CN110673484A - Control system for self-adaptive energy-saving operation of optimal working condition of industrial furnace - Google Patents
Control system for self-adaptive energy-saving operation of optimal working condition of industrial furnace Download PDFInfo
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Abstract
The embodiment of the invention relates to a control system for self-adaptive energy-saving operation of an industrial furnace under the optimal working condition, which comprises: the material input module is used for inputting raw materials, fuels and auxiliary materials; the data acquisition module is used for acquiring through metering equipment of raw materials, fuels, auxiliary materials and products; the data point marking module is used for marking the raw material input quantity, the fuel input quantity and the auxiliary material input quantity and storing the data point marking module in the data point storage module; the data point storage module is used for storing the raw material input quantity, the fuel input quantity and the auxiliary material input quantity of the data point marking module; the big data processing module is used for calling the raw material input quantity, the fuel input quantity and the auxiliary material input quantity stored by the data point storage module to perform operation processing and analyze results, and transmitting a control signal to the control module; the control module is used for generating an execution signal according to the control signal provided by the big data processing module and controlling the raw material input quantity, the fuel input quantity and the auxiliary material input quantity in the material input module.
Description
Technical Field
The embodiment of the invention relates to the technical field of energy-saving intelligent control processing of industrial furnaces, in particular to a control system for self-adaptive energy-saving operation of the optimal working condition of an industrial furnace.
Background
With the macroscopic regulation and control of national economic policies, backward enterprises with serious pollution are eliminated through capacity production, and the development of green, environment-friendly and energy-saving industry is promoted. The industrial furnace is used as a domestic equipment manufacturing industry to upgrade and transform. In the long run, the industrial furnace can produce qualified high-end products only by abandoning the old mode of relying on manual operation and worker experience and developing towards the intelligent automation, energy conservation and environmental protection, thereby improving the market competitiveness. From the consideration of enterprise operation cost accounting and return rate, the intelligent industrial furnace can save energy, and generally can save 5-30% of energy (different with different equipment) through comprehensive management control; the labor can be saved, the integrated control management of equipment operation and production organization is realized, and even an unmanned production system is formed; the investment can be reduced, and the equipment number is reduced due to reasonable allocation of equipment load.
The traditional industrial furnace basically adopts manual or semi-automatic operation for material inlet and outlet, so that the efficiency is low, on one hand, the dependence of ingredient components on the experience of workers is serious, the operating level of the workers is different, so that the product qualification rate is uncontrollable, on the other hand, the workers only pay attention to the stable and normal operation of the industrial process, and the high efficiency and energy conservation of the industrial process are neglected.
Disclosure of Invention
In view of this, in order to solve the problems in the prior art, embodiments of the present invention provide a control system for adaptive energy-saving operation of an industrial furnace under optimal operating conditions.
In a first aspect, an embodiment of the present invention provides a control system for adaptive energy-saving operation of an industrial furnace under optimal conditions, where the system includes:
the device comprises a material input module, a data acquisition module, a data point marking module, a data point storage module, a big data processing module and a control module, wherein the material input module, the data acquisition module, the data point marking module, the data point storage module, the big data processing module and the control module are sequentially connected;
the material input module is used for inputting raw materials, fuels and auxiliary materials;
the data acquisition module is used for acquiring through metering equipment of raw materials, fuels, auxiliary materials and products;
the data point marking module is used for marking the raw material input quantity, the fuel input quantity and the auxiliary material input quantity and storing the raw material input quantity, the fuel input quantity and the auxiliary material input quantity in the data point storage module;
the data point storage module is used for storing the raw material input quantity, the fuel input quantity and the auxiliary material input quantity of the data point marking module;
the big data processing module is used for calling the raw material input quantity, the fuel input quantity and the auxiliary material input quantity stored by the data point storage module to perform operation processing and analyze results, and transmitting a control signal to the control module;
the control module is used for generating an execution signal according to the control signal provided by the big data processing module and controlling the raw material input quantity, the fuel input quantity and the auxiliary material input quantity in the material input module.
In one possible embodiment, the data acquisition module is further configured to obtain an average lag time, match raw material input, fuel input, auxiliary material input, plant equipment power data with the product of the average lag time, and collect the data by grouping the data into one data point.
In one possible embodiment, the data acquisition module is further configured to obtain the average lag time through field measurement or process simulation, match the raw material input amount, the fuel input amount, the auxiliary material input amount, and the power consumption data of the plant equipment with the products separated by the average lag time, and collect the data by grouping the data into one data point.
In one possible embodiment, the data point marking module is configured to mark the raw material input amount, the fuel input amount, and the auxiliary material input amount according to input time of the raw material, the fuel, and the auxiliary material, and store the raw material input amount, the fuel input amount, and the auxiliary material input amount in the data point storage module.
In a possible embodiment, the data point storage module is specifically configured to store the raw material input amount, the fuel input amount, and the auxiliary material input amount of the data point marking module according to the time sequence of the marking.
In one possible embodiment, the big data processing module is specifically configured to perform operation processing and analysis on the raw material input amount, the fuel input amount and the auxiliary material input amount stored in the data point storage module by using a general multi-objective optimization theory, which includes a raw material, fuel and auxiliary material input minimum control theory, a yield output maximum control theory, a plant equipment electrode minimum control theory and a multi-objective optimization processing;
wherein the target is a raw fuel material cost minimization target under the constraint conditions of equipment capacity and product quality, an energy consumption minimization target, or a comprehensive target considered by the both.
In one possible implementation mode, the primary combustion material cost minimization target fully considers the time difference of purchase or sale of bulk goods and the price fluctuation problem, establishes the influence relation of the two constraints on the actual economic effect, namely the cost and the price, and takes the influence relation as a correction coefficient into the input condition.
In one possible embodiment, the energy consumption minimization objective is a comprehensive principle that considers both the minimization of material input per unit of production value and the minimization of power consumption of plant equipment.
In one possible embodiment, the overall objectives considered by both are: and based on the original combustion material cost minimization target and the energy consumption minimization target, performing multi-target optimization again, designing a weight corresponding to the original combustion material cost minimization target result and the energy consumption minimization target result according to the actual situation of the industrial furnace, taking a formula of the sum of the products of the two results and the weight as a basic algorithm of a comprehensive target considered by the two results and the weight, taking the weighted average value as a measurement standard, and taking the comprehensive index with the lowest original combustion material cost and the lowest energy consumption as an optimization target.
In one possible implementation mode, the control module adopts a cloud server, the raw material input amount, the fuel input amount and the auxiliary material input amount in the material input module are transmitted to the cloud server, the raw material input amount, the fuel input amount and the auxiliary material input amount are transmitted to the industrial furnace client through the cloud server, and the control program and parameters in the cloud server are directly updated through the Ethernet according to the field requirements.
The control system for the self-adaptive energy-saving operation of the optimal working condition of the industrial furnace provided by the embodiment of the invention realizes the searching and maintaining of the optimal working condition, improves the precision of the production flow, shortens the lag time, improves the operation reliability, has the good characteristics of simplicity and economy, improves the processing capacity and effect in emergency situations, provides a new idea for the intelligent energy saving of the industrial furnace in the new era, is beneficial to reducing the cost of enterprises and obtains better economic benefit.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the embodiments of the present invention, and it is also possible for a person skilled in the art to obtain other drawings based on the drawings.
Fig. 1 is a schematic structural diagram of a control system for adaptive energy-saving operation of an industrial furnace under an optimal working condition according to an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
For the convenience of understanding of the embodiments of the present invention, the following description will be further explained with reference to specific embodiments, which are not to be construed as limiting the embodiments of the present invention.
In the embodiment of the invention, the control system for the self-adaptive energy-saving operation of the optimal working condition of the industrial furnace kiln is provided, the input proportion of raw materials, fuels and auxiliary materials is controlled by researching the component proportion interval of products such as cement clinker, the intelligent system monitors and records all experienced working conditions on the basis of obtaining the cement clinker with equal quality reaching the standard according to the same production flow and equipment operation, all the working conditions are analyzed through big data, the input formula of the raw materials, the fuels or the auxiliary materials with the lowest raw material cost or the lowest energy consumption is sought, the input material proportion is automatically regulated and controlled according to the formula, the system stably operates nearby the working condition for a long time, and the optimal working condition is compared with the current optimal working condition in real time, so that the advantages and the disadvantages are overcome, the formula is updated in real time, and the optimal energy-saving working condition is automatically obtained and stably operates. Meanwhile, because the yield of the industrial furnace fluctuates in a certain range, the operation working condition of the highest yield or the most power-saving factory equipment can be searched through big data analysis, so that the system can operate near the working condition of the highest yield or the most power-saving factory equipment, the yield or power consumption interval is reduced, and the yield or power consumption is controlled more accurately. The instability of manual control is overcome, the operation stability of the whole production process is ensured, and the method is safe, stable and efficient.
The data includes raw material input, fuel input, auxiliary material input, output, plant equipment power consumption, etc.
The yield data is influenced by multiple parameters, besides input materials, input wind, pressure, temperature and the like, has a certain fluctuation range, and is compared by adopting a yield data mean value under the same material input working condition; yields fluctuate above and below the mean and are considered stable without exceeding a certain range. The system can keep the yield within the allowable deviation range all the time, and the yield is stably kept at a higher level.
As shown in fig. 1, a schematic structural diagram of a control system for adaptive energy-saving operation of an industrial furnace according to an embodiment of the present invention includes: the device comprises a material input module, a data acquisition module, a data point marking module, a data point storage module, a big data processing module and a control module, wherein the material input module, the data acquisition module, the data point marking module, the data point storage module, the big data processing module and the control module are sequentially connected;
the material input module is used for inputting raw materials, fuels and auxiliary materials;
the data acquisition module is used for acquiring through metering equipment of raw materials, fuels, auxiliary materials and products;
the data point marking module is used for marking the raw material input quantity, the fuel input quantity and the auxiliary material input quantity and storing the raw material input quantity, the fuel input quantity and the auxiliary material input quantity in the data point storage module;
the data point storage module is used for storing the raw material input quantity, the fuel input quantity and the auxiliary material input quantity of the data point marking module;
the big data processing module is used for calling the raw material input quantity, the fuel input quantity and the auxiliary material input quantity stored by the data point storage module to perform operation processing and analyze results, and transmitting a control signal to the control module;
the control module is used for generating an execution signal according to the control signal provided by the big data processing module and controlling the raw material input quantity, the fuel input quantity and the auxiliary material input quantity in the material input module so as to realize that the production flow is maintained at the optimal matching operation condition of the design target. And realizing the accurate control of the material flow based on the control rule self-adjustment.
For the data acquisition module, considering that the production flow is long, the input quantities of raw materials, fuels and auxiliary materials are not in one-to-one correspondence with the output at the same moment, the data acquisition module is also used for acquiring the average lag time, matching the input quantities of the raw materials, the input quantities of the fuels, the input quantities of the auxiliary materials and the power utilization data of the plant equipment with the products spaced by the average lag time, and collecting the data by grouping the data into a data point, wherein the data acquisition module corresponds the data one to one, and is favorable for classifying, sorting, analyzing and calling the data. These components can be obtained by on-line monitoring of the data or by off-line monitoring of the data. In the embodiment of the present invention, the average lag time may be obtained by field measurement or process simulation.
The data point marking module is specifically used for marking the raw material input quantity, the fuel input quantity and the auxiliary material input quantity according to the input time of the raw material, the fuel and the auxiliary material, and storing the raw material input quantity, the fuel input quantity and the auxiliary material input quantity in the data point storage module. The data points (raw material input quantity, fuel input quantity and auxiliary material input quantity) are marked according to time, so that the classification, the analysis, the comparison and the calling of the data are facilitated, and the short-term and long-term yield is predicted.
And the data point storage module is specifically used for storing the raw material input quantity, the fuel input quantity and the auxiliary material input quantity of the data point marking module according to the marked time sequence. Automatically establishing a new folder every day to store the data of the day, and sequentially marking and storing the folders according to specific years, months and days; a new folder is automatically created each day to store the best data point for that day. The data classification, analysis, comparison and calling are facilitated.
The big data processing module is specifically used for carrying out operation processing and analyzing results by calling the raw material input quantity, the fuel input quantity and the auxiliary material input quantity stored by the data point storage module by using a general multi-objective optimization theory comprising a raw material, fuel and auxiliary material input minimum control theory, a yield output maximum control theory, a factory equipment electrode minimum control theory and multi-objective optimization processing;
wherein the target is a raw fuel material cost minimization target under the constraint conditions of equipment capacity and product quality, an energy consumption minimization target, or a comprehensive target considered by the both. The three targets are considered from the aspects of enterprise cost/gross profit, national improvement on the energy consumption standard of the industrial furnace and long-term efficient green development of the industry.
The cost minimization target of the raw combustion materials fully considers the problems of time difference and price fluctuation of purchase or sale of bulk goods, establishes the influence relationship of the two constraints on the actual economic effect, namely the cost and the price, and takes the influence relationship as a correction coefficient into consideration in the input condition. The data source may rely on futures market or economic data. In practice, risk control of production benefits can also be performed by the system and with futures market operations. The method comprises the steps of inputting minimum control theories to raw materials, fuels and auxiliary materials, designing weights corresponding to the input quantities of the raw materials, the fuels and the auxiliary materials according to local material prices, taking a formula of the sum of the products of the three qualities and unit prices as a basic algorithm of the minimum control theories, and taking a weighted average value as a measurement standard, namely taking the lowest cost of the three items as one of optimization targets. The method takes the yield as a measurement standard, namely the maximum output as one of optimization targets. And (3) multi-objective optimization processing, namely designing weights of input quantity and output quantity according to local material prices, taking a formula of difference between total product price (product of quality and unit price) and material input weighted average value as a basic algorithm of the multi-objective optimization processing, and taking the new weighted average value as a measurement standard, namely taking maximum gross profit as an overall optimization target. And comparing the latest data point with the new weighted average value of the original optimal data point, eliminating the data points with small new weighted average value, selecting the data points with large new weighted average value, storing the data points in the optimal data point folder, calling the input data of the raw materials, the fuels and the auxiliary materials in the data points, and transmitting the input data to the control system. Initially, two data points at the previous and subsequent times are compared, and the data point with the larger new weighted average is defined as the optimal data point and stored in the optimal data point folder. The goal is favorable for enterprises to reduce cost, improve profit and improve market competitiveness.
The minimum energy consumption target is a comprehensive principle which simultaneously considers the minimum material input of unit output value and the minimum power consumption of plant equipment. Mainly divided into three parts. The method comprises the steps of inputting minimum control theories of raw materials, fuels and auxiliary materials, designing weights corresponding to the input quantities of the raw materials, the fuels and the auxiliary materials according to material energy consumption, taking a formula of the sum of the products of the three qualities and the energy consumption as a basic algorithm of the minimum control theories, and taking a weighted average value as a measurement standard, namely, taking the minimum energy consumption of the three items as one of optimization targets. The method takes the yield as a measurement standard, namely the maximum output as one of optimization targets. According to the minimum value control theory of the power consumption of the plant equipment, the method takes the power consumption condition of the plant equipment under the working condition as a measurement standard, namely the minimum power consumption is taken as one of optimization targets. And (3) multi-objective optimization processing, namely designing weights corresponding to two items of the material input weighted average value, the yield ratio and the power consumption according to the material energy consumption, taking a formula of the weighted average of the two items as a basic algorithm of the multi-objective optimization processing, and taking the new weighted average value as a measurement standard, namely taking the minimum energy consumption of unit yield as an overall optimization target. And comparing the new weighted average value of the latest data point and the original optimal data point, eliminating the data point with the large new weighted average value, selecting the data point with the small new weighted average value, storing the data point in the optimal data point folder, and transferring the input data of raw materials, fuels and auxiliary materials, the output data of products and the power consumption data of plant equipment in the data point to a control system. Initially, two data points at the previous and subsequent times are compared, the data point with the smaller new weighted average is defined as the optimal data point, and is stored in the optimal data point folder. As the national energy consumption standard of the industrial furnace is continuously improved, the target is favorable for reducing the unit yield energy consumption of enterprises, and removes the restriction factors of national energy conservation and emission reduction on the enterprises, so that the enterprises develop green and healthy.
The comprehensive target considered by the two is based on the original combustion material cost minimization target and the energy consumption minimization target, multi-objective optimization processing is carried out again, a weight corresponding to the original combustion material cost minimization target result and the energy consumption minimization target result is designed according to the actual situation of the industrial furnace, a formula of the sum of the products of the two results and the weight is used as a basic algorithm of the comprehensive target considered by the two, the method takes a weighted average value as a measurement standard, and the comprehensive index with the lowest original combustion material cost and the lowest energy consumption is used as an optimization target. The target considers the cost/gross profit of enterprises and the improvement of the national energy consumption standard of the industrial furnace, can regulate and control two sub-targets by controlling the weight according to local conditions, and meets the requirements of various aspects of long-term high-efficiency green development of the industry. The new weighted average value of the latest data point and the original optimal data point can be compared, the data are superior and inferior, the data are stored in the optimal data point folder, and the input data of raw materials, fuels and auxiliary materials in the data point are called and transmitted to the control system. And initially comparing two data points at the moment before and after, defining a better data point as an optimal data point, and storing the optimal data point in an optimal data point folder.
Adopt the high in the clouds server to control module, raw materials input, fuel input, the auxiliary material input among the material input module transmit to the high in the clouds server, through the high in the clouds server with raw materials input, fuel input, auxiliary material input transmit to the industrial furnace kiln customer end, directly through control program and the parameter in the ethernet update high in the clouds server according to the on-the-spot demand, the high in the clouds server is connected in order to reach the control to material input system body through ethernet and controller, and does not need maintainer to go to the on-the-spot update, and the flexibility is strong.
In addition, the control module basically adopts an intelligent instrument mode to control material flow, the instrument is used for requiring an operator to input parameters in sequence strictly according to a specification interface, and most parameter codes are English symbols; meanwhile, the instrument operation interface is small, and the key operation is easy to make mistakes. The touch screen control system of the full-Chinese interface integrated furnace and the full-color Chinese interface can completely replace an intelligent instrument configuration recorder mode, and are convenient to operate, high in precision, simple and easy to learn.
Because the fluctuation of material data (raw material input quantity, fuel input quantity and auxiliary material input quantity) is large, a general fluctuation formula of the material data is obtained by a field measurement or process simulation mode, and the yield and the power consumption can be predicted according to the input trend of the raw material, the fuel and the auxiliary material input quantity to a certain extent.
The material data fluctuation has random fluctuation and real fluctuation, a method for distinguishing the random fluctuation from the real fluctuation is obtained through field measurement or a process simulation mode, filtering is completed, and self-adaption is achieved.
Taking a cement kiln as an example, the control system for the self-adaptive energy-saving operation of the optimal working condition of the industrial kiln mainly comprises an energy-saving control platform of the cement kiln, and comprises a key technology for acquiring and exchanging energy data of the cement kiln and acquisition software, real-time monitoring and remote control software in the production process of the cement kiln, a graded early warning and cooperative processing system of the energy operation state, and finally the energy-saving control platform which is efficiently matched with the material flow, the energy flow and the information flow of a cement process is built. The platform has the advantages of energy conservation, emission reduction and intelligent control, and can improve the material utilization efficiency and reduce the material usage amount.
Furthermore, the energy-saving management and control platform of the cement kiln collects system operation data by using a sensing technology, transmits control and sampling signals in a communication mode, performs big data analysis on the operation data, and intelligently adjusts and controls the operation of the system. The kiln condition of the whole cement production line can be stabilized, the period of maintaining the kiln is prolonged, the maintenance cost is saved, the ignition cost is reduced, the temperatures of the rotary kiln and the decomposing furnace are stabilized, the service life of refractory bricks is prolonged, the cement strength is improved, a high-temperature variable-frequency fan is adjusted, the electricity is saved, the faults caused by manual operation are reduced, the labor intensity of operation workers is reduced, the reaction speed of the whole system is high, and the control precision is high.
Furthermore, the energy-saving control platform of the cement kiln can perform intelligent closed-loop control on material consumption, yield, kiln temperature and pressure, sudden faults and the like through a preset program;
furthermore, the intelligent control module of the energy-saving management and control platform of the cement kiln carries out information communication with an operator through a human-computer interaction device, receives sensor data through a programmable logic control device, and adjusts the material ratio through an execution module after comparison and calculation; when the data exceeds the limit, an alarm is sent out through an alarm device and protective measures are automatically taken; each module can be independently integrated and selectively used.
The control system for the self-adaptive energy-saving operation of the industrial furnace under the optimal working condition, provided by the embodiment of the invention, has the following beneficial effects:
(1) the invention can automatically work to realize the measurement, monitoring, control and recording of each main device of the industrial furnace under the optimal working condition and the self-adaptive energy-saving operation. The intellectualization of the industrial furnace is realized through the control system, the operation requirements of different types of industrial furnaces can be met, and the applicability range of the method and the equipment is improved;
(2) the invention can autonomously seek the optimal material input working condition of the industrial furnace, control the production flow to operate according to the working condition, and improve the material use efficiency, save energy, reduce the production cost and increase the profit by controlling the material input system;
(3) the invention can meet the requirements of different processing capacities through parameter design and intelligent control;
(4) the invention can save manpower by comprehensive management control, carry out equipment operation and production organization integrated control management, and even form an unmanned production system;
(5) the invention can reasonably allocate the equipment load, reduce the equipment quantity and reduce the investment.
Those of skill would further appreciate that the various illustrative components and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied in hardware, a software system executed by a processor, or a combination of the two. The software system may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. A control system for adaptive energy-saving operation of an industrial furnace under optimal working conditions is characterized by comprising:
the device comprises a material input module, a data acquisition module, a data point marking module, a data point storage module, a big data processing module and a control module, wherein the material input module, the data acquisition module, the data point marking module, the data point storage module, the big data processing module and the control module are sequentially connected;
the material input module is used for inputting raw materials, fuels and auxiliary materials;
the data acquisition module is used for acquiring through metering equipment of raw materials, fuels, auxiliary materials and products;
the data point marking module is used for marking the raw material input quantity, the fuel input quantity and the auxiliary material input quantity and storing the raw material input quantity, the fuel input quantity and the auxiliary material input quantity in the data point storage module;
the data point storage module is used for storing the raw material input quantity, the fuel input quantity and the auxiliary material input quantity of the data point marking module;
the big data processing module is used for calling the raw material input quantity, the fuel input quantity and the auxiliary material input quantity stored by the data point storage module to perform operation processing and analyze results, and transmitting a control signal to the control module;
the control module is used for generating an execution signal according to the control signal provided by the big data processing module and controlling the raw material input quantity, the fuel input quantity and the auxiliary material input quantity in the material input module.
2. The system of claim 1, wherein the data acquisition module is further configured to obtain an average lag time, match raw material input, fuel input, auxiliary material input, plant equipment power usage data with products spaced apart by the average lag time, and collect the data by grouping the data into one data point.
3. The system of claim 2, wherein the data collection module is further configured to obtain the average lag time by field measurement or process simulation, and match the raw material input amount, the fuel input amount, the auxiliary material input amount, and the plant equipment electricity data with the product of the average lag time and collect the data by grouping the data into one data point.
4. The system of claim 1, wherein the data point marking module is configured to mark the raw material input amount, the fuel input amount, and the auxiliary material input amount according to input time of the raw material, the fuel, and the auxiliary material, and store the raw material input amount, the fuel input amount, and the auxiliary material input amount in the data point storage module.
5. The system of claim 1, wherein the data point storage module is specifically configured to store the raw material input amount, the fuel input amount, and the auxiliary material input amount of the data point marking module according to a time sequence of the marking.
6. The system of claim 1, wherein the big data processing module is specifically configured to perform an operation processing and analysis on the input amount of raw materials, the input amount of fuel, and the input amount of auxiliary materials stored in the data point storage module by using a general multi-objective optimization theory including a raw material, fuel, and auxiliary material input minimum control theory, a yield output maximum control theory, a plant equipment electrode minimum control theory, and a multi-objective optimization processing;
wherein the target is a raw fuel material cost minimization target under the constraint conditions of equipment capacity and product quality, an energy consumption minimization target, or a comprehensive target considered by the both.
7. The system of claim 6, wherein the primary combustion material cost minimization target fully considers the time difference of bulk material purchase or sale and price fluctuation problems, establishes the influence relationship of the two constraints on the actual economic effect, namely cost and price, and takes the influence relationship into consideration as the correction coefficient into the input conditions.
8. The system of claim 6, wherein the energy consumption minimization objective is a comprehensive principle that considers both the minimization of material input per unit of production and the minimization of electricity usage by plant equipment.
9. The system of claim 6, wherein the synthetic objectives considered by both are: and based on the original combustion material cost minimization target and the energy consumption minimization target, performing multi-target optimization again, designing a weight corresponding to the original combustion material cost minimization target result and the energy consumption minimization target result according to the actual situation of the industrial furnace, taking a formula of the sum of the products of the two results and the weight as a basic algorithm of a comprehensive target considered by the two results and the weight, taking the weighted average value as a measurement standard, and taking the comprehensive index with the lowest original combustion material cost and the lowest energy consumption as an optimization target.
10. The system of claim 1, wherein the control module adopts a cloud server, the raw material input amount, the fuel input amount and the auxiliary material input amount in the material input module are transmitted to the cloud server, the raw material input amount, the fuel input amount and the auxiliary material input amount are transmitted to the industrial furnace client through the cloud server, and the control program and parameters in the cloud server are directly updated through the Ethernet according to the field requirements.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116628576A (en) * | 2023-07-26 | 2023-08-22 | 中南大学 | Intelligent production yield monitoring method for heat carrier lime kiln |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1396770A1 (en) * | 2002-08-16 | 2004-03-10 | Powitec Intelligent Technologies GmbH | Method of regulating a thermodynamic process |
| CN202486576U (en) * | 2011-09-20 | 2012-10-10 | 北京凯盛建材工程有限公司 | Cement raw material blending control device |
| CN103092171A (en) * | 2013-01-09 | 2013-05-08 | 武安市新峰水泥有限责任公司 | Cement digital management and control system |
| CN104913658A (en) * | 2015-06-05 | 2015-09-16 | 广东中鹏热能科技有限公司 | Production management system of ceramic tile kiln and control method of production management system |
| CN109190846A (en) * | 2018-10-08 | 2019-01-11 | 华东理工大学 | A kind of new dry process rotary kiln furnace calcination process Multipurpose Optimal Method |
-
2019
- 2019-10-18 CN CN201910991404.4A patent/CN110673484A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1396770A1 (en) * | 2002-08-16 | 2004-03-10 | Powitec Intelligent Technologies GmbH | Method of regulating a thermodynamic process |
| CN202486576U (en) * | 2011-09-20 | 2012-10-10 | 北京凯盛建材工程有限公司 | Cement raw material blending control device |
| CN103092171A (en) * | 2013-01-09 | 2013-05-08 | 武安市新峰水泥有限责任公司 | Cement digital management and control system |
| CN104913658A (en) * | 2015-06-05 | 2015-09-16 | 广东中鹏热能科技有限公司 | Production management system of ceramic tile kiln and control method of production management system |
| CN109190846A (en) * | 2018-10-08 | 2019-01-11 | 华东理工大学 | A kind of new dry process rotary kiln furnace calcination process Multipurpose Optimal Method |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116628576A (en) * | 2023-07-26 | 2023-08-22 | 中南大学 | Intelligent production yield monitoring method for heat carrier lime kiln |
| CN116628576B (en) * | 2023-07-26 | 2023-10-13 | 中南大学 | Intelligent production yield monitoring method for heat carrier lime kiln |
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