Multi-parameter coupled intelligent operation regulation and control method and system for wet CO2 mineralization of base solid waste

Abstract

The invention belongs to the technical field of carbon dioxide capture, utilization and storage (CCUS), and discloses a multi-parameter coupled intelligent operation regulation and control method and system for wet CO2 mineralization of base solid waste. The intelligent operation regulation and control method comprises the following steps: monitoring real-time data in a flue gas CO2 generation mechanism, and constructing a CO2 concentration prediction model; calculating the mass of the basic group solid waste required in the mineralization reaction process; preparing base solid waste slurry according to a calculation result, and mixing the base solid waste slurry with flue gas to be treated to perform CO2 mineralization reaction; the change of physical quantity in the mineralization reaction process is monitored in real time, and the amount of base solid waste slurry reacting with the to-be-treated flue gas is finely adjusted in real time according to the monitored result, so that intelligent regulation and control of the CO2 mineralization process are realized. According to the invention, prediction of key operation parameters of the CO2 mineralization system and intelligent regulation and control of key equipment are realized, and a foundation is laid for large-scale CO2 mineralization and storage by using the base solid waste.

Description

Technical Field

[0001] The present invention belongs to the technical field related to carbon dioxide capture, utilization and storage (CCUS), and more specifically, to a multi-parameter coupled alkaline solid waste wet CO 2 Intelligent operation and control method and system for mineralization. Background Art

[0002] Thermal power plants, steel mills, cement plants and other industries emit large amounts of CO 2 At the same time, a large amount of alkaline solid waste is generated. The accumulation and discharge of these solid wastes not only occupy a large amount of land resources, but also the excessive leaching of heavy metals easily causes environmental pollution problems such as soil, surface water and groundwater.

[0003] CO 2 Mineralization utilizes alkaline substances such as CaO and MgO in alkaline solid waste to generate stable carbonates. On the one hand, it can achieve permanent storage of carbon dioxide; on the other hand, it can also de-alkali and detoxify the alkaline solid waste, which is beneficial to its subsequent utilization. It is an important synergistic technology for waste reduction and carbon reduction.

[0004] However, there is currently no research on the use of alkaline solid waste for CO 2 The invention can fill the gap in the current CO 2 The gap in the intelligent control system for mineralization storage provides a technical reference for the current high-carbon emission industries and alkaline solid waste treatment. Summary of the invention

[0005] In view of the above defects or improvement needs of the prior art, the present invention provides a multi-parameter coupled alkali solid waste wet CO 2 Intelligent operation and control method and system for mineralization to solve the problem of CO 2 The technical problem of mineral storage being unable to be intelligently regulated.

[0006] To achieve the above object, according to one aspect of the present invention, a multi-parameter coupled alkaline solid waste wet CO 2 The method for intelligent operation and control of mineralization comprises the following steps:

[0007] Monitoring flue gas CO 2 Generate real-time data in the organization and predict the flue gas CO generated based on the real-time data 2 concentration, analyze the content of alkaline substances in alkali solid waste, and use the predicted flue gas CO 2 The mass of the alkaline solid waste required in the mineralization reaction process is calculated based on the concentration and content of alkaline substances in the alkaline solid waste;

[0008] According to the required mass of the alkali solid waste, it is mixed with the solution to form an alkali solid waste slurry, and the alkali solid waste slurry is mixed with the flue gas to be treated for CO 2 Mineralization reaction, real-time monitoring of the changes in physical quantities during the mineralization reaction, and the use of monitoring results to fine-tune the amount of slurry of alkaline solid waste that reacts with the flue gas to be treated in real time, so as to achieve CO 2 Intelligent regulation of mineralization process.

[0009] Further preferably, the CO 2 Real-time data generated by the mechanism include fuel carbon content, fuel consumption, air excess factor, fuel utilization and fuel carbon oxidation rate.

[0010] Further preferably, the CO 2 The physical quantities in the mineralization reaction process include flue gas inlet / outlet flow rate, flue gas inlet / outlet temperature, flue gas inlet / outlet CO 2 Concentration, slurry density, slurry pH, slurry level, slurry temperature, alkali solid waste slurry pump flow and solution pump flow.

[0011] Further preferably, the alkaline substances in the alkaline solid waste include CaO, MgO, Fe 2 O 3 and Na 2 O.

[0012] According to another aspect of the present invention, there is provided a method for controlling CO 2 The system of intelligent operation and control of mineralization includes material analysis module, online monitoring module, intelligent control module and CO 2 Mineralization module, where:

[0013] The material analysis module is used to analyze the content of alkaline substances in alkali solid waste;

[0014] The online monitoring module is used to monitor flue gas CO 2 Generate real-time data in the mechanism and monitor the changes of physical quantities in the mineralization process in real time, and feed back the monitored real-time data and changes of physical quantities to the intelligent control module;

[0015] The CO 2 The mineralization module is used to mix the alkali solid waste slurry with the flue gas to be treated to CO 2 Mineralization reaction;

[0016] The intelligent control module is used to monitor the flue gas CO in real time. 2 Real-time data from the production organization predicts the flue gas CO generated 2 concentration, using the predicted flue gas CO 2The mass of the alkali solid waste required in the mineralization reaction process is calculated based on the concentration and content of alkaline substances in the alkali solid waste, and the amount of slurry of the alkali solid waste reacting with the flue gas to be treated is fine-tuned in real time according to the changes in physical quantities in the mineralization reaction process monitored in real time.

[0017] Further preferably, the intelligent control module includes a data acquisition unit, a machine learning unit and an automatic control unit, wherein the data acquisition unit is connected to the online monitoring module to receive the monitoring results of the online monitoring unit in real time, and a plurality of prediction and control models are arranged in the machine learning unit, and the machine learning unit is connected to the data acquisition unit, and the prediction model uses the data from the data acquisition unit to learn and update the output; the automatic control unit fine-tunes the amount of slurry of the alkaline solid waste that reacts with the flue gas to be treated in real time according to the changes in physical quantities in the mineralization reaction process monitored in real time.

[0018] Further preferably, the machine learning unit is provided with a CO 2 Concentration prediction and control model, the CO 2 The concentration prediction and control model is used to predict the generated flue gas CO based on the real-time monitoring data. 2 concentration, and based on this predicted flue gas CO 2 The amount of alkali solid waste slurry required in the mineralization reaction process is calculated based on the concentration and content of alkaline substances in the alkali solid waste, so as to achieve the regulation of the quality of the alkali solid waste.

[0019] Further preferably, the CO 2 The mineralization module is equipped with a mineralization reaction tower for CO 2 mineralization reaction.

[0020] Further preferably, a pH control model is also provided in the machine learning unit, the input parameter is the real-time pH of the slurry in the mineralization reaction tower, and the output is the amount of slurry of alkaline solid waste required to maintain a preset pH.

[0021] According to another aspect of the present invention, a multi-parameter coupled alkaline solid waste wet CO 2 The intelligent operation and control system for mineralization includes an actuator for executing the above-mentioned multi-parameter coupled alkaline solid waste wet CO 2 Intelligent operation and control method of mineralization.

[0022] In general, the above technical solutions conceived by the present invention have the following beneficial effects compared with the prior art:

[0023] 1. The present invention provides a wet CO 2 The intelligent control concept of mineralization uses the predicted flue gas CO 2The mass of the alkaline solid waste required in the mineralization reaction can be calculated based on the concentration and the content of alkaline substances in the alkaline solid waste, so as to realize the pre-regulation of the amount of the alkaline solid waste in the mineralization reaction. Then, the amount of the alkaline solid waste in the reaction can be fine-tuned in real time by real-time monitoring of the mineralization reaction process, so as to realize the intelligent regulation of the amount of the alkaline solid waste in the mineralization process. This invention can fill the current gap in the use of CO 2 The gap in the mineralization storage system provides a technical reference for the current high-carbon emission industry and alkaline solid waste treatment;

[0024] 2. The present invention is to treat CO 2 Monitoring and regulation of multi-parameter coupling during mineralization to achieve CO 2 Prediction of key operating parameters of mineralization systems and intelligent control of key equipment;

[0025] 3. The present invention sets multiple models in the intelligent control module, including but not limited to CO 2 The application of concentration prediction and control model, slurry pH control model and multiple models can provide the system with strong data support and intelligent decision-making capabilities, greatly improve the overall performance and stability of the system, optimize resource utilization and reduce operational risks;

[0026] 4. The present invention realizes closed-loop control of the entire control process by online monitoring of real-time results and feeding back the monitoring results to the intelligent control module. It has a high level of intelligent and precise control and can solve the problem of CO2 removal from alkali solid waste after application. 2 The technical problem of mineral storage being unable to be intelligently regulated can be solved, effectively reducing the system’s operating energy consumption and operating costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a multi-parameter coupled alkaline solid waste wet CO 2 Flow chart of the intelligent operation and control system for mineralization;

[0028] Figure 2 It is a multi-parameter coupled alkaline solid waste wet CO 2 Schematic diagram of the structure of the intelligent operation and control system of mineralization.

[0029] Throughout the drawings, the same reference numerals are used to denote the same elements or structures, wherein:

[0030] 1- Flue gas inlet, 2- Fan, 3- Flue gas temperature / CO 2 Concentration sensor, 4-bubbler, 5-stirring paddle, 6-demister, 7-slurry pH meter, 8-discharge pump, 9-flue gas outlet, 10-slurry pump, 11-solution pump, 12-mixing tank, 13-material inlet, 14-solution tank, 15-solid-liquid separator, 16-material outlet. DETAILED DESCRIPTION

[0031] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0032] like Figure 1 and 2 As shown in the figure, a multi-parameter coupled alkaline solid waste wet CO 2 The intelligent operation and control system for mineralization includes a material analysis module for analyzing the composition and content of alkali solid waste, a module for monitoring boilers and CO 2 The online monitoring module of the mineralization system operation parameters, the intelligent control module that builds data models and automatically controls the system, 2 CO that provides a chemical reaction site with alkaline solid waste 2 Mineralization module;

[0033] The material analysis module is used to conduct qualitative and quantitative analysis on the physical and chemical properties of alkali solid waste, and detect solid waste CaO, MgO, etc. for CO 2 The content of alkaline substances sealed by mineralization, and the data is stored and transmitted to the intelligent control module;

[0034] The material analysis module covers samplers, preprocessors, detection systems, data storage and transmission equipment for analysis of alkaline solid waste components and contents, so as to achieve a comprehensive analysis of alkaline solid waste components and contents.

[0035] Online monitoring module is used to monitor boiler and CO 2 Comprehensive real-time monitoring of the mineralization system operating parameters, data collection, integration, storage and transmission to the intelligent control module;

[0036] The online monitoring module mainly includes various online monitoring devices related to the physical and chemical properties of flue gas and slurry. The real-time monitoring parameters include but are not limited to fuel carbon content, fuel consumption, air excess coefficient, fuel utilization rate, fuel carbon oxidation rate, CO in the inlet / outlet flue gas of the mineralization tower, and the amount of carbon in the flue gas. 2 Concentration, flue gas flow, slurry pH value, slurry density, slurry level, and slurry circulation pump flow.

[0037] The intelligent control module includes a data acquisition unit, a machine learning unit and an automatic control unit. By receiving data from the material analysis module and the online monitoring module, the machine learning method is used to establish an alkaline material database, and construct a slurry pH control, CO 2Prediction and control models are used to intelligently calculate and predict the amount of alkaline slurry replenishment, solution replenishment, and circulating slurry, so as to achieve CO 2 Intelligent control of mineralization tower;

[0038] The intelligent control module uses machine learning and CO driven by big data. 2 The basic principle of mineralization reaction is to establish CO 2 Prediction and control model, based on which CO 2 Precise control of slurry pumps, slurry discharge pumps and other equipment in the mineralization module.

[0039] CO 2 The mineralization module includes the main / auxiliary mineralization tower and auxiliary equipment, including flue gas temperature sensor, CO 2 The controlled components include concentration sensors, pH sensors, slurry pumps, slurry discharge pumps, solution pumps, stirring paddles, etc., and also include slurry pools and bubblers arranged at the bottom of the main / auxiliary mineralization towers, demisters arranged above the main / auxiliary mineralization towers, and slurry pumps, solution pumps, slurry discharge pumps and other equipment outside the main / auxiliary mineralization towers. 2 The main sites for mineralization storage;

[0040] The main equipment of the mineralization tower includes 1 flue gas inlet, 2 fans, 3 flue gas temperature / CO 2 Concentration sensor, 4 bubbler, 5 stirring paddle, 6 demister, 7 slurry pH meter, 8 slurry pump, 9 flue gas outlet, 10 slurry pump, 11 solution pump, 12 slurry mixing tank, 13 material inlet, 14 solution tank, 15 solid-liquid separator, 16 material outlet; the working process on the slurry side is as follows: the alkali solid waste enters the slurry mixing tank 12 through the material inlet 13 after pretreatment such as crushing and screening, and is mixed with the solution and process water from the solution tank 14 to form alkali solid waste slurry. The evenly mixed slurry is transported to the main / auxiliary mineralization tower through the slurry pump 10, and is continuously stirred and mixed by the stirring paddle 5. The slurry absorbs CO in the flue gas in the mineralization tower. 2 The chemical reaction takes place, and the slurry after the reaction is transported out of the mineralization tower through the slurry pump 8, and passes through the solid-liquid separator 15, wherein the solid product is discharged through the material outlet 16, and the separated solution is transported to the solution pool 14 through the solution pump 11; the working process on the flue gas side is as follows: CO 2 The flue gas enters the bottom of the main mineralization tower through the flue gas inlet 1 and the fan 2, and is passed into the mineralization tower through the bubbler 4 to contact with the alkali solid waste slurry to CO 2 The flue gas after the reaction is then passed into the secondary mineralization tower for secondary mineralization reaction to ensure CO 2 Mineralization efficiency, the net flue gas after the reaction is discharged through 9 flue gas outlets, during operation through 3 flue gas temperature / CO 2 Concentration sensors and 7 slurry pH meters continuously monitor material parameters.

[0041] Based on the online monitoring module and the actual operating parameters of the boiler, machine learning is used to establish the CO 2 Concentration prediction model; at the same time, based on the material analysis module and CO 2 The historical operation data of the mineralization module is used to establish the CO 2 Inlet concentration, alkaline solid waste slurry pump flow, solution pump flow, slurry pH and outlet CO 2 Multi-parameter CO concentration 2 Control coupling model, combined with CO 2 Mineralization mechanism calculation of slurry replenishment.

[0042] Based on CO 2 Concentration prediction and CO 2 The control of the two processes, the establishment of CO 2 Prediction and control models to achieve CO 2 Precise regulation of the controlled components of the mineralization module.

[0043] Intelligent control module realizes CO 2 The precise control of the mineralization module requires the following steps:

[0044] Step S1: The material analysis module analyzes the alkaline solid waste analysis results, including CaO, MgO, Fe 2 O 3 and Na 2 The content of substances such as O is transmitted to the intelligent control module;

[0045] Step S2: Use the online detection module to collect boiler and CO 2 The main parameters of the mineralization module operation include but are not limited to fuel carbon content, fuel consumption, air excess coefficient, fuel utilization rate, fuel carbon oxidation rate, flue gas inlet / outlet flow rate, flue gas inlet / outlet temperature, flue gas inlet / outlet CO 2 concentration, slurry density, slurry pH, slurry level, slurry temperature, slurry pump flow, solution pump flow, and transmit them to the intelligent control module;

[0046] Step S3: The intelligent control module establishes CO based on big data calculation through the data acquisition unit and the machine learning unit. 2 Concentration prediction and control model, pH control model, under the current setting of CO 2 Mineralization tower outlet CO 2 concentration and discharge pH conditions, combined with CO 2 The mineralization mechanism calculates the required slurry volume and the automatic control unit controls the CO 2 The mineralization tower slurry pump, slurry discharge pump and other controlled components are regulated;

[0047] Step S4: When the boiler load increases and the gas flow rate at the mineralization tower inlet increases, the intelligent control system will timely perform frequency conversion adjustment on the slurry pump. When the predicted value exceeds the rated flow rate of the slurry pump, the standby slurry pump will be turned on to ensure that the CO at the outlet of the mineralization tower is 2 The concentration reaches the set standard;

[0048] Step S5: Collect CO using the online detection system 2 Mineralization tower inlet / outlet flow and CO 2 The concentration is calculated and the collected data is transmitted to the trained model in step S3, which is mainly used for proofreading and feedback correction of the above prediction and control values.

[0049] Technical features not described in the present invention can be achieved through or by adopting existing technologies and will not be elaborated here.

[0050] It will be easily understood by those skilled in the art that 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 should be included in the protection scope of the present invention.

Claims

1. A multi-parameter coupled intelligent operation and control method for wet CO2 mineralization of alkaline solid waste, characterized in that: The method comprises the following steps: Monitor the real-time data in the flue gas CO2 generating mechanism, and predict the flue gas CO2 concentration generated according to the real-time data, analyze the content of alkaline substances in the alkaline solid waste, and calculate the mass of the alkaline solid waste required in the mineralization reaction process using the predicted flue gas CO2 concentration and the content of alkaline substances in the alkaline solid waste; According to the required mass of the alkali solid waste, it is mixed with the solution to form an alkali solid waste slurry, which is then mixed with the flue gas to be treated to carry out a CO2 mineralization reaction. The changes in physical quantities during the mineralization reaction are monitored in real time, and the amount of the alkali solid waste slurry that reacts with the flue gas to be treated is fine-tuned in real time using the monitoring results, thereby achieving intelligent regulation of the CO2 mineralization process.

2. The intelligent operation and control method of multi-parameter coupled alkali solid waste wet CO2 mineralization according to claim 1 is characterized in that: The real-time data in the CO2 generation mechanism include fuel carbon content, fuel consumption, air excess coefficient, fuel utilization rate and fuel carbon oxidation rate.

3. A multi-parameter coupled alkali solid waste wet CO2 mineralization intelligent operation and control method as claimed in claim 1 or 2, characterized in that: The physical quantities in the CO2 mineralization reaction process include flue gas inlet / outlet flow, flue gas inlet / outlet temperature, flue gas inlet / outlet CO2 concentration, slurry density, slurry pH, slurry level, slurry temperature, alkali solid waste slurry pump flow and solution pump flow.

4. A multi-parameter coupled alkali solid waste wet CO2 mineralization intelligent operation and control method as claimed in claim 1 or 2, characterized in that: The alkaline substances in the alkaline solid waste include CaO, MgO, Fe2O3 and Na2O.

5. A system for intelligent operation and control of CO2 mineralization using the control method according to any one of claims 1 to 4, characterized in that: The system includes material analysis module, online monitoring module, intelligent control module and CO2 mineralization module, among which: The material analysis module is used to analyze the content of alkaline substances in alkali solid waste; The online monitoring module is used to monitor the real-time data in the flue gas CO2 generating mechanism and the changes in physical quantities during the mineralization process, and feed back the monitored real-time data and changes in physical quantities to the intelligent control module; The CO2 mineralization module is used to mix the alkali solid waste slurry with the flue gas to be treated to carry out a CO2 mineralization reaction; The intelligent control module is used to predict the concentration of flue gas CO2 generated according to the real-time data in the flue gas CO2 generating mechanism monitored in real time, calculate the mass of the alkaline solid waste required in the mineralization reaction process using the predicted flue gas CO2 concentration and the content of alkaline substances in the alkaline solid waste, and fine-tune the amount of slurry of the alkaline solid waste that reacts with the flue gas to be treated in real time according to the changes in physical quantities in the mineralization reaction process monitored in real time.

6. A multi-parameter coupled alkali solid waste wet CO2 mineralization intelligent operation and control system as claimed in claim 5, characterized in that: The intelligent control module includes a data acquisition unit, a machine learning unit and an automatic control unit, wherein the data acquisition unit is connected to the online monitoring module to receive the monitoring results of the online monitoring unit in real time. A plurality of prediction and control models are arranged in the machine learning unit, and the machine learning unit is connected to the data acquisition unit. The prediction model uses the data from the data acquisition unit to learn and update the output. The automatic control unit fine-tunes the amount of slurry of the alkali solid waste that reacts with the flue gas to be treated in real time according to the changes in physical quantities in the mineralization reaction process monitored in real time.

7. The multi-parameter coupled alkali solid waste wet CO2 mineralization intelligent operation and control system according to claim 6, characterized in that: A CO2 concentration prediction and control model is provided in the machine learning unit. The CO2 concentration prediction and control model is used to predict the generated flue gas CO2 concentration based on the monitored real-time data, and calculate the amount of alkali solid waste slurry required in the mineralization reaction process based on the predicted flue gas CO2 concentration and the content of alkaline substances in the alkali solid waste, so as to achieve regulation of the quality of the alkali solid waste.

8. A multi-parameter coupled alkali solid waste wet CO2 mineralization intelligent operation and control system as described in claim 5 or 6, characterized in that: The CO2 mineralization module is provided with a mineralization reaction tower for performing a CO2 mineralization reaction.

9. A multi-parameter coupled alkali solid waste wet CO2 mineralization intelligent operation and control system as claimed in claim 5 or 6, characterized in that: The machine learning unit is also provided with a pH control model, the input parameter of which is the real-time pH of the slurry in the mineralization reaction tower, and the output is the amount of slurry of alkaline solid waste required to maintain the preset pH.

10. A multi-parameter coupled alkali solid waste wet CO2 mineralization intelligent operation and control system, characterized in that: The system includes an actuator, which is used to execute a multi-parameter coupled alkali solid waste wet CO2 mineralization intelligent operation and control method according to any one of claims 1 to 4.

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