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%.

Influence of Varying Al/Ni Thicknesses Ratio on Reaction Behaviors and Mechanical Properties of Al/Ni Energetic Laminates

Mingzhi Wang,Anqi Xu,Ruolin Peng,Liangsheng Qiu,Jie Tao 대한금속·재료학회 2021 METALS AND MATERIALS International Vol.27 No.11

Al/Ni energetic laminates with various Al/Ni thickness ratios were prepared by electrodeposition and hot pressing method toinvestigate the effect of the thickness ratios on mechanical properties and exothermic energy of the composites. In this study,the different Al/Ni thickness ratios of 9:4, 9:6, 9:8 and 9:10 were determined by varying the initial thickness of Ni layers. Theresults showed that the Al/Ni laminate with the thickness ratio of 9:4 had the largest exothermic heat of 1139 J/g, while thelaminate with 9:10 possessed the optimum mechanical properties, such as the tensile strength of 395.8 MPa, the elongationof 13.3% and the bending strength of 706 MPa. It was found that the tensile properties and plastic forming properties of thelaminates were enhanced continuously with the increasing proportion of nickel layers due to higher strength and the improvingcollaborative deformation of Al/Ni multilayers, when the Al/Ni thickness ratio altered from 9:4 to 9:10. Meanwhile,the exothermic reaction energy was declined significantly because of the increasing proportion of the exothermic reactionto form AlNi3compounds, which possessed the lower energy density. In addition, the effect of Al/Ni thickness ratio on thereaction paths in the continuous heating process was also studied.

Phosphorylation of Cellulose from Oak Wood Chips and wastepapers

( Wang Mingzhi ),( Modabber Ahmed Khan ),( Bu Kug Lim ),( Jong Yoon Lee ) 韓國木材工學會 2007 한국목재공학회 학술발표논문집 Vol.2007 No.-

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>

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>

Research on the “Wonder-advocating” Thought in Gao Qipei's Finger Painting

侯明志(Mingzhi HOU) Korea Institute for Humanities and Social Sciences 2023 Journal of Global Arts Studies (JGAS) Vol.1 No.1

[Background] During the early and middle Qing Dynasty when Gao Qipei was living in, he largely inherited the old system of the Ming Dynasty. In the late Ming Dynasty, influenced by Wang Yangming’s Philosophy, individual willpower and subject spirit began to awaken, and he pursued novel ideas in his artistic creations. [Objective] In the history of art, some artists are in a state of being ignored, which is both normal and abnormal. The normal refers to that an art history is a history with both masking and exposure, while the abnormal means that some important artists are excluded from the vision of art history. [Method] Based on literature research methods, we organized articles and literature materials related to Gao Qipei, explored the origin of Gao Qipei's “Wonder-advocating” Thought in his painting from the perspectives of his career and personal experience and analyzed the “Wonder-advocating” Thought from multiple angles. [Result] There was an expression form of “Wonder-advocating” art in the Ming and Qing Dynasties, and Gao Qipei reflected the pursuit of “curiosity” taste from artists in the Qing Dynasty. Starting from Gao Qipei, the finger and pen have a distinction in painting. In his concept, the finger had many advantages over the pen, which prompted him to use the finger for painting and formed his own unique painting style. [Conclusion] It is revealed that Gao Qipei had a “brand new” understanding of painting art under the condition of “Wonder-advocating” Thought, which brought new vitality to Chinese painting art. 【背景】高其佩所处的清前中期,大体上承袭了明朝的旧制,明晚期,受阳明心学的影响,个性意思和主体精神开始觉醒,在艺术造诣上追求着新奇的思想。【目的】在艺术史上,有些艺术家处于遗漏状态,这即正常又不正常。正常的是一部艺术史就是一部遮蔽与显露的历史,不正常是一些重要的艺术家被排除于艺术史的视野之外。【方法】通过文献研究法整理有关高其佩的文章与文献资料,在以仕途和个人经历的角度探究高其佩绘画尚奇思想的渊源,以多种角度去分析“尚奇”思想。【结果】明清时期有尚奇艺术的表现形式,高其佩体现了清朝时期艺术家对于“奇趣”品味的追求。自高其佩开始,绘画中有了指与笔的区分,在他的观念中,指有众多高于笔的优点,这促使他运指作画,形成自己独特是绘画风格。【结论】窥探出高其佩在“尚奇思想”的条件下对绘画艺术有“崭新”的认识,为中国绘画艺术带来新的活力。

Analysis of the Dynamic Diameter of Superthin Diamond Blades in the High Speed and Precison Dicing Process

Kenan Li,Mingzhi Wang,Feng Chen,Ning Yan,Qin Zou,Yucheng Zhao,Jianmin Li,Fu Zhao 한국정밀공학회 2019 International Journal of Precision Engineering and Vol.20 No.7

During the planar lightwave circuit (PLC) splitter manufacturing process, high precision is essential. Controlling machining precision is extremely difficult during the high-speed-and-precision dicing process, which is empirically related to the rotational speed, static diameter and elastic modulus of diamond dicing blades and other factors. However, the effect of changes in the outer diameter of blades on machining precision has been disregarded in the PLC manufacturing process. In this research, we proposed a dynamic diameter (Dd) for describing changes in a blade’s outer diameter during the high-speed machining process. Dd is positively correlated with machining precision. Here, we derived a formula for calculating Dd that is related to the rotational speed, static diameter, elastic modulus, Poisson’s ratio, density, cutting length and radial wear rate of diamond dicing blades. Furthermore, a series of experimental Dd values was obtained by changing the rotational speed, static diameter and elastic modulus of diamond dicing blades. These values are highly consistent with the calculated results. Our findings not only provide clues to compensate for diameter during high-speed-and-precision dicing process, but also offer guidelines for a new design route of diamond tools.

Robust Gesture Recognition with Kinect Data Acquisition

Jinghui Wang,Mingzhi Niu 보안공학연구지원센터 2016 International Journal of Signal Processing, Image Vol.9 No.10

To realize the gesture recognition of high precision ratio, the gesture recognition method of multi-model data fusion based on Kinect depth image is proposed, to implement the automatic splicing of models. First of all, the feature package model uses the speeded up robust feature (SURF) algorithm to replace the scale invariant feature transform (SIFT) algorithm to extract features, improve the real-time performance. Secondly, Hu moment is introduced to describe the global gesture features, further improving the recognition rate, the ray casting is used finally, and the obtained coordinate information is used to solve the rigid transformation between two point cloud models. Finally, the proposed data fusion method is verified through two experiments, the algorithm in this paper is better than the traditional support vector machine (SVM) method both in real time performance and recognition rate, and obtains better model splicing effect.

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.