JP7384476B2 - Optimization method and system for ventilation structure for boiler combustion - Google Patents

Description

The present invention relates to the technical field of boiler control, and specifically relates to a method for optimizing a boiler combustion ventilation structure and a system for optimizing a boiler combustion ventilation structure.

The combustion efficiency of a boiler varies depending on the amount of air supply and the position of the supply air, and has a direct impact on a boiler company's profits. In related art, supply air parameters are typically adjusted manually according to rules of thumb. However, considering the complex chemical reactions and severe temperature hysteresis during the combustion process, the objective of optimizing the ventilation structure cannot be achieved after adjusting the air supply parameters.

In order to solve the above technical problems, the present invention provides a method for optimizing boiler combustion, which greatly improves the efficiency of boiler combustion and at the same time increases the profit of boiler by optimizing the ventilation structure. be able to.

The technical solutions used in the present invention are as follows:

A method for optimizing a ventilation structure for boiler combustion, the method comprising:
determining variables to be optimized for the ventilation structure for boiler combustion, wherein the variables to be optimized include on/off amounts and adjustment amounts of the primary air valve and the secondary air valve, and the objective optimization for the annual cost of the boiler is determined; calculating according to the objective optimization function and the constraints to obtain the variable to be optimized that satisfies the constraints and has the lowest annual cost; and generating an optimization control method for a boiler combustion ventilation structure that satisfies the constraint conditions and has the lowest annual cost in accordance with the variable to be optimized that satisfies the constraint conditions and has the lowest annual cost.

ここで、

は供給機器iの電力消費量、Ｌ_ｉは機器損失、

は供給燃料rの使用量、

は前記供給機器iの供給燃料rの使用量に対応するコスト係数である。
供給機器の電力消費量は、以下の式で生成され、

ここで、

はボイラーの燃焼効率に直接影響を与える機器の電力消費量、

はボイラーの燃焼効率に間接的に影響を与える機器の電力消費量、δとσは重み係数である。 The objective optimization function is minJ=OP+CAP,
Here, J is annual cost, OP is annual operating cost, and CAP is annual fixed investment cost.
The annual operating cost is calculated using the following formula,

here,

is the power consumption of supply device i, L _i is the device loss,

is the consumption of supplied fuel r,

is a cost coefficient corresponding to the amount of fuel r used by the supply device i.
The power consumption of the supply equipment is generated by the following formula,

here,

is the power consumption of equipment that directly affects the combustion efficiency of the boiler,

is the power consumption of equipment that indirectly affects the combustion efficiency of the boiler, and δ and σ are weighting factors.

Constraints include the amount of adjustment of the primary air valve, the amount of adjustment of the secondary air valve, and the combinatorial relationship between the transfer variables.

Constraints include the amount of adjustment of the primary air valve, the amount of adjustment of the secondary air valve, and the combinatorial relationship between the transfer variables.

、前記二次空気弁の調節上限と下限

、風量変動の上限と下限Ｄ^Ｕ≦Ｄ≦Ｄ^Ｌを含む。
ボイラー燃焼用通風構造の最適化システムであって、それは、決定モジュールであって、ボイラー燃焼用通風構造の最適化すべき変数を決定するために使用され、ここで、前記最適化すべき変数が一次空気弁と二次空気弁のオンオフ量と調節量を含む決定モジュールと、確立モジュールであって、ボイラーの年間コストに関する目的最適化関数とそれに対応する制約条件を確立するために使用される確立モジュールと、取得モジュールであって、前記目的最適化関数と前記制約条件に従って計算し、制約条件を満たし、かつ、年間コストが最も低い前記最適化すべき変数を取得するために使用される取得モジュールと、生成モジュールであって、取得された制約条件を満たし、かつ年間コストが最も低い前記最適化すべき変数に応じて、制約条件を満たし、かつ年間コストが最も低いボイラー燃焼用通風構造の最適化制御方式を生成するために使用される生成モジュールとを含む。 Constraints further include variable trade-off relationships, upper and lower limits of adjustment of the primary air valve.

, the upper and lower adjustment limits of the secondary air valve.

, including the upper and lower limits of air volume fluctuation D ^U ≦ D ≦ ^DL .
A system for optimizing a boiler combustion ventilation structure, the determination module being used to determine a variable to be optimized for a boiler combustion ventilation structure, wherein the variable to be optimized is a primary air a determination module including on/off amounts and adjustment amounts for the valves and secondary air valves; and an establishment module used to establish an objective optimization function and corresponding constraints regarding the annual cost of the boiler. , an acquisition module used to calculate according to the objective optimization function and the constraints, and to acquire the variable to be optimized that satisfies the constraints and has the lowest annual cost; An optimization control method for a boiler combustion ventilation structure that satisfies the obtained constraints and has the lowest annual cost in accordance with the variable to be optimized that is a module and that satisfies the acquired constraints and has the lowest annual cost. and a generation module used to generate.

The beneficial effects of the present invention are as follows:
By optimizing the ventilation structure, the present invention can significantly improve the efficiency of boiler combustion and at the same time increase the profitability of the boiler.

FIG. 1 is a flowchart of a method for optimizing a boiler combustion ventilation structure in an embodiment of the present invention. FIG. 2 is a diagram illustrating the combination of primary ventilation adjustment variables, secondary ventilation adjustment variables, and transfer variables in one embodiment of the present invention. FIG. 3 is a schematic diagram of online prediction of additional quantities using a rolling optimization strategy in one embodiment of the present invention. FIG. 4 is a schematic block diagram of a boiler combustion ventilation structure optimization system according to an embodiment of the present invention.

The following clearly and completely describes the technical solutions in the embodiments of the invention with reference to the accompanying drawings in the embodiments of the invention, but the described embodiments are only part of the embodiments of the invention. It is clear that this is not the whole story. All other embodiments that can be obtained by those skilled in the art based on the embodiments of the present invention without creative efforts will fall within the protection scope of the present invention.

FIG. 1 is a flowchart of a method for optimizing a boiler combustion ventilation structure according to an embodiment of the present invention.

Specifically, as shown in FIG. 1, the method for optimizing the ventilation structure for boiler combustion in the embodiment of the present invention may include the following steps:
S1: Determine the variables to be optimized for the ventilation structure for boiler combustion.
Here, the variables to be optimized include on/off amounts and adjustment amounts of the primary air valve and the secondary air valve.
S2, establish the objective optimization function and its corresponding constraints for the annual cost of the boiler;

According to one embodiment of the invention, the objective optimization function is:
minJ=OP+CAP(1)
Here, J is annual cost, OP is annual operating cost, and CAP is annual fixed investment cost.
In other words, we replace the traditional complicated and inaccurate combustion efficiency mechanism calculation formula with a general formula, and instead of calculating complicated combustion efficiency and profits, we use economical calculations that take all factors into account. After obtaining the final optimization results, the total combustion efficiency and annual profit of the boiler can be determined by simple conversion.

ここで、

は供給機器iの電力消費量、Ｌ_ｉは機器損失、

は供給燃料rの使用量、

は供給機器iの供給燃料rの使用量に対応するコスト係数であり、下付き文字ｒは燃料ｒを表し、ｉとｒを二重下付き文字にすることでボイラーのどこかに位置決めすることができる。 According to one embodiment of the present invention, the annual operating cost can be generated using the following formula:

here,

is the power consumption of supply device i, L _i is the device loss,

is the consumption of supplied fuel r,

is the cost coefficient corresponding to the consumption of supplied fuel r of supply equipment i, where the subscript r represents the fuel r, and by making i and r a double subscript, it can be positioned somewhere in the boiler. Can be done.

ここで、

はボイラーの燃焼効率に直接影響を与える機器の電力消費量、

はボイラーの燃焼効率に間接的に影響を与える機器の電力消費量、δとσは重み係数である。 Here, the power consumption of the supply equipment can be generated using the following formula,

here,

is the power consumption of equipment that directly affects the combustion efficiency of the boiler,

is the power consumption of equipment that indirectly affects the combustion efficiency of the boiler, and δ and σ are weighting factors.

Here, the subscript i represents the equipment, i∈I, and the subscripts x and Nx represent the equipment that has a direct and indirect impact on the combustion efficiency of the boiler, respectively, and the combination of the two represents the Construct a set, I={1, 2, . ．． ．． x, N1, N2, . ．． ．． , Nx}. δ and σ are weighting coefficients, their values are determined by heuristic rules, and larger coefficients can be obtained for devices that directly affect them.

ここで、Ｍは新しく追加された機器の年間コスト、下付き文字ｋは新しく追加された機器を表す。 Note that CAP in equation (1) can be calculated using the following equation.

Here, M represents the annual cost of newly added equipment, and the subscript k represents the newly added equipment.

Here, the constraint conditions may include the following several conditions.
According to one embodiment of the invention, the constraints include a combinatorial relationship between the primary air valve adjustment, the secondary air valve adjustment, and the transfer variable.

（一次送風調節変数）、二次空気弁の調節量μ_ｂ（二次送風調節変数）、および伝達変数γ_{ｍａｘ｛ｌ，ｂ｝＋ｖ}間の組み合わせ関係を確立することが可能である。本発明の一実施例において、すべての可能な関係の組み合わせを列挙することができ、一次送風調節変数の変化が伝達変数に、次に伝達変数から二次送風調節変数に伝送し、変数の変化を「真の」中間変数と見なされ、変数間の複雑な相互関係を整理する必要なく、変数の変化伝送のプロセスは、関係割り当てのプロセスであり、そうすると、送風調節量と関連する変数の間のメカニズム関係を正確に説明できない元の状況を変えることができる。モデルの説明は以下のとおりであり、

ここで、上記モデルは、ボイラー燃焼過程における変数の変化を記述するために使用される、送風関連変数の全要素入力生成モデルである。ここで、γ_{ｍａｘ｛ｌ，ｂ｝＋ｖ}（ｔ+１）とμ_ｂ（ｔ+１）はそれぞれ次の瞬間の伝達と二次送風機の出力変数で、

とγ_{ｍａｘ｛ｌ，ｂ｝＋ｖ}（ｔ）は前の瞬間の入力変数である。

は改良されたファンクションチェーン人工ニューラルネットワークによってトレーニングされた全要素入力モデルである。 Specifically, as shown in Figure 2, the amount of adjustment of the primary air valve

It is possible to establish a combinatorial relationship between (primary airflow adjustment variable), the adjustment amount μ _b of the secondary air valve (secondary airflow adjustment variable), and the transfer variable γ _{max {l,b}+v} . In one embodiment of the invention, all possible relationship combinations can be enumerated, such that a change in the primary airflow regulation variable is transmitted to a transfer variable, which in turn is transferred to a secondary airflow adjustment variable, and a change in the variable is considered a "true" intermediate variable, without the need to organize complex interrelationships between variables, the process of variable transmission is a process of relational assignment, so that between the air adjustment amount and the associated variable It is possible to change the original situation in which the mechanistic relationship cannot be accurately explained. The model description is as follows,

Here, the above model is an all-element input generation model of ventilation-related variables, which is used to describe changes in variables in the boiler combustion process. Here, γ _{max {l, b}+v} (t+1) and μ _b (t+1) are the next instantaneous transmission and secondary blower output variables, respectively;

and γ _max{l,b}+v (t) are the input variables at the previous moment.

is a full-element input model trained by an improved function chain artificial neural network.

According to another embodiment of the present invention, the constraint condition further includes a combination of the adjustment amount of the primary air valve, the adjustment amount of the secondary air valve, and the on/off amount.

ここで、ｚは整数変数である。 Specifically, the on-off problem has a one-to-one correspondence with the above model, i.e., if a certain airflow has an amount of regulation, the corresponding switch must be closed, where the model description is as follows: and

Here, z is an integer variable.

と、（３）二次空気弁の調節量の上限と下限

と、（４）風量変動の上限と下限Ｄ^Ｕ≦Ｄ≦Ｄ^Ｌとを含む。ここで、制約条件（１）に対して、材料層の厚さを以下のように記述し、
Ｍ＝Ｆｈρ、（９）
ここで、Ｍは炉内のベッド材の総量、ｈは材料層の厚さ、Ｆは炉床の面積、ρはベッド材の積み上げ密度である。また、平均滞留時間ｔ＝Ｍ／ｗを考慮し、ここで、ｗはスラグ排出速度である。 According to a further embodiment of the invention, the constraint is:
(1) a variable trade-off relationship (the previous all-element input generation model allows us to know the air volume produced by any blower in any period of time) and (2) upper and lower bounds on the amount of adjustment of the primary air valve.

and (3) the upper and lower limits of the adjustment amount of the secondary air valve.

and (4) an upper limit and a lower limit of air volume fluctuation D ^U ≦D≦ ^DL . Here, for constraint (1), the thickness of the material layer is described as follows,
M=Fhρ, (9)
Here, M is the total amount of bed material in the furnace, h is the thickness of the material layer, F is the area of the hearth, and ρ is the stacking density of the bed material. Also, consider the average residence time t=M/w, where w is the slag discharge rate.

注意すべきは、収量Ｂはデータ自体の量が多く、データポイントが間違っていたり無効であったりするなど不確定要素が多く存在する可能性があるため、データを直接導入して最適化問題を解くと、結局、最適解が得られない、あるいは解けないということになり、したがって、感度分析は、最適化問題を解く前の準備作業に過ぎず、最適化の反復中に実行する必要はない。 However, since all the variables and parameters in equation (9) are related to boiler combustion, and their actual changes cannot be explored by simple mechanism equations, here we also include information-granularity time series regression prediction by support vector machine (SVM). However, unlike the above process, here we need to consider the framework of the mechanism equation above, that is, it is necessary to take initial values and fix some of the parameters and input weights of the mechanism equation. . Here, the fixed parameters selected by the data-driven sensitivity evaluation method: the parameters corresponding to small sensitivity of yield B are as shown in the following equation:

It should be noted that yield B requires a large amount of data and there may be many uncertainties such as wrong or invalid data points, so it is not possible to directly introduce the data and solve the optimization problem. If we solve the problem, the result is that the optimal solution will not be obtained or cannot be solved, so sensitivity analysis is only a preliminary step before solving the optimization problem and does not need to be performed during optimization iterations. .

それをメカニズムモデルに組み込んだ後、次のようになり、

それにより、流動化状態での材料層の厚さの関係式を求めることができる。 The basic idea of fuzzy particles is satisfied, the fuzzy particles can represent the original data reasonably, and at the same time, the need is met that fuzzy particles should have a certain degree of specificity. Then, establish a function regarding X as follows,

After incorporating it into the mechanism model, it looks like this,

Thereby, a relational expression for the thickness of the material layer in the fluidized state can be determined.

ここで、ｈｇは流動化状態での材料層の厚さの初期値である静止材料層の厚さ、ｇは重力加速度、λは材料層の抵抗，

は炭種によって決定されるスケールファクターである。
式（１３）から、材料層の抵抗は静止材料層の厚さに比例することが分かり、すなわち材料層が厚いほど流動化抵抗は大きくなる。同時に、送風量と流動化状態は以下の平衡関係を満たし、
ｌ＝ｖ＋λ（１４）
ここで、ｌは空気室の静圧、ｖは通風板の抵抗である。 Furthermore, the relationship between the thickness of the stationary material layer and the air volume is as follows:

Here, hg is the thickness of the static material layer which is the initial value of the thickness of the material layer in the fluidized state, g is the gravitational acceleration, λ is the resistance of the material layer,

is a scale factor determined by the coal type.
From equation (13) it can be seen that the resistance of the material layer is proportional to the thickness of the static material layer, ie the thicker the material layer, the greater the fluidization resistance. At the same time, the air flow rate and fluidization state satisfy the following equilibrium relationship,
l=v+λ(14)
Here, l is the static pressure of the air chamber, and v is the resistance of the ventilation plate.

一次送風量を増やすと、ボイラー内の流動化状態が変化し、底層の流動化速度が加速し、ボイラーの燃焼効率を向上させるためには、底層材料の滞留時間を長くする必要があり、すなわち、前記ボイラー燃焼の最適化に関する研究成果には火格子供給量

が追加され、該追加量は一次送風調節量

に直接関係するが、明確なメカニズム的関係を確立することは困難であり、したがって、ローリング最適化戦略を使用して追加量をオンラインで予測し、その具体的なプロセスは図３に示すとおりである。 In actual applications, there is a direct relationship between air flow rate and air pressure, and the performance of an air blower is usually expressed by a relationship curve between the two. Therefore, we collected data to establish a database between material layer resistance and air flow rate, and still predicted the relationship between the two with information-granular time series regression based on support vector machine (SVM).

Increasing the primary air flow will change the fluidization state in the boiler, accelerate the fluidization speed of the bottom layer, and in order to improve the combustion efficiency of the boiler, the residence time of the bottom layer material needs to be increased, i.e. , the research results on optimization of boiler combustion include the grate supply amount.

is added, and the additional amount is the primary ventilation adjustment amount.

Although it is directly related to the above, it is difficult to establish a clear mechanistic relationship, so a rolling optimization strategy is used to predict the additional amount online, and the specific process is as shown in Figure 3. be.

を相関させるために、一般化された線形状態空間モデルを確立する。該モデルは、離散時間線形状態空間モデルに基づいている。 According to the proposed rolling optimization strategy, the present invention can calculate the additional grate supply amount of adjacent moments

We establish a generalized linear state-space model to correlate the The model is based on a discrete-time linear state-space model.

のように記述される。ここで、

は瞬間ｍでの追加の火格子供給量であり、該変数の将来の状態も表す。該追加の火格子供給量に関連する現在の状態ベクトルＸ^ｍ，ｉは、一次送風調節量で構成される。

は、関連する入力変数を表す。ＡとＢはそれぞれ状態行列と入力行列である。該モデルは正確で連続的なモデルの離散化形式であるため、該モデルとボイラー燃焼力学モデルとの不一致はない。このように、上記離散モデルは、追加の火格子供給量と一次送風調節量を相関させる。 For example, a discrete-time linear state-space model is

It is written like this. here,

is the additional grate supply at instant m and also represents the future state of this variable. The current state vector X ^m,i associated with the additional grate supply consists of the primary airflow adjustments.

represents the relevant input variable. A and B are the state matrix and input matrix, respectively. Since the model is an accurate and continuous discretized form of the model, there is no discrepancy between the model and the boiler combustion dynamics model. Thus, the discrete model correlates the additional grate feed with the primary air adjustment.

のように記述される。ここで、

、追加の火格子供給量を調節するための一次送風調節量の制御動作を表し、かつ、それは、追加の火格子供給量の将来の状態に影響を与える。Ａ^*とＢ^*はそれぞれ将来の状態での追加の火格子供給量と追加の火格子供給量に対する制御動作の影響を表す。そして、ローリング最適化戦略を上記モデルに導入することで、

のようになる。ここで、Ａ^**とＢ^**は、追加の火格子供給量に対する前回の調整がこの時点での状態に与える影響を表し、Ｃ^**の意味も同じように理解できる。

はそれぞれ、瞬間ｍ－１での追加の火格子供給量の将来状態での一次送風調節量とその制御動作を表す。ソリューションの品質の観点から、ワンショットのオフライン最適化プロセスは、各瞬間にこの瞬間に対する最適なパフォーマンスを取得することを目的としているが、その制限は、取得される最適ではないソリューションにある。逆に、ローリング最適化は、時間範囲で繰り返しオンラインで実行され、現時点での特定の変数で将来の時点のキー変数を決定するため、ボイラー燃焼の動的モデルの不一致、時変外乱、ランダム外乱などの要因を考慮し、時間的に更新および修正してプロセス全体の動的最適化を達成することができる。 Since the additional grate supply reflects the primary air adjustment amount, the relationship between the current and future state of the additional grate supply is extracted from the above model,

It is written like this. here,

, represents a control action of the primary airflow adjustment to adjust the additional grate feed, and it affects the future state of the additional grate feed. A ^* and B ^* represent the additional grate feed in the future state and the effect of the control action on the additional grate feed, respectively. Then, by introducing a rolling optimization strategy to the above model,

become that way. Here, A ^** and B ^** represent the effect of the previous adjustment to the additional grate feed on the current state, and the meaning of C ^** can be understood in the same way.

represent the primary air adjustment amount and its control action in the future state of the additional grate supply at the moment m-1, respectively. From the perspective of solution quality, the one-shot offline optimization process aims to obtain the optimal performance for this moment at each moment, but its limitation lies in the suboptimal solutions obtained. Rolling optimization, on the contrary, is performed online repeatedly over a time range to determine key variables at future points in time with certain variables at the current moment, thus eliminating inconsistencies, time-varying disturbances, and random disturbances in the dynamic model of boiler combustion. It can be updated and modified over time to achieve dynamic optimization of the entire process.

However, if the additional grate supply amount is too large as adjusted by the primary airflow rate, the fluctuation of the gas-solid mixture will be large, which is directly linked to the change of the secondary airflow ventilation structure, so the combination of the above airflow adjustment amount The introduction of optimization relationships optimizes the secondary air distribution, stabilizes the ventilation structure, ensures complete combustion, and improves boiler efficiency.

S3: Calculate according to the objective optimization function and constraints to obtain a variable to be optimized that satisfies the constraints and has the lowest annual cost.

S4: Generate an optimization control method for the boiler combustion ventilation structure that satisfies the obtained constraints and has the lowest annual cost, according to the variable to be optimized that satisfies the obtained constraints and has the lowest annual cost.

Specifically, after determining the objective optimization function and constraints using the above method, calculations are performed according to the objective optimization function and constraints to find the variable to be optimized that satisfies the constraints and has the lowest annual cost. , it is possible to generate an optimized control method for boiler combustion that satisfies the constraints and has the lowest annual cost. As a result, not only can boiler combustion efficiency and profitability be improved, but the challenge of optimizing boiler ventilation structures can be solved by taking into account the correlation between key variables.

In summary, according to the method for optimizing a ventilation structure for boiler combustion according to an embodiment of the present invention, the variables to be optimized for the ventilation structure for boiler combustion are determined, and the objective optimization function for the annual cost of the boiler and its corresponding and calculate according to the objective optimization function and the constraints to obtain the variable to be optimized that satisfies the constraints and has the lowest annual cost. An optimized control method for a boiler combustion ventilation structure that satisfies the constraints and has the lowest annual cost is generated according to the variable to be optimized that satisfies the constraint and has the lowest annual cost. As a result, by optimizing the ventilation structure, the combustion efficiency of the boiler can be significantly improved, and at the same time, the profitability of the boiler can be increased.
Corresponding to the boiler combustion ventilation structure optimization method of the above embodiment, the present invention also provides a boiler combustion ventilation structure optimization system.

As shown in FIG. 4, the boiler combustion ventilation structure optimization system may include a determination module 100, an establishment module 200, an acquisition module 300, and a generation module 400.

Here, the determination module 100 is used to determine variables to be optimized for the ventilation structure for boiler combustion, and the variables to be optimized include on/off amounts and adjustment amounts of the primary air valve and the secondary air valve. , the establishment module 200 is used to establish an objective optimization function and corresponding constraints regarding the annual cost of the boiler, the acquisition module 300 calculates according to the objective optimization function and the constraints, and satisfies the constraints; and is used to obtain a variable to be optimized with the lowest annual cost, and the generation module 400 satisfies the obtained constraint and satisfies the constraint according to the variable to be optimized with the lowest annual cost, It is used to generate an optimized control scheme for boiler combustion ventilation structure with the lowest annual cost.

For a more specific embodiment of the system for optimizing the ventilation structure for boiler combustion in the embodiment of the present invention, reference can be made to the embodiment of the method for optimizing the ventilation structure for boiler combustion, and will not be repeated here. Please note.

According to the system for optimizing the ventilation structure for boiler combustion in the embodiment of the present invention, the determination module determines the variables to be optimized for the ventilation structure for boiler combustion, and the establishment module determines the objective optimization function regarding the annual cost of the boiler. and the corresponding constraints are established, the acquisition module calculates the objective optimization function according to the constraints, and the variable to be optimized that satisfies the constraints and has the lowest annual cost is obtained, and the generation module calculates According to the variable to be optimized that satisfies the obtained constraints and has the lowest annual cost, an optimization control method for the boiler combustion ventilation structure that satisfies the constraints and has the lowest annual cost is generated. As a result, by optimizing the ventilation structure, the combustion efficiency of the boiler can be significantly improved, and at the same time, the profitability of the boiler can be increased.

In the description of the invention, the terms "first" and "second" are used for descriptive purposes only and as indicating or implying relative importance or number of technical features presented. It cannot be understood as implied. Therefore, features defined as "first" and "second" may include one or more of the features explicitly or implicitly. "Plurality" means two or more, unless explicitly limited.

In the present invention, unless otherwise specified and limited, terms such as "attachment", "connection", "connection", "fixation" etc. should be understood in a broad sense, such as fixed connection, removable connection, etc. or may be an integral connection, a mechanical connection or an electrical connection, a direct connection, an indirect connection through an intermediate medium, a connection within two elements or an interaction relationship between two elements. It may be. For those skilled in the art, the specific meaning of the above terms in the context of the present invention can be understood according to the particular situation.

In the present invention, unless otherwise specified and limited, a first feature that is "above" or "below" a second feature may be a direct contact between the first feature and the second feature, or an intermediary There may also be indirect contact between the first feature and the second feature via. Furthermore, a first feature being "on", "above", or "on top of" a second feature means that the first feature is directly above or diagonally above the second feature, or simply that the first feature It can mean that a feature is horizontally taller than a second feature. A first feature being "below", "beneath", or "beneath" a second feature means that the first feature is directly below or diagonally below the second feature, or simply that the first feature is It can mean that the horizontal height is lower than the second feature.

In the description herein, references to terms such as "one embodiment," "some embodiments," "example," "particular example," or "some examples" refer to the A particular feature, structure, material or characteristic described in connection with an embodiment or example is meant to be included in at least one embodiment or example of the invention. As used herein, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the particular features, structures, materials or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, those skilled in the art can combine different embodiments or examples and features of different embodiments or examples described herein without contradicting each other.

In the flowchart illustrations or herein, the illustrations of any process or method may be illustrated as modules, segments, or portions of code containing one or more executable instructions for implementing particular logical functions or process steps. , and the scope of the preferred embodiments of the invention includes additional implementations, depending on the functionality involved, as understood by those skilled in the art to which embodiments of the invention belong. , functions may be performed out of the order shown or discussed, including in a substantially simultaneous manner or in reverse order.

The logic and/or steps illustrated in the flowcharts or described herein may be implemented, for example, in an instruction execution system, device, or apparatus (e.g., a computer-based system, a system that includes a processor, or an instruction execution system, device, or apparatus). considered as a contiguous list of executable instructions for performing a logical function that can be tangibly implemented in any computer-readable medium for use by or in conjunction with other systems capable of fetching and executing instructions from a device be able to. As used herein, "computer-readable medium" refers to any computer-readable medium that can contain, store, communicate, transmit, or transfer a program for use by or in conjunction with an instruction execution system, device, or equipment. device. More specific examples (non-exhaustive list) of computer readable media include electrical connections having one or more wires (electronic devices), portable computer disk cartridges (magnetic devices), random access memory (RAM). , read-only memory (ROM), erasable editable read-only memory (EPROM or flash memory), fiber optic devices, and portable compact disk read-only memory (CDROM). Furthermore, a computer-readable medium is a computer-readable medium in which said program is obtained electronically, for example by optical scanning of paper or other medium, and subsequently edited, decoded, or otherwise processed as necessary in a computer-readable medium. Since it is stored in memory, the program may be on printable paper or other suitable media.

It should be understood that various parts of the invention can be implemented in hardware, software, firmware, or a combination thereof. In the embodiments described above, the steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, when implemented in hardware, any or a combination of the following techniques well known in the art may be used, as another implementation, for data signals. Discrete logic circuits with logic gates to realize logic functions, application-specific integrated circuits with appropriate combinatorial logic gates, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

Those skilled in the art will be able to instruct relevant hardware to perform all or some of the steps performed by the method of the above embodiments by a program, and the program can be stored on a computer-readable storage medium; When the program is executed, it can be understood to include one or a combination of the steps of the method embodiments.

Further, each functional unit in each embodiment of the present invention may be integrated into one processing module, each unit may physically exist alone, or two or more units may be integrated into one processing module. may be integrated into. The integration module described above can be implemented in the form of hardware, or can also be implemented in the form of software functional modules. The integration module may also be stored on a computer-readable storage medium when implemented in the form of a software functional module and sold or used as a separate product.

The storage medium may be a read-only memory, a magnetic disk, an optical disk, or the like. Although the embodiments of the present invention have been shown and described above, the above embodiments are merely illustrative and are not to be construed as limiting the present invention, and variations, modifications, substitutions, and modifications to the above embodiments are It is to be understood that what can be done by one skilled in the art is within the scope of the invention.

Claims (4)

A method for optimizing a ventilation structure for boiler combustion, the method comprising:
determining variables to be optimized of a boiler combustion ventilation structure, wherein the variables to be optimized include adjustments of primary air valves and secondary air valves;
establishing an objective optimization function and corresponding constraints for the annual cost of the boiler;
calculating according to the objective optimization function and the constraints to obtain the variable to be optimized that satisfies the constraints and has the lowest annual cost;
generating an optimization control method for a boiler combustion ventilation structure that satisfies the obtained constraints and has the lowest annual cost, according to the variable to be optimized that satisfies the obtained constraints and has the lowest annual cost; including,
The objective optimization function is minJ=OP+CAP,
Here, J is annual cost, OP is annual operating cost, CAP is annual fixed investment cost,
The annual operating cost is calculated using the following formula,

here,

is the power consumption of supply device i, L _i is the device loss,

is the consumption of supplied fuel r,

is the cost coefficient corresponding to the usage amount of the supply fuel r of the supply equipment i
A method for optimizing a ventilation structure for boiler combustion, characterized by: The power consumption of the supply equipment is generated by the following formula,

here,

is the power consumption of equipment that directly affects the combustion efficiency of the boiler,

2. The method for optimizing a ventilation structure for boiler combustion according to claim 1 , wherein: is the power consumption of equipment that indirectly affects the combustion efficiency of the boiler, and δ and σ are weighting coefficients. The method of optimizing a ventilation structure for boiler combustion according to claim 1, wherein the constraint conditions include an adjustment amount of the primary air valve, an adjustment amount of the secondary air valve, and a combination relationship between transfer variables. . An optimization system for ventilation structure for boiler combustion ,
a determination module used to determine variables to be optimized of a boiler combustion ventilation structure, wherein the variables to be optimized include adjustments of a primary air valve and a secondary air valve;
an establishment module used to establish an objective optimization function and corresponding constraints regarding the annual cost of the boiler;
an acquisition module that is used to calculate according to the objective optimization function and the constraint conditions, and to acquire the variable to be optimized that satisfies the constraint conditions and has the lowest annual cost;
An optimization control method for a boiler combustion ventilation structure that satisfies the acquired constraints and has the lowest annual cost according to the variable to be optimized that is a generation module and that satisfies the acquired constraints and has the lowest annual cost. and a generation module used to generate the
The objective optimization function is minJ=OP+CAP,
Here, J is annual cost, OP is annual operating cost, CAP is annual fixed investment cost,
The annual operating cost is calculated using the following formula,

here,

is the power consumption of supply device i, L _i is the device loss,

is the consumption of supplied fuel r,

is the cost coefficient corresponding to the usage amount of the supply fuel r of the supply equipment i
An optimization system for ventilation structures for boiler combustion, which is characterized by:

Images