CN105912824A - A2O biological tank process model building method - Google Patents

Abstract

The invention relates to the field of sewage treatment, and provides an A2O biological tank process model building method. The method comprises the following steps: setting input parameters and output parameters; building an A2O biological tank process model according to the input parameters, the output parameters, an ASM2D (Activated Sludge Model No.2D) model and a material balance equation, wherein the A2O biological tank process model comprises an anaerobic tank process model, an anoxic tank process model and an aerobic tank process model; the output parameters represent the concentration variation rate of components. According to the A2O biological tank process model building method, the A2O biological tank process model is simple and can be built through Matlab simulation software; the A2O biological tank process model can be used for simulating the A2O biological tank process, so as to provide foundation for later control strategy study; meanwhile, purchase of foreign ASMs-like series simulation software which is high in price can be avoided, so that the cost of sewage treatment of the A2O biological tank process can be reduced.

Description

A kind of A2O biological tank Technology Modeling method

Technical field

The present invention relates to sewage treatment area, particularly relate to a kind of A2O biological tank Technology Modeling method.

Background technology

A2O method is also known as AAO method, it it is the abbreviation (anaerobic-anoxic-oxic method) of English Anaerobic-Anoxic-Oxic first letter, it is that a kind of conventional B-grade sewage processes technique, this technique has been widely applied to municipal sewage plant, can be used for B-grade sewage to process or three grades of sewage disposals, it uses activated sludge process to be also the sewage water treatment method that the world is commonly used now, has good Nitrogen/Phosphorus Removal.Along with the application in sewage treatment process of mathematical modelling and computer technology is the most active, for activated Sludge System exploitation mathematical modelling simulation software become possibility, also the operation for activated sludge model provides good basis.Such as, foreign study personnel are using ASMs series analog software as general-purpose platform, by correction and the simplification of analog parameter, develop many application programs and software.And the domestic research in this field is started late, and software development research is the most less, make domestic during using A2O sewage treatment process, if desired for the process of analog simulation A2O sewage treatment process, would have to the substantial amounts of fund purchase of flower words and be similar to ASMs series analog software.

Therefore, being necessary to provide a kind of A2O biological tank Technology Modeling method, the method can have simple the Realization of Simulation, thus solves the most domestic research in terms of biological tank Technology Modeling starting late, the less problem of associated analog software, the control strategy research for the later stage provides analog simulation basis.

Summary of the invention

It is an object of the invention to provide a kind of A2O biological tank Technology Modeling method, the method can realize through simulation software, and in order to analog simulation A2O biological tank technique, the control strategy research for the later stage provides the foundation.

In order to solve above-mentioned technical problem, the present invention realizes by providing a kind of A2O biological tank Technology Modeling method, and the method realizes based on simulation software, and described A2O biological tank Technology Modeling method includes:

Set input parameter and output parameter；

Setting up A2O biological tank process modeling according to described input parameter, output parameter, ASM2D model and material balance equation, described A2O biological tank process modeling includes anaerobic pond process modeling, anoxic pond process modeling and Aerobic Pond process modeling；

Wherein, described material balance equation is:

$\frac{{\mathrm{\ΔZ}}_i}{\mathrm{\Δ}t}=\frac{1}{V}(Q_{in}{Z}_{in}+r_{i}V-Q_{out}{Z}_{out})---\left(1\right)$

Wherein, △ Z_iFor concentration of component variable quantity, △ t is time variation amount, and V is the volume in this pond, Q_inFor entering the water yield, Z_inFor entering concentration of component, r_iFor affecting the process rate of concentration of component change, Q_outFor flowing out the water yield, Z_outFor flowing out concentration of component；

Described anaerobic pond process modeling includes the following differential equation:

$\begin{array}{c}\frac{{\mathrm{dS}}_{O2,1}}{dt}=\frac{Q_e}{V_y}{S}_{O2,0}+\frac{Q_r}{V_y}{S}_{O2,4}-\frac{Q_1}{V_y}{S}_{O2,1}-0.6{\mathrm{\ρ}}_4-0.6{\mathrm{\ρ}}_5-0.2{\mathrm{\ρ}}_{11}\\ -0.6{\mathrm{\ρ}}_{13}-18{\mathrm{\ρ}}_{18}\end{array}---\left(2\right)$

$\begin{array}{c}\frac{{\mathrm{dS}}_{A,1}}{dt}=\frac{Q_e}{V_y}{S}_{A,0}+\frac{Q_r}{V_y}{S}_{A,4}-\frac{Q_1}{V_y}{S}_{A,1}-1.6{\mathrm{\ρ}}_5-1.6{\mathrm{\ρ}}_7+{\mathrm{\ρ}}_8\\ -{\mathrm{\ρ}}_{10}+{\mathrm{\ρ}}_{17}\end{array}---\left(3\right)$

$\begin{array}{c}\frac{{\mathrm{dS}}_{F,1}}{dt}=\frac{Q_e}{V_y}{S}_{F,0}+\frac{Q_r}{V_y}{S}_{F,4}-\frac{Q_1}{V_y}{S}_{F,1}+{\mathrm{\ρ}}_1+{\mathrm{\ρ}}_2+{\mathrm{\ρ}}_3-1.6{\mathrm{\ρ}}_4\\ -1.6{\mathrm{\ρ}}_6-{\mathrm{\ρ}}_8\end{array}---\left(4\right)$

$\begin{array}{c}\frac{{\mathrm{dS}}_{{\mathrm{NH}}_4,1}}{dt}=\frac{Q_e}{V_y}{S}_{{\mathrm{NH}}_4,0}+\frac{Q_r}{V_y}{S}_{{\mathrm{NH}}_4,4}-\frac{Q_1}{V_y}{S}_{{\mathrm{NH}}_4,1}+0.01{\mathrm{\ρ}}_1+0.01{\mathrm{\ρ}}_2\\ +0.01{\mathrm{\ρ}}_3-0.022{\mathrm{\ρ}}_4-0.07{\mathrm{\ρ}}_5-0.022{\mathrm{\ρ}}_6-0.07{\mathrm{\ρ}}_7\\ +0.03{\mathrm{\ρ}}_8+0.03{\mathrm{\ρ}}_9-0.07{\mathrm{\ρ}}_{13}-0.07{\mathrm{\ρ}}_{14}+0.031{\mathrm{\ρ}}_{15}\\ -4.24{\mathrm{\ρ}}_{18}+0.031{\mathrm{\ρ}}_{19}\end{array}---\left(5\right)$

$\begin{array}{c}\frac{{\mathrm{dS}}_{{\mathrm{NO}}_3,1}}{dt}=\frac{Q_e}{V_y}{S}_{{\mathrm{NO}}_3,0}+\frac{Q_r}{V_y}{S}_{{\mathrm{NO}}_3,4}-\frac{Q_1}{V_y}{S}_{{\mathrm{NO}}_3,1}-0.21{\mathrm{\ρ}}_6-0.21{\mathrm{\ρ}}_7\\ -0.07{\mathrm{\ρ}}_{12}-0.21{\mathrm{\ρ}}_{14}+4.17{\mathrm{\ρ}}_{18}\end{array}---\left(6\right)$

$\begin{array}{c}\frac{{\mathrm{dS}}_{{\mathrm{PO}}_4,1}}{dt}=\frac{Q_e}{V_y}{S}_{{\mathrm{PO}}_4,0}+\frac{Q_r}{V_y}{S}_{{\mathrm{PO}}_4,4}-\frac{Q_1}{V_y}{S}_{{\mathrm{PO}}_4,1}-0.004{\mathrm{\ρ}}_4-0.02{\mathrm{\ρ}}_5\\ -0.004{\mathrm{\ρ}}_6-0.02{\mathrm{\ρ}}_7+0.01{\mathrm{\ρ}}_8+0.01{\mathrm{\ρ}}_9+0.04{\mathrm{\ρ}}_{10}\\ -{\mathrm{\ρ}}_{11}-{\mathrm{\ρ}}_{12}-0.02{\mathrm{\ρ}}_{13}+0.01{\mathrm{\ρ}}_{15}+{\mathrm{\ρ}}_{16}-0.02{\mathrm{\ρ}}_{19}+0.01{\mathrm{\ρ}}_{19}\end{array}---\left(7\right)$

$\frac{{\mathrm{dX}}_{I,1}}{dt}=\frac{Q_e}{V_y}{X}_{I,0}+\frac{Q_r}{V_y}{X}_{I,4}-\frac{Q_1}{V_y}{X}_{I,1}+0.1{\mathrm{\ρ}}_9+0.1{\mathrm{\ρ}}_{15}+0.1{\mathrm{\ρ}}_{19}---\left(8\right)$

$\begin{array}{c}\frac{{\mathrm{dX}}_{S,1}}{dt}=\frac{Q_e}{V_y}{X}_{S,0}+\frac{Q_r}{V_y}{X}_{S,4}-\frac{Q_1}{V_y}{X}_{S,1}-{\mathrm{\ρ}}_1-{\mathrm{\ρ}}_2-{\mathrm{\ρ}}_3+0.9{\mathrm{\ρ}}_9\\ +0.9{\mathrm{\ρ}}_{15}+0.9{\mathrm{\ρ}}_{19}\end{array}---\left(9\right)$

$\frac{{\mathrm{dX}}_{H,1}}{dt}=\frac{Q_e}{V_y}{X}_{H,0}+\frac{Q_r}{V_y}{X}_{H,4}-\frac{Q_1}{V_y}{X}_{H,1}+{\mathrm{\ρ}}_4+{\mathrm{\ρ}}_5+{\mathrm{\ρ}}_6+{\mathrm{\ρ}}_7-{\mathrm{\ρ}}_9---\left(10\right)$

$\frac{{\mathrm{dX}}_{PAO,1}}{dt}=\frac{Q_e}{V_y}{X}_{PAO,0}+\frac{Q_r}{V_y}{X}_{PAO,4}-\frac{Q_1}{V_y}{X}_{PAO,1}+{\mathrm{\ρ}}_{13}+{\mathrm{\ρ}}_{14}-{\mathrm{\ρ}}_{15}---\left(11\right)$

$\frac{{\mathrm{dX}}_{PP,1}}{dt}=\frac{Q_e}{V_y}{X}_{PP,0}+\frac{Q_r}{V_y}{X}_{PP,4}-\frac{Q_1}{V_y}{X}_{PP,1}-0.4{\mathrm{\ρ}}_{10}+{\mathrm{\ρ}}_{11}+{\mathrm{\ρ}}_{12}-{\mathrm{\ρ}}_{16}---\left(12\right)$

$\frac{{\mathrm{dX}}_{PHA,1}}{dt}=\frac{Q_e}{V_y}{X}_{PHA,0}+\frac{Q_r}{V_y}{X}_{PHA,4}-\frac{Q_1}{V_y}{X}_{PHA,1}+{\mathrm{\ρ}}_{10}-{\mathrm{\ρ}}_{17}---\left(13\right)$

$\frac{{\mathrm{dX}}_{AUT,1}}{dt}=\frac{Q_e}{V_y}{X}_{AUT,0}+\frac{Q_r}{V_y}{X}_{AUT,4}-\frac{Q_1}{V_y}{X}_{AUT,1}-{\mathrm{\ρ}}_{19}---\left(14\right)$

Described anoxic pond process modeling includes the following differential equation:

$\begin{array}{c}\frac{{\mathrm{dS}}_{O_2,2}}{dt}=\frac{Q_1}{V_q}{S}_{O2,1}+\frac{Q_t}{V_q}{S}_{O2,3}-\frac{Q_2}{V_q}{S}_{O2,2}-0.6{\mathrm{\ρ}}_4-0.6{\mathrm{\ρ}}_5-0.2{\mathrm{\ρ}}_{11}\\ -0.6{\mathrm{\ρ}}_{13}-18{\mathrm{\ρ}}_{18}\end{array}---\left(15\right)$

$\begin{array}{c}\frac{{\mathrm{dS}}_{A,2}}{dt}=\frac{Q_1}{V_q}{S}_{A,1}+\frac{Q_t}{V_q}{S}_{A,3}-\frac{Q_2}{V_q}{S}_{A,2}-1.6{\mathrm{\ρ}}_5-1.6{\mathrm{\ρ}}_7+{\mathrm{\ρ}}_8\\ -{\mathrm{\ρ}}_{10}+{\mathrm{\ρ}}_{17}\end{array}---\left(16\right)$

$\begin{array}{c}\frac{{\mathrm{dS}}_{F,2}}{dt}=\frac{Q_1}{V_q}{S}_{F,1}+\frac{Q_t}{V_q}{S}_{F,3}-\frac{Q_2}{V_q}{S}_{F,2}+{\mathrm{\ρ}}_1+{\mathrm{\ρ}}_2+{\mathrm{\ρ}}_3-1.6{\mathrm{\ρ}}_4\\ -1.6{\mathrm{\ρ}}_6-{\mathrm{\ρ}}_8\end{array}---\left(17\right)$

$\begin{array}{c}\frac{{\mathrm{dS}}_{{\mathrm{NH}}_4,2}}{dt}=\frac{Q_1}{V_q}{S}_{{\mathrm{NH}}_4,1}+\frac{Q_t}{V_q}{S}_{{\mathrm{NH}}_4,3}-\frac{Q_2}{V_q}{S}_{{\mathrm{NH}}_4,2}+0.01{\mathrm{\ρ}}_1+0.01{\mathrm{\ρ}}_2\\ +0.01{\mathrm{\ρ}}_3-0.022{\mathrm{\ρ}}_4-0.07{\mathrm{\ρ}}_5-0.022{\mathrm{\ρ}}_6-0.07{\mathrm{\ρ}}_7\\ +0.03{\mathrm{\ρ}}_8+0.03{\mathrm{\ρ}}_9-0.07{\mathrm{\ρ}}_{13}-0.07{\mathrm{\ρ}}_{14}+0.031{\mathrm{\ρ}}_{15}\\ -4.24{\mathrm{\ρ}}_{18}+0.031{\mathrm{\ρ}}_{19}\end{array}---\left(\mathrm{18}\right)$

$\begin{array}{c}\frac{{\mathrm{dS}}_{{\mathrm{NO}}_3,2}}{dt}=\frac{Q_1}{V_q}{S}_{{\mathrm{NO}}_3,1}+\frac{Q_t}{V_q}{S}_{{\mathrm{NO}}_3,3}-\frac{Q_2}{V_q}{S}_{{\mathrm{NO}}_3,2}-0.21{\mathrm{\ρ}}_6-0.21{\mathrm{\ρ}}_7-0.07{\mathrm{\ρ}}_{12}\\ -0.21{\mathrm{\ρ}}_{14}+4.17{\mathrm{\ρ}}_{18}\end{array}---\left(\mathrm{19}\right)$

$\begin{array}{c}\frac{{\mathrm{dS}}_{{\mathrm{PO}}_4,2}}{dt}=\frac{Q_1}{V_q}{S}_{{\mathrm{PO}}_4,1}+\frac{Q_t}{V_q}{S}_{{\mathrm{PO}}_4,3}-\frac{Q_2}{V_q}{S}_{{\mathrm{PO}}_4,2}-0.004{\mathrm{\ρ}}_4-0.02{\mathrm{\ρ}}_5-0.004{\mathrm{\ρ}}_6\\ -0.02{\mathrm{\ρ}}_7+0.01{\mathrm{\ρ}}_8+0.01{\mathrm{\ρ}}_9+0.04{\mathrm{\ρ}}_{10}-{\mathrm{\ρ}}_{11}-{\mathrm{\ρ}}_{12}-0.02{\mathrm{\ρ}}_{13}\\ +0.01{\mathrm{\ρ}}_{15}+{\mathrm{\ρ}}_{16}-0.02{\mathrm{\ρ}}_{18}+0.01{\mathrm{\ρ}}_{19}\end{array}---\left(\mathrm{20}\right)$

$\frac{{\mathrm{dX}}_{I,2}}{dt}=\frac{Q_1}{V_q}{X}_{I,1}+\frac{Q_t}{V_q}{X}_{I,3}-\frac{Q_2}{V_q}{X}_{I,2}+0.1{\mathrm{\ρ}}_9+0.1{\mathrm{\ρ}}_{15}+0.1{\mathrm{\ρ}}_{19}---\left(\mathrm{21}\right)$

$\begin{array}{c}\frac{{\mathrm{dX}}_{S,2}}{dt}=\frac{Q_1}{V_q}{X}_{S,1}+\frac{Q_t}{V_q}{X}_{S,3}-\frac{Q_2}{V_q}{X}_{S,2}-{\mathrm{\ρ}}_1-{\mathrm{\ρ}}_2-{\mathrm{\ρ}}_3+0.9{\mathrm{\ρ}}_9\\ +0.9{\mathrm{\ρ}}_{15}+0.9{\mathrm{\ρ}}_{19}\end{array}---\left(\mathrm{22}\right)$

$\frac{{\mathrm{dX}}_{H,2}}{dt}=\frac{Q_1}{V_q}{X}_{H,1}+\frac{Q_t}{V_q}{X}_{H,3}-\frac{Q_2}{V_q}{X}_{H,2}+{\mathrm{\ρ}}_4+{\mathrm{\ρ}}_5+{\mathrm{\ρ}}_6+{\mathrm{\ρ}}_7-{\mathrm{\ρ}}_9---\left(\mathrm{23}\right)$

$\frac{{\mathrm{dX}}_{PAO,2}}{dt}=\frac{Q_t}{V_q}{X}_{PAO,1}+\frac{Q_t}{V_q}{X}_{PAO,3}-\frac{Q_2}{V_q}{X}_{PAO,2}+{\mathrm{\ρ}}_{13}+{\mathrm{\ρ}}_{14}-{\mathrm{\ρ}}_{15}---\left(24\right)$

$\frac{{\mathrm{dX}}_{PP,2}}{dt}=\frac{Q_t}{V_q}{X}_{PP,1}+\frac{Q_t}{V_q}{X}_{PP,3}-\frac{Q_2}{V_q}{X}_{PP,2}-0.4{\mathrm{\ρ}}_{10}+{\mathrm{\ρ}}_{11}+{\mathrm{\ρ}}_{12}-{\mathrm{\ρ}}_{16}---\left(25\right)$

$\frac{{\mathrm{dX}}_{PHA,2}}{dt}=\frac{Q_t}{V_q}{X}_{PHA,1}+\frac{Q_t}{V_q}{X}_{PHA,3}-\frac{Q_2}{V_q}{X}_{PHA,2}+{\mathrm{\ρ}}_{10}-{\mathrm{\ρ}}_{17}---\left(26\right)$

$\frac{{\mathrm{dX}}_{AUT,2}}{dt}=\frac{Q_1}{V_q}{X}_{AUT,1}+\frac{Q_t}{V_q}{X}_{AUT,3}-\frac{Q_2}{V_q}{X}_{AUT,2}-{\mathrm{\ρ}}_{19}---\left(27\right)$

Described Aerobic Pond process modeling includes the following differential equation:

$\begin{array}{c}\frac{{\mathrm{dS}}_{O_2,3}}{dt}=\frac{Q_2}{V_h}{S}_{O2,2}-\frac{Q_t+Q_3+Q_r+Q_w}{V_h}{S}_{O2,3}-0.6{\mathrm{\ρ}}_4-0.6{\mathrm{\ρ}}_5\\ -0.2{\mathrm{\ρ}}_{11}-0.6{\mathrm{\ρ}}_{13}-18{\mathrm{\ρ}}_{18}+KLa(8.56-S_{O2,3})\end{array}---\left(28\right)$

$\begin{array}{c}\frac{{\mathrm{dS}}_{A,3}}{dt}=\frac{Q_2}{V_h}{S}_{A,2}-\frac{Q_t+Q_3+Q_r+Q_w}{V_h}{S}_{A,3}-1.6{\mathrm{\ρ}}_5-1.6{\mathrm{\ρ}}_7\\ +{\mathrm{\ρ}}_8-{\mathrm{\ρ}}_{10}+{\mathrm{\ρ}}_{17}\end{array}---\left(29\right)$

$\begin{array}{c}\frac{{\mathrm{dS}}_{F,3}}{dt}=\frac{Q_2}{V_h}{S}_{F,2}-\frac{Q_t+Q_3+Q_r+Q_w}{V_h}{S}_{F,3}+{\mathrm{\ρ}}_1+{\mathrm{\ρ}}_2+{\mathrm{\ρ}}_3\\ -1.6{\mathrm{\ρ}}_4-1.6{\mathrm{\ρ}}_6-{\mathrm{\ρ}}_8\end{array}---\left(30\right)$

$\begin{array}{c}\frac{{\mathrm{dS}}_{{\mathrm{NH}}_4,3}}{dt}=\frac{Q_2}{V_h}{S}_{{\mathrm{NH}}_4,2}-\frac{Q_t+Q_3+Q_r+Q_w}{V_h}{S}_{{\mathrm{NH}}_4,3}+0.01{\mathrm{\ρ}}_1+0.01{\mathrm{\ρ}}_2\\ +0.01{\mathrm{\ρ}}_3-0.022{\mathrm{\ρ}}_4-0.07\mathrm{\ρ}5-0.022\mathrm{\ρ}6-0.07{\mathrm{\ρ}}_7\\ +0.03{\mathrm{\ρ}}_8+0.03{\mathrm{\ρ}}_9-0.07{\mathrm{\ρ}}_{13}-0.07{\mathrm{\ρ}}_{14}+0.031{\mathrm{\ρ}}_{15}\\ -4.24{\mathrm{\ρ}}_{18}+0.031{\mathrm{\ρ}}_{19}\end{array}---\left(31\right)$

$\begin{array}{c}\frac{{\mathrm{dS}}_{{\mathrm{NO}}_3,3}}{dt}=\frac{Q_2}{V_h}{S}_{{\mathrm{NO}}_3,2}-\frac{Q_t+Q_3+Q_r+Q_w}{V_h}{S}_{{\mathrm{NO}}_3,3}-0.21{\mathrm{\ρ}}_6-0.21{\mathrm{\ρ}}_7\\ -0.07{\mathrm{\ρ}}_{12}-0.21{\mathrm{\ρ}}_{14}+4.17{\mathrm{\ρ}}_{18}\end{array}---\left(\mathrm{32}\right)$

$\begin{array}{c}\frac{{\mathrm{dS}}_{{\mathrm{PO}}_4,3}}{dt}=\frac{Q_2}{V_h}{S}_{{\mathrm{PO}}_4,2}-\frac{Q_t+Q_3+Q_r+Q_w}{V_h}{S}_{{\mathrm{PO}}_4,3}-0.004{\mathrm{\ρ}}_4-0.02{\mathrm{\ρ}}_5\\ -0.004{\mathrm{\ρ}}_6-0.02{\mathrm{\ρ}}_7++0.01{\mathrm{\ρ}}_8+0.01{\mathrm{\ρ}}_9+0.4{\mathrm{\ρ}}_{10}-{\mathrm{\ρ}}_{11}\\ -{\mathrm{\ρ}}_{12}-0.02{\mathrm{\ρ}}_{13}+0.01{\mathrm{\ρ}}_{15}++{\mathrm{\ρ}}_{16}-0.02{\mathrm{\ρ}}_{18}+0.01{\mathrm{\ρ}}_{19}\end{array}---\left(\mathrm{33}\right)$

$\begin{array}{c}\frac{{\mathrm{dX}}_{I,3}}{dt}=\frac{Q_2}{V_h}{X}_{I,2}-\frac{Q_t+Q_3+Q_r+Q_w}{V_h}{X}_{I,3}+0.1{\mathrm{\ρ}}_9\\ +0.1{\mathrm{\ρ}}_{15}+0.1{\mathrm{\ρ}}_{19}\end{array}---\left(34\right)$

$\begin{array}{c}\frac{{\mathrm{dX}}_{S,3}}{dt}=\frac{Q_2}{V_h}{X}_{S,2}-\frac{Q_t+Q_3+Q_r+Q_w}{V_h}{X}_{S,3}-{\mathrm{\ρ}}_1-{\mathrm{\ρ}}_2-{\mathrm{\ρ}}_3\\ +0.9{\mathrm{\ρ}}_9+0.9{\mathrm{\ρ}}_{15}+0.9{\mathrm{\ρ}}_{19}\end{array}---\left(35\right)$

$\begin{array}{c}\frac{{\mathrm{dX}}_{H,3}}{dt}=\frac{Q_2}{V_h}{X}_{H,2}-\frac{Q_t+Q_3+Q_r+Q_w}{V_h}{X}_{H,3}+{\mathrm{\ρ}}_4+{\mathrm{\ρ}}_5\\ +{\mathrm{\ρ}}_6+{\mathrm{\ρ}}_7-{\mathrm{\ρ}}_9\end{array}---\left(\mathrm{36}\right)$

$\begin{array}{c}\frac{{\mathrm{dX}}_{PAO,3}}{dt}=\frac{Q_2}{V_h}{X}_{PAO,2}-\frac{Q_t+Q_3+Q_r+Q_w}{V_h}{X}_{PAO,3}+{\mathrm{\ρ}}_{13}\\ +{\mathrm{\ρ}}_{14}-{\mathrm{\ρ}}_{15}\end{array}---\left(37\right)$

$\begin{array}{c}\frac{{\mathrm{dX}}_{PP,3}}{dt}=\frac{Q_2}{V_h}{X}_{PP,2}-\frac{Q_t+Q_3+Q_r+Q_w}{V_h}{X}_{PP,3}-0.4{\mathrm{\ρ}}_{10}+{\mathrm{\ρ}}_{11}\\ +{\mathrm{\ρ}}_{12}-{\mathrm{\ρ}}_{16}\end{array}---\left(38\right)$

$\frac{{\mathrm{dX}}_{PHA,3}}{dt}=\frac{Q_2}{V_h}{X}_{PHA,2}-\frac{Q_t+Q_3+Q_r+Q_w}{V_h}{X}_{PHA,3}+{\mathrm{\ρ}}_{10}-{\mathrm{\ρ}}_{17}---\left(39\right)$

$\frac{{\mathrm{dX}}_{AUT,3}}{dt}=\frac{Q_2}{V_h}{X}_{AUT,2}-\frac{Q_t+Q_3+Q_r+Q_w}{V_h}{X}_{AUT,3}-{\mathrm{\ρ}}_{19}---\left(40\right)$

In formula 2-40, Q_eFor biological tank flow of inlet water, Q_rFor sludge reflux amount, Q₁For anaerobic pond water yield, Q₂For anoxic pond water yield, Q₃For Aerobic Pond water yield, Q_tMixed-liquor return amount, Q_wFor sludge volume, wherein Q₁=Q_e+Q_r,Q₂=Q₁+Q_t,Q₃=Q₂-Q_t-Q_r-Q_w,V_yFor the volume of anaerobic pond, V_qFor the volume of anoxic pond, V_hFor the volume of Aerobic Pond, t is the response time, and KLa is oxygen mass transfer coefficients, S_O2、S_F、S_A、S_NH4、S_NO3And S_PO4It is dissolubility component, S_O2、S_F、S_A、S_NH4、S_NO3And S_PO4Represent dissolved oxygen, fermentable easily biological-degradable Organic substance, tunning, ammonia nitrogen, nitrate nitrogen and nitrite nitrogen, dissolved inorganic phosphorus, ρ respectively₁, ρ₂..., ρ₁₉For the process rate in ASM2D model, X_I、X_s、X_H、X_PAO、X_PP、X_PHAAnd X_AUTIt is graininess component, X_I、X_s、X_H、X_PAO、X_PP、X_PHAAnd X_AUTRepresent inert particle Organic substance, at a slow speed degradable substrate, heterotrophic microorganism, polyP bacteria PAO, Quadrafos, the intracellular stock of polyP bacteria PAO, nitrifier, S in formula 2-40 respectively_O2、 S_F、S_A、S_NH4、S_NO3、S_PO4、S_O2、S_F、S_A、S_NH4、S_NO3And S_PO4Subscript in 0 expression biological tank after comma, subscript 1,2,3,4 represents anaerobic pond, anoxic pond, Aerobic Pond and second pond after comma respectively.

Preferably, described input parameter includes: biological tank flow of inlet water Q_e, sludge reflux amount Q_r, mixed-liquor return amount Q_t, sludge volume Q_wAnd biological tank water inlet concentration of component；Described output parameter is concentration of component rate of change.

Preferably, when actually used A2O biological tank process modeling, model restrictive condition is also included.

Preferably, described model restrictive condition includes:

Described A2O biological tank process modeling is only applicable to biological wastewater treatment systems simulation；

The temperature that described A2O biological tank process modeling is suitable for is 10～25 DEG C.

Further, described ρ₁, ρ₂..., ρ₁₉Process rate uses the coefficient table that international water association promulgates.

Preferably, described A2O biological tank Technology Modeling method realizes based on Matlab software emulation.

Preferably, step-length ode4 solver is chosen in described emulation, and the step-length of described step-length ode4 solver is 0.0001, and simulation time is 61 days.

Preferably, described biological tank water inlet concentration of component includes: COD concentration, ammonia nitrogen concentration, nitrate concentration and phosphate concn.

Compared to existing technology, the beneficial effects of the present invention is: The embodiment provides a kind of A2O biological tank Technology Modeling method, the method includes: set input parameter and output parameter；A2O biological tank process modeling is set up according to described input parameter, output parameter, ASM2D model and material balance equation, described A2O biological tank process modeling includes anaerobic pond process modeling, Aerobic Pond process modeling and anoxic pond process modeling, and described output parameter is concentration of component rate of change；The method can realize based on simulation software, and in order to analog simulation A2O biological tank technique, the control strategy research for the later stage provides the foundation.

Accompanying drawing explanation

For the technical scheme being illustrated more clearly that in the embodiment of the present invention, in describing embodiment below, the required accompanying drawing used is briefly introduced, obviously, accompanying drawing in describing below is only some embodiments of the present invention, from the point of view of those of ordinary skill in the art, do not pay creative and laborious on the premise of, it is also possible to obtain other accompanying drawing according to these accompanying drawings.

Fig. 1 is the flow chart of the A2O biological tank Technology Modeling method that one embodiment of the invention provides；

Fig. 2 be one embodiment of the invention provide based on A2O biological tank Technology Modeling method simulated effect figure；

Fig. 3 be one embodiment of the invention provide based on A2O biological tank Technology Modeling method simulated effect figure.

Detailed description of the invention

In order to make the purpose of the present invention, technical scheme and advantage clearer, below in conjunction with drawings and Examples, the present invention is further elaborated.Should be appreciated that specific embodiment described herein, only in order to explain the present invention, is not intended to limit the present invention.

Fig. 1 shows the flow chart of the A2O biological tank Technology Modeling method that one embodiment of the invention provides, as it is shown in figure 1, described method includes:

S101, setting input parameter and output parameter；

In this step, input parameter includes biological tank flow of inlet water Q_e, sludge reflux amount Q_r, mixed-liquor return amount Q_t, sludge volume Q_wAnd biological tank water inlet concentration of component；Output parameter is concentration of component rate of change, and wherein biological tank water inlet concentration of component includes: COD concentration, ammonia nitrogen concentration, nitrate concentration and phosphate concn.

S102, setting up A2O biological tank process modeling according to described input parameter, output parameter, ASM2D model and material balance equation, described A2O biological tank process modeling includes anaerobic pond process modeling, anoxic pond process modeling and Aerobic Pond process modeling.

Model in this embodiment uses the ASM2D model that international water association promulgates, this ASM2D model is become by 19 first order differential equation system, it is the most all necessary owing to 19 first order differential equation system become at A2O biological tank Technology Modeling, therefore according to practical situation, carry out reasonably it is assumed that this ASM2D model can be optimized.Setting up model hypothesis condition, this model hypothesis condition includes: assume that pH value is constant and close neutral；Assume that the coefficient in speed expression formula is steady state value；Hypothesized model does not consider the restriction that organic matter removal and cell are grown by other inorganic nutrients things in addition to N, P；Heterotrophic bacteria in hypothesized model grows under conditions of aerobic, anoxia, anaerobic fermentation；Assume that tunning is the unique organic substrate that can be absorbed by polyP bacteria；Assume that polyP bacteria can only depend on the PHA of storage rather than directly with SA as substrate, grow under aerobic condition；Assume that polyP bacteria does not possess denitrifying ability；Assume in poly (hydroxyalkanoate) representative model all of carbon storage material in polyP bacteria cell.According to above-mentioned assumed condition, simplifying ASM2D model, this simplification includes: remove the second pond in ASM2D model；Dissolved oxygen concentration is set to steady state value；Do not consider the basicity impact on process rate；Remove total suspended solid and S_IInertia dissolved matter；And do not consider two processes of chemical dephosphorization in ASM2D model.Thus combining material balance equation and can draw this A2O biological tank process modeling, wherein material balance equation is:

$\frac{{\mathrm{\ΔZ}}_i}{\mathrm{\Δ}t}=\frac{1}{V}(Q_{in}{Z}_{in}+r_{i}V-Q_{out}{Z}_{out})---\left(1\right)$

Wherein, △ Z_iFor concentration of component variable quantity, △ t is time variation amount, and V is the volume in this pond, Q_inFor entering the water yield, Z_inFor entering concentration of component, r_iFor affecting the process rate of concentration of component change, Q_outFor flowing out the water yield, Z_outFor flowing out concentration of component.

Described anaerobic pond process modeling includes the following differential equation:

$\begin{array}{c}\frac{{\mathrm{dS}}_{O2,1}}{dt}=\frac{Q_e}{V_y}{S}_{O2,0}+\frac{Q_r}{V_y}{S}_{O2,4}-\frac{Q_1}{V_y}{S}_{O2,1}-0.6{\mathrm{\ρ}}_4-0.6{\mathrm{\ρ}}_5-0.2{\mathrm{\ρ}}_{11}\\ -0.6{\mathrm{\ρ}}_{13}-18{\mathrm{\ρ}}_{18}\end{array}---\left(2\right)$

$\begin{array}{c}\frac{{\mathrm{dS}}_{A,1}}{dt}=\frac{Q_e}{V_y}{S}_{A,0}+\frac{Q_r}{V_y}{S}_{A,4}-\frac{Q_1}{V_y}{S}_{A,1}-1.6{\mathrm{\ρ}}_5-1.6{\mathrm{\ρ}}_7+{\mathrm{\ρ}}_8\\ -{\mathrm{\ρ}}_{10}+{\mathrm{\ρ}}_{17}\end{array}---\left(3\right)$

$\begin{array}{c}\frac{{\mathrm{dS}}_{F,1}}{dt}=\frac{Q_e}{V_y}{S}_{F,0}+\frac{Q_r}{V_y}{S}_{F,4}-\frac{Q_1}{V_y}{S}_{F,1}+{\mathrm{\ρ}}_1+{\mathrm{\ρ}}_2+{\mathrm{\ρ}}_3-1.6{\mathrm{\ρ}}_4\\ -1.6{\mathrm{\ρ}}_6-{\mathrm{\ρ}}_8\end{array}---\left(4\right)$

$\begin{array}{c}\frac{{\mathrm{dS}}_{{\mathrm{NH}}_4,1}}{dt}=\frac{Q_e}{V_y}{S}_{{\mathrm{NH}}_4,0}+\frac{Q_r}{V_y}{S}_{{\mathrm{NH}}_4,4}-\frac{Q_1}{V_y}{S}_{{\mathrm{NH}}_4,1}+0.01{\mathrm{\ρ}}_1+0.01{\mathrm{\ρ}}_2\\ +0.01{\mathrm{\ρ}}_3-0.022{\mathrm{\ρ}}_4-0.07{\mathrm{\ρ}}_5-0.022{\mathrm{\ρ}}_6-0.07{\mathrm{\ρ}}_7\\ +0.03{\mathrm{\ρ}}_8+0.03{\mathrm{\ρ}}_9-0.07{\mathrm{\ρ}}_{13}-0.07{\mathrm{\ρ}}_{14}+0.031{\mathrm{\ρ}}_{15}\\ -4.24{\mathrm{\ρ}}_{18}+0.031{\mathrm{\ρ}}_{19}\end{array}---\left(5\right)$

$\begin{array}{c}\frac{{\mathrm{dS}}_{{\mathrm{NO}}_3,1}}{dt}=\frac{Q_e}{V_y}{S}_{{\mathrm{NO}}_3,0}+\frac{Q_r}{V_y}{S}_{{\mathrm{NO}}_3,4}-\frac{Q_1}{V_y}{S}_{{\mathrm{NO}}_3,1}-0.21{\mathrm{\ρ}}_6-0.21{\mathrm{\ρ}}_7\\ -0.07{\mathrm{\ρ}}_{12}-0.21{\mathrm{\ρ}}_{14}+4.17{\mathrm{\ρ}}_{18}\end{array}---\left(6\right)$

$\begin{array}{c}\frac{{\mathrm{dS}}_{{\mathrm{PO}}_4,1}}{dt}=\frac{Q_e}{V_y}{S}_{{\mathrm{PO}}_4,0}+\frac{Q_r}{V_y}{S}_{{\mathrm{PO}}_4,4}-\frac{Q_1}{V_y}{S}_{{\mathrm{PO}}_4,1}-0.004{\mathrm{\ρ}}_4-0.02{\mathrm{\ρ}}_5\\ -0.004{\mathrm{\ρ}}_6-0.02{\mathrm{\ρ}}_7+0.01{\mathrm{\ρ}}_8+0.01{\mathrm{\ρ}}_9+0.04{\mathrm{\ρ}}_{10}\\ -{\mathrm{\ρ}}_{11}-{\mathrm{\ρ}}_{12}-0.02{\mathrm{\ρ}}_{13}+0.01{\mathrm{\ρ}}_{15}+{\mathrm{\ρ}}_{16}-0.02{\mathrm{\ρ}}_{19}+0.01{\mathrm{\ρ}}_{19}\end{array}---\left(7\right)$

$\frac{{\mathrm{dX}}_{I,1}}{dt}=\frac{Q_e}{V_y}{X}_{I,0}+\frac{Q_r}{V_y}{X}_{I,4}-\frac{Q_1}{V_y}{X}_{I,1}+0.1{\mathrm{\ρ}}_9+0.1{\mathrm{\ρ}}_{15}+0.1{\mathrm{\ρ}}_{19}---\left(8\right)$

$\begin{array}{c}\frac{{\mathrm{dX}}_{S,1}}{dt}=\frac{Q_e}{V_y}{X}_{S,0}+\frac{Q_r}{V_y}{X}_{S,4}-\frac{Q_1}{V_y}{X}_{S,1}-{\mathrm{\ρ}}_1-{\mathrm{\ρ}}_2-{\mathrm{\ρ}}_3+0.9{\mathrm{\ρ}}_9\\ +0.9{\mathrm{\ρ}}_{15}+0.9{\mathrm{\ρ}}_{19}\end{array}---\left(9\right)$

$\frac{{\mathrm{dX}}_{H,1}}{dt}=\frac{Q_e}{V_y}{X}_{H,0}+\frac{Q_r}{V_y}{X}_{H,4}-\frac{Q_1}{V_y}{X}_{H,1}+{\mathrm{\ρ}}_4+{\mathrm{\ρ}}_5+{\mathrm{\ρ}}_6+{\mathrm{\ρ}}_7-{\mathrm{\ρ}}_9---\left(10\right)$

$\frac{{\mathrm{dX}}_{PAO,1}}{dt}=\frac{Q_e}{V_y}{X}_{PAO,0}+\frac{Q_r}{V_y}{X}_{PAO,4}-\frac{Q_1}{V_y}{X}_{PAO,1}+{\mathrm{\ρ}}_{13}+{\mathrm{\ρ}}_{14}-{\mathrm{\ρ}}_{15}---\left(11\right)$

$\frac{{\mathrm{dX}}_{PP,1}}{dt}=\frac{Q_e}{V_y}{X}_{PP,0}+\frac{Q_r}{V_y}{X}_{PP,4}-\frac{Q_1}{V_y}{X}_{PP,1}-0.4{\mathrm{\ρ}}_{10}+{\mathrm{\ρ}}_{11}+{\mathrm{\ρ}}_{12}-{\mathrm{\ρ}}_{16}---\left(12\right)$

$\frac{{\mathrm{dX}}_{PHA,1}}{dt}=\frac{Q_e}{V_y}{X}_{PHA,0}+\frac{Q_r}{V_y}{X}_{PHA,4}-\frac{Q_1}{V_y}{X}_{PHA,1}+{\mathrm{\ρ}}_{10}-{\mathrm{\ρ}}_{17}---\left(13\right)$

$\frac{{\mathrm{dX}}_{AUT,1}}{dt}=\frac{Q_e}{V_y}{X}_{AUT,0}+\frac{Q_r}{V_y}{X}_{AUT,4}-\frac{Q_1}{V_y}{X}_{AUT,1}-{\mathrm{\ρ}}_{19}---\left(14\right)$

Described anoxic pond process modeling includes the following differential equation:

$\begin{array}{c}\frac{{\mathrm{dS}}_{O_2,2}}{dt}=\frac{Q_1}{V_q}{S}_{O2,1}+\frac{Q_t}{V_q}{S}_{O2,3}-\frac{Q_2}{V_q}{S}_{O2,2}-0.6{\mathrm{\ρ}}_4-0.6{\mathrm{\ρ}}_5-0.2{\mathrm{\ρ}}_{11}\\ -0.6{\mathrm{\ρ}}_{13}-18{\mathrm{\ρ}}_{18}\end{array}---\left(15\right)$

$\begin{array}{c}\frac{{\mathrm{dS}}_{A,2}}{dt}=\frac{Q_1}{V_q}{S}_{A,1}+\frac{Q_t}{V_q}{S}_{A,3}-\frac{Q_2}{V_q}{S}_{A,2}-1.6{\mathrm{\ρ}}_5-1.6{\mathrm{\ρ}}_7+{\mathrm{\ρ}}_8\\ -{\mathrm{\ρ}}_{10}+{\mathrm{\ρ}}_{17}\end{array}---\left(16\right)$

$\begin{array}{c}\frac{{\mathrm{dS}}_{F,2}}{dt}=\frac{Q_1}{V_q}{S}_{F,1}+\frac{Q_t}{V_q}{S}_{F,3}-\frac{Q_2}{V_q}{S}_{F,2}+{\mathrm{\ρ}}_1+{\mathrm{\ρ}}_2+{\mathrm{\ρ}}_3-1.6{\mathrm{\ρ}}_4\\ -1.6{\mathrm{\ρ}}_6-{\mathrm{\ρ}}_8\end{array}---\left(17\right)$

$\begin{array}{c}\frac{{\mathrm{dS}}_{{\mathrm{NH}}_4,2}}{dt}=\frac{Q_1}{V_q}{S}_{{\mathrm{NH}}_4,1}+\frac{Q_t}{V_q}{S}_{{\mathrm{NH}}_4,3}-\frac{Q_2}{V_q}{S}_{{\mathrm{NH}}_4,2}+0.01{\mathrm{\ρ}}_1+0.01{\mathrm{\ρ}}_2\\ +0.01{\mathrm{\ρ}}_3-0.022{\mathrm{\ρ}}_4-0.07{\mathrm{\ρ}}_5-0.022{\mathrm{\ρ}}_6-0.07{\mathrm{\ρ}}_7\\ +0.03{\mathrm{\ρ}}_8+0.03{\mathrm{\ρ}}_9-0.07{\mathrm{\ρ}}_{13}-0.07{\mathrm{\ρ}}_{14}+0.031{\mathrm{\ρ}}_{15}\\ -4.24{\mathrm{\ρ}}_{18}+0.031{\mathrm{\ρ}}_{19}\end{array}---\left(\mathrm{18}\right)$

$\begin{array}{c}\frac{{\mathrm{dS}}_{{\mathrm{NO}}_3,2}}{dt}=\frac{Q_1}{V_q}{S}_{{\mathrm{NO}}_3,1}+\frac{Q_t}{V_q}{S}_{{\mathrm{NO}}_3,3}-\frac{Q_2}{V_q}{S}_{{\mathrm{NO}}_3,2}-0.21{\mathrm{\ρ}}_6-0.21{\mathrm{\ρ}}_7-0.07{\mathrm{\ρ}}_{12}\\ -0.21{\mathrm{\ρ}}_{14}+4.17{\mathrm{\ρ}}_{18}\end{array}---\left(\mathrm{19}\right)$

$\begin{array}{c}\frac{{\mathrm{dS}}_{{\mathrm{PO}}_4,2}}{dt}=\frac{Q_1}{V_q}{S}_{{\mathrm{PO}}_4,1}+\frac{Q_t}{V_q}{S}_{{\mathrm{PO}}_4,3}-\frac{Q_2}{V_q}{S}_{{\mathrm{PO}}_4,2}-0.004{\mathrm{\ρ}}_4-0.02{\mathrm{\ρ}}_5-0.004{\mathrm{\ρ}}_6\\ -0.02{\mathrm{\ρ}}_7+0.01{\mathrm{\ρ}}_8+0.01{\mathrm{\ρ}}_9+0.4{\mathrm{\ρ}}_{10}-{\mathrm{\ρ}}_{11}-{\mathrm{\ρ}}_{12}-0.02{\mathrm{\ρ}}_{13}\\ +0.01{\mathrm{\ρ}}_{15}+{\mathrm{\ρ}}_{16}-0.02{\mathrm{\ρ}}_{18}+0.01{\mathrm{\ρ}}_{19}\end{array}---\left(\mathrm{20}\right)$

$\frac{{\mathrm{dX}}_{I,2}}{dt}=\frac{Q_1}{V_q}{X}_{I,1}+\frac{Q_t}{V_q}{X}_{I,3}-\frac{Q_2}{V_q}{X}_{I,2}+0.1{\mathrm{\ρ}}_9+0.1{\mathrm{\ρ}}_{15}+0.1{\mathrm{\ρ}}_{19}---\left(\mathrm{21}\right)$

$\begin{array}{c}\frac{{\mathrm{dX}}_{S,2}}{dt}=\frac{Q_1}{V_y}{X}_{S,1}+\frac{Q_t}{V_q}{X}_{S,3}-\frac{Q_2}{V_q}{X}_{S,2}-{\mathrm{\ρ}}_1-{\mathrm{\ρ}}_2-{\mathrm{\ρ}}_3+0.9{\mathrm{\ρ}}_9\\ +0.9{\mathrm{\ρ}}_{15}+0.9{\mathrm{\ρ}}_{19}\end{array}---\left(\mathrm{22}\right)$

$\frac{{\mathrm{dX}}_{H,2}}{dt}=\frac{Q_1}{V_q}{X}_{H,1}+\frac{Q_t}{V_q}{X}_{H,3}-\frac{Q_2}{V_q}{X}_{H,2}+{\mathrm{\ρ}}_4+{\mathrm{\ρ}}_5+{\mathrm{\ρ}}_6+{\mathrm{\ρ}}_7-{\mathrm{\ρ}}_9---\left(\mathrm{23}\right)$

$\frac{{\mathrm{dX}}_{PAO,2}}{dt}=\frac{Q_t}{V_q}{X}_{PAO,1}+\frac{Q_t}{V_q}{X}_{PAO,3}-\frac{Q_2}{V_q}{X}_{PAO,2}+{\mathrm{\ρ}}_{13}+{\mathrm{\ρ}}_{14}-{\mathrm{\ρ}}_{15}---\left(24\right)$

$\frac{{\mathrm{dX}}_{PP,2}}{dt}=\frac{Q_t}{V_q}{X}_{PP,1}+\frac{Q_t}{V_q}{X}_{PP,3}-\frac{Q_2}{V_q}{X}_{PP,2}-0.4{\mathrm{\ρ}}_{10}+{\mathrm{\ρ}}_{11}+{\mathrm{\ρ}}_{12}-{\mathrm{\ρ}}_{16}---\left(25\right)$

$\frac{{\mathrm{dX}}_{PHA,2}}{dt}=\frac{Q_t}{V_q}{X}_{PHA,1}+\frac{Q_t}{V_q}{X}_{PHA,3}-\frac{Q_2}{V_q}{X}_{PHA,2}+{\mathrm{\ρ}}_{10}-{\mathrm{\ρ}}_{17}---\left(26\right)$

$\frac{{\mathrm{dX}}_{AUT,2}}{dt}=\frac{Q_1}{V_q}{X}_{AUT,1}+\frac{Q_t}{V_q}{X}_{AUT,3}-\frac{Q_2}{V_q}{X}_{AUT,2}-{\mathrm{\ρ}}_{19}---\left(27\right)$

Described Aerobic Pond process modeling includes the following differential equation:

$\begin{array}{c}\frac{{\mathrm{dS}}_{O_2,3}}{dt}=\frac{Q_2}{V_h}{S}_{O2,2}-\frac{Q_t+Q_3+Q_r+Q_w}{V_h}{S}_{O2,3}-0.6{\mathrm{\ρ}}_4-0.6{\mathrm{\ρ}}_5\\ -0.2{\mathrm{\ρ}}_{11}-0.6{\mathrm{\ρ}}_{13}-18{\mathrm{\ρ}}_{18}++KLa(8.56-S_{O2,3})\end{array}---\left(28\right)$

$\begin{array}{c}\frac{{\mathrm{dS}}_{A,3}}{dt}=\frac{Q_2}{V_h}{S}_{A,2}-\frac{Q_t+Q_3+Q_r+Q_w}{V_h}{S}_{A,3}-1.6{\mathrm{\ρ}}_5-1.6{\mathrm{\ρ}}_7\\ +{\mathrm{\ρ}}_8-{\mathrm{\ρ}}_{10}+{\mathrm{\ρ}}_{17}\end{array}---\left(29\right)$

$\begin{array}{c}\frac{{\mathrm{dS}}_{F,3}}{dt}=\frac{Q_2}{V_h}{S}_{F,2}-\frac{Q_t+Q_3+Q_r+Q_w}{V_h}{S}_{F,3}+{\mathrm{\ρ}}_1+{\mathrm{\ρ}}_2+{\mathrm{\ρ}}_3\\ -1.6{\mathrm{\ρ}}_4-1.6{\mathrm{\ρ}}_6-{\mathrm{\ρ}}_8\end{array}---\left(30\right)$

$\begin{array}{c}\frac{{\mathrm{dS}}_{{\mathrm{NH}}_4,3}}{dt}=\frac{Q_2}{V_h}{S}_{{\mathrm{NH}}_4,2}-\frac{Q_t+Q_3+Q_r+Q_w}{V_h}{S}_{{\mathrm{NH}}_4,3}+0.01{\mathrm{\ρ}}_1+0.01{\mathrm{\ρ}}_2\\ +0.01{\mathrm{\ρ}}_3-0.022{\mathrm{\ρ}}_4-0.07\mathrm{\ρ}5-0.022\mathrm{\ρ}6-0.07{\mathrm{\ρ}}_7\\ +0.03{\mathrm{\ρ}}_8+0.03{\mathrm{\ρ}}_9-0.07{\mathrm{\ρ}}_{13}-0.07{\mathrm{\ρ}}_{14}+0.031{\mathrm{\ρ}}_{15}\\ -4.24{\mathrm{\ρ}}_{18}+0.031{\mathrm{\ρ}}_{19}\end{array}---\left(31\right)$

$\begin{array}{c}\frac{{\mathrm{dS}}_{{\mathrm{NO}}_3,3}}{dt}=\frac{Q_2}{V_h}{S}_{{\mathrm{NO}}_3,2}-\frac{Q_t+Q_3+Q_r+Q_w}{V_h}{S}_{{\mathrm{NO}}_3,3}-0.21{\mathrm{\ρ}}_6-0.21{\mathrm{\ρ}}_7\\ -0.07{\mathrm{\ρ}}_{12}-0.21{\mathrm{\ρ}}_{14}+4.17{\mathrm{\ρ}}_{18}\end{array}---\left(\mathrm{32}\right)$

$\begin{array}{c}\frac{{\mathrm{dS}}_{{\mathrm{PO}}_4,3}}{dt}=\frac{Q_2}{V_h}{S}_{{\mathrm{PO}}_4,2}-\frac{Q_t+Q_3+Q_r+Q_w}{V_h}{S}_{{\mathrm{PO}}_4,3}-0.04{\mathrm{\ρ}}_4-0.02{\mathrm{\ρ}}_5\\ -0.004{\mathrm{\ρ}}_6-0.02{\mathrm{\ρ}}_7++0.01{\mathrm{\ρ}}_8+0.01{\mathrm{\ρ}}_9+0.04{\mathrm{\ρ}}_{10}-{\mathrm{\ρ}}_{11}\\ -{\mathrm{\ρ}}_{12}-0.02{\mathrm{\ρ}}_{13}+0.01{\mathrm{\ρ}}_{15}++{\mathrm{\ρ}}_{16}-0.02{\mathrm{\ρ}}_{18}+0.01{\mathrm{\ρ}}_{19}\end{array}---\left(\mathrm{33}\right)$

$\begin{array}{c}\frac{{\mathrm{dX}}_{I,3}}{dt}=\frac{Q_2}{V_h}{X}_{I,2}-\frac{Q_t+Q_3+Q_r+Q_w}{V_h}{X}_{I,3}+0.1{\mathrm{\ρ}}_9\\ +0.1{\mathrm{\ρ}}_{15}+0.1{\mathrm{\ρ}}_{19}\end{array}---\left(34\right)$

$\begin{array}{c}\frac{{\mathrm{dX}}_{S,3}}{dt}=\frac{Q_2}{V_h}{X}_{S,2}-\frac{Q_t+Q_3+Q_r+Q_w}{V_h}{X}_{S,3}-{\mathrm{\ρ}}_1-{\mathrm{\ρ}}_2-{\mathrm{\ρ}}_3\\ +0.9{\mathrm{\ρ}}_9+0.9{\mathrm{\ρ}}_{15}+0.9{\mathrm{\ρ}}_{19}\end{array}---\left(35\right)$

$\begin{array}{c}\frac{{\mathrm{dX}}_{H,3}}{dt}=\frac{Q_2}{V_h}{X}_{H,2}-\frac{Q_t+Q_3+Q_r+Q_w}{V_h}{X}_{H,3}+{\mathrm{\ρ}}_4+{\mathrm{\ρ}}_5\\ +{\mathrm{\ρ}}_6+{\mathrm{\ρ}}_7-{\mathrm{\ρ}}_9\end{array}---\left(\mathrm{36}\right)$

$\begin{array}{c}\frac{{\mathrm{dX}}_{PAO,3}}{dt}=\frac{Q_2}{V_h}{X}_{PAO,2}-\frac{Q_t+Q_3+Q_r+Q_w}{V_h}{X}_{PAO,3}+{\mathrm{\ρ}}_{13}\\ +{\mathrm{\ρ}}_{14}-{\mathrm{\ρ}}_{15}\end{array}---\left(37\right)$

$\begin{array}{c}\frac{{\mathrm{dX}}_{PP,3}}{dt}=\frac{Q_2}{V_h}{X}_{PP,2}-\frac{Q_t+Q_3+Q_r+Q_w}{V_h}{X}_{PP,3}-0.4{\mathrm{\ρ}}_{10}+{\mathrm{\ρ}}_{11}\\ +{\mathrm{\ρ}}_{12}-{\mathrm{\ρ}}_{16}\end{array}---\left(38\right)$

$\frac{{\mathrm{dX}}_{PHA,3}}{dt}=\frac{Q_2}{V_h}{X}_{PHA,2}-\frac{Q_t+Q_3+Q_r+Q_w}{V_h}{X}_{PHA,3}+{\mathrm{\ρ}}_{10}-{\mathrm{\ρ}}_{17}---\left(39\right)$

$\frac{{\mathrm{dX}}_{AUT,3}}{dt}=\frac{Q_2}{V_h}{X}_{AUT,2}-\frac{Q_t+Q_3+Q_r+Q_w}{V_h}{X}_{AUT,3}-{\mathrm{\ρ}}_{19}---\left(40\right)$

In formula 2-40, Q_eFor biological tank flow of inlet water, Q_rFor sludge reflux amount, Q₁For anaerobic pond water yield, Q₂For anoxic pond water yield, Q₃For Aerobic Pond water yield, Q_tMixed-liquor return amount, Q_wFor sludge volume, wherein Q₁=Q_e+Q_r,Q₂=Q₁+Q_t,Q₃=Q₂-Q_t-Q_r-Q_w,V_yFor the volume of anaerobic pond, V_qFor the volume of anoxic pond, V_hFor the volume of Aerobic Pond, t is the response time, and KLa is oxygen mass transfer coefficients, S_O2、S_F、S_A、S_NH4、S_NO3And S_PO4It is dissolubility component, S_O2、S_F、S_A、S_NH4、S_NO3And S_PO4Represent dissolved oxygen, fermentable easily biological-degradable Organic substance, tunning, ammonia nitrogen, nitrate nitrogen and nitrite nitrogen, dissolved inorganic phosphorus, ρ respectively₁, ρ₂..., ρ₁₉For the process rate in ASM2D model, X_I、X_s、X_H、X_PAO、X_PP、X_PHAAnd X_AUTIt is graininess component, X_I、X_s、X_H、X_PAO、X_PP、X_PHAAnd X_AUTRepresent inert particle Organic substance, at a slow speed degradable substrate, heterotrophic microorganism, polyP bacteria PAO, Quadrafos, the intracellular stock of polyP bacteria PAO, nitrifier, S in formula 2-40 respectively_O2、S_F、S_A、S_NH4、S_NO3、S_PO4、S_O2、S_F、S_A、S_NH4、S_NO3And S_PO4Subscript in 0 expression biological tank after comma, subscript 1,2,3,4 represents anaerobic pond, anoxic pond, Aerobic Pond and second pond after comma respectively.

Above formula model, i.e. formula 2-40 is all differential equation of first order, it is respectively anaerobic pond, anoxic pond and the formula model of Aerobic Pond, owing to utilizing material balance formula to draw, also referred to as anaerobic pond, the material balance equation formula of anoxic pond and Aerobic Pond, by anaerobic pond, anoxic pond, the formula model simultaneous of Aerobic Pond can draw A2O biological tank process modeling, this A2O biological tank process modeling can be realized by Matlab simulation software, in order to analog simulation A2O biological tank technique, thus the control strategy research for the later stage provides the foundation, avoid to buy simultaneously and external be similar to the ASMs high expense of series analog software, save the cost of A2O biological tank PROCESS FOR TREATMENT sewage.

It addition, the A2O biological tank process modeling provided in this embodiment, including well model restrictive condition, this model restrictive condition includes: this A2O biological tank process modeling is only applicable to biological wastewater treatment systems simulation；The temperature that described A2O biological tank process modeling is suitable for is 10～25 DEG C.This restrictive condition can specify the range of this A2O biological tank process modeling, this A2O biological tank process modeling analog simulation A2O biological tank technique of more good utilisation, thus the control strategy research for the later stage provides control basis.

Fig. 2 and Fig. 3 all illustrate one embodiment of the invention provide based on A2O biological tank Technology Modeling method simulated effect figure, Fig. 3 is according to certain Sewage Plant practical situation, take biological tank water inlet intraday effect Qe (m³/ d), biological tank water inlet concentration of component (COD, ammonia nitrogen, nitrate, phosphate), sludge reflux amount Q_rTaking definite value is 23010 (m³/ d), mixed-liquor return amount Q_tTaking definite value is 93150 (m³/ d), sludge volume Q_wTaking definite value is 750 (m³/ d), anaerobic pond volume be V_y=3110 (m³), anoxic pond volume be V_q=7358 (m³), Aerobic Pond volume be V_h=14500 (m³).The process rate ρ of ASM2D model₁, ρ₂..., ρ₁₉The coefficient table promulgated with reference to international water association.Formula model 2 to 40 is programmed in the S function text of matlab, and call under simulink environment, fixed step size ode4 solver is chosen in emulation, step-length is set to 0.0001, simulation time is set to 61 days, input Sewage Plant actual water inlet data, finally obtains simulation result, for simulation result, originally it is preferably carried out middle A2O biological tank process modeling and uses water outlet total nitrogen TN and/or water outlet COD COD to characterize.From figure 2 it can be seen that the analogue value of water outlet TN variation tendency basic with its actual value is identical, and deviation is less；As can be seen from Figure 3, the analogue value of water outlet COD and its actual value other values in addition to indivedual points are pressed close to substantially, above simulation result illustrates this A2O biological tank process modeling, and A2O technique can be preferably simulated by the analog systems set up based on Matlab software.

The foregoing is only presently preferred embodiments of the present invention, not in order to limit the present invention, all any amendment, equivalent and improvement etc. made within the spirit and principles in the present invention, should be included within the scope of the present invention.

Claims (8)

1. an A2O biological tank Technology Modeling method, the method realizes based on simulation software, its It is characterised by, said method comprising the steps of:

Set input parameter and output parameter；

Build according to described input parameter, output parameter, ASM2D model and material balance equation Vertical A2O biological tank process modeling, described A2O biological tank process modeling includes anaerobic pond technique mould Type, anoxic pond process modeling and Aerobic Pond process modeling；

Wherein, described material balance equation is:

$\frac{{\mathrm{\ΔZ}}_i}{\mathrm{\Δ}t}=\frac{1}{V}(Q_{in}{Z}_{in}+r_{i}V-Q_{out}{Z}_{out})---\left(1\right)$

Wherein, △ Z_iFor concentration of component variable quantity, △ t is time variation amount, and V is the body in this pond Long-pending, Q_inFor entering the water yield, Z_inFor entering concentration of component, r_iFor affecting concentration of component change Process rate, Q_outFor flowing out the water yield, Z_outFor flowing out concentration of component；

Described anaerobic pond process modeling includes the following differential equation:

$\begin{array}{c}\frac{{\mathrm{dS}}_{O2,1}}{dt}=\frac{Q_e}{V_y}{S}_{O2,0}+\frac{Q_r}{V_y}{S}_{O2,4}-\frac{Q_1}{V_y}{S}_{O2,1}-0.6{\mathrm{\ρ}}_4-0.6{\mathrm{\ρ}}_5-0.2{\mathrm{\ρ}}_{11}\\ -0.6{\mathrm{\ρ}}_{13}-18{\mathrm{\ρ}}_{18}\end{array}---\left(2\right)$

$\begin{array}{c}\frac{{\mathrm{dS}}_{A,1}}{dt}=\frac{Q_e}{V_y}{S}_{A,0}+\frac{Q_r}{V_y}{S}_{A,4}-\frac{Q_1}{V_y}{S}_{A,1}-1.6{\mathrm{\ρ}}_5-1.6{\mathrm{\ρ}}_7+{\mathrm{\ρ}}_8\\ -{\mathrm{\ρ}}_{10}+{\mathrm{\ρ}}_{17}\end{array}---\left(3\right)$

$\begin{array}{c}\frac{{\mathrm{dS}}_{F,1}}{dt}=\frac{Q_e}{V_y}{S}_{F,0}+\frac{Q_r}{V_y}{S}_{F,4}-\frac{Q_1}{V_y}{S}_{F,1}+{\mathrm{\ρ}}_1+{\mathrm{\ρ}}_2+{\mathrm{\ρ}}_3-1.6{\mathrm{\ρ}}_4\\ -1.6{\mathrm{\ρ}}_6-{\mathrm{\ρ}}_8\end{array}---\left(4\right)$

$\begin{array}{c}\frac{{\mathrm{dS}}_{{\mathrm{NH}}_4,1}}{dt}=\frac{Q_e}{V_y}{S}_{{\mathrm{NH}}_4,0}+\frac{Q_r}{V_y}{S}_{{\mathrm{NH}}_4,4}-\frac{Q_1}{V_y}{S}_{{\mathrm{NH}}_4,1}+0.01{\mathrm{\ρ}}_1+0.01{\mathrm{\ρ}}_2\\ +0.01{\mathrm{\ρ}}_3-0.022{\mathrm{\ρ}}_4-0.07{\mathrm{\ρ}}_5-0.022{\mathrm{\ρ}}_6-0.07{\mathrm{\ρ}}_7\\ +0.03{\mathrm{\ρ}}_8+0.03{\mathrm{\ρ}}_9-0.07{\mathrm{\ρ}}_{13}-0.07{\mathrm{\ρ}}_{14}+0.031{\mathrm{\ρ}}_{15}\\ -4.24{\mathrm{\ρ}}_{18}+0.031{\mathrm{\ρ}}_{19}\end{array}---\left(5\right)$

$\begin{array}{c}\frac{{\mathrm{dS}}_{{\mathrm{NO}}_3,1}}{dt}=\frac{Q_e}{V_y}{S}_{{\mathrm{NO}}_3,0}+\frac{Q_r}{V_y}{S}_{{\mathrm{NO}}_3,4}-\frac{Q_1}{V_y}{S}_{{\mathrm{NO}}_3,1}-0.21{\mathrm{\ρ}}_6-0.21{\mathrm{\ρ}}_7\\ -0.07{\mathrm{\ρ}}_{12}-0.21{\mathrm{\ρ}}_{14}+4.17{\mathrm{\ρ}}_{18}\end{array}---\left(6\right)$

$\begin{array}{c}\frac{{\mathrm{dS}}_{{\mathrm{PO}}_4,1}}{dt}=\frac{Q_e}{V_y}{S}_{{\mathrm{PO}}_4,0}+\frac{Q_r}{V_y}{S}_{{\mathrm{PO}}_4,4}-\frac{Q_1}{V_y}{S}_{{\mathrm{PO}}_4,1}-0.004{\mathrm{\ρ}}_4-0.02{\mathrm{\ρ}}_5\\ -0.004{\mathrm{\ρ}}_6-0.02{\mathrm{\ρ}}_7+0.01{\mathrm{\ρ}}_8+0.01{\mathrm{\ρ}}_9+0.04{\mathrm{\ρ}}_{10}\\ -{\mathrm{\ρ}}_{11}-{\mathrm{\ρ}}_{12}-0.02{\mathrm{\ρ}}_{13}+0.01{\mathrm{\ρ}}_{15}+{\mathrm{\ρ}}_{16}-0.02{\mathrm{\ρ}}_{19}+0.01{\mathrm{\ρ}}_{19}\end{array}---\left(7\right)$

$\frac{{\mathrm{dX}}_{I,1}}{dt}=\frac{Q_e}{V_y}{X}_{I,0}+\frac{Q_r}{V_y}{X}_{I,4}-\frac{Q_1}{V_y}{X}_{I,1}+0.1{\mathrm{\ρ}}_9+0.1{\mathrm{\ρ}}_{15}+0.1{\mathrm{\ρ}}_{19}---\left(8\right)$

$\begin{array}{c}\frac{{\mathrm{dX}}_{S,1}}{dt}=\frac{Q_e}{V_y}{X}_{S,0}+\frac{Q_r}{V_y}{X}_{S,4}-\frac{Q_1}{V_y}{X}_{S,1}-{\mathrm{\ρ}}_1-{\mathrm{\ρ}}_2-{\mathrm{\ρ}}_3+0.9{\mathrm{\ρ}}_9\\ +0.9{\mathrm{\ρ}}_{15}+0.9{\mathrm{\ρ}}_{19}\end{array}---\left(9\right)$

$\frac{{\mathrm{dX}}_{H,1}}{dt}=\frac{Q_e}{V_y}{X}_{H,0}+\frac{Q_r}{V_y}{X}_{H,4}-\frac{Q_1}{V_y}{X}_{H,1}+{\mathrm{\ρ}}_4+{\mathrm{\ρ}}_5+{\mathrm{\ρ}}_6+{\mathrm{\ρ}}_7-{\mathrm{\ρ}}_9---\left(10\right)$

$\frac{{\mathrm{dX}}_{PAO,1}}{dt}=\frac{Q_e}{V_y}{X}_{PAO,0}+\frac{Q_r}{V_y}{X}_{PAO,4}-\frac{Q_1}{V_y}{X}_{PAO,1}+{\mathrm{\ρ}}_{13}+{\mathrm{\ρ}}_{14}-{\mathrm{\ρ}}_{15}---\left(11\right)$

$\frac{{\mathrm{dX}}_{PP,1}}{dt}=\frac{Q_e}{V_y}{X}_{PP,0}+\frac{Q_r}{V_y}{X}_{PP,4}-\frac{Q_1}{V_y}{X}_{PP,1}-0.4{\mathrm{\ρ}}_{10}+{\mathrm{\ρ}}_{11}+{\mathrm{\ρ}}_{12}-{\mathrm{\ρ}}_{16}---\left(12\right)$

$\frac{{\mathrm{dX}}_{PHA,1}}{dt}=\frac{Q_e}{V_y}{X}_{PHA,0}+\frac{Q_r}{V_y}{X}_{PHA,4}-\frac{Q_1}{V_y}{X}_{PHA,1}+{\mathrm{\ρ}}_{10}-{\mathrm{\ρ}}_{17}---\left(13\right)$

$\frac{{\mathrm{dX}}_{AUT,1}}{dt}=\frac{Q_e}{V_y}{X}_{AUT,0}+\frac{Q_r}{V_y}{X}_{AUT,4}-\frac{Q_1}{V_y}{X}_{AUT,1}-{\mathrm{\ρ}}_{19}---\left(14\right)$

Described anoxic pond process modeling includes the following differential equation:

$\begin{array}{c}\frac{{\mathrm{dS}}_{O_2,2}}{dt}=\frac{Q_1}{V_q}{S}_{O2,1}+\frac{Q_t}{V_q}{S}_{O2,3}-\frac{Q_2}{V_q}{S}_{O2,2}-0.6{\mathrm{\ρ}}_4-0.6{\mathrm{\ρ}}_5-0.2{\mathrm{\ρ}}_{11}\\ -0.6{\mathrm{\ρ}}_{13}-18{\mathrm{\ρ}}_{18}\end{array}---\left(15\right)$

$\begin{array}{c}\frac{{\mathrm{dS}}_{A,2}}{dt}=\frac{Q_1}{V_q}{S}_{A,1}+\frac{Q_t}{V_q}{S}_{A,3}-\frac{Q_2}{V_q}{S}_{A,2}-1.6{\mathrm{\ρ}}_5-1.6{\mathrm{\ρ}}_7+{\mathrm{\ρ}}_8\\ -{\mathrm{\ρ}}_{10}+{\mathrm{\ρ}}_{17}\end{array}---\left(16\right)$

$\begin{array}{c}\frac{{\mathrm{dS}}_{F,2}}{dt}=\frac{Q_1}{V_q}{S}_{F,1}+\frac{Q_t}{V_q}{S}_{F,3}-\frac{Q_2}{V_q}{S}_{F,2}+{\mathrm{\ρ}}_1+{\mathrm{\ρ}}_2+{\mathrm{\ρ}}_3-1.6{\mathrm{\ρ}}_4\\ -1.6{\mathrm{\ρ}}_6-{\mathrm{\ρ}}_8\end{array}---\left(17\right)$

$\begin{array}{c}\frac{{\mathrm{dS}}_{{\mathrm{NH}}_4,2}}{dt}=\frac{Q_1}{V_q}{S}_{{\mathrm{NH}}_4,1}+\frac{Q_t}{V_q}{S}_{{\mathrm{NH}}_4,3}-\frac{Q_2}{V_q}{S}_{{\mathrm{NH}}_4,2}+0.01{\mathrm{\ρ}}_1+0.01{\mathrm{\ρ}}_2\\ +0.01{\mathrm{\ρ}}_3-0.022{\mathrm{\ρ}}_4-0.07{\mathrm{\ρ}}_5-0.022{\mathrm{\ρ}}_6-0.07{\mathrm{\ρ}}_7\\ +0.03{\mathrm{\ρ}}_8+0.03{\mathrm{\ρ}}_9-0.07{\mathrm{\ρ}}_{13}-0.07{\mathrm{\ρ}}_{14}+0.031{\mathrm{\ρ}}_{15}\\ -4.24{\mathrm{\ρ}}_{18}+0.031{\mathrm{\ρ}}_{19}\end{array}---\left(18\right)$

$\begin{array}{c}\frac{{\mathrm{dS}}_{{\mathrm{NO}}_3,2}}{dt}=\frac{Q_1}{V_q}{S}_{{\mathrm{NO}}_3,1}+\frac{Q_t}{V_q}{S}_{{\mathrm{NO}}_3,3}-\frac{Q_2}{V_q}{S}_{{\mathrm{NO}}_3,2}-0.21{\mathrm{\ρ}}_6-0.21{\mathrm{\ρ}}_7-0.07{\mathrm{\ρ}}_{12}\\ -0.21{\mathrm{\ρ}}_{14}+4.17{\mathrm{\ρ}}_{18}\end{array}---\left(19\right)$

$\begin{array}{c}\frac{{\mathrm{dS}}_{{\mathrm{PO}}_4,2}}{dt}=\frac{Q_1}{V_q}{S}_{{\mathrm{PO}}_4,1}+\frac{Q_t}{V_q}{S}_{{\mathrm{PO}}_4,3}-\frac{Q_2}{V_q}{S}_{{\mathrm{PO}}_4,2}-0.004{\mathrm{\ρ}}_4-0.02{\mathrm{\ρ}}_5-0.004{\mathrm{\ρ}}_6\\ -0.02{\mathrm{\ρ}}_7+0.01{\mathrm{\ρ}}_8+0.01{\mathrm{\ρ}}_9+0.4{\mathrm{\ρ}}_{10}-{\mathrm{\ρ}}_{11}-{\mathrm{\ρ}}_{12}-0.02{\mathrm{\ρ}}_{13}\\ +0.01{\mathrm{\ρ}}_{15}+{\mathrm{\ρ}}_{16}-0.02{\mathrm{\ρ}}_{18}+0.01{\mathrm{\ρ}}_{19}\end{array}---\left(20\right)$

$\frac{{\mathrm{dX}}_{I,2}}{dt}=\frac{Q_1}{V_q}{X}_{I,1}+\frac{Q_t}{V_q}{X}_{I,3}-\frac{Q_2}{V_q}{X}_{I,2}+0.1{\mathrm{\ρ}}_9+0.1{\mathrm{\ρ}}_{15}+0.1{\mathrm{\ρ}}_{19}---\left(21\right)$

$\begin{array}{c}\frac{{\mathrm{dX}}_{S,2}}{dt}=\frac{Q_1}{V_y}{X}_{S,1}+\frac{Q_t}{V_q}{X}_{S,3}-\frac{Q_2}{V_q}{X}_{S,2}-{\mathrm{\ρ}}_1-{\mathrm{\ρ}}_2-{\mathrm{\ρ}}_3+0.9{\mathrm{\ρ}}_9\\ +0.9{\mathrm{\ρ}}_{15}+0.9{\mathrm{\ρ}}_{19}\end{array}---\left(22\right)$

$\frac{{\mathrm{dX}}_{H,2}}{dt}=\frac{Q_1}{V_q}{X}_{H,1}+\frac{Q_t}{V_q}{X}_{H,3}-\frac{Q_2}{V_q}{X}_{H,2}+{\mathrm{\ρ}}_4+{\mathrm{\ρ}}_5+{\mathrm{\ρ}}_6+{\mathrm{\ρ}}_7-{\mathrm{\ρ}}_9---\left(23\right)$

$\frac{{\mathrm{dX}}_{PAO,2}}{dt}=\frac{Q_t}{V_q}{X}_{PAO,1}+\frac{Q_t}{V_q}{X}_{PAO,3}-\frac{Q_2}{V_q}{X}_{PAO,2}+{\mathrm{\ρ}}_{13}+{\mathrm{\ρ}}_{14}-{\mathrm{\ρ}}_{15}---\left(24\right)$

$\frac{{\mathrm{dX}}_{PP,2}}{dt}=\frac{Q_t}{V_q}{X}_{PP,1}+\frac{Q_t}{V_q}{X}_{PP,3}-\frac{Q_2}{V_q}{X}_{PP,2}-0.4{\mathrm{\ρ}}_{10}+{\mathrm{\ρ}}_{11}+{\mathrm{\ρ}}_{12}-{\mathrm{\ρ}}_{16}---\left(25\right)$

$\frac{{\mathrm{dX}}_{PHA,2}}{dt}=\frac{Q_t}{V_q}{X}_{PHA,1}+\frac{Q_t}{V_q}{X}_{PHA,3}-\frac{Q_2}{V_q}{X}_{PHA,2}+{\mathrm{\ρ}}_{10}-{\mathrm{\ρ}}_{17}---\left(26\right)$

$\frac{{\mathrm{dX}}_{AUT,2}}{dt}=\frac{Q_1}{V_q}{X}_{AUT,1}+\frac{Q_t}{V_q}{X}_{AUT,3}-\frac{Q_2}{V_q}{X}_{AUT,2}-{\mathrm{\ρ}}_{19}---\left(27\right)$

Described Aerobic Pond process modeling includes the following differential equation:

$\begin{array}{c}\frac{{\mathrm{dS}}_{O_2,3}}{dt}=\frac{Q_2}{V_h}{S}_{O2,2}-\frac{Q_t+Q_3+Q_r+Q_w}{V_h}{S}_{O2,3}-0.6{\mathrm{\ρ}}_4-0.6{\mathrm{\ρ}}_5\\ -0.2{\mathrm{\ρ}}_{11}-0.6{\mathrm{\ρ}}_{13}-18{\mathrm{\ρ}}_{18}+KLa\left(8.56-S_{O2,3}\right)\end{array}---\left(28\right)$

$\begin{array}{c}\frac{{\mathrm{dS}}_{A,3}}{dt}=\frac{Q_2}{V_h}{S}_{A,2}-\frac{Q_t+Q_3+Q_r+Q_w}{V_h}{S}_{A,3}-1.6{\mathrm{\ρ}}_5-1.6{\mathrm{\ρ}}_7\\ +{\mathrm{\ρ}}_8-{\mathrm{\ρ}}_{10}+{\mathrm{\ρ}}_{17}\end{array}---\left(29\right)$

$\begin{array}{c}\frac{{\mathrm{dS}}_{F,3}}{dt}=\frac{Q_2}{V_h}{S}_{F,2}-\frac{Q_t+Q_3+Q_r+Q_w}{V_h}{S}_{F,3}+{\mathrm{\ρ}}_1+{\mathrm{\ρ}}_2+{\mathrm{\ρ}}_3\\ -1.6{\mathrm{\ρ}}_4-1.6{\mathrm{\ρ}}_6-{\mathrm{\ρ}}_8\end{array}---\left(30\right)$

$\begin{array}{c}\frac{{\mathrm{dS}}_{{\mathrm{NH}}_4,3}}{dt}=\frac{Q_2}{V_h}{S}_{{\mathrm{NH}}_4,2}-\frac{Q_t+Q_3+Q_r+Q_w}{V_h}{S}_{{\mathrm{NH}}_4,3}+0.01{\mathrm{\ρ}}_1+0.01{\mathrm{\ρ}}_2\\ +0.01{\mathrm{\ρ}}_3-0.022{\mathrm{\ρ}}_4-0.07\mathrm{\ρ}5-0.022\mathrm{\ρ}6-0.07{\mathrm{\ρ}}_7\\ +0.03{\mathrm{\ρ}}_8+0.03{\mathrm{\ρ}}_9-0.07{\mathrm{\ρ}}_{13}-0.07{\mathrm{\ρ}}_{14}+0.031{\mathrm{\ρ}}_{15}\\ -4.24{\mathrm{\ρ}}_{18}+0.031{\mathrm{\ρ}}_{19}\end{array}---\left(31\right)$

$\begin{array}{c}\frac{{\mathrm{dS}}_{{\mathrm{NO}}_3,3}}{dt}=\frac{Q_2}{V_h}{S}_{{\mathrm{NO}}_3,2}-\frac{Q_t+Q_3+Q_r+Q_w}{V_h}{S}_{{\mathrm{NO}}_3,3}-0.21{\mathrm{\ρ}}_6-0.21{\mathrm{\ρ}}_7\\ -0.07{\mathrm{\ρ}}_{12}-0.21{\mathrm{\ρ}}_{14}+4.17{\mathrm{\ρ}}_{18}\end{array}---\left(32\right)$

$\begin{array}{c}\frac{{\mathrm{dS}}_{{\mathrm{PO}}_4,3}}{dt}=\frac{Q_2}{V_h}{S}_{{\mathrm{PO}}_4,2}-\frac{Q_t+Q_3+Q_r+Q_w}{V_h}{S}_{{\mathrm{PO}}_4,3}-0.004{\mathrm{\ρ}}_4-0.02{\mathrm{\ρ}}_5\\ -0.004{\mathrm{\ρ}}_6-0.02{\mathrm{\ρ}}_7++0.01{\mathrm{\ρ}}_8+0.01{\mathrm{\ρ}}_9+0.4{\mathrm{\ρ}}_{10}-{\mathrm{\ρ}}_{11}\\ -{\mathrm{\ρ}}_{12}-0.02{\mathrm{\ρ}}_{13}+0.01{\mathrm{\ρ}}_{15}++{\mathrm{\ρ}}_{16}-0.02{\mathrm{\ρ}}_{18}+0.01{\mathrm{\ρ}}_{19}\end{array}---\left(33\right)$

$\begin{array}{c}\frac{{\mathrm{dX}}_{I,3}}{dt}=\frac{Q_2}{V_h}{X}_{I,2}-\frac{Q_t+Q_3+Q_r+Q_w}{V_h}{X}_{I,3}+0.1{\mathrm{\ρ}}_9\\ +0.1{\mathrm{\ρ}}_{15}+0.1{\mathrm{\ρ}}_{19}\end{array}---\left(34\right)$

$\begin{array}{c}\frac{{\mathrm{dX}}_{S,3}}{dt}=\frac{Q_2}{V_h}{X}_{S,2}-\frac{Q_t+Q_3+Q_r+Q_w}{V_h}{X}_{S,3}-{\mathrm{\ρ}}_1-{\mathrm{\ρ}}_2-{\mathrm{\ρ}}_3\\ +0.9{\mathrm{\ρ}}_9+0.9{\mathrm{\ρ}}_{15}+0.9{\mathrm{\ρ}}_{19}\end{array}---\left(35\right)$

$\begin{array}{c}\frac{{\mathrm{dX}}_{H,3}}{dt}=\frac{Q_2}{V_h}{X}_{H,2}-\frac{Q_t+Q_3+Q_r+Q_w}{V_h}{X}_{H,3}+{\mathrm{\ρ}}_4+{\mathrm{\ρ}}_5\\ +{\mathrm{\ρ}}_6+{\mathrm{\ρ}}_7-{\mathrm{\ρ}}_9\end{array}---\left(36\right)$

$\begin{array}{c}\frac{{\mathrm{dX}}_{PAO,3}}{dt}=\frac{Q_2}{V_h}{X}_{PAO,2}-\frac{Q_t+Q_3+Q_r+Q_w}{V_h}{X}_{PAO,3}+{\mathrm{\ρ}}_{13}\\ +{\mathrm{\ρ}}_{14}-{\mathrm{\ρ}}_{15}\end{array}---\left(37\right)$

$\begin{array}{c}\frac{{\mathrm{dX}}_{PP,3}}{dt}=\frac{Q_2}{V_h}{X}_{PP,2}-\frac{Q_t+Q_3+Q_r+Q_w}{V_h}{X}_{PP,3}-0.4{\mathrm{\ρ}}_{10}+{\mathrm{\ρ}}_{11}\\ +{\mathrm{\ρ}}_{12}-{\mathrm{\ρ}}_{16}\end{array}---\left(38\right)$

$\frac{{\mathrm{dX}}_{PHA,3}}{dt}=\frac{Q_2}{V_h}{X}_{PHA,2}-\frac{Q_t+Q_3+Q_r+Q_w}{V_h}{X}_{PHA,3}+{\mathrm{\ρ}}_{10}-{\mathrm{\ρ}}_{17}---\left(39\right)$

$\frac{{\mathrm{dX}}_{AUT,3}}{dt}=\frac{Q_2}{V_h}{X}_{AUT,2}-\frac{Q_t+Q_3+Q_r+Q_w}{V_h}{X}_{AUT,3}-{\mathrm{\ρ}}_{19}---\left(40\right)$

In formula 2-40, Q_eFor biological tank flow of inlet water, Q_rFor sludge reflux amount, Q₁For anaerobism Pond water yield, Q₂For anoxic pond water yield, Q₃For Aerobic Pond water yield, Q_tMixed-liquor return amount, Q_wFor sludge volume, wherein Q₁=Q_e+Q_r,Q₂=Q₁+Q_t,Q₃=Q₂-Q_t-Q_r-Q_w,V_y For the volume of anaerobic pond, V_qFor the volume of anoxic pond, V_hFor the volume of Aerobic Pond, t is reaction Time, KLa is oxygen mass transfer coefficients, S_O2、S_F、S_A、S_NH4、S_NO3And S_PO4It is dissolubility Component, S_O2、S_F、S_A、S_NH4、S_NO3And S_PO4Represent dissolved oxygen, fermentable easy life respectively Thing degradation of organic substances, tunning, ammonia nitrogen, nitrate nitrogen and nitrite nitrogen, the dissolved without Machine phosphorus, ρ₁, ρ₂..., ρ₁₉For the process rate in ASM2D model, X_I、X_s、X_H、X_PAO、 X_PP、X_PHAAnd X_AUTIt is graininess component, X_I、X_s、X_H、X_PAO、X_PP、X_PHAAnd X_AUT Represent inert particle Organic substance, at a slow speed degradable substrate, heterotrophic microorganism, polyP bacteria respectively PAO, Quadrafos, the intracellular stock of polyP bacteria PAO, nitrifier, S in formula 2-40_O2、 S_F、S_A、S_NH4、S_NO3、S_PO4、S_O2、S_F、S_A、S_NH4、S_NO3And S_PO4Subscript in funny After number 0 expression biological tank, in subscript after comma 1,2,3,4 represent respectively anaerobic pond, Anoxic pond, Aerobic Pond and second pond.

A2O biological tank Technology Modeling method the most according to claim 1, it is characterised in that Described input parameter includes: biological tank flow of inlet water Q_e, sludge reflux amount Q_r, mixed-liquor return Amount Q_t, sludge volume Q_wAnd biological tank water inlet concentration of component；Described output parameter is concentration of component Rate of change.

A2O biological tank Technology Modeling method the most according to claim 2, it is characterised in that When actually used A2O biological tank process modeling, also include model restrictive condition.

A2O biological tank Technology Modeling method the most according to claim 3, it is characterised in that Described model restrictive condition includes:

Described A2O biological tank process modeling is only applicable to biological wastewater treatment systems simulation；

The temperature that described A2O biological tank process modeling is suitable for is 10～25 DEG C.

A2O biological tank Technology Modeling method the most according to claim 1, it is characterised in that Described ρ₁, ρ₂..., ρ₁₉Process rate uses the coefficient table that international water association promulgates.

A2O biological tank Technology Modeling method the most according to claim 1, it is characterised in that Described A2O biological tank Technology Modeling method realizes based on Matlab software emulation.

A2O biological tank Technology Modeling method the most according to claim 6, it is characterised in that Step-length ode4 solver is chosen in described emulation, and the step-length of described step-length ode4 solver is 0.0001, simulation time is 61 days.

A2O biological tank Technology Modeling method the most according to claim 2, it is characterised in that Described biological tank water inlet concentration of component include: COD concentration, ammonia nitrogen concentration, nitrate concentration and Phosphate concn.