Intelligent control system and method for coal gasification ash co-production of cement

Abstract

The present application belongs to the technical field of industrial solid waste resource utilization and cement production, and particularly relates to an intelligent control system and method for coal gasification ash slag co-production of cement, which comprises an online detection module and an intelligent control module, wherein the online detection module comprises a carbon online detection unit, an ash component detection unit and a clinker mineral detection unit, and the intelligent control module comprises a pretreatment control unit, an optimized raw material proportioning unit and a calcination parameter control unit. The present application provides an intelligent control system and method for coal gasification ash slag co-production of cement.

Description

Technical Field

[0001] This invention belongs to the field of industrial solid waste resource utilization and cement production technology, and particularly relates to an intelligent control system and method for co-producing cement from coal gasification ash and slag. Background Technology

[0002] China is the world's largest producer and consumer of coal. In 2023, the country's raw coal output reached 4.71 billion tons, and coal consumption increased by 5.6% year-on-year, accounting for 55.3% of total energy consumption. China's energy structure, characterized by abundant coal, scarce oil, and limited natural gas, has long made coal my country's dominant energy source and a crucial pillar of its national economic development. Vigorously promoting the clean and efficient utilization of coal is an important task in driving my country's green and low-carbon energy transformation, and also an inevitable choice for accelerating ecological civilization construction.

[0003] Coal gasification technology is the leading technology for the clean and efficient utilization of coal. Currently, it is growing rapidly in my country at a compound annual growth rate of 25% to 30%, and has formed regional clusters of new coal chemical industries in coal-rich regions such as Hebei, Shaanxi, Inner Mongolia, Shanxi, Xinjiang, and Ningxia, consuming nearly 300 million tons of coal annually. With the rapid development of coal gasification and its upstream and downstream industries, the amount of coal gasification ash generated in the industrial process is also increasing rapidly, exceeding 60 million tons annually. Currently, my country's effective utilization rate of coal gasification ash is extremely low, with ash disposal in slag dumps or landfills still the main methods, resulting in a series of environmental problems such as land resource waste, surface dust, and heavy metal pollution of water and soil. On January 27, 2022, eight departments, including the Ministry of Industry and Information Technology, the National Development and Reform Commission, and the Ministry of Science and Technology, issued the "Implementation Plan on Accelerating the Comprehensive Utilization of Industrial Resources," proposing to "actively promote the efficient and comprehensive utilization of gasification ash, increase the development of large-scale utilization technologies and equipment, and construct a number of efficient utilization projects such as the production of cementitious materials from gasification ash." Therefore, accelerating the research on basic theories and key technologies for the safe and harmless disposal and large-scale, efficient resource utilization of coal gasification ash is of great significance for promoting my country's development of a circular economy and the construction of "zero-waste cities".

[0004] In recent years, research on using coal gasification ash as a substitute for cement raw materials has gradually increased, but its practical industrial application still faces significant technical bottlenecks: (1) Fluctuations in composition lead to unstable clinker quality The unburned carbon content is significantly affected by the gasifier type (such as fluidized bed or fixed bed) and operating parameters, and studies have shown that its fluctuation range can reach 5% to 15%. Excessive unburned carbon will interfere with the burnability of raw materials, leading to excessive free lime (f-CaO) in clinker (>2.5%), and reducing cement strength.

[0005] The fluctuation of the SiO2 / Al2O3 ratio in ash (1.5~4.0) affects the formation of silicate minerals (C3S, C2S), requiring frequent adjustments to the raw material ratio. However, existing processes rely on manual sampling and offline testing (such as XRF, chemical titration), with feedback cycles as long as 2~4 hours, resulting in a standard deviation of clinker compressive strength of over 3.5 MPa after 28 days.

[0006] (2) The sorting and batching control is extensive. Traditional separation technologies (such as air separation and magnetic separation) have low efficiency in separating unburned carbon (<70%) and are not integrated with cement production systems. Some scholars have proposed grinding and modifying coal gasification slag before performing airflow separation on the modified slag for efficient carbon-ash separation; however, this separation efficiency is significantly affected by the moisture content of the coal gasification ash and lacks an integrated online detection module. In the batching stage, fixed proportions (e.g., 10%~15%) are often used, lacking dynamic optimization. Studies have also shown that when the content of coal gasification ash exceeds 15%, the risk of ring formation in the kiln increases by 30% if calcination parameters are not adjusted in real time.

[0007] (3) Insufficient system coordination The pretreatment, raw meal preparation, and clinker calcination of coal gasification ash and slag operate in isolation, lacking a global optimization model. Existing cement production DCS systems can only achieve single-stage control and cannot achieve multi-objective coordination through data-driven approaches, such as maximizing the amount of coal gasification ash and slag and minimizing energy consumption.

[0008] Therefore, the main shortcomings of the existing technology can be summarized as follows: detection lag, offline analysis leads to control response delay, and it cannot adapt to the high-frequency fluctuation of coal gasification ash composition; the control strategy is simple, and traditional PID algorithm is difficult to handle multi-variable and nonlinear coupled calcination process; resource utilization is low, and due to the lack of collaborative control, the amount of coal gasification ash has been limited to below 5% for a long time, failing to realize its potential as a substitute raw material. Summary of the Invention

[0009] The purpose of this application is to provide an intelligent control system and method for co-producing cement from coal gasification ash and slag in order to solve the technical problems in the prior art.

[0010] The technical solution adopted in this application embodiment to solve the technical problems existing in the prior art is as follows: An intelligent control system for co-producing cement from coal gasification ash and slag, comprising an online detection module and an intelligent control module, wherein the online detection module includes an online carbon content detection unit, an ash content detection unit, and a clinker mineral detection unit, and the intelligent control module includes a pretreatment control unit, an optimized raw material ratio unit, and a calcination parameter control unit, wherein: The online carbon content detection unit uses near-infrared spectroscopy combined with a partial least squares regression prediction model to detect the unburned carbon content in the ash slag in real time and transmits the data to the pretreatment control unit. The pretreatment control unit controls the ash slag separation intensity by adjusting the air classifier speed and the sieve particle size to ensure that the unburned carbon content in the output ash slag is <3%. The ash composition detection unit uses X-ray fluorescence spectroscopy and laser-induced breakdown spectroscopy in combination. A weighted fusion algorithm is used to integrate the X-ray fluorescence spectroscopy and laser-induced breakdown spectroscopy data to obtain the content of key components in coal gasification ash. The key component content includes CaO, SiO2, Al2O3, Fe2O3, and carbon content. This key component content is then transmitted to the raw material ratio optimization unit. The objective function of the raw material ratio optimization unit includes: lime saturation coefficient LSF = 95±1, silicate ratio SM = 2.6±0.1, and ash content constraint of 0%~20%. The raw material ratio is dynamically calculated based on the multi-objective genetic algorithm MOGA. The clinker mineral detection unit uses online XRD and thermogravimetric analysis to output the C3S and C2S content and free lime data in the clinker to the calcination parameter control unit in real time. The calcination parameter control unit uses a fuzzy PID algorithm to stabilize the free calcium f-CaO in the clinker to ≤1.2% by adjusting the rotary kiln motor speed, the rotary kiln calcination temperature control and the cooler air volume.

[0011] The embodiments of this application may also employ the following technical solutions: In the aforementioned intelligent control system for co-producing cement from coal gasification ash, the near-infrared spectroscopy NIRS probe is further installed above the coal gasification ash conveyor belt. The quantitative relationship formula between near-infrared spectroscopy and unburned carbon content is as follows:

[0012] in: The predicted carbon content of the i-th sample; The model intercept, and the specific parameters are determined based on the coal quality used in the factory; , which corresponds to the regression coefficient of the j-th wavelength variable or latent variable; The absorbance value of the i-th sample at the j-th wavelength point after preprocessing; n: The number of wavelength variables selected, where n is an integer from 1 to n.

[0013] In the aforementioned intelligent control system for co-producing cement from coal gasification ash residue, further, when the online carbon detection unit detects that the unburned carbon content in the ash residue is ≥3%, the pretreatment control unit adjusts the air classifier speed and the sieve particle size, outputs the ash residue, and returns it to the online carbon detection unit for a second detection. When the online carbon detection unit detects that the unburned carbon content in the ash is less than 3%, the pretreatment control unit outputs qualified ash.

[0014] In the aforementioned intelligent control system for co-producing cement from coal gasification ash and slag, the air separation velocity is further negatively correlated with the carbon content. The sieved particle size is negatively correlated with the carbon content; The relationship between particle size and wind speed is as follows:

[0015] d 50 Characteristic particle size (μm) of air-separated products; ν: Wind-selected wind speed (m / s); k: Equipment structural constants related to impeller geometry, material density, etc.; n: the exponent, which is between 0.5 and 2.

[0016] In the aforementioned intelligent control system for the co-production of cement from coal gasification ash and slag, the excitation voltage of the X-ray fluorescence spectrum is 50kV, and the contents of CaO, SiO2, Al2O3, and Fe2O3 are measured. The pulsed laser energy for laser-induced breakdown spectroscopy is 80 mJ, and the spectral range is 200~900 nm. The content of light elements C, H, O, and N is measured. XRF and LIBS data are integrated using a weighted fusion algorithm.

[0017] In the aforementioned intelligent control system for co-producing cement from coal gasification ash and slag, the optimized raw material proportioning unit further includes the following steps: Step 1: Set the lime saturation coefficient LSF=95±1, silica content SM=2.6±0.1, and limit the coal gasification ash content to 0%~20%; LSF=100×CaO / (2.8SiO2+1.18Al2O3+0.65Fe2O3); SM = SiO2 / (Al2O3+Fe2O3); Step 2: Generate an initial mix design based on the ash composition, calculate fitness, select, cross over, and mutate to generate offspring mix designs, iterate until convergence, with a maximum of 40-50 iterations, and output the optimal mix design.

[0018] In the aforementioned intelligent control system for co-producing cement from coal gasification ash and slag, the online XRD uses a Cu-Kα ray source with λ=1.54 Å and a scanning rate of 4° / min to measure the contents of C3S, C2S, and f-CaO. Thermogravimetric analysis was performed by heating the clinker sample to 1000℃ under a nitrogen atmosphere and calculating the f-CaO content using the mass loss curve.

[0019] In the aforementioned intelligent control system for co-producing cement from coal gasification ash and slag, the calcination parameter control unit further sets up a fuzzy rule base based on the clinker f-CaO content, the calcination zone temperature deviation ΔT, and the kiln tail CO concentration obtained by a real-time CO analyzer. If ΔT > 10℃ and f-CaO > 1.5%, then increase the kiln speed and reduce the secondary air volume.

[0020] If the CO concentration is >500 ppm, increase the rotary kiln calcination temperature setpoint and decrease the heat source flow valve.

[0021] T set Rotary kiln calcination temperature setpoint, °C; α: Influence coefficient, 0.02-0.06; Q CO Measured CO concentration, ppm.

[0022] In the aforementioned intelligent control system for co-producing cement from coal gasification ash and slag, the intelligent control system further includes a data integration and visualization platform, which includes a digital twin model and a visualization platform.

[0023] An intelligent control method for co-producing cement from coal gasification ash and slag, wherein the intelligent control method for co-producing cement from coal gasification ash and slag adopts the intelligent control system for co-producing cement from coal gasification ash and slag described in any of the above-mentioned embodiments.

[0024] One or more technical solutions provided in the embodiments of this application have at least the following beneficial effects: 1. This invention is applicable to online detection, precise blending, and whole-process optimization control of coal gasification ash and slag. Through online detection, multi-objective optimization algorithms, and segmented-overall collaborative control strategies, this invention achieves precise ash and slag batching, stable clinker quality, and minimized energy consumption.

[0025] 2. This invention utilizes online detection and the multi-objective genetic algorithm MOGA to apply coal gasification ash in cement production, achieving a 20% admixture ratio and reducing the consumption of natural raw materials (clay, iron powder) by 15%~20%, significantly improving resource utilization. It can increase the annual amount of coal gasification ash processed from 80,000 tons to 120,000 tons, reducing raw material costs by 12%. With a separation efficiency of ≥90%, the separated carbonaceous material can be reused as fuel (calorific value ≥20 MJ / kg), and the ash residue can be directly used as a blending material, achieving full utilization of resources.

[0026] 3. The online detection module (NIRS-XRF-LIBS) of this invention shortens the feedback cycle of coal gasification ash composition from the traditional 2-4 hours to ≤30 seconds. Combined with the MOGA dynamic optimization ratio of the optimized raw material ratio unit, the standard deviation of clinker 28-day compressive strength is reduced from 3.5 MPa to 1.6-1.8 MPa. In laboratory simulations of ash SiO2 fluctuations (±5%), the LSF deviation is stable within ±0.3, and the clinker C3S content fluctuation is ≤2%. The fuzzy PID algorithm controls the calcination parameters in real time, stabilizing the f-CaO content to ≤1.2%, increasing the clinker stability pass rate from 85% to ≥98%, and enhancing the clinker quality stability.

[0027] 4. This invention reduces raw material costs by replacing coal gasification ash with other raw materials. Combined with reduced coal consumption, the overall cost per ton of clinker is reduced by 15% to 20%. The online detection module supports rapid replacement of detection probes and controllers, with an average fault repair time of less than 2 hours, and maintenance costs are reduced by 30% compared to traditional DCS systems.

[0028] 5. This invention employs a collaborative control system of calcination and global optimization. By iteratively adjusting calcination temperature, kiln speed, and cooling air volume, the coal consumption per ton of clinker is reduced from the traditional 110-115 kg to ≤105 kg, achieving an energy saving rate of 4%-5%. Simultaneously, considering the different proportions of coarse and fine slag (typically coarse slag accounts for about 70-80%, and fine slag accounts for 20-30%), the overall utilization rate of the entire industry is approximately 65%. Using this technology, the comprehensive utilization rate of coal gasification ash can be increased to approximately 80-85%. Furthermore, it reduces landfill space requirements and lowers CO2 emissions per ton of clinker, contributing to the cement industry's "dual carbon" goals and demonstrating excellent environmental and social benefits. Attached Figure Description

[0029] The technical solutions of the embodiments of this application will be further described in detail below with reference to the accompanying drawings. However, it should be understood that these drawings are designed for illustrative purposes only and are not intended to limit the scope of this application. In addition, unless otherwise specified, these drawings are only intended to conceptually illustrate the structural construction described herein and are not necessarily drawn to scale.

[0030] Figure 1 This is an overall architecture diagram of the intelligent control system for the co-production of cement from coal gasification ash and slag in the embodiment.

[0031] Figure 2 This is the control logic diagram of the preprocessing control unit in the embodiment.

[0032] Figure 3 This is a control flowchart of the raw material ratio optimization unit in the embodiment.

[0033] Figure 4 This is a schematic diagram of the control unit for calcination parameters in the embodiment. Detailed Implementation

[0034] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0035] Example 1 This embodiment includes: 1. Online detection module, including carbon content detection unit, ash content detection unit and clinker mineral detection unit, used to acquire data on unburned carbon content, ash chemical composition and clinker mineral composition of ash slag in real time.

[0036] The carbon content online detection unit uses near-infrared spectroscopy (NIRS) and a chemometric model to detect the unburned carbon content in ash with an accuracy of ±0.5%. The ash content detection unit uses X-ray fluorescence spectroscopy (XRF) coupled with laser-induced breakdown spectroscopy (LIBS) with a detection cycle of ≤30 seconds, covering major components such as CaO, SiO2, Al2O3, Fe2O3, and carbon content. The clinker mineral detection unit integrates online XRD and thermogravimetric analysis (TGA) to output free lime (f-CaO) data in clinker in real time.

[0037] 1.1 Online Carbon Detection Unit Near-infrared spectroscopy (NIRS) technology, combined with a partial least squares regression prediction model, is used to detect the unburned carbon content in ash slag in real time. The specific implementation method is as follows: 1) Install the NIRS probe above the coal gasification ash conveyor belt, with a scanning wavelength range of 900~1700 nm and a spectral resolution of ≤5 nm.

[0038] 2) Based on laboratory-calibrated samples (carbon content 0%~20%), a quantitative relationship model between NIRS spectra and carbon content was established, with a model accuracy of ±0.5%.

[0039] The quantitative relationship between NIRS spectra and carbon content is expressed by the following formula:

[0040] , the predicted carbon content of the i-th sample.

[0041] The model intercept, and the specific parameters are determined based on the coal quality used in the factory.

[0042] , which is the regression coefficient corresponding to the j-th wavelength variable (or latent variable).

[0043] The absorbance value of the i-th sample at the j-th wavelength point after preprocessing.

[0044] n: The number of wavelength variables selected, where n is an integer from 1 to n.

[0045] 3) Spectral data is collected every 10 seconds, and the carbon content is calculated in real time by the embedded processor and transmitted to the preprocessing control unit.

[0046] 1.2 Ash Component Detection Unit A rapid analysis of CaO, SiO2, Al2O3, Fe2O3, and carbon content in coal gasification ash was achieved by combining X-ray fluorescence spectroscopy (XRF) with laser-induced breakdown spectroscopy (LIBS). Specific methods include: 1) An XRF module is used with an excitation voltage of 50 kV and a detection limit of ≤0.1% for quantitative analysis of major elements (Si, Al, Fe, Ca).

[0047] 2) The LIBS module is used with a pulsed laser energy of 80 mJ and a spectral range of 200~900 nm for the auxiliary detection of trace elements (such as K and Na).

[0048] 3) Then, XRF and LIBS data are integrated using a weighted fusion algorithm, with a detection cycle of ≤30 seconds, to ensure the comprehensiveness and timeliness of component analysis. The data is then transmitted to the raw material ratio optimization unit.

[0049] X-ray radiofrequency (XRF) can determine the content of CaO, SiO2, Al2O3, and Fe2O3. It is a mature, highly accurate, and contactless technology, but it cannot measure light elements such as C, H, O, and N. Lithium-ion Biopsy (LIBS) can determine the content of CaO, SiO2, Al2O3, Fe2O3, and light elements such as C, H, O, and N, but its quantitative accuracy is relatively low. The two devices can also corroborate each other's results. Therefore, simply put, the method involves combining the CaO, SiO2, Al2O3, and Fe2O3 content obtained from XRF with the C, H, O, and N content obtained from LIBS. In other words, the test data from both XRF and LIBS devices are cross-referenced, and then partial data from each are combined.

[0050] 1.3 Clinker Mineral Detection Unit Integrating online X-ray diffraction (XRD) and thermogravimetric analysis (TGA), the mineral composition and free calcium (f-CaO) content of clinker are monitored in real time. Specific implementation methods include: 1) Set up online XRD using a Cu-Kα X-ray source (λ=1.54 Å) and a scanning rate of 4° / min to analyze the contents of C3S, C2S and f-CaO in real time.

[0051] 2) Set up the TGA module and heat the clinker sample to 1000℃ in a nitrogen atmosphere. Calculate the f-CaO content through the mass loss curve. The detection error is ≤0.1%.

[0052] 3) The monitoring results are transmitted to the calcination parameter control unit via industrial Ethernet for closed-loop control.

[0053] 2. Intelligent control module, including pretreatment control unit, raw material ratio optimization unit and calcination parameter control unit, which are used to adjust the ash and slag sorting intensity, optimize the raw material ratio and regulate the calcination process control parameters, respectively.

[0054] The pretreatment control unit controls the ash and slag separation intensity by adjusting the air separation speed and the sieve particle size, achieving a separation efficiency of ≥80% and an output ash and slag unburned carbon content of <3%.

[0055] The optimized raw material ratio unit dynamically calculates the raw material ratio based on a multi-objective genetic algorithm (MOGA). The objective functions include lime saturation coefficient (LSF=95±1), silica content (SM=2.6±0.1), and ash content constraint (0%~20%), and control the raw material delivery error to ≤±0.5%.

[0056] When calculating the objective function: LSF=100×CaO / (2.8SiO2+1.18Al2O3+0.65Fe2O3) SM = SiO2 / (Al2O3+Fe2O3) The calcination parameter control unit adopts a fuzzy PID algorithm to stabilize the free calcium (f-CaO) in the clinker to ≤1.2% by adjusting the rotary kiln speed (range 0~5.0rpm), the rotary kiln calcination temperature control, and the cooler air volume.

[0057] 2.1 Preprocessing Control Unit Based on the carbon content detection results of coal gasification ash obtained from the online carbon content detection unit, the system will dynamically adjust the air separation speed and screening particle size control parameters.

[0058] The wind speed is controlled by a variable frequency fan and can be adjusted within the range of 5~30m / s. It is negatively correlated with the carbon content, meaning that the higher the carbon content, the lower the wind speed setting.

[0059] The particle size is determined by adjusting the rotation speed of the separator to ensure that the screening efficiency is not less than 80% and that the unburned carbon content after separation is less than 3%.

[0060] The higher the wind speed in air separation, the larger the particle size and the lower the carbon content; the lower the wind speed, the smaller the particle size and the higher the carbon content.

[0061] The relationship between particle size and wind speed is as follows:

[0062] d 50Characteristic particle size (μm) of air-separated products; ν: Wind-selected wind speed (m / s); k: Equipment structural constant (related to impeller geometry, material density, etc., and requires actual calibration); n: the exponent, between 0.5 and 2.

[0063] To ensure the sorting effect, the sorted ash will be subjected to secondary near-infrared spectroscopy (NIRS) detection; if the detection result shows that the carbon content exceeds the standard, the system will automatically trigger the sorting parameter self-correction algorithm for optimization and adjustment.

[0064] 2.2 Optimization of raw material proportioning unit A multi-objective genetic algorithm (MOGA) is used to achieve automatic optimization and control of raw material ratio. The specific implementation steps are as follows: The first step is to set the target function.

[0065] 1) Set the lime saturation coefficient (LSF=95±1) to ensure the molar ratio of CaO to SiO2 and Al2O3 is balanced.

[0066] 2) Set the silica ratio (SM=2.6±0.1) and optimize the SiO2 / (Al2O3+Fe2O3) ratio to improve the activity of clinker minerals.

[0067] 3) Set a limit on the amount of coal gasification ash residue (0%~20%) to maximize resource utilization while avoiding process risks.

[0068] The second step is to optimize the process.

[0069] 1) Input the composition of ash and slag and the raw material database data.

[0070] 2) Generate the initial 100 sets of proportioning schemes.

[0071] 3) Perform fitness calculations on the mixing scheme.

[0072] 4) Through selection, crossover, mutation and fitness screening (fitness = LSF bias + SM bias + ash content weight), iterative optimization is performed until convergence, with a maximum number of iterations of 40-50.

[0073] 5) Output the optimal ratio and control the electronic belt scale, with a conveying error of ≤±0.5%.

[0074] 2.3 Calcination Parameter Control Unit A fuzzy PID algorithm is used to design and realize the automatic control of the calcination process. The specific implementation steps are as follows: The first is the input variables, including the clinker f-CaO content, the temperature deviation of the firing zone (ΔT), and the CO concentration at the kiln tail.

[0075] Second, set up a fuzzy rule base: If ΔT > 10℃ and f-CaO > 1.5%, then increase the kiln speed and reduce the secondary air volume.

[0076] If the CO concentration is greater than 500 ppm, increase the rotary kiln calcination temperature setpoint while simultaneously reducing the heat source flow valve. This means increasing the set temperature and decreasing the valve opening to ensure complete fuel combustion.

[0077]

[0078] T set Rotary kiln calcination temperature setpoint, °C; α: Influence coefficient, 0.02-0.06; Q CO Measured CO concentration, ppm.

[0079] Fourth, output control is set: the rotary kiln speed adjustment range is 0~5.0 rpm, with a resolution of 0.1 rpm; the rotary kiln calcination temperature control accuracy is ±10℃, using dynamic temperature adjustment via injection valves; the air volume of the cooler is adjusted based on feedback from the clinker outlet temperature to ensure the rapid cooling effect of the clinker. This ensures that the free calcium (f-CaO) in the clinker is stable at ≤1.2%.

[0080] 3. Data integration and visualization platform, used to integrate production data and build digital twin models to achieve full-process simulation and dynamic optimization.

[0081] The data integration and visualization platform integrates multi-source data from distributed control systems (DCS), manufacturing execution systems (MES), and online testing equipment through industrial bus protocols. It dynamically collects raw material ratios and clinker quality parameters, and uses a digital twin model to simulate production status in real time and issue warnings for the following anomalies: excessive ash content (>20% or <5%); clinker 28-day compressive strength deviation >2MPa.

[0082] 3.1 Constructing a digital twin model By integrating multi-source data from distributed control systems (DCS), manufacturing execution systems (MES), and online testing equipment through industrial bus protocols, raw material ratios, calcination temperature curves, real-time energy consumption indicators, and clinker quality parameters are dynamically acquired.

[0083] The multi-level coupled model architecture mainly includes: 1. Physical layer. Based on CFD simulation of gas-solid flow and heat transfer processes inside the kiln.

[0084] 2. Data Layer. Machine learning (LSTM network) is used to predict clinker intensity and coal consumption trends.

[0085] 3. Optimization layer. Particle swarm optimization (PSO) algorithm is used to generate global optimization suggestions (such as upper limit of coal gasification ash and slag batching, and threshold of rotary kiln calcination temperature).

[0086] 4. Visual interface. Real-time display of coal gasification ash carbon content, raw material ratio, kiln temperature distribution, and clinker quality thermogram.

[0087] 5. Abnormal Warning. A red alarm will be triggered when the content of coal gasification ash exceeds the limit (>20% or <5%) or the clinker strength deviation is >2MPa.

[0088] 3.2 Collaborative Control First, there is dynamic and coordinated control of pretreatment and proportioning. The composition data of coal gasification ash after sorting is transmitted in real time to the raw material optimization and proportioning unit via the Industrial Internet of Things. Based on the fluctuations of active components such as SiO2 and Al2O3 in the ash, the weight coefficient allocation strategy of the multi-objective genetic algorithm (MOGA) is dynamically corrected to achieve adaptive matching between the raw material proportion and the characteristics of the ash.

[0089] Second, there is the collaborative control of calcination and global optimization. Quality data such as the free calcium oxide (f-CaO) content and mineral phase composition of each batch of clinker are fed back to the control room, and the particle swarm optimization (PSO) algorithm is reconstructed to generate global optimization suggestions, such as the upper limit of coal gasification ash and slag batching and the threshold of rotary kiln calcination temperature, driving the process parameters to continuously approach the global optimum.

[0090] 4. Hardware configuration and process compatibility 4.1 Core Hardware Combined testing equipment: NIRS-XRF-LIBS integrated device, IP65 protection level, suitable for high dust environment in cement plant.

[0091] Industrial PC: Supports real-time computation of MOGA and fuzzy PID algorithms.

[0092] Monitoring unit: Online XRD and TGA are integrated into the rotary kiln outlet chute, with a high temperature resistant design (≤1400℃).

[0093] First, a combined component analyzer. This integrates three detection modules—near-infrared spectroscopy (NIRS), X-ray fluorescence (XRF), and laser-induced breakdown spectroscopy (LIBS)—into a single device. It boasts IP65 protection and explosion-proof certification, and is directly installed on the side of the conveyor belt at the sorting system's outlet, withstanding high dust levels (>50mg / m³) in cement plants. 3 High vibration environment.

[0094] Second, edge computing industrial PCs. Equipped with multi-core processors and FPGA acceleration cards, and with a built-in real-time operating system (RTOS), they meet the millisecond-level response requirements of multi-objective genetic algorithm (MOGA) iterative calculations and fuzzy PID controllers.

[0095] Thirdly, the high-temperature online monitoring unit. It adopts a combination probe of X-ray diffractometer (XRD) and thermogravimetric analyzer (TGA) protected by a water-cooled jacket, which is embedded in the top of the grate cooler outlet to continuously monitor clinker minerals. It has a temperature resistance of up to 800℃ and a temperature measurement error of ≤±1.5%.

[0096] 4.2 Process adaptability This system constructs a dynamic mapping model of rotary kiln calcination temperature and ash content, which is adapted to new dry-process cement kilns equipped with external decomposition furnaces and multi-stage preheaters. When the ash content of coal gasification varies in the range of 5% to 20%, the temperature compensation value of the kiln head firing zone (±30℃) is positively increased or decreased with the increase or decrease of the ash content, thereby controlling the calcination temperature, ensuring the crystallization rate of C3S minerals, and breaking through the process limitations of the traditional fixed temperature control mode on ash blending.

[0097] Real-time data communication between the raw material grinding system and the MOGA proportioning module is achieved based on the OPCUA protocol. The three-parameter decoupled control is driven by the ash activity index: dynamically adjusting the vertical mill grinding pressure, feed rate compensation and classifier speed, and coordinating the optimization of raw material fineness and homogenization coefficient to solve the problem of grinding efficiency reduction caused by ash admixture.

[0098] This embodiment supports the coordinated control of ash and slag blending and cement production, including: closed-loop feedback between the pretreatment stage and the raw meal preparation stage; and dynamic parameter iteration between the calcination stage and the global optimization stage.

[0099] The digital twin model generates optimization suggestions based on historical data and energy consumption models, including: adaptive and automatic adjustment of ash and slag sorting intensity; real-time correction and optimization of the proportion of corrective material in the raw material mix; and coordinated control thresholds for firing zone temperature and kiln speed.

[0100] The system's hardware configuration includes: an NIRS-XRF-LIBS combined detector installed on the ash conveyor belt; an industrial PC equipped with a MOGA and fuzzy PID algorithm controller; and a clinker mineral detection unit integrated at the grate cooler outlet.

[0101] The system is suitable for cement clinker production scenarios, especially for production processes involving coal gasification ash.

[0102] In summary, the module division and function definition in this embodiment, through multispectral online detection, segmented-overall collaborative control, and digital twin technology, achieve full-process optimization of coal gasification ash batching and cement production.

[0103] Example 2 During the production process, a cement plant experienced significant fluctuations in the properties of its coal gasification ash. The baseline value for unburned carbon content in the ash was 8%, but it suddenly surged to 12%. Simultaneously, the ratio of SiO2 to Al2O3 in the ash deviated from the normal range (2.5-3.0), abnormally fluctuating to 3.8. Under traditional production processes, based on this ash condition, the free calcium oxide (f-CaO) content in the clinker is expected to exceed 2.5%, which will severely impact clinker quality and adversely affect subsequent production stages.

[0104] In the online detection module, the NIRS probe above the belt conveyor (accuracy ±0.5%) captured the carbon content of 12.1% in real time; in terms of composition analysis, the XRF-LIBS combined equipment output SiO2 / Al2O3=3.82 in the ash slag within 25 seconds; clinker monitoring showed that the online XRD at the kiln mouth showed that the C3S content dropped to 58% (target ≥60%), and the TGA detection showed that f-CaO rose to 1.8%.

[0105] The pretreatment unit automatically increased the air classifier velocity to 28 m / s (from 20 m / s), and the carbon content was reduced to 2.9% after secondary NIRS verification. In terms of proportion optimization, MOGA aimed for LSF=94.8, SM=2.62, and ash content of 18%, dynamically increasing limestone by 3% and reducing clay by 1.5%, with a conveying error of ±0.3%. During calcination control, the fuzzy PID increased the kiln speed to 4.2 rpm (from 3.5 rpm) based on the f-CaO exceeding signal, adjusted the calcination zone temperature to 1480℃, and increased the cooling air volume by 15%.

[0106] The entire control response took less than 5 minutes and achieved significant results. After adjustment, the f-CaO content in the clinker stabilized at 1.1%, reaching the target value of ≤1.2%; the standard deviation of the 28-day compressive strength was 1.7 MPa, a 51% reduction compared to the 3.5 MPa of the traditional process, significantly improving strength stability; while maintaining the ash content at 18%, the consumption of natural raw materials was reduced by 18%, and the coal consumption per ton of clinker was reduced to 103 kg, achieving cost reduction and efficiency improvement while ensuring quality.

[0107] Example 3 During the rainy season, the moisture content of ash and slag increases by 20%. It is necessary to work together to maximize the amount of ash and slag added to the coal gasification batch and minimize the coal consumption per ton of clinker (≤105kg). Under the traditional process, an addition of more than 15% will cause kiln ring formation.

[0108] In the data integration platform, the digital twin model retrieves historical data from the DCS and identifies a baseline coal consumption of 108 kg under a humidity of 12% working conditions; during online monitoring, LIBS detects in real time that the moisture content of ash and slag rises to 14.5%, and XRF feedback shows that CaO fluctuates by ±0.8%.

[0109] In the pretreatment self-correction stage, the humidity compensation algorithm is triggered, and the air separation velocity is reduced to 22m / s (to prevent ash loss), while the separation efficiency remains at 92%. In terms of collaborative optimization, MOGA uses coal consumption as a weighting factor to output a ratio scheme with LSF=95.2, SM=2.58, and ash content of 19.5%. The fuzzy PID is based on the CO concentration at the kiln tail of 480ppm, and the firing zone temperature is finely adjusted to 1465℃ (±10℃) and the kiln speed is reduced to 3.8rpm. In the global iteration, the digital twin model (PSO algorithm) monitors the coal consumption per ton of coal at 106kg, dynamically tightens the temperature threshold by ±5℃ and adjusts the content back to 18.8%.

[0110] Despite the unfavorable conditions of increased moisture content in coal gasification ash during the rainy season, the ash content in the feedstock successfully reached 18.8%, approaching the design limit of 20%. This breakthrough significantly increased the annual coal gasification ash disposal capacity to 140,000 tons, effectively alleviating the pressure on solid waste treatment. Simultaneously, coal consumption per ton of clinker decreased to 104.5 kg, a 3.2% reduction from the previous benchmark of 108 kg. Based on annual production capacity, this translates to an annual reduction of 12,000 tons of carbon dioxide emissions, demonstrating both economic and environmental benefits. Regarding clinker quality, the C3S content remained stable at 62%, free calcium oxide (f-CaO) was controlled at 1.0%, and the standard deviation of 28-day compressive strength was only 1.6 MPa. This not only meets the national standard requirement of ≤2.5 MPa but also reflects the high stability of the production process and the reliability of product quality.

[0111] In summary, this invention provides an intelligent control system and method for the co-production of cement from coal gasification ash and slag.

[0112] The above embodiments have provided a detailed description of the present invention, but the content described is only a preferred embodiment of the present invention and should not be considered as limiting the scope of the present invention. All equivalent variations and improvements made within the scope of the present invention should still fall within the patent coverage of the present invention.

Claims

1. An intelligent control system for the co-production of cement from coal gasification ash and slag, characterized in that: The intelligent control system for the co-production of cement from coal gasification ash includes an online detection module and an intelligent control module. The online detection module includes an online carbon content detection unit, an ash content detection unit, and a clinker mineral detection unit. The intelligent control module includes a pretreatment control unit, an optimized raw material ratio unit, and a calcination parameter control unit. The online carbon content detection unit uses near-infrared spectroscopy combined with a partial least squares regression prediction model to detect the unburned carbon content in the ash slag in real time and transmits the data to the pretreatment control unit. The pretreatment control unit controls the ash slag separation intensity by adjusting the air classifier speed and the sieve particle size to ensure that the unburned carbon content in the output ash slag is <3%. The ash composition detection unit uses X-ray fluorescence spectroscopy and laser-induced breakdown spectroscopy in combination. A weighted fusion algorithm is used to integrate the X-ray fluorescence spectroscopy and laser-induced breakdown spectroscopy data to obtain the content of key components in coal gasification ash. The key component content includes CaO, SiO2, Al2O3, Fe2O3, and carbon content. This key component content is then transmitted to the raw material ratio optimization unit. The objective function of the raw material ratio optimization unit includes: lime saturation coefficient LSF = 95±1, silicate ratio SM = 2.6±0.1, and ash content constraint of 0%~20%. The raw material ratio is dynamically calculated based on the multi-objective genetic algorithm MOGA. The clinker mineral detection unit uses online XRD and thermogravimetric analysis to output the C3S and C2S content and free lime data in the clinker to the calcination parameter control unit in real time. The calcination parameter control unit uses a fuzzy PID algorithm to stabilize the free calcium f-CaO in the clinker to ≤1.2% by adjusting the rotary kiln motor speed, the rotary kiln calcination temperature control and the cooler air volume.

2. The intelligent control system for co-producing cement from coal gasification ash and slag according to claim 1, characterized in that: The near-infrared spectroscopy NIRS probe is installed above the coal gasification ash conveyor belt. The quantitative relationship between near-infrared spectroscopy and unburned carbon content is expressed by the following formula: in: The predicted carbon content of the i-th sample; The model intercept, and the specific parameters are determined based on the quality of the coal used in the factory; , which corresponds to the regression coefficient of the j-th wavelength variable or latent variable; The absorbance value of the i-th sample at the j-th wavelength point after preprocessing; n: The number of wavelength variables selected, where n is an integer from 1 to n.

3. The intelligent control system for co-producing cement from coal gasification ash and slag according to claim 1, characterized in that: When the online carbon detection unit detects that the unburned carbon content in the ash is ≥3%, the pretreatment control unit adjusts the air classifier speed and the sieve particle size, outputs the ash, and returns it to the online carbon detection unit for a second detection. When the online carbon detection unit detects that the unburned carbon content in the ash is less than 3%, the pretreatment control unit outputs qualified ash.

4. The intelligent control system for co-producing cement from coal gasification ash and slag according to claim 3, characterized in that: The air separation velocity is negatively correlated with the carbon content; The sieved particle size is negatively correlated with the carbon content; The relationship between particle size and wind speed is as follows: d 50 Characteristic particle size (μm) of air-separated products; ν: Wind-selected wind speed (m / s); k: Equipment structural constants related to impeller geometry, material density, etc.; n: the exponent, which is between 0.5 and 2.

5. The intelligent control system for co-producing cement from coal gasification ash and slag according to claim 1, characterized in that: The X-ray fluorescence spectrum was excited at a voltage of 50 kV, and the contents of CaO, SiO2, Al2O3, and Fe2O3 were measured. The pulsed laser energy for laser-induced breakdown spectroscopy is 80 mJ, and the spectral range is 200~900 nm. The content of light elements C, H, O, and N is measured. XRF and LIBS data are integrated using a weighted fusion algorithm.

6. The intelligent control system for co-producing cement from coal gasification ash and slag according to claim 1, characterized in that: The optimized raw material ratio unit includes the following steps: Step 1: Set the lime saturation coefficient LSF=95±1, silica content SM=2.6±0.1, and limit the coal gasification ash content to 0%~20%; LSF=100×CaO / (2.8SiO2+1.18Al2O3+0.65Fe2O3); SM = SiO2 / (Al2O3+Fe2O3); Step 2: Generate an initial mix design based on the ash composition, calculate fitness, select, cross over, and mutate to generate offspring mix designs, iterate until convergence, with a maximum of 40-50 iterations, and output the optimal mix design.

7. The intelligent control system for co-producing cement from coal gasification ash and slag according to claim 1, characterized in that: The online XRD used a Cu-Kα ray source, λ=1.54 Å, with a scan rate of 4° / min, to measure the contents of C3S, C2S, and f-CaO. Thermogravimetric analysis was performed by heating the clinker sample to 1000℃ under a nitrogen atmosphere and calculating the f-CaO content using the mass loss curve.

8. The intelligent control system for co-producing cement from coal gasification ash and slag according to claim 1, characterized in that: The calcination parameter control unit sets up a fuzzy rule base based on the clinker f-CaO content, the calcination zone temperature deviation ΔT, and the kiln tail CO concentration obtained by a real-time CO analyzer. If ΔT > 10℃ and f-CaO > 1.5%, then increase the kiln speed and reduce the secondary air volume. If the CO concentration is >500 ppm, increase the rotary kiln calcination temperature setpoint and decrease the heat source flow valve. T set Rotary kiln calcination temperature setpoint, °C; α: Influence coefficient, 0.02-0.06; Q CO Measured CO concentration, ppm.

9. The intelligent control system for co-producing cement from coal gasification ash and slag according to claim 1, characterized in that: The intelligent control system for the co-production of cement from coal gasification ash and slag also includes a data integration and visualization platform, which comprises a digital twin model and a visualization platform.

10. An intelligent control method for co-producing cement from coal gasification ash and slag, characterized in that: The intelligent control method for co-producing cement from coal gasification ash and slag adopts the intelligent control system for co-producing cement from coal gasification ash and slag as described in any one of claims 1-9.

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