A method and system for optimizing the proportion of industrial waste materials mixed in a cement kiln

Abstract

This invention discloses a method and system for optimizing the proportion of industrial waste materials co-fired in cement kilns, relating to the field of raw material optimization technology. The method includes: acquiring multi-dimensional test data of industrial waste, including calorific value, heavy metal content, moisture content, and ash content; classifying industrial waste based on this multi-dimensional test data; labeling each category as either permitted or prohibited from co-firing; analyzing the impact trends of permitted co-firing categories on cement kiln energy consumption and pollutant emissions based on multi-dimensional test data; determining a recommended co-firing ratio range with the lowest total energy consumption and compliance with heavy metal emission standards; and implementing the recommended ratio by sending it to the production line control system for execution, while collecting real-time key process parameters for feedback optimization of the recommended co-firing ratio range. This invention achieves the calculation and dynamic optimization of the co-firing ratio, improving the intelligent level of cement kiln waste disposal.

Description

Technical Field

[0001] This invention belongs to the field of raw material optimization technology, and relates to a method and system for optimizing the proportion of industrial waste materials co-fired in cement kilns. Background Technology

[0002] With the rapid development of the industrial sector, the amount of industrial waste generated is increasing daily. Improper handling of this waste not only occupies significant land resources but also poses environmental risks due to heavy metal leakage and the release of harmful gases. Against this backdrop, cement kilns, with their advantages of high-temperature calcination, stable operation, and the ability to co-degrade pollutants, have become a viable approach for treating industrial waste. By analyzing the characteristics of the waste and optimizing its co-firing ratio in cement kilns, fossil fuel consumption can be effectively reduced.

[0003] Although existing technologies have been applied in the co-processing of industrial waste in cement kilns, they still have the following shortcomings: First, existing technologies for assessing industrial waste focus excessively on the single indicator of calorific value to predict coal-saving effects, failing to comprehensively consider the complex impact of other key characteristics on the overall energy consumption of cement kilns. This results in actual operating energy efficiency decreasing rather than increasing, and threatens the stability of kiln conditions. On the other hand, environmental risk assessments are often limited to heavy metal content, failing to cover other harmful substances that may be generated at high temperatures and cause excessive emissions. This creates loopholes in emission supervision, leading to a lack of comprehensiveness in co-firing decisions and difficulty in balancing energy consumption and emission targets.

[0004] Secondly, existing co-firing process control is mostly limited to material addition and basic parameter monitoring. In actual operation, fluctuations in kiln combustion, instantaneous exceedances of flue gas standards, or batch differences in material characteristics can all cause short-term anomalies in energy efficiency and environmental indicators. Current technologies, which mainly rely on post-event alarm control, have significant limitations and cannot achieve dynamic adjustment and closed-loop optimization of the co-firing ratio based on real-time process parameters. Summary of the Invention

[0005] In view of this, in order to solve the problems mentioned in the background art, the present invention provides a method and system for optimizing the proportion of industrial waste materials co-fired in cement kilns.

[0006] The objective of this invention can be achieved through the following technical solutions: In the first aspect, this invention provides a method for optimizing the proportion of industrial waste materials co-fired in cement kilns, comprising: acquiring multi-dimensional test data of the batch of industrial waste to be treated, including calorific value, heavy metal content, moisture content and ash content ratio, and preprocessing the multi-dimensional test data to generate a batch material data archive.

[0007] Industrial waste is categorized based on batch material data archives, and each category is labeled with a decision label indicating whether co-combustion is permitted or not.

[0008] For the categories of materials that are permitted to be blended, the impact trend on cement kiln energy consumption and pollutant emissions is analyzed by combining multi-dimensional test data. The recommended blending ratio range is calculated with the dual constraints of the lowest total energy consumption and compliance with heavy metal emissions.

[0009] The recommended blending ratio range is sent to the production line control system to trigger the blending action, and a prohibition on blending action is triggered for material categories that are not allowed to be blended.

[0010] Real-time key process parameters of the co-firing process are collected, and the co-firing ratio range is calculated based on the feedback of real-time key process parameters.

[0011] Secondly, the present invention provides a system for optimizing the proportion of industrial waste materials co-fired in cement kilns, comprising the following modules: a data preprocessing module, used to acquire multi-dimensional test data of industrial waste and generate batch material data archives.

[0012] The material classification decision module is used to classify materials based on the files and label the blending decision based on the safety threshold of heavy metal content and the blending benefit index.

[0013] The Influence Trend Analysis module is used to analyze the targeted impact trends of multi-dimensional data on cement kiln energy consumption and pollutant emissions for the categories of materials that are permitted to be co-fired.

[0014] The proportion calculation and issuance module is used to calculate the recommended blending ratio range with constraints of minimum total energy consumption and compliance with heavy metal emission standards, and then issue the decision to the production line control system for execution.

[0015] The real-time feedback optimization module is used to collect data on heavy metal concentration in kiln tail flue gas and temperature in the firing zone. Through parameter trend analysis and correlation analysis with the blending ratio, it dynamically optimizes and recommends the blending ratio range.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) The present invention obtains multi-dimensional test data of industrial waste, including calorific value, heavy metal content, moisture content and ash ratio, avoiding the problem of increased energy consumption and environmental risk omission caused by ignoring the influence of other key characteristics when only focusing on calorific value and heavy metal content, ensuring the accuracy of energy consumption control, and ensuring from the source that the co-firing decision can take into account both energy consumption and environmental protection goals, providing accurate material basis for subsequent co-firing actions.

[0017] (2) This invention analyzes the impact trends of the material categories permitted for co-firing on cement kiln energy consumption and pollutant emissions by combining multi-dimensional test data. With the lowest total energy consumption and compliance with heavy metal emission standards as dual constraints, a recommended co-firing ratio range is calculated. This avoids problems such as excessive heavy metals in kiln tail flue gas and temperature fluctuations in the burning zone affecting clinker quality due to excessively high co-firing ratios without quantitative guidance, or insufficient utilization of waste calorific value and high fossil fuel consumption due to excessively low co-firing ratios. This provides a quantitative basis for subsequent optimization feedback.

[0018] (3) This invention constructs a closed-loop control system from decision-making to execution by issuing the recommended ratio to the production line control system and collecting real-time key process parameters for feedback optimization of the recommended blending ratio range. It can dynamically adjust the blending ratio range based on the changing trend of real-time process parameters, thereby realizing immediate response and proactive intervention to combustion fluctuations and material batch differences, effectively overcoming the control lag of relying on post-event alarms. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a diagram illustrating the implementation steps of the method of the present invention.

[0021] Figure 2 This is a flowchart illustrating the multi-dimensional material data classification process of this invention.

[0022] Figure 3 This is a schematic diagram showing the connections of the various modules in the system of the present invention. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] Please see Figure 1 As shown, the first aspect of the present invention provides a method for optimizing the proportion of industrial waste materials co-fired in cement kilns, comprising: S1, acquiring multi-dimensional test data of the batch of industrial waste to be processed, including calorific value, heavy metal content, moisture content and ash content, and preprocessing the multi-dimensional test data to generate a batch material data archive.

[0025] A specific embodiment of obtaining multi-dimensional test data of the batch of industrial waste to be treated, including calorific value, heavy metal content, moisture content and ash ratio, is as follows: First, sampling points are set for the batch of industrial waste to be treated, and then sample preparation is carried out.

[0026] Among them, the calorific value is determined by weighing an appropriate amount of sample and placing it in an oxygen bomb calorimeter. The heat released is measured by burning the sample in an oxygen-filled environment. The experiment is repeated 2-3 times and the average value is taken to ensure that the error is less than 5%.

[0027] Heavy metal content determination: The sample is first digested and pretreated to remove interfering impurities. Then, atomic absorption spectrometry is used for quantitative detection to obtain heavy metal content data.

[0028] Moisture content determination: Weigh a certain amount of the original sample and record the initial weight. Place it in an oven at a specified temperature to dry to constant weight. Weigh the sample again after drying and calculate the moisture content of the sample by the difference between the weights before and after drying.

[0029] Ash content determination: Weigh an appropriate amount of dried sample and record the initial weight. Place the sample in a muffle furnace and ignite it at the specified temperature for 2 hours. After the sample cools to room temperature, weigh the residue. Calculate the ash content by the weight ratio of the residue to the dried sample.

[0030] In summary, the calorific value, heavy metal content, moisture content, and ash ratio of industrial waste directly affect the energy consumption and pollutant emissions of cement kilns. For example, a higher calorific value allows for the substitution of more fossil fuels, directly reducing fuel consumption and total energy consumption in cement kilns. Conversely, a lower calorific value necessitates additional fuel to maintain the calcination temperature, leading to increased energy consumption. When the heavy metal content exceeds the safety threshold, some heavy metals will volatilize and enter the kiln tail flue gas at high temperatures, resulting in excessive heavy metal emissions and environmental risks. High-moisture waste requires additional heat to evaporate moisture after entering the kiln, consuming effective heat and increasing total energy consumption. Low-moisture waste does not require additional heat, which helps maintain stable kiln temperature, reducing energy consumption and ensuring clinker calcination quality. An excessively high ash ratio alters the proportions of key components such as silicon, aluminum, and iron in cement raw materials, requiring additional corrective raw materials for adjustment, increasing auxiliary material consumption and energy consumption. Obtaining multi-dimensional test data is crucial for comprehensively assessing the potential impact of waste and ensuring that subsequent classification, decision-making, and proportion calculations are based on objective data, not empirical estimates.

[0031] S2. Classify industrial waste based on batch material data archives, and label each category with a decision label indicating whether co-firing is permitted or not.

[0032] refer to Figure 2As shown, a specific implementation process for classifying batch material data files into industrial waste categories is as follows: corresponding to the four dimensions of calorific value, heavy metal content, moisture content, and ash content, multi-dimensional test data are extracted from the batch material data files, and four sets of dimensional data sequences are formed based on the multi-dimensional test data.

[0033] The data sequences of each dimension are divided into several continuous intervals, and the number of material batches in each continuous interval under different dimensions is counted.

[0034] Calculate the proportion of the number of material batches to the total number of batches in the corresponding dimension, and use the ratio of this proportion to the interval width as the distribution density value of the interval, generating distribution density data for all intervals under each dimension.

[0035] It should be noted that the above distribution density data essentially represents the concentration of material batches within a unit data width. The higher the density value, the more material is present within that range, and the more concentrated its characteristics.

[0036] Traverse all intervals except the first and last intervals. If the distribution density value of a certain interval is greater than the distribution density values ​​of the two adjacent intervals, then mark the interval as a local maximum interval. Merge the local maximum interval and its adjacent continuous intervals to form the core dense intervals of each dimension.

[0037] Batches of materials falling within the same core dense area are grouped into an initial category group.

[0038] For material batches that overlap in multiple core dense regions, these overlapping material batches will be grouped into the same material category.

[0039] The above steps ensure that the members of the final material category are similar in most key characteristics, thus ensuring the homogeneity of materials within the category.

[0040] Considering the differences in origin and production processes among different batches of materials, variations in calorific value, heavy metal content, moisture content, and ash content may occur. For example, one batch of material may have a low calorific value, but its heavy metal content, moisture content, and ash content all fall within their respective core concentration ranges. Another batch may have a medium calorific value, but its heavy metal content, moisture content, and ash content also fall within the same core concentration range. These two batches of material will be classified into the same category because they have low heavy metal content, low moisture content, and moderate ash content. Therefore, this classification method ensures the homogeneity of materials within a category in most key characteristics.

[0041] As cement kilns are large-scale industrial facilities that operate continuously, their waste disposal operations must be based on clear and specific instructions, and the disposal of waste must ensure safety and environmental protection, with pollutants never exceeding the standards. On this basis, efforts should be made to save energy and reduce costs.

[0042] Therefore, the specific content of the decision label for each category indicating whether blending is permitted or not is as follows: For each material category, its heavy metal content is extracted. If the heavy metal content of the material in this category exceeds the safety threshold, the decision label for this category is directly marked as not permitted to be blended.

[0043] Because some heavy metals will volatilize into the flue gas at high temperatures in cement kilns, if the concentration of heavy metals in the flue gas exceeds the standard, it will directly lead to excessive emissions of air pollutants. At the same time, heavy metals remaining in cement products will be slowly released into the environment as the products are used, causing long-term pollution to soil and groundwater and endangering public health.

[0044] It should be noted that the safety threshold is first based on the heavy metal emission limit of kiln tail flue gas stipulated in national regulations as the benchmark value. Then, the actual emission concentration of flue gas under different heavy metal contents is tested by experiments to establish the correspondence between heavy metal content and actual emission concentration of flue gas. Finally, the heavy metal content corresponding to the actual emission concentration in the correspondence is equal to the benchmark value as the safety threshold, ensuring that co-firing exceeding this threshold will directly lead to excessive heavy metal emissions.

[0045] Standardize the multi-dimensional test data for material categories whose heavy metal content does not exceed the safety threshold.

[0046] The standardized multi-dimensional test data were weighted and fused to obtain the blending benefit index.

[0047] The formula for calculating the above-mentioned blending yield index is as follows: .

[0048] Where I represents the blending yield index, This indicates that the calorific value replaces the weight. Indicates environmental and safety weight. Indicates the weight of water energy consumption. Indicates the weight of ash separation process, satisfying N(x) represents the standardization function that maps the data to the interval (0, 1), C represents the calorific value, H represents the heavy metal content, M represents the moisture content, and A represents the ash content.

[0049] The above , , , The settings can be configured based on industry experience; alternatively, they can be obtained through a limited number of experimental data: First, collect multi-dimensional test data of historical batches of industrial waste, including calorific value, heavy metal content, moisture content, ash ratio, and corresponding actual co-firing effect data, including coal savings per unit co-firing ratio, changes in total energy consumption, heavy metal emission concentration, etc. Then, calculate the correlation coefficient between each dimension of data and the co-firing benefit index, use multiple regression analysis to determine the contribution of each dimension to the co-firing benefit index, and finally, after normalization, convert the contribution into a preset weight coefficient with a sum of 1.

[0050] The above formula is further explained as follows: This represents the thermal energy revenue item, which assesses the direct value of waste as fuel. The higher the calorific value of the waste, the more coal it can replace, and the more fuel costs the company can save.

[0051] The environmental safety item represents a further refined assessment of the material's environmental friendliness within safety thresholds. It is presented in reciprocal form. The lower the heavy metal content, the higher the score. Even if the heavy metal content is within the safe threshold, a lower content provides a larger buffer for process control and reduces the risk of exceeding the standard.

[0052] This represents the energy cost item, assessing the additional energy costs required to treat waste, using... The reciprocal form ensures that the lower the moisture content, the higher the score. High-moisture materials consume a lot of heat during evaporation in the kiln, increasing system energy consumption and reducing overall thermal efficiency.

[0053] This section represents the process impact items, assessing the influence of ash content on cement production processes and ingredient proportions. The reciprocal form ensures that the lower the ash content, the higher the score. High ash content will change the raw meal ratio, which may affect cement quality and kiln operation stability, and increase the cost of adjusting auxiliary materials.

[0054] The blending benefit index of all material categories is sorted in descending order. Material categories whose ranking is within the quartile of the number of items in the ranking are marked as permitted for blending, while those whose ranking is outside the quartile are marked as not permitted for blending.

[0055] The blending benefit index is obtained through weighted fusion. The higher the value, the more significant the positive benefits of blending this type of material in reducing energy consumption and improving operational economy. By dividing by quartiles, the material categories with the highest benefit performance can be screened out from all candidate categories. These materials can maximize the value of blending. Categories ranked outside the quartiles have lower blending benefit indices, which means that the positive effects of blending on energy consumption optimization are weaker, and may even have insufficient benefits to offset the potential benefits of blending. Therefore, this is used as the boundary to divide the categories that are permitted and not permitted to be blended.

[0056] S3. For the categories of materials that are permitted to be blended, analyze the impact trend on cement kiln energy consumption and pollutant emissions by combining multi-dimensional test data. With the lowest total energy consumption and compliance with heavy metal emissions as dual constraints, calculate the recommended blending ratio range.

[0057] The specific details of the impact trend on cement kiln energy consumption and pollutant emissions analyzed by combining multi-dimensional test data are as follows: For material categories marked as permitted for co-firing, correlation data sequences were constructed according to the batch sequence of the material, namely, calorific value and theoretical coal saving, moisture content and theoretical evaporation energy consumption, ash ratio and theoretical auxiliary material addition, and total heavy metal content and theoretical flue gas emission concentration.

[0058] Curve fitting is performed on the associated data sequence to generate continuous analysis curves showing the correspondence between the characteristics of each material and energy consumption and emission parameters.

[0059] Calculate the rate of change of each point on the continuous analysis curve, and determine the continuous segment with a positive rate of change as a monotonically increasing interval, and the continuous segment with a negative rate of change as a monotonically decreasing interval.

[0060] Based on the monotonically increasing and monotonically decreasing intervals, the directional impact trends of each dimension of test data on the total energy consumption of cement kilns and on the directional impact trends on pollutant emission concentrations are determined.

[0061] Specifically, based on the monotonically increasing and monotonically decreasing intervals, the above-mentioned determination of the directional impact trends of each dimension of test data on the total energy consumption of cement kilns and on the directional impact trends on pollutant emission concentrations is as follows: When the curves of calorific value and theoretical coal saving, moisture content and theoretical evaporation energy consumption, and ash ratio and theoretical auxiliary material addition show monotonically increasing characteristics, it is determined that calorific value has a positive impact on reducing total energy consumption, while moisture content and ash ratio have a negative impact on increasing total energy consumption.

[0062] When the curve of total heavy metal content and theoretical amount of additives shows a monotonically increasing characteristic, it is determined that the heavy metal content constitutes a key constraint on the compliance of pollutant emissions.

[0063] For the energy consumption-related curves, the calorific value and theoretical coal saving amount increase monotonically. The higher the calorific value, the more coal can be replaced, thus reducing coal consumption. Therefore, the calorific value has a positive impact on reducing total energy consumption. The moisture content and theoretical evaporation energy consumption, as well as the ash ratio and theoretical auxiliary material addition amount, all increase monotonically. Higher moisture content requires more energy for evaporation, and higher ash content requires more auxiliary materials to balance the composition, both of which increase energy consumption. Therefore, both have a negative impact on total energy consumption.

[0064] For pollutant emission-related curves, the total heavy metal content increases monotonically with the theoretical amount of additives. The higher the heavy metal content, the higher the risk of residue or emission. Moreover, there is an upper limit to the amount of additives that can be added, which cannot infinitely offset the problem of exceeding the standard and is easy to exceed the emission standard. Therefore, the heavy metal content constitutes a key constraint on the compliance of pollutant emissions.

[0065] The specific process for obtaining the above-mentioned recommended blending ratio range is as follows: By using the blending experiment data of historical batches of materials, a pollutant emission result database with heavy metal content as the key variable is established. The maximum blending ratio of various materials allowed to ensure that pollutant emissions meet the standards is directly correlated from the pollutant emission result database. This maximum blending ratio is determined as the upper limit of the blending ratio of the current material category.

[0066] The maximum co-firing ratio, as shown above, is the permissible limit for ensuring pollutant emissions meet standards, derived from historical experimental databases. This indicates that exceeding this ratio will drastically increase the probability of heavy metal emissions exceeding standards. In engineering design, for risks that may cause serious consequences, such as pollutant exceedances, directly using this experimentally determined limit as the upper limit for production control sets the strictest safety margin regarding heavy metal emissions.

[0067] Under the premise of satisfying the upper limit of the blending ratio, based on the influence trend of calorific value, moisture content and ash ratio on total energy consumption, a total energy consumption function in the form of a quadratic function with the blending ratio as the variable is constructed. The point where the first derivative of the total energy consumption function with respect to the blending ratio is zero is solved to obtain the optimal blending ratio point that minimizes the total energy consumption.

[0068] Specifically, the total energy consumption function is expressed as: .

[0069] Where E(P) is the total energy consumption, P is the blending ratio, and the coefficients a, b, and c are obtained by fitting the trend data of the influence of calorific value, moisture content, and ash content on energy consumption.

[0070] By finding the minimum point of the total energy consumption function, the optimal co-firing ratio point that minimizes the total energy consumption is obtained. The minimum point is calculated by differentiation: .

[0071] Based on the optimized blending ratio point, and combined with the operating control tolerance of the cement kiln system, an operating control tolerance is symmetrically extended to both sides to form a recommended blending ratio range with the midpoint as the core.

[0072] An example of the recommended blending ratio range mentioned above is as follows: with 12% as the core midpoint, the control tolerance is symmetrically extended to both sides by 2%, and the final recommended blending ratio range is [10%, 14%].

[0073] It should be added that the operating control tolerance of the cement kiln system is determined according to relevant national standards.

[0074] S4. Send the recommended blending ratio range to the production line control system to trigger the blending action, and trigger the prohibition of blending action for material categories that are not allowed to be blended.

[0075] Since the recommended blending ratio range is for material categories that are permitted to be blended, and material categories that are not permitted to be blended are either prohibited due to heavy metal content exceeding safety thresholds or because their blending benefit index ranking is outside the quartile of the ranking, issuing the recommended blending ratio range to the production line control system and triggering blending and prohibition actions respectively is a necessary step in translating the optimization decisions derived from the preliminary analysis into actual production execution instructions. This ensures the safety and compliance of blending operations and avoids abnormal energy consumption or excessive emissions caused by human error.

[0076] S5. Collect real-time key process parameters during the blending process, and optimize and recommend the blending ratio range based on the feedback of real-time key process parameters.

[0077] Given that heavy metals may accumulate and circulate within the kiln during the actual cement kiln co-firing process, real-time monitoring of the kiln tail flue gas concentration can detect a continuous upward trend before it approaches the red line for exceeding the standard, thereby taking action in advance to reduce the co-firing ratio and avoid environmental accidents.

[0078] Furthermore, given the time delay between material feeding and complete combustion and heat release, if the material's calorific value is lower than expected, the temperature in the firing zone will show a slow downward trend. By the time the temperature has already dropped significantly, it will be too late to adjust it, and the quality of the clinker and the stability of the kiln condition will have been affected.

[0079] Therefore, the real-time key process parameters include: heavy metal emission concentration data in kiln tail flue gas and firing zone temperature data.

[0080] The above-mentioned feedback optimization recommended blending ratio range calculation content is as follows: extract the heavy metal emission concentration data of kiln tail flue gas and the temperature data of the firing zone from the real-time key process parameters collected from the production line, and organize them into a continuous parameter monitoring sequence according to the time sequence.

[0081] Set a sliding window on the parameter monitoring sequence that contains the most recent consecutive monitoring points.

[0082] Calculate the difference between all adjacent monitoring points within the sliding window. If all differences are greater than zero, the sequence within the window is determined to show a continuous upward trend. If all differences are less than zero, the sequence within the window is determined to show a continuous downward trend.

[0083] When the monitoring sequence of the firing zone temperature shows a continuous downward trend, the impact of the calorific value or moisture content of the current blended material on energy consumption is correlated, and optimization suggestions for adjusting the recommended blending ratio range downward are generated.

[0084] When the monitoring sequence of heavy metal emission concentration in kiln tail flue gas shows a continuous upward trend, the impact of the heavy metal content of the current blended materials on emissions is correlated to generate an optimization suggestion to adjust the upper limit of the recommended blending ratio range downward.

[0085] It should be noted that calorific value has a positive impact on reducing total energy consumption, while moisture content has a negative impact on increasing total energy consumption. When the temperature monitoring sequence of the firing zone continues to decline, it means that the current blended material may have a low calorific value, which means it cannot provide enough heat to maintain the temperature inside the kiln, or a high moisture content, which leads to insufficient heat supply inside the kiln. In this case, adjusting the recommended blending ratio range downward can reduce the amount of such material blended to alleviate the heat imbalance problem.

[0086] Meanwhile, since the content of heavy metals is a key constraint on the compliance of pollutant emissions, when the concentration of heavy metal emissions from the kiln tail flue gas continues to rise, it indicates that the heavy metal content of the current blended materials has already shown a trend of exceeding the emission standards, even at the original ratio. Adjusting the upper limit of the recommended blending ratio range downward can control the total input of heavy metals by reducing the amount of such materials blended, thus preventing the emission concentration from further exceeding the standard. Ultimately, this achieves precise control of the blending ratio based on real-time parameter feedback, ensuring that the energy consumption and emissions of cement kilns are stably compliant.

[0087] Finally, the recommended blending ratio range was optimized and adjusted based on the optimization suggestions.

[0088] Specifically, the optimization and adjustment of the recommended blending ratio range based on the optimization suggestions are as follows: If the optimization suggestion is to adjust the recommended blending ratio range downward, then the entire range will be shifted downward by a fixed step based on the current range.

[0089] It should be added that the process of determining the fixed step size is as follows: First, extract the historical data of the target material category in the past 3 months, screen out the effective control cases under the corresponding optimization suggestions, and calculate the average step size of a single adjustment; then refer to the maximum threshold for single adjustment of the blending ratio specified in the relevant national cement kiln process control standards. If the average step size calculated above exceeds the maximum threshold, the maximum threshold is used as the fixed step size, otherwise the average step size is used as the fixed step size.

[0090] If the optimization suggestion is to adjust the upper limit of the recommended blending ratio range downwards, then the upper limit of the range will be moved downwards by a fixed step, while the lower limit will remain unchanged.

[0091] If the monitoring sequence of the firing zone temperature shows a continuous upward trend, or the monitoring sequence of the heavy metal emission concentration in the kiln tail flue gas shows a continuous downward trend, then based on the current recommended blending ratio range, the entire range should be shifted upward by a fixed step, or its upper limit value should be shifted upward by a fixed step.

[0092] refer to Figure 3 As shown, a second aspect of the present invention provides a system for optimizing the proportion of industrial waste materials co-fired in cement kilns, comprising a data preprocessing module, a material classification decision module, an influence trend analysis module, a proportion calculation and issuance module, and a real-time feedback optimization module. All modules are connected in the order described above.

[0093] Specifically, the data preprocessing module is used to acquire multi-dimensional test data of industrial waste and generate batch material data archives.

[0094] The material classification decision module is used to classify materials based on the files and label the blending decision based on the safety threshold of heavy metal content and the blending benefit index.

[0095] The Influence Trend Analysis module is used to analyze the targeted impact trends of multi-dimensional data on cement kiln energy consumption and pollutant emissions for the categories of materials that are permitted to be co-fired.

[0096] The proportion calculation and issuance module is used to calculate the recommended blending ratio range with constraints of minimum total energy consumption and compliance with heavy metal emission standards, and then issue the decision to the production line control system for execution.

[0097] The real-time feedback optimization module is used to collect data on heavy metal concentration in kiln tail flue gas and temperature in the firing zone. Through parameter trend analysis and correlation analysis with the blending ratio, it dynamically optimizes and recommends the blending ratio range.

[0098] The parameters involved in the above formula are all dimensionless and calculated numerically. The formula is a formula obtained from the most recent real situation by collecting a large amount of data and simulating it with software. The preset parameters in the formula are set by those skilled in the art according to the actual situation.

[0099] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0100] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0101] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0102] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0103] Finally, 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, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for optimizing the proportion of industrial waste materials co-fired in cement kilns, characterized in that: include: Obtain multi-dimensional test data of the batch of industrial waste to be processed, including calorific value, heavy metal content, moisture content and ash ratio, and preprocess the multi-dimensional test data to generate batch material data archives; Industrial waste is categorized based on batch material data archives, and each category is labeled with a decision label indicating whether co-combustion is permitted or not. For the material categories that are permitted to be blended, the impact trend on cement kiln energy consumption and pollutant emissions is analyzed by combining multi-dimensional test data. The recommended blending ratio range is calculated with the dual constraints of the lowest total energy consumption and compliance with heavy metal emissions. The recommended blending ratio range is sent to the production line control system to trigger the blending action, and the blending prohibition action is triggered for material categories that are not allowed to be blended. Collect real-time key process parameters during the blending process, and optimize and recommend the blending ratio range based on the feedback of real-time key process parameters. The specific details of classifying industrial waste based on batch material data archives are as follows: For the four dimensions of calorific value, heavy metal content, moisture content, and ash content, multi-dimensional test data are extracted from batch material data archives, forming four sets of dimensional data sequences. Each dimensional data sequence is divided into several continuous intervals, and the number of material batches in each continuous interval under different dimensions is counted. The proportion of the number of material batches to the total number of batches in the corresponding dimension is calculated, and the ratio of this proportion to the interval width is used as the distribution density value of the interval, generating distribution density data for all intervals under each dimension. The intervals are traversed except for the first and last intervals. If the distribution density value of an interval is greater than the distribution density values ​​of the two adjacent intervals, the interval is marked as a local maximum interval. The local maximum interval and its adjacent continuous intervals are merged to form the core dense intervals of each dimension. Material batches falling within the same core dense interval are grouped into an initial category group. For material batches that overlap in the core dense intervals of multiple dimensions, the material batches that overlap are merged into the same material category.

2. The method for optimizing the proportion of industrial waste materials co-fired in cement kilns according to claim 1, characterized in that: The specific details of the decision labels for each category indicating whether blending is permitted or prohibited are as follows: For each material category, extract its heavy metal content. If the heavy metal content of the material in that category exceeds the safety threshold, then directly label the category as not allowed to be blended. Standardize the multi-dimensional test data for material categories whose heavy metal content does not exceed the safety threshold; The standardized, multi-dimensional test data were weighted and fused to obtain the blending yield index. The blending benefit index of all material categories is sorted in descending order. Material categories whose ranking is within the quartile of the number of items in the ranking are marked as permitted for blending, while those whose ranking is outside the quartile are marked as not permitted for blending.

3. The method for optimizing the proportion of industrial waste materials co-fired in cement kilns according to claim 1, characterized in that: The specific details of the analysis of the impact trends on cement kiln energy consumption and pollutant emissions based on multi-dimensional laboratory data are as follows: For material categories marked as permitted for co-firing, correlation data sequences were constructed according to the material batch sequence, including calorific value and theoretical coal saving, moisture content and theoretical evaporation energy consumption, ash ratio and theoretical auxiliary material addition, and total heavy metal content and theoretical flue gas emission concentration. Curve fitting is performed on the associated data sequence to generate continuous analysis curves showing the correspondence between the characteristics of each material and energy consumption and emission parameters; Calculate the rate of change of each point on the continuous analysis curve, and determine the continuous segment with a positive rate of change as a monotonically increasing interval, and the continuous segment with a negative rate of change as a monotonically decreasing interval. Based on the monotonically increasing and monotonically decreasing intervals, the directional impact trends of each dimension of test data on the total energy consumption of cement kilns and on the directional impact trends on pollutant emission concentrations are determined.

4. The method for optimizing the proportion of industrial waste materials co-fired in cement kilns according to claim 3, characterized in that: The determination of the directional impact trends of each dimension of test data on the total energy consumption of cement kilns and on the directional impact trends on pollutant emission concentrations, based on the monotonically increasing and monotonically decreasing intervals, is as follows: When the curves of calorific value versus theoretical coal saving, moisture content versus theoretical evaporation energy consumption, and ash ratio versus theoretical auxiliary material addition show a monotonically increasing trend, it is determined that calorific value has a positive impact on reducing total energy consumption, while moisture content and ash ratio have a negative impact on increasing total energy consumption. When the curve of total heavy metal content and theoretical amount of additives shows a monotonically increasing characteristic, it is determined that the heavy metal content constitutes a key constraint on the compliance of pollutant emissions.

5. The method for optimizing the proportion of industrial waste materials co-fired in cement kilns according to claim 1, characterized in that: The specific process for obtaining the recommended blending ratio range is as follows: By using historical batches of material co-firing test data, a pollutant emission result database with heavy metal content as the key variable is established. The maximum co-firing ratio of various materials allowed to ensure that pollutant emissions meet the standards is directly linked from the pollutant emission result database. This maximum co-firing ratio is determined as the upper limit of the co-firing ratio of the current material category. Under the premise of satisfying the upper limit of the blending ratio, based on the influence trend of calorific value, moisture content and ash ratio on total energy consumption, a total energy consumption function in the form of a quadratic function with blending ratio as the variable is constructed. The point where the first derivative of the total energy consumption function with respect to the blending ratio is zero is solved to obtain the optimal blending ratio point that minimizes the total energy consumption. Based on the optimized blending ratio point, and combined with the operating control tolerance of the cement kiln system, an operating control tolerance is symmetrically extended to both sides to form a recommended blending ratio range with the midpoint as the core.

6. The method for optimizing the proportion of industrial waste materials co-fired in cement kilns according to claim 1, characterized in that: The real-time key process parameters include: heavy metal emission concentration data in kiln tail flue gas and firing zone temperature data.

7. The method for optimizing the proportion of industrial waste materials co-fired in cement kilns according to claim 6, characterized in that: The calculation of the recommended blending ratio range for feedback optimization is as follows: Data on heavy metal emission concentrations in kiln tail flue gas and temperature data in the firing zone are extracted from real-time key process parameters collected from the production line and organized into a continuous parameter monitoring sequence in chronological order. Set a sliding window on the parameter monitoring sequence that contains multiple most recent consecutive monitoring points; Calculate the difference between all adjacent monitoring points within the sliding window. If all differences are greater than zero, the sequence within the window is determined to show a continuous upward trend. If all differences are less than zero, the sequence within the window is determined to show a continuous downward trend. When the monitoring sequence of the firing zone temperature shows a continuous downward trend, the impact of the calorific value or moisture content of the current blended material on energy consumption is correlated, and an optimization suggestion to adjust the recommended blending ratio range downward is generated. When the monitoring sequence of heavy metal emission concentration in kiln tail flue gas shows a continuous upward trend, the impact of the heavy metal content of the current blended materials on emissions is correlated to generate an optimization suggestion to adjust the upper limit of the recommended blending ratio range downward. The recommended blending ratio range was optimized and adjusted based on the optimization suggestions.

8. The method for optimizing the proportion of industrial waste materials co-fired in cement kilns according to claim 7, characterized in that: The specific details of optimizing and adjusting the recommended blending ratio range based on the optimization suggestions are as follows: If the optimization suggestion is to adjust the recommended blending ratio range downwards, then the entire range will be shifted downwards by a fixed step based on the current range. If the optimization suggestion is to adjust the upper limit of the recommended blending ratio range downward, then the upper limit of the range will be moved downward by a fixed step based on the current range, while the lower limit will remain unchanged. If the monitoring sequence of the firing zone temperature shows a continuous upward trend, or the monitoring sequence of the heavy metal emission concentration in the kiln tail flue gas shows a continuous downward trend, then based on the current recommended blending ratio range, the entire range should be shifted upward by a fixed step, or its upper limit value should be shifted upward by a fixed step.

9. A system for optimizing the proportion of industrial waste materials co-fired in cement kilns, used to execute the steps in the method for optimizing the proportion of industrial waste materials co-fired in cement kilns as described in any one of claims 1-8, characterized in that: include: The data preprocessing module is used to acquire multi-dimensional test data of industrial waste and generate batch material data archives; The material classification decision module is used to classify materials based on the files and label the blending decision based on the safety threshold of heavy metal content and the blending benefit index. The impact trend analysis module is used to analyze the targeted impact trends of multi-dimensional data on cement kiln energy consumption and pollutant emissions for the categories of materials that are permitted to be co-fired. The ratio calculation and issuance module is used to calculate the recommended blending ratio range with the constraints of minimum total energy consumption and compliance with heavy metal emission standards, and then issue it to the production line control system for execution. The real-time feedback optimization module is used to collect data on heavy metal concentration in kiln tail flue gas and temperature in the firing zone. Through parameter trend analysis and correlation analysis with the blending ratio, it dynamically optimizes and recommends the blending ratio range.

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