Energy efficiency evaluation method for large liquefied natural gas device

Abstract

The invention discloses an energy efficiency evaluation method for a large liquefied natural gas device, which relates to the technical field of natural gas and comprises the following steps: constructing an evaluation index system; constructing an index matrix R, and standardizing the index matrix R; the entropy ej of the indexes is calculated, the objective weight wj of each index is calculated according to the entropy ej, and an objective weight matrix w is constructed; constructing a judgment matrix A by using an analytic hierarchy process, calculating a subjective weight bj of each index through an entropy weight analysis method, and combining the subjective weight bj and the objective weight wj to obtain a comprehensive weight matrix Z; dynamically adjusting the weight of each index based on a feedback mechanism; a TOPSIS method is adopted to calculate the relative proximity; obtaining a comprehensive evaluation score S through fuzzy comprehensive evaluation; and sorting the evaluation indexes according to the comprehensive evaluation score S. According to the evaluation method provided by the invention, the accuracy of energy efficiency evaluation can be improved, so that the energy utilization efficiency of the device is improved, the energy consumption is reduced, and the environmental pollution is reduced.

Description

Technical Field

[0001] The present invention relates to the field of natural gas technology, and more particularly to an energy efficiency evaluation method for a large-scale liquefied natural gas device. Background Art

[0002] In the global energy structure, natural gas has become an important energy source due to its clean and efficient characteristics. In particular, liquefied natural gas (LNG) has become an important part of international energy trade due to its unique advantages in storage and transportation. With the growing global demand for energy and the improvement of environmental protection awareness, improving the energy efficiency of LNG devices has become a goal pursued by the industry. Therefore, a method is needed to evaluate the energy efficiency of the device, so as to feedback the energy efficiency level of the device and formulate energy efficiency improvement measures.

[0003] Traditional natural gas energy efficiency evaluation methods often lack a systematic framework and scientific evaluation tools, which leads to insufficient accuracy and reliability of evaluation results. At the same time, these methods usually rely on manual experience and lack data support, so the evaluation process is not efficient and scientific enough.

[0004] Chinese patent CN110779275A discloses a method for improving the energy efficiency of a natural gas liquefaction device. The method uses a planning solution method to obtain the regression coefficients of a high-order regression equation, and obtains the optimal ratio of refrigerant in the natural gas liquefaction process, which effectively improves the energy utilization rate of the natural gas liquefaction device. However, there is a lack of a scientific and reasonable energy efficiency evaluation system and evaluation method.

[0005] Chinese patent CN115204636A discloses a comprehensive evaluation method for driving behavior based on multi-index fusion, which uses entropy weight analysis method-hierarchy analysis method to extract weight matrix, and uses TOPSIS method and fuzzy comprehensive evaluation method to evaluate driving behavior. However, the original data is not standardized to eliminate the influence of dimension, and the model is complex and computationally intensive, and subjective elements may also introduce deviations, affecting the overall evaluation.

[0006] Chinese patent CN117251757A discloses a fuzzy comprehensive evaluation method and system based on intelligent classification, which uses entropy weight analysis method-TOPSIS method-fuzzy comprehensive evaluation method for evaluation. It mainly relies on deep learning classifiers, does not involve the construction of a specific energy efficiency evaluation index system, and does not describe the weight allocation and comprehensive evaluation method of multiple indicators in detail.

[0007] Chinese patent CN116720782A discloses a method and device for evaluating the credibility of flexible load response, a storage medium, and a computer device. The method obtains the load to be evaluated and constructs an index system, uses the fuzzy analytic hierarchy process-entropy weight analysis method to determine the comprehensive weight value in the index system, and uses the fuzzy comprehensive evaluation method for comprehensive evaluation. However, the original data is not standardized, and the fuzzy comprehensive evaluation process is not described.

[0008] Chinese patent CN115764979A discloses a method for quantitatively evaluating the stability of a distributed photovoltaic system taking into account communication delay. This method obtains evaluation indicators by calculating relevant attributes, obtains subjective and objective weights by using the analytic hierarchy process and entropy weight analysis method, and evaluates the system stability by using a fuzzy comprehensive quantitative calculation method. However, this method is mainly based on static indicators and cannot better reflect the relative advantages and disadvantages of each evaluation object.

[0009] Therefore, it is necessary to propose an energy efficiency evaluation method for large-scale liquefied natural gas plants to solve the above problems. Summary of the invention

[0010] The purpose of the present invention is to solve the problem that the existing evaluation method cannot better reflect the relative advantages and disadvantages of each evaluation object.

[0011] In order to achieve the above-mentioned purpose, the present invention specifically adopts the following technical solutions:

[0012] A method for evaluating energy efficiency of a large-scale liquefied natural gas device comprises the following steps:

[0013] Step S1, constructing an evaluation index system, including indicators of energy consumption, product output, energy utilization, system stability, environmental emissions, waste generation, product quality, safety and production efficiency;

[0014] Step S2, constructing an indicator matrix R, and performing standardization processing on the indicator matrix R;

[0015] Step S3: Calculate the entropy value e of the index j , to evaluate the information entropy value of each indicator, according to the entropy value e j , calculate the objective weight w of each indicator j , construct the objective weight matrix w;

[0016] Step S4: Use the hierarchical analysis method to construct the judgment matrix A, and calculate the subjective weight b of each indicator through the entropy weight analysis method. j , combined with the subjective weight b j and objective weight w j , get the comprehensive weight matrix Z;

[0017] Step S5: dynamically adjust the weight of each indicator based on the feedback mechanism;

[0018] Step S6, using the TOPSIS method to calculate relative proximity;

[0019] Step S7, through fuzzy comprehensive evaluation, the fuzzy evaluation results of each indicator are integrated to obtain a comprehensive evaluation score S;

[0020] Step S8: Sort the evaluation indicators according to the comprehensive evaluation score S.

[0021] Furthermore, the step S2 specifically includes the following steps:

[0022] Step S20: construct an indicator matrix R, where the rows represent evaluation indicators, the columns represent evaluation objects in different time periods, and the matrix elements r ij Represents the original data of the i-th evaluation object on the j-th indicator;

[0023] Use median and interquartile range to standardize indicator matrix R

[0024]

[0025]

[0026] Where: x ij and x′ ij is the data value after normalization, and the normalized indicator matrix R′ is obtained.

[0027] Furthermore, the step S3 specifically includes the following steps:

[0028] Step S30: Calculate the entropy value e of the index j , to evaluate the information entropy value of each indicator, reflect its distribution uniformity in different samples, and calculate the entropy matrix E;

[0029]

[0030]

[0031]

[0032] Where: p ij is the weight of indicator j on evaluation object i, m is the number of evaluation objects; x ij is the element in the i-th row and j-th column of the normalized indicator matrix;

[0033] Step S31: According to the entropy value e j , calculate the objective weight w of each indicator j , and construct the objective weight matrix w, and dynamically adjust it according to the actual situation of the evaluation object.

[0034]

[0035] w j (t) = αw j +(1-α)w j (t-1)

[0036] Where: w j (t) represents the weight at time t, w j is the currently calculated entropy weight, w j (t-1) is the weight at the previous moment, and α is the adjustment coefficient (0<α<1).

[0037] Furthermore, the step S4 specifically includes the following steps:

[0038] Step S40: construct a hierarchical structure of indicators:

[0039] Target layer: comprehensive energy efficiency evaluation; Criteria layer: energy consumption, product output, energy utilization rate; Indicator layer: electricity consumption, natural gas consumption, LNG output, energy utilization rate;

[0040] According to the importance of indicators, pairwise comparisons are made to construct the judgment matrix A;

[0041] Step S41: Perform consistency check and calculate the maximum eigenvalue λ of the judgment matrix A max , consistency coefficient CI, consistency ratio CR, CR < 0.1, indicating that the matrix has satisfactory consistency;

[0042] Step S42: normalize the judgment matrix A to obtain a normalized matrix A′, and use arithmetic mean to calculate the subjective weight b j , obtain the subjective weight matrix B, combined with the subjective weight b j and objective weight w j , and the comprehensive weight z is obtained j , and then get the comprehensive weight matrix Z,

[0043] z j =α·b j +(1-α)·w j

[0044] Where: α is the weight adjustment coefficient, and its value range is 0-1.

[0045] Furthermore, the step S5 specifically includes the following steps:

[0046] Step S50: setting a fixed feedback cycle to collect energy efficiency index feedback data;

[0047] Step S51, analyzing feedback data to identify key indicators that affect system energy efficiency;

[0048] Step S52: adjust the objective weight of each indicator according to the analysis result, and apply the adjusted objective weight to the next feedback cycle.

[0049] Furthermore, the specific steps of step S6 are as follows:

[0050] Step S60: Construct ideal solution A + and negative ideal solution A - ,

[0051] A + ={max(x ij )|j∈I + ;min(x ij )|j∈I -}

[0052] A - ={min(x ij )|j∈I + ;max(x ij )|j∈I -}

[0053] Where: I + is the set of indicators that need to be maximized; I - is the set of indicators that need to be minimized;

[0054] Step S61: Calculate the distance between each solution and the positive ideal solution and the negative ideal solution and

[0055]

[0056]

[0057] Where: are the membership degrees of the positive ideal solution and the negative ideal solution respectively.

[0058] Step S62: Calculate relative proximity

[0059]

[0060] Furthermore, the specific steps of step S7 are as follows:

[0061] Step S70: According to the actual situation and expert opinions, four evaluation levels and their corresponding membership functions are set.

[0062] Step S71, calculating the membership matrix U according to the original data in the indicator matrix R and the preset membership function, reflecting the membership of each evaluation object on each indicator;

[0063] Calculate the fuzzy comprehensive evaluation score S,

[0064]

[0065] Where: Z is the comprehensive weight matrix; U is the membership matrix.

[0066] Compared with the prior art, the present invention has the following beneficial effects:

[0067] The evaluation method provided by the present invention can improve the accuracy of energy efficiency evaluation, thereby improving the energy utilization efficiency of the device, reducing energy consumption, and reducing environmental pollution. DETAILED DESCRIPTION

[0068] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0069] A method for evaluating the energy efficiency of a large-scale liquefied natural gas plant.

[0070] Example 1

[0071] Step S1: Constructing an evaluation index system:

[0072] Key indicators: energy consumption (including electricity consumption, natural gas consumption, etc.), product output, energy utilization rate, etc.

[0073] Methods: This example focuses on the relationship between energy consumption and product output.

[0074] Energy consumption:

[0075] Electricity consumption: usually measured in kilowatt-hours (kWh), which can be obtained directly from the meter reading or calculated using the following formula:

[0076] E 电 =P×t

[0077] Among them, E 电 is the electricity consumption, in kWh; P is the power, in kW; t is the time, in h.

[0078] Natural gas consumption: usually in m 3 Or kg, which can be obtained from the gas flow meter reading or calculated by the following formula:

[0079] E 气 =V×ρ×CV

[0080] Among them, E 气 is the natural gas consumption, in MJ or kWh; V is the volume flow, in m 3 or L; ρ is the density of natural gas, unit is kg / m 3 ; CV is the calorific value of natural gas, in MJ / kg or kWh / kg.

[0081] Product output is usually measured in terms of unit product quantity or weight, and the specific formula depends on the type of product and the production process. Liquefied natural gas (LNG) output can be calculated using the following formula:

[0082] P LNG =V LNG ×ρ LNG

[0083] Among them, P LNG is the LNG production, in kg or t; V LNG is the volume of LNG, in m 3 or L; ρ LNG is the density of LNG, in kg / m 3 .

[0084] Energy utilization rate refers to the ratio of effectively used energy to total consumed energy, which can be calculated by the following formula:

[0085]

[0086] Where, η is the energy utilization rate (%), E 有效 is the effective energy consumption, E 总 is the total energy consumption.

[0087] Step S2: Construct the indicator matrix R:

[0088] Step S20: construct an indicator matrix R, where the rows represent evaluation indicators, the columns represent evaluation objects in different time periods, and the matrix elements r ij Represents the original data of the i-th evaluation object on the j-th indicator.

[0089]

[0090] The left column is: Time Period 1, and the right column is: Time Period 2.

[0091] Step S21: Use the median and interquartile range to standardize the indicator matrix R to eliminate the dimension effect and improve the comparability of the data:

[0092]

[0093]

[0094] The standardized indicator matrix R′:

[0095]

[0096] Step S30, calculation of entropy matrix E: calculation of the entropy value e of the index j , to evaluate the information entropy value of each indicator, reflecting its uniform distribution in different samples:

[0097]

[0098]

[0099]

[0100]

[0101] Where: p ij is the weight of indicator j on evaluation object i, m is the number of evaluation objects; x ij is the element in the i-th row and j-th column of the normalized indicator matrix.

[0102] Step S31: According to the entropy value e j , calculate the objective weight w of each indicator j , and construct the objective weight matrix w, and dynamically adjust it according to the actual situation of the evaluation object.

[0103]

[0104] w j (t) = αw j +(1-α)w j (t-1)

[0105]

[0106] Where: w j (t) represents the weight at time t, w j is the currently calculated entropy weight, w j (t-1) is the weight at the previous moment, and α is the adjustment coefficient (0<α<1).

[0107] Step S40: construct a hierarchical structure of indicators:

[0108] Target layer: comprehensive energy efficiency evaluation;

[0109] Criteria layer: energy consumption, product output, energy utilization rate;

[0110] Indicator layer: electricity consumption, natural gas consumption, LNG production, and energy utilization rate.

[0111] Invite experts to compare the indicators in pairs according to their importance and construct a judgment matrix A. The expert's judgment matrix is ​​as follows:

[0112]

[0113] Step S41: Perform consistency check and calculate the maximum eigenvalue λ of the judgment matrix max , consistency coefficient CI, consistency ratio CR, CR < 0.1, indicating that the matrix has satisfactory consistency;

[0114] Step S42: normalize the judgment matrix A to obtain a normalized matrix A′.

[0115]

[0116] Use the arithmetic mean to find the subjective weight b j , get the subjective weight matrix B,

[0117] B=[b 11 , b 12 , b 13 , b 14 ]

[0118] Combined with subjective weight w j and objective weight b j , get the comprehensive weight matrix Z,

[0119] z j =α·b j +(1-α)·w j

[0120] Where: z j is the comprehensive weight, α is the weight adjustment coefficient, and its value range is 0-1.

[0121] Step S5: dynamically adjusting the weight of each indicator based on the feedback mechanism, including the following steps:

[0122] Step S50: setting a fixed feedback cycle to collect energy efficiency index feedback data;

[0123] Step S51, analyzing feedback data to identify key indicators that affect system energy efficiency;

[0124] Step S52: adjust the objective weight of each indicator according to the analysis result, and apply the adjusted objective weight to the next feedback cycle.

[0125] Step S6: Calculate the relative closeness using the TOPSIS method:

[0126] Step S60: construct an ideal solution A+ and a negative ideal solution A-.

[0127] A + ={max(x ij )|j∈I + ;min(x ij )|j∈I -}

[0128] A - ={min(x ij )|j∈I + ;max(x ij )|j∈I -}

[0129] Where: I + It is a collection of indicators that need to be maximized, such as income indicators; I - It is a collection of indicators that need to be minimized, such as cost indicators;

[0130]

[0131]

[0132] Step S61: Calculate the distance between each solution and the positive ideal solution and the negative ideal solution and

[0133]

[0134]

[0135] Where: are the membership degrees of the positive ideal solution and the negative ideal solution respectively.

[0136] Step S62: Calculate relative proximity

[0137]

[0138] Step S7: Through fuzzy comprehensive evaluation, the fuzzy evaluation results of each indicator are integrated to obtain a comprehensive evaluation score S:

[0139] Step S70, determine the evaluation level and membership function: according to the actual situation and expert opinions, set multiple evaluation levels (such as excellent, good, medium, poor) and their corresponding membership functions.

[0140] Excellent: The membership degree is exist When, with As , the membership increases linearly from 0 to 1.

[0141] Good: The membership degree is exist When it is close to 0.5, the membership reaches the maximum value of 1, and when it is far away from 0.5, the membership gradually decreases.

[0142] Middle: The membership degree is exist When, with decreases, and the membership increases linearly from 0 to 1.

[0143] Bad: The membership is always 0.

[0144] Step S71, establish the membership matrix U: The membership matrix U is calculated based on the data in the indicator matrix R and the pre-set membership function, reflecting the membership of each evaluation object on each indicator. The membership indicates the degree to which the performance of an evaluation object on a certain indicator belongs to a certain evaluation level.

[0145] U1=[u 11 ,u 12 ,u 13 ,u 14 ]

[0146] U2=[u 21 ,u 22 ,u 23 ,u 24 ]

[0147] Calculate the fuzzy comprehensive evaluation score S:

[0148]

[0149] Among them, Z is the comprehensive weight matrix; U is the membership matrix.

[0150] Step S8, sorting the evaluation indicators according to the comprehensive score: sorting the evaluation objects according to the comprehensive score S, obtaining the energy efficiency ranking, and providing data support for energy efficiency optimization.

[0151] Regarding one of the results: Time period 1 performed better than time period 2 in all indicators.

[0152] The comprehensive evaluation results of time period 1 show that the overall evaluation result of time period 1 is good. After comprehensive consideration, the performance of time period 1 in the key indicators of electricity consumption, natural gas consumption, product output and energy utilization rate has obtained a relatively high comprehensive score.

[0153] The comprehensive evaluation results of time period 2 show that the overall evaluation results of time period 2 are average. The comprehensive evaluation results of time period 2 are low. Although it is closer to the positive ideal solution than the negative ideal solution in relative proximity, it does not reach a high level in the comprehensive evaluation of each indicator. There may be room for improvement in terms of electricity consumption, natural gas consumption, product output, and energy utilization.

[0154] In summary, the comprehensive evaluation method demonstrated in this embodiment can more comprehensively and objectively evaluate the energy efficiency performance of large-scale liquefied natural gas plants, and provide guidance and basis for optimization and improvement. The analysis of the evaluation results helps to identify which aspects need to be focused on and improved to improve the energy efficiency and economic benefits of the plant.

[0155] Example 2

[0156] Step S1: Constructing an evaluation index system:

[0157] This embodiment describes an application practice of an energy efficiency evaluation method for a large-scale liquefied natural gas (LNG) plant. The method integrates multi-dimensional indicators to achieve a comprehensive evaluation of the energy efficiency of the LNG plant.

[0158] Construction of energy efficiency evaluation index system:

[0159] Key indicators: system stability, environmental emissions (including greenhouse gases, nitrogen oxides, etc.), waste generation, etc.

[0160] Method: This example focuses on the system stability and environmental impact of the device. In addition to key indicators such as energy consumption (including electricity consumption, natural gas consumption, etc.), product output, and energy utilization, the energy efficiency of the device is evaluated by monitoring and analyzing key indicators such as system stability, environmental emissions, and waste generation. Environmental emissions and waste generation quantify the degree of impact of the device on the environment. By weighing these indicators, a comprehensive energy efficiency evaluation index system is established.

[0161] System stability: The reliability of the natural gas pipeline network system can be divided into mechanical reliability, hydraulic reliability and gas supply reliability.

[0162] The availability (A0) indicator is used to measure the mechanical reliability of the system:

[0163]

[0164] Where MTTF is the mean time between failures, and MDT is the mean maintenance downtime, which includes proactive maintenance time, support delay time, and management delay time.

[0165] Using indicator K rel Evaluation of hydraulic reliability of long-distance natural gas pipeline system:

[0166]

[0167] Where Mξ is the expected gas flow rate of the system, q o is the nominal gas delivery volume of the system.

[0168] Evaluate gas supply reliability from two dimensions: gas volume and time:

[0169]

[0170] Where, T is the task time, the unit is d or h; C i Indicates the satisfaction of demand on the i-th day or hour.

[0171] Environmental emissions:

[0172] Greenhouse gas emissions = activity data × emission factor

[0173] Activity data refers to the amount of production or consumption activities that lead to greenhouse gas emissions. Emission factors are coefficients corresponding to activity level data.

[0174] Nitrogen oxide emissions (kg) = fuel consumption (t) × emission coefficient (kg / t) 3 .

[0175] Waste generation: Waste generation includes solid waste, liquid waste and gaseous waste.

[0176] The calculation formula for the amount of solid waste generated is:

[0177] The amount of solid waste generated = (the amount of comprehensive utilization - the amount of storage for comprehensive utilization in previous years) + the amount of storage + (the amount of disposal - the amount of storage for disposal in previous years) + the amount of dumping and discarding.

[0178] The calculation formula for liquid waste generation is:

[0179] G=Q×C×T

[0180] Among them, G is the emission of a pollutant in wastewater or waste gas, in kg; Q is the emission of wastewater or waste gas per unit time, in m 3 / h; C is the concentration of pollutants; T is time, the unit is h.

[0181] The calculation formula for the amount of gaseous waste generated is: For gaseous waste, the amount of waste gas emissions is:

[0182] Q 年 =Q 时 ×B 年 / B 时 / 10000

[0183] Among them, Q 年 is the annual exhaust gas emissions, Q 时 is the hourly exhaust gas emission, B 年 and B 时 is the time factor.

[0184] Step S2: Construct the indicator matrix R:

[0185] Step S20: construct an indicator matrix R, where the rows represent evaluation indicators, the columns represent evaluation objects in different time periods, and the matrix elements r ij Represents the original data of the i-th evaluation object on the j-th indicator.

[0186]

[0187] The left column is: Time Period 1, and the right column is: Time Period 2.

[0188] Step S21: Use the median and interquartile range to standardize the indicator matrix R to eliminate the dimension effect and improve the comparability of the data:

[0189]

[0190]

[0191] The standardized indicator matrix R′:

[0192]

[0193] Step S30, calculation of entropy matrix E: calculation of the entropy value e of the index j , to evaluate the information entropy value of each indicator, reflecting its uniform distribution in different samples:

[0194]

[0195]

[0196]

[0197]

[0198] Where: p ij is the weight of indicator j on evaluation object i, m is the number of evaluation objects; x ij is the element in the i-th row and j-th column of the normalized indicator matrix.

[0199] Step S31: According to the entropy value e j , calculate the objective weight w of each indicator j , and construct the objective weight matrix w, and dynamically adjust it according to the actual situation of the evaluation object.

[0200]

[0201] w j (t) = αw j +(1-α)w j (t-1)

[0202]

[0203] Where: w j (t) represents the weight at time t, w j is the currently calculated entropy weight, w j (t-1) is the weight at the previous moment, and α is the adjustment coefficient (0<α<1).

[0204] Step S40: construct a hierarchical structure of indicators:

[0205] Target layer: comprehensive energy efficiency evaluation;

[0206] Criteria layer: energy consumption, product output, energy utilization rate;

[0207] Indicator layer: electricity consumption, natural gas consumption, LNG production, and energy utilization rate.

[0208] Invite experts to compare the indicators in pairs according to their importance and construct a judgment matrix A. The expert's judgment matrix is ​​as follows:

[0209]

[0210] Step S41: Perform consistency check and calculate the maximum eigenvalue λ of the judgment matrix max , consistency coefficient CI, consistency ratio CR, CR < 0.1, indicating that the matrix has satisfactory consistency;

[0211] Step S42: normalize the judgment matrix A to obtain a normalized matrix A′.

[0212]

[0213] Use arithmetic mean to find the objective weight b j , get the subjective weight matrix B,

[0214] B=[b 11 , b 12 , b 13 , b 14 , b 15 , b 16 , b 17 ]

[0215] Combined with subjective weight w j and objective weight b j , get the comprehensive weight matrix Z,

[0216] z j =α·b j +(1-α)·w j

[0217] Where: z j is the comprehensive weight, α is the weight adjustment coefficient, and its value range is 0-1.

[0218] Step S5: dynamically adjusting the weight of each indicator based on the feedback mechanism, including the following steps:

[0219] Step S50: setting a fixed feedback cycle to collect energy efficiency index feedback data;

[0220] Step S51, analyzing feedback data to identify key indicators that affect system energy efficiency;

[0221] Step S52: adjust the objective weight of each indicator according to the analysis result, and apply the adjusted objective weight to the next feedback cycle.

[0222] Step S6: Calculate the relative closeness using the TOPSIS method:

[0223] Step S60: Construct ideal solution A + and negative ideal solution A - ,

[0224] A + ={max(x ij )|j∈I + ;min(x ij )|j∈I -}

[0225] A - ={min(x ij )|j∈I + ;max(x ij )|j∈I -}

[0226] Where: I + It is a collection of indicators that need to be maximized, such as income indicators; I - It is a collection of indicators that need to be minimized, such as cost indicators;

[0227]

[0228]

[0229] Step S61: Calculate the distance between each solution and the positive ideal solution and the negative ideal solution and

[0230]

[0231]

[0232] Where: are the membership degrees of the positive ideal solution and the negative ideal solution respectively.

[0233] Step S62: Calculate relative proximity

[0234]

[0235] Step S7: Through fuzzy comprehensive evaluation, the fuzzy evaluation results of each indicator are integrated to obtain a comprehensive evaluation score S:

[0236] Step S70, determine the evaluation level and membership function: according to the actual situation and expert opinions, set multiple evaluation levels (such as excellent, good, medium, poor) and their corresponding membership functions.

[0237] Good: The membership function is a triangle with the center point The width is 0.1.

[0238] Good: The membership function is a triangle with the center point The width is 0.1.

[0239] Middle: The membership function is a triangle with the center point The width is 0.1.

[0240] Difference: The membership function is a triangle with the center point being The width is 0.1.

[0241] Step S71, establish the membership matrix U: The membership matrix U is calculated based on the data in the indicator matrix R and the pre-set membership function, reflecting the membership of each evaluation object on each indicator. The membership indicates the degree to which the performance of an evaluation object on a certain indicator belongs to a certain evaluation level.

[0242] U1=[u 11 ,u 12 ,u 13 ,u 14 ,u 15 ,u 16 ,u 17 ]

[0243] U2=[u 21 ,u 22 ,u 23 ,u 24 ,u 25 ,u 26 ,u 27 ]

[0244] Calculate the fuzzy comprehensive evaluation score S:

[0245]

[0246] Among them, Z is the comprehensive weight matrix; U is the membership matrix.

[0247] Step S8, sorting the evaluation indicators according to the comprehensive score: sorting the evaluation objects according to the comprehensive score S, obtaining the energy efficiency ranking, and providing data support for energy efficiency optimization.

[0248] For one of the results: When the comprehensive evaluation score of time period 1 is lower than that of time period 2, it is still at a relatively good level. There is some room for improvement in time period 1. It is recommended to further optimize the electricity consumption and natural gas consumption to improve energy utilization and reduce resource waste, thereby improving energy efficiency.

[0249] The comprehensive evaluation score of time period 2 is at a relatively high level, indicating that it performs well in the comprehensive evaluation of energy efficiency. Time period 2 performs better than time period 1 in terms of electricity consumption, natural gas consumption, product output, and energy utilization. It can be inferred that the reason for the higher efficiency of time period 2 is that some effective energy efficiency optimization measures have been taken. It is recommended to continue to monitor and maintain these optimization measures to maintain a high level of energy efficiency performance.

[0250] In summary, this embodiment can provide data support and guidance for energy efficiency optimization based on the results of fuzzy comprehensive evaluation, and help decision makers identify and optimize key issues related to energy efficiency to achieve higher energy efficiency levels and economic benefits.

[0251] Example 3

[0252] Step S1: Constructing an evaluation index system:

[0253] Key indicators: product quality, safety, production efficiency, etc.

[0254] Method: In addition to key indicators such as energy consumption (including electricity consumption, natural gas consumption, etc.), product output, and energy utilization rate, this embodiment also comprehensively considers the energy efficiency of the device by comprehensively evaluating key indicators such as product quality, safety, and production efficiency.

[0255] Product quality is usually related to the purity of the natural gas and the efficiency of the liquefaction process. The liquefaction efficiency can be calculated by the ratio of the cooling capacity provided by the liquefier to the cooling capacity required:

[0256]

[0257] Production efficiency can be calculated in many ways. A common calculation formula is:

[0258]

[0259] Step S2: Construct the indicator matrix R:

[0260] Step S20: construct an indicator matrix R, where the rows represent evaluation indicators, the columns represent evaluation objects in different time periods, and the matrix elements r ij Represents the original data of the i-th evaluation object on the j-th indicator.

[0261]

[0262] The left column is: Time Period 1, and the right column is: Time Period 2.

[0263] Step S21: Use the median and interquartile range to standardize the indicator matrix R to eliminate the dimension effect and improve the comparability of the data:

[0264]

[0265]

[0266] The standardized indicator matrix R′:

[0267]

[0268] Step S30, calculation of entropy matrix E: calculation of the entropy value e of the index j , to evaluate the information entropy value of each indicator, reflecting its uniform distribution in different samples:

[0269]

[0270]

[0271]

[0272]

[0273] Where: p ij is the weight of indicator j on evaluation object i, m is the number of evaluation objects; x ij is the element in the i-th row and j-th column of the normalized indicator matrix.

[0274] Step S31: According to the entropy value e j , calculate the objective weight w of each indicator j , and construct the objective weight matrix w, and dynamically adjust it according to the actual situation of the evaluation object.

[0275]

[0276] w j (t) = αw j +(1-α)w j (t-1)

[0277]

[0278] Where: w j (t) represents the weight at time t, w j is the currently calculated entropy weight, w j (t-1) is the weight at the previous moment, and α is the adjustment coefficient (0<α<1).

[0279] Step S40: construct a hierarchical structure of indicators:

[0280] Target layer: comprehensive energy efficiency evaluation;

[0281] Criteria layer: energy consumption, product output, energy utilization rate;

[0282] Indicator layer: electricity consumption, natural gas consumption, LNG production, and energy utilization rate.

[0283] Invite experts to compare the indicators in pairs according to their importance and construct a judgment matrix A. The expert's judgment matrix is ​​as follows:

[0284]

[0285] Step S41: Perform consistency check and calculate the maximum eigenvalue λ of the judgment matrix max , consistency coefficient CI, consistency ratio CR, CR < 0.1, indicating that the matrix has satisfactory consistency;

[0286] Step S42: normalize the judgment matrix A to obtain a normalized matrix A′.

[0287]

[0288] Use arithmetic mean to find the objective weight b j , get the subjective weight matrix B,

[0289] B=[b 11 , b 12 , b 13 , b 14 , b 15 , b 16 , b 17 , b 18 , b 19 , b 110 ]

[0290] Combined with subjective weight w j and objective weight b j , get the comprehensive weight matrix Z,

[0291] z j =α·bj +(1-α)·w j

[0292] Where: z j is the comprehensive weight, α is the weight adjustment coefficient, and its value range is 0-1.

[0293] Step S5: dynamically adjusting the weight of each indicator based on the feedback mechanism, including the following steps:

[0294] Step S50: setting a fixed feedback cycle to collect energy efficiency index feedback data;

[0295] Step S51, analyzing feedback data to identify key indicators that affect system energy efficiency;

[0296] Step S52: adjust the objective weight of each indicator according to the analysis result, and apply the adjusted objective weight to the next feedback cycle.

[0297] Step S6: Calculate the relative closeness using the TOPSIS method:

[0298] Step S60: Construct ideal solution A + and negative ideal solution A - ,

[0299] A + ={max(x ij )|j∈I + ;min(x ij )|j∈I -}

[0300] A - ={min(x ij )|j∈I + ;max(x ij )|j∈I -}

[0301] Where: I + It is a collection of indicators that need to be maximized, such as income indicators; I - It is a collection of indicators that need to be minimized, such as cost indicators;

[0302]

[0303]

[0304] Step S61: Calculate the distance between each solution and the positive ideal solution and the negative ideal solution and

[0305]

[0306]

[0307] Where: are the membership degrees of the positive ideal solution and the negative ideal solution respectively.

[0308] Step S62: Calculate relative proximity

[0309]

[0310] Step S7: Through fuzzy comprehensive evaluation, the fuzzy evaluation results of each indicator are integrated to obtain a comprehensive evaluation score S:

[0311] Step S70, determine the evaluation level and membership function: according to the actual situation and expert opinions, set multiple evaluation levels (such as excellent, good, medium, poor) and their corresponding membership functions. For the membership function (a, b, c), a represents the value at which the membership starts to rise, b represents the vertex where the membership reaches the maximum value, and c represents the value at which the membership starts to decrease.

[0312] Excellent: (0.7, 0.85, 1). When the standardized value of the evaluation index is between 0.7 and 1, the closer it is to 0.85, the more it tends to be considered "excellent".

[0313] Good: (0.5, 0.7, 0.85). When the standardized value of the evaluation index is between 0.5 and 0.85, the closer it is to 0.7, the more it tends to be considered "good".

[0314] Medium: (0.3, 0.5, 0.7). When the standardized value of the evaluation index is between 0.3 and 0.7, the closer it is to 0.5, the more it tends to be considered "medium".

[0315] Poor: (0, 0.3, 0.5). When the standardized value of the evaluation index is between 0 and 0.5, the closer it is to 0.3, the more it tends to be considered "poor".

[0316] Step S71, establish the membership matrix U: The membership matrix U is calculated based on the data in the indicator matrix R and the pre-set membership function, reflecting the membership of each evaluation object on each indicator. The membership indicates the degree to which the performance of an evaluation object on a certain indicator belongs to a certain evaluation level.

[0317] U1=[u 11 ,u 12 ,u 13 ,u 14 ,u 15 ,u 16 ,u 17 ,u 18 ,u 19 ,u 110]

[0318] U2=[u 21 ,u 22 ,u 23 ,u 24 ,u 25 ,u 26 ,u 27 ,u 28 ,u 29 ,u 210 ]

[0319] Calculate the fuzzy comprehensive evaluation score S:

[0320]

[0321] Among them, Z is the comprehensive weight matrix; U is the membership matrix.

[0322] Step S8, sorting the evaluation indicators according to the comprehensive score: sorting the evaluation objects according to the comprehensive score S, obtaining the energy efficiency ranking, and providing data support for energy efficiency optimization.

[0323] Regarding one of the results: when the comprehensive evaluation score of time period 1 is higher than that of time period 2, it indicates that the performance in the comprehensive evaluation of energy efficiency is relatively good.

[0324] When the comprehensive evaluation score of time period 2 is higher than that of time period 1, it means that under the set evaluation indicators and weights, the comprehensive energy efficiency performance of time period 2 is better than that of time period 1. This result can provide data support for energy efficiency optimization, and it is recommended to focus on and optimize the operating conditions and strategies in time period 2 in future energy efficiency management to further improve the comprehensive energy efficiency performance.

[0325] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. The patent protection scope of the present invention shall be based on the claims. All equivalent structural changes made using the description of the present invention should also be included in the protection scope of the present invention.

Claims

1. A method for evaluating energy efficiency of a large-scale liquefied natural gas plant, characterized in that: The following steps are involved: Step S1, constructing an evaluation index system, including indicators of energy consumption, product output, energy utilization, system stability, environmental emissions, waste generation, product quality, safety and production efficiency; Step S2, constructing an indicator matrix R, and performing standardization processing on the indicator matrix R; Step S3: Calculate the entropy value e of the index j , to evaluate the information entropy value of each indicator, according to the entropy value e j , calculate the objective weight w of each indicator j , construct the objective weight matrix w; Step S4: Use the hierarchical analysis method to construct the judgment matrix A, and calculate the subjective weight b of each indicator through the entropy weight analysis method. j , combined with the subjective weight b j and objective weight w j , get the comprehensive weight matrix Z; Step S5: dynamically adjust the weight of each indicator based on the feedback mechanism; Step S6, using the TOPSIS method to calculate relative proximity; Step S7, through fuzzy comprehensive evaluation, the fuzzy evaluation results of each indicator are integrated to obtain a comprehensive evaluation score S; Step S8: Sort the evaluation indicators according to the comprehensive evaluation score S.

2. A large-scale liquefied natural gas plant energy efficiency evaluation method according to claim 1, characterized in that: The step S2 specifically includes the following steps: Step S20: construct an indicator matrix R, where the rows represent evaluation indicators, the columns represent evaluation objects in different time periods, and the matrix elements r ij Represents the original data of the i-th evaluation object on the j-th indicator; Use median and interquartile range to standardize indicator matrix R Where: x ij and x′ ij is the data value after normalization, and the normalized indicator matrix R′ is obtained.

3. The method for evaluating energy efficiency of a large-scale liquefied natural gas plant according to claim 1, characterized in that: The step S3 specifically comprises the following steps: Step S30: Calculate the entropy value e of the index j , to evaluate the information entropy value of each indicator, reflect its distribution uniformity in different samples, and calculate the entropy matrix E; Where: p ij is the weight of indicator j on evaluation object i, m is the number of evaluation objects; x ij is the element in the i-th row and j-th column of the normalized indicator matrix; Step S31: According to the entropy value e j , calculate the objective weight w of each indicator j , and construct the objective weight matrix w, and dynamically adjust it according to the actual situation of the evaluation object. w j (t)=αw j +(1-α)w j (t-1) Where: w j (t) represents the weight at time t, w j is the currently calculated entropy weight, w j (t-1) is the weight at the previous moment, and α is the adjustment coefficient: 0<α<1.

4. The method for evaluating energy efficiency of a large-scale liquefied natural gas plant according to claim 1, characterized in that: The step S4 specifically comprises the following steps: Step S40: construct a hierarchical structure of indicators: Target layer: comprehensive energy efficiency evaluation; Criteria layer: energy consumption, product output, energy utilization rate; Indicator layer: electricity consumption, natural gas consumption, LNG output, energy utilization rate; According to the importance of indicators, pairwise comparisons are made to construct the judgment matrix A; Step S41: Perform consistency check and calculate the maximum eigenvalue λ of the judgment matrix A max , consistency coefficient CI, consistency ratio CR, CR < 0.1, indicating that the matrix has satisfactory consistency; Step S42: normalize the judgment matrix A to obtain a normalized matrix A′, and use arithmetic mean to calculate the subjective weight b j , obtain the subjective weight matrix B, combined with the subjective weight b j and objective weight w j , and the comprehensive weight z is obtained j , and then get the comprehensive weight matrix Z, z j =a·b j +(1-a)·w j Where: α is the weight adjustment coefficient, and its value range is 0.

1.

5. The method for evaluating energy efficiency of a large-scale liquefied natural gas plant according to claim 1, characterized in that: The step S5 specifically comprises the following steps: Step S50: setting a fixed feedback cycle to collect energy efficiency index feedback data; Step S51, analyzing feedback data to identify key indicators that affect system energy efficiency; Step S52: adjust the objective weight of each indicator according to the analysis result, and apply the adjusted objective weight to the next feedback cycle.

6. The method for evaluating energy efficiency of a large-scale liquefied natural gas plant according to claim 1, characterized in that: The specific steps of step S6 are as follows: Step S60: construct an ideal solution A+ and a negative ideal solution A-. A + ={max(x ij )|j∈I + ; min(x ij )|j∈I - } A - ={min(x ij )|j∈I + ;max(x ij )|j∈I - } Where: I + is the set of indicators that need to be maximized; I- is the set of indicators that need to be minimized; Step S61: Calculate the distance between each solution and the positive ideal solution and the negative ideal solution and Where: are the membership degrees of the positive ideal solution and the negative ideal solution respectively. Step S62: Calculate relative proximity 7. The method for evaluating energy efficiency of a large-scale liquefied natural gas plant according to claim 1, characterized in that: The specific steps of step S7 are as follows: Step S70: According to the actual situation and expert opinions, four evaluation levels and their corresponding membership functions are set. Step S71, calculating the membership matrix U according to the original data in the indicator matrix R and the preset membership function, reflecting the membership of each evaluation object on each indicator; Calculate the fuzzy comprehensive evaluation score S, Where: Z is the comprehensive weight matrix; U is the membership matrix.

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