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    • 1. 发明申请
    • BIOCHEMICAL REACTOR
    • 生化反应器
    • US20120015391A1
    • 2012-01-19
    • US12871539
    • 2010-08-30
    • Siliang ZhangYuefang ZhangJian LiuXujun YuanMeijin GuoLiqing HeGongjian ChenMing ZhangGuoqiang SunWenfeng MaJu ChuYingping ZhuangYonghong WangMingzhi HuangHaifeng HangJianye Xia
    • Siliang ZhangYuefang ZhangJian LiuXujun YuanMeijin GuoLiqing HeGongjian ChenMing ZhangGuoqiang SunWenfeng MaJu ChuYingping ZhuangYonghong WangMingzhi HuangHaifeng HangJianye Xia
    • C12M1/34C12Q1/02
    • G01N21/6428C12M27/02C12M41/06C12M41/36C12M41/46G01N21/253G02B21/0016
    • A biochemical reactor involves an online cell examination microscope (4) thereon. Said online cell examination microscope (4) comprises a main body (43), an objective lens (24), an observation entrance window (22), a sampling device (40), an external light source system (60). Said observation entrance window (22) lies in front of said main body (43) and the sampling device (40) lies in front of said observation entrance window (22). An objective lens (24) and said external light source system (60) lie behind the observation entrance window (22) in the main body (43). A reflector prism (25) is installed between the external light source system (60) and the objective lens (24). A reflector (23) lies behind the objective lens (24) and in front of the observation entrance window (22). An annular diaphragm (26) is placed in front of the reflecting prism (25). A CCD or an area image sensor (30) is arranged on the upper side of the reflectance prism (25). The sampling device (40) includes a sampling piece (21), an elastic element (38) and a mobile device. Said elastic element (38) is installed between the sampling piece (21) and the drive shaft (51) of the mobile device. A space between the head end of the sampling piece (21) and the observation entrance window (22) develops the sampling pool (50).
    • 生化反应器包括在线细胞检查显微镜(4)。 所述在线细胞检查显微镜(4)包括主体(43),物镜(24),观察入口窗(22),取样装置(40),外部光源系统(60)。 所述观察入口窗(22)位于所述主体(43)的前方,所述取样装置(40)位于所述观察入口窗(22)的前方。 物镜(24)和外部光源系统(60)位于主体(43)中的观察入口窗(22)的后面。 反射棱镜(25)安装在外部光源系统(60)和物镜(24)之间。 反射器(23)位于物镜(24)的后面并且在观察入口窗口(22)的前方。 环形隔膜(26)放置在反射棱镜(25)的前面。 CCD或区域图像传感器(30)布置在反射棱镜(25)的上侧。 采样装置(40)包括采样片(21),弹性元件(38)和移动装置。 所述弹性元件(38)安装在移动装置的取样片(21)和驱动轴(51)之间。 取样片(21)的头端与观察入口窗(22)之间的空间形成采样池(50)。
    • 4. 发明申请
    • METHOD AND SYSTEM FOR WASTEWATER TREATMENT BASED ON DISSOLVED OXYGEN CONTROL BY FUZZY NEURAL NETWORK
    • 基于FUZZY神经网络解决氧气控制的废水处理方法与系统
    • US20140052422A1
    • 2014-02-20
    • US13985482
    • 2011-09-22
    • Jinquan WanMingzhi HuangYongwen MaYan Wang
    • Jinquan WanMingzhi HuangYongwen MaYan Wang
    • G06N3/04
    • G06N3/0436C02F3/006C02F3/30C02F2209/001C02F2209/003C02F2209/005C02F2209/04C02F2209/08C02F2209/22C02F2209/40G05B13/048Y02W10/15
    • A method and system for wastewater treatment based on dissolved oxygen control by a fuzzy neural network, the method for wastewater treatment comprising the following steps: (1) measuring art inlet water flow rate, an ORP value in an anaerobic tank, a DO value in an aerobic tank, an inlet water COD value, and an actual outlet water COD value; (2) collecting the measured sample data and sending them via a computer to a COD fuzzy neural network predictive model, so as to establish an outlet water COD predicted value, (3) comparing the outlet COD predicted value with the outlet water COD set value, so as to obtain an error and an error change rate, and using them as two input variables to adjust a suitable dissolved oxygen concentration. Accordingly, the on-line prediction and real-time control of dissolved oxygen wastewater treatment are achieved. The accurate control of dissolved oxygen concentration by the present method for wastewater treatment can achieve a saving in energy consumption while ensuring stable running of the sewage treatment system, and the outlet water quality meets the national emission standards.
    • 一种基于模糊神经网络溶解氧控制的污水处理方法和系统,废水处理方法包括以下步骤:(1)测量艺术入口水流量,厌氧池中的ORP值,DO值 有氧罐,进水COD值和实际出水COD值; (2)收集测量样本数据并通过计算机将其发送到COD模糊神经网络预测模型,以建立出口水COD预测值,(3)将出口COD预测值与出口水COD设定值进行比较 ,以获得误差和误差变化率,并将其用作两个输入变量来调节合适的溶解氧浓度。 因此,实现了在线预测和实时控制溶解氧废水处理。 通过本方法对污水处理的溶解氧浓度进行准确控制可以节省能源消耗,同时确保污水处理系统的稳定运行,出水水质符合国家排放标准。