JP3123167B2 - Nitrification and denitrification process simulator - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a control device for an activated sludge process in sewage treatment, and more particularly to a simulator for determining an optimum value of a control factor for a nitrification and denitrification process.

[0002]

2. Description of the Related Art Nitrogen compounds (ammonia nitrogen (NH ₄ —N), nitrite nitrogen (NO ₂ −
N), and organic nitrogen (ORN-N)] are discharged from a sewage treatment facility to rivers, seas, lakes and marshes without treatment, and it is known that various environmental problems occur. Therefore, various biological or physicochemical methods have been proposed as methods for removing nitrogen from sewage. Among these, the biological nitrification and denitrification process that utilizes the metabolic activity of microorganisms has become the mainstream of sewage treatment because it can be processed in large quantities and at low cost and is relatively easy to maintain. . An overview of the biological nitrification denitrification process is shown in FIG. As shown in the figure, after inflow water is subjected to nitrification and denitrification treatment in the aeration tank 1, the final sedimentation of SS
Perform removal. Sludge settled in the final sedimentation tank 2 is discharged as surplus sludge, and a part of the discharged sludge is returned to the aeration tank 1 as activated sludge. Here, the aeration tank 1 is divided into a denitrification tank (anoxic state) 3 and a nitrification tank (aerobic state) 4,
In that the nitrification liquid is circulated from the outlet 1b to the inlet 1a,
Different from conventional standard activated sludge method.

FIG. 11 shows a control system for this recirculating nitrification and denitrification process. The nitrification tank 4 of the aeration tank 1 is aerated by a blower 5.
Is controlled by a nitrification tank dissolved oxygen concentration control section (DOC) 7. The amount of sludge discharged from the final sedimentation basin 2 by the excess sludge pump 8 is determined by an average sludge residence time control unit (SRT) 9.
Is controlled by Reference numeral 10 denotes a return sludge pump for returning sludge to the aeration tank 1, and reference numeral 11 denotes a circulation pump for circulating treated water in the aeration tank 1.

In the control of the circulation pump 11, important controllable factors include dissolved nitric oxide (DO) concentration, average sludge residence time (SRT), circulating water amount (circulation ratio; circulation ratio to inflow water amount), and A / O. Ratio (volume ratio between the denitrification tank and the nitrification tank). At present, these four items are all controlled constantly. For example, DO = 2 (mg / l),
SRT = 10 (days), R2 (circulation ratio) = 2, A / O =
It is controlled at 1: 1. Generally, each management target value is determined based on the experience of the operator.

[0005]

The above four control factors DO, SRT, R2 and A / O ratio affect the nitrification reaction rate and the denitrification reaction rate, respectively. For example, in the case of the DO concentration, the nitrification reaction rate increases as the DO concentration increases. However, the DO concentration in the circulating water and the DO concentration in the denitrification tank also increase, so that the denitrification rate decreases. Therefore, when the DO concentration is increased, whether the TN removal rate eventually becomes higher or lower depends on the range of the increase in the DO concentration and the D to nitrification and denitrification.
Since it changes depending on the intensity of the O concentration control or the like, it cannot be determined unconditionally. By increasing the DO concentration, other control (operation) factors (SRT, R2, A / O ratio) may deviate from appropriate values.

[0006] Thus, this type of process is a conventional B
The number of control factors is larger than that of the standard activated sludge method, which mainly removes OD (Biochemical Oxygen Demand), and these factors interfere with each other. The current situation is to decide.

In view of such circumstances, the present invention provides an optimum value (optimum operation) of various control factors in the control of an activated sludge process in sewage treatment, in particular, a treatment process of nitrifying and denitrifying by combining anaerobic and aerobic treatments. The purpose of the present invention is to provide a simulator for assisting driving by estimating the amount.

[0008]

SUMMARY OF THE INVENTION The present invention is directed to a process for performing nitrification denitrification by combining anaerobic and aerobic treatments in order to achieve the above object. Is provided as a nitrification denitrification process simulator that requires the following.

(1) A process model unit having a processing process model unit and a control process model unit, and performing a mutual operation between the two model units. The processing model part simulates the processing by replacing the simultaneous nonlinear differential equations representing the main reaction network of the processing with a linear form. The control process model unit simulates a processing process model unit and a control algorithm.

(2) A steady solution of the simulation output of the processing process is obtained from the set values of the processing process state variables and the control factors. A steady-state analysis unit that performs calculations and uses the convergence value as the steady-state solution;

(3) A control factor optimizing operation unit that performs a control factor optimizing operation by repeatedly operating the steady-state analysis unit while changing the set value of the control factor.

[0012]

As described above, in this type of treatment process, control factors interfere with each other. Therefore, in the present invention, in simulating the processing process, a method of performing an operation using a numerical convergence calculation is employed. That is, when a main reaction network of the treatment process is mathematically modeled by an appropriate method, it is represented by a system of nonlinear differential equations. Since a steady-state solution cannot be obtained with this mathematical model, the simultaneous nonlinear differential equations described above are replaced with a linear form, and the calculation is repeatedly performed, and the numerical value is converged to obtain an analysis result.

In this way, a process model is constructed,
The optimization calculation of the control factor is performed using this model. That is, by changing the set value of the control factor to be given to the process model and repeatedly making the process model perform the calculation, the optimum operation amount of the control factor is obtained. Optimization operations include:
Well-known techniques can be employed.

[0014]

DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, an operation support system for an activated sludge process according to an embodiment of the present invention will be described. As shown in FIG. 2, this system is mainly composed of a process model unit 12 and an analysis unit 13 in terms of its functions. The process model unit 12 includes a processing process model unit 14 and a control process model unit 15. The processing process model unit 6 is obtained by modeling the actual processing process 8 using a mathematical model, and performs analysis using state variables and kinetic parameters provided from the state variable storage unit 16 and the kinetic parameter storage unit 17. Do. The control process model unit 7 is a model of a control algorithm, and performs an analysis using the control factor target value provided from the control factor target value storage unit 18. The analysis unit 2 includes a stationary analysis unit 19 and an optimization calculation unit 20. The optimization calculation unit 20 includes an operation condition optimization unit 2 that optimizes operation conditions.
1 and a dynamic parameter optimizing unit 22 for optimizing dynamic parameters. Hereinafter, each component of the driving support system will be described in detail.

1. Process Model Unit In determining a mathematical model in the process model unit 14, a network of state variables and main reactions of the circulating nitrification and denitrification method was assumed. As shown in FIG. 2, the organic substances present in the inflow water and in the reaction tank are roughly classified into soluble organic substances and floating organic substances. Dissolve organic matter into soluble BOD
(S) and soluble organic nitrogen (ORN). The planktonic organic matter is assumed to be composed of BOD-utilizing bacteria (X) and nitrifying bacteria (XN), and changes to soluble BOD and soluble organic nitrogen by hydrolysis.

In view of the fact that many activated sludge bacteria have been reported to be facultative anaerobic bacteria, denitrifying bacteria were replaced with BO.
Equated with D-assimilating bacterium, depending on the degree of DO concentration
D removal and denitrification reactions shall proceed. Also, when the soluble BOD component is degraded by BOD-utilizing bacteria,
It is assumed that the soluble organic nitrogen component (ORN) is also decomposed into ammonia nitrogen (NH ₄ —N) at the same time. This deamination reaction does not involve oxygen consumption. Ammonia nitrogen (generated by inflow and deamination) is oxidized to nitrate nitrogen (NO ₃ -N) by nitrifying bacteria under aerobic conditions, and the growth of BOD-utilizing bacteria (denitrifying bacteria) and nitrifying bacteria At this time, it shall be used as a nitrogen source. In addition,
In this model, a hydraulic model in which N complete mixing tanks are connected in series as a reaction tank is assumed.

Table 1 shows the rate-determining items and their formulas in the above-mentioned model, which take into account the main reactions of BOD removal, nitrification and denitrification.

[0018]

[Table 1]

As shown in this table, the removal of soluble BOD and the nitrification reaction are limited by DO in the form of the Monod equation.
It was assumed that the denitrification reaction was rate-limited by DO in the form of C-Monod. The C-Monod equation is (1-x / (k +
x)). Here, x is a rate limiting factor, and k is a saturation constant. The pH was calculated from a relational expression with alkalinity. In addition, in the nitrification reaction and the denitrification reaction, the pH changes with consumption and production of alkalinity.
The effect of pH was taken into account using the relational expression represented by g et al. This relational expression is represented by (1-a (b-pH)). Here, a and b are constants. The temperature dependence of the BOD removal rate coefficient, the maximum specific nitrification rate coefficient, and the maximum specific denitrification rate coefficient was determined according to the Arrhenius equation.
This equation is represented by a ₂₀ · θ ^(T-20) . Where a ₂₀ , θ
Is a constant.

The control process model unit 15 constructs a model for each controllable factor. Here, the control items include nitrification rate control, DO control, nitrification liquid circulation ratio and A / O ratio control, pH control and SRT control of each circuit of the denitrification tank and the nitrification tank.

2. Analysis unit 2.1. Steady-state analysis part Steady-state analysis is important in terms of grasping the steady-state characteristics of state variables including control factors, and is also important as a tool for optimization or a means for determining initial values for dynamic simulation. The process model is a simultaneous nonlinear differential equation as can be seen from FIG. 2 and Table 1, and a steady-state solution cannot be obtained algebraically. Therefore, the model was modified by the following method, and a steady solution was determined by numerical convergence calculation.

A) The differential term of the system of nonlinear equations is set to 0 by steady-state analysis.

B) Solve the equations for each variable.

C) When solving the equation in b), the variables in the denominator of the hyperbolic function (Monod equation), which is a nonlinear element, are:
The true value is converged by leaving it as it is and performing repeated calculations.

Specifically, the calculation is performed according to the following procedure.

A) Input the water temperature of the reaction tank (denitrification tank and nitrification tank), inflow load and initial values of each state variable. Further, an initial value of a control target value such as DO control or SRT control is input.

B) Each control target value is compared with a calculated value obtained by repeated calculation, and each manipulated variable is obtained by PID, fuzzy calculation, or the like so as to eliminate the deviation.

C) comparing the previous variable value with the current value,
The convergence is made when the change falls within the allowable range.

2.2. Optimizer The function of the optimizer is roughly divided into two. One is optimization of driving operation conditions, and the other is optimization of kinetic parameters.

(1) Optimization of driving operation conditions An evaluation criterion and a constraint condition are set, and an optimal operation amount that satisfies these is determined. For example, the evaluation criterion is “maximize the TN removal rate”, and a SIMPLEX method can be used as a search method for an optimum solution that satisfies this, for example. Parameters for which optimization is desired can be arbitrarily selected.

(2) Optimization of kinetic parameters In the same manner as in (1), evaluation criteria and constraints are set, and optimal kinetic parameters satisfying the criteria are determined. Generally, the optimal value of the dynamic parameter fluctuates due to long-term load fluctuation, water temperature fluctuation, and the like. Therefore, in order to operate the system stably for a long time as a driving support system, it is necessary to periodically review dynamic parameters.

For example, nitrate nitrogen concentration in a denitrification tank, ammonia nitrogen concentration in a nitrification tank, BOD concentration, MLSS concentration,
And measured values such as nitrifying bacteria concentration, and corresponding steady-state analysis is the output computed value and evaluation criteria that the sum of squares to minimize the deviation, denitrification rate constant (K _D), nitrification reaction rate constant (K It is possible to obtain optimal solutions of kinetic parameters such as _N ) and BOD removal rate constant (K _L ).

3. Operation Procedure An outline of the operation procedure of this system is shown in FIG. First, initial values of state variables necessary for steady-state analysis and optimization, initial values of dynamic parameters, control target values, and the like are set in the process model unit 12 (S1). Next, the process model unit 12 repeatedly starts the calculation based on the initial set value, and continues the calculation until the steady analysis unit 19 satisfies the convergence condition (S2, 3).

When the convergence condition is satisfied (S3; Yes),
The steady-state analysis unit 19 sets the calculation result at that time as a steady solution, and evaluates the evaluation reference value of 2 as the optimum solution from among them.
Determine the item. The optimizing unit 20 uses the evaluation reference value output from the steady-state analysis unit 19 to determine whether the current steady-state solution is the optimum value (or converges on the optimum value) (S4). If the optimal solution has not been obtained yet,
Each manipulated variable is assigned to a predetermined algorithm (for example, SIMPL
EX method), and repeat the steady analysis / optimization calculation (S5). In the case of optimizing kinetic parameters, calculation is performed in the same procedure.

4. Specific Examples (1) Steady State Analysis FIGS. 4 to 8 show one example of the result of the steady state analysis. In this example, the water temperature is 20 ° C., the nitrification rate at the outlet of the nitrification tank is 90%, and the A / O ratio is 1:
3. The case where the residence time in the reaction tank is 6 hours. From these results, the TN removal rate, nitrifying bacteria concentration (XN), MLSS concentration (X), and air volume (G
s) becomes clear. In the case of TN, it is found that the removal rate is improved by operating the SRT longer and lowering the DO concentration, but it is understood that the required airflow increases with 15 days or more.

(2) Optimization Based on the results of the steady-state analysis, it is considered that a necessary airflow (a measure of power consumption) and the like are required as evaluation criteria for optimization in addition to the TN removal rate. As one example, the MLSS concentration at the nitrification tank outlet was 1,000 mg /
The optimal combination of the DO concentration set value and the circulation rate when operating at 1, 2,000 mg / l, and 3,000 mg / l was examined. FIG. 9 shows the result. However, the water temperature in the reaction tank was 20 ° C., the A / O ratio was 1: 3, and the residence time in the reaction tank was 6 hours. MLSS concentration of 1,000mg / l
It is better to lower the DO set value and increase the circulation ratio (R) as
This is advantageous in improving the removal rate. This is MLS
When the S concentration is low, increasing the DO set value to improve the nitrification rate is more effective for removing TN than increasing the circulation ratio and increasing the amount of NO ₃ -N to the denitrification tank. I have. The reason why the circulation ratio can be kept low is that an increase in the DO set value causes an increase in the amount of DO flowing into the denitrification (anaerobic) tank by circulation, which suppresses the denitrification reaction. On the other hand, it is possible to suppress the MLSS concentration (nitrifying bacteria concentration) by lengthening the SRT.

[0037]

As described above, according to the present invention, a model of a process is constructed by replacing a simultaneous nonlinear differential equation representing a network of a main reaction of a treatment process with a linear form, and the model is repeatedly calculated. In addition, a steady-state solution of the process simulation output is obtained by performing numerical convergence calculation, and the calculation of the steady-state solution is repeated while changing the set value of the control factor given to the process model, thereby controlling based on arbitrary operating conditions. Find the optimal manipulated variable for the factor. As a result, even a plurality of operation control parameters that interfere with each other can be determined quantitatively so as to satisfy an arbitrary standard criterion or constraint, instead of being determined by trial and error.

Therefore, this apparatus can be operated to output an optimal solution at regular intervals, for example, and can be used to support the operation of the nitrification / denitration process, thereby achieving the following effects.

(1) Since more appropriate operation of the nitrification / denitration process becomes possible, the quality of treated water is improved, and the influence on the discharge environment can be minimized.

(2) Efficient operation is possible, which is advantageous in terms of operation cost.

(3) Since operation can be performed easily and accurately without skill, human resources can be efficiently used, which contributes to labor saving.

[Brief description of the drawings]

FIG. 1 is a functional block diagram of an operation support system for an activated sludge process according to one embodiment of the present invention.

FIG. 2 is a diagram showing a network of state variables and main reactions.

FIG. 3 is a flowchart showing the operation of the system of FIG. 1;

FIG. 4 is a graph showing the results of steady-state analysis.

FIG. 5 is a graph showing the results of steady-state analysis.

FIG. 6 is a graph showing the results of steady-state analysis.

FIG. 7 is a graph showing the results of steady-state analysis.

FIG. 8 is a graph showing the results of steady-state analysis.

FIG. 9 is a graph showing optimization calculation results.

FIG. 10 is an explanatory diagram showing an outline of a circulating nitrification / denitration process.

FIG. 11 is an explanatory diagram showing an outline of a control unit of a circulation type nitrification / denitration process.

[Explanation of symbols]

 DESCRIPTION OF SYMBOLS 12 ... Process model part 13 ... Analysis part 14 ... Processing process model part 15 ... Control process model part 19 ... Steady state analysis part 20 ... Optimization operation part 21 ... Operating condition optimization part 22 ... Dynamic parameter optimization part

──────────────────────────────────────────────────続 き Continued on the front page (58) Field surveyed (Int.Cl. ⁷ , DB name) G02F 3/34 101 G02F 3/12

Claims (2)

(57) [Claims] An apparatus for determining an optimal value of a control factor for a nitrification denitrification process by combining anaerobic and aerobic processes, wherein a simultaneous nonlinear differential equation representing a main reaction network of the process is provided. A process model unit that simulates a processing process by replacing with a linear format, and a control process model unit that simulates a control algorithm, and a process model unit that performs mutual operations in both model units; a process process state variable and a control factor A steady solution of the process simulation output is obtained from the set value of the above.The process model section is repeatedly calculated, and a numerical convergence calculation is performed using the calculation result. The control is performed by repeatedly operating the steady-state analysis unit and the set value of the control factor while changing the set value of the control factor. A nitrification denitrification process simulator, comprising: a control factor optimization operation unit for performing an optimization operation of a control factor. 2. The nitrification and denitrification process simulator according to claim 1, wherein the process model unit is repeatedly operated while changing a dynamic parameter used for constructing the processing process model unit. 2. The nitrification denitrification process simulator according to claim 1, further comprising a kinetic parameter optimization calculation unit for performing a parameter optimization calculation.