An intelligent optimization control system and method for park energy consumption and carbon emission

Abstract

The present application belongs to the technical field of park carbon emission management, in particular to the industrial park carbon emission management technology, and specifically discloses a park energy consumption and carbon emission intelligent optimization control system and method, which performs carbon emission intensity statistics on the energy consumption and production of various products in the industrial park by using historical production records, thereby screening out products that need to be focused on emission reduction, and predicting high emission bottlenecks of these products in the production process, and based on this, implementing emission reduction optimization measures for specific high emission bottlenecks, so as to realize precise and detailed control of the carbon emission of the industrial park. This method can not only implement on-demand emission reduction without affecting normal production, but also significantly improve the emission reduction effect, and ensure the effective use of resources and the realization of environmental targets.

Description

Technical Field

[0001] This invention belongs to the field of carbon emission management technology in industrial parks, specifically relating to an intelligent optimization control system and method for energy consumption and carbon emissions in industrial parks. Background Technology

[0002] With the growing prominence of climate change, carbon emission management in people's production and daily lives has become crucial. As major energy consumers and sources of greenhouse gas emissions, energy consumption analysis and carbon emission control in industrial parks have become key measures to mitigate the impacts of climate change.

[0003] Since energy consumption in industrial parks is mainly concentrated in the industrial production process, their energy consumption analysis and carbon emission control usually focus on monitoring and managing specific carbon emission sources in the production process.

[0004] However, existing emission reduction control methods that focus on carbon emission sources often only focus on the carbon emission sources themselves, failing to fully consider the actual source of carbon emissions—that is, specific products. Because industrial parks produce a wide variety of industrial products, the carbon emissions from different products, even from the same emission source, can vary significantly. Simply relying on data from carbon emission sources for emission reduction control usually only focuses on specific emitting equipment or processes, without considering the specific performance of these devices or processes in the production of different products. For example, a boiler may consume more energy when producing product A, but less when producing product B. This results in overly general emission reduction measures, potentially treating all products the same and leading to resource waste. Furthermore, because the carbon emission characteristics of different products are not differentiated, emission reduction measures are often generic and non-personalized, making it difficult to optimize for specific products or processes. This may result in some high-carbon emission products not being effectively controlled, while low-carbon emission products are subject to unnecessary restrictions. Additionally, failing to fully consider the actual needs of product production may cause unnecessary disruption to the production line, affecting normal production.

[0005] In summary, relying solely on carbon emission sources for emission reduction control cannot accurately and deeply reflect emission reduction needs. This may lead to a lack of targeted emission reduction measures, resulting in poor overall emission reduction effects and making it difficult to achieve the predetermined emission reduction targets. Summary of the Invention

[0006] The purpose of this invention is to improve the shortcomings of the existing technology and provide an intelligent optimization control system and method for energy consumption and carbon emissions in industrial parks. By focusing on the source of carbon emissions in the production process of industrial parks—products—it carries out detailed emission reduction control based on the carbon emission characteristics of different products, effectively solving the problems mentioned in the background technology.

[0007] The objective of this invention can be achieved through the following technical solution: The first aspect of this invention provides an intelligent optimization control system for energy consumption and carbon emissions in a park, comprising the following modules: The Energy and Product Statistics module is used to statistically analyze the types of energy and products produced in the production areas of industrial parks.

[0008] The historical energy consumption extraction module is used to retrieve the historical production records of various products within a specified time period, and extract the production volume and energy consumption from the production records.

[0009] The unit carbon emission intensity statistics module is used to calculate the unit carbon emission intensity of various products based on the production volume of various products in each production record and the consumption of various energy sources, combined with the emission factors of each energy source.

[0010] The demand reduction product screening module is used to screen demand reduction products based on the unit carbon emission intensity of various products.

[0011] The high emission bottleneck prediction module is used to predict the high emission bottleneck of demand-reduced emission products by comparing demand-reduced emission products with non-demand-reduced emission products.

[0012] The reference database stores the baseline energy conversion efficiency of each production equipment in the industrial park and the allowable waste generation rate of each process.

[0013] The energy consumption and emission reduction optimization module is used to assess the energy efficiency of production equipment in the production process of products with high emission reduction demand when the high emission bottleneck is predicted to be excessive energy consumption, thereby identifying and addressing inefficient production equipment.

[0014] The production efficiency and emission reduction optimization module is used to collect waste data generated by the products in each process flow when the high emission bottleneck of the products with high emission reduction demand is due to low production efficiency, thereby identifying high-waste production processes for process optimization.

[0015] The second aspect of the present invention proposes an intelligent optimization control method for energy consumption and carbon emissions in industrial parks, comprising the following steps: Step 1, statistically analyzing the energy categories and product types present in the production area of ​​the industrial park, retrieving historical production records of various products within a specified time period, and extracting the production volume and energy consumption from the production records.

[0016] Step 2: Based on the production volume of various products in each production record and the consumption of various energy sources, combined with the emission factors of each energy source, calculate the unit carbon emission intensity of various products.

[0017] Step 3: Select emission reduction products based on the unit carbon emission intensity of various products.

[0018] Step 4: Compare demand-reduced emission reduction products with non-demand-reduced emission reduction products to predict the high emission bottleneck of demand-reduced emission reduction products. If the predicted high emission bottleneck of demand-reduced emission reduction products is excessive energy consumption, proceed to steps 5-6. If the predicted high emission bottleneck of demand-reduced emission reduction products is excessively low production efficiency, proceed to steps 7-8.

[0019] Step 5: Conduct an energy efficiency assessment of the production equipment used in the production process of products with reduced emissions.

[0020] Step 6: Identify and address inefficient production equipment.

[0021] Step 7: Collect data on waste generated by the products requiring emission reduction in each process flow.

[0022] Step 8: Identify high-waste production processes and optimize them.

[0023] Combining all the above technical solutions, the positive effects of this invention are as follows: By statistically analyzing the carbon emission intensity of various products in the industrial park through historical production records, this invention identifies products that require key emission reduction and predicts the high emission bottlenecks in the production process of these products. Based on this, optimization measures are implemented for specific high emission bottlenecks, achieving precise and detailed control of carbon emissions in the industrial park. This method not only enables on-demand emission reduction without affecting normal production, but also significantly improves the emission reduction effect, ensuring the effective use of resources and the achievement of environmental goals. Attached Figure Description

[0024] The present invention will be further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present invention. For those skilled in the art, other drawings can be obtained based on the following drawings without creative effort.

[0025] Figure 1 This is a schematic diagram of the system module connections of the present invention.

[0026] Figure 2 This is a schematic diagram illustrating the energy consumption analysis and carbon emission control operations of products in an industrial park in this invention.

[0027] Figure 3 This is a diagram illustrating the implementation steps of the method of the present invention. Detailed Implementation

[0028] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] Example 1 This invention provides an intelligent optimization and control system for energy consumption and carbon emissions in industrial parks, including an energy and product statistics module, a historical energy consumption extraction module, a unit carbon emission intensity statistics module, a demand-reduction emission reduction product screening module, a high emission bottleneck prediction module, a reference database, an energy consumption emission reduction optimization module, and an output efficiency emission reduction optimization module.

[0030] See Figure 1 As shown, the energy and product statistics module is connected to the historical energy consumption extraction module, the historical energy consumption extraction module is connected to the unit carbon emission intensity statistics module, the unit carbon emission intensity statistics module is connected to the demand emission reduction product screening module, the demand emission reduction product screening module is connected to the high emission bottleneck prediction module, the high emission bottleneck prediction module is connected to the energy consumption emission reduction optimization module and the production efficiency emission reduction optimization module respectively, and the reference database is connected to the energy consumption emission reduction optimization module and the production efficiency emission reduction optimization module respectively.

[0031] The energy and product statistics module is used to statistically analyze the types of energy and products produced in the production areas of industrial parks.

[0032] In the examples of the above implementations, the energy category can be electricity, natural gas, coal, etc.

[0033] The historical energy consumption extraction module is used to retrieve the historical production records of various products within a specified time period, and extract the production volume and the consumption of each energy source from the production records.

[0034] The specified time period mentioned above must be based on the current time, and the time span can be up to six months.

[0035] It should be added that, during the product manufacturing process, in order to ensure traceability and provide detailed traceability data, companies typically maintain detailed production records. These records cover several key aspects, including but not limited to the following: raw material consumption, various energy consumption, production volume, process flow data (such as key parameters of each process, such as temperature, pressure, time, flow rate, etc., as well as the production time and waste generation of each process step), production equipment operating status information (such as operating indicators of production equipment, such as speed, current, vibration, temperature, etc., as well as the equipment's operating time, etc.), and operator information, etc.

[0036] The unit carbon emission intensity statistics module is used to calculate the unit carbon emission intensity of various products based on the production volume of various products in each production record and the consumption of various energy sources, combined with the emission factors of each energy source. The specific statistics are as follows: the consumption of each energy source for the same product in each production record is multiplied by the corresponding energy emission factor and summed to obtain the total carbon emission of various products in each production record, and then divided by the production volume in the production record to obtain the unit carbon emission intensity of various products in each production record.

[0037] It's important to understand that emission factors refer to the amount of greenhouse gas emissions produced per unit mass or volume of a particular energy source or activity. They are typically expressed as the amount of carbon dioxide emissions per unit of energy consumed, reflecting the carbon emission intensity of different energy sources and serving as the fundamental data for calculating carbon emissions. For electricity, the emission factor is the electricity emission factor, which refers to the amount of carbon dioxide emissions corresponding to the consumption of 1 kWh of electricity. For natural gas, the emission factor is the natural gas emission factor, which refers to the amount of carbon dioxide emissions corresponding to the consumption of 1 m³ of natural gas. 3 The carbon dioxide emissions associated with natural gas. For coal, the emission factor, or coal emission factor, refers to the carbon dioxide emissions associated with consuming one ton of coal.

[0038] It should be added that the emission factors for various energy sources can be referenced from the national emission factor standards. For example, the emission factor for electricity is 0.6 kg CO2 / kWh, and the emission factor for natural gas is 2.0 kg CO2 / m³. 3 Coal emission factor: 2.5 kg CO2 / kg.

[0039] This invention quantifies carbon emission intensity by combining the energy consumption of each component in the product manufacturing process with corresponding emission factors, allowing enterprises to comprehensively understand their carbon emission footprint. More importantly, this quantification method unifies different types of energy consumption under the same standard (i.e., the carbon dioxide equivalent per unit of product output), thus providing a standardized benchmark for the subsequent selection of emission-reducing products.

[0040] The emission reduction product screening module is used to screen emission reduction products based on the unit carbon emission intensity of various products. The specific screening is as follows: the unit carbon emission intensity of various products in each production record is summarized to calculate the average unit carbon emission intensity, and the maximum unit carbon emission intensity is extracted.

[0041] Arrange the various products in ascending order of average unit carbon emission intensity to obtain the average carbon emission ranking of the various products.

[0042] Arranging various products in ascending order of maximum unit carbon emission intensity yields the ranking of the highest carbon emissions for each product.

[0043] The ranking numbers of various products were extracted from the average carbon emission ranking and the highest carbon emission ranking, respectively.

[0044] The standard deviation is calculated using the unit carbon emission intensity of various products in each production record.

[0045] The production frequency of each product is calculated by counting the number of production records retrieved for each product and obtaining the interval between adjacent production records. The specific calculation formula is as follows: In the formula Indicates the first The frequency of production of this product Indicates the first The average interval between adjacent production records for this product Indicates the product category number. , Indicates the quantity of product types. Indicates the duration of a specified time period. Indicates the first The number of production records retrieved for this product.

[0046] As can be seen from the above formula for calculating production frequency, the more production records a product retrieves and the shorter the interval between adjacent production records, the greater the production frequency. Production frequency reflects the production frequency of the product. The greater the production frequency, the more frequent the energy consumption and carbon emissions.

[0047] The high carbon emission tendency of various products is calculated by combining the average carbon emission ranking number and the highest carbon emission ranking number of each product with the standard deviation and production frequency. The specific calculation formula is as follows: In the formula Indicates the first The high carbon emission tendency of this product Indicates the first The carbon emission standards of these products differ. Indicates the first The average carbon intensity per unit of this product , They represent the first The average carbon emission ranking number and the highest carbon emission ranking number for each product.

[0048] It's important to understand that when analyzing high carbon emission propensity based on a product's average and highest carbon emission ranking numbers, standard deviation is used for number weighting. A smaller standard deviation in a product's unit carbon emission intensity indicates relatively stable unit carbon emission intensity with minimal fluctuations. In this case, the average carbon emission ranking number better represents the product's overall carbon emission level and should therefore be given higher weight, as the average value better reflects the typical carbon emissions across different production batches. Conversely, a larger standard deviation in a product's unit carbon emission intensity indicates greater fluctuations and the potential for extreme values ​​(i.e., some batches having carbon emissions significantly higher or lower than the average). In this case, the highest carbon emission ranking number should be given higher weight to ensure the analysis captures potential high carbon emission risks. The highest value reveals the most unfavorable carbon emission scenarios, helping companies identify products that may have a significant environmental impact.

[0049] This invention, when analyzing high carbon emission propensity based on the unit carbon emission intensity of a product across various production records, finds an optimal balance between stability and volatility by combining the average carbon emission ranking number and the highest carbon emission ranking number, and adjusting the weight of the numbers using standard deviation. Furthermore, production frequency reflects the future carbon emission trend of a product; therefore, combining historical data with future trends for multi-dimensional high carbon emission propensity analysis significantly improves the rationality and reliability of the analysis results.

[0050] The high carbon emission tendency of various products is compared with a preset threshold. For example, the preset threshold is 0.9. Products with a high carbon emission tendency that reaches the threshold are selected as products for demand-based emission reduction. If no products that reach the threshold are selected, the product with the highest carbon emission tendency is selected as a product for demand-based emission reduction, and the product with the lowest high carbon emission tendency is selected as a product for non-demand-based emission reduction.

[0051] This invention compares the high carbon emission propensity of various products with preset thresholds. By setting these thresholds, companies can quickly identify high-carbon-emission products and concentrate resources on addressing those products with the greatest impact on carbon emissions, ensuring the effectiveness and targeted nature of emission reduction measures. Even if no product meets the threshold, companies can select the product with the highest carbon emission propensity as a demand-based emission reduction product, ensuring that no significant carbon emission sources are overlooked. Simultaneously, classifying products with the lowest high carbon emission propensity as non-demand-based emission reduction products facilitates targeted analysis of the high-carbon emission bottlenecks of demand-based emission reduction products by comparing the differences between demand-based and non-demand-based emission reduction products.

[0052] The high emission bottleneck prediction module is used to predict the high emission bottleneck of the product with demand emission reduction by comparing the product with the product without demand emission reduction. The specific operation is as follows: the average carbon emission is calculated by averaging the total carbon emissions of the product with demand emission reduction and the product without demand emission reduction in each production record.

[0053] The average production volume is calculated by averaging the production volumes of demand-reduced emission reduction products and non-demand-reduced emission reduction products across all production records.

[0054] The high energy consumption index of products with reduced emissions due to demand is calculated by proportionally comparing the average carbon emissions of products with those without. ,in Indicates a high energy consumption index. , These represent the average carbon emissions of demand-driven emission reduction products and non-demand-driven emission reduction products, respectively.

[0055] The low production index for demand-reducing products is obtained by proportionally calculating the average production volume of non-demand-reducing emission reduction products to that of demand-reducing emission reduction products. In the formula This indicates a low production index. , These represent the average production volume of demand-driven emission reduction products and non-demand-driven emission reduction products, respectively.

[0056] By comparing the high energy consumption index and the low production index of products with demand for emission reduction, if the high energy consumption index is greater than the low production index, the high emission bottleneck of products with demand for emission reduction is predicted to be excessive energy consumption; otherwise, the high emission bottleneck is predicted to be excessively low production efficiency.

[0057] It's important to understand that when selecting products for emission reduction and predicting high-emission bottlenecks, the selection criterion is carbon intensity per unit, calculated by dividing total carbon emissions by production volume. Therefore, high-emission bottlenecks for products for emission reduction may stem from two main factors: high total carbon emissions (i.e., high energy consumption leading to increased carbon intensity per unit) and low production volume (i.e., low production efficiency resulting in relatively high carbon intensity per unit). Analyzing from these two perspectives can clearly identify the specific causes of high-emission bottlenecks and provide targeted guidance for subsequent emission reduction measures.

[0058] Specifically, when the average carbon emissions of demand-reducing products are higher than the average carbon emissions of non-demand products, there is still room for improvement in the energy consumption of demand-reducing products. When the production volume of demand-reducing products is lower than the average production volume of non-demand products, it indicates that the production efficiency of demand-reducing products is low and production capacity needs to be increased.

[0059] The reference database is used to store the benchmark energy conversion efficiency of each production equipment in the production of various products in the industrial park and the allowable waste generation rate of each process.

[0060] The energy consumption reduction and optimization module is used to assess the energy efficiency of production equipment in the production process of the product when the high emission bottleneck of the product is predicted to be excessive energy consumption, thereby identifying and dealing with inefficient production equipment.

[0061] In the optimized implementation of the above scheme, the energy efficiency assessment of the production equipment in the production process of demand reduction emission reduction products is carried out as follows: the consumption of the same energy in each production record of demand reduction emission reduction products is added up and multiplied by the emission factor of the corresponding energy to obtain the total carbon emissions of each energy.

[0062] Dividing the total carbon emissions from all energy sources by the sum of production volumes in all production records yields the unit carbon emission intensity corresponding to each energy source during the production process of the product requiring emission reduction.

[0063] It should be noted that by decomposing the unit carbon emission intensity of demand-reducing products into the unit carbon emission intensity corresponding to each energy source, the carbon emission status corresponding to each energy source can be quantified.

[0064] Obtain the energy category to which each production equipment belongs, and determine the order of energy efficiency assessment for each production equipment by combining the energy category with the unit carbon emission intensity corresponding to each energy source.

[0065] It's important to add that various production equipment uses different energy sources during product manufacturing. Based on the diversity of their energy sources, production equipment can be divided into two categories: single-energy equipment and multi-energy equipment. Single-energy equipment relies on only one type of energy during operation. Examples of this type include boilers and electric motors. Boilers primarily use coal or natural gas as fuel to produce steam or hot water, while electric motors are mainly driven by electricity. Multi-energy equipment can use two or more energy sources during operation, offering greater flexibility and adaptability. Examples of this type include combined cycle generator sets and combined heat and power (CHP) systems, which require both natural gas and electricity during operation.

[0066] The specific implementation order for determining the energy efficiency assessment of production equipment is as follows: the unit carbon emission intensity corresponding to the energy to which a single energy equipment belongs is directly used as the comprehensive unit carbon emission intensity of the corresponding production equipment.

[0067] The comprehensive unit carbon emission intensity of the corresponding production equipment is obtained by summing the unit carbon emission intensity of each energy source for multi-energy equipment.

[0068] Extract the runtime of each production device from the production records, and divide it by the production volume to calculate the average unit runtime of each production device as the weighting factor for each production device.

[0069] It is important to understand that the weighting factors mentioned above reflect the frequency of equipment use in the production process, and can better reflect the actual contribution of the equipment.

[0070] The energy efficiency assessment order of production equipment is obtained by arranging each piece of equipment in descending order according to the weighted average calculation result of the comprehensive unit carbon emission intensity and weighting factors.

[0071] The weighted average calculation mentioned above can comprehensively consider the carbon emission intensity and usage frequency of the equipment, ensuring that the assessment results are more comprehensive and objective. The descending order helps to identify key equipment with high carbon emissions and frequent use.

[0072] This invention determines the energy efficiency assessment order by arranging production equipment in descending order of comprehensive unit carbon emission intensity. This operation can effectively identify equipment with a greater impact on carbon emissions, prioritize their assessment and optimization, and thus achieve the goal of energy conservation and emission reduction.

[0073] According to the order of energy efficiency assessment of production equipment, retrieve the energy consumption and useful output energy of each production equipment in each production record.

[0074] It's important to understand that useful output energy refers to the useful output of production equipment in each production record, including mechanical energy, thermal energy, and product output. Specifically, when the useful output is mechanical energy (e.g., an electric motor converts consumed electrical energy into mechanical energy), the output power can be measured using a power meter or torque sensor. For useful output being thermal energy (e.g., a boiler converts consumed natural gas into thermal energy), the output heat can be measured using temperature sensors, flow meters, etc.

[0075] It should be added that useful output energy can also be extracted from production records.

[0076] The number of energy categories to which each production equipment belongs is counted, thereby classifying the production equipment into single-energy equipment and multi-energy equipment.

[0077] For a single energy device, the energy conversion efficiency of the single energy device in each production record is obtained by comparing the useful output energy of the single energy device with the associated energy consumption and taking the percentage.

[0078] For multi-energy equipment, the energy conversion efficiency of the multi-energy equipment in each production record is calculated as a percentage by summing the useful output energy of the multi-energy equipment in each production record with the energy consumption of each energy source. This applies when the production equipment uses both electricity and natural gas. .

[0079] It is important to note that electricity consumption and natural heat consumption need to be converted according to their respective energy values ​​to ensure that the units are consistent.

[0080] In the further optimization and implementation of the above scheme, inefficient production equipment is identified and processed as follows: the energy conversion efficiency of the same production equipment in each production record is compared with the benchmark energy conversion efficiency of the corresponding production equipment in the reference database, and the proportion of production records with energy conversion efficiency lower than the benchmark energy conversion efficiency is recorded as the inefficient proportion.

[0081] The benchmark energy conversion efficiency of the production equipment mentioned above can be referenced from industry standards. For example, according to industry standards, the average energy conversion efficiency of boilers is 45%-50%.

[0082] The inefficiency rate of each production equipment is compared with the preset critical rate. For example, the critical rate is 0.7. If the inefficiency rate of a certain production equipment reaches the critical rate, it means that the equipment has failed to achieve the expected energy efficiency level in a large number of production records and has obvious energy efficiency problems. Then the production equipment is regarded as an inefficient production equipment.

[0083] Extract operational indicators from each production record corresponding to inefficient production equipment to identify whether there is an operational anomaly. If there is an operational anomaly, the inefficient production equipment is handled by adjusting the operational indicators; otherwise, it is handled by replacing or upgrading the equipment.

[0084] It's important to understand that the aforementioned operational indicators are key parameters reflecting the equipment's operating status and can help diagnose whether the equipment is malfunctioning or operating abnormally. Anomaly identification can be achieved by comparing whether the operational indicators are within the normal operating range.

[0085] If the operating indicators of inefficient production equipment are abnormal, it indicates that the equipment may be operating abnormally, leading to decreased energy efficiency. In this case, the operating parameters of the equipment should be adjusted first to ensure it operates under optimal conditions. If the operating indicators of the inefficient production equipment are normal, but energy efficiency problems still exist, it indicates that the equipment itself may have design flaws or aging issues, making it difficult to improve energy efficiency by adjusting parameters. In this case, it is necessary to replace or upgrade the equipment, selecting newer, more efficient, and energy-saving equipment to completely solve the energy efficiency problem. Upgrade measures may include: replacing old equipment, phasing out inefficient old equipment, and replacing it with newer, more energy-efficient equipment.

[0086] This invention effectively identifies inefficient production equipment by comparing the energy conversion efficiency of production equipment in each production record with a benchmark value and then comparing this ratio with a preset critical ratio. Further analysis of the operational indicators of inefficient equipment determines whether there are any operational anomalies, allowing for the development of targeted solutions. If an operational anomaly is detected, adjustments to the operational indicators are prioritized; if the equipment is operating normally but with low energy efficiency, replacement or upgrading is considered. This systematic assessment method helps companies promptly identify energy efficiency issues, take effective improvement measures, enhance overall energy efficiency, and reduce carbon emissions.

[0087] The production efficiency-oriented emission reduction optimization module is used to collect waste data generated by the products in each process flow when the high emission bottleneck of the predicted emission reduction products is due to low production efficiency, thereby identifying high-waste production processes for process optimization.

[0088] The specific operation is as follows: extract the amount of waste and processing time of each process from each production record corresponding to the product with demand reduction, and then calculate the waste generation rate of each process in each production record.

[0089] The waste generation rate of each process in each production record is compared with the allowable waste generation rate of the corresponding process in the reference database. The production ratio of processes with a waste generation rate higher than the allowable waste generation rate is recorded as the high waste ratio of the process.

[0090] Compare the high waste ratio of each process with the preset critical ratio. If the high waste ratio of a certain process reaches the critical ratio, it indicates that the process has failed to effectively control waste generation in a large number of production records and there is an obvious waste management problem. Therefore, the process is regarded as a high waste process.

[0091] When identifying high-waste processes and optimizing them, methods such as adjusting process parameters, improving equipment maintenance, and optimizing raw material selection can be adopted to reduce waste generation.

[0092] This invention compares the actual waste generation rate of each process in each production record with the allowable waste generation rate to calculate the high waste ratio, and then compares this high waste ratio with a preset critical ratio. This effectively identifies high-waste processes that need optimization. This systematic evaluation method helps companies promptly identify problems in waste management, take effective improvement measures, reduce waste generation, improve resource utilization efficiency, and reduce environmental impact.

[0093] For the energy consumption analysis and carbon emission control operations of the industrial park products mentioned above, please refer to [link / reference]. Figure 2 As shown.

[0094] Example 2 Reference Figure 3 As shown, this invention proposes an intelligent optimization control method for energy consumption and carbon emissions in industrial parks, including the following steps: Step 1: Statistically analyze the energy categories and product types present in the production area of ​​the industrial park, retrieve the historical production records of various products within a specified time period, and extract the production volume and energy consumption from the production records.

[0095] Step 2: Based on the production volume of various products in each production record and the consumption of various energy sources, combined with the emission factors of each energy source, calculate the unit carbon emission intensity of various products.

[0096] Step 3: Select emission reduction products based on the unit carbon emission intensity of various products.

[0097] Step 4: Compare demand-reduced emission reduction products with non-demand-reduced emission reduction products to predict the high emission bottleneck of demand-reduced emission reduction products. If the predicted high emission bottleneck of demand-reduced emission reduction products is excessive energy consumption, proceed to steps 5-6. If the predicted high emission bottleneck of demand-reduced emission reduction products is excessively low production efficiency, proceed to steps 7-8.

[0098] Step 5: Conduct an energy efficiency assessment of the production equipment used in the production process of products requiring emission reduction; Step 6: Identify and address inefficient production equipment.

[0099] Step 7: Collect data on waste generated by the products requiring emission reduction in each process flow.

[0100] Step 8: Identify high-waste production processes and optimize them.

[0101] The above description is merely an example and illustration of the structure of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. A smart optimization control system for energy consumption and carbon emissions in a park, characterized in that, Includes the following modules: The Energy and Product Statistics module is used to statistically analyze the types of energy present in the production areas of industrial parks and the types of products produced. The historical energy consumption extraction module is used to retrieve the historical production records of various products within a specified period and extract the production volume and energy consumption from the production records. The unit carbon emission intensity statistics module is used to calculate the unit carbon emission intensity of various products based on the production volume of various products in each production record and the consumption of various energy sources, combined with the emission factors of each energy source. The demand reduction product screening module is used to screen demand reduction products based on the unit carbon emission intensity of various products. The high emission bottleneck prediction module is used to predict the high emission bottleneck of demand-reducing products by comparing demand-reducing emission reduction products with non-demand-reducing emission reduction products. Reference database is used to store the benchmark energy conversion efficiency of each production equipment in the production of various products in the industrial park and the allowable waste generation rate of each process. The energy consumption and emission reduction optimization module is used to assess the energy efficiency of production equipment in the production process of products with high emission reduction demand when the high emission bottleneck is predicted to be excessive energy consumption, thereby identifying and addressing inefficient production equipment. The production efficiency and emission reduction optimization module is used to collect waste data generated by the products in each process flow when the high emission bottleneck of the products with high emission reduction demand is due to low production efficiency, thereby identifying high-waste production processes for process optimization.

2. The intelligent optimization control system for park energy consumption and carbon emissions as described in claim 1, characterized in that: The statistical analysis of the unit carbon emission intensity of various products is described in the following process: The total carbon emissions of each product in each production record are obtained by multiplying the energy consumption of each product in each production record by the corresponding energy emission factor and summing them up. Then, the total carbon emissions of each product in each production record are obtained by dividing the total production volume in each production record.

3. The intelligent optimization control system for park energy consumption and carbon emissions as described in claim 2, characterized in that: The process for selecting emission reduction products based on demand is as follows: The average unit carbon emission intensity is calculated by summarizing the unit carbon emission intensity of various products in each production record, and the maximum unit carbon emission intensity is extracted. The products are sorted in ascending order by average unit carbon emission intensity and maximum unit carbon emission intensity to obtain the average carbon emission ranking and the highest carbon emission ranking of the products. Extract the ranking numbers of various products from the average carbon emission ranking and the highest carbon emission ranking, respectively; The standard deviation is calculated using the unit carbon emission intensity of various products in each production record; The production frequency of each product is calculated by counting the number of production records retrieved for each product and obtaining the interval between adjacent production records. The specific calculation formula is as follows: In the formula Indicates the first The frequency of production of this product Indicates the first The average interval between adjacent production records for this product Indicates the product category number. , Indicates the quantity of product types. Indicates the duration of a specified time period. Indicates the first The number of production records retrieved for this product category; The high carbon emission tendency of each product is calculated by combining its average carbon emission ranking number and highest carbon emission ranking number with its standard deviation and production frequency. The specific calculation formula is as follows: In the formula Indicates the first The carbon emission standards of these products differ. Indicates the first The average carbon intensity per unit of this product , They represent the first Ranking number of average carbon emissions and ranking number of highest carbon emissions for each product; The high carbon emission tendency of various products is compared with a preset threshold. Products that meet the threshold are selected as demand-driven emission reduction products, and products with the lowest high carbon emission tendency are selected as non-demand-driven emission reduction products. If no products that meet the threshold are selected, the product with the highest carbon emission tendency is selected as demand-driven emission reduction product.

4. The intelligent optimization control system for park energy consumption and carbon emissions as described in claim 3, characterized in that: The high emission bottleneck of the predicted demand reduction products is as follows: The average carbon emissions and average production volume are calculated by averaging the total carbon emissions and production volume of demand-reduced and non-demand-reduced products in each production record. The high energy consumption index of products with reduced emissions due to demand is calculated by proportionally comparing the average carbon emissions of products with reduced emissions due to demand with those without. The low production index of demand-reducing products is obtained by proportionally calculating the average production volume of non-demand-reducing emission reduction products to that of demand-reducing emission reduction products. By comparing the high energy consumption index and the low production index of products with demand for emission reduction, if the high energy consumption index is greater than the low production index, the high emission bottleneck of products with demand for emission reduction is predicted to be excessive energy consumption; otherwise, the high emission bottleneck is predicted to be excessively low production efficiency.

5. The intelligent optimization control system for park energy consumption and carbon emissions as described in claim 1, characterized in that: The energy efficiency assessment follows the procedure described below: The total carbon emissions of each energy source are obtained by summing up the consumption of the same energy source in each production record for the product with reduced emissions and multiplying it by the emission factor of the corresponding energy source. Divide the total carbon emissions of each energy source by the sum of the production volume in all production records to obtain the unit carbon emission intensity of each energy source in the production process of the product requiring emission reduction. Obtain the energy category to which each production equipment belongs, and determine the order of energy efficiency assessment for each production equipment by combining the unit carbon emission intensity corresponding to each energy source. According to the order of energy efficiency assessment of production equipment, retrieve the energy consumption and useful output energy of each production equipment in each production record; The number of energy categories to which each production equipment belongs is counted, thereby classifying the production equipment into single-energy equipment and multi-energy equipment; The energy conversion efficiency of a single energy device in each production record is obtained by comparing the useful output energy of the single energy device with the energy consumption of the corresponding energy in each production record as a percentage. The energy conversion efficiency of the multi-energy device in each production record is obtained by comparing the total energy consumption obtained by adding the useful output energy of the multi-energy device to the energy consumption of each energy source in each production record, and taking the percentage.

6. The intelligent optimization control system for park energy consumption and carbon emissions as described in claim 5, characterized in that: The determination of the order of energy efficiency assessment for production equipment is implemented as follows: The unit carbon emission intensity of a single energy device corresponding to its energy source is directly used as the comprehensive unit carbon emission intensity of the corresponding production equipment. The comprehensive unit carbon emission intensity of the corresponding production equipment is obtained by summing the unit carbon emission intensity of each energy source corresponding to the multi-energy equipment. Extract the runtime of each production device from the production records, and divide it by the production volume to calculate the average unit runtime of each production device as the weighting factor for each production device. The energy efficiency assessment order of production equipment is obtained by arranging each piece of equipment in descending order according to the weighted average calculation result of the comprehensive unit carbon emission intensity and weighting factors.

7. The intelligent optimization control system for park energy consumption and carbon emissions as described in claim 5, characterized in that: The process for identifying and handling inefficient production equipment is as follows: The energy conversion efficiency of the same production equipment in each production record is compared with the benchmark energy conversion efficiency of the corresponding production equipment in the reference database. The percentage of production records with energy conversion efficiency lower than the benchmark energy conversion efficiency is recorded as the inefficient percentage. Compare the inefficiency rate of each production equipment with the preset critical rate. If the inefficiency rate of a certain production equipment reaches the critical rate, then the production equipment is regarded as an inefficient production equipment. Extract operational indicators from each production record corresponding to inefficient production equipment to identify whether there is an operational anomaly. If there is an operational anomaly, the inefficient production equipment is handled by adjusting the operational indicators; otherwise, it is handled by replacing or upgrading the equipment.

8. The intelligent optimization control system for energy consumption and carbon emissions in a park as described in claim 1, characterized in that: The process for collecting waste data generated in each process flow of the product requiring emission reduction is as follows: Extract the waste amount and processing time of each process from each production record corresponding to the product with reduced emissions demand, and then calculate the waste generation rate of each process in each production record.

9. The intelligent optimization control system for park energy consumption and carbon emissions as described in claim 8, characterized in that: The process for identifying high-waste production processes is described below: The waste generation rate of each process in each production record is compared with the allowable waste generation rate of the corresponding process in the reference database. The production ratio of processes with a waste generation rate higher than the allowable waste generation rate is recorded as the high waste ratio of the process. The high waste ratio of each process is compared with the preset critical ratio. If the high waste ratio of a certain process reaches the critical ratio, then the process is designated as a high waste process.

10. A method for intelligent optimization control of energy consumption and carbon emissions in a park, characterized in that, Includes the following steps: Step 1: Analyze the types of energy and products produced in the industrial park's production area, retrieve historical production records for each product within a specified time period, and extract the production volume and energy consumption from the production records. Step 2: Based on the production volume of various products in each production record and the consumption of various energy sources, combined with the emission factors of each energy source, calculate the unit carbon emission intensity of various products. Step 3: Select emission reduction products based on the unit carbon emission intensity of various products; Step 4: Compare demand-reduced emission reduction products with non-demand-reduced emission reduction products to predict the high emission bottleneck of demand-reduced emission reduction products. If the predicted high emission bottleneck of demand-reduced emission reduction products is excessive energy consumption, proceed to steps 5-6. If the predicted high emission bottleneck of demand-reduced emission reduction products is excessively low production efficiency, proceed to steps 7-8. Step 5: Conduct an energy efficiency assessment of the production equipment used in the production process of products requiring emission reduction; Step 6: Identify and address inefficient production equipment; Step 7: Collect waste data generated by the products requiring emission reduction in each process flow; Step 8: Identify high-waste production processes and optimize them.

Images