[0020] The following is a detailed description of specific embodiments of the energy management system for rubber refining production in conjunction with the accompanying drawings. It should be understood that the disclosed embodiments are merely examples of ways in which certain aspects of the present application can be implemented, and do not represent an exhaustive list of all the ways in which the present invention can be implemented. Any specific structural and functional details disclosed herein should not be construed as limiting.
[0021] reference figure 1 An energy management system for tire rubber production is shown, which includes a cloud service platform and a client. One or more clients access the cloud service platform, and the cloud service platform and the client perform data interaction.
[0022] Combine figure 2 The cloud service platform includes a site management unit, a data collection unit, and a data analysis unit, and the client includes a data display unit.
[0023] The site management unit is used for accessing sites and managing the accessed sites. The sites are systems or equipment on the tire rubber production line that need to monitor energy consumption.
[0024] In tire rubber production management, sites can be independent systems involving energy consumption on the tire rubber production line, such as power systems, air compressor systems, boiler systems, water supply systems, and nitrogen generation systems. Through the site management unit, users can add or delete sites, perform site equipment management, view site information, and configure site information according to actual needs.
[0025] The data collection unit is used to obtain monitored energy consumption data from the accessed site.
[0026] The energy consumption data collected by the data collection unit is specifically the energy consumption type involved in the access site and the energy consumption type concerned by the user. For example, for each system on the tire rubber production line mentioned above, when it is connected to the cloud service platform as the monitored site, for the power system, the data collection unit mainly collects the power consumption of the power-consuming equipment in the tire production process. The energy consumption data includes: various power parameters, power quality parameters, power consumption accumulation, etc.; for air compressor systems, the data collection unit collects at least one level of compressed air temperature, pressure, flow, humidity and other indicators related to the production process; For the boiler system, the data acquisition unit collects at least the real-time parameters of each boiler and steam delivery system; for the water supply system, the data acquisition unit collects at least water supply related data; for the nitrogen generation system, the data acquisition unit mainly collects the nitrogen supply and recovery process Temperature, pressure, instantaneous flow, accumulation and other parameters.
[0027] Preferably, a collector is configured at each site, and the energy consumption data of each site is collected through the collector and transmitted to the data collection unit of the cloud service platform. Collectors are set separately for each site. According to the difference in energy consumption of each pair of sites, you can set collectors for collecting different energy consumption data. For each site of the aforementioned tire compounding production line, the collector can be a collection device that collects electricity, pressure, humidity and other related data, and the collectors that collect different types of energy consumption data at the same site can be integrated in the same In the collector, it can also be set separately.
[0028] Preferably, a collection front-end processor is set at each site, and the collection front-end processor communicates with the cloud service platform through a bus. The collection front-end processor can be a data processing device such as a PC. The collector provides a channel interface, and the collection front-end processor slave channel The energy consumption data collected by it is received in the interface and uploaded to the cloud service platform through the bus. As an optional real-time method, the acquisition front-end processor and the cloud service platform can also communicate through other wired or wireless methods.
[0029] The data analysis unit is used for data analysis based on the energy consumption data obtained by the data acquisition unit. The data analysis unit includes at least one of an energy consumption prediction analysis unit, an abnormal energy consumption detection analysis unit, and a production scheduling optimization analysis unit. The energy consumption prediction and analysis unit, including an energy consumption prediction model, is used to analyze and process the collected energy consumption data using the energy consumption prediction model to obtain an energy consumption prediction interval for a specific period; the energy consumption abnormality detection and analysis unit includes an abnormality The detection model is used to compare the actual energy consumption data of a specific period with the corresponding energy consumption prediction interval of the corresponding period to determine whether there is an abnormality; the production scheduling optimization analysis unit, including the low-carbon production scheduling model, is used to use the The low-carbon production scheduling model provides a production scheduling optimization plan based on the collected energy consumption data.
[0030] Energy consumption statistics is the basic function of the data analysis unit. The system can provide multiple data statistics methods, including statistics by region, energy consumption type, and equipment type. The system provides a variety of analysis algorithms, such as year-on-year, ring-on-quarter, ranking, etc. The data analysis unit is also used to analyze regional energy consumption, specific energy consumption types, and equipment energy consumption types based on these algorithms. The analysis period can provide daily analysis and monthly analysis. Analysis, annual analysis, and data analysis within any specified time period.
[0031] In order to realize low-carbon production scheduling optimization, energy anomaly detection and other functions that are conducive to production management based on the analysis and processing of energy consumption data, firstly build theoretical models such as energy consumption prediction model, anomaly detection model, and low-carbon production scheduling model, and Use actual energy consumption data to train these models so that these models can respectively realize the functions of energy consumption prediction, anomaly detection, and generation scheduling optimization. And, the system uses a three-tier architecture to apply these theoretical models. reference image 3 , The three-tier system structure includes: a data warehouse layer, a service processing layer and a user layer.
[0032] The data warehouse layer includes the data warehouse. The energy consumption data collected by the data collection unit is verified, filtered and sorted and then loaded into the data warehouse; the data in the data warehouse is used as the basic data of the energy analysis unit. The data warehouse provides the data foundation for the above theoretical model.
[0033] At the service processing layer, the system uses online analytical processing (OLAP) and related theoretical models to process the multi-dimensional energy consumption data of the data warehouse to obtain analysis, optimization or detection results. The analysis result of the service processing layer is sent to the user layer through the human-computer interaction interface.
[0034] The user layer is a human-computer interaction environment provided to decision-making users. The analysis results are presented in the form of alarms, graphics, and tables to help users make decisions.
[0035] In this embodiment, an energy consumption prediction model is constructed based on the energy consumption prediction algorithm of the BP neural network. The activation function in the selected neuron model is: According to the neural network prediction model, the predicted value of energy demand is 3.5% by incorporating relevant data. According to the BP neural network model, the energy demand of enterprises in 2020 can be predicted.
[0036] Forecast calculation method:
[0037] GM(1,1) model prediction. Before the grey model forecast, the total energy demand time series X=(x(1),..........,x(k),...x(9)) needs to be compared Judgment, where k represents different time intervals, calculate the smoothing ratio ρ(k)∈[0,***] of the time series, if the smoothing ratio is less than <0.5, it means that the original sequence of energy demand has a quasi-exponential law.
[0038] Example: Take the data from 2011 to 2019 as the original data sequence: X(0)(t)={x(0)(2011),x(0)(2012),......x (0)( 2019)}={3651,3784,...3968};
[0039] The one-time accumulation sequence is: x(1)(k)={3651,3784,×××3968}, k=9;
[0040] According to the formula [a, b] T =(btb) -1 B T yn, the values of a and b can be solved, and then substituted into the prediction model to calculate the energy demand in 2020.
[0041] Preferably, this application constructs a low-carbon production scheduling model based on genetic algorithms, so as to optimize the production scheduling of the enterprise.
[0042] Preferably, the system includes an abnormality alarm unit. When the abnormal energy consumption is detected by the abnormal energy consumption detection and analysis unit of the data analysis unit, an alarm message is sent to the designated user. In the embodiment, in order to achieve a variety of abnormal energy consumption alarms, the system presets the abnormal limit value of the energy consumption index, and monitors all the energy consumption information points in the rubber mixing area in real time. Once it is detected that the corresponding energy consumption index collected exceeds all If the set energy consumption index is abnormally limited, an abnormal alarm notification will be issued. The alarm level can be divided in advance according to the actual situation, and the management staff can be reminded to deal with the priority according to the alarm level.
[0043] Preferably, the energy consumption abnormality detection and analysis unit can locate the specific abnormality site, and the alarm information given by the abnormality alarm unit includes site information and energy consumption abnormality type information. When an alarm occurs, the system records detailed information such as location, energy consumption type, time, abnormal value, etc.
[0044] Preferably, the system further includes a system management unit, which is used to manage users, including but not limited to users designated to receive alarm information. Alarm events can be set by means of SMS, e-mail, etc., and sent to designated managers. When an alarm occurs, it is preferable to give an audible and visual alarm prompt, for example, a screen flashing and audible reminder on the client of the manager, or an audible and visual alarm set at the corresponding site to give a reminder.
[0045] The system management unit is also used for the organization management of the enterprise and divides the sites connected to the cloud service platform according to the corresponding organization. For example, enterprise A has multiple departments such as the first rubber refining branch and the second rubber refining branch, and each department has multiple production lines or energy supply lines such as A1, A2...B1, B2... , All production lines or energy supply lines are connected to the cloud service platform, and the sites are divided according to the above organizations. In this way, the energy consumption of different levels or different departments can be viewed, and the energy consumption data at all levels can be processed according to the organization; for the departments, horizontal and vertical analysis and processing can be carried out, which is convenient for the unified management of the enterprise.
[0046] Take "power consumption" as an example to illustrate, you can display the relevant electricity consumption statistics by department, and calculate the electricity consumption of each department during this period, which is convenient for the energy consumption management of each department.
[0047] The data display unit of the client is used to provide a user interaction interface, which is an interface connecting the above-mentioned service processing layer and the user layer. The data display unit obtains corresponding data analysis results from the cloud service platform in response to the user's request, and displays it through the user interactive interface.
[0048] Combine figure 2 , In order to display the data analysis results to users more intuitively, the cloud service platform also includes a report analysis unit. The report analysis unit is used to perform statistics on the collected energy consumption data according to a selected time period to obtain a multi-dimensional energy consumption statistical result, and generate a report based on the energy consumption statistical result and/or other user data. The system prestores various forms of report templates, and provides options for generating various report templates in the user interaction interface. When the cloud service platform receives a request from the user to generate a specified type of report, the system obtains the corresponding data analysis unit As a result, the report template corresponding to the report generation request is called, and the corresponding report is generated based on the corresponding result of the data analysis unit and the corresponding report template, and returned to the user.
[0049] In the embodiment, the output report can be saved as an Excel document format, and provide a direct printing function; it also provides a variety of graphic representation methods such as curve, bar graph, pie chart, etc., saved into a document format and printing function.
[0050] As a preference, the system can provide a variety of data statistics methods, such as regional statistics, energy consumption types, and equipment types. The system can provide statistical methods for various time periods such as daily statistics, weekly statistics, monthly statistics, and annual statistics. Correspondingly, the user interaction interface also provides the selection of different time statistical units (year/month/day, etc.), as well as the selection of statistical start time and statistical end time. The data analysis unit counts energy consumption according to the time period selected by the user. And give the corresponding report.
[0051] In order to more intuitively display the energy consumption of each department of the unit, the report analysis unit is also used to provide the results of comparison and analysis of the energy consumption of the site according to the divided organization. E.g: Figure 4 A schematic diagram of the results of comparative analysis of energy consumption is given. Click the "Steam"-"Comparative Analysis" menu to display the tree structure of each branch and the steam line under the branch. Check the branch or line name (multiple choices available), and click The "Comparative Analysis" button displays the comparison curve of the checked lines.
[0052] The client that communicates with the cloud service platform can be a PC or a mobile terminal such as a mobile phone or a tablet. When the client is a mobile terminal, the cloud service platform pushes data results or alarm information to the client in the form of SMS, email or APP data push service. Further, in this case, the user can configure the account that receives the information sent by the cloud service platform through the user interaction interface.
[0053] In other embodiments, the system management further includes a log management unit, which is used to generate a system log to record various operations related to information modification, so as to facilitate security audits.
[0054] In summary, the above system can manage all aspects of energy consumption in the tire rubber production process, can automatically collect energy consumption data at the access site, perform automatic analysis and intelligent diagnosis on it, and provide users with energy-saving optimization scheduling Program. At the same time, through multi-dimensional statistical analysis, it is convenient for users to manage the energy consumption in the generation process.
