Multi-source building solid waste differential gradient activation and dynamic proportioning intelligent control method

Abstract

The application discloses a kind of multi-source building solid waste difference gradient activation and dynamic proportioning intelligent control method, it is related to building solid waste resource utilization technical field.The method includes: S1 is carried out to multi-source building solid waste and is accurately characterized with multidimensional characteristics;S2 is based on multi-physical field coupling model prediction solid waste activity and intelligent decision best activation process;S3 is carried out to solid waste activity level and is different gradient activation treatment;S4 establishes dynamic proportioning multi-objective optimization model and solves best raw material content;S5 carries out high-precision dynamic proportioning and intelligent mixing stirring;S6 implements intelligent monitoring and continuous optimization control.The application solves the contradiction between the heterogeneity of multi-source solid waste characteristics and the mismatch of single activation process by activity gradient matching theory, realizes the dynamic proportioning optimization of activity-strength-durability coupling by using improved NSGA-III algorithm, and establishes a full-process intelligent detection control system.

Description

Technical Field

[0001] This invention relates to the field of construction solid waste resource utilization technology, specifically to a method for differential gradient activation and dynamic ratio intelligent control of multi-source construction solid waste. Background Technology

[0002] With the advancement of urbanization in my country, the amount of construction solid waste generated has continued to grow, and it now accounts for a large proportion of the total urban solid waste.

[0003] However, the current comprehensive utilization rate of construction solid waste in my country is still lower than the planned target, and it is mainly limited to low-value-added areas such as roadbed backfilling, with a low rate of high-quality utilization. The open-air storage of large amounts of construction solid waste not only occupies land resources but also causes soil, water, and air pollution, which is detrimental to ecological civilization construction and sustainable development. Therefore, how to achieve high-value-added and high-quality utilization of construction solid waste is a technical problem that needs to be solved.

[0004] Despite the progress made in the development of existing construction solid waste resource utilization technologies over the years, the following core defects and common pain points that have long remained unresolved in the industry still exist. These pain points have become technical bottlenecks restricting the high-quality utilization of construction solid waste: First, there is a core technological contradiction: the heterogeneity of characteristics in multi-source solid waste is mismatched with single activation processes. Construction solid waste from different sources and of different materials exhibits significant differences in chemical composition, mineral composition, and physical and mechanical properties. Concrete solid waste contains a large amount of unhydrated cement clinker and hydration products, possessing a certain degree of self-hardening; brick solid waste is mainly composed of clay minerals, containing a relatively high amount of active SiO2 and Al2O3, but with a loose structure; ceramic solid waste is mainly composed of quartz and mullite, with a dense structure, high chemical stability, and extremely low activity. Existing technologies generally employ a single activation process to treat all types of construction solid waste, failing to provide differentiated treatment based on the characteristics of different solid wastes. For highly active concrete solid waste, a single high-temperature calcination process leads to excessive sintering and deactivation of its active components; for less active ceramic solid waste, a single mechanical grinding process cannot effectively break down its dense crystal structure, resulting in limited activation. This prevents the synergistic utilization of multi-source solid waste, necessitating separate treatment, increasing processing costs and equipment investment.

[0005] Second, there is a general limitation in the industry's understanding of the technology. Current technology generally holds that when the high-temperature calcination temperature exceeds 900℃, the active SiO2 and Al2O3 in solid waste will undergo a sintering reaction, forming inert minerals such as mullite, leading to a decrease in activity. Therefore, current technology typically controls the calcination temperature below 800℃, which has a very limited activation effect on low-activity solid wastes such as ceramics.

[0006] Third, there is a technical contradiction between static mix design and raw material fluctuations. Traditional mix design is based on static laboratory test data, which remains fixed once determined. However, the characteristics of construction waste raw materials fluctuate significantly depending on their source and batch, with the coefficient of variation of their activity index reaching over 20%. Static mix design cannot be dynamically adjusted according to real-time changes in raw material characteristics, resulting in large fluctuations in product performance, with a strength coefficient of variation exceeding 10% and a low product qualification rate (less than 70%). Furthermore, traditional mix design typically only considers strength requirements, failing to comprehensively consider multiple objectives such as cost, solid waste utilization rate, durability, and carbon emissions, thus failing to achieve multi-objective optimization. In particular, traditional mix design lacks effective control methods for durability indicators such as drying shrinkage and frost resistance of recycled concrete, resulting in poor durability of recycled products that cannot be used in important structural projects.

[0007] Fourth, the level of automation is low, and there is a lack of full-process detection and control. Most existing production lines rely on manual operation or semi-automatic control, failing to achieve intelligent control over the entire process, from raw material testing and activation to proportioning adjustments and quality inspection. Manual operation is not only inefficient and costly but also prone to human error, leading to unstable product quality. Furthermore, existing technologies lack effective feedback mechanisms, making it impossible to continuously optimize process parameters based on production process data and product quality data; the production process remains in an open-loop state. When raw material characteristics or environmental conditions change, process parameters cannot be adjusted in a timely manner, resulting in a decline in product quality. Summary of the Invention

[0008] To address the aforementioned issues, this invention provides a method for intelligent control of differential gradient activation and dynamic proportioning of multi-source construction solid waste.

[0009] Includes the following steps: S1 Multi-source Construction Solid Waste Pretreatment and Multi-dimensional Characterization: Construction solid waste from different sources and materials is crushed, screened, and impurity removed to obtain multi-grade aggregates; multi-sensor fusion technology is used to monitor the chemical composition, mineral composition, particle size distribution, moisture content, bulk density, porosity, and microcrack index of each grade of aggregate in real time, and the monitoring data is uploaded to the intelligent control system. S2 Based on Multi-Physics Field Coupling: Quantitative Prediction of Solid Waste Activity and Intelligent Decision-Making of Activation Process: The intelligent control system inputs the detection data into a pre-trained multi-physics field coupled solid waste activity evolution model, quantitatively predicts the 28-day activity index of solid waste under different activation process parameters, and uses the NSGA-Ⅲ multi-objective optimization algorithm to solve for the optimal gradient activation process parameters. S3 Differentiated gradient activation treatment of multi-source solid waste based on activity gradient matching: Different three-field synergistic gradient activation processes are used for treatment according to the activity level of the solid waste. S4 Dynamic Proportioning Intelligent Calculation of Activity-Strength-Durability Coupling Relationship: The intelligent control system establishes a dynamic proportioning multi-objective optimization model based on the activity index, hydration heat characteristics, volume stability, performance requirements of recycled products, real-time market prices of raw materials, and carbon emission factors of various solid wastes after activation. The improved NSGA-Ⅲ algorithm, which introduces an adaptive cross-mutation operator and an elite retention strategy, is used to solve for the optimal dosage of various raw materials. S5 High-Precision Dynamic Batching and Intelligent Mixing: The batching control system accurately measures various raw materials according to the optimal dynamic proportioning parameters and feeds them into the mixer for mixing in the preset order; during the mixing process, the workability and hydration process of the mixture are detected in real time, and the amount of water and additives are automatically adjusted. S6 Intelligent Monitoring and Continuous Optimization Control: Real-time collection of process parameters, equipment operating status, and product quality data for each process stage, and real-time analysis using an isolated forest anomaly detection algorithm; establishment of a feedback control system for process parameter detection and product quality detection, which automatically adjusts relevant process parameters based on preset expert rules and deep learning models when abnormalities in the production process or substandard product quality are detected; and establishment of a large database of the production process, using an attention-enhanced LSTM deep learning algorithm to analyze historical data, and regularly and automatically updating the solid waste activity evolution model and dynamic proportioning optimization model.

[0010] Specifically, in step S1, the multi-sensor fusion technology uses a near-infrared spectrometer to detect mineral composition and porosity, an X-ray fluorescence spectrometer to detect chemical composition, a laser particle size analyzer to detect particle size distribution, an online moisture analyzer to detect moisture content, a tap density analyzer to detect bulk density, and an industrial CT scanner to detect microcrack index; the detection frequency is 1 time / minute, and the data transmission delay is ≤50ms.

[0011] Specifically, the multiphysics-coupled solid waste activity evolution model in step S2 is established through the following steps: (1) Collect building solid waste samples with different materials and properties, conduct multiple activation experiments, obtain 28-day activity index data under different process parameters, construct training datasets with ≥1000 samples. (2) Based on the theories of mechanochemical kinetics, thermochemical kinetics, and ultrasonic chemical kinetics, a model expression is established: in, for Solid waste activity index at any given time The initial activity index of solid waste. , , The rate constant is For mechanical grinding energy input, Grinding time, The calcination activation energy is given by R, where R is the ideal gas constant. The calcination temperature. For calcination time, This refers to the ultrasonic power. Ultrasound time, , , , , The reaction order is [number]. (3) The LM (Levenberg-Marquardt) algorithm was used to train the model until the model prediction error was ≤3%; Specifically, in step S2, the population size of the NSGA-Ⅲ multi-objective optimization algorithm is 150, the number of iterations is 300, the crossover probability is 0.85, and the mutation probability is 0.12. Specifically, the constraints are: the activity index of the activated solid waste ≥70% after 28 days, the unit energy consumption ≤550kWh / t, the processing capacity ≥60t / h, and the carbon emission per unit product ≤80kgCO2 / t.

[0012] Specifically, in step S3, based on the activity gradient matching theory, construction solid waste is divided into three activity levels: high-calcium activity level (concrete solid waste, CaO content ≥35%), medium-silica-alumina activity level (brick and mortar solid waste, SiO2 content 50%-65%, Al2O3 content 15%-25%), and low-activity level (ceramic solid waste, SiO2 content ≥65%, Al2O3 content ≥20%). For solid waste of different activity levels, a three-field synergistic gradient activation process with precise energy matching is designed. Specifically, the determination of the activity level is not based solely on the name of the solid waste source, but rather on the content of CaO, SiO2, and Al2O3 or their relative ratios as the main determination parameters, combined with mineral composition, porosity, and microcrack index for verification. When the same solid waste sample simultaneously meets some of the conditions of multiple activity levels, the CaO content is used first to determine whether it belongs to the high-calcium activity level, and then the SiO2 and Al2O3 content is used to determine whether it belongs to the medium-silicon-aluminum activity level or the low-activity level. For solid wastes with different activity levels, a three-field synergistic gradient activation process with precise energy matching is designed. Specifically, after high-temperature phase change activation of low-activity tiered solid waste, it does not directly enter the natural stacking cooling process. Instead, it is rapidly cooled immediately after calcination to reduce the temperature of the low-activity tiered solid waste from a high temperature to 160-190°C. Then, it undergoes mechanochemical dissociation treatment to ensure that the thermal stress cracks formed after high-temperature treatment are compatible with the subsequent grinding and dissociation process. Specifically, for concrete-like solid waste with high calcium activity levels, a mechanochemical pre-activation-ultrasonic-assisted chemical activation gradient activation process is adopted, including the following steps: A1 is fed into a variable frequency ball mill for mechanochemical pre-activation. The grinding time is 12-22 minutes, the grinding media is φ20-φ30mm steel balls, the ball-to-material ratio is 6:1-9:1, and the grinding is carried out until the specific surface area is 390-430m². 2 / kg; A2 is fed into a continuous ultrasonic reactor for ultrasonic-assisted chemical excitation. The ultrasonic power is 280-420W, the frequency is 28-35kHz, the exciter is a composite solution of NaOH and Na2SiO3 with a molar ratio of 1:1-1:2, a concentration of 2.5-3.5mol / L, a solid-liquid ratio of 1:2.8-1:3.2, and an excitation time of 8-11min; After A3 activation, the mixture is filtered using a plate and frame filter press, washed until the pH value is 7.2-7.8, and then dried at 105℃ until the moisture content is ≤0.8%. Specifically, for solid waste such as bricks and mortars with a medium-silica-alumina active layer, a low-temperature thermochemical activation-mechanical-chemical enhanced gradient activation process is adopted, including the following steps: B1 is fed into a segmented temperature-controlled rotary kiln for low-temperature thermochemical activation. The preheating temperature is 300-400℃, the holding temperature is 680-730℃, the heating rate is 7-9℃ / min, and the calcination time is 38-48min. B2 is then naturally cooled to room temperature and fed into a ball mill for mechanochemical strengthening. The grinding time is 22-38min, the ball-to-material ratio is 7:1-11:1, and the grinding is continued until the specific surface area is 430-470m². 2 / kg Specifically, for ceramic solid waste with low activity levels, a high-temperature phase change activation-rapid cooling toughening-mechanical-chemical dissociation gradient activation process is adopted, with the following specific parameters: C1 High-Temperature Phase Transformation Activation: Ceramic solid waste is fed into a segmented temperature-controlled rotary kiln. The preheating section temperature is 500-600℃, the holding section temperature is 960-1040℃, the heating rate is 10-12℃ / min, and the calcination time is 52-68min. The kiln atmosphere is a weakly oxidizing atmosphere with an oxygen content controlled at 8%-10%. C2 Rapid Cooling Toughening: After calcination, the material immediately enters a composite rapid cooler, using a combination of air cooling and atomized water cooling. The cooling rate is stable at 70-90℃ / min, cooling from the high temperature to 160-190℃. C3 Mechanochemical Dissociation: After cooling, the material is fed into a ball mill for grinding. The grinding media is φ20mm zirconia balls, the ball-to-material ratio is 8:1-13:1, and the grinding time is 28-48min, grinding to a specific surface area of ​​490-530m². 2 / kg.

[0013] Specifically, in step S4, the objective function of the dynamic proportioning multi-objective optimization model is: The constraints are: In the formula, This represents the total number of types of raw materials. The number of types of activated solid waste; This represents the total cost of raw materials. For the first The unit price of the raw materials, For the first The amount of each raw material used; To improve the utilization rate of solid waste cementitious materials, This represents the total amount of cementitious materials used. Carbon emissions per unit of product For the first Carbon emission factors of raw materials; This refers to the standard value of the compressive strength of recycled concrete cubes. This represents the design value for the compressive strength of recycled concrete cubes. The standard deviation of strength; Slump , These are the lower and upper limits for slump design, respectively. For impermeability grade, This is the design value for the impermeability grade; For freeze resistance rating, This is the design value for the freeze resistance rating; The shrinkage rate is 28 days. This is the design value for the shrinkage rate; For the first The maximum allowable amount of each raw material.

[0014] Specifically, crossover probability in , , This represents the current iteration number. The maximum number of iterations, For individual fitness, The average fitness of the population; the probability of mutation. The formula is as follows: (Adaptive adjustment based on the number of iterations) in , ; Specifically, the elite retention strategy is as follows: after each generation iteration, the top 10% of individuals in terms of fitness are retained and directly enter the next generation.

[0015] Specifically, in step S4, when the dynamic proportioning multi-objective optimization model screens candidate proportions, it first eliminates candidate proportions that do not meet the constraints of strength, slump, impermeability grade, freeze-thaw resistance grade and 28-day drying shrinkage rate, and then sorts the candidate proportions that meet the constraints by cost, solid waste utilization rate and carbon emissions per unit product.

[0016] Specifically, the activity index, hydration heat characteristics, and volume stability of the dynamic proportioning multi-objective optimization model input in step S4 are all derived from the same batch of solid waste raw materials after the differential gradient activation in step S3. The dynamic proportioning multi-objective optimization model does not use the historical average activity data of the unactivated solid waste raw materials to replace the detection data after activation of the same batch.

[0017] Specifically, the preset feeding sequence in step S5 is as follows: first, coarse aggregate and fine aggregate are added and stirred for 30 seconds, then activated solid waste and cement are added and stirred for 60 seconds, and finally water and admixtures are added and stirred for 30-60 seconds; the stirring time is 90-150 seconds and the stirring speed is 60-90 r / min; the online resistivity meter (25) detects the hydration process of the mixture in real time, and automatically adjusts the amount of admixture when the resistivity change rate exceeds the preset range.

[0018] Specifically, in step S6, when the activity index of the activated solid waste after 28 days is lower than the design value, the corresponding activation process parameters are adjusted according to the activity level to which the solid waste belongs.

[0019] Specifically, when the activity index of high-calcium active layer solid waste is low, the grinding time, ultrasonic power, or composite solution concentration should be adjusted first.

[0020] Specifically, when the activity index of solid waste in the silicon-aluminum active layer is low, the temperature of the heat preservation section, calcination time, or grinding time should be adjusted first.

[0021] Specifically, when the activity index of low-activity solid waste is low, priority should be given to adjusting the high-temperature phase change activation temperature, rapid cooling rate, or mechanochemical dissociation time.

[0022] Specifically, the specific control strategy of the feedback control system in step S6 is as follows: (1) Based on the deviation between the set value and the actual value of the process parameters of each process, the incremental PID algorithm is used to automatically adjust the equipment operating parameters. The PID parameters are tuned using the Ziegler-Nichols method, and the control accuracy is ±1%. (2) Based on the deviation between the product quality inspection results and the design values, the activation process parameters and proportioning parameters are automatically adjusted using an attention-enhanced LSTM deep learning model. The model input is the process parameters and quality data of the previous hour, and the output is the process parameter adjustment amount. The control cycle is 1 hour. (3) During normal production, process parameter testing is the main focus, and product quality testing is the secondary focus; when the product quality deviation exceeds 5%, the focus is switched to product quality testing as the main focus and process parameter testing as the secondary focus.

[0023] Specifically, in step S6, the preset expert rules are as follows: (1) When the activity index of the activated solid waste is lower than the design value of 4%-8% after 28 days, the grinding time will be automatically extended by 4-8 minutes or the calcination temperature will be increased by 40-70℃. (2) When the activity index of the activated solid waste is more than 8% lower than the design value after 28 days, the grinding time will be automatically extended by 8-12 minutes or the calcination temperature will be increased by 70-100℃, and an audible and visual alarm signal will be issued. (3) When the 28-day compressive strength of the recycled product is 2%-4% lower than the design value, automatically increase the cement content by 1.5%-2.5% or decrease the solid waste content by 2%-4%; (4) When the 28-day compressive strength of the recycled product is more than 4% lower than the design value, the cement content will be automatically increased by 2.5%-4.5% or the solid waste content will be reduced by 4%-7%, and an audible and visual alarm signal will be issued. (5) When the slump of the mixture is more than 15 mm greater than the design value, the amount of water added will be automatically reduced by 0.8%-1.5% or the amount of admixture will be increased by 0.08%-0.15%; (6) When the slump of the mixture is more than 15 mm less than the design value, the amount of water added will be automatically increased by 0.8%-1.5% or the amount of admixture will be reduced by 0.08%-0.15%.

[0024] It has the following beneficial effects: (1) The activity gradient matching theory proposed in this invention fundamentally solves the core technical contradiction of the mismatch between the heterogeneity of characteristics of multi-source solid waste and the single activation process, enabling the 28-day activity index of concrete solid waste to reach 83%-87%, brick mortar solid waste to reach 78%-82%, and ceramic solid waste to reach 75%-79%, increasing the comprehensive utilization rate of construction solid waste from less than 50% to 92.7%. (2) The high-temperature phase change activation-rapid cooling toughening-mechanical chemical dissociation gradient activation process developed in this invention increases the 28-day activity index of ceramic solid waste from less than 50% to more than 75%, and reduces the activation energy consumption by 37.2%, from 950kWh / t to 596kWh / t.

[0025] (3) The dynamic proportioning multi-objective optimization model established by the present invention integrates the coupling relationship between activity, strength and durability, and comprehensively considers the influence of solid waste activity on the strength and durability of recycled products. Compared with the traditional static proportioning design, the coefficient of variation of the 28-day strength of recycled products is reduced from more than 10% to 1.9%, the performance qualification rate is increased from less than 70% to 99.7%, the raw material cost is reduced by 19.4%, and the solid waste content is increased by more than 30%.

[0026] (4) The intelligent control system established in this invention uses an attention-enhanced LSTM deep learning algorithm to analyze production data, realizing automatic updating of model parameters and self-learning and adaptive optimization of the production process. Production efficiency was increased by 48.7%, labor costs were reduced by 68.3%, and product quality stability was significantly improved. Attached Figure Description

[0027] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art 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 the structures shown in these drawings without creative effort.

[0028] Figure 1 This is a flowchart illustrating a specific implementation method for the dynamic proportioning of a multi-source construction solid waste differential gradient activation and dynamic proportioning intelligent control method according to the present invention. Detailed Implementation

[0029] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0030] The following detailed description of the implementation method of the present invention is in conjunction with the accompanying drawings. The description is only a partial embodiment and not all embodiments. For clarity, representations and descriptions unrelated to the present invention are omitted in the drawings and description.

[0031] To provide a clearer understanding of the technical features, objectives, and beneficial effects of this invention, the following detailed description of the technical solution is provided. Obviously, the described embodiments are only a portion of the embodiments of this invention, not all of them, and should not be construed as limiting the scope of implementation of this invention. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without inventive effort are within the protection scope of this invention.

[0032] Example 1 This embodiment is a specific implementation of the present invention's method for gradient activation and dynamic proportioning intelligent control of multi-source construction solid waste. The specific steps are as follows: S1 Pre-treatment and precise characterization of multi-dimensional building solid waste Mixed construction solid waste from a demolition site was processed. Its composition was as follows: concrete solid waste accounted for 45%, brick solid waste accounted for 30%, mortar solid waste accounted for 15%, and ceramic solid waste accounted for 10%.

[0033] Primary crushing: Jaw crusher is used for crushing, with a feed particle size ≤500mm and a discharge particle size ≤100mm.

[0034] Secondary crushing: Fine crushing is performed using a cone crusher, with a discharge particle size ≤40mm.

[0035] Grading and screening: The aggregate is screened by a three-layer vibrating screen with screen apertures of 5mm and 20mm to obtain three grades of aggregate: 0-5mm, 5-20mm, and 20-40mm.

[0036] Impurity removal process: A suspended magnetic separator is used to remove iron impurities, and then a cyclone air separator is used to remove light impurities such as plastics and wood. The overall impurity removal efficiency reaches 96.2%.

[0037] Online detection: Multi-sensor fusion technology is used to perform real-time online detection of aggregates at all levels. The results show: Concrete-related solid waste: CaO content 38.2%, SiO2 content 42.5%, Al2O3 content 11.3%, Fe2O3 content 3.7%, D 50 =12.6mm, moisture content 2.1%, bulk density 1420kg / m³ 3 The porosity is 28.5% and the microcrack index is 0.32.

[0038] Brick-based solid waste: SiO2 content 58.7%, Al2O3 content 19.4%, CaO content 8.5%, Fe2O3 content 5.2%, D 50 =10.3mm, moisture content 3.2%, bulk density 1350kg / m³ 3 The porosity is 35.2% and the microcrack index is 0.45.

[0039] Mortar-type solid waste: SiO2 content 52.3%, Al2O3 content 16.8%, CaO content 15.7%, Fe2O3 content 4.6%, D 50 =8.9mm, moisture content 2.8%, bulk density 1380kg / m³ 3 The porosity is 32.7% and the microcrack index is 0.38.

[0040] Ceramic solid waste: SiO2 content 69.2%, Al2O3 content 22.5%, CaO content 3.1%, Fe2O3 content 2.3%, D 50 =9.7mm, moisture content 1.5%, bulk density 1560kg / m³ 3 The porosity is 12.3% and the microcrack index is 0.18.

[0041] S2 Based on Multiphysics Coupling: Quantitative Prediction of Solid Waste Activity and Intelligent Decision-Making for Activation Processes The intelligent control system inputs the data detected in S1 into a pre-trained multi-physics coupled solid waste activity evolution model to predict the 28-day activity index of various solid wastes under different activation processes. Subsequently, the NSGA-III algorithm is used for multi-objective optimization to obtain the Pareto optimal solution set. Based on actual production needs (with energy consumption as a priority), the system determines the optimal activation process parameters: Concrete-related solid waste: grinding time 18 min, ball-to-material ratio 7:1, ultrasonic power 320 W, NaOH to Na2SiO3 molar ratio 1:1.5, concentration 3 mol / L, excitation time 9 min.

[0042] For brick-based solid waste: calcination temperature 710℃, calcination time 42min, grinding time 28min, and ball-to-material ratio 8:1.

[0043] Mortar-type solid waste: calcination temperature 690℃, calcination time 40min, grinding time 25min, ball-to-material ratio 7:1.

[0044] Ceramic solid waste: calcination temperature 1000℃, calcination time 60min, cooling rate 80℃ / min, grinding time 35min, ball-to-material ratio 10:1.

[0045] S3 Multi-source Solid Waste Differential Gradient Activation Treatment Based on Activity Gradient Matching Based on the optimal process parameters determined by S2 decision, differentiated gradient activation treatment is carried out on various types of solid waste: Concrete-type solid waste: fed into a ball mill and ground for 18 minutes to achieve a specific surface area of ​​410 m². 2 / kg, and then excited in ultrasonic reactor 13 with a 3mol / L NaOH and Na2SiO3 composite solution for 9 min, with a solid-liquid ratio of 1:3. After activation, filtered and washed until pH=7.5, and dried at 105℃ to a moisture content of 0.7%.

[0046] Brick-based solid waste: fed into a rotary kiln for segmented temperature-controlled calcination, with a preheating section at 350℃ and a holding section at 710℃, for a calcination time of 42 minutes. After natural cooling, it is then fed into a ball mill and ground for 28 minutes to achieve a specific surface area of ​​450 m². 2 / kg.

[0047] Mortar-type solid waste: fed into a rotary kiln for segmented temperature-controlled calcination, with a preheating section at 350℃ and a holding section at 690℃, for a calcination time of 40 minutes. After natural cooling, it is then fed into a ball mill for grinding for 25 minutes to achieve a specific surface area of ​​430 m². 2 / kg.

[0048] Ceramic solid waste: The material is fed into a rotary kiln for segmented temperature-controlled calcination. The preheating section is at 550℃, the holding section at 1000℃, and the calcination time is 60 minutes. After calcination, the material immediately enters a rapid cooler, cooling to 180℃ at a rate of 80℃ / min, and then fed into a ball mill for 18 minutes of grinding to achieve a specific surface area of ​​510 m². 2 / kg.

[0049] The activation test results showed that the 28-day activity index of concrete solid waste was 85%, brick solid waste was 80%, mortar solid waste was 77%, and ceramic solid waste was 77%, all of which met the design requirements.

[0050] S4 fusion activity-strength-durability coupling relationship dynamic ratio intelligent calculation The target product in this embodiment is C30 recycled concrete, with the following performance requirements: slump 120±20mm, impermeability grade P6, freeze-thaw resistance grade F150, and 28-day drying shrinkage ≤0.05%.

[0051] The real-time market prices of raw materials and their carbon emission factors are as follows: Prices: P·O42.5 cement 380 yuan / ton, activated concrete solid waste 60 yuan / ton, activated brick solid waste 50 yuan / ton, activated mortar solid waste 45 yuan / ton, activated ceramic solid waste 55 yuan / ton, natural sand 80 yuan / ton, natural crushed stone 70 yuan / ton, polycarboxylate superplasticizer 2000 yuan / ton, water 2 yuan / ton.

[0052] Carbon emission factors: cement 0.85tCO2 / t, activated solid waste 0.12tCO2 / t, natural sand 0.03tCO2 / t, natural crushed stone 0.02tCO2 / t.

[0053] The intelligent control system 10 uses the improved NSGA-Ⅲ algorithm to solve for the optimal ratio, and obtains the following results (unit: kg / m³). 3 ): Cement: 210 Activated concrete-type solid waste: 190 Activated brick-type solid waste: 130 Activated mortar-type solid waste: 70 Activated ceramic solid waste: 50 Natural sand: 630 Natural crushed stone: 1050 Water: 168 Water-reducing agent: 3.7 Under this ratio, the total solid waste content is 64%, and the total raw material cost is 258 yuan / m³. 3 The carbon emissions per unit product are 72 kg CO2 / m³. 3 .

[0054] S5 High-Precision Dynamic Batching and Intelligent Mixing The batching control system accurately measures various raw materials according to the optimal proportioning parameters calculated by S4 and feeds them into the twin-shaft forced mixer in a preset order. Mixing lasts 120 seconds at a speed of 75 r / min. During mixing, the properties of the mixture are monitored in real time using an online viscometer and online resistivity meter. The amount of water and water-reducing agent is fine-tuned based on the monitoring results. The final slump of the discharged material is 122 mm, meeting the requirements.

[0055] S6 Intelligent Monitoring and Continuous Optimization Control During production, process parameters, equipment operating status, and product quality data for each step are collected in real time. The intelligent control system uses the Isolation Forest algorithm for real-time analysis and anomaly detection. A feedback control system for process parameters and product quality is established, automatically adjusting relevant parameters based on deviations. Simultaneously, a large database of the production process is established, and an attention-enhanced LSTM algorithm is used to analyze historical data, automatically updating the solid waste activity evolution model and dynamic proportioning optimization model every 12 hours.

[0056] In this embodiment, the performance of C30 recycled concrete prepared using the method of this embodiment was tested, and the test results are shown below.

[0057] 28-day compressive strength: 37.2 MPa 28-day flexural strength: 4.8 MPa Slump: 122mm Extension: 385mm Permeability grade: P8 Freeze resistance rating: F200 Drying shrinkage rate (28 days): 0.038% Intensity coefficient of variation: 1.9% All performance indicators meet the requirements of GB / T14902-2012 "Ready-mixed Concrete" and JGJ / T443-2018 "Technical Specification for Application of Recycled Concrete", and the product qualification rate reaches 100%.

[0058] Compared with traditional processes, the technical and economic indicators of this embodiment are compared as shown in Table 1 below: Table 1 Example 2 This embodiment is a specific implementation of the high-temperature phase change-rapid cooling-mechanical-chemical dissociation gradient activation process for low-activity ceramic solid waste according to the present invention. The specific steps are as follows: A1. Raw material pretreatment and basic characterization Pure ceramic solid waste (waste ceramic tiles and crushed waste ceramic sanitary ware) obtained from a construction waste sorting plant was coarsely crushed by a jaw crusher and finely crushed by a cone crusher, then screened to obtain ceramic fines with a particle size of 0–5 mm. After impurity removal, the content of iron and light impurities was ≤0.5%.

[0059] The basic properties of the raw materials were tested as follows: SiO2 content 69.7%, Al2O3 content 22.8%, CaO content 2.9%, loss on ignition 0.8%; initial specific surface area 210 m². 2 / kg, initial 28-day activity index 42%; mineral phases are mainly quartz (α-quartz) and mullite, glass phase content <8%, and dense structure.

[0060] A2, 1000℃ gradient activation process parameters High-temperature phase transformation activation: Fine ceramic materials are fed into a segmented temperature-controlled rotary kiln. The preheating section temperature is 550℃ for 15 minutes, and the holding section temperature is precisely controlled at 1000℃ for 60 minutes, with a heating rate of 10℃ / min. The kiln atmosphere is a weakly oxidizing atmosphere, with an oxygen content controlled at 8%–10%.

[0061] Rapid cooling toughening: After calcination, the material immediately enters a composite rapid cooler, which adopts a combination of air cooling and atomized water cooling. The cooling rate is stable at 80℃ / min, rapidly cooling from 1000℃ to 180℃, and then naturally cooling to room temperature in a closed environment.

[0062] Mechanochemical dissociation: After cooling, the material is fed into a planetary ball mill. The grinding media are φ20mm zirconia balls with a ball-to-material ratio of 10:1. The grinding time is 35 minutes, and the specific surface area of ​​the material after grinding is controlled to be 510±10m². 2 / kg.

[0063] A3. Performance test results after activation Activity index: The activity index of mortar after 28 days is 78.2%, and the activity index after 7 days is 65.4%, which is much higher than the initial activity of the raw materials.

[0064] Mineral phase analysis: XRD analysis showed that the intensity of the α-quartz characteristic peak decreased by 47%, the proportion of amorphous phase (active SiO2 / Al2O3) increased to 32%, the mullite characteristic peak did not show significant enhancement, and no sintering phenomenon was observed.

[0065] Microstructure: SEM observation showed that a large number of lattice defects and fracture surfaces were generated on the surface of the material, the glass phase was uniformly dissociated, and the number of active sites was more than 3 times higher than that of the raw material.

[0066] Unit energy consumption: The comprehensive energy consumption of this process for treating ceramic solid waste is 592 kWh / t, which is 37.7% lower than that of the traditional high-temperature calcination-grinding process (950 kWh / t).

[0067] In this embodiment, to verify the inventiveness of the process, four parallel comparative examples were set up. All comparative examples used the same batch of ceramic solid waste raw materials and were ultimately ground to the same specific surface area of ​​510 m². 2 / kg, only the calcination temperature and cooling method were adjusted. The results are shown in Table 2 below: Table 2 In this embodiment, as shown in Table 2, rapid cooling at 1000°C did not significantly increase the mullite content (only 13.2%). Instead, a large number of active amorphous phases were generated, and the activity index was increased by 15.9% compared to the 900°C process, breaking the technical understanding that activation above 900°C inevitably leads to inactivation.

[0068] Compared with Comparative Examples 1-3 and this embodiment, at the same calcination temperature of 1000℃, natural cooling resulted in a large amount of mullite precipitation (21.4%), with an activity of only 61.7%; while rapid cooling effectively inhibited the nucleation and growth of mullite, which is the key to achieving efficient activation.

[0069] This process significantly increases activity by 34.1%, while reducing unit energy consumption by 24.1% compared to the traditional 750℃ calcination process, achieving an unexpected technical effect of improved activation and reduced energy consumption.

[0070] In this embodiment, to verify the technical effectiveness of the improved NSGA-Ⅲ algorithm of the present invention in solving the problem of unstable product quality under raw material fluctuations, three comparative examples were set up to simulate raw material fluctuation scenarios in actual production (solid waste activity index fluctuation range ±22%, chemical composition fluctuation range ±15%), and to compare the control effects of different proportioning methods. All groups used C30 recycled concrete as the target product, and 10 batches of fluctuating raw materials were continuously collected for verification. The results are shown in Table 3 below: Table 3 In this embodiment, as shown in Table 2, the improved NSGA-Ⅲ algorithm of the present invention is not a simple transplantation of a general algorithm. Its input layer directly couples 12 specific characteristic parameters of construction solid waste, and the objective function embeds a coupled evolution model of solid waste activity-cementing system strength-long-term durability. It is a special algorithm to solve the specific technical problem of unstable product quality caused by raw material fluctuations.

[0071] To address the significant batch fluctuations in solid waste raw materials, the algorithm incorporates a dynamically adjusted crossover and mutation probability based on the number of iterations and individual fitness. Compared to the standard NSGA-III, this algorithm further reduces the coefficient of variation by 54.8% and improves the pass rate by 12 percentage points in scenarios with substantial raw material fluctuations, effectively resolving a key industry pain point.

[0072] The algorithm of this invention can achieve a multi-objective balance of maximizing solid waste content, minimizing cost, and minimizing carbon emissions while meeting all performance constraints such as strength, impermeability, freeze resistance, and shrinkage, resulting in a significant improvement in overall benefits.

[0073] The above description is merely a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the forms disclosed herein and should not be construed as excluding other embodiments. It can be used in various other combinations, modifications, and environments, and can be altered within the scope of the concept described herein through the above teachings or related technologies or knowledge. Modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention should be within the protection scope of the appended claims.

Claims

1. A method for differential gradient activation and dynamic proportioning intelligent control of multi-source construction solid waste, characterized in that, Includes the following steps: S1 Multi-source Construction Solid Waste Pretreatment and Multi-dimensional Characterization: Construction solid waste from different sources and materials is crushed, screened, and impurity removed to obtain multi-grade aggregates; multi-sensor fusion technology is used to monitor the chemical composition, mineral composition, particle size distribution, moisture content, bulk density, porosity, and microcrack index of each grade of aggregate in real time, and the monitoring data is uploaded to the intelligent control system. S2 Based on Multi-Physics Field Coupling: Quantitative Prediction of Solid Waste Activity and Intelligent Decision-Making of Activation Process: The intelligent control system inputs the detection data into a pre-trained multi-physics field coupled solid waste activity evolution model, quantitatively predicts the 28-day activity index of solid waste under different activation process parameters, and uses the NSGA-Ⅲ multi-objective optimization algorithm to solve for the optimal gradient activation process parameters. S3 Differentiated gradient activation treatment of multi-source solid waste based on activity gradient matching: Different three-field synergistic gradient activation processes are used for treatment according to the activity level of the solid waste. S4 Dynamic Proportioning Intelligent Calculation of Activity-Strength-Durability Coupling Relationship: The intelligent control system establishes a dynamic proportioning multi-objective optimization model based on the activity index, hydration heat characteristics, volume stability, performance requirements of recycled products, real-time market prices of raw materials, and carbon emission factors of various solid wastes after activation. The improved NSGA-Ⅲ algorithm, which introduces an adaptive cross-mutation operator and an elite retention strategy, is used to solve for the optimal dosage of various raw materials. S5 High-Precision Dynamic Batching and Intelligent Mixing: The batching control system accurately measures various raw materials according to the optimal dynamic proportioning parameters and feeds them into the mixer for mixing in the preset order; during the mixing process, the workability and hydration process of the mixture are detected in real time, and the amount of water and additives are automatically adjusted. S6 Intelligent Monitoring and Continuous Optimization Control: Real-time collection of process parameters, equipment operating status, and product quality data for each process stage, and real-time analysis using an isolated forest anomaly detection algorithm; establishment of a feedback control system for process parameter detection and product quality detection, which automatically adjusts relevant process parameters based on preset expert rules and deep learning models when abnormalities in the production process or substandard product quality are detected; and establishment of a large database of the production process, using an attention-enhanced LSTM deep learning algorithm to analyze historical data, and regularly and automatically updating the solid waste activity evolution model and dynamic proportioning optimization model.

2. The method for differential gradient activation and dynamic proportioning intelligent control of multi-source construction solid waste according to claim 1, characterized in that, In step S1, the multi-sensor fusion technology uses a near-infrared spectrometer to detect mineral composition and porosity, an X-ray fluorescence spectrometer to detect chemical composition, a laser particle size analyzer to detect particle size distribution, an online moisture analyzer to detect moisture content, a tap density analyzer to detect bulk density, and an industrial CT scanner to detect microcrack index; the detection frequency is 1 time / minute, and the data transmission delay is ≤50ms.

3. The method for differential gradient activation and dynamic proportioning intelligent control of multi-source construction solid waste according to claim 1, characterized in that, The multiphysics-coupled solid waste activity evolution model in step S2 is established through the following steps: (1) Collect building solid waste samples with different materials and characteristics, conduct multiple activation experiments, obtain 28-day activity index data under different process parameters, construct training datasets, with a sample size of ≥1000 groups; (2) Based on the theories of mechanochemical kinetics, thermochemical kinetics, and sonicochemical kinetics, the model expression is established: in, for Solid waste activity index at any given time The initial activity index of solid waste. , , The rate constant is For mechanical grinding energy input, Grinding time, The calcination activation energy is given by R, where R is the ideal gas constant. The calcination temperature. For calcination time, This refers to the ultrasonic power. Ultrasound time, , , , , The reaction order is [number]. (3) The LM (Levenberg-Marquardt) algorithm is used to train the model until the model prediction error is ≤3%.

4. The method for differential gradient activation and dynamic proportioning intelligent control of multi-source construction solid waste according to claim 1, characterized in that, In step S2, the population size of the NSGA-Ⅲ multi-objective optimization algorithm is 150, the number of iterations is 300, the crossover probability is 0.85, and the mutation probability is 0.

12. The constraints are: the activity index of the activated solid waste ≥70% after 28 days, the unit energy consumption ≤550kWh / t, the processing capacity ≥60t / h, and the carbon emission per unit product ≤80kgCO2 / t.

5. The method for differential gradient activation and dynamic proportioning intelligent control of multi-source construction solid waste according to claim 1, characterized in that, In step S3, based on the activity gradient matching theory, construction solid waste is divided into three activity levels: high-calcium activity level (concrete solid waste, CaO content ≥35%), medium-silica-alumina activity level (brick and mortar solid waste, SiO2 content 50%-65%, Al2O3 content 15%-25%), and low-activity level (ceramic solid waste, SiO2 content ≥65%, Al2O3 content ≥20%). The determination of the activity level is not based solely on the source name of the solid waste, but rather on the content of CaO, SiO2, and Al2O3 or their relative ratios as the main determination parameters, combined with mineral composition, porosity, and microcrack index for verification. When the same solid waste sample simultaneously meets some conditions of multiple activity levels, the CaO content is used to determine whether it belongs to the high-calcium activity level, and the SiO2 and Al2O3 content is used to determine whether it belongs to the medium-silicon-aluminum activity level or the low-activity level. For solid wastes of different activity levels, a three-field synergistic gradient activation process with precise energy matching is designed. After high-temperature phase change activation of low-activity level solid waste, it does not directly enter the natural stacking cooling process, but is immediately rapidly cooled after calcination to cool the low-activity level solid waste from a high temperature state to 160-190℃, and then undergoes mechanochemical dissociation treatment so that the thermal stress cracks formed after high-temperature treatment can be coordinated with the subsequent grinding and dissociation process. For concrete-like solid waste with high calcium activity levels, a mechanochemical pre-activation-ultrasonic-assisted chemical activation gradient activation process is adopted, including the following steps: A1 is fed into a variable frequency ball mill for mechanochemical pre-activation. The grinding time is 12-22 minutes, the grinding media is φ20-φ30mm steel balls, the ball-to-material ratio is 6:1-9:1, and the grinding is carried out until the specific surface area is 390-430m². 2 / kg; A2 is fed into a continuous ultrasonic reactor for ultrasonic-assisted chemical excitation. The ultrasonic power is 280-420W, the frequency is 28-35kHz, the exciter is a composite solution of NaOH and Na2SiO3 with a molar ratio of 1:1-1:2, a concentration of 2.5-3.5mol / L, a solid-liquid ratio of 1:2.8-1:3.2, and an excitation time of 8-11min; After A3 activation, the mixture is filtered using a plate and frame filter press, washed until the pH value is 7.2-7.8, and then dried at 105℃ until the moisture content is ≤0.8%. For solid waste such as bricks and mortars with medium silicon-aluminum active layers, a low-temperature thermochemical activation-mechanical-chemical enhanced gradient activation process is adopted, including the following steps: B1 is fed into a segmented temperature-controlled rotary kiln for low-temperature thermochemical activation. The preheating temperature is 300-400℃, the holding temperature is 680-730℃, the heating rate is 7-9℃ / min, and the calcination time is 38-48min. B2 is then naturally cooled to room temperature and fed into a ball mill for mechanochemical strengthening. The grinding time is 22-38min, the ball-to-material ratio is 7:1-11:1, and the grinding is continued until the specific surface area is 430-470m². 2 / kg For ceramic solid waste with low activity levels, a gradient activation process of high-temperature phase change activation, rapid cooling toughening, and mechanochemical dissociation is adopted, with specific parameters as follows: C1 High-Temperature Phase Transformation Activation: Ceramic solid waste is fed into a segmented temperature-controlled rotary kiln. The preheating section temperature is 500-600℃, the holding section temperature is 960-1040℃, the heating rate is 10-12℃ / min, and the calcination time is 52-68min. The kiln atmosphere is a weakly oxidizing atmosphere with an oxygen content controlled at 8%-10%. C2 Rapid Cooling Toughening: After calcination, the material immediately enters a composite rapid cooler, using a combination of air cooling and atomized water cooling. The cooling rate is stable at 70-90℃ / min, cooling from the high temperature to 160-190℃. C3 Mechanochemical Dissociation: After cooling, the material is fed into a ball mill for grinding. The grinding media is φ20mm zirconia balls, the ball-to-material ratio is 8:1-13:1, and the grinding time is 28-48min, grinding to a specific surface area of ​​490-530m². 2 / kg.

6. The method for differential gradient activation and dynamic proportioning intelligent control of multi-source construction solid waste according to claim 1, characterized in that, In step S4, the objective function of the dynamic proportioning multi-objective optimization model is: The constraints are: In the formula, This represents the total number of types of raw materials. This represents the number of types of activated solid waste; This represents the total cost of raw materials. For the first The unit price of the raw materials, For the first The amount of each raw material used; To improve the utilization rate of solid waste cementitious materials, This represents the total amount of cementitious materials used. Carbon emissions per unit of product For the first Carbon emission factors of raw materials; This refers to the standard value of the compressive strength of recycled concrete cubes. This represents the design value for the compressive strength of recycled concrete cubes. The standard deviation of strength; Slump , These are the lower and upper limits for slump design, respectively. For impermeability grade, This is the design value for the impermeability grade; For freeze resistance rating, This is the design value for the freeze resistance rating; The shrinkage rate over 28 days is [missing information]. This is the design value for the shrinkage rate; For the first The maximum allowable amount of each raw material; the adaptive crossover and mutation operator of the improved NSGA-Ⅲ algorithm specifically refers to the crossover probability. in , , This represents the current iteration number. The maximum number of iterations, For individual fitness, The average fitness of the population; the probability of mutation. The formula is as follows: (Adaptive adjustment based on the number of iterations) in , The elite retention strategy specifically involves retaining the top 10% of individuals by fitness after each generation iteration, allowing them to directly enter the next generation.

7. The method for differential gradient activation and dynamic proportioning intelligent control of multi-source construction solid waste according to claim 1, characterized in that, In step S4, when the dynamic proportioning multi-objective optimization model screens candidate proportions, it first eliminates candidate proportions that do not meet the constraints of strength, slump, impermeability grade, freeze-thaw resistance grade, and 28-day drying shrinkage rate. Then, among the candidate proportions that meet the constraints, it sorts them non-dominatedly according to cost, solid waste utilization rate, and carbon emissions per unit product. The activity index, hydration heat characteristics, and volume stability input to the dynamic proportioning multi-objective optimization model in step S4 are all from the same batch of solid waste raw materials after the differentiated gradient activation in step S3. The dynamic proportioning multi-objective optimization model does not use the historical average activity data of unactivated solid waste raw materials to replace the detection data after activation of the same batch.

8. The method for intelligent control of differential gradient activation and dynamic proportioning of multi-source construction solid waste according to claim 1, characterized in that, The preset feeding sequence in step S5 is as follows: first, coarse aggregate and fine aggregate are added and stirred for 30s, then activated solid waste and cement are added and stirred for 60s, and finally water and admixtures are added and stirred for 30-60s; the stirring time is 90-150s, and the stirring speed is 60-90r / min; the online resistivity meter (25) detects the hydration process of the mixture in real time, and automatically adjusts the amount of admixture when the resistivity change rate exceeds the preset range.

9. The method for intelligent control of differential gradient activation and dynamic proportioning of multi-source construction solid waste according to claim 1, characterized in that, In step S6, when the activity index of the activated solid waste after 28 days is lower than the design value, the corresponding activation process parameters are adjusted according to the activity level of the solid waste. When the activity index of the high-calcium activity level solid waste is low, the grinding time, ultrasonic power, or composite solution concentration is adjusted. When the activity index of the silicon-aluminum activity level solid waste is low, the temperature of the heat preservation section, calcination time, or grinding time is adjusted. When the activity index of the low-activity level solid waste is low, the high-temperature phase change activation temperature, rapid cooling rate, or mechanochemical dissociation time is adjusted. The specific control strategy of the feedback control system in step S6 is as follows: (1) Based on the deviation between the set value and the actual value of the process parameters of each process, the incremental PID algorithm is used to automatically adjust the equipment operating parameters. The PID parameters are tuned using the Ziegler-Nichols method, and the control accuracy is ±1%. (2) Based on the deviation between the product quality inspection results and the design values, the activation process parameters and proportioning parameters are automatically adjusted using an attention-enhanced LSTM deep learning model. The model input is the process parameters and quality data of the previous hour, and the output is the process parameter adjustment amount. The control cycle is 1 hour. (3) During normal production, process parameter testing is the main focus, and product quality testing is the secondary focus; when the product quality deviation exceeds 5%, the focus is switched to product quality testing as the main focus and process parameter testing as the secondary focus.

10. The method for differential gradient activation and dynamic proportioning intelligent control of multi-source construction solid waste according to claim 1, characterized in that, In step S6, the preset expert rules are specifically as follows: (1) When the activity index of the activated solid waste is lower than the design value of 4%-8% after 28 days, the grinding time will be automatically extended by 4-8 minutes or the calcination temperature will be increased by 40-70℃. (2) When the activity index of the activated solid waste is more than 8% lower than the design value after 28 days, the grinding time will be automatically extended by 8-12 minutes or the calcination temperature will be increased by 70-100℃, and an audible and visual alarm signal will be issued. (3) When the 28-day compressive strength of the recycled product is 2%-4% lower than the design value, automatically increase the cement content by 1.5%-2.5% or decrease the solid waste content by 2%-4%; (4) When the 28-day compressive strength of the recycled product is more than 4% lower than the design value, the cement content will be automatically increased by 2.5%-4.5% or the solid waste content will be reduced by 4%-7%, and an audible and visual alarm signal will be issued. (5) When the slump of the mixture is more than 15 mm greater than the design value, the amount of water added will be automatically reduced by 0.8%-1.5% or the amount of admixture will be increased by 0.08%-0.15%; (6) When the slump of the mixture is more than 15 mm less than the design value, the amount of water added will be automatically increased by 0.8%-1.5% or the amount of admixture will be reduced by 0.08%-0.15%.

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