JP3123167B2 - Nitrification and denitrification process simulator - Google Patents
Nitrification and denitrification process simulatorInfo
- Publication number
- JP3123167B2 JP3123167B2 JP03334614A JP33461491A JP3123167B2 JP 3123167 B2 JP3123167 B2 JP 3123167B2 JP 03334614 A JP03334614 A JP 03334614A JP 33461491 A JP33461491 A JP 33461491A JP 3123167 B2 JP3123167 B2 JP 3123167B2
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- Prior art keywords
- control
- nitrification
- unit
- steady
- control factor
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Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02W—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO WASTEWATER TREATMENT OR WASTE MANAGEMENT
- Y02W10/00—Technologies for wastewater treatment
- Y02W10/10—Biological treatment of water, waste water, or sewage
Landscapes
- Purification Treatments By Anaerobic Or Anaerobic And Aerobic Bacteria Or Animals (AREA)
- Feedback Control In General (AREA)
- Activated Sludge Processes (AREA)
Description
【0001】[0001]
【産業上の利用分野】この発明は、下水処理における活
性汚泥プロセスの制御装置に係わり、特に硝化脱窒プロ
セスの制御因子の最適値を求めるシミュレータに関す
る。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】[0002]
【従来の技術】下排水中に含まれる窒素化合物〔アンモ
ニア態窒素(NH4−N)、亜硝酸態窒素(NO2−
N)、有機態窒素(ORN−N)〕が未処理のまま下水
処理施設から河川、海、湖沼等へ放流されると、種々の
環境問題が発生することが知られている。そこで下排水
から窒素を除去する方法として、生物学的あるいは物理
化学的な方法が種々提案されている。これらのうち、微
生物の代謝活性を応用した生物学的硝化脱窒プロセスは
大量かつ安価に処理を行うことができ、しかも維持管理
が比較的容易であることから、下水処理の主流となって
いる。生物学的硝化脱窒プロセスの概要を図10に示
す。図に示すように、流入水に曝気槽1で硝化脱窒処理
を施した後、最終沈殿池2でSS成分の最終的な沈殿・
除去を行う。最終沈殿池2に沈殿した汚泥は余剰汚泥と
して排出され、排出された汚泥の一部は活性汚泥として
曝気槽1に返送される。ここで曝気槽1が脱窒槽(無酸
素状態)3と硝化槽(好気状態)4に別れている点や、
流出口1bから流入口1aに硝化液を循環させる点で、
従来の標準活性汚泥法と異なる。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.
【0003】この循環式硝化脱窒プロセスの制御システ
ムを図11に示す。曝気槽1の硝化槽4にはブロワ5に
より曝気が行われるが、この送風量を調節する制御弁6
は硝化槽溶存酸素濃度制御部(DOC)7により制御さ
れる。また余剰汚泥ポンプ8による最終沈殿池2からの
汚泥排出量は、汚泥の平均滞留時間制御部(SRT)9
により制御される。10は汚泥を曝気槽1に返送する返
送汚泥ポンプ、11は曝気槽1内で処理水を循環させる
循環ポンプである。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.
【0004】循環ポンプ11の制御では、重要な可制御
因子として硝化槽溶存酸素(DO)濃度、汚泥の平均滞
留時間(SRT)、循環水量(循環比;流入水量に対す
る循環比率)、A/O比(脱窒槽および硝化槽の容積
比)などが挙げられる。現在これら4項目は、すべて一
定に制御されている。たとえばDO=2(mg/l)、
SRT=10(日)、R2(循環比)=2、A/O=
1:1で制御される。一般に、この各管理目標値は運転
員の経験によって決められている。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】[0005]
【発明が解決しようとする課題】前述した4項目の制御
因子DO,SRT,R2,A/O比はそれぞれ硝化反応
速度や脱窒反応速度に影響を与える。たとえばDO濃度
の場合、DO濃度が高まるに従って硝化反応速度が高く
なるが、循環水中のDO濃度および脱窒槽中のDO濃度
も上昇するため、脱窒速度は逆に低下する。したがって
DO濃度を上げた場合、最終的にT−N除去率が高くな
るか低下するかは、DO濃度の上げ幅や硝化脱窒へのD
O濃度律速の強さなどにより変化するため、一概に判断
できない。またDO濃度を上げることによって、他の制
御(操作)因子(SRT,R2,A/O比)が適正値か
らずれてくる場合がある。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】このようにこの種のプロセスは、従来のB
OD(生物化学的酸素要求量)除去を主たる目的とした
標準活性汚泥法に比べて制御因子数が多く、しかもそれ
らの因子は相互に干渉するために各操作量は運転員が試
行錯誤的に決定して行くのが現状である。[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.
【0007】この発明は、このような事情に鑑み、下水
処理における活性汚泥プロセス、特に嫌気・好気処理を
組み合わせて硝化脱窒を行う処理プロセスの制御におい
て、各種制御因子の最適値(最適操作量)を推定して運
転を支援するシミュレータを提供することを目的とす
る。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】[0008]
【課題を解決するための手段】この発明は、上記の目的
を達成するために、嫌気・好気処理を組み合わせて硝化
脱窒を行う処理プロセスを対象とし、この処理プロセス
の制御因子の最適値を求める硝化脱窒プロセスシミュレ
ータとして、次の演算要素を有するものを提供する。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.
【0009】(1)処理プロセスモデル部および制御プ
ロセスモデル部を有し、両モデル部で相互に演算を行う
プロセスモデル部。処理プロセスモデル部は、処理プロ
セスの主要反応ネットワークを表す連立非線形微分方程
式を線形式に置き換えて処理プロセスを模擬したもので
ある。制御プロセスモデル部は、処理プロセスモデル部
および制御アルゴリズムを模擬したものである。(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.
【0010】(2)処理プロセス状態変数および制御因
子の設定値から処理プロセス模擬出力の定常解を求める
ものであって、前記プロセスモデル部に繰り返し計算を
行わせ、この計算結果を用いて数値収束計算を行い、こ
の収束値を前記定常解とする定常解析部。(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;
【0011】(3)制御因子の設定値を変更しつつ、定
常解析部を繰り返し動作させることにより、制御因子の
最適化演算を行う制御因子最適化演算部。(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】[0012]
【作用】前述のように、この種の処理プロセスでは制御
因子が相互に干渉する。そこでこの発明では、処理プロ
セスを模擬するにあたって、数値収束計算を用いて演算
を行う手法をとる。すなわち、処理プロセスの主要反応
ネットワークを適当な手法で数式モデル化すると、連立
非線形微分方程式により表される。この数式モデルでは
定常解が得られないので、上記の連立非線形微分方程式
を線形式に置き換えて繰り返し計算を行い、その数値を
収束させて解析結果を得る。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.
【0013】このようにしてプロセスモデルを構築し、
このモデルを用いて制御因子の最適化演算を行う。つま
り、プロセスモデルに与える制御因子の設定値を変更し
つつプロセスモデルに繰り返し計算を行わせることによ
り、制御因子の最適操作量を求める。最適化演算には、
周知の手法を採用することができる。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】[0014]
【実施例】以下、この発明の実施例に係る活性汚泥プロ
セスの運転支援システムを説明する。このシステムはそ
の機能上、図2に示すように、大きく分けてプロセスモ
デル部12と解析部13とから構成されている。プロセ
スモデル部12は、処理プロセスモデル部14と制御プ
ロセスモデル部15とから構成される。処理プロセスモ
デル部6は、実処理プロセス8を数式モデルによりモデ
ル化したものであり、状態変数格納部16および動力学
的パラメータ格納部17から与えられる状態変数および
動力学的パラメータを用いて解析を行う。制御プロセス
モデル部7は、制御アルゴリズムをモデル化したもので
あり、制御因子目標値格納部18から与えられる制御因
子目標値を用いて解析を行う。解析部2は、定常解析部
19と最適化演算部20とから構成される。最適化演算
部20は、運転条件の最適化を行う運転条件最適化部2
1と、動力学的パラメータの最適化を行う動力学的パラ
メータ最適化部22とから構成される。以下、この運転
支援システムの各構成要素について詳細に説明する。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.
【0015】1.プロセスモデル部 処理プロセスモデル部14における数式モデルを決定す
るにあたって、循環式硝化脱窒法の状態変数と主要反応
のネットワークを仮定した。図2に示すように、流入水
中および反応槽内に存在する有機物を溶解性有機物と浮
遊性有機物に大別する。溶解性有機物を溶解性BOD
(S)と溶解性有機窒素(ORN)に分ける。浮遊性有
機物は、BOD資化細菌(X)と硝化菌(XN)で構成
されるとし、加水分解により溶解性BODと溶解性有機
窒素に変化するものとする。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.
【0016】また、活性汚泥細菌の多くは通性嫌気性菌
であることが報告されていることに鑑み、脱窒菌をBO
D資化細菌と同一視し、DO濃度律速の程度によりBO
D除去および脱窒反応が進むものとする。また、溶解性
BOD成分がBOD資化細菌によって分解されるとき、
溶解性有機窒素成分(ORN)も同時に分解されてアン
モニア態窒素(NH4−N)になると想定する。この脱
アミノ反応は酸素消費を伴わないものとする。アンモニ
ア態窒素(流入および脱アミノ反応による生成)は、好
気的条件で硝化菌により硝酸態窒素(NO3−N)に酸
化されると共に、BOD資化細菌(脱窒菌)や硝化菌の
増殖の際、窒素源として利用されるものとする。なお、
このモデルでは、反応槽としてN個の完全混合槽を直列
した水理モデルを仮定している。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.
【0017】以上のモデルにおいて、主要な反応である
BOD除去、硝化および脱窒反応について考慮した律速
項目とその式を表1に示す。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】[0018]
【表1】 [Table 1]
【0019】この表に示すように、溶解性BOD除去お
よび硝化反応はDOにMonod式の形で律速を受け、
脱窒反応はDOにC−Monodの形で律速を受けるも
のと仮定した。C−Monod式は、(1−x/(k+
x))で表される。ここでxは律速因子、kは飽和定数
である。また、pHはアルカリ度との関係式より算出し
た。また硝化反応および脱窒反応においては、アルカリ
度の消費生成に伴ってpHが変化するのでDownin
gらにより表されている関係式を用いてpHの影響を考
慮した。この関係式は、(1−a(b−pH))で表さ
れる。ここでa,bは定数である。また、BOD除去速
度係数、最大比硝化速度係数、最大比脱窒速度係数の温
度依存性は、Arrhenius式に従うものとした。
この式は、a20・θ(T-20)で表される。ここでa20,θ
は定数である。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.
【0020】制御プロセスモデル部15では、制御可能
な因子についてそれぞれモデルを構築した。ここで制御
項目は、硝化槽の硝化率制御、DO制御、硝化液の循環
比およびA/O比制御、脱窒槽および硝化槽の各回路の
pH制御およびSRT制御が挙げられる。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.
【0021】2.解析部 2.1.定常解析部 定常解析は、制御因子を含む状態変数の定常特性を把握
する意味から、また最適化のツールや動的シミュレーシ
ョンの初期値の決定手段として重要である。プロセスモ
デルは、図2や表1から判るように連立非線形微分方程
式であり、定常解を代数的に求めることはできない。そ
こで下記の方法でモデルを変形し、定常解を数値収束計
算で求めることにした。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.
【0022】a)定常解析により非線形連立方程式の微
分項を0とおく。A) The differential term of the system of nonlinear equations is set to 0 by steady-state analysis.
【0023】b)方程式を各々の変数に関して解く。B) Solve the equations for each variable.
【0024】c)方程式をb)で解く際、非線形要素で
ある双曲線関数(Monod式)の分母にある変数は、
そのままの形で残し、繰り返し計算を行うことにより真
値収束させる。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.
【0025】具体的には、次の手順により演算を行う。Specifically, the calculation is performed according to the following procedure.
【0026】a)反応槽(脱窒槽および硝化槽)の水
温、流入負荷および各状態変数の初期値を入力する。ま
た、DO制御やSRT制御などの制御目標値の初期値を
入力する。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.
【0027】b)各制御目標値と繰り返し計算で得られ
る計算値を比較し、その偏差がなくなるようにPIDや
ファジィ演算等で各操作量を求める。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.
【0028】c)前回の変数値と今回の値とを比較し、
その変化が許容範囲内になる時点で収束とする。C) comparing the previous variable value with the current value,
The convergence is made when the change falls within the allowable range.
【0029】2.2.最適化部 最適化部の機能は大きく分けて2つある。1つは運転操
作条件の最適化であり、他の1つは動力学的パラメータ
の最適化である。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.
【0030】(1)運転操作条件の最適化 評価基準および制約条件を設定し、これを満たす最適操
作量を決定する。たとえば評価基準は「T−N除去率を
最大にする」とし、これを満たすような最適解の探索手
法としてはたとえばSIMPLEX法を用いることがで
きる。最適化を希望するパラメータは任意に選択するこ
とができる。(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.
【0031】(2)動力学的パラメータの最適化 (1)と同様に評価基準および制約条件を設定し、これ
を満たす最適な動力学的パラメータを決定する。一般
に、長期的な負荷変動や水温変動等により動力学的パラ
メータの最適値は変動する。したがって、運転支援シス
テムとして長期間安定してシステムを稼働させるために
は定期的に動力学的パラメータを見直すことが必要とな
る。(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.
【0032】たとえば脱窒槽の硝酸態窒素濃度、硝化槽
のアンモニア態窒素濃度、BOD濃度、MLSS濃度、
硝化菌濃度等の実測値と、対応する定常解析出力である
計算値との偏差の二乗和を最小にすることを評価基準と
し、脱窒反応速度定数(KD)、硝化反応速度定数
(KN)、BOD除去速度定数(KL)等の動力学的パラ
メータの最適解を得ることができる。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 ).
【0033】3.運用手順 このシステムの運用手順の概略を図3に示す。まずプロ
セスモデル部12に、定常解析や最適化に必要な各状態
変数の初期値や、動力学的パラメータ、制御目標値等の
初期値などを設定する(S1)。次にこの初期設定値に
基づいてプロセスモデル部12が繰り返し計算を開始
し、定常解析部19で収束条件を満たすまで計算を続け
る(S2,3)。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).
【0034】収束条件を満足したら(S3;Yes)、
定常解析部19はその時点の計算結果を定常解とし、こ
の中から最適解かどうか判断するための評価基準値の2
項目を決定する。最適化部20では、定常解析部19か
ら出力される評価基準値を使用し、現在の定常解が最適
値であるか(あるいは最適値に収束しているか)を判断
する(S4)。まだ最適解が得られていない場合には、
各操作変数を所定のアルゴリズム(たとえばSIMPL
EX法)に従って変更し、定常解析・最適化の演算を再
度繰り返す(S5)。動力学的パラメータの最適化の場
合も同様の手順で演算を行う。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.
【0035】4.具体例 (1)定常解析 図4〜8は定常解析結果の1例を示す。この例は、水温
20℃、硝化槽流出口の硝化率90%、A/O比1:
3、反応槽滞留時間6時間の場合である。この結果か
ら、DO−SRT特性と対応するT−N除去率、硝化菌
濃度(XN)、MLSS濃度(X)および送風量(G
s)との関係が明らかとなる。T−Nの場合、SRTを
長くDO濃度を低く操作したほうが除去率が向上する
が、15日以上とするとそれに伴って必要送風量が増大
することがわかる。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.
【0036】(2)最適化 定常解析の結果より、最適化の評価基準としてT−N除
去率以外に必要送風量(電力量の目安になる)等も必要
と考えられるが、ここでは最適化の1例として硝化槽流
出口におけるMLSS濃度をそれぞれ1,000mg/
l、2,000mg/l、3,000mg/lで運転し
た場合のDO濃度設定値と循環率の最適な組み合わせに
ついて検討した。その結果を図9に示す。ただし、反応
槽の水温は20℃、A/O比は1:3、反応槽の滞留時
間は6時間とした。MLSS濃度が1,000mg/l
から3,000mg/lと高くなるに従い、DO設定値
を下げ、循環比(R)を挙げて運転するほうが、T−N
除去率を向上させるうえで有利である。これは、MLS
S濃度が低い場合、DO設定値を上げて硝化率を向上さ
せるほうが、循環比を上げて脱窒槽へのNO3−N量を
増加するよりもT−N除去に有効であることを示してい
る。循環比が低く抑えられる理由は、DO設定値の上昇
が循環による脱窒(嫌気)槽への流入DO量の増大を招
き、これが脱窒反応を抑制することによる。これに対し
て、SRTを長くしてMLSS濃度(硝化菌濃度)を抑
制することが可能である。(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】[0037]
【発明の効果】以上説明したように、この発明によれ
ば、処理プロセスの主要反応のネットワークを表す連立
非線形微分方程式を線形式に置き換えてプロセスのモデ
ルを構築し、このモデルに繰り返し計算を行わせて数値
収束計算を行うことによりプロセス模擬出力の定常解を
求め、さらにプロセスモデルに与える制御因子の設定値
を変更しつつ定常解の算出を繰り返すことにより、任意
の運転条件等に基づいて制御因子の最適操作量を求め
る。これにより、複数の相互に干渉する運転制御パラメ
ータであっても、試行錯誤的に決定するのではなく、任
意の標準基準や制約条件を満たすように定量的に決定す
ることが可能となる。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.
【0038】したがってこの装置を例えば一定周期毎に
最適解を出力させるように運用して硝化・脱硝プロセス
の運転支援に供することが可能となり、これにより次の
ような諸効果を奏する。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.
【0039】(1)硝化・脱硝プロセスのより適切な運
転が可能となるので処理水質が向上し、放流先環境への
影響を最大限に抑えることができる。(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.
【0040】(2)効率的な運転が可能となるので、運
転コストの面でも有利となる。(2) Efficient operation is possible, which is advantageous in terms of operation cost.
【0041】(3)熟練を要さず容易かつ正確に運転が
できるので、人的資源の効率的運用が可能となり省力化
に寄与する。(3) Since operation can be performed easily and accurately without skill, human resources can be efficiently used, which contributes to labor saving.
【図1】この発明の一実施例に係る活性汚泥プロセスの
運転支援システムの機能ブロック図。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.
【図2】状態変数と主要反応のネットワークを示す線
図。FIG. 2 is a diagram showing a network of state variables and main reactions.
【図3】図1のシステムの動作を示すフローチャート。FIG. 3 is a flowchart showing the operation of the system of FIG. 1;
【図4】定常解析結果を示すグラフ。FIG. 4 is a graph showing the results of steady-state analysis.
【図5】定常解析結果を示すグラフ。FIG. 5 is a graph showing the results of steady-state analysis.
【図6】定常解析結果を示すグラフ。FIG. 6 is a graph showing the results of steady-state analysis.
【図7】定常解析結果を示すグラフ。FIG. 7 is a graph showing the results of steady-state analysis.
【図8】定常解析結果を示すグラフ。FIG. 8 is a graph showing the results of steady-state analysis.
【図9】最適化演算結果を示すグラフ。FIG. 9 is a graph showing optimization calculation results.
【図10】循環式硝化・脱硝プロセスの概要を示す説明
図。FIG. 10 is an explanatory diagram showing an outline of a circulating nitrification / denitration process.
【図11】循環式硝化・脱硝プロセスの制御部の概要を
示す説明図。FIG. 11 is an explanatory diagram showing an outline of a control unit of a circulation type nitrification / denitration process.
12…プロセスモデル部 13…解析部 14…処理プロセスモデル部 15…制御プロセスモデル部 19…定常解析部 20…最適化演算部 21…運転条件最適化部 22…動力学的パラメータ最適化部 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
───────────────────────────────────────────────────── フロントページの続き (58)調査した分野(Int.Cl.7,DB名) G02F 3/34 101 G02F 3/12 ──────────────────────────────────────────────────続 き Continued on the front page (58) Field surveyed (Int.Cl. 7 , DB name) G02F 3/34 101 G02F 3/12
Claims (2)
を行う処理プロセスを対象とし、この処理プロセスの制
御因子の最適値を求める装置において、 処理プロセスの主要反応ネットワークを表す連立非線形
微分方程式を線形式に置き換えて処理プロセスを模擬し
た処理プロセスモデル部および制御アルゴリズムを模擬
した制御プロセスモデル部を有し、両モデル部で相互に
演算を行うプロセスモデル部と、 処理プロセス状態変数および制御因子の設定値から処理
プロセス模擬出力の定常解を求めるものであって、前記
プロセスモデル部に繰り返し計算を行わせ、この計算結
果を用いて数値収束計算を行い、この収束値を前記定常
解とする定常解析部と、 前記制御因子の設定値を変更しつつ、定常解析部を繰り
返し動作させることにより、制御因子の最適化演算を行
う制御因子最適化演算部とを備えたことを特徴とする硝
化脱窒プロセスシミュレータ。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.
レータにおいて、前記処理プロセスモデル部の構築に使
用される動力学的パラメータを変更しつつ、プロセスモ
デル部を繰り返し動作させることにより、動力学的パラ
メータの最適化演算を行う動力学的パラメータ最適化演
算部を備えたことを特徴とする請求項1記載の硝化脱窒
プロセスシミュレータ。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.
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JP03334614A JP3123167B2 (en) | 1991-12-18 | 1991-12-18 | Nitrification and denitrification process simulator |
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JP3123167B2 true JP3123167B2 (en) | 2001-01-09 |
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JP5614630B2 (en) * | 2010-07-05 | 2014-10-29 | 横河電機株式会社 | Plant operation support apparatus and plant operation support method |
JP6250388B2 (en) * | 2013-12-27 | 2017-12-20 | 川崎重工業株式会社 | Operating condition calculation device and water treatment system provided with the same |
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CN107986441A (en) * | 2017-12-20 | 2018-05-04 | 安徽大学 | The Forecasting Methodology of nano-ZnO exposure level in anaerobic waste water biological treatment system |
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