CN102249411A - Method for optimizing sewage treatment process - Google Patents

Abstract

The invention discloses a method for optimizing a sewage treatment process in an urban sewage treatment factory, which comprises the following steps of: 1) generating n operating conditions of an anaerobic-anoxic-oxic (A<2>O) process in the urban sewage treatment factory by a stochastic simulation method; 2) simulating effluent quality under the n stochastically generated process operating conditions by using an activated sludge mathematical model of the A<2>O process; 3) establishing a relationship between the operating conditions obtained in the step 1) and the effluent quality obtained in the step 2) by using a support vector machine to obtain a support vector machine model; and 4) optimizing the support vector machine model obtained in the step 3) by using an accelerated genetic algorithm to obtain the optimal operating condition of the sewage treatment process. Under the optimal process operating condition obtained by the method, the internal reflux ratio is reduced, and the hydraulic retention time of an anoxic tank is shortened simultaneously, so that operating cost is saved while the requirement on the effluent quality is met.

Description

A kind of optimization method of sewage treatment process

Technical field

The invention belongs to the biological wastewater treatment technology field, relate to a kind of sewage disposal A ²The optimization method of O technology.

Background technology

A ²O technology (also claims A-A-O technology, being the abbreviation (biological carbon and phosphorous removal) of English first letter of Anaerobic-Anoxic-Oxic, being also referred to as anaerobic-anoxic-aerobic method) function of denitrogenation dephosphorizing becomes the widely used biological process of wastewater treatment in present municipal sewage plant owing to having simultaneously.Because A ²The effluent quality of O technology and working cost are subjected to the influence of a plurality of factors, as internal reflux ratio, hydraulic detention time, sludge retention time, aeration speed etc., therefore need to seek the technology operational conditions of optimizing, make guaranteeing that effluent quality can reduce working cost again in up to standard.The general method of seeking optimal conditions is the experiment of a series of different process operational conditionss of design, obtains A under the different process operational conditions by experiment ²The effluent quality of O technology obtains A by optimization method then ²The optimum operating condition of O technology.But because A ²O technology more complicated if the method for design obtains optimum operating condition by experiment, needs to consume a large amount of time and expense.

Summary of the invention

The purpose of this invention is to provide a kind of sewage disposal A ²The optimization method of O technology.

Sewage disposal A provided by the invention ²The optimization method of O technology comprises the steps:

1) adopt the method for stochastic simulation to produce n sewage disposal A ²The operational conditions of O technology;

2) adopt A ²The activated sludge model of O technology obtains each sewage disposal A of step 1) gained ²Effluent quality under the O technology operational conditions;

3) with step 1) gained operational conditions and step 2) relation between the gained effluent quality is optimized, and obtains described sewage disposal A ²The optimum operating condition of O technology.

In the described step 1), owing to obtain municipal sewage plant A by the series of optimum experiment ²The optimum operating condition of O technology is difficult to realize, therefore adopts method for computer simulation to seek the optimised process operational conditions.In this step, described operational conditions comprises the hydraulic detention time (HRT) of anaerobic pond, the hydraulic detention time of anoxic pond, hydraulic detention time, internal reflux ratio, Aerobic Pond oxygen mass transfer coefficients and the solid retention time (SRT) of Aerobic Pond.In described stochastic simulation step, can set the different range of above-mentioned operational conditions according to the actual treatment ability of different cities sewage work, so that the operational conditions after optimizing is within reasonable process range.

Described step 2) in, the activated sludge model of described sewage treatment process is any one or the revised mathematical model of following any one mathematical model in the following mathematical model: No. 1 model of active sludge, No. 2 models of active sludge, No. 3 models of active sludge and active sludge 2d model.Above-mentioned four kinds of models are the conventional activated sludge mathematical model, referring to " activated sludge model ", and international water association's biological wastewater treatment design and operation mathematical model seminar work, Zhang Yalei, Li Yongmei translates.In actual applications, can above-mentioned four kinds of mathematical models be revised according to the different situations of the sewage technology of concrete processing, various modification methods commonly used all are applicable to the present invention.The detection index of described effluent quality comprises water outlet COD value, NH ₄ ⁺Concentration, the concentration of total nitrogen (TN) and the concentration of total phosphorus (TP).This step is specially: with a said n used A of different technology operational conditionss difference substitution ²The activated sludge model of O technology obtains A under the different process operational conditions by corresponding mathematical model ²The effluent quality of O technology.

In the described step 3), the method for optimization comprises the steps:

1) adopt SVMs to set up described step 1) gained operational conditions and step 2) relation between the gained effluent quality, supported vector machine model;

2) adopt the described supporting vector machine model of acceleration genetic algorithm optimization, obtain described sewage disposal A ²The optimum operating condition of O technology.

Described step 1) gained operational conditions and described step 2) pass between the gained effluent quality is nonlinear function; In SVMs, the nonlinear function of setting up between input variable and the output variable is to solve regression problem, at this A ²The optimization method of O technology, input variable are step 1) gained operational conditions, and output variable is step 2) the gained effluent quality.

By introducing slack variable ξ _iAnd ξ _i ^*, regression problem can be converted into formula (1):

$\underset{w,b,\mathrm{\&xi;},{\mathrm{\&xi;}}^{*}}{\min }\frac{1}{2}{\mathrm{<figure-callout\; id="2"\; label="w"\; filenames="HDA0000061697380000012.png"\; state="\{\{state\}\}">w</figure-callout>}}^2+C\underset{i=1}{\overset{l}{\mathrm{\&Sigma;}}}{\mathrm{\&xi;}}_i+C\underset{i=1}{\overset{l}{\mathrm{\&Sigma;}}}{\mathrm{\&xi;}}_i^{*}---\left(1\right)$

Constraint condition is: $\begin{array}{c}(w\&CenterDot;\mathrm{\&phi;}\left(x_i\right)+b)-z_i\&le;\mathrm{\&epsiv;}+{\mathrm{\&xi;}}_i\\ (z_i-w\&CenterDot;\mathrm{\&phi;}\left(x_i\right))-b\&le;\mathrm{\&epsiv;}+\mathrm{\&xi;}\\ {\mathrm{\&xi;}}_i,{\mathrm{\&xi;}}_i^{*}\&GreaterEqual;0,i=1,...,l\end{array}$

Lagrange function face is incorporated into above-mentioned protruding double optimization problem, and then formula (1) becomes:

$\underset{\mathrm{\&alpha;},{\mathrm{\&alpha;}}^{*}}{\min }\frac{1}{2}\underset{i,j=1}{\overset{l}{\mathrm{\&Sigma;}}}({\mathrm{\&alpha;}}_i-{\mathrm{\&alpha;}}_i^{*})({\mathrm{\&alpha;}}_i-{\mathrm{\&alpha;}}_i^{*})K(x_i,x_j)+\mathrm{\&epsiv;}\underset{i=1}{\overset{l}{\mathrm{\&Sigma;}}}({\mathrm{\&alpha;}}_i+{\mathrm{\&alpha;}}_i^{*})-\underset{i=1}{\overset{l}{\mathrm{\&Sigma;}}}{z}_i({\mathrm{\&alpha;}}_i-{\mathrm{\&alpha;}}_i^{*})---\left(2\right)$

Constraint condition is: $\begin{array}{c}\underset{i=1}{\overset{l}{\mathrm{\&Sigma;}}}({\mathrm{\&alpha;}}_i-{\mathrm{\&alpha;}}_i^{*})=0\\ 0\&le;{\mathrm{\&alpha;}}_i,{\mathrm{\&alpha;}}_i^{*}\&le;\mathrm{Ci}=1,...,l\end{array}$

Wherein: K (x _i, x _j)=φ (x _i) φ (x _j) be kernel function, generally get polynomial kernel function or RBF kernel function etc.

By finding the solution formula (2), the nonlinear function between different process operational conditions of can supported vector machine setting up and the effluent quality.

The present invention also provides a kind of sewage treatment process of optimization, comprise following technology operational conditions: the Aerobic Pond oxygen mass transfer coefficients is 32/h, solid retention time is 15 days, internal reflux ratio is 2.6, and the hydraulic detention time of Aerobic Pond, anaerobic pond and anoxic pond was respectively 10.8 hours, 1.5 hours and 2.9 hours.

In addition, supporting vector machine model and the application of acceleration genetic algorithm in the optimization method of sewage treatment process also belong to protection scope of the present invention.

The present invention obtains municipal sewage plant A by mathematical model and method for computer simulation ²The optimised process operational conditions of O technology.The technology operational conditions of gained the best has reduced internal reflux ratio, has shortened the hydraulic detention time of anoxic pond simultaneously, makes to save working cost when satisfying effluent quality.This method has important practical value.

Description of drawings

Fig. 1 is municipal sewage plant A ²O technology Inlet and outlet water water quality (former processing condition) (A)-(D) is followed successively by into COD value, the NH of water and water outlet ₄ ⁺The concentration of concentration, total nitrogen (TN) and the concentration of total phosphorus (TP) with the variation relation figure of the working time of sewage work.

Nonlinear function between different process operational conditions that Fig. 2 sets up for SVMs and the effluent quality (A) is water outlet COD value, (B) is NH ₄ ⁺Concentration, (C) be the concentration of total nitrogen (TN), (D) be the concentration of total phosphorus (TP).

Embodiment

The invention will be further described below in conjunction with specific embodiment, but the present invention is not limited to following examples.No. 3, used activated sludge model is referring to " activated sludge model " among the following embodiment, biological wastewater treatment design of international water association and operation mathematical model seminar work, and Zhang Yalei, Li Yongmei translates; EAWAG biological phosphorus model is referring to Rieger L, Koch G, Kuhni M, Gujer W, Siegrist H.The EAWAG Bio-P module for activated sludge model No.3.Water Research 35 (2001) 3887-3903.

Embodiment 1

Municipal sewage plant A ²The hydraulic detention time of anaerobic pond, anoxic pond and Aerobic Pond was respectively 1.5 hours, 3.2 hours and 10.8 hours in the O technology, and solid retention time is 13 days, and external reflux ratio (return sludge ratio) is 80%, and internal reflux ratio (return current ratio of the mixed liquid) is 3.0.The Inlet and outlet water water quality of sewage work is (the X-coordinate time is the working time of sewage work among Fig. 1) as shown in Figure 1.Municipal sewage plant A ²The step of O process optimization technology is as follows:

Step 1: adopt the method for stochastic simulation to produce 100 groups of different municipal sewage plant A ²The operational conditions (table 2) of O technology, the technology operational conditions comprises the hydraulic detention time (HRT) of Aerobic Pond oxygen mass transfer coefficients, solid retention time (SRT), internal reflux ratio, Aerobic Pond, anaerobic pond and anoxic pond, wherein, the setting range of Aerobic Pond oxygen mass transfer coefficients is 10-60/h, the setting range of solid retention time (SRT) is 5-50 days, the setting range of internal reflux ratio is 0.5-5, and the setting range of the hydraulic detention time of Aerobic Pond, anaerobic pond and anoxic pond (HRT) was respectively 1.5-10.5 hour, 0.5-1.5 hour, 1.5-3 hour.

Step 2: No. 3,100 groups of different process operational conditionss difference substitution activated sludge models and EAWAG biological phosphorus model with step 1) produces at random obtain A under each technology operational conditions by this mathematical model ²The effluent quality of O technology comprises water outlet COD, NH ₄ ⁺, TN and TP concentration (as shown in table 3).

In No. 3, this activated sludge model, consider 4 kinds of living microorganisms: common heterotrophic bacterium (X _H), ammonia oxidizing bacteria (X _AOB), nitrite-oxidizing bacteria (X _NOB) and polyP bacteria (X _PAO).In addition, this model also comprises common heterotrophism mycetocyte internal memory storing (X _STO), polyP bacteria born of the same parents internal memory storing (X _PHA) and polyphosphoric acid salt (X _PP), and because the inert particle organic matter (X that the microorganism endogenous respiration produces _I).This model comprises 32 biochemical reaction dynamic processes.Because the sewage treatment plant inflow complicated component is at first considered hydrolytic process (1 process); For heterotrophic bacterium (X _H), comprise 11 processes in the model; For ammonia oxidizing bacteria (X _AOB) and nitrite-oxidizing bacteria (X _NOB), comprise 6 processes in the model altogether; For polyP bacteria (X _PAO), comprise 14 processes in the model.The kinetic equation of 4 kinds of living microorganisms and hydrolytic process sees Table 1 in the model.

Step 3: adopt SVMs to set up technology operational conditions (hydraulic detention time (HRT) of Aerobic Pond oxygen mass transfer coefficients, solid retention time (SRT), internal reflux ratio, Aerobic Pond, anaerobic pond and anoxic pond) and effluent quality (water outlet COD, NH ₄ ⁺, TN and TP concentration) between nonlinear function, obtain supporting vector machine model.

In SVMs, the nonlinear function of setting up between input variable and the output variable is the solution regression problem.Herein, input variable is technology operational conditions (hydraulic detention time (HRT) of Aerobic Pond oxygen mass transfer coefficients, solid retention time (SRT), internal reflux ratio, Aerobic Pond, anaerobic pond and anoxic pond), amounts to 6 input variables; Output variable is effluent quality (water outlet COD, NH ₄ ⁺, TN and TP concentration), amount to 4 output variables.

By introducing slack variable ξ _iAnd ξ _i ^*, regression problem can be converted into formula (1):

$\underset{w,b,\mathrm{\&xi;},{\mathrm{\&xi;}}^{*}}{\min }\frac{1}{2}{\mathrm{<figure-callout\; id="2"\; label="w"\; filenames="HDA0000061697380000012.png"\; state="\{\{state\}\}">w</figure-callout>}}^2+C\underset{i=1}{\overset{l}{\mathrm{\&Sigma;}}}{\mathrm{\&xi;}}_i+C\underset{i=1}{\overset{l}{\mathrm{\&Sigma;}}}{\mathrm{\&xi;}}_i^{*}---\left(1\right)$

Constraint condition is: $\begin{array}{c}(w\&CenterDot;\mathrm{\&phi;}\left(x_i\right)+b)-z_i\&le;\mathrm{\&epsiv;}+{\mathrm{\&xi;}}_i\\ (z_i-w\&CenterDot;\mathrm{\&phi;}\left(x_i\right))-b\&le;\mathrm{\&epsiv;}+\mathrm{\&xi;}\\ {\mathrm{\&xi;}}_i,{\mathrm{\&xi;}}_i^{*}\&GreaterEqual;0,i=1,...,l\end{array}$

Wherein, C is a penalty coefficient, gets 10, x _iBe input, z _iBe work output, ε, w and b are coefficient.Lagrange function face is incorporated into above-mentioned protruding double optimization problem, and then formula (1) becomes:

$\underset{\mathrm{\&alpha;},{\mathrm{\&alpha;}}^{*}}{\min }\frac{1}{2}\underset{i,j=1}{\overset{l}{\mathrm{\&Sigma;}}}({\mathrm{\&alpha;}}_i-{\mathrm{\&alpha;}}_i^{*})({\mathrm{\&alpha;}}_i-{\mathrm{\&alpha;}}_i^{*})K(x_i,x_j)+\mathrm{\&epsiv;}\underset{i=1}{\overset{l}{\mathrm{\&Sigma;}}}({\mathrm{\&alpha;}}_i+{\mathrm{\&alpha;}}_i^{*})-\underset{i=1}{\overset{l}{\mathrm{\&Sigma;}}}{z}_i({\mathrm{\&alpha;}}_i-{\mathrm{\&alpha;}}_i^{*})---\left(2\right)$

Constraint condition is: $\begin{array}{c}\underset{i=1}{\overset{l}{\mathrm{\&Sigma;}}}({\mathrm{\&alpha;}}_i-{\mathrm{\&alpha;}}_i^{*})=0\\ 0\&le;{\mathrm{\&alpha;}}_i,{\mathrm{\&alpha;}}_i^{*}\&le;\mathrm{Ci}=1,...,l\end{array}$

Wherein, α _iWith

Be Lagrange's multiplier; K (x _i, x _j)=φ (x _i) φ (x _j) be kernel function, select the RBF kernel function herein for use.

By finding the solution formula (2), can be supported the different process operational conditions set up of vector machine and effluent quality between 4 nonlinear functions, as shown in Figure 2, wherein, Training refers to be used to set up the data that supporting vector machine model adopts, Testing refers to be used to test the data that SVMs adopts, the effluent quality that the effluent quality that Stimulated Values refers to adopt aforesaid activated sludge model to obtain, Predicted Values refer to adopt the supporting vector machine model of foundation to obtain.As shown in Figure 2, supporting vector machine model can be set up the correlationship between technology operational conditions and the effluent quality preferably.

Step 4: adopt and quicken genetic algorithm optimization above-mentioned steps 3) 4 supporting vector machine models setting up are to obtain municipal sewage plant A ²The optimised process operational conditions of O technology.

Adopt and quicken in the genetic algorithm optimization step, parameter setting is as follows: population size 400,20 of excellent individual numbers, acceleration cycle number of times 100 times.This optimization step of quickening genetic algorithm is as follows:

1) coding.Utilizing formula is the initial change interval that [a (j), b (j)] interval j optimization variable x (j) corresponds to the real number y (j) on [0,1] interval.Through coding, the span of all optimization variable all unifies to be [0,1] interval.

x(j)＝a(j)+y(j)[b(j)-a(j)] (3)

2) initialize of parent colony.If population size is n, generate the uniform random number on n group [0,1] interval, every group has p, i.e. { u (j, i) } (j=1～p, i=1～n), each { u (j, i) } as the parent individual values y of initial population (j, i).(j, i) substitution formula (3) is optimized, and (j, i), the function of substitution optimization aim again obtains corresponding target function value f (i) to variate-value x y.{ f (i), i=1～n} sorts from small to large, corresponding individual y (j, i) also and then ordering.Claim that top ns the individuality in ordering back is excellent individual.

3) fitness evaluation of parent colony.This individual fitness value of the more little expression of target function value f (i) value is high more, and vice versa.Based on this, the fitness function value F (i) of i the parent individuality in definition ordering back is:

F(i)＝1/[f(i)×f(i)+0.001] (4)

0.001 being that experience is provided with in the denominator wherein, is 0 situation to avoid f (i) value.

4) select.First progeny population { y ₁(j, i) | j=1～p, i=1～n} produces by selection operation.Get the ratio selection mode, then the individual y of parent (j, selection Probability p s (i) i) is:

P (2)] ..., [p (n-1), p (n)], (j i) sets up one-to-one relationship to these sub-ranges and n the individual y of parent.

5) intersect.Second { y of filial generation colony ₂(j, i) | j=1～p, i=1～n} produces by selection operation.The purpose of intersecting is that to seek the parent parents existing but fail the gene information rationally utilized.The GA of standard, its interlace operation is to be divided into several sections at random on a pair of parent parents chromosome chain, mutual then exchange forms.But in AGA, in order to keep the diversity of colony, its interlace operation is to select the individual y of a pair of parent at random according to the selection probability of formula (5) ₂(j, i ₁) and y ₂(j, i ₂) as parents, and carry out following combination at random, produce the individual y of a filial generation ₂(j, i):

$\left\{\begin{array}{c}{y}_2(j,i)=u_{1}y(j,i_1)+(1-u_1)y(j,i_2),u_3<0.5\\ y_2(j,i)=u_{2}y(j,i_1)+(1-u_2)y(j,i_2),u_3\&GreaterEqual;0.5\end{array}\right.---\left(6\right)$

Wherein: u ₁, u ₂And u ₃It all is randomized number.

6) variation.The 3rd { y of filial generation colony ₃(j, i) | j=1～p, i=1～n} produces by mutation operation.The purpose of variation is in order to introduce new gene, to strengthen the diversity of colony.The GA of standard, its mutation operation are with a small probability p to any 1 or any 2 genic value on the karyomit(e) of each parent individuality _m(probability promptly makes a variation) overturns.And in AGA, (j, i), if its fitness function value F (i) is more little, promptly it selects Probability p to the individual y of any one parent _s(i) more little, the Probability p that this individuality is made a variation then _m(i) big more.The variation probability is calculated by formula (7):

p _m(i)＝1-p _S(i) (7)

Adopt p randomized number with p _m(i) probability replace individual y (j i), thereby obtains the 3rd filial generation individuality, also promptly:

$\left\{\begin{array}{c}{y}_3(j,i)=u\left(j\right),u_m<p_m\left(i\right)\\ y_3(j,i)=y(j,i),u_m\&GreaterEqual;p_m\left(i\right)\end{array}\right.---\left(8\right)$

Wherein: u (j) (j=1～p) and u _mBe randomized number.

7) evolution iteration.3n filial generation individuality by step 4 to the step 6 of front obtains sorts from big to small according to its fitness function value, gets and comes top n filial generation individuality as new parent colony.Algorithm changes step 3 over to, carries out next round evolution iteration, again parent colony is estimated, selects, hybridizes and makes a variation, and develops so repeatedly.

8) acceleration cycle.Acceleration cycle is that as the new initial change interval of variable, then algorithm changes step 1 over to the first time, the pairing variable change of this sub-group of the excellent individual interval that iteration produced that develops for the second time.Acceleration cycle like this, the constant interval of excellent individual will progressively be adjusted and shrink, with optimum point the distance will be more and more nearer, target function value until optimum individual reaches predetermined (circulation) number of times that quickens less than a certain set(ting)value or algorithm operation, finish the operation of whole algorithm, and the mean value of optimized individual or excellent individual in the current colony is decided to be the optimum result of AGA.

The optimised process operational conditions that is solved is: the Aerobic Pond oxygen mass transfer coefficients is 32/h, and solid retention time (SRT) is 15d, and internal reflux ratio is 2.6, and the hydraulic detention time of Aerobic Pond, anaerobic pond and anoxic pond is respectively 10.8h, 1.5h and 2.9h.In No. 3, the used activated sludge model of this technology operational conditions substitution, obtain A under this optimised process operational conditions ²The effluent quality of O technology is: water outlet COD, NH ₄ ⁺, TN and TP concentration is respectively 23.1mg/L, 4.75mg/L, 5.95mg/L and 0.04mg/L.Compare with former technology operational conditions, utilize the technology operational conditions of the best that optimization method provided by the invention obtains, reduced internal reflux ratio, shortened the hydraulic detention time of anoxic pond simultaneously, make and when satisfying effluent quality, save working cost.

Table 1, reaction kinetics equation

Parameter declaration:

S _S: the readily biodegradable organic concentration

S _O: concentration of oxygen

S _NH4: the concentration of ammonia nitrogen

S _NO2: the concentration of nitrite

S _NO3: the concentration of nitrate

S _PO4: phosphatic concentration

X _H: the concentration of heterotrophic bacterium

X _AOB: the concentration of nitrite bacteria

X _NOB: the concentration of nitrobacteria

X _PAO: the concentration of polyP bacteria

X _STO: the concentration of stock in the born of the same parents in the heterotrophic bacterium

X _PHA: the concentration of intracellular PHA in the polyP bacteria

X _PP: the concentration of PP in the born of the same parents in the polyP bacteria

k _STO: X _STOMemory rate

K _{H, S}: S _SThe semi-saturation constant (to X _H)

K _{H, O}: S _OThe semi-saturation constant (to X _H)

η _{H, NOx}: X _HThe anoxic correction factor

K _NOx: S _NOxThe semi-saturation constant (to X _H)

K _{H, NO2}: S _NO2The semi-saturation constant (to X _H)

K _{H, NO3}: S _NO3The semi-saturation constant (to X _H)

K _{H, NH4}: S _NH4The semi-saturation constant (to X _H)

K _{H, PO4}: S _PO4The semi-saturation constant (to X _H)

μ _H: X _HMaximum specific growth rate

b _{H, O}: X _HRate of decay under the aerobic condition

b _{H, NOx}: X _HRate of decay under the anoxia condition

b _{STO, O}: X _STORate of decay under the aerobic condition

b _{STO, NOx}: X _STORate of decay under the anoxia condition

μ _AOB: X _AOBMaximum specific growth rate

μ _NOB: X _NOBMaximum specific growth rate

K _{AOB, O}: S _OThe semi-saturation constant (to X _AOB)

K _{NOB, O}: S _OThe semi-saturation constant (to X _NOB)

K _{AOB, NH4}: S _NH4The semi-saturation constant (to X _AOB)

K _{AOB, NO2}: S _NO2The semi-saturation constant (to X _AOB)

K _{NOB, NO2}: S _NO2The semi-saturation constant (to X _NOB)

K _{NOB, NO3}: S _NO3The semi-saturation constant (to X _NOB)

K _{AOB, PO4}: S _PO4The semi-saturation constant (to X _AOB)

K _{NOB, PO4}: S _PO4The semi-saturation constant (to X _NOB)

b _{AOB, O}: X _AOBRate of decay under the aerobic condition

b _{AOB, NO2}: X _AOBRate of decay under the anoxia condition

b _{NOB, O}: X _NOBRate of decay under the aerobic condition

b _{NOB, NO3}: X _NOBRate of decay under the anoxia condition

q _PHA: X _PHAThe memory rate constant

q _PP: X _PPThe memory rate constant

μ _PAO: X _PAOMaximum specific growth rate

η _{PAO, NOx}: X _PAOThe anoxic correction factor

b _PAO: X _PAORate of decay

b _PP: X _PPRate of decay

b _PHA: X _PHARate of decay

K _{PAO, S}: S _SThe semi-saturation constant (to X _PAO)

K _{PAO, O}: S _OThe semi-saturation constant (to X _PAO)

K _{PAO, PP}: X _PP/ X _PAOThe semi-saturation constant

K _{Max, PAO}: X _PP/ X _PAOMaximum value

K _{IPP, PAO}: (k _{Max, PAO}-X _PP/ X _PAO) the semi-saturation constant

K _PHA: X _PHA/ X _PAOThe semi-saturation constant

K _{PAO, NH4}: S _NH4The semi-saturation constant (to X _PAO)

K _{PAO, NOx}: S _NOxThe semi-saturation constant (to X _PAO)

K _{PAO, PO4}: S _PO4The semi-saturation constant (to X _PAO)

Table 2,100 groups of different municipal sewage plant A ²The operational conditions of O technology

A under table 3, each technology operational conditions ²The effluent quality of O technology

Claims (7)

1. the sewage treatment process of an optimization, comprise following technology operational conditions: the Aerobic Pond oxygen mass transfer coefficients is 32/h, solid retention time is 15 days, and internal reflux ratio is 2.6, and the hydraulic detention time of Aerobic Pond, anaerobic pond and anoxic pond was respectively 10.8 hours, 1.5 hours and 2.9 hours.

2. supporting vector machine model and the application of acceleration genetic algorithm in the optimization method of sewage treatment process.

3. sewage disposal A ²The optimization method of O technology comprises the steps:

1) adopt the method for stochastic simulation to produce n sewage disposal A ²The operational conditions of O technology;

2) adopt A ²The activated sludge model of O technology obtains each sewage disposal A of step 1) gained ²Effluent quality under the O technology operational conditions;

3) with step 1) gained operational conditions and step 2) relation between the gained effluent quality is optimized, and obtains described sewage disposal A ²The optimum operating condition of O technology.

4. method according to claim 3, it is characterized in that: in the described step 1), described operational conditions comprises the hydraulic detention time of anaerobic pond, the hydraulic detention time of anoxic pond, hydraulic detention time, internal reflux ratio, Aerobic Pond oxygen mass transfer coefficients and the solid retention time of Aerobic Pond.

5. according to claim 3 or 4 described methods, it is characterized in that: described step 2), the detection index of described effluent quality comprises water outlet COD value, NH ₄ ⁺Concentration, the concentration of total nitrogen and the concentration of total phosphorus.

6. according to the arbitrary described method of claim 3-5, it is characterized in that: described step 2), the activated sludge model of described sewage treatment process is any one or the revised mathematical model of following any one mathematical model in the following mathematical model: No. 1 model of active sludge, No. 2 models of active sludge, No. 3 models of active sludge and active sludge 2d model.

7. according to the arbitrary described method of claim 3-6, it is characterized in that: the method for described step 3) optimization comprises the steps:

1) adopt SVMs to set up described step 1) gained operational conditions and step 2) relation between the gained effluent quality, supported vector machine model;

2) adopt the described supporting vector machine model of acceleration genetic algorithm optimization, obtain described sewage disposal A ²The optimum operating condition of O technology.

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