Heating furnace energy efficiency prediction and adjustment method based on subjective and objective combination weighting

Abstract

The invention provides a heating furnace energy efficiency prediction and adjustment method based on subjective and objective combination weighting, and belongs to the technical field of oil and gas production. The method comprises the following steps: calculating a subjective weight of a heating furnace energy efficiency evaluation index by adopting an analytic hierarchy process; calculating objective weights of the heating furnace energy efficiency evaluation indexes by adopting an entropy weight evaluation method; calculating the comprehensive weight of the oil field heating furnace energy efficiency evaluation index based on a combined weighting method; performing linear regression on the heating furnace evaluation index comprehensive influence factor and the heating furnace thermal efficiency, and fitting to obtain a heating furnace thermal efficiency prediction model; judging whether the precision of the heating furnace thermal efficiency prediction model meets precision requirements. Accurate prediction of the heating furnace thermal efficiency is achieved, quantitative adjustment is conducted on the heating furnace air-fuel ratio, the heating furnace thermal efficiency calculation method is simplified, and the detection cost is saved.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas production, and in particular to a method for predicting and regulating the energy efficiency of a heating furnace based on subjective and objective combined weighting. Background Art

[0002] Oilfield heating furnaces are the main thermal equipment for oil and gas production, and are one of the important production facilities that are indispensable in the ground gathering, processing and transportation. The purpose of carrying out energy efficiency monitoring of oilfield heating furnaces is to accurately evaluate the operating status of the equipment, diagnose the working conditions of each link, promptly discover problems, and guide the economic operation of the heating furnace. Oilfield heating furnaces have the characteristics of a large number of equipment, small heat load per unit, large flow rate of the heated medium, small required pressure drop and temperature rise of the heated medium, large fluctuations in heat load, and complex coupling of operating parameters. These characteristics result in difficulties in manual analysis and diagnosis, high professional quality requirements, inability to draw diagnostic conclusions in a timely manner, inability to guide economic operation in a timely manner, and inability to quantify adjustments based on experience.

[0003] At the same time, oilfield heating furnace equipment usually optimizes and controls operation by adjusting the air volume, fuel volume and their ratio of the heating furnace. It is a complex industrial process control system with many variable parameters and nonlinear calculation models. The adjustment of gas pressure, composition, calorific value and other factors will cause a large change in the air-fuel ratio. It is a highly complex nonlinear system with large hysteresis and large delay characteristics. The conventional single operating parameter feedback control system cannot achieve optimal operation control. In addition, according to the existing measurement technology, the gas heating furnace needs to measure or test: fuel gas composition, calorific value, fuel gas consumption, medium flow, inlet and outlet medium temperature, furnace body outer surface temperature, exhaust gas composition, exhaust oxygen content, measurement and testing parameters are more, the detection and testing cycle is long, equipment installation and testing are difficult, and the investment is high. With the deepening of digital oilfield construction and the application of Internet of Things technology in oilfield production, there is no solution for analysis, diagnosis and optimal control under the background of online monitoring data of oilfield heating furnace energy efficiency (full time, large data volume, relatively rough).

[0004] Oilfield heating furnace equipment is a complex industrial process control system, involving many variable parameters, nonlinear calculation models and other characteristics. The adjustment of gas pressure, composition, calorific value and other factors causes a large change in the air-fuel ratio, making it even more difficult to optimize the air-fuel ratio of the heating furnace by adjusting the gas pressure, composition and calorific value. It is difficult to accurately find the value of the optimal air-fuel ratio of the oilfield heating furnace equipment, whether it is to establish a mathematical model or adjust the control. The ultimate goal of the heating furnace air-fuel ratio optimization control is to improve the production thermal efficiency of the heating furnace.

[0005] Chinese patent 201610986990.X discloses a gas heating furnace air-fuel ratio optimization control system, the input parameters are used to calculate the oxygen content of the heating furnace, and the oxygen content in the flue gas and the ideal air-fuel ratio are determined by the formula under the conditions of the current indoor temperature and the gas pipeline flow. The above method determines the air-fuel ratio of the heating furnace by calculating the residual oxygen content, and does not reflect the impact of adjusting the parameter value on the thermal efficiency operating conditions of the heating furnace, and cannot determine the optimal thermal efficiency value. It is necessary to test the exhaust components and flue gas oxygen content parameters, which is difficult to operate and requires high professionalism. Summary of the invention

[0006] In order to solve the problems existing in the prior art, the present invention provides a method for predicting and adjusting the energy efficiency of a heating furnace based on subjective and objective combined weighting, and the prediction method comprises the following steps: acquiring collected energy efficiency parameter information from an oilfield energy efficiency acquisition parameter database; using a hierarchical analysis evaluation method to calculate the subjective weight of the heating furnace energy efficiency evaluation index; using an entropy weight evaluation method to calculate the objective weight of the heating furnace energy efficiency evaluation index; based on a combined weighting method, calculating the comprehensive weight of the oilfield heating furnace energy efficiency evaluation index; considering the influence of the weight of each heating furnace energy efficiency evaluation index on the heating furnace thermal efficiency, normalizing the comprehensive weight of the energy efficiency evaluation index, and obtaining the comprehensive influencing factor of the heating furnace evaluation index according to the normalized comprehensive weight; performing linear regression on the comprehensive influencing factor of the heating furnace evaluation index and the heating furnace thermal efficiency, and fitting to obtain a heating furnace thermal efficiency prediction model; judging whether the accuracy of the heating furnace thermal efficiency prediction model meets the accuracy requirements, if the accuracy requirements are met, it means that the heating furnace thermal efficiency prediction model is accurate; if the accuracy requirements are not met, continuously adding energy efficiency parameters to the oilfield energy efficiency acquisition parameter database, and repeating the above steps until the accuracy requirements are met. The present invention can accurately predict the thermal efficiency of a heating furnace, solve the problems of the air-fuel ratio of a heating furnace being difficult to calculate, being unable to quantify and adjust, and having control delays, and simplify the calculation method of the thermal efficiency of a heating furnace, reduce the types of test parameters and the amount of data, and effectively solve the problems of the difficulty in obtaining thermal efficiency calculation parameters and the complexity of the calculation process of existing heating furnaces, while saving equipment installation costs and laboratory testing costs.

[0007] The present invention provides a method for predicting energy efficiency of a heating furnace based on subjective and objective combined weighting, comprising the following steps:

[0008] S100, acquiring collected energy efficiency parameter information from an oilfield energy efficiency collection parameter database, and using the energy efficiency parameter as a heating furnace energy efficiency evaluation index to calculate the heating furnace thermal efficiency, including the following steps:

[0009] S101, calculate the smoke exhaust loss q2;

[0010] S102, calculate the heat loss q3 of incomplete combustion of gas;

[0011] S103, calculating the heat dissipation loss q5;

[0012] S104, calculating the heating furnace thermal efficiency η2 based on the smoke exhaust loss q2, the gas incomplete combustion heat loss q3 and the heat dissipation loss q5;

[0013] S200, using the analytic hierarchy process to calculate the subjective weights of the heating furnace energy efficiency evaluation indicators;

[0014] S300, using the entropy weight evaluation method to calculate the objective weight of the heating furnace energy efficiency evaluation index;

[0015] S400, based on the combined weighting method, calculates the comprehensive weight of the energy efficiency evaluation index of oilfield heating furnaces;

[0016] S500, considering the influence of the weight of each heating furnace energy efficiency evaluation index on the thermal efficiency of the heating furnace, normalizing the comprehensive weight of the energy efficiency evaluation index, and obtaining the comprehensive influence factor of the heating furnace evaluation index according to the normalized comprehensive weight;

[0017] S600, linear regression is performed on the comprehensive influencing factors of the heating furnace evaluation index and the heating furnace thermal efficiency to obtain a heating furnace thermal efficiency prediction model;

[0018] S700, judging whether the accuracy of the heating furnace thermal efficiency prediction model meets the accuracy requirements. If the accuracy requirements are met, it means that the heating furnace thermal efficiency prediction model is accurate. If the accuracy requirements are not met, energy efficiency parameters are continuously added to the oilfield energy efficiency acquisition parameter database, and steps S100-S600 are repeated until the accuracy requirements are met.

[0019] Preferably, the energy efficiency parameter information includes the enthalpy at the flue gas emission outlet of the heating furnace, the enthalpy at the air inlet of the heating furnace, the input heat, the dry gas volume at the flue gas emission outlet of the heating furnace, the volume fraction of carbon monoxide and each hydrocarbon component in the flue gas emitted by the heating furnace, the theoretical heat dissipation loss of the heating furnace, the surface temperature of the furnace body of the heating furnace, the air temperature at the air inlet of the heating furnace, the thermal efficiency of the heating furnace, and the excess air coefficient.

[0020] Preferably, the hierarchical analysis evaluation method is used to calculate the subjective weight of the heating furnace energy efficiency evaluation index, including the following steps:

[0021] S2001, constructing a hierarchical evaluation model for heating furnaces;

[0022] S2002, using set value iteration method to determine the indicator sequence;

[0023] S2003, construct the adjacent criteria comparison matrix;

[0024] S2004, determine the subjective weight of the evaluation criteria.

[0025] Preferably, the hierarchical analysis evaluation method is used to calculate the subjective weight of the heating furnace energy efficiency evaluation index, including the following steps:

[0026] S2001, based on the heating furnace energy efficiency evaluation index, construct a heating furnace hierarchical evaluation model;

[0027] S2002, use the set value iteration method to determine the indicator sequence set, specifically:

[0028] Assume that the heating furnace has m energy efficiency evaluation indicators, there are P experts to evaluate and score the heating furnace energy efficiency, the kth (1≤k≤P) expert scores the heating furnace energy efficiency evaluation indicator, x is the score of a heating furnace energy efficiency evaluation indicator, and the energy efficiency evaluation indicator set of the heating furnace is composed of

[0029] Each evaluation index is formed into different score levels, and the full score of index m is 100 points;

[0030] The expert weight λ is introduced. According to the data analysis results of the fit between the expert’s historical evaluation and the objective indicators, a weight value is assigned to each expert, which is denoted as λ1=(λ1,λ2,…,λ P );

[0031] A questionnaire on expert evaluation ability is issued to a preset number of people, and a weight value is assigned to each expert according to the questionnaire results, denoted as λ2=(λ1,λ2,…,λ P ), and use the weighted average method to process λ1 and λ2 to form an expert weight set λ=(λ1,λ2,…,λ P );

[0032] The energy efficiency evaluation indicators of heating furnaces in the energy efficiency evaluation indicator subset of heating furnaces are processed in descending order to form a new sequence set of energy efficiency evaluation indicators of heating furnaces.

[0033] S2003, constructing an adjacent criterion comparison matrix, specifically including:

[0034] The indicator sequence set determined by set value iteration method Based on the fuzzy interval value method, the value r of the kth factor in the adjacent criterion comparison matrix is ​​obtained. k :where r k =1.0, indicating and The importance of k =1.2, indicating Compare Slightly important, k =1.4, Compare Obviously important, k =1.6, indicating Compare Strongly important; and are the new heating furnace energy efficiency evaluation index set X after being arranged in descending order * The k-1th index and the kth index in the equation are used; here, the order relationship analysis method is used to calculate the subjective weight, which is the most commonly used and recognized value. k Numerical value;

[0035] P experts compare the importance of adjacent indicators and obtain the element [r ji ′,r ji ″], the set of elements constitutes the adjacent criterion comparison matrix;

[0036] S2004, determine the subjective weights of the evaluation criteria, as follows:

[0037] The element pair [r ji ′,r ji The interval value of ″] is converted into the interval point value. The conversion formula is as follows:

[0038]

[0039] According to the interval point value, the following formula is used to calculate the subjective weight set of each energy efficiency evaluation index of the heating furnace: ω′=(ω′1,ω′2,…,ω′ m ) is the subjective weight of the j-th energy efficiency evaluation index:

[0040]

[0041] in,

[0042] k is the element number in the adjacent criterion comparison matrix;

[0043] r i It is the interval point value after the transformation of the element in the j-th row and the i-th column of the adjacent criterion comparison matrix.

[0044] Preferably, the entropy weight evaluation method is used to calculate the objective weight of the heating furnace energy efficiency evaluation index, including the following steps:

[0045] S3001, assume that there are n heating furnaces as the evaluation objects, m energy efficiency evaluation indicators, x ij is the value of the jth index of the i-th evaluation object, and the index matrix of the evaluated heating furnace is constructed as follows:

[0046] X=(x ij ) nm (i=1,2,…,n; j=1,2,…,m)

[0047] S3002, normalize the indicator matrix X of the evaluation object to obtain a normalized judgment matrix Y:

[0048] Y=(y ij ) nm

[0049] Among them, y ij is the element in row i and column j in Y;

[0050] S3003, calculate the proportion of the heating furnace energy efficiency evaluation index value of the i-th evaluation object under the j-th index:

[0051]

[0052] S3004, calculate the entropy value e of the jth evaluation index j (e j ≥0):

[0053]

[0054] S3005, calculate the entropy redundancy of the jth evaluation index:

[0055] d j =1-e j , j=1,…,m

[0056] S3006, calculating the objective weight ω″ of each evaluation index j :

[0057]

[0058] Preferably, based on the combined weighting method, the comprehensive weight of the oilfield heating furnace energy efficiency evaluation index is calculated, which specifically includes the following steps:

[0059] S4001, sort the subjective weight and objective weight of each energy efficiency evaluation index respectively, and verify whether the subjective weight and objective weight can be combined and weighted by calculating the consistency coefficient, as follows:

[0060] Calculate the Spearman consistency coefficient ρ of the objective weight of the subjective weight. ρ is calculated as follows:

[0061]

[0062] in,

[0063] is the jth subjective weight after re-ranking;

[0064] is the jth objective weight after re-ranking;

[0065] When ρ∈[-1,0], it is considered that the subjective weight and the objective weight are inconsistent and cannot be combined for weighting; when ρ∈(0,1], it is considered that the subjective weight and the objective weight are consistent and can be combined for weighting, and step S4002 is executed;

[0066] S4002, determine the combination weight, as follows:

[0067] By using q weighting methods to calculate the weights of p heating furnace energy efficiency evaluation indicators, q basic weight vector sets ω can be obtained: k =(w k1 ,w k2 ,…,w kp ), k=1,2,…,q;

[0068] The subjective weight vector and the objective weight vector are point-multiplied and normalized to obtain the new weight vector set ω=(ω1,ω2,…,ω j ), j = 1, 2, ..., n, any linear combination of n weight vector sets can be:

[0069] Among them, ε j is the combination coefficient;

[0070] Establish the following optimization model to solve the optimal combination coefficient The weight vector set ω after combining subjective weight and objective weight is equal to the basic weight vector set ω i The deviation difference and minimum:

[0071]

[0072]

[0073] Solving the problem yields the optimal combination coefficient

[0074]

[0075] Substitute the solved optimal combination coefficient into any linear combination expression that can be composed of n weight vector sets to solve the new weight vector composed of the optimized subjective weight and objective weight, and obtain the comprehensive weight of the evaluation index:

[0076]

[0077] Preferably, obtaining the comprehensive impact factor of the indicator according to the normalized comprehensive weight specifically includes the following steps:

[0078] Considering the impact of the comprehensive weight of the evaluation index on the thermal efficiency of the heating furnace, the comprehensive weight of the energy efficiency evaluation index is normalized and the comprehensive impact factor w of the index is introduced;

[0079]

[0080] Where:

[0081] A is the benchmark value of heating furnace energy efficiency index;

[0082] T is the test value of the heating furnace energy efficiency index;

[0083] w j It is the comprehensive weight of heating furnace energy efficiency index.

[0084] Preferably, the energy efficiency evaluation indicators include: exhaust temperature, excess air coefficient, furnace body outer surface temperature and thermal efficiency.

[0085] Preferably, the smoke exhaust loss is calculated using the following formula:

[0086]

[0087] Where:

[0088] K q4 K is the heat loss adjustment factor for incomplete combustion of fuel, q4 =1;

[0089] H py is the enthalpy at the flue gas outlet of the heating furnace, kJ / kg;

[0090] H lk is the enthalpy at the air inlet of the heating furnace, kJ / kg;

[0091] Q in is the heat input, kJ / kg.

[0092] Preferably, the heat loss of incomplete combustion of gas is calculated using the following formula:

[0093]

[0094] Where:

[0095] Q in is the input heat, kJ / kg;

[0096] V gy is the dry gas volume at the flue gas outlet of the heating furnace, m 3 / kg;

[0097] CO, C m H nis the volume fraction, %, of carbon monoxide and each hydrocarbon component in the flue gas discharged from the heating furnace, where m and n are the numbers of carbon atoms and hydrogen atoms in the hydrocarbon, representing a class of substances, for example, including CH4, C2H8, etc.

[0098] Preferably, the heat loss due to heat dissipation is calculated using the following formula:

[0099]

[0100] In the formula:

[0101] q′5 is the theoretical heat loss due to heat dissipation of the heating furnace, %;

[0102] T b is the surface temperature of the furnace body of the heating furnace, °C;

[0103] T is the air temperature at the air inlet position of the heating furnace, °C.

[0104] Preferably, the actual thermal efficiency of the heating furnace is calculated using the following formula:

[0105] η2 = 100% - (q2 + q3 + q5).

[0106] Preferably, a linear regression is performed on the comprehensive influence factor and the thermal efficiency of the heating furnace to obtain η = a0w + b0;

[0107] Wherein,

[0108] η is the thermal efficiency of the linear regression;

[0109] a0 and b0 are the coefficients of the linear regression equation.

[0110] Preferably, in step S700, it is judged whether |η - η2| < t. If the above formula is satisfied, the accuracy requirement is met; if the above formula is not satisfied, the accuracy requirement is not met, where t is the accuracy requirement.

[0111] The present invention provides a method for adjusting the energy efficiency of a heating furnace based on a combination of subjective and objective weighting, characterized in that it is adjusted based on the prediction result of any one of the above-mentioned heating furnace energy efficiency prediction methods based on a combination of subjective and objective weighting. The method includes the following steps:

[0112] S111. Obtain the monitored fuel gas flow through a flow sensor and keep this flow unchanged;

[0113] S112. Assume that the system starts to work from the position D1(α1, η1), and take a step forward or backward at an interval of t time, that is, adjust the air intake amount by Δα;

[0114] S113. Every time a step is taken, the computer is used to measure the thermal efficiency index of the control and calculate the air thermal efficiency difference Δη;

[0115] S114. When the air thermal efficiency difference Δη is greater than zero, the adjustment direction is correct, and the next step is to adjust in the original direction;

[0116] S115. When it is determined that the air thermal efficiency difference Δη is less than zero, the computer issues an adjustment command in the opposite direction, and the next step is to adjust in the opposite direction;

[0117] in,

[0118] α1 is the air intake volume at the initial position of D1;

[0119] η1 is the thermal efficiency at the initial position of D1;

[0120] Δα is the difference between the current air intake volume and the previous step length;

[0121] η t is the thermal efficiency of the heating furnace at time t;

[0122] Δη is the difference between the current air thermal efficiency and the previous step length.

[0123] Preferably, the initialization parameters of the system include: optimization period TOP, optimization step length SOP and optimization starting point EOP, and the initial values ​​of the optimization period, optimization step length and optimization starting point are respectively the time from the start of heating to the end of heating in the heating furnace, the time interval t, and the position of D1 (α1, η1).

[0124] Preferably, during system startup, changes in thermal efficiency are monitored to optimize fuel gas increment, air increment and air-fuel ratio.

[0125] Preferably, the method further includes: Step S116. If the optimal value of the heating furnace equipment system fluctuates within a preset range, the ratio of the air volume to the gas volume at this time is the optimal air-fuel ratio of the system.

[0126] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0127] (1) The present invention provides a method for predicting the thermal efficiency of a gas heating furnace based on a subjective and objective comprehensive evaluation. The method uses a combination of subjective and objective methods to calculate the comprehensive weight of the evaluation index, introduces a comprehensive weight influencing factor, and realizes accurate prediction of the thermal efficiency of the heating furnace.

[0128] (2) The present invention provides a method for adjusting the air-fuel ratio of a gas-fired heating furnace. By adjusting the air volume of the heating furnace, the method solves the problems of the air-fuel ratio of the heating furnace being difficult to calculate, unable to be quantitatively adjusted, and having control delay, thereby improving the real-time thermal efficiency of the heating furnace.

[0129] (3) The present invention provides an improved method for calculating the thermal efficiency of a gas-fired heating furnace. Based on the counter-balance calculation method, the method innovatively simplifies the method for calculating the thermal efficiency of a heating furnace, reduces the types of test parameters and the amount of data, and effectively solves the problems of difficulty in obtaining thermal efficiency calculation parameters and complex calculation process of existing heating furnaces, while saving equipment installation costs and laboratory testing costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0130] Figure 1 This is a flow chart of a heating furnace energy efficiency adjustment method based on subjective and objective combined weighting according to an embodiment of the present invention;

[0131] Figure 2 The heating furnace energy efficiency adjustment principle diagram of one embodiment of the present invention is shown in the figure, a) is a heating furnace thermal efficiency diagram under different air flow rates; b) is a heating furnace thermal efficiency diagram under different time periods; c) is an air flow diagram under different time periods;

[0132] Figure 3 The block diagram is a heating furnace energy efficiency adjustment based on subjective and objective combined weighting according to an embodiment of the present invention. DETAILED DESCRIPTION

[0133] The specific implementation modes of the present invention are described in detail below in conjunction with the accompanying drawings.

[0134] The present invention provides a method for predicting energy efficiency of a heating furnace based on subjective and objective combined weighting, comprising the following steps:

[0135] S100, acquiring collected energy efficiency parameter information from an oilfield energy efficiency collection parameter database, using the energy efficiency parameter as a heating furnace energy efficiency evaluation index, and calculating the heating furnace thermal efficiency, including the following steps:

[0136] S101, calculate the smoke exhaust loss q2;

[0137] S102, calculate the heat loss q3 of incomplete combustion of gas;

[0138] S103, calculating the heat dissipation loss q5;

[0139] S104, calculating the heating furnace thermal efficiency η2 based on the smoke exhaust loss q2, the gas incomplete combustion heat loss q3 and the heat dissipation loss q5;

[0140] S200, using the analytic hierarchy process to calculate the subjective weights of the heating furnace energy efficiency evaluation indicators;

[0141] S300, using the entropy weight evaluation method to calculate the objective weight of the heating furnace energy efficiency evaluation index;

[0142] S400, based on the combined weighting method, calculates the comprehensive weight of the energy efficiency evaluation index of oilfield heating furnaces;

[0143] S500, considering the influence of the weight of each heating furnace energy efficiency evaluation index on the thermal efficiency of the heating furnace, normalizing the comprehensive weight of the energy efficiency evaluation index, and obtaining the comprehensive influence factor of the heating furnace evaluation index according to the normalized comprehensive weight;

[0144] S600, linear regression is performed on the comprehensive influencing factors of the heating furnace evaluation index and the heating furnace thermal efficiency to obtain a heating furnace thermal efficiency prediction model;

[0145] S700, judging whether the accuracy of the heating furnace thermal efficiency prediction model meets the accuracy requirements. If the accuracy requirements are met, it means that the heating furnace thermal efficiency prediction model is accurate. If the accuracy requirements are not met, energy efficiency parameters are continuously added to the oilfield energy efficiency acquisition parameter database, and steps S100-S600 are repeated until the accuracy requirements are met.

[0146] According to a specific embodiment of the present invention, the subjective weight of the heating furnace energy efficiency evaluation index is calculated by using a hierarchical analysis evaluation method, which includes the following steps:

[0147] S2001, constructing a hierarchical evaluation model for heating furnaces;

[0148] S2002, using set value iteration method to determine the indicator sequence;

[0149] S2003, construct the adjacent criteria comparison matrix;

[0150] S2004, determine the subjective weight of the evaluation criteria.

[0151] According to a specific embodiment of the present invention, the subjective weight of the heating furnace energy efficiency evaluation index is calculated by using a hierarchical analysis evaluation method, which includes the following steps:

[0152] S2001, based on the heating furnace energy efficiency evaluation index, construct a heating furnace hierarchical evaluation model;

[0153] S2002, use the set value iteration method to determine the indicator sequence set, specifically:

[0154] Assume that the heating furnace has m energy efficiency evaluation indicators, there are P experts to evaluate and score the heating furnace energy efficiency, the kth (1≤k≤P) expert scores the heating furnace energy efficiency evaluation indicator, x is the score of a heating furnace energy efficiency evaluation indicator, and the energy efficiency evaluation indicator set of the heating furnace is composed of

[0155] Each evaluation index is formed into different score levels, and the full score of index m is 100 points;

[0156] The expert weight λ is introduced. According to the data analysis results of the fit between the expert’s historical evaluation and the objective indicators, a weight value is assigned to each expert, which is denoted as λ1=(λ1,λ2,…,λ P );

[0157] According to the historical performance of experts, (1) the number of times participating in the heating furnace energy efficiency evaluation a≥10 and the number of times the evaluation failed ≤10%; (2) the number of times participating in the heating furnace energy efficiency evaluation a≥10 and the number of times the evaluation failed ≥10% and ≤30%; (3) the number of times participating in the heating furnace energy efficiency evaluation a≥10 and the number of times the evaluation failed ≥30%; (4) the number of times participating in the heating furnace energy efficiency evaluation a≤10 and a≥5 and no number of times the evaluation failed; (5) the number of times participating in the heating furnace energy efficiency evaluation a≤10 and a≥5 and there were times the evaluation failed; (6) the number of times participating in the heating furnace energy efficiency evaluation a≤5 and no number of times the evaluation failed; (7) the number of times participating in the heating furnace energy efficiency evaluation a≤5 and there were times the evaluation failed. The weights of (1) to (7) are {0.25, 0.23, 0.1, 0.2, 0.05, 0.15, 0.02} respectively.

[0158] A questionnaire on expert evaluation ability is issued to a preset number of people, and a weight value is assigned to each expert according to the questionnaire results, denoted as λ2=(λ1,λ2,…,λ P ), and use the weighted average method to process λ1 and λ2 to form an expert weight set λ=(λ1,λ2,…,λ P ) ; experts were assigned values ​​by means of a questionnaire, the content of which was set as: Which expert do you think is experienced in this field? Values ​​were assigned based on the votes received by each expert.

[0159] The energy efficiency evaluation indicators of heating furnaces in the energy efficiency evaluation indicator subset of heating furnaces are processed in descending order to form a new sequence set of energy efficiency evaluation indicators of heating furnaces.

[0160] S2003, constructing an adjacent criterion comparison matrix, specifically including:

[0161] The indicator sequence set determined by set value iteration method Based on the fuzzy interval value method, the value r of the kth factor in the adjacent criterion comparison matrix is ​​obtained. k :where r k =1.0, indicating and The importance of k =1.2, indicating Compare Slightly important, k =1.4, Compare Obviously important, k =1.6, indicating Compare Strongly important; and are the new heating furnace energy efficiency evaluation index set X after being arranged in descending order * The k-1th index and the kth index in ;

[0162] P experts compare the importance of adjacent indicators and obtain the element [r ji ′,r ji ″], the set of elements constitutes the adjacent criterion comparison matrix;

[0163] For ease of understanding, an example of the neighbor criterion comparison matrix is ​​as follows:

[0164]

[0165] S2004, determine the subjective weights of the evaluation criteria, as follows:

[0166] The element pair [r ji ′,r ji The interval value of ″] is converted into the interval point value. The conversion formula is as follows:

[0167]

[0168] According to the interval point value, the following formula is used to calculate the subjective weight set of each energy efficiency evaluation index of the heating furnace: ω′=(ω′1,ω′2,…,ω′ m ) is the subjective weight of the j-th energy efficiency evaluation index:

[0169]

[0170] in,

[0171] k is the element number in the adjacent criterion comparison matrix;

[0172] r i It is the interval point value after the transformation of the element in the j-th row and the i-th column of the adjacent criterion comparison matrix.

[0173] According to a specific embodiment of the present invention, the entropy weight evaluation method is used to calculate the objective weight of the heating furnace energy efficiency evaluation index, including the following steps:

[0174] S3001, assume that there are n heating furnaces as the evaluation objects, m energy efficiency evaluation indicators, x ij is the value of the jth index of the i-th evaluation object, and the index matrix of the evaluated heating furnace is constructed as follows:

[0175] X=(x ij ) nm(i=1,2,…,n; j=1,2,…,m)

[0176] S3002, normalize the indicator matrix X of the evaluation object to obtain a normalized judgment matrix Y:

[0177] Y=(y ij ) nm

[0178] Among them, y ij is the element in row i and column j in Y;

[0179] S3003, calculate the proportion of the heating furnace energy efficiency evaluation index value of the i-th evaluation object under the j-th index:

[0180]

[0181] S3004, calculate the entropy value e of the jth evaluation index j (e j ≥0):

[0182]

[0183] S3005, calculate the entropy redundancy of the jth evaluation index:

[0184] d j =1-e j ,j=1,…,m

[0185] S3006, calculating the objective weight ω″ of each evaluation index j :

[0186]

[0187] According to a specific embodiment of the present invention, based on the combined weighting method, the comprehensive weight of the energy efficiency evaluation index of the oilfield heating furnace is calculated, which specifically includes the following steps:

[0188] S4001, sort the subjective weight and objective weight of each energy efficiency evaluation index respectively, and verify whether the subjective weight and objective weight can be combined and weighted by calculating the consistency coefficient, as follows:

[0189] Calculate the Spearman consistency coefficient ρ of the objective weight of the subjective weight. ρ is calculated as follows:

[0190]

[0191] in,

[0192] is the jth subjective weight after re-ranking;

[0193] is the jth objective weight after re-ranking;

[0194] When ρ∈[-1,0], it is considered that the subjective weight and the objective weight are inconsistent and cannot be combined for weighting; when ρ∈(0,1], it is considered that the subjective weight and the objective weight are consistent and can be combined for weighting, and step S4002 is executed;

[0195] S4002, determine the combination weight, as follows:

[0196] By using q weighting methods to calculate the weights of p heating furnace energy efficiency evaluation indicators, q basic weight vector sets ω can be obtained: k =(w k1 ,w k2 ,…,w kp ), k=1,2,…,q;

[0197] The subjective weight and objective weight are integrated through the combined weighting method to form a new weight vector set ω=(ω1,ω2,…,ω j ), j = 1, 2, ..., n, any linear combination of n weight vector sets can be:

[0198] Among them, ε j is the combination coefficient;

[0199] Establish the following optimization model to solve the optimal combination coefficient The weight vector set ω after combining subjective weight and objective weight is equal to the basic weight vector set ω i The deviation difference and minimum:

[0200]

[0201] Solving the problem yields the optimal combination coefficient

[0202]

[0203] Substitute the solved optimal combination coefficient into any linear combination expression that can be composed of n weight vector sets to solve the new weight vector composed of the optimized subjective weight and objective weight, and obtain the comprehensive weight of the evaluation index:

[0204]

[0205] According to a specific embodiment of the present invention, obtaining the index comprehensive impact factor according to the normalized comprehensive weight specifically includes the following steps:

[0206] Considering the impact of the comprehensive weight of the evaluation index on the thermal efficiency of the heating furnace, the comprehensive weight of the energy efficiency evaluation index is normalized and the comprehensive impact factor w of the index is introduced;

[0207]

[0208] Where:

[0209] A is the benchmark value of heating furnace energy efficiency index;

[0210] T is the test value of the heating furnace energy efficiency index;

[0211] w j It is the comprehensive weight of heating furnace energy efficiency index.

[0212] According to a specific embodiment of the present invention, the energy efficiency evaluation indexes include: exhaust gas temperature, excess air coefficient, furnace body outer surface temperature and thermal efficiency.

[0213] According to a specific embodiment of the present invention, the calculation of the thermal efficiency of the heating furnace includes the following steps:

[0214] S101, calculate the smoke exhaust loss q2;

[0215] S102, calculate the heat loss q3 of incomplete combustion of gas;

[0216] S103, calculating the heat dissipation loss q5;

[0217] S104, calculating the heating furnace thermal efficiency η2 based on the smoke exhaust loss q2, the gas incomplete combustion heat loss q3 and the heat dissipation loss q5.

[0218] According to a specific embodiment of the present invention, the smoke exhaust loss is calculated using the following formula:

[0219]

[0220] Where:

[0221] K q4 K is the heat loss adjustment factor for incomplete combustion of fuel, q4 =1;

[0222] H py is the enthalpy at the flue gas outlet of the heating furnace, kJ / kg;

[0223] H lk is the enthalpy at the air inlet of the heating furnace, kJ / kg;

[0224] Q in is the heat input, kJ / kg.

[0225] According to a specific embodiment of the present invention, the heat loss of incomplete combustion of gas is calculated using the following formula:

[0226]

[0227] Where:

[0228] Q in is the input heat, kJ / kg;

[0229] V gy is the dry gas volume at the flue gas outlet of the heating furnace, m 3 / kg;

[0230] CO, C m H n is the volume fraction of carbon monoxide and various hydrocarbon components in the flue gas discharged from the heating furnace, %, where m and n are the number of carbon atoms and hydrogen atoms in the hydrocarbons, representing a class of substances, such as CH 4 , C2H8, etc., has no effect on the form of the formula.

[0231] According to a specific embodiment of the present invention, the heat dissipation loss is calculated using the following formula:

[0232]

[0233] Where:

[0234] q′5 is the theoretical heat dissipation loss of the heating furnace, %;

[0235] T b is the surface temperature of the heating furnace, °C;

[0236] T is the air temperature at the air inlet of the heating furnace, ℃.

[0237] According to a specific embodiment of the present invention, the actual heating furnace thermal efficiency is calculated using the following formula:

[0238] η2=100%-(q2+q3+q5).

[0239] According to a specific embodiment of the present invention, a linear regression is performed on the comprehensive influencing factor and the thermal efficiency of the heating furnace to obtain η=a0w+b0;

[0240] in,

[0241] η is the thermal efficiency of linear regression;

[0242] a0 and b0 are the coefficients of the linear regression equation.

[0243] According to a specific embodiment of the present invention, in step S700, it is determined whether |η - η2| < t. If the above formula is satisfied, the accuracy requirement is met; if the above formula is not satisfied, the accuracy requirement is not met, where t is the accuracy requirement.

[0244] The present invention provides a method for regulating the energy efficiency of a heating furnace based on combined subjective and objective weighting, characterized in that it is regulated based on the prediction result of any one of the above-mentioned heating furnace energy efficiency prediction methods based on combined subjective and objective weighting. The method includes the following steps:

[0245] S111. Obtain the monitored fuel gas flow through a flow sensor and keep this flow constant.

[0246] S112. Assume that the system starts working at the position D1(α1, η1), and move one step forward or backward at an interval of t time, that is, adjust the air intake volume by Δα.

[0247] S113. Every time a step length is taken, use a computer to measure the controlled thermal efficiency index and calculate the air thermal efficiency difference Δη.

[0248] S114. When the air thermal efficiency difference Δη is greater than zero, the adjustment direction is correct, so the next step should be adjusted in the original direction.

[0249] S115. Determine that when the air thermal efficiency difference Δη is less than zero, the computer issues an adjustment command in the opposite direction, and the next step should be adjusted in the opposite direction.

[0250] Wherein,

[0251] α1 is the air intake volume at the initial position D1.

[0252] η1 is the thermal efficiency at the initial position D1.

[0253] Δα is the difference between the current air intake volume and that at the previous step length.

[0254] η t is the thermal efficiency of the heating furnace at time t.

[0255] Δη is the difference between the current air thermal efficiency and that at the previous step length.

[0256] According to a specific embodiment of the present invention, the initialization parameters of the system include: optimization period TOP, optimization step length SOP, and optimization starting point EOP. The initial values of the optimization period, optimization step length, and optimization starting point are respectively the duration from the start of heating to the end of heating of the heating furnace, the time interval t, and the position D1(α1, η1).

[0257] According to a specific embodiment of the present invention, during the system startup process, the change of thermal efficiency is monitored, and the fuel gas increment, air amount increment and air-fuel ratio are optimized.

[0258] According to a specific embodiment of the present invention, it also includes: Step S116. If the optimal value of the heating furnace equipment system fluctuates within a preset range, the ratio of the air volume to the gas volume at this time is the optimal air-fuel ratio of the system.

[0259] Example 1

[0260] According to a specific embodiment of the present invention, the heating furnace energy efficiency prediction method based on subjective and objective combined weighting of the present invention is described in detail below.

[0261] The present invention provides a method for predicting energy efficiency of a heating furnace based on subjective and objective combined weighting, comprising the following steps:

[0262] S100, acquiring collected energy efficiency parameter information from an oilfield energy efficiency collection parameter database, using the energy efficiency parameter as a heating furnace energy efficiency evaluation index, and calculating the heating furnace thermal efficiency, including the following steps:

[0263] S101, calculate the smoke exhaust loss q2;

[0264] S102, calculate the heat loss q3 of incomplete combustion of gas;

[0265] S103, calculating the heat dissipation loss q5;

[0266] S104, calculating the heating furnace thermal efficiency η2 based on the smoke exhaust loss q2, the gas incomplete combustion heat loss q3 and the heat dissipation loss q5;

[0267] S200, using the analytic hierarchy process to calculate the subjective weights of the heating furnace energy efficiency evaluation indicators;

[0268] S300, using the entropy weight evaluation method to calculate the objective weight of the heating furnace energy efficiency evaluation index;

[0269] S400, based on the combined weighting method, calculates the comprehensive weight of the energy efficiency evaluation index of oilfield heating furnaces;

[0270] S500, considering the influence of the weight of each heating furnace energy efficiency evaluation index on the thermal efficiency of the heating furnace, normalizing the comprehensive weight of the energy efficiency evaluation index, and obtaining the comprehensive influence factor of the heating furnace evaluation index according to the normalized comprehensive weight;

[0271] S600, linear regression is performed on the comprehensive influencing factors of the heating furnace evaluation index and the heating furnace thermal efficiency to obtain a heating furnace thermal efficiency prediction model;

[0272] S700, judging whether the accuracy of the heating furnace thermal efficiency prediction model meets the accuracy requirements. If the accuracy requirements are met, it means that the heating furnace thermal efficiency prediction model is accurate. If the accuracy requirements are not met, energy efficiency parameters are continuously added to the oilfield energy efficiency acquisition parameter database, and steps S100-S600 are repeated until the accuracy requirements are met.

[0273] Among them, the hierarchical analysis evaluation method is used to calculate the subjective weight of the heating furnace energy efficiency evaluation index, including the following steps:

[0274] S2001, constructing a hierarchical evaluation model for heating furnaces;

[0275] S2002, using set value iteration method to determine the indicator sequence;

[0276] S2003, construct the adjacent criteria comparison matrix;

[0277] S2004, determine the subjective weight of the evaluation criteria.

[0278] Among them, the hierarchical analysis evaluation method is used to calculate the subjective weight of the heating furnace energy efficiency evaluation index, including the following steps:

[0279] S2001, based on the heating furnace energy efficiency evaluation index, construct a heating furnace hierarchical evaluation model;

[0280] S2002, use the set value iteration method to determine the indicator sequence set, specifically:

[0281] Assume that the heating furnace has m energy efficiency evaluation indicators, there are P experts to evaluate and score the heating furnace energy efficiency, the kth (1≤k≤P) expert scores the heating furnace energy efficiency evaluation indicator, x is the score of a heating furnace energy efficiency evaluation indicator, and the energy efficiency evaluation indicator set of the heating furnace is composed of

[0282] Each evaluation index is formed into different score levels, and the full score of index m is 100 points;

[0283] The expert weight λ is introduced. According to the data analysis results of the fit between the expert’s historical evaluation and the objective indicators, a weight value is assigned to each expert, which is denoted as λ1=(λ1,λ2,…,λ P );

[0284] A questionnaire on expert evaluation ability is issued to a preset number of people, and a weight value is assigned to each expert according to the questionnaire results, denoted as λ2=(λ1,λ2,…,λ P ), and use the weighted average method to process λ1 and λ2 to form an expert weight set λ=(λ1,λ2,…,λ P );

[0285] The energy efficiency evaluation indicators of heating furnaces in the energy efficiency evaluation indicator subset of heating furnaces are processed in descending order to form a new sequence set of energy efficiency evaluation indicators of heating furnaces.

[0286] S2003, constructing an adjacent criterion comparison matrix, specifically including:

[0287] The indicator sequence set determined by set value iteration method Based on the fuzzy interval value method, the value r of the kth factor in the adjacent criterion comparison matrix is ​​obtained. k :where r k =1.0, indicating and The importance of k =1.2, indicating Compare Slightly important, k =1.4, Compare Obviously important, k =1.6, indicating Compare Strongly important; and are the new heating furnace energy efficiency evaluation index set X after being arranged in descending order * The k-1th index and the kth index in ;

[0288] P experts compare the importance of adjacent indicators and obtain the element [r ji ′,r ji ″], the set of elements constitutes the adjacent criterion comparison matrix.

[0289] S2004, determine the subjective weights of the evaluation criteria, as follows:

[0290] The element pair [rj ji ′,rj ji The interval value of ″] is converted into the interval point value. The conversion formula is as follows:

[0291]

[0292] According to the interval point value, the following formula is used to calculate the subjective weight set of each energy efficiency evaluation index of the heating furnace: ω′=(ω′1,ω′2,…,ω′ m ) is the subjective weight of the j-th energy efficiency evaluation index:

[0293]

[0294] in,

[0295] k is the element number in the adjacent criterion comparison matrix;

[0296] r i It is the interval point value after the transformation of the element in the j-th row and the i-th column of the adjacent criterion comparison matrix.

[0297] Among them, the entropy weight evaluation method is used to calculate the objective weight of the heating furnace energy efficiency evaluation index, including the following steps:

[0298] S3001, assume that there are n heating furnaces as the evaluation objects, m energy efficiency evaluation indicators, x ij is the value of the jth index of the i-th evaluation object, and the index matrix of the evaluated heating furnace is constructed as follows:

[0299] X=(x ij ) nm (i=1,2,…,n; j=1,2,…,m)

[0300] S3002, normalize the indicator matrix X of the evaluation object to obtain a normalized judgment matrix Y:

[0301] Y=(y ij ) nm

[0302] Among them, y ij is the element in row i and column j in Y;

[0303] S3003, calculate the proportion of the heating furnace energy efficiency evaluation index value of the i-th evaluation object under the j-th index:

[0304]

[0305] S3004, calculate the entropy value e of the jth evaluation index j (e j ≥0):

[0306]

[0307] S3005, calculate the entropy redundancy d of the jth evaluation index j :

[0308] d j =1-e j ,j=1,…,m

[0309] S3006, calculate the objective weight ω′ of each evaluation index j :

[0310]

[0311] Among them, based on the combined weighting method, the comprehensive weight of the energy efficiency evaluation index of the oilfield heating furnace is calculated, which specifically includes the following steps:

[0312] S4001, sort the subjective weight and objective weight of each energy efficiency evaluation index respectively, and verify whether the subjective weight and objective weight can be combined and weighted by calculating the consistency coefficient, as follows:

[0313] Calculate the Spearman consistency coefficient ρ of the objective weight of the subjective weight. ρ is calculated as follows:

[0314]

[0315] in,

[0316] is the jth subjective weight after re-ranking;

[0317] is the jth objective weight after re-ranking;

[0318] When ρ∈[-1,0], it is considered that the subjective weight and the objective weight are inconsistent and cannot be combined for weighting;

[0319] When ρ∈(0, 1], it is considered that the subjective weight and the objective weight are consistent and can be combined and weighted, and step S4002 is executed;

[0320] S4002, determine the combination weight, as follows:

[0321] By using q weighting methods to calculate the weights of p heating furnace energy efficiency evaluation indicators, q basic weight vector sets ω can be obtained: k =(w k1 ,w k2 ,…,w kp ), k=1,2,…,q;

[0322] The subjective weight vector and the objective weight vector are point-multiplied and normalized to obtain the new weight vector set ω=(ω1,ω2,…,ω j ), j = 1, 2, ..., n, any linear combination of n weight vector sets can be:

[0323] Among them, ε j is the combination coefficient;

[0324] Establish the following optimization model to solve the optimal combination coefficient The weight vector set ω after combining subjective weight and objective weight is equal to the basic weight vector set ω k The deviation difference and minimum:

[0325]

[0326]

[0327] The optimal combination coefficient can be obtained by solving

[0328]

[0329] Substitute the obtained optimal combination coefficient into any linear combination expression that can be formed by the set of n weight vectors to solve the new weight vector composed of the optimized subjective weight and objective weight, and obtain the comprehensive weight of the evaluation index:

[0330]

[0331] Among them, according to the normalized comprehensive weight, the comprehensive influence factor of the index is obtained, and the specific steps are as follows:

[0332] Considering the influence of the comprehensive weight of the evaluation index on the thermal efficiency of the heating furnace, normalize the comprehensive weight of the energy efficiency evaluation index, and introduce the comprehensive influence factor w of the index;

[0333]

[0334] In the formula:

[0335] A is the benchmark value of the energy efficiency index of the heating furnace;

[0336] T is the test value of the energy efficiency index of the heating furnace;

[0337] w j is the comprehensive weight of the energy efficiency index of the heating furnace.

[0338] Among them, the energy efficiency evaluation index includes: flue gas temperature, excess air coefficient, outer surface temperature of the furnace body, and thermal efficiency.

[0339] Among them, a linear regression is performed on the comprehensive influence factor and the thermal efficiency of the heating furnace to obtain η = a0w + b0;

[0340] Among them,

[0341] η is the thermal efficiency of the linear regression;

[0342] a0 and b0 are the coefficients of the linear regression equation.

[0343] Among them, in step S700, it is judged that |η - η2| < t. If the above formula is satisfied, the accuracy requirement is met; if the above formula is not satisfied, the accuracy requirement is not met, where t is the accuracy requirement.

[0344] Example 2

[0345] According to a specific embodiment of the present invention, the heating furnace energy efficiency prediction method based on subjective and objective combined weighting of the present invention is described in detail below.

[0346] The present invention provides a method for predicting energy efficiency of a heating furnace based on subjective and objective combined weighting, comprising the following steps:

[0347] S100, acquiring collected energy efficiency parameter information from an oilfield energy efficiency collection parameter database, using the energy efficiency parameter as a heating furnace energy efficiency evaluation index, and calculating the heating furnace thermal efficiency, including the following steps:

[0348] S101, calculate the smoke exhaust loss q2;

[0349] S102, calculate the heat loss q3 of incomplete combustion of gas;

[0350] S103, calculating the heat dissipation loss q5;

[0351] S104, calculating the heating furnace thermal efficiency η2 based on the smoke exhaust loss q2, the gas incomplete combustion heat loss q3 and the heat dissipation loss q5;

[0352] The smoke exhaust loss is calculated using the following formula:

[0353]

[0354] Where:

[0355] K q4 K is the heat loss adjustment factor for incomplete combustion of fuel, q4 =1;

[0356] H py is the enthalpy at the flue gas outlet of the heating furnace, kJ / kg;

[0357] H lk is the enthalpy at the air inlet of the heating furnace, kJ / kg;

[0358] Q in is the heat input, kJ / kg.

[0359] Among them, the heat loss of incomplete combustion of gas is calculated using the following formula:

[0360]

[0361] Where:

[0362] Q in is the input heat, kJ / kg;

[0363] V gy is the dry gas volume at the flue gas outlet of the heating furnace, m 3 / kg;

[0364] CO, C m H n is the volume fraction of carbon monoxide and various hydrocarbon components in the flue gas discharged from the heating furnace, %, where m and n are the number of carbon atoms and hydrogen atoms in the hydrocarbons, representing a class of substances, such as CH 4 , C2H8, etc., has no effect on the form of the formula.

[0365] The heat dissipation loss is calculated using the following formula:

[0366]

[0367] Where:

[0368] q′5 is the theoretical heat dissipation loss of the heating furnace, %;

[0369] T b is the surface temperature of the heating furnace, °C;

[0370] T is the air temperature at the air inlet of the heating furnace, ℃.

[0371] Among them, the actual heating furnace thermal efficiency is calculated using the following formula:

[0372] η2=100%-(q2+q3+q5);

[0373] S200, using the analytic hierarchy process to calculate the subjective weights of the heating furnace energy efficiency evaluation indicators;

[0374] S300, using the entropy weight evaluation method to calculate the objective weight of the heating furnace energy efficiency evaluation index;

[0375] S400, based on the combined weighting method, calculates the comprehensive weight of the energy efficiency evaluation index of oilfield heating furnaces;

[0376] S500, considering the influence of the weight of each heating furnace energy efficiency evaluation index on the thermal efficiency of the heating furnace, normalizing the comprehensive weight of the energy efficiency evaluation index, and obtaining the comprehensive influence factor of the heating furnace evaluation index according to the normalized comprehensive weight;

[0377] S600, linear regression is performed on the comprehensive influencing factors of the heating furnace evaluation index and the heating furnace thermal efficiency to obtain a heating furnace thermal efficiency prediction model;

[0378] S700, judging whether the accuracy of the heating furnace thermal efficiency prediction model meets the accuracy requirements. If the accuracy requirements are met, it means that the heating furnace thermal efficiency prediction model is accurate. If the accuracy requirements are not met, energy efficiency parameters are continuously added to the oilfield energy efficiency acquisition parameter database, and steps S100-S600 are repeated until the accuracy requirements are met.

[0379] Among them, the hierarchical analysis evaluation method is used to calculate the subjective weight of the heating furnace energy efficiency evaluation index, including the following steps:

[0380] S2001, constructing a hierarchical evaluation model for heating furnaces;

[0381] S2002, using set value iteration method to determine the indicator sequence;

[0382] S2003, construct the adjacent criteria comparison matrix;

[0383] S2004, determine the subjective weight of the evaluation criteria.

[0384] Among them, the hierarchical analysis evaluation method is used to calculate the subjective weight of the heating furnace energy efficiency evaluation index, including the following steps:

[0385] S2001, based on the heating furnace energy efficiency evaluation index, construct a heating furnace hierarchical evaluation model;

[0386] S2002, use the set value iteration method to determine the indicator sequence set, specifically:

[0387] Assume that the heating furnace has m energy efficiency evaluation indicators, there are P experts to evaluate and score the heating furnace energy efficiency, the kth (1≤k≤P) expert scores the heating furnace energy efficiency evaluation indicator, x is the score of a heating furnace energy efficiency evaluation indicator, and the energy efficiency evaluation indicator set of the heating furnace is composed of

[0388] Each evaluation index is formed into different score levels, and the full score of index m is 100 points;

[0389] The expert weight λ is introduced. According to the data analysis results of the fit between the expert’s historical evaluation and the objective indicators, a weight value is assigned to each expert, which is denoted as λ1=(λ1,λ2,…,λ P );

[0390] A questionnaire on expert evaluation ability is issued to a preset number of people, and a weight value is assigned to each expert according to the questionnaire results, denoted as λ2=(λ1,λ2,…,λ P ), and use the weighted average method to process λ1 and λ2 to form an expert weight set λ=(λ1,λ2,…,λ P );

[0391] The energy efficiency evaluation indicators of heating furnaces in the energy efficiency evaluation indicator subset of heating furnaces are processed in descending order to form a new sequence set of energy efficiency evaluation indicators of heating furnaces.

[0392] S2003, constructing an adjacent criterion comparison matrix, specifically including:

[0393] The indicator sequence set determined by set value iteration method Based on the fuzzy interval value method, the value r of the kth factor in the adjacent criterion comparison matrix is ​​obtained. k :where r k =1.0, indicating and The importance of k =1.2, indicating Compare Slightly important, k =1.4, Compare Obviously important, k =1.6, indicating Compare Strongly important; and are the new heating furnace energy efficiency evaluation index set X after being arranged in descending order * The k-1th index and the kth index in ;

[0394] P experts compare the importance of adjacent indicators and obtain the element [r ji ′,r ji ″], the set of elements constitutes the adjacent criterion comparison matrix;

[0395] S2004, determine the subjective weights of the evaluation criteria, as follows:

[0396] The element pair [r ji ′,r ji The interval value of ″] is converted into the interval point value. The conversion formula is as follows:

[0397]

[0398] According to the interval point value, the following formula is used to calculate the subjective weight set of each energy efficiency evaluation index of the heating furnace: ω′=(ω′1,ω′2,…,ω′ m ) is the subjective weight of the j-th energy efficiency evaluation index:

[0399]

[0400] in,

[0401] k is the element number in the adjacent criterion comparison matrix;

[0402] r i It is the interval point value after the transformation of the element in the j-th row and the i-th column of the adjacent criterion comparison matrix.

[0403] Among them, the entropy weight evaluation method is used to calculate the objective weight of the heating furnace energy efficiency evaluation index, including the following steps:

[0404] S3001, assume that there are n heating furnaces as the evaluation objects, m energy efficiency evaluation indicators, x ij is the value of the jth index of the i-th evaluation object, and the index matrix of the evaluated heating furnace is constructed as follows:

[0405] X=(x ij ) nm (i=1,2,…,n; j=1,2,…,m)

[0406] S3002, normalize the indicator matrix X of the evaluation object to obtain a normalized judgment matrix Y:

[0407] Y=(y ij ) nm

[0408] Among them, y ij is the element in row i and column j in Y;

[0409] S3003, calculate the proportion of the heating furnace energy efficiency evaluation index value of the i-th evaluation object under the j-th index:

[0410]

[0411] S3004, calculate the entropy value e of the jth evaluation index j (e j ≥0):

[0412]

[0413] S3005, calculate the entropy redundancy d of the jth evaluation index j :

[0414] d j =1-e j ,j=1,…,m

[0415] S3006, calculating the objective weight ω″ of each evaluation index j :

[0416]

[0417] Among them, based on the combined weighting method, the comprehensive weight of the energy efficiency evaluation index of the oilfield heating furnace is calculated, which specifically includes the following steps:

[0418] S4001, sort the subjective weight and objective weight of each energy efficiency evaluation index respectively, and verify whether the subjective weight and objective weight can be combined and weighted by calculating the consistency coefficient, as follows:

[0419] Calculate the Spearman consistency coefficient ρ of the objective weight of the subjective weight. ρ is calculated as follows:

[0420]

[0421] in,

[0422] is the jth subjective weight after re-ranking;

[0423] is the jth objective weight after re-ranking;

[0424] When ρ∈[-1,0], it is considered that the subjective weight and the objective weight are inconsistent and cannot be combined for weighting;

[0425] When ρ∈(0, 1], it is considered that the subjective weight and the objective weight are consistent and can be combined and weighted, and step S4002 is executed;

[0426] S4002, determine the combination weight, as follows:

[0427] By using q weighting methods to calculate the weights of p heating furnace energy efficiency evaluation indicators, q basic weight vector sets ω can be obtained: k =(w k1 ,w k2 ,…,w kp ), k=1,2,…,q;

[0428] The subjective weight vector and the objective weight vector are point-multiplied and normalized to obtain the new weight vector set ω=(ω1,ω2,…,ω j ), j = 1, 2, ..., n, any linear combination of n weight vector sets can be:

[0429] Among them, ε j is the combination coefficient;

[0430] Establish the following optimization model to solve the optimal combination coefficient The weight vector set ω after combining subjective weight and objective weight is equal to the basic weight vector set ω k The deviation difference and minimum:

[0431]

[0432]

[0433] Solving the problem yields the optimal combination coefficient

[0434]

[0435] Substitute the optimal combination coefficients to be solved into any linear combination expression that can be formed by the set of n weight vectors to solve the new weight vector composed of the optimized subjective weight and objective weight, and obtain the comprehensive weight of the evaluation index:

[0436]

[0437] Among them, according to the normalized comprehensive weight, the comprehensive influence factor of the index is obtained, and the specific steps are as follows:

[0438] Considering the influence of the comprehensive weight of the evaluation index on the thermal efficiency of the heating furnace, normalize the comprehensive weight of the energy efficiency evaluation index, and introduce the comprehensive influence factor w of the index;

[0439]

[0440] In the formula:

[0441] A is the benchmark value of the energy efficiency index of the heating furnace;

[0442] T is the test value of the energy efficiency index of the heating furnace;

[0443] w j is the comprehensive weight of the energy efficiency index of the heating furnace.

[0444] Among them, the energy efficiency evaluation index includes: flue gas temperature, excess air coefficient, outer surface temperature of the furnace body, and thermal efficiency.

[0445] Among them, perform a linear regression on the comprehensive influence factor and the thermal efficiency of the heating furnace to obtain η = a0w + b0;

[0446] Among them,

[0447] η is the thermal efficiency of the linear regression;

[0448] a0 and b0 are the coefficients of the linear regression equation.

[0449] Among them, in step S700, judge |η - η2| < t. If the above formula is satisfied, the accuracy requirement is met; if the above formula is not satisfied, the accuracy requirement is not met, where t is the accuracy requirement.

[0450] Embodiment 3

[0451] According to a specific implementation manner of the present invention, the method for adjusting the energy efficiency of a heating furnace based on combined subjective and objective weighting of the present invention will be described in detail below.

[0452] The present invention provides a method for adjusting the energy efficiency of a heating furnace based on combined subjective and objective weighting, which is adjusted based on the prediction result of any one of the above-mentioned heating furnace energy efficiency prediction methods based on combined subjective and objective weighting. The method includes the following steps:

[0453] S111. Obtain the monitored fuel gas flow rate through the flow sensor and fix the flow rate unchanged;

[0454] S112. Assume that the system starts working from the position D1 (α1, η1), and moves forward or backward one step at an interval of t, that is, adjusts the air intake volume Δα;

[0455] S113. Every time a step is taken, the computer is used to measure the thermal efficiency index of the control and calculate the air thermal efficiency difference Δη;

[0456] S114. When the air thermal efficiency difference Δη is greater than zero, the adjustment direction is correct, and the next step is to adjust in the original direction;

[0457] S115. When it is determined that the air thermal efficiency difference Δη is less than zero, the computer issues an adjustment command in the opposite direction, and the next step is to adjust in the opposite direction;

[0458] in,

[0459] α1 is the air intake volume at the initial position of D1;

[0460] η1 is the thermal efficiency at the initial position of D1;

[0461] Δα is the difference between the current air intake volume and the previous step length;

[0462] η t is the thermal efficiency of the heating furnace at time t;

[0463] Δη is the difference between the current air thermal efficiency and the previous step length.

[0464] Among them, the initialization parameters of the system include: optimization period TOP, optimization step length SOP and optimization starting point EOP. The initial values ​​of the optimization period, optimization step length and optimization starting point are the time from the start of heating to the end of heating in the heating furnace, the time interval t, and the D1 (α1, η1) position.

[0465] During the system startup process, the changes in thermal efficiency are monitored, and the fuel gas increase, air volume increase and air-fuel ratio are optimized.

[0466] Among them, it also includes: Step S116. If the optimal value of the heating furnace equipment system fluctuates within a preset range, then the ratio of the air volume to the gas volume at this time is the optimal air-fuel ratio of the system.

[0467] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention are included in the protection scope of the present invention.

Claims

1. A heating furnace energy efficiency prediction method based on subjective and objective combined weighting, characterized in that: The steps include: S100, acquiring collected energy efficiency parameter information from an oilfield energy efficiency collection parameter database, using the energy efficiency parameter as a heating furnace energy efficiency evaluation index, and calculating the heating furnace thermal efficiency, including the following steps: S101, calculate the smoke exhaust loss q2; S102, calculate the heat loss q3 of incomplete combustion of gas; S103, calculating the heat dissipation loss q5; S104, calculating the heating furnace thermal efficiency η2 based on the smoke exhaust loss q2, the gas incomplete combustion heat loss q3 and the heat dissipation loss q5; S200, using the analytic hierarchy process to calculate the subjective weights of the heating furnace energy efficiency evaluation indicators; S300, using the entropy weight evaluation method to calculate the objective weight of the heating furnace energy efficiency evaluation index; S400, based on the combined weighting method, calculates the comprehensive weight of the energy efficiency evaluation index of oilfield heating furnaces; S500, considering the influence of the weight of each heating furnace energy efficiency evaluation index on the thermal efficiency of the heating furnace, normalizing the comprehensive weight of the energy efficiency evaluation index, and obtaining the comprehensive influence factor of the heating furnace evaluation index according to the normalized comprehensive weight; S600, linear regression is performed on the comprehensive influencing factors of the heating furnace evaluation index and the heating furnace thermal efficiency to obtain a heating furnace thermal efficiency prediction model; S700, judging whether the accuracy of the heating furnace thermal efficiency prediction model meets the accuracy requirements. If the accuracy requirements are met, it means that the heating furnace thermal efficiency prediction model is accurate. If the accuracy requirements are not met, energy efficiency parameters are continuously added to the oilfield energy efficiency acquisition parameter database, and steps S100-S600 are repeated until the accuracy requirements are met.

2. The heating furnace energy efficiency prediction method based on subjective and objective combined weighting according to claim 1 is characterized in that: Using the hierarchical analysis evaluation method, the calculation of the subjective weight of the heating furnace energy efficiency evaluation index includes the following steps: S2001, constructing a hierarchical evaluation model for heating furnaces; S2002, using set value iteration method to determine the indicator sequence; S2003, construct the adjacent criteria comparison matrix; S2004, determine the subjective weight of the evaluation criteria.

3. The heating furnace energy efficiency prediction method based on subjective and objective combined weighting according to claim 2 is characterized in that: Using the hierarchical analysis evaluation method, the calculation of the subjective weight of the heating furnace energy efficiency evaluation index includes the following steps: S2001, based on the heating furnace energy efficiency evaluation index, construct a heating furnace hierarchical evaluation model; S2002, use the set value iteration method to determine the indicator sequence set, specifically: Assume that the heating furnace has m energy efficiency evaluation indicators, there are P experts to evaluate and score the heating furnace energy efficiency, the kth (1≤k≤P) expert scores the heating furnace energy efficiency evaluation indicator, x is the score of a heating furnace energy efficiency evaluation indicator, and the energy efficiency evaluation indicator set of the heating furnace is composed of Each evaluation index is formed into different score levels, and the full score of index m is 100 points; The expert weight λ is introduced. According to the data analysis results of the fit between the expert’s historical evaluation and the objective indicators, a weight value is assigned to each expert, which is denoted as λ1=(λ1,λ2,…,λ P ); A questionnaire on expert evaluation ability is issued to a preset number of people, and a weight value is assigned to each expert according to the questionnaire results, denoted as λ2=(λ1,λ2,…,λ P ), and use the weighted average method to process λ1 and λ2 to form an expert weight set λ=(λ1,λ2,…,λ P ); The energy efficiency evaluation indicators of heating furnaces in the energy efficiency evaluation indicator subset of heating furnaces are processed in descending order to form a new sequence set of energy efficiency evaluation indicators of heating furnaces. S2003, constructing an adjacent criterion comparison matrix, specifically including: The indicator sequence set determined by the set value iteration method Based on the fuzzy interval value method, the value r of the kth factor in the adjacent criterion comparison matrix is ​​obtained. k :where r k =1.0, indicating and The importance of k =1.2, indicating Compare Slightly important, k =1.4, Compare Obviously important, k =1.6, indicating Compare Strongly important; and are the new heating furnace energy efficiency evaluation index set X after being arranged in descending order * The k-1th index and the kth index in ; P experts compare the importance of adjacent indicators and obtain the element [r′ ji ,r″ ji ], the set of elements constitutes the adjacent criterion comparison matrix; S2004, determine the subjective weights of the evaluation criteria, as follows: The element [r′ in the jth row and ith column of the adjacent criterion comparison matrix ji ,r″ ji ] is converted into interval point value, the conversion formula is as follows: According to the interval point value, the following formula is used to calculate the subjective weight set of each energy efficiency evaluation index of the heating furnace: ω′=(ω′1,ω′2,…,ω′ m ) is the subjective weight of the j-th energy efficiency evaluation index: in, k is the element number in the adjacent criterion comparison matrix; r i It is the interval point value after the transformation of the element in the j-th row and the i-th column of the adjacent criterion comparison matrix.

4. The heating furnace energy efficiency prediction method based on subjective and objective combined weighting according to claim 1 is characterized in that: Using the entropy weight evaluation method, the objective weights of the heating furnace energy efficiency evaluation indicators are calculated, including the following steps: S3001, assume that there are n heating furnaces as the evaluation objects, m energy efficiency evaluation indicators, x ij is the value of the jth index of the i-th evaluation object, and the index matrix of the evaluated heating furnace is constructed as follows: X=(x ij ) m (i=1,2,…,n;j=1,2,…,m) S3002, normalize the indicator matrix X of the evaluation object to obtain a normalized judgment matrix Y: And=(and ij ) nm Among them, y ij is the element in row i and column j in Y; S3003, calculate the proportion of the heating furnace energy efficiency evaluation index value of the i-th evaluation object under the j-th index: S3004, calculate the entropy value e of the jth evaluation index j (e j ≥0): S3005, calculate the entropy redundancy d of the jth evaluation index j : d j =1-e j ,j=1,…,m S3006, calculating the objective weight ω″ of each evaluation index j :

5. The heating furnace energy efficiency prediction method based on subjective and objective combined weighting according to claim 1 is characterized in that: Based on the combined weighting method, the comprehensive weight of the energy efficiency evaluation index of the oilfield heating furnace is calculated, which specifically includes the following steps: S4001, sort the subjective weight and objective weight of each energy efficiency evaluation index respectively, and verify whether the subjective weight and objective weight can be combined and weighted by calculating the consistency coefficient, as follows: Calculate the Spearman consistency coefficient ρ of the subjective weight and the objective weight. The calculation of ρ is as follows: Where: ω′ j * is the jth subjective weight after re-ranking; ω″ j * is the jth objective weight after re-ranking; When ρ ∈ [-1, 0], it is considered that the subjective weight and the objective weight do not have consistency and cannot be combined for weighting; When ρ ∈ (0, 1], it is considered that the subjective weight and the objective weight have consistency and can be combined for weighting, and step S4002 is executed; S4002. Determine the combined weighting weight, specifically as follows: By using q weighting methods to calculate the weights of p heating furnace energy efficiency evaluation indicators, q basic weight vector sets ωk=(w k1 , w k2 ,…,w kp ), k = 1, 2, ..., q; The subjective weight vector and the objective weight vector are point-multiplied and normalized to obtain the new weight vector set ω=(ω1,ω2,…,ω j ), j = 1, 2, ..., n, any linear combination of n weight vector sets can be: Among them, ε j is the combination coefficient; Establish the following optimization model to solve the optimal combination coefficient The weight vector set ω after combining subjective weight and objective weight is equal to the basic weight vector set ω k The deviation difference and minimum: Solving the problem yields the optimal combination coefficient Substitute the solved optimal combination coefficient into any linear combination expression that can be formed by the n weight vector sets to solve the new weight vector composed of the optimized subjective weight and objective weight, and obtain the comprehensive weight of the evaluation index:

6. The heating furnace energy efficiency prediction method based on subjective and objective combined weighting according to any one of claims 1 to 5, characterized in that: According to the normalized comprehensive weight, obtain the comprehensive influence factor of the index, which specifically includes the following steps: Considering the influence of the comprehensive weight of the evaluation index on the thermal efficiency of the heating furnace, normalize the comprehensive weight of the energy efficiency evaluation index, and introduce the comprehensive influence factor w of the index; In the formula: A is the benchmark value of the heating furnace energy efficiency index; T is the test value of the heating furnace energy efficiency index; w j It is the comprehensive weight of heating furnace energy efficiency index.

7. The heating furnace energy efficiency prediction method based on subjective and objective combined weighting according to claim 6 is characterized in that: The energy efficiency evaluation index includes: flue gas temperature, excess air coefficient, outer surface temperature of the furnace body, and thermal efficiency..

8. The heating furnace energy efficiency prediction method based on subjective and objective combined weighting according to claim 7 is characterized in that: Perform linear regression on the comprehensive influence factor and the heating furnace thermal efficiency to obtain η = a0w + b0; Where: η is the thermal efficiency of the linear regression; a0 and b0 are the coefficients of the linear regression equation.

9. The heating furnace energy efficiency prediction method based on subjective and objective combined weighting according to claim 8 is characterized in that: In step S700, judge |η - η2| < t. If the above formula is satisfied, the accuracy requirement is met; if the above formula is not satisfied, the accuracy requirement is not met, where t is the accuracy requirement.

10. A heating furnace energy efficiency adjustment method based on subjective and objective combined weighting, characterized in that: Based on the prediction result of the heating furnace energy efficiency prediction method based on subjective and objective combined weighting according to any one of claims 1-9, the method includes the following steps: S111. Obtain the monitored fuel gas flow through the flow sensor and keep the flow unchanged; S112. Assume that the system starts to work from the position D1(α1, η1), and take a step forward or backward at an interval of t time, that is, adjust the air intake amount by Δα; S113. Every time a step is taken, use the computer to measure the controlled thermal efficiency index and calculate the air thermal efficiency difference Δη; S114. When the air thermal efficiency difference Δη is greater than zero, the adjustment direction is correct, so the next step should be adjusted in the original direction; S115. Judge that when the air thermal efficiency difference Δη is less than zero, the computer issues an adjustment command in the opposite direction, and the next step should be adjusted in the opposite direction; Where: α1 is the air intake amount at the initial position D1; η1 is the thermal efficiency at the initial position D1; Δα is the difference between the current air intake amount and the previous step; η t is the thermal efficiency of the heating furnace at time t; Δη is the difference between the current air thermal efficiency and the previous step.

11. The heating furnace energy efficiency adjustment method based on subjective and objective combined weighting according to claim 10 is characterized in that: The initialization parameters of the system include: optimization period, optimization step size, and optimization starting point. The initial values of the optimization period, optimization step size, and optimization starting point are respectively the duration from the start of heating to the end of heating of the heating furnace, the time interval t, and the position D1(α1, η1).

12. The heating furnace energy efficiency adjustment method based on subjective and objective combined weighting according to claim 11 is characterized in that: During the startup process of the system, monitor the change of the thermal efficiency and optimize the fuel gas increment, air volume increment, and air-fuel ratio.

13. The heating furnace energy efficiency adjustment method based on subjective and objective combined weighting according to claim 12 is characterized in that: It also includes: Step S116. If the optimal value of the heating furnace equipment system fluctuates within a preset range, the ratio of the air volume to the gas volume at this time is the optimal air-fuel ratio of the system.

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