Lee, Bo-Kyung,Kim, Mee-Jeong,Jung, Jee H.,Im, Dong-Soon The Korean Society of Applied Pharmacology 2018 Biomolecules & Therapeutics(구 응용약물학회지) Vol.26 No.6

4-Hydroxy-2-(4-hydroxyphenethyl)isoindoline-1,3-dione (PD1) is a synthetic phthalimide derivative of a marine compound. PD1 has peroxisome proliferator-activated receptor (PPAR) ${\gamma}$ agonistic and anti-inflammatory effects. This study aimed to investigate the effect of PD1 on allergic asthma using rat basophilic leukemia (RBL)-2H3 mast cells and an ovalbumin (OVA)-induced asthma mouse model. In vitro, PD1 suppressed ${\beta}$-hexosaminidase activity in RBL-2H3 cells. In the OVA-induced allergic asthma mouse model, increased inflammatory cells and elevated Th2 and Th1 cytokine levels were observed in bronchoalveolar lavage fluid (BALF) and lung tissue. PD1 administration decreased the numbers of inflammatory cells, especially eosinophils, and reduced the mRNA and protein levels of the Th2 cytokines including interleukin (IL)-4 and IL-13, in BALF and lung tissue. The severity of inflammation and mucin secretion in the lungs of PD1-treated mice was also less. These findings indicate that PD1 could be a potential compound for anti-allergic therapy.

Suppressive Effect of 4-Hydroxy-2-(4-Hydroxyphenethyl) Isoindoline-1,3-Dione on Ovalbumin-Induced Allergic Asthma

( Jin Huang ),( Mingzhi Su ),( Bo-kyung Lee ),( Mee-jeong Kim ),( Jee H. Jung ),( Dong-soon Im ) 한국응용약물학회 2018 Biomolecules & Therapeutics(구 응용약물학회지) Vol.26 No.6

4-Hydroxy-2-(4-hydroxyphenethyl)isoindoline-1,3-dione (PD1) is a synthetic phthalimide derivative of a marine compound. PD1 has peroxisome proliferator-activated receptor (PPAR) γ agonistic and anti-inflammatory effects. This study aimed to investigate the effect of PD1 on allergic asthma using rat basophilic leukemia (RBL)-2H3 mast cells and an ovalbumin (OVA)-induced asthma mouse model. In vitro, PD1 suppressed β-hexosaminidase activity in RBL-2H3 cells. In the OVA-induced allergic asthma mouse model, increased inflammatory cells and elevated Th2 and Th1 cytokine levels were observed in bronchoalveolar lavage fluid (BALF) and lung tissue. PD1 administration decreased the numbers of inflammatory cells, especially eosinophils, and reduced the mRNA and protein levels of the Th2 cytokines including interleukin (IL)-4 and IL-13, in BALF and lung tissue. The severity of inflammation and mucin secretion in the lungs of PD1-treated mice was also less. These findings indicate that PD1 could be a potential compound for anti-allergic therapy.

Adaptive neuro-fuzzy inference system based faulty sensor monitoring of indoor air quality in a subway station

유창규,류홍빈,Mingzhi Huang,김정태 한국화학공학회 2013 Korean Journal of Chemical Engineering Vol.30 No.3

A new faulty sensor monitoring method based on an adaptive neuro-fuzzy inference system (ANFIS) is proposed to improve the monitoring performance of indoor air quality (IAQ) in subway stations. To enhance network performance, a data preprocessing step for detecting outliers and treating missing data is implemented before building the monitoring models. A squared prediction error (SPE) monitoring index based on the ANFIS prediction model is proposed to detect sensor faults, where the confidence limit for the SPE index is determined by using the kernel density estimation method. The proposed monitoring approach is applied to detect four typical kinds of sensor faults that may happen in the indoor space of a subway. The prediction results in the subway system indicate that the prediction accuracy of an ANFIS structure with 15 clusters is superior to that of an appropriate artificial neural network structure. Specifically, when detecting one kind of complete failure fault that happened within the normal range, the detection performance of ANFIS-based SPE outperforms that of a traditional principal component analysis method. The developed sensor monitoring technique could work well for other kinds of sensor faults resulting from a noxious underground environment.

Faulty Sensor Detection, Identification and Reconstruction of Indoor Air Quality Measurements in a Subway Station

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.

A fuzzy neural network-based soft sensor for modeling nutrient removal mechanism in a full-scale wastewater treatment system

Liu, Hongbin,Huang, Mingzhi,Yoo, ChangKyoo Taylor Francis 2013 Desalination and water treatment Vol.51 No.31

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>

Dry reforming of methane over Ni/SBA-15 catalysts prepared by homogeneous precipitation method

Qiulin Zhang,Jing Wang,Ping Ning,Tengfei Zhang,Mingzhi Wang,Kaixian Long,Jianhong Huang 한국화학공학회 2017 Korean Journal of Chemical Engineering Vol.34 No.11

Ni/SBA-15 catalyst was prepared by homogeneous precipitation method (Ni-HP) and used for dry reforming of methane (DRM). The related characterization results indicated that the Ni particles were highly dispersed with a size range of 2-5 nm. Compared with Ni/SBA-15 catalyst prepared by impregnation (Ni-IM), the reduction temperature of Ni-HP obtained from H2-TPR was greatly improved, suggesting the stronger metal-support interaction. After reacting at 700 oC for 100 h, the CH4 conversion of DRM over Ni-HP catalyst slightly decreased from 74.5% to 73.8%. While, for the Ni-IM catalyst, the CH4 conversion dropped from 61.7% to 37.3%. Furthermore, the average particle size of Ni-HP was 3.7 nm and 4.7 nm before and after the long-time stability test, respectively, ascribed to the good antisintering property. Although a certain amount of coke was produced, mainly with disorder filamentous carbon of basegrowth, the Ni/SBA-15 prepared by homogeneous precipitation exhibited excellent catalytic activity and stability.