Intelligent air compressor energy-saving transformation recommendation algorithm

Abstract

The invention discloses an intelligent air compressor energy-saving transformation recommendation algorithm which comprises the following steps: estimating full-load gas production, estimating the sum of the full-load gas production and recommended equipment quantity, and determining an optimal scheme. According to the intelligent air compressor energy-saving transformation recommendation algorithm, flow management from the earlier stage to the after-sales stage is provided for service providers, flow negotiation schemes are provided, and more professional services are provided for clients; a transformation scheme report is automatically generated, and the working efficiency and timeliness of service providers are improved; an energy-saving transformation report is intelligently generated, the project value is highlighted, and the order forming rate is improved; energy cost is saved, industrial electric equipment usually consumes large power, if energy consumption can be reduced through energy-saving transformation, energy cost expenditure can be reduced, equipment efficiency can be improved, and by adopting advanced new equipment, energy efficiency of the industrial electric equipment can be improved, energy waste can be reduced, and production efficiency and equipment utilization rate can be improved.

Description

Technical Field

[0001] The invention relates to the technical field of energy-saving transformation of air compressors, and in particular to an intelligent energy-saving transformation recommendation algorithm for air compressors. Background Art

[0002] Air compressors are important gas compression equipment in the industrial field, and are widely used in many industries such as machinery, electronics, chemicals, and food. However, traditional air compressors have problems such as high energy consumption and low efficiency. Therefore, energy-saving transformation of air compressors has become the focus of attention in the industrial field.

[0003] Existing energy-saving transformation methods for air compressors are usually based on manual experience and are not intelligent or adaptive. Summary of the invention

[0004] In view of the above-mentioned deficiencies in the prior art, the present invention provides an intelligent air compressor energy-saving transformation recommendation algorithm, which aims to provide an intelligent air compressor energy-saving transformation recommendation algorithm for evaluating the air compressor production efficiency of an enterprise, and then intelligently recommending a set of energy-saving transformation plans that meet the needs of the enterprise, estimating the annual income after the transformation, and helping the enterprise achieve the goal of energy conservation and emission reduction, while improving the project transaction rate of sales personnel.

[0005] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is:

[0006] An intelligent air compressor energy-saving transformation recommendation algorithm includes the following steps:

[0007] Step 1: Estimate the full load gas production:

[0008] Estimated full load gas production = exhaust volume (m 3 / min)*0.95;

[0009] Step 2: S1: When the sum of the estimated full-load gas production is less than 15, recommend a solution, i.e., 1 device;

[0010] S2: When the sum of the estimated full-load gas production is greater than or equal to 15 and less than 50, three options are recommended, namely, 1 device, 2 different devices, and 2 same devices;

[0011] S3: When the sum of the estimated full-load gas production is greater than or equal to 50, four solutions are recommended, including 2 different devices, 2 same devices, 3 different devices and 3 same devices, or 3 different devices, 3 same devices, 4 different devices and 4 same devices;

[0012] When the total estimated full-load gas production is ≥50, the number of recommended equipment is calculated using the following formula:

[0013] Number of equipment = sum of estimated full-load gas production ÷ maximum exhaust volume of current series equipment

[0014] Step 3: Calculate the exhaust volume of the recommended equipment, select the qualified equipment and provide the best solution:

[0015] S1: Recommended for the same equipment: Exhaust volume of a single equipment (m 3 / min) = the sum of estimated full-load gas production / number of equipment;

[0016] S2: When recommending different equipment: Search for all equipment in each equipment series whose exhaust volume is less than the full-load gas production of the customer's factory, arrange and combine these equipment, and after the arrangement and combination is completed, add up the exhaust volume of the equipment in the combination. The combination that is greater than and closest to the full-load gas production of the customer is the optimal solution;

[0017] Step 4: After determining the optimal solution, display the equipment information and estimated energy-saving benefits of all recommended solutions in the research report.

[0018] Furthermore, the equipment information includes equipment model, exhaust volume, prototype mode, specific power, exhaust pressure, rated power and equipment energy efficiency grade.

[0019] Furthermore, the estimated energy-saving benefits include the estimated power saving before and after the transformation (kW / (m3 / min) comparison, the nameplate total power (KW) comparison before and after the transformation, the estimated unit energy consumption (kW·h / Nm3) comparison before and after the transformation, the annual operating time (H), the annual total gas production (Nm3), the annual total electricity (KWh) comparison before and after the transformation, the annual total electricity bill (Yuan) comparison before and after the transformation, the average electricity price (Yuan), the annual electricity saving (KWh) comparison before and after the transformation, and the estimated energy saving rate comparison before and after the transformation.

[0020] The beneficial effects of the present invention are:

[0021] The intelligent energy-saving transformation recommendation algorithm of the present invention provides service providers with process management from the early stage to after-sales, process negotiation of solutions, and provides customers with more professional services;

[0022] Automatically generate transformation plan reports to improve service providers' work efficiency and timeliness;

[0023] Intelligently generate energy-saving transformation reports, highlight project value, and increase order success rate;

[0024] Save energy costs. Industrial electrical equipment usually consumes a lot of electricity. If energy consumption can be reduced through energy-saving transformation, energy cost expenditure can be reduced.

[0025] Improving equipment efficiency. By adopting advanced new equipment, the energy efficiency of industrial electrical equipment can be improved, energy waste can be reduced, and production efficiency and equipment utilization can be improved.

[0026] Improve the economic development environment: Energy-saving transformation can save energy consumption, reduce energy input, and save a lot of enterprise costs;

[0027] Reduce environmental impact: The consumption of large amounts of energy will have a serious impact on the environment, including greenhouse gas emissions and air pollution. Through energy-saving transformation, the company's carbon emissions and environmental pollution can be reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 This is a flow chart of the algorithm recommended for energy-saving transformation of intelligent air compressors according to the present invention. DETAILED DESCRIPTION

[0029] The technical solutions in the embodiments of the present invention are clearly and completely described below in conjunction with the accompanying drawings.

[0030] like Figure 1 As shown in the figure, an intelligent energy-saving modification recommendation algorithm for air compressors is a tool that intelligently recommends energy-saving modification plans based on customer equipment data. Salesmen fill in customer information, on-site equipment information parameters, and on-site equipment conditions into the system's survey form through field visits and interviews. The system automatically recommends on-sale air compressors that meet the actual situation of the customer's factory based on the key information in the survey form, and combines the performance data of these air compressors with the factory's energy consumption data to calculate the considerable profits that can be brought by using new machines within one year.

[0031] The survey form contains the following contents:

[0032] Customer information;

[0033] Project information;

[0034] Energy saving research;

[0035] Quality research;

[0036] Station survey;

[0037] Cost research;

[0038] Pain point research;

[0039] On-site recordings and station pictures.

[0040] After filling in the above information in the survey form, you can click to generate a survey report, which mainly includes the following contents:

[0041] On-site survey report: Contains information about existing project equipment, such as the number of air compressors and the number of air compressors in use; contains analysis of existing pain points. The system will automatically match and display possible pain points based on the pain points filled in the survey form.

[0042] Energy-saving recommendation plan: includes information about equipment that needs to be modified, recommended equipment information, and the estimated annual energy-saving benefits after using the new equipment.

[0043] First, the system will recommend the best equipment and estimate the annual energy saving benefits based on the equipment parameters filled in the survey form, such as specific power, rated power, exhaust volume, pressure and other indicators. The algorithm steps of the recommendation plan are as follows:

[0044] The formula for estimating full load gas production is as follows:

[0045] Estimated full load gas production = exhaust volume (m 3 / min)*0.95

[0046] The recommended number of equipment is calculated based on the full-load gas production. In order to reserve some spare resources and ensure the health of the equipment, the full-load gas production calculated below is based on the original full-load gas production plus 3 (m 3 / min):

[0047] When the sum of the estimated full-load gas production is less than 15, only one solution is recommended, that is, one device is sufficient.

[0048] When the sum of the estimated full-load gas production is greater than or equal to 15 and less than 50, three options are recommended, namely 1 device, 2 different devices, and 2 of the same device.

[0049] When the sum of the estimated full-load gas production is ≥50, four options are recommended, such as 2 different devices, 2 same devices, 3 different devices, 3 same devices, or 3 different devices, 3 same devices, 4 different devices, and 4 same devices.

[0050] When the total estimated full-load gas production is ≥50, there are many recommended solutions. The number of recommended equipment is calculated according to the following formula:

[0051] Number of equipment = sum of estimated full-load gas production ÷ maximum exhaust volume of current series equipment

[0052] Different equipment has different corresponding maximum exhaust volume values. The recommended solution can retrieve the equipment data that matches all equipment series, or only retrieve the equipment series that the company mainly promotes. Each equipment series generates a recommended solution for selection.

[0053] Example 1: The maximum exhaust volume of a series of equipment is 21.76 (m 3 / min), the exhaust volume of a customer's factory is 45 (m3 / min), then the estimated full load gas production is calculated = exhaust volume (m3 / min)*0.95=45*0.95=42.75 (m 3 / min), then the number of devices to be recommended = the sum of the estimated full-load gas production ÷ the maximum exhaust volume value of the current series of equipment = 42.75 ÷ 21.76 = 1.9646, then the number of devices to be recommended is 2 (here only the integer is entered without rounding).

[0054] Calculate the recommended equipment exhaust volume, select qualified equipment and provide the best solution:

[0055] When the same equipment is recommended: Exhaust volume of a single equipment (m 3 / min) = sum of estimated full load gas production ÷ number of equipment

[0056] Example 2: (Continue the conditions of Example 1): Exhaust volume of a single device (m 3 / min) = the sum of estimated full-load gas production / number of equipment = 42.75 (m 3 / min)÷2=21.375(m 3 / min), that is, the exhaust volume of a single device must be at least greater than 21.375 (m 3 / min) are eligible, and many exhaust volumes are greater than 21.375 (m 3 / min), the system will recommend the device closest to this value, that is, to avoid waste as much as possible while meeting the needs.

[0057] When recommending different equipment: the system will automatically retrieve all equipment in each equipment series whose exhaust volume is less than the full-load gas production of the customer's factory, and then arrange and combine these equipment. After the arrangement and combination is completed, the exhaust volume of the equipment in the combination is added up. The combination that is greater than and closest to the customer's full-load gas production is the optimal solution.

[0058] Example 3 (Continuing from the conditions of Example 1): The full load exhaust volume of the customer's factory is 42.75 (m 3 / min), the number of recommended devices is calculated to be 2. After searching for devices in a certain equipment series, it is found that there are 4 devices with exhaust volumes less than 42.75, namely device A is 9.5, device B is 11.2, device C is 28, and device D is 33.6. After permutation and combination, the combinations greater than 42.75 are A+D=43.1, B+D=44.8, and C+D=61.6. Among the above 3 combinations, the combination closest to 42.75 is A+D=43.5, so the optimal solution for this series is A+D.

[0059] After determining the optimal solution, the system will automatically display the equipment information and estimated energy-saving benefits of all recommended solutions in the research report.

[0060] The device information module contains the following:

[0061] Main information such as equipment model, exhaust volume, prototype method, specific power, exhaust pressure, rated power, etc.

[0062] Equipment energy efficiency level. The system calculates the corresponding energy efficiency level based on the four conditions of the recommended equipment: "rated power", "exhaust pressure", "cooling type" and "energy efficiency level", combined with the national standards GB 19153-2019 "Energy efficiency limit values ​​and energy efficiency levels for positive displacement air compressors" and GB 19153-2009 "Energy efficiency limit values ​​and energy efficiency levels for positive displacement air compressors".

[0063] The Energy Saving Estimated Benefits module will display the data comparison before and after the change and the estimated benefit amount, including the following:

[0064] Estimated electricity cost savings;

[0065] Comparison of estimated specific power (kW / (m3 / min) before and after the transformation;

[0066] Comparison of total nameplate power (KW) before and after the transformation;

[0067] Comparison of estimated unit energy consumption (kW·h / Nm3) before and after the transformation;

[0068] Annual operating time (H);

[0069] Total annual gas production (Nm3);

[0070] Comparison of total annual electricity consumption (KWh) before and after the transformation;

[0071] Comparison of total annual electricity costs (yuan) before and after the transformation;

[0072] Average electricity price (yuan);

[0073] Comparison of annual electricity savings (KWh) before and after the transformation;

[0074] Comparison of estimated energy savings before and after renovation.

[0075] An intelligent air compressor energy-saving transformation recommendation algorithm of the present invention provides enterprises with process management of service providers from the early stage to after-sales, process negotiation of solutions, provides customers with more professional services, automatically generates transformation solution reports, improves the work efficiency and timeliness of service providers; intelligently generates energy-saving transformation reports, highlights project value, and improves order success rate.

[0076] For customers, it saves energy costs. Industrial electrical equipment usually consumes a lot of electricity. If energy consumption can be reduced through energy-saving transformation, energy cost expenditure can be reduced, the competitiveness of the enterprise can be improved, and equipment efficiency can be improved. By adopting advanced new equipment, the energy efficiency of industrial electrical equipment can be improved, energy waste can be reduced, and production efficiency and equipment utilization can be improved.

[0077] For society, improve the economic development environment: energy-saving transformation can save energy consumption, reduce energy input, save a lot of enterprise costs, improve the economic benefits of enterprises, and reduce environmental impact: large amounts of energy consumption will have a serious impact on the environment, including greenhouse gas emissions and air pollution. Through energy-saving transformation, the carbon emissions and environmental pollution of enterprises can be reduced, which is conducive to sustainable development and promotes sustainable development of the economy and society: energy-saving transformation aims to reduce the consumption of coal and other fossil energy and reduce the use of renewable resources, which is conducive to protecting the ecological environment and improving the sustainable development of the social economy.

[0078] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

Claims

1. An intelligent air compressor energy-saving transformation recommendation algorithm, characterized in that: The following steps are involved: Step 1: Estimate the full load gas production: Estimated full load gas production = exhaust volume (m 3 / min)*0.95; Step 2: S1: When the sum of the estimated full-load gas production is less than 15, recommend a solution, i.e., 1 device; S2: When the sum of the estimated full-load gas production is greater than or equal to 15 and less than 50, three options are recommended, namely, 1 device, 2 different devices, and 2 same devices; S3: When the sum of the estimated full-load gas production is greater than or equal to 50, four solutions are recommended, including 2 different devices, 2 same devices, 3 different devices and 3 same devices, or 3 different devices, 3 same devices, 4 different devices and 4 same devices; When the total estimated full-load gas production is ≥50, the number of recommended equipment is calculated using the following formula: Number of equipment = sum of estimated full-load gas production ÷ maximum exhaust volume of current series equipment Step 3: Calculate the exhaust volume of the recommended equipment, select the qualified equipment and provide the best solution: S1: Recommended for the same equipment: Exhaust volume of a single equipment (m 3 / min) = the sum of the estimated full-load gas production / number of equipment; S2: When recommending different equipment: Search for all equipment in each equipment series whose exhaust volume is less than the full-load gas production of the customer's factory, arrange and combine these equipment, and after the arrangement and combination is completed, add up the exhaust volume of the equipment in the combination. The combination that is greater than and closest to the customer's full-load gas production is the optimal solution; Step 4: After determining the optimal solution, display the equipment information and estimated energy-saving benefits of all recommended solutions in the research report.

2. The intelligent air compressor energy-saving transformation recommendation algorithm according to claim 1 is characterized by: The equipment information includes equipment model, exhaust volume, prototype mode, specific power, exhaust pressure, rated power and equipment energy efficiency grade.

3. The intelligent air compressor energy-saving transformation recommendation algorithm according to claim 1 is characterized by: The estimated energy-saving benefits include the estimated power saving before and after the transformation (kW / (m3 / min) comparison, the nameplate total power (KW) comparison before and after the transformation, the estimated unit energy consumption (kW·h / Nm3) comparison before and after the transformation, the annual operating time (H), the annual total gas production (Nm3), the annual total electricity (KWh) comparison before and after the transformation, the annual total electricity bill (Yuan) comparison before and after the transformation, the average electricity price (Yuan), the annual electricity saving (KWh) comparison before and after the transformation, and the estimated energy saving rate comparison before and after the transformation.

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