Injection molding cooling water control system and method

Abstract

The present disclosure relates to an injection molding cooling water control system and method. The system includes a SaaS cloud platform connected to a meteorological big data platform and a cooling control unit, wherein the SaaS cloud platform monitors and controls the control parameters of the cooling control unit; the cooling control unit is connected to the cooling unit and records and controls the operating parameters of the cooling unit; the SaaS cloud platform obtains daily local meteorological condition data and daily power consumption of the cooling unit during stable operation from the meteorological big data platform and the cooling control unit, respectively, compares the process temperature and daily power consumption corresponding to at least 10 dates with similar meteorological conditions, and determines the actual meteorological maximum temperature on the day with the minimum power consumption as the cooling target temperature of the cooling unit corresponding to the meteorological condition; when the meteorological condition changes, the SaaS cloud platform feeds back the new cooling target temperature corresponding to the new meteorological condition to the cooling control unit, and the cooling control unit adjusts the cooling temperature of the cooling unit to the new cooling target temperature.

Description

Technical Field

[0001] The present invention relates to the technical field of injection molding cooling technology, and in particular to an injection molding cooling water control system and method. Background Art

[0002] Conventional injection molding cooling water system mainly uses the convection heat transfer principle of the cooling tower in normal temperature air to further reduce the temperature of the cooling water. The system includes low-temperature cooling water and normal temperature cooling water. The reasonable temperature of low-temperature chilled water is between 5 and 22°C, and the reasonable temperature of normal temperature cooling water is between 28 and 32°C.

[0003] Existing cooling systems use hysteresis control or PID (Proportion Integration Differentiation) regulation control for cooling towers or refrigerators to control cooling and heat dissipation. In actual on-site applications, there is no actual monitoring of ambient temperature and equipment input conditions, resulting in unreasonable unit operating target parameters.

[0004] For example, in most parts of my country, the temperature difference between summer and winter is significant. Consequently, the actual temperature of ambient cooling water often falls below the target in winter. For some systems requiring low-temperature chilled water, ambient cooling water can even meet the required temperature. In summer, however, due to poor cooling water system management, the wet-bulb temperature of the cooling tower often rises far above the target, rendering effective cooling impossible regardless of cooling tower operation. Both of these conditions negatively impact the cooling efficiency of the injection molding cooling water system, with the latter resulting in significant waste of electricity resources.

[0005] If the temperature of the cooling water in the injection molding cooling water system can be adjusted in time according to the ambient temperature, the working efficiency of the cooling water system can be greatly improved, energy consumption can be reduced, and costs can be saved. Summary of the Invention

[0006] Based on this, one of the objectives of the present disclosure is to provide an injection molding cooling water control system with high cooling efficiency and energy saving.

[0007] The injection molding cooling water control system includes a SaaS cloud platform connected to a meteorological big data platform and a cooling control unit, respectively, wherein the SaaS cloud platform monitors and controls the control parameters of the cooling control unit; the cooling control unit is connected to the cooling unit and records and controls the operating parameters of the cooling unit, and the cooling unit cools the injection molding system;

[0008] The SaaS cloud platform obtains daily local meteorological condition data including actual meteorological temperature, and the process temperature and daily power consumption of the cooling unit during stable operation from the meteorological big data platform and the cooling control unit, compares the daily power consumption corresponding to at least 10 dates with similar meteorological conditions, and determines the process temperature on the day with the minimum daily power consumption as the cooling target temperature of the cooling unit corresponding to the meteorological conditions;

[0009] When the meteorological conditions change, the SaaS cloud platform feeds back the new cooling target temperature corresponding to the new meteorological conditions to the cooling control unit, and the cooling control unit adjusts the cooling temperature of the cooling unit to the new cooling target temperature.

[0010] Preferably, the system further comprises an on-site temperature monitoring unit provided near the cooling unit, the on-site temperature monitoring unit being connected to the cooling control unit and transmitting the monitored on-site actual temperature to the cooling control unit in real time;

[0011] The cooling control unit feeds back the actual on-site temperature to the SaaS cloud platform, and the SaaS cloud platform replaces the actual meteorological temperature with the actual on-site temperature as the actual ambient temperature.

[0012] Preferably, the cooling unit includes multiple cooling towers and multiple refrigerators.

[0013] More preferably, the cooling unit is connected to the user's MES platform or the BI platform; the MES platform or the BI platform is connected to the SaaS cloud platform, and the start-up rate of the cooling tower and the refrigerator is fed back to the SaaS cloud platform. The SaaS cloud platform calculates the load of the cooling unit and adjusts the start-stop number and load ratio of the cooling tower and refrigerator by adjusting the control parameters of the cooling control unit according to the size of the load.

[0014] Another object of the present disclosure is to provide an injection molding cooling water control method with high cooling efficiency and energy saving.

[0015] The method comprises the following steps:

[0016] S1: The SaaS cloud platform obtains and records daily local meteorological conditions from the meteorological big data platform, and obtains and records the process temperature and daily power consumption of the cooling unit when it is in stable operation;

[0017] S2: The SaaS cloud platform compares the daily power consumption corresponding to at least 10 dates with similar meteorological conditions, and determines the actual process temperature on the day with the minimum daily power consumption as the cooling target temperature of the cooling unit corresponding to the meteorological conditions;

[0018] S3: When the meteorological conditions change, the SaaS cloud platform feeds back the new cooling target temperature corresponding to the new meteorological conditions to the cooling control unit, and the cooling control unit adjusts the cooling temperature of the cooling unit to the new cooling target temperature.

[0019] Preferably, the meteorological conditions include the forecasted and actual maximum temperature, the forecasted and actual minimum temperature, sunny weather, rain or snow, and severe convection.

[0020] Preferably, the method further comprises the step of obtaining the actual daily on-site temperature from the on-site temperature monitoring unit;

[0021] When the actual temperature on site is inconsistent with the actual meteorological temperature, the actual temperature on site is used as the actual ambient temperature to replace the actual meteorological temperature.

[0022] Preferably, when the SaaS cloud platform fails to obtain and record a sufficient number of similar meteorological conditions and process temperature and daily power consumption data corresponding to the meteorological conditions:

[0023] Cooling target temperature = maximum forecast temperature of the day - (maximum forecast temperature of the previous day - maximum on-site temperature of the previous day) - cooling temperature difference of the cooling unit, and the cooling temperature difference of the cooling unit is 6 to 8°C.

[0024] Preferably, when the cooling unit includes multiple cooling towers and multiple refrigerators, the SaaS cloud platform obtains the startup rate of the cooling unit from the user's MES platform or BI platform and calculates the load of the cooling unit, and controls the start-stop number and load ratio of the cooling tower and refrigerator by adjusting the control parameters of the cooling control unit.

[0025] Preferably, after the SaaS cloud platform records the cooling target of the cooling unit's daily operation for a complete four-season cycle, it simulates and calculates the cooling target temperature for the next four-season cycle and sends the simulation calculation results to the user for reference in advance.

[0026] The technical solution claimed in this disclosure has achieved the following beneficial effects:

[0027] 1) By deploying an injection molding cooling water control system based on the SaaS cloud platform, the injection molding cooling unit is monitored and intelligently controlled in real time. The cooling target temperature of the cooling unit under different meteorological conditions is determined based on the actual ambient temperature around the cooling unit, the corresponding cooling unit process temperature, and the daily power consumption of the cooling unit. Therefore, different cooling temperatures are automatically used under different meteorological conditions, which maximizes the working efficiency of the injection molding cooling unit and achieves energy conservation and consumption reduction.

[0028] 2) The on-site temperature monitoring unit can obtain a more accurate ambient temperature around the cooling unit. Correcting the actual meteorological temperature from the Meteorological Bureau with the actual on-site temperature measured by the on-site temperature detection unit can obtain a more accurate ambient temperature range, thereby obtaining a more accurate cooling target temperature that matches a certain meteorological condition. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings in the following description are merely embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without any creative work.

[0030] Figure 1 Schematic diagram of the injection molding cooling control system.

[0031] Figure 2 This is a schematic diagram of the meteorological temperature curve for a certain day. DETAILED DESCRIPTION

[0032] To make the purpose, technical solutions, and beneficial effects of the embodiments of the present disclosure more clear, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments of the present disclosure, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present disclosure.

[0033] Example 1

[0034] like Figure 1 As shown, this embodiment discloses an injection molding cooling water control system, which is managed and controlled by a SaaS cloud platform. The SaaS cloud platform is connected to the meteorological big data platform of the China Meteorological Administration through an API (Application Programming Interface) interface or a CLI (Command Line Interface) interface, and is connected to the cooling control unit of the injection molding system through a 4G or 5G smart gateway. The cooling control unit is specifically a PLC (Programmable Logic Controller) in the cooling system control cabinet. The SaaS cloud platform monitors and controls the control parameters of the PLC, wherein the 4G or 5G smart gateway is also provided in the control cabinet. The cooling control unit is connected to the cooling unit to record and control and modify various operating parameters of the cooling unit. The cooling unit is provided with a cooling tower and a freezer for cooling the injection molding system to cool the injection molding system.

[0035] The SaaS cloud platform obtains daily local meteorological condition data, including actual meteorological temperature, and the process temperature and daily power consumption of the cooling unit during stable operation from the meteorological big data platform and the cooling control unit, respectively. It compares the daily power consumption corresponding to at least 10 dates with similar meteorological conditions, and determines the process temperature on the day with the lowest daily power consumption as the cooling target temperature of the cooling unit corresponding to the meteorological conditions. The daily power consumption of the cooling unit is calculated once a day for 24 hours using the total power meter installed in the control cabinet. When the meteorological conditions change, the SaaS cloud platform feeds back the new cooling target temperature corresponding to the new meteorological conditions to the cooling control unit, and the cooling control unit adjusts the cooling temperature of the cooling unit to the new cooling target temperature, thereby achieving the purpose of intelligent control of the cooling temperature of the cooling unit and energy saving.

[0036] In a preferred solution, the system further comprises an on-site temperature monitoring unit (which can be set as a micro-meteorological station) arranged near the cooling unit, the on-site temperature monitoring unit is communicatively connected to the cooling control unit, and the cooling control unit performs real-time communication and collection of the temperature monitored by the on-site temperature monitoring unit; the cooling control unit sends the actual on-site temperature to the SaaS cloud platform, and when the actual on-site temperature is inconsistent with the actual meteorological temperature, the SaaS cloud platform replaces the actual meteorological temperature with the actual on-site temperature as the actual ambient temperature (see Appendix). Figure 2 (This is a schematic diagram of the meteorological temperature curve for a certain day, which includes the meteorological forecast temperature, the actual meteorological temperature, and the actual on-site temperature). That is, when the actual on-site temperature (including the actual maximum temperature on-site) is different from the actual meteorological temperature, the actual on-site temperature shall prevail. The actual ambient temperature range that can be accurately obtained is further corrected to obtain a more accurate cooling target temperature that matches a certain meteorological condition.

[0037] In a preferred solution, when the cooling unit includes multiple cooling towers and multiple refrigerators, the cooling unit is connected to the user's MES platform or the BI platform; the MES platform or the BI platform is connected to the SaaS cloud platform through an API interface or a CLI interface, and the startup rates of the cooling towers and the refrigerators are fed back to the SaaS cloud platform. The SaaS cloud platform calculates the load of the cooling unit based on this, and adjusts the control parameters of the cooling control unit for the cooling towers and refrigerators according to the calculated load results, thereby controlling the start-stop number and load ratio of the cooling towers and refrigerators, so that the cooling unit is always in the most energy-saving and optimal operating state.

[0038] Example 2

[0039] This embodiment provides an injection molding cooling water control method performed in the system of embodiment 1, comprising the following steps:

[0040] S1: The SaaS cloud platform obtains and records the local daily meteorological conditions from the meteorological big data platform, and obtains and records the process temperature and daily power consumption of the cooling unit during stable operation every day;

[0041] S2: The SaaS cloud platform compares the daily power consumption corresponding to at least 10 similar meteorological conditions, and determines the process temperature on the day with the minimum daily power consumption as the cooling target temperature of the cooling unit corresponding to the meteorological condition;

[0042] S3: When the meteorological conditions change, the SaaS cloud platform feeds back the cooling target temperature corresponding to the new meteorological conditions to the cooling control unit, and the cooling control unit adjusts the cooling temperature of the cooling unit to the new cooling target temperature.

[0043] The meteorological data includes the forecasted and actual maximum temperature, the forecasted and actual minimum temperature, sunny weather, rain and snow, and severe convection. In actual operation, three weather conditions are generally divided into: sunny weather without severe convection, sunny weather with severe convection, and rain and snow. Sunny weather includes cloudy and overcast weather. Severe convection is preferably only recorded without calculation or comparison. The similar meteorological conditions preferably refer to situations where the maximum and minimum temperatures of the day are similar.

[0044] In a preferred embodiment, the method further includes obtaining the daily actual on-site temperature from the on-site temperature monitoring unit; when the actual on-site temperature is inconsistent with the actual meteorological temperature, the actual on-site temperature is used as the actual ambient temperature to replace the actual meteorological temperature.

[0045] In a preferred solution, when the SaaS cloud platform does not obtain and record a sufficient number of meteorological conditions and process temperature and daily power consumption data corresponding to the meteorological conditions, the cooling target temperature is calculated as follows:

[0046] Cooling target temperature = maximum forecast temperature of the day - (maximum forecast temperature of the previous day - maximum on-site temperature of the previous day) - cooling temperature difference of the cooling unit, and the cooling temperature difference of the cooling unit is 6 to 8°C.

[0047] In a preferred solution, when the cooling unit includes multiple cooling towers and multiple refrigerators, the SaaS cloud platform obtains the startup rate of the cooling unit from the user's MES platform or BI platform and calculates the load of the cooling unit, and controls the start-stop number and load ratio of the cooling tower and refrigerator by adjusting the parameters of the cooling control unit according to the load size, so that the cooling unit operates within a reasonable load range.

[0048] In a preferred solution, the SaaS cloud platform records the actual maximum temperature, actual minimum temperature and cooling target temperature of the cooling unit during a complete four-season cycle, simulates and calculates the cooling target temperature for the next four-season cycle, and sends the simulation calculation results to the user for reference in advance.

[0049] The embodiments and application examples described above are merely illustrative descriptions of the present disclosure and do not limit the scope of the present disclosure. Without departing from the design spirit of the present disclosure, various modifications and improvements made to the technical solutions of the present disclosure by ordinary technicians in this field should fall within the scope of protection determined by the claims of the present disclosure.

Claims

1. An injection molding cooling water control system, characterized in that: It includes a SaaS cloud platform connected to a meteorological big data platform and a cooling control unit, wherein the SaaS cloud platform monitors and controls the control parameters of the cooling control unit; the cooling control unit is connected to a cooling unit and records and controls the operating parameters of the cooling unit, and the cooling unit cools the injection molding system; The SaaS cloud platform obtains daily local meteorological condition data including actual meteorological temperature, and the process temperature and daily power consumption of the cooling unit during stable operation from the meteorological big data platform and the cooling control unit, compares the daily power consumption corresponding to at least 10 dates with similar meteorological conditions, and determines the process temperature on the day with the minimum daily power consumption as the cooling target temperature of the cooling unit corresponding to the meteorological conditions; When the meteorological conditions change, the SaaS cloud platform feeds back the new cooling target temperature corresponding to the new meteorological conditions to the cooling control unit, and the cooling control unit adjusts the cooling temperature of the cooling unit to the new cooling target temperature.

2. The injection molding cooling water control system according to claim 1, characterized in that: The system further comprises an on-site temperature monitoring unit arranged near the cooling unit, the on-site temperature monitoring unit being connected to the cooling control unit and sending the monitored on-site actual temperature to the cooling control unit in real time; The cooling control unit feeds back the actual on-site temperature to the SaaS cloud platform. When the actual on-site temperature is inconsistent with the actual meteorological temperature, the SaaS cloud platform replaces the actual meteorological temperature with the actual on-site temperature as the actual ambient temperature.

3. The injection molding cooling water control system according to claim 1 or 2, characterized in that: The cooling unit includes multiple cooling towers and multiple refrigerators.

4. The injection molding cooling water control system according to claim 3, characterized in that: The cooling unit is connected to the user's MES platform or BI platform; the MES platform or the BI platform is connected to the SaaS cloud platform, and the startup rate of the cooling tower and the refrigerator is fed back to the SaaS cloud platform. The SaaS cloud platform calculates the load of the cooling unit and controls the start-stop number and load ratio of the cooling tower and refrigerator by adjusting the control parameters of the cooling control unit according to the size of the load.

5. A method for controlling cooling water for injection molding using the system according to any one of claims 1 to 4, characterized in that: The following steps are involved: S1: The SaaS cloud platform obtains and records daily local meteorological conditions from the meteorological big data platform, and obtains and records the process temperature and daily power consumption of the cooling unit when it is in stable operation; S2: The SaaS cloud platform compares the daily power consumption corresponding to at least 10 dates with similar meteorological conditions, and determines the process temperature on the day with the minimum daily power consumption as the cooling target temperature of the cooling unit corresponding to the meteorological conditions; S3: When the meteorological conditions change, the SaaS cloud platform feeds back the new cooling target temperature corresponding to the new meteorological conditions to the cooling control unit, and the cooling control unit adjusts the cooling temperature of the cooling unit to the new cooling target temperature.

6. The injection molding cooling water control method according to claim 5, characterized in that: The meteorological conditions include the forecast and actual maximum temperature, the forecast and actual minimum temperature, sunny weather, rain or snow, and severe convection.

7. The injection molding cooling water control method according to claim 6, characterized in that: The method further includes the step of obtaining the actual daily on-site temperature from the on-site temperature monitoring unit; when the actual on-site temperature is inconsistent with the actual meteorological temperature, replacing the actual meteorological temperature with the actual on-site temperature as the actual ambient temperature.

8. The injection molding cooling water control method according to claim 7, characterized in that: When the SaaS cloud platform does not obtain and record a sufficient number of similar meteorological conditions and process temperature and daily power consumption data corresponding to the meteorological conditions: Cooling target temperature = maximum forecast temperature of the day - (maximum forecast temperature of the previous day - maximum on-site temperature of the previous day) - cooling temperature difference of the cooling unit, and the cooling temperature difference of the cooling unit is 6~8℃.

9. The injection molding cooling water control method according to claim 5, characterized in that: When the cooling unit includes multiple cooling towers and multiple refrigerators, the SaaS cloud platform obtains the startup rate of the cooling unit from the user's MES platform or BI platform and calculates the load of the cooling unit, and controls the start-stop number and load ratio of the cooling tower and refrigerator by adjusting the control parameters of the cooling control unit.

10. The injection molding cooling water control method according to any one of claims 7 to 8, characterized in that: After the SaaS cloud platform records the cooling target temperature of the cooling unit during a complete four-season cycle, it simulates and calculates the cooling target temperature for the next four-season cycle and sends the simulation calculation results to the user for reference in advance.

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