Rapid production system for low-odor high-flame-retardant PVC (polyvinyl chloride) pipe

Abstract

The invention relates to the technical field of PVC pipe production, in particular to a low-odor high-flame-retardant PVC pipe rapid production system which comprises a raw material proportion analysis module, a real-time proportion adjustment module, an extrusion process optimization module and a product quality test module. According to the invention, by adjusting the production process of the PVC pipe, in particular to management of additive ratio and extrusion process parameters, a dynamic adjustment and real-time feedback mechanism is introduced, so that the adaptability and accuracy of the production process are effectively improved, the production parameters are allowed to be adjusted in real time through refined control, the consistency of product quality is improved, and the production cost is reduced. The material waste caused by parameter deviation is also reduced, through real-time data analysis, it is ensured that each batch of pipes can meet strict environmental protection and safety standards, and especially in terms of flame retardance and low-odor performance, the product can better adapt to markets with special requirements for high-safety performance, such as high-rise buildings or dense equipment areas.

Description

Technical Field

[0001] This invention relates to the field of PVC pipe production technology, and in particular to a rapid production system for low-odor, high-flame-retardant PVC pipes. Background Technology

[0002] The field of PVC pipe manufacturing technology involves the processing and manufacturing of polyvinyl chloride (PVC), especially the technology used in producing pipes of various sizes and applications. Due to its cost-effectiveness, chemical stability and durability, PVC has become the material of choice for manufacturing water supply pipes, drainage pipes, electrical conduits and other industrial applications. This field includes PVC pipe design, raw material preparation, formulation adjustment (additives such as stabilizers, plasticizers and antioxidants), extrusion molding technology, cooling and cutting technology and post-processing.

[0003] The low-odor, high-flame-retardant PVC pipe rapid production system aims to improve the production efficiency of PVC pipes while ensuring that the products have low odor and high flame-retardant properties. This type of pipe is mainly used in environments with high fire safety requirements, such as high-rise buildings, underground facilities, and dense equipment areas. Through specific formulations and production processes, the system can reduce odor emissions during the production process, improve environmental quality, and meet the growing market demand for high-performance PVC pipes while ensuring material performance.

[0004] Traditional systems rely on fixed production formulas and parameter settings. When the quality of raw materials changes from batch to batch or the production environment changes, it is difficult to maintain the consistency of product performance, which can easily lead to batch-to-batch quality fluctuations. This inaccuracy in quality control not only increases production costs but also affects the market competitiveness of products. In addition, traditional systems have relatively limited ability to reduce odor emissions during the production process, which leads to non-compliance risks under increasingly stringent environmental standards. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and propose a rapid production system for low-odor, high-flame-retardant PVC pipes.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a rapid production system for low-odor, high-flame-retardant PVC pipes, the system comprising:

[0007] The raw material ratio analysis module, based on the input quality data of PVC raw materials and additives, compares the historical performance data of additive ratios, calculates the contribution rate of additives to odor and flame retardancy, obtains the additive performance analysis results, and adjusts the raw material ratios according to the analysis results to obtain raw material formulation information.

[0008] The real-time proportioning adjustment module dynamically adjusts the additive input ratio based on the raw material blending information and real-time production line feedback data, controls the raw material mixing process, monitors quality, records proportion changes, and generates proportioning adjustment monitoring information.

[0009] The extrusion process optimization module adjusts the monitoring information according to the ratio, combines the extrusion parameter data, analyzes the changes in temperature, speed and pressure during the extrusion process, predicts the impact on pipe quality, and optimizes the extrusion parameters to obtain the process optimization parameters.

[0010] Based on the process optimization parameters, the product quality testing module tests the low odor and high flame retardant properties of the finished PVC pipes, obtains test data, analyzes the deviation between the test data and the target performance standards, and obtains the product performance evaluation results.

[0011] The present invention is improved in that the calculation steps for the contribution rate of the additive to odor and flame retardancy are specifically as follows:

[0012] Based on the input quality data of PVC raw materials and additives, the additive ratio is calculated using the formula:

[0013]

[0014] Where, m PVC m represents the quality of PVC raw materials ωdd R represents the quality of the additive, and R represents the proportion of the additive in the raw material.

[0015] Calculate the historical average contribution rate using historical performance data, using the formula:

[0016]

[0017] Among them, w i It is the weight of each data point in the historical data, e i This is the corresponding performance value, v i It is the attenuation coefficient associated with the time of the data point, and C represents the historical average contribution rate;

[0018] The current additive ratio R is compared with the historical average contribution rate C, using the formula:

[0019]

[0020] Calculate the expected contribution rate C under the current ratio. expected , where α and β are regression coefficients, and γ is the normalization coefficient.

[0021] The present invention is improved in that the step of adjusting the raw material ratio according to the analysis results is specifically as follows:

[0022] The contribution of the additives to odor and flame retardancy was assessed by comparing the expected contribution rate with the performance target, and the difference in contribution rate was calculated using the formula:

[0023] ΔC=|C expected -C target |

[0024] Among them, C expected For the expected contribution rate of additives, C target ΔC represents the target contribution rate, and ΔC is the absolute difference between the expected contribution rate and the target contribution rate.

[0025] The new additive ratio is calculated based on the contribution rate difference ΔC and historical adjustment data, using the formula:

[0026]

[0027] Where k is the adjustment coefficient, h is the attenuation coefficient of historical adjustment data, and R is the original additive ratio. new This is the adjusted new proportion;

[0028] The new proportion R new When applied to the production process, it monitors the effectiveness of the new formulation, determines whether the expected goals have been achieved, and obtains information on raw material allocation.

[0029] The present invention is improved in that the dynamic adjustment step of the additive input ratio is specifically as follows:

[0030] Based on the raw material blending information, and according to the real-time monitoring data of the production line, the current usage data of additives and PVC raw materials are obtained, and the formula is used:

[0031]

[0032] Obtain the real-time additive input ratio R current , where m add，current For the current quality of the additive, m PVC，current For the quality of PVC raw materials, γ and λ are adjustment coefficients used to balance the properties and flowability of the raw materials;

[0033] Compare the real-time additive input ratio R current The ratio to the target additive is determined using the formula:

[0034]

[0035] The deviation in the additive input ratio ΔR is obtained, where R target For the target additive ratio, calculate the absolute value of the emphasis deviation;

[0036] Based on the additive addition ratio deviation ΔR, use the formula:

[0037]

[0038] Obtain the adjusted additive input ratio R new1 , where k is the linear adjustment factor and h is the nonlinear adjustment parameter.

[0039] The present invention is improved in that the step of obtaining the ratio adjustment monitoring information is specifically as follows:

[0040] Based on the dynamically adjusted additive input ratio, multiple parameters during the mixing process are detected using sensors and monitoring equipment, and the formula is applied:

[0041] M = f(T, P, U, V)

[0042] Calculate the mixing quality index M to obtain the mixing process monitoring data, where T is temperature, P is pressure, U is mixing uniformity, V is flowability, and f is a composite function;

[0043] Based on the monitoring data of the mixing process, analyze the quality and efficiency of the mixing process to determine whether it meets production standards. If M meets the production standards, no adjustment is needed; otherwise, calculate the required adjustment range using the following formula:

[0044]

[0045] The mixed mass adjustment requirement ΔM is obtained, where M target is the target mixed quality index, and M is the mixed quality index;

[0046] Based on the mixing process monitoring data and mixing quality adjustment requirements, use the following formula:

[0047] C info =g(R) new M, ΔM, S)

[0048] Obtain monitoring information on ratio adjustment C info Where M is the mixed quality index, ΔM is the mixed quality adjustment requirement, S is the stability system parameter, and R... new The new ratio is given, and g is the aggregation function.

[0049] The present invention is improved in that the steps for obtaining the process optimization parameters are specifically as follows:

[0050] Based on the stated ratio, adjust the monitoring information, combine it with extrusion parameter data, analyze the changes in temperature, speed, and pressure during the extrusion process, and use the formula:

[0051]

[0052] The dynamic analysis data D of the extrusion process is obtained, where T is the temperature value during the extrusion process, V is the material flow rate during the extrusion process, P is the pressure value during the extrusion process, α is the weighting coefficient for temperature data, β is the influence index for adjusting temperature data, γ is the weighting coefficient for velocity data, δ is the influence index for adjusting velocity data, ∈ is the weighting coefficient for the pressure-temperature ratio, and ζ is the pressure proportionality constant for adjusting the influence of temperature.

[0053] Based on dynamic analysis data D from the extrusion process, the potential impact on product quality is predicted using the following formula:

[0054]

[0055] The predicted impact of pipe quality Q is generated, where D is the dynamic analysis data of the extrusion process, λ is the weighting coefficient for the nonlinear effect of D, μ is the influence index for adjusting D, ν is the weighting coefficient for the square root inverse effect of D, and ξ is the constant for adjusting the value of D in the square root.

[0056] Based on the predicted impact of pipe quality Q, extrusion parameters are adjusted and product quality is optimized using the formula:

[0057]

[0058] Obtain the process optimization parameter P opt , where ρ and τ are optimization coefficients used for the weighting of Q-inverse ratio and logarithm, and σ and ω are constants.

[0059] The present invention is improved in that the test steps for low odor and high flame retardant performance are specifically as follows:

[0060] Based on the aforementioned process optimization parameters, finished PVC pipe samples were randomly selected from the production line. The pipe samples were then tested for low odor and high flame retardancy, and the test results were recorded. For low odor performance, the formula was used:

[0061]

[0062] For high flame retardant performance, the formula is as follows:

[0063]

[0064] Test data on low odor and high flame retardant performance were obtained, including O i C is the odor intensity index of sample i. i It is the measured concentration of volatile chemical substances, α and β are adjustment coefficients, F i T is the flame retardancy index of sample i. bi It is the time when combustion begins, T fiThis refers to the complete combustion time, where γ, δ, and ∈ are adjustment coefficients;

[0065] Summarize the test data for low odor and high flame retardancy performance, calculate the average performance index and coefficient of variation, using the formula:

[0066]

[0067] and

[0068]

[0069] Calculate the average low odor index O avg and average high flame retardancy index F avg , of which O i F is the odor intensity index of sample i. i is the flame retardancy index of sample i, and n is the total number of test samples.

[0070] The present invention is improved in that the steps for obtaining the product performance evaluation results are specifically as follows:

[0071] Based on the test data, the performance of the finished PVC pipes was iteratively analyzed, and the standard deviations of low odor and high flame retardant performance were calculated.

[0072] Compare and analyze data with market or industry performance standards using the following formula:

[0073]

[0074] Obtain the deviation of performance from the standard, ΔP, where P avg P is the average performance index of the test. std It is an industry standard, λ is the weighting coefficient, and μ is the deviation adjustment coefficient;

[0075] Based on the deviation ΔP between the performance and the standard, assess whether the product meets the quality requirements and obtain the product performance evaluation results.

[0076] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0077] In this invention, by adjusting the production process of PVC pipes, especially in the management of additive ratios and extrusion process parameters, a dynamic adjustment and real-time feedback mechanism is introduced, which effectively improves the adaptability and accuracy of the production process. The refined control allows for immediate adjustment of production parameters, which not only improves the consistency of product quality but also reduces material waste caused by parameter deviations. Through real-time data analysis, it is ensured that each batch of pipes meets strict environmental and safety standards, especially in terms of flame retardancy and low odor performance, enabling the product to better adapt to markets with special requirements for high safety performance, such as high-rise buildings or areas with dense equipment. Attached Figure Description

[0078] Figure 1 This invention provides a modular diagram of a rapid production system for low-odor, high-flame-retardant PVC pipes.

[0079] Figure 2 This invention provides a flowchart for calculating the contribution rate of additives to odor and flame retardancy in a rapid production system for low-odor, high-flame-retardant PVC pipes.

[0080] Figure 3 This invention provides a flowchart of the steps for adjusting the raw material ratio based on analysis results in a rapid production system for low-odor, high-flame-retardant PVC pipes.

[0081] Figure 4 This invention provides a flowchart for dynamically adjusting the additive input ratio in a rapid production system for low-odor, high-flame-retardant PVC pipes.

[0082] Figure 5 This invention provides a flowchart for obtaining proportioning adjustment monitoring information in a rapid production system for low-odor, high-flame-retardant PVC pipes;

[0083] Figure 6 This invention provides a flowchart for obtaining process optimization parameters in a rapid production system for low-odor, high-flame-retardant PVC pipes.

[0084] Figure 7 This invention provides a test flowchart for low odor and high flame retardant performance in a rapid production system for low-odor, high-flame-retardant PVC pipes.

[0085] Figure 8 This invention provides a flowchart for obtaining product performance evaluation results in a rapid production system for low-odor, high-flame-retardant PVC pipes. Detailed Implementation

[0086] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0087] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0088] Example

[0089] Please see Figure 1 This invention provides a technical solution: a rapid production system for low-odor, high-flame-retardant PVC pipes, comprising:

[0090] The raw material ratio analysis module, based on the input quality data of PVC raw materials and additives, compares the historical performance data of additive ratios, calculates the contribution rate of additives to odor and flame retardancy, obtains the additive performance analysis results, and adjusts the raw material ratios according to the analysis results to obtain raw material formulation information.

[0091] The real-time proportioning adjustment module dynamically adjusts the additive input ratio based on raw material blending information and real-time production line feedback data, controls the raw material mixing process, monitors quality, records proportion changes, and generates proportioning adjustment monitoring information.

[0092] The extrusion process optimization module adjusts the monitoring information based on the ratio, combines the extrusion parameter data, analyzes the changes in temperature, speed and pressure during the extrusion process, predicts the impact on pipe quality, and optimizes the extrusion parameters to obtain the process optimization parameters.

[0093] The product quality testing module, based on process optimization parameters, tests the low odor and high flame retardant properties of finished PVC pipes, acquires test data, analyzes the deviation between the test data and the target performance standards, and obtains product performance evaluation results.

[0094] Raw material formulation information includes the new proportions of stabilizers, plasticizers, and antioxidants; formulation adjustment monitoring information includes the dynamic proportions of additives, real-time quality feedback information, and formulation stability data; process optimization parameters include the adjusted extrusion temperature, adjusted extrusion speed, and adjusted extrusion pressure; and product performance evaluation results include odor detection level, flame retardancy performance level, and deviation from the standard.

[0095] Please see Figure 2 The specific steps for calculating the contribution of additives to odor and flame retardancy are as follows:

[0096] Based on the input quality data of PVC raw materials and additives, the additive ratio is calculated using the formula:

[0097]

[0098] Where, m PVC m represents the quality of PVC raw materials ωdd R represents the quality of the additive, and R represents the proportion of the additive in the raw material.

[0099] Calculate the historical average contribution rate using historical performance data, using the formula:

[0100]

[0101] Among them, w i It is the weight of each data point in the historical data, e i This is the corresponding performance value, v i It is the attenuation coefficient associated with the time of the data point, and C represents the historical average contribution rate;

[0102] The current additive ratio R is compared with the historical average contribution rate C, using the formula:

[0103]

[0104] Calculate the expected contribution rate C under the current ratio. expected , where α and β are regression coefficients, and γ is the normalization coefficient.

[0105] In calculating the additive ratio, the following assumptions are made:

[0106] m PVC Assuming it is 1000 grams;

[0107] m add Assuming it is 100 grams;

[0108] First, obtain the mass of PVC raw materials and additives, then divide the mass of additives by the mass of PVC raw materials to calculate the ratio.

[0109] Calculation process:

[0110] When m PVC =1000 grams, m add =100 grams, then:

[0111]

[0112] In calculating the historical average contribution rate, the following assumptions are made:

[0113] w i Assume equal weights of 1;

[0114] e i This corresponds to a performance value, such as a rating for odor and flame retardancy;

[0115] v i The decay coefficient is time-dependent on the data point and is used to account for the decay of the performance value over time. Assuming the data is relatively new, the coefficient is 0.9.

[0116] For each historical data point, calculate w. i ·e i ·v i ;

[0117] Perform a weighted average over all data points, adjusting the weights using a time decay factor.

[0118] calculate:

[0119] Suppose there are 3 data points, and e for each point i The values ​​are 0.8, 0.6, and 0.7 respectively, and the w value at each point is... i =1, v i If it is 0.9, then:

[0120] The molecule is:

[0121] 1·0.8·0.9 + 1·0.6·0.9 + 1·0.7·0.9 = 1.89

[0122] The denominator is:

[0123] 1·0.9 + 1·0.9 + 1·0.9 = 2.7

[0124]

[0125] In calculating the expected contribution rate under the current ratio, the following assumptions are made:

[0126] α, β, and γ are regression coefficients obtained through data analysis, assumed to be 2, 3, and 1.5, respectively.

[0127] R is the proportion obtained from the above;

[0128] C is the historical average contribution rate obtained from the above.

[0129] Use R and square the result, take the square root of the result C, and combine the above result with the regression coefficient to calculate:

[0130] If R = 0.1 and C = 0.7, then:

[0131] α·R 2 =2·(0.1) 2 =0.02

[0132]

[0133]

[0134] Please see Figure 3 The specific steps for adjusting the raw material ratio based on the analysis results are as follows:

[0135] To assess the contribution of additives to odor and flame retardancy, compare the expected contribution rate with the performance target, calculate the difference in contribution rate, and use the following formula:

[0136] ΔC=|C expected -C target |

[0137] Among them, C expected For the expected contribution rate of additives, C target ΔC represents the target contribution rate, and ΔC is the absolute difference between the expected contribution rate and the target contribution rate.

[0138] The new additive ratio is calculated based on the contribution rate difference ΔC and historical adjustment data, using the formula:

[0139]

[0140] Where k is the adjustment coefficient, h is the attenuation coefficient of historical adjustment data, and R is the original additive ratio. new This is the adjusted new proportion;

[0141] The new proportion R new When applied to the production process, it monitors the effectiveness of the new formulation, determines whether the expected goals have been achieved, and obtains information on raw material allocation.

[0142] In calculating the difference in contribution rates, the following assumptions are made:

[0143] C expected =0.65 (i.e., the predicted contribution of the additive to performance given by the prediction model is 65%), C target =0.70 (i.e., the target contribution rate required by the market or product design is 70%).

[0144] The calculation is as follows:

[0145] ΔC = |0.65 - 0.70| = 0.05

[0146] This indicates that the current contribution rate of the additive is 5% lower than the target contribution rate.

[0147] In calculating the new additive ratio, the following assumptions are made:

[0148] R = 0.1 (original ratio is 10%);

[0149] k = 0.5 (assuming 0.5 indicates that a small difference in contribution rate can cause a large adjustment in the ratio);

[0150] h = 0.2 (representing the impact of historical data on the current adjustment);

[0151] ΔC = 0.05 (derived from the above calculation).

[0152] The calculation process is as follows:

[0153]

[0154] This suggests that the new additive ratio should be adjusted to approximately 12.475% to get closer to the target contribution rate.

[0155] Please see Figure 4 The specific steps for dynamically adjusting the additive input ratio are as follows:

[0156] Based on raw material allocation information and real-time monitoring data from the production line, the current usage data of additives and PVC raw materials are obtained, and the following formula is used:

[0157]

[0158] Obtain the real-time additive input ratio R current , where m add，current For the current quality of the additive, m PVC，current For the quality of PVC raw materials, γ and λ are adjustment coefficients used to balance the properties and flowability of the raw materials;

[0159] Compare the real-time additive input ratio R current The ratio to the target additive is determined using the formula:

[0160]

[0161] The deviation in the additive input ratio ΔR is obtained, where R target For the target additive ratio, calculate the absolute value of the emphasis deviation and eliminate the influence of negative deviation;

[0162] Based on the additive addition ratio deviation ΔR, use the formula:

[0163]

[0164] Obtain the adjusted additive input ratio R new1 , where k is the linear adjustment factor and · is the nonlinear adjustment parameter used to reduce oscillations and over-adjustment during the adjustment process.

[0165] In obtaining the real-time usage ratio of additives and PVC raw materials, the following assumptions are made:

[0166] m add，current Assuming the production line currently uses 50 kg;

[0167] m PVC，current Assuming the production line currently uses 500 kg;

[0168] γ and λ are assumed to be 1.1 and 1.05, respectively, to adjust the ratio according to the properties of the additives and PVC raw materials.

[0169] Using the data assumed above, calculate the current additive input ratio:

[0170]

[0171] This indicates that the current additive usage ratio is approximately 10.48%.

[0172] In calculating the deviation from the target ratio, the following assumptions are made:

[0173] R target Set it to 10% or 0.1.

[0174] The target proportion using the above results and assumptions:

[0175]

[0176] This indicates a deviation of approximately 0.48% between the current input ratio and the target ratio.

[0177] During the proportional adjustment, the following assumptions are made:

[0178] k is 0.5;

[0179] h is set to 10 to reduce oscillations and over-adjustment during the adjustment process.

[0180] calculate:

[0181] Using the above results:

[0182]

[0183] R new1 ≈0.1024

[0184] This indicates that the adjusted additive input ratio is approximately 10.24%, which is close to the target ratio and reduces the deviation.

[0185] Please see Figure 5 The specific steps for obtaining monitoring information on ratio adjustment are as follows:

[0186] Based on dynamically adjusted additive ratios, multiple parameters during the mixing process are detected using sensors and monitoring equipment, and the formula is used:

[0187] M = f(T, P, U, V)

[0188] Calculate the mixing quality index M to obtain the mixing process monitoring data, where T is temperature, P is pressure, U is mixing uniformity, V is flowability, and f is a composite function;

[0189] Based on the monitoring data of the mixing process, analyze the quality and efficiency of the mixing process to determine whether it meets production standards. If M meets the production standards, no adjustment is needed; otherwise, calculate the required adjustment range using the following formula:

[0190]

[0191] The mixed mass adjustment requirement ΔM is obtained, where M target is the target mixed quality index, and M is the mixed quality index;

[0192] Based on the mixing process monitoring data and mixing quality adjustment requirements, use the following formula:

[0193] C info =g(R) new M, ΔM, S)

[0194] Obtain monitoring information on ratio adjustment C info Where M is the mixed quality index, ΔM is the mixed quality adjustment requirement, S is the stability system parameter, and R... new The new ratio is given, and g is the aggregation function.

[0195] During the monitoring of raw material mixing process, it is assumed that:

[0196] T is assumed to be 100°C during the mixing process;

[0197] P is assumed to be 200 kPa;

[0198] U is assumed to be 0.9 (ranging from 0 to 1, where 1 indicates perfect uniformity);

[0199] The V assumption is 0.8 (ranging from 0 to 1, where 1 represents perfect flow).

[0200] Assume the function f is a weighted average, with the weights depending on the influence of the process parameters:

[0201]

[0202] In analyzing the data from the mixing process, the following assumptions are made:

[0203] M is the obtained mixed quality index, which is 100.26;

[0204] M target Let's assume it's 100.

[0205] calculate:

[0206]

[0207] ΔM = 0.1326

[0208] In the monitoring information for ratio adjustment, it is assumed that:

[0209] R new Assume the adjusted value is 0.105;

[0210] M is 100.26;

[0211] ΔM is 0.1326;

[0212] The S-hypothesis is 0.95.

[0213] Calculation process:

[0214] Assume the function g is a simple summation:

[0215] C info =R new +M+ΔM+S

[0216] C info = 0.105 + 100.26 + 0.1326 + 0.95

[0217] C info =101.4476

[0218] Please see Figure 6 The specific steps for obtaining process optimization parameters are as follows:

[0219] Based on the adjustment of the formulation and monitoring information, combined with extrusion parameter data, analyze the changes in temperature, speed, and pressure during the extrusion process, using the formula:

[0220]

[0221] The dynamic analysis data D of the extrusion process is obtained, where T is the temperature value during the extrusion process, V is the material flow rate during the extrusion process, P is the pressure value during the extrusion process, α is the weighting coefficient for temperature data, β is the influence index for adjusting temperature data, γ is the weighting coefficient for velocity data, δ is the influence index for adjusting velocity data, ∈ is the weighting coefficient for the pressure-temperature ratio, and ζ is the pressure proportionality constant for adjusting the influence of temperature.

[0222] Based on dynamic analysis data D from the extrusion process, the potential impact on product quality is predicted using the following formula:

[0223]

[0224] The predicted impact of pipe quality Q is generated, where D is the dynamic analysis data of the extrusion process, λ is the weighting coefficient for the nonlinear effect of D, μ is the influence index for adjusting D, ν is the weighting coefficient for the square root inverse effect of D, and ξ is the constant for adjusting the value of D in the square root.

[0225] Based on the predicted impact of pipe quality Q, extrusion parameters are adjusted and product quality is optimized using the formula:

[0226]

[0227] Obtain the process optimization parameter P opt, where ρ and τ are optimization coefficients used for the weighting of Q-inverse ratio and logarithm, and σ and ω are constants used to avoid zero denominators and ensure computational validity.

[0228] In analyzing the temperature, speed, and pressure changes during the extrusion process, the following assumptions are made:

[0229] α=0.2, β=1.5, γ=0.3, δ=2.5, ∈=0.5, ζ=10

[0230] T = 200 degrees, V = 1.5 m / s, P = 150 bar

[0231] Calculate D:

[0232]

[0233] D = 565.6854 + 0.85908 + 0.35714

[0234] D = 566.9

[0235] In predicting the impact on pipe quality, the following assumptions are made:

[0236] λ=0.6, μ=2.2, ν=0.4, ξ=5

[0237] Using the above calculation, D = 566.90164.

[0238] Calculate Q:

[0239]

[0240] Q = 141717.204 - 0.4 * 0.0347105

[0241] Q≈141717.2

[0242] In optimizing extrusion parameters, we assume:

[0243] ρ=1.5, σ=1, τ=0.75, ω=10

[0244] Using the above calculation, Q = 141717.188.

[0245] Calculate P opt :

[0246]

[0247] P opt =1.5·0.000007+0.75·11.861

[0248] P opt =0.00001+8.89575

[0249] P opt =8.89576

[0250] Please see Figure 7 The specific testing steps for low odor and high flame retardant properties are as follows:

[0251] Based on process optimization parameters, samples of finished PVC pipes were randomly selected from the production line. The samples were then tested for low odor and high flame retardancy, and the test results were recorded. For low odor performance, the formula was used:

[0252]

[0253] For high flame retardant performance, the formula is as follows:

[0254]

[0255] Test data on low odor and high flame retardant performance were obtained, including O i C is the odor intensity index of sample i. i This refers to the measured concentration of volatile chemical substances. α and β are adjustment coefficients used to adjust the influence of the chemical substance concentration. F i T is the flame retardancy index of sample i. bi It is the time when combustion begins, T fi It is the complete combustion time, and γ, δ, and ∈ are adjustment coefficients, which are the weights affecting the time index;

[0256] Summarize the test data for low odor and high flame retardancy performance, calculate the average performance index and coefficient of variation, using the formula:

[0257]

[0258] and

[0259]

[0260] Calculate the average low odor index O avg and average high flame retardancy index F avg , of which O i F is the odor intensity index of sample i. i is the flame retardancy index of sample i, and n is the total number of test samples.

[0261] In the low odor and high flame retardant performance test, the following assumptions are made:

[0262] O i This represents the odor intensity index of sample i, with a low value indicating a low odor level.

[0263] C i This indicates the concentration of volatile chemical substances measured in sample i;

[0264] α and β are adjustment coefficients used to adjust the effect of chemical concentration on odor intensity. These coefficients are usually obtained through regression analysis of historical data to ensure that the model's predictions are consistent with actual measurements.

[0265] F i This represents the flame retardancy index of sample i, with a value close to 1 indicating high flame retardancy.

[0266] T bi It is the time when combustion begins;

[0267] T fi It is the time for complete combustion;

[0268] γ, δ, and ∈ are adjustment coefficients used to adjust the effect of time parameters on flame retardancy.

[0269] Assuming a PVC pipe sample, the following are the specific measurement values ​​and parameter settings:

[0270] C i =50ppm (concentration of volatile substances);

[0271] α = 0.03, β = 2 (parameters obtained through regression analysis);

[0272] T bi =30 seconds, T fi = 300 seconds;

[0273] γ=0.05, δ=1, ∈=1.

[0274] Calculate O i :

[0275]

[0276] O i =e -75

[0277] O i ≈3.73×10 -33 (Extremely low odor intensity)

[0278] Calculate F i :

[0279]

[0280] F i =1-0.005

[0281] F i =0.995 (extremely high flame retardancy)

[0282] In summarizing the test data, the following assumptions are made:

[0283] Assuming three PVC pipe samples were tested, and the O value of one sample has been calculated... i and F i Now assume that the O values ​​of the other two samples are... i and F i The following are examples:

[0284] Sample 2:

[0285] O2 = 4.11 × 10 -33 F2 = 0.994

[0286] Sample 3:

[0287] O3 = 3.20 × 10 -33 F3 = 0.993

[0288] Calculate the average low odor index O avg and average high flame retardancy index F avg :

[0289]

[0290] O avg ≈3.68×10 -33 (Average low odor index)

[0291]

[0292] F avg ≈0.994 (average high flame retardancy index)

[0293] Please see Figure 8 The specific steps for obtaining product performance evaluation results are as follows:

[0294] Based on the test data, the performance of the finished PVC pipes was iteratively analyzed, and the standard deviations of low odor and high flame retardant performance were calculated.

[0295] Compare and analyze data with market or industry performance standards using the following formula:

[0296]

[0297] Obtain the deviation of performance from the standard, ΔP, where P avg P is the average performance index of the test. std It is an industry standard, where λ is the weighting coefficient and μ is the deviation adjustment coefficient, used to increase the flexibility and accuracy of the calculation;

[0298] Based on the deviation ΔP between the performance and the standard, assess whether the product meets the quality requirements and obtain the product performance evaluation results.

[0299] Suppose that the following data was obtained in a certain test batch:

[0300] P avg =85, representing the average performance index obtained from the test;

[0301] P std =90, the target performance index set by industry standards;

[0302] λ = 0.1, the selected weighting coefficient;

[0303] μ = 5, deviation adjustment coefficient.

[0304] Substitute the value into the formula to calculate the performance deviation ΔP:

[0305]

[0306] ΔP≈2.74

[0307] Assuming the threshold is 3.0, the resulting ΔP is approximately 2.74. A judgment is then made:

[0308] ΔP ≤ threshold, meets the standard

[0309] ΔP > threshold, does not meet the standard.

[0310] Since ΔP is approximately 2.74, which is less than the threshold, it meets the standard.

[0311] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A rapid production system for low-odor, high-flame-retardant PVC pipes, characterized in that, The system includes: The raw material ratio analysis module, based on the input quality data of PVC raw materials and additives, compares the historical performance data of additive ratios, calculates the contribution rate of additives to odor and flame retardancy, obtains the additive performance analysis results, and adjusts the raw material ratios according to the analysis results to obtain raw material formulation information. The real-time proportioning adjustment module dynamically adjusts the additive input ratio based on the raw material blending information and real-time production line feedback data, controls the raw material mixing process, monitors quality, records proportion changes, and generates proportioning adjustment monitoring information. The extrusion process optimization module adjusts the monitoring information according to the ratio, combines the extrusion parameter data, analyzes the changes in temperature, speed and pressure during the extrusion process, predicts the impact on pipe quality, and optimizes the extrusion parameters to obtain the process optimization parameters. Based on the process optimization parameters, the product quality testing module tests the low odor and high flame retardant properties of the finished PVC pipes, obtains test data, analyzes the deviation between the test data and the target performance standards, and obtains the product performance evaluation results.

2. The rapid production system for low-odor, high-flame-retardant PVC pipes according to claim 1, characterized in that, The specific steps for calculating the contribution of the additive to odor and flame retardancy are as follows: Based on the input quality data of PVC raw materials and additives, the additive ratio is calculated using the formula: Where, m PVC m represents the quality of PVC raw materials ωdd R represents the quality of the additive, and R represents the proportion of the additive in the raw material. Calculate the historical average contribution rate using historical performance data, using the formula: Among them, w i It is the weight of each data point in the historical data, e i This is the corresponding performance value, v i It is the attenuation coefficient associated with the time of the data point, and C represents the historical average contribution rate; The current additive ratio R is compared with the historical average contribution rate C, using the formula: Calculate the expected contribution rate C under the current ratio. expected , where α and β are regression coefficients, and γ is the normalization coefficient.

3. The rapid production system for low-odor, high-flame-retardant PVC pipes according to claim 1, characterized in that, The specific steps for adjusting the raw material ratio based on the analysis results are as follows: The contribution of the additives to odor and flame retardancy was assessed by comparing the expected contribution rate with the performance target, and the difference in contribution rate was calculated using the formula: ΔC=|C expected -C target | Among them, C expected For the expected contribution rate of additives, C target ΔC represents the target contribution rate, and ΔC is the absolute difference between the expected contribution rate and the target contribution rate. The new additive ratio is calculated based on the contribution rate difference ΔC and historical adjustment data, using the formula: Where k is the adjustment coefficient, h is the attenuation coefficient of historical adjustment data, and R is the original additive ratio. new This is the adjusted new proportion; The new proportion R new When applied to the production process, it monitors the effectiveness of the new formulation, determines whether the expected goals have been achieved, and obtains information on raw material allocation.

4. The rapid production system for low-odor, high-flame-retardant PVC pipes according to claim 1, characterized in that, The specific steps for dynamically adjusting the additive input ratio are as follows: Based on the raw material blending information, and according to the real-time monitoring data of the production line, the current usage data of additives and PVC raw materials are obtained, and the formula is used: Obtain the real-time additive input ratio R current , where m add，current For the current quality of the additive, m PVC，current For the quality of PVC raw materials, γ and λ are adjustment coefficients used to balance the properties and flowability of the raw materials; Compare the real-time additive input ratio R current The ratio to the target additive is determined using the formula: The deviation in the additive input ratio ΔR is obtained, where R target For the target additive ratio, calculate the absolute value of the emphasis deviation; Based on the additive addition ratio deviation ΔR, use the formula: Obtain the adjusted additive input ratio R new1 , where k is the linear adjustment factor and h is the nonlinear adjustment parameter.

5. The rapid production system for low-odor, high-flame-retardant PVC pipes according to claim 1, characterized in that, The specific steps for obtaining the ratio adjustment monitoring information are as follows: Based on the dynamically adjusted additive input ratio, multiple parameters during the mixing process are detected using sensors and monitoring equipment, and the formula is used: M = f(T, P, U, V) Calculate the mixing quality index M to obtain the mixing process monitoring data, where T is temperature, P is pressure, U is mixing uniformity, V is flowability, and f is a composite function; Based on the monitoring data of the mixing process, analyze the quality and efficiency of the mixing process to determine whether it meets production standards. If M meets the production standards, no adjustment is needed; otherwise, calculate the required adjustment range using the following formula: The mixed mass adjustment requirement ΔM is obtained, where M target is the target mixed quality index, and M is the mixed quality index; Based on the mixing process monitoring data and mixing quality adjustment requirements, use the following formula: C info =g(R new ,M,ΔM,S) Obtain monitoring information on ratio adjustment C info Where M is the mixed quality index, ΔM is the mixed quality adjustment requirement, S is the stability system parameter, and R... new The new ratio is given, and g is the aggregation function.

6. The rapid production system for low-odor, high-flame-retardant PVC pipes according to claim 1, characterized in that, The specific steps for obtaining the process optimization parameters are as follows: Based on the stated ratio, adjust the monitoring information, combine it with extrusion parameter data, analyze the changes in temperature, speed, and pressure during the extrusion process, and use the formula: The dynamic analysis data D of the extrusion process is obtained, where T is the temperature value during the extrusion process, V is the material flow rate during the extrusion process, P is the pressure value during the extrusion process, α is the weighting coefficient for temperature data, β is the influence index for adjusting temperature data, γ is the weighting coefficient for velocity data, δ is the influence index for adjusting velocity data, ∈ is the weighting coefficient for the pressure-temperature ratio, and ζ is the pressure proportionality constant for adjusting the influence of temperature. Based on dynamic analysis data D from the extrusion process, the potential impact on product quality is predicted using the following formula: The predicted impact of pipe quality Q is generated, where D is the dynamic analysis data of the extrusion process, λ is the weighting coefficient for the nonlinear effect of D, μ is the influence index for adjusting D, ν is the weighting coefficient for the square root inverse effect of D, and ξ is the constant for adjusting the value of D in the square root. Based on the predicted impact of pipe quality Q, extrusion parameters are adjusted and product quality is optimized using the formula: Obtain the process optimization parameter P opt , where ρ and τ are optimization coefficients used for the weighting of Q-inverse ratio and logarithm, and σ and ω are constants.

7. The rapid production system for low-odor, high-flame-retardant PVC pipes according to claim 1, characterized in that, The specific test steps for low odor and high flame retardant performance are as follows: Based on the aforementioned process optimization parameters, finished PVC pipe samples were randomly selected from the production line. The pipe samples were then tested for low odor and high flame retardancy, and the test results were recorded. For low odor performance, the formula was used: For high flame retardant performance, the formula is as follows: Test data on low odor and high flame retardant performance were obtained, including O i C is the odor intensity index of sample i. i It is the measured concentration of volatile chemical substances, α and β are adjustment coefficients, F i T is the flame retardancy index of sample i. bi It is the time when combustion begins, T fi This refers to the complete combustion time, where γ, δ, and ∈ are adjustment coefficients; Summarize the test data for low odor and high flame retardancy performance, calculate the average performance index and coefficient of variation, using the formula: and Calculate the average low odor index O avg and average high flame retardancy index F avg , of which O i F is the odor intensity index of sample i. i is the flame retardancy index of sample i, and n is the total number of test samples.

8. The rapid production system for low-odor, high-flame-retardant PVC pipes according to claim 1, characterized in that, The specific steps for obtaining the product performance evaluation results are as follows: Based on the test data, the performance of the finished PVC pipes was iteratively analyzed, and the standard deviations of low odor and high flame retardant performance were calculated. Compare and analyze data with market or industry performance standards using the following formula: Obtain the deviation of performance from the standard, ΔP, where P avg P is the average performance index of the test. std It is an industry standard, λ is the weighting coefficient, and μ is the deviation adjustment coefficient; Based on the deviation ΔP between the performance and the standard, assess whether the product meets the quality requirements and obtain the product performance evaluation results.

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