Energy efficiency metering method for fuel gas ceramic kiln

Abstract

The invention relates to an energy efficiency metering method for a fuel gas ceramic kiln, which comprises the following steps: S1, calculating the energy consumption change rate of the fuel gas ceramic kiln, alpha (d, t) being the energy consumption change rate of the fuel gas ceramic kiln at the moment t of the d day, and L (d, t) being the unit time energy consumption value of the fuel gas ceramic kiln at the moment t of the d day; s2, judging whether the energy consumption record data is reasonable or not; and S3, collecting each calculated energy consumption value calculated in unit time into a set to form a calculated value data set. According to the invention, the energy efficiency of the fuel gas ceramic kiln can be accurately metered and predicted.

Description

Technical Field

[0001] The invention relates to the technical field of energy efficiency measurement of gas-fired ceramic kilns, and in particular to an energy efficiency measurement method for gas-fired ceramic kilns. Background Art

[0002] Gas-fired ceramic kilns use gas as a heat source. The combustion of gas generates a large amount of heat, which heats the ceramics through radiation, conduction, and convection. Under high temperature conditions, the moisture and organic matter in ceramic products will evaporate or be burned. As the temperature rises, the crystal water and chemically bound water in the ceramic material will gradually be released.

[0003] During the entire firing process, the temperature distribution inside the kiln is uneven, and reasonable design and control are required to achieve uniform temperature distribution inside the kiln. At the same time, the oxygen content and air flow speed in the kiln also need to be controlled to ensure that the composition and color of the ceramic products meet the expected requirements.

[0004] The firing process of gas-fired ceramic kilns is a high-energy consumption process, so it is necessary to monitor the energy efficiency level of gas-fired ceramic kilns in real time during their operation, so as to adjust the equipment parameters in a timely manner to achieve energy conservation and emission reduction. There are five main heat expenditures in the energy consumption of gas-fired ceramic kilns: 1. Sensible heat brought out by the product; 2. Heat consumed by evaporation of water in the green body and heating water vapor; 3. Sensible heat brought out by flue gas; 4. Sensible heat brought out by water vapor in flue gas; 5. Heat loss on the surface of the kiln. Due to the different types of ceramic products, types of gas, and air flow speeds, the unit time values ​​of the above five main heat expenditures are different. Furthermore, in order to control the uniform temperature in the kiln, it is necessary to adjust the oxygen content and air flow speed in the kiln in real time. Therefore, even if the same ceramic product is fired, the unit time values ​​of the above five main heat expenditures of multiple kilns of the same model are different. It is difficult to judge the degree of correlation between a certain heat expenditure and the energy consumption of gas-fired ceramic kilns. Therefore, it is difficult to predict and measure the energy consumption of gas-fired ceramic kilns. Summary of the invention

[0005] The purpose of the present invention is to provide an energy efficiency measurement method for a gas-fired ceramic kiln. The method analyzes the correlation between the five main heat expenditures and the energy consumption of the gas-fired ceramic kiln by comparing the data of the five main heat expenditures and the energy consumption of the gas-fired ceramic kiln, thereby accurately measuring and predicting the energy efficiency of the gas-fired ceramic kiln.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A method for measuring the energy efficiency of a gas-fired ceramic kiln comprises the following steps:

[0008] S1: The energy consumption change rate calculation formula of gas-fired ceramic kiln is as follows:

[0009]

[0010] Among them, α(d,t) is the energy consumption change rate of the gas-fired ceramic kiln at time t on the dth day, and L(d,t) is the energy consumption per unit time of the gas-fired ceramic kiln at time t on the dth day;

[0011] The maximum value of the energy consumption change rate of the gas-fired ceramic kiln is obtained by the following formula:

[0012]

[0013] in, is the maximum value of the energy consumption change rate at time t within the n days before day d, i represents the number of days between day d and that day, L(di, t-1) is the energy consumption record data at time t-1 on day d, and there are q+1 records every day;

[0014] S2: Determine whether the energy consumption record data is reasonable. The judgment conditions are as follows:

[0015]

[0016] The energy consumption per unit time value at the current moment is compared with the energy consumption per unit time value at the previous moment in chronological order. When an unreasonable energy consumption per unit time value is detected, it is immediately calculated and changed, and used as the comparison value of the energy consumption per unit time value at the next moment. The unreasonable energy consumption data is calculated using the data of the last m days. The calculation formula is as follows:

[0017]

[0018] Among them, j is the influence coefficient, which is used to indicate the influence of the energy consumption per unit time at time t on the dmth day on the energy consumption per unit time at time t on the dth day; ε is the smoothing coefficient, and L(dm, t) is the energy consumption per unit time of the gas ceramic kiln at time dm.

[0019] S3: Collect the estimated values ​​of the energy consumption per unit time obtained by each calculation into a set to form an estimated value data set X 0 , X 0 ={x 01 , x 02 , x 03 ,…,x 0m};

[0020] During the operation of a gas-fired ceramic kiln, the five main thermal expenditures that affect the energy consumption of the kiln are:

[0021] Sensible heat from the product;

[0022] The water in the green body evaporates and the heat is consumed by the heating steam;

[0023] Sensible heat brought by flue gas;

[0024] The sensible heat brought by water vapor in the flue gas;

[0025] Heat dissipation loss on the kiln surface;

[0026] During the operation of the gas-fired ceramic kiln, the unit time heat expenditure values ​​of the above five heat expenditures are monitored in real time. Every time an unreasonable unit time energy consumption value is detected, an estimated value x is calculated. 0j When the detection time is reached, the heat expenditure values ​​of the above five heat expenditures in the unit time before the detection time are collected respectively, and the collected values ​​of the five heat expenditures are arranged into data sets in the corresponding order:

[0027] Data set X of collected values ​​of sensible heat brought out by products 1 ={x 11 , x 12 , x 13 ,…,x 1m};

[0028] Data set X for the collected values ​​of the evaporation of water in the green body and the heat consumption of the heating water vapor 2 ={x 21 , x 22 , x 23 ,…,x 2m};

[0029] Data set X of collected values ​​of sensible heat brought out by flue gas 3 ={x 31 , x 32 , x 33 ,…,x 3m};

[0030] Data set X of the collected values ​​of the sensible heat brought by water vapor in the flue gas 4 ={x 41 , x 42 , x 43 ,…,x 4m};

[0031] Dataset of collected values ​​of heat loss on the kiln surface X 5 ={x 51 , x 52 , x 53 ,…,x 5m};

[0032] Combine the above six sets of numbers into a comparison matrix X:

[0033]

[0034] S4: parameter matrix X ij Each element in is standardized, and the formula is:

[0035]

[0036] In the above formula, X ij is the quantity value, y ij is the standard value, y ij The value of is between 0.1 and 1.

[0037] After the contrast matrix X is standardized, the contrast matrix Y is obtained:

[0038]

[0039] Among them, the first row y 0j The number set is the reference number set, and the second to sixth rows y ij The five number sets are comparison number sets;

[0040] Comparison number set y ij With the reference number set y 0j The mutual influence coefficient δ of the hth element 0i The calculation formula of (h) is as follows:

[0041]

[0042] Among them, min i min j |y ij -y 0j | is the minimum difference between the two levels, max i max j |y ij -y 0j | is the minimum difference between the two levels; is the discriminant coefficient;

[0043] The mutual influence coefficient judgment matrix D is calculated:

[0044]

[0045] Among them, δ 00 (j) is the reference number set y 0 The correlation coefficient with its own j-th element is 1;

[0046] S5: Assign weights to the column vectors of the comparison matrix Y, excluding the first row elements, using the following formula:

[0047]

[0048] Among them, e j represents the information entropy of the j-th column data, w jRepresents the weight of the j-th column data, and obtains the weighted relevance judgment matrix F:

[0049]

[0050] Among them, w j δ 0i (j) is the set of numbers y j With y 0 The weighted correlation coefficient of the jth element;

[0051] S6: Calculate the correlation degree value, the formula is as follows:

[0052]

[0053] Among them, G i Represents the collected value data set X i Correlation with energy consumption of gas-fired ceramic kilns, G i The value of G is between 0 and 1. i The closer the value of is to 1, the greater the correlation is; i The closer the value is to 0, the smaller the correlation is;

[0054] G i Data values ​​less than 0.2 are excluded, and the remaining data values ​​are used to calculate the energy consumption of the gas-fired ceramic kiln, thereby calculating the energy efficiency of the gas-fired ceramic kiln.

[0055] Specifically, let P i，d (t) is the unit time value of the heat expenditure of the ith type at time t on the dth day. The first-order curve y=p+q·d in the least squares model is used to calculate the historical data of the previous n days. According to the relevant formula of the least squares method, it can be obtained:

[0056]

[0057] The first-order curve y=p+q·d is obtained. This curve can reflect the variation law of the unit time value of the i-th type of heat expenditure in the previous n days. Using this variation law, the value of the d+1 day can be predicted as:

[0058] P i，n+1 (t) = p + q (n + 1)

[0059] Then, the prediction allocation coefficient K for predicting the heat expenditure of the i-th type at time t on day n+1 is calculated. i，d+1 (t), the formula is as follows:

[0060]

[0061] Where c is the number of main heat expenditure types of gas-fired ceramic kilns, and c is 5.

[0062] Compared with the prior art, the present invention has the following beneficial effects:

[0063] In S6, G i Data values ​​less than 0.2 are heat expenditure values ​​that are greatly affected by the oxygen content in the furnace, the air flow velocity in the furnace and the type of ceramic products. They are special data measured under special working conditions of a small number of gas-fired ceramic kilns, such as gas-fired ceramic kilns in large-scale gas addition, too fast green body conveying speed, too high green body moisture content, etc. The corresponding heat expenditure values ​​measured under the above working conditions have a low correlation with the common energy consumption characteristics of multiple gas-fired ceramic kilns. Therefore, they are excluded, and the remaining heat expenditure values ​​are used to predict and measure the energy consumption of gas-fired ceramic kilns.

[0064] By analyzing the variation pattern of the unit time value of the i-th type of heat expenditure in the previous n days, the predicted allocation coefficient of the i-th type of heat expenditure at time t is calculated. The predicted allocation coefficient takes into account and compensates for the changing factors of the gas-fired ceramic kiln, such as the real-time adjustment of the oxygen content, airflow velocity, etc. to maintain uniform internal temperature, so that the values ​​of various heat expenditures can be accurately predicted and measured, thereby accurately predicting and measuring the energy efficiency of the gas-fired ceramic kiln. DETAILED DESCRIPTION

[0065] The technical solutions in the embodiments of the present invention will be clearly and completely described below in combination with the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0066] A method for measuring energy efficiency of a gas-fired ceramic kiln, characterized in that it comprises the following steps:

[0067] S1: The energy consumption change rate calculation formula of gas-fired ceramic kiln is as follows:

[0068]

[0069] Among them, α(d,t) is the energy consumption change rate of the gas-fired ceramic kiln at time t on the dth day, and L(d,t) is the energy consumption per unit time of the gas-fired ceramic kiln at time t on the dth day;

[0070] The maximum value of the energy consumption change rate of the gas-fired ceramic kiln is obtained by the following formula:

[0071]

[0072] in, is the maximum value of the energy consumption change rate at time t within the n days before the dth day, i represents the number of days between the day d and the current day, L(di, t-1) is the energy consumption record data at time t-1 on the dith day, and there are q+1 records every day.

[0073] S2: Determine whether the energy consumption record data is reasonable. The judgment conditions are as follows:

[0074]

[0075] The energy consumption per unit time value at the current moment is compared with the energy consumption per unit time value at the previous moment in chronological order. When an unreasonable energy consumption per unit time value is detected, it is immediately calculated and changed, and used as the comparison value of the energy consumption per unit time value at the next moment. The unreasonable energy consumption data is calculated using the data of the last m days. The calculation formula is as follows:

[0076]

[0077] Among them, j is the influence coefficient, which is used to indicate the influence of the energy consumption per unit time at time t on the dmth day on the energy consumption per unit time at time t on the dth day; ε is the smoothing coefficient, and L(dm, t) is the energy consumption per unit time of the gas ceramic kiln at time dm.

[0078] The entire process in S2 is carried out in chronological order, and the energy consumption per unit time value at each moment is only compared with the energy consumption per unit time value at the previous moment. Therefore, the entire detection process algorithm is simple and occupies little memory, making the program run smoothly and not prone to jamming or freezing, which is suitable for long-term monitoring. When abnormal data is detected, it is immediately calculated and changed and used as the comparison value of the data at the next moment. The process is reliable and efficient.

[0079] S3: Collect the estimated values ​​of the energy consumption per unit time obtained by each calculation into a set to form an estimated value data set X 0 , X 0 ={x 01 , x 02 , x 03 ,…,x 0m};

[0080] During the operation of a gas-fired ceramic kiln, the five main thermal expenditures that affect the energy consumption of the kiln are:

[0081] Sensible heat from the product;

[0082] The water in the green body evaporates and the heat is consumed by the heating steam;

[0083] Sensible heat brought by flue gas;

[0084] The sensible heat brought by water vapor in the flue gas;

[0085] Heat dissipation loss on the kiln surface;

[0086] During the operation of the gas-fired ceramic kiln, the unit time heat expenditure values ​​of the above five heat expenditures are monitored in real time. Every time an unreasonable unit time energy consumption value is detected, an estimated value x is calculated. 0j When the detection time is reached, the heat expenditure values ​​of the above five heat expenditures in the unit time before the detection time are collected respectively, and the collected values ​​of the five heat expenditures are arranged into data sets in the corresponding order:

[0087] Data set X of collected values ​​of sensible heat brought out by products 1 ={x 11 , x 12 , x 13 ,…,x 1m};

[0088] Data set X for the collected values ​​of the evaporation of water in the green body and the heat consumption of the heating water vapor 2 ={x 21 , x 22 , x 23 ,…,x 2m};

[0089] Data set X of collected values ​​of sensible heat brought out by flue gas 3 ={x 31 , x 32 , x 33 ,…,x 3m};

[0090] Data set X of the collected values ​​of the sensible heat brought by water vapor in the flue gas 4 ={x 41 , x 42 , x 43 ,…,x 4m};

[0091] Dataset of collected values ​​of heat loss on the kiln surface X 5 ={x 51 , x 52 , x 53 ,…,x 5m};

[0092] Combine the above six sets of numbers into a comparison matrix X:

[0093]

[0094] S4: parameter matrix X ij Each element in is standardized, and the formula is:

[0095]

[0096] In the above formula, X ij is the quantity value, y ij is the standard value, yij The value of is between 0.1 and 1.

[0097] After the contrast matrix X is standardized, the contrast matrix Y is obtained:

[0098]

[0099] Among them, the first row y 0j The number set is the reference number set, and the second to sixth rows y ij The five number sets are comparison number sets;

[0100] Comparison number set y ij With the reference number set y 0j The mutual influence coefficient δ of the hth element 0i The calculation formula of (h) is as follows:

[0101]

[0102] Among them, min i min j |y ij -y 0j | is the minimum difference between the two levels, max i max j |y ij -y 0j | is the minimum difference between the two levels; is the discriminant coefficient;

[0103] The mutual influence coefficient judgment matrix D is calculated:

[0104]

[0105] Among them, δ 00 (j) is the reference number set y 0 The correlation coefficient with its own j-th element is 1;

[0106] S5: Assign weights to the column vectors of the comparison matrix Y, excluding the first row elements, using the following formula:

[0107]

[0108] Among them, e j represents the information entropy of the j-th column data, w j Represents the weight of the j-th column data, and obtains the weighted relevance judgment matrix F:

[0109]

[0110] Among them, w j δ 0i (j) is the set of numbers y j With y 0The weighted correlation coefficient of the jth element;

[0111] S6: Calculate the correlation degree value, the formula is as follows:

[0112]

[0113] Among them, G i Represents the collected value data set X i Correlation with energy consumption of gas-fired ceramic kilns, G i The value of G is between 0 and 1. i The closer the value of is to 1, the greater the correlation is; i The closer the value is to 0, the smaller the correlation is;

[0114] G i Data values ​​less than 0.2 are excluded, and the remaining data values ​​are used to calculate the energy consumption of the gas-fired ceramic kiln, thereby calculating the energy efficiency of the gas-fired ceramic kiln.

[0115] Specifically, let P i，d (t) is the unit time value of the heat expenditure of the ith type at time t on the dth day. The first-order curve y=p+q· in the least squares model is used to calculate the historical data of the previous n days. According to the relevant formula of the least squares method, it can be obtained:

[0116]

[0117]

[0118] The first-order curve y=p+q· is obtained. This curve can reflect the variation law of the unit time value of the i-th type of heat expenditure in the previous n days. Using this variation law, the value of the d+1 day can be predicted as:

[0119] P i，n+1 (t) = p + q (n + 1)

[0120] Then, the prediction allocation coefficient K for predicting the heat expenditure of the i-th type at time t on day n+1 is calculated. i，d+1 (t), the formula is as follows:

[0121]

[0122] Where c is the number of main heat expenditure types of gas-fired ceramic kilns, and c is 5.

[0123] The technical effects of the energy efficiency measurement method of the gas-fired ceramic kiln of the present invention are:

[0124] In S6, G iData values ​​less than 0.2 are heat expenditure values ​​that are greatly affected by the oxygen content in the furnace, the air flow velocity in the furnace and the type of ceramic products. They are special data measured under special working conditions of a small number of gas-fired ceramic kilns, such as gas-fired ceramic kilns in large-scale gas addition, too fast green body conveying speed, too high green body moisture content, etc. The corresponding heat expenditure values ​​measured under the above working conditions have a low correlation with the common energy consumption characteristics of multiple gas-fired ceramic kilns. Therefore, they are excluded, and the remaining heat expenditure values ​​are used to predict and measure the energy consumption of gas-fired ceramic kilns.

[0125] By analyzing the variation pattern of the unit time value of the i-th type of heat expenditure in the previous n days, the predicted allocation coefficient of the i-th type of heat expenditure at time t is calculated. The predicted allocation coefficient takes into account and compensates for the changing factors of the gas-fired ceramic kiln, such as the real-time adjustment of the oxygen content, airflow velocity, etc. to maintain uniform internal temperature, so that the values ​​of various heat expenditures can be accurately predicted and measured, thereby accurately predicting and measuring the energy efficiency of the gas-fired ceramic kiln.

[0126] The above are only preferred embodiments of the present invention, and are not intended to limit the present invention in any form. Although the present invention has been disclosed as above in the form of preferred embodiments, it is not intended to limit the present invention. Any technical personnel in the field can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A method for measuring the energy efficiency of a gas-fired ceramic kiln, characterized in that: The following steps are involved: S1: The energy consumption change rate calculation formula of gas-fired ceramic kiln is as follows: Among them, α(d,t) is the energy consumption change rate of the gas-fired ceramic kiln at time t on the dth day, and L(d,t) is the energy consumption per unit time of the gas-fired ceramic kiln at time t on the dth day; The maximum value of the energy consumption change rate of the gas-fired ceramic kiln is obtained by the following formula: in, is the maximum value of the energy consumption change rate at time t within the n days before day d, i represents the number of days between day d and that day, L(di, t-1) is the energy consumption record data at time t-1 on day d, and there are q+1 records every day; S2: Determine whether the energy consumption record data is reasonable. The judgment conditions are as follows: The energy consumption per unit time value at the current moment is compared with the energy consumption per unit time value at the previous moment in chronological order. When an unreasonable energy consumption per unit time value is detected, it is immediately calculated and changed, and used as the comparison value of the energy consumption per unit time value at the next moment. The unreasonable energy consumption data is calculated using the data of the last m days. The calculation formula is as follows: Among them, j is the influence coefficient, which is used to indicate the influence of the energy consumption per unit time at time t on the dmth day on the energy consumption per unit time at time t on the dth day; ε is the smoothing coefficient, and L(dm, t) is the energy consumption per unit time of the gas ceramic kiln at time dm. S3: Collect the estimated values ​​of the energy consumption per unit time obtained by each calculation into a set to form an estimated value data set X0, X0 = {x 01 , x 02 , x 03 ,…,x 0m }; During the operation of a gas-fired ceramic kiln, the five main thermal expenditures that affect the energy consumption of the kiln are: Sensible heat from the product; The water in the green body evaporates and the heat is consumed by the heating steam; Sensible heat brought by flue gas; The sensible heat brought by water vapor in the flue gas; Heat dissipation loss on the kiln surface; During the operation of the gas-fired ceramic kiln, the unit time heat expenditure values ​​of the above five heat expenditures are monitored in real time. Every time an unreasonable unit time energy consumption value is detected, an estimated value x is calculated. 0j When the detection time is reached, the heat expenditure values ​​of the above five heat expenditures in the unit time before the detection time are collected respectively, and the collected values ​​of the five heat expenditures are arranged into data sets in the corresponding order: The data set of collected values ​​of the sensible heat brought out by the product is X1 = {x 11 , x 12 , x 13 ,…,x 1m }; Data set X2 = {x 21 , x 22 , x 23 ,…,x 2m }; The data set X3 of the collected values ​​of the sensible heat brought out by the flue gas is {x 31 , x 32 , x 33 ,…,x 3m }; The data set X4 of the sensible heat brought by water vapor in the flue gas is 41 , x 42 , x 43 ,…,x 4m }; Data set X5 = {x 51 , x 52 , x 53 ,…,x 5m }; Combine the above six sets of numbers into a comparison matrix X: S4: parameter matrix X ij Each element in is standardized, and the formula is: In the above formula, X ij is the quantity value, y ij is the standard value, y ij The value of is between 0.1 and 1. After the contrast matrix X is standardized, the contrast matrix Y is obtained: Among them, the first row y 0j The number set is the reference number set, and the second to sixth rows y ij The five number sets are comparison number sets; Comparison number set y ij With the reference number set y 0j The mutual influence coefficient δ of the hth element 0i The calculation formula of (h) is as follows: i=1, 2, 3, 4, 5; j=1, 2,..., m; Among them, min i min j |y jj -y 0j | is the minimum difference between the two levels, max i max j |y ij -y 0j | is the minimum difference between the two levels; is the discriminant coefficient; The mutual influence coefficient judgment matrix D is calculated: Among them, δ 00 (j) is the correlation coefficient between the reference number set y0 and its j-th element, and its value is 1; S5: Assign weights to the column vectors of the comparison matrix Y, excluding the first row elements, using the following formula: Among them, e j represents the information entropy of the j-th column data, w j Represents the weight of the j-th column data, and obtains the weighted relevance judgment matrix F: Among them, w j δ 0i (j) is the set of numbers y j The weighted correlation coefficient with the j-th element of y0; S6: Calculate the correlation degree value, the formula is as follows: Among them, G i Represents the collected value data set X i Correlation with energy consumption of gas-fired ceramic kilns, G i The value of G is between 0 and 1. i The closer the value of is to 1, the greater the correlation is; i The closer the value of is to 0, the smaller the correlation is: G i Data values ​​less than 0.2 are excluded, and the remaining data values ​​are used to calculate the energy consumption of the gas-fired ceramic kiln, thereby calculating the energy efficiency of the gas-fired ceramic kiln.

2. The energy efficiency measurement method for a gas-fired ceramic kiln according to claim 1 is characterized in that: P i，d (t) is the unit time value of the heat expenditure of the ith type at time t on the dth day. The first-order curve y=p+q·d in the least squares model is used to calculate the historical data of the previous n days. According to the relevant formula of the least squares method, it can be obtained: The first-order curve y=p+q·d is obtained. This curve can reflect the variation law of the unit time value of the i-th type of heat expenditure in the previous n days. Using this variation law, the value of the d+1 day can be predicted as: P i，n+1 (t)=p+q(n+1) Then, the prediction allocation coefficient K for predicting the heat expenditure of the i-th type at time t on day n+1 is calculated. i，d+1 (t), the formula is as follows: Where c is the number of main heat expenditure types of gas-fired ceramic kilns, and c is 5.

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