Fermentation device for producing compound fertilizer

Abstract

The invention discloses a fermentation device for compound fertilizer production and a control method thereof, and belongs to the technical field of compound fertilizer production, the fermentation device comprises a device body and a control system, and the control system comprises an efficiency analysis module, a quality analysis module, a benefit analysis module, a reliability analysis module, a temperature and oxygen condition analysis module and an aeration power consumption adjustment module. By constructing a processing efficiency model, a product quality model, an economic model, a system reliability model, a temperature-oxygen adaptation model and an aeration power consumption ratio optimization model, a processing efficiency coefficient, a product quality coefficient, an economic coefficient, a system reliability coefficient, a temperature-oxygen adaptation degree and a target aeration system power consumption ratio are respectively output. Through multi-module collaborative analysis and modeling calculation, self-adaptive adjustment of the power consumption ratio of the aeration system is realized, and the problem that an existing fermentation device is difficult to collaboratively optimize between energy consumption control and fermentation process requirements is effectively solved.

Description

Technical Field

[0001] This invention belongs to the field of compound fertilizer production technology, and particularly relates to a fermentation device for compound fertilizer production. Background Technology

[0002] In the production of compound fertilizers, the fermentation of organic materials is a crucial step, and its effectiveness directly affects the quality of the final fertilizer and the production cost.

[0003] Currently, existing compound fertilizer production fermentation equipment typically focuses on achieving basic fermentation functions, such as creating a suitable environment for microorganisms to decompose organic matter through stirring, aeration, and temperature control. In terms of control, most devices employ simple control strategies based on a single or a few parameters (such as temperature or time). While some improved devices have introduced automated control elements, their control logic remains relatively independent, failing to deeply integrate and analyze multiple dimensions of indicators, including overall fermentation efficiency, final product quality, economic cost, and system reliability. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a fermentation device for compound fertilizer production, which solves the aforementioned problems.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a fermentation device for compound fertilizer production, comprising a device body, and further comprising: The control system, used to regulate fermentation energy consumption, includes: The efficiency analysis module constructs a treatment efficiency model based on the fermentation cycle, volatile solids degradation rate, and average daily processing capacity, and outputs the treatment efficiency coefficient. The quality analysis module constructs a product quality model based on seed germination index, nutrient retention rate, and pest inactivation rate, and outputs product quality coefficients. The benefit analysis module constructs an economic model based on power consumption, water consumption, and turning uniformity per ton of processing and outputs economic coefficients. The reliability analysis module constructs a system reliability model based on the fully automatic operation rate, effective sensor data acquisition rate, and average repair time for a single fault, and outputs the system reliability coefficient. The temperature and oxygen conditions analysis module constructs a temperature-oxygen adaptation model based on the average fermentation temperature and average fermentation oxygen concentration under the processing efficiency coefficient and product quality coefficient, and outputs the temperature-oxygen adaptation degree. The aeration power consumption adjustment module constructs an aeration power consumption ratio optimization model based on temperature-oxygen compatibility, economic coefficient, system reliability coefficient, and aeration system power consumption ratio. It outputs the target aeration system power consumption ratio and adjusts the aeration system power consumption ratio to the target aeration system power consumption ratio.

[0006] On the basis of the above technical solutions, the application further provides the following optional technical solutions. Further technical solutions: the working content of the aeration power consumption adjustment module includes: Based on the temperature-oxygen adaptation degree, the economic coefficient, and the system reliability coefficient, a proportional gain model is constructed to obtain an adaptive proportional gain coefficient, and the proportional gain model is represented as: Among them, represents the adaptive proportional gain coefficient, represents the basic proportional gain coefficient, represents the adaptive gain adjustment coefficient, represents the temperature-oxygen adaptation degree, represents the economic coefficient, represents the system reliability coefficient. Based on the proportional gain coefficient, the temperature-oxygen adaptation degree, the economic coefficient, and the system reliability coefficient, a regulation amount model is constructed to obtain an electric consumption proportion regulation amount, and the regulation amount model is represented as: Among them, represents the electric consumption proportion regulation amount, represents the adaptive proportional gain coefficient, represents the target temperature-oxygen adaptation degree, represents the temperature-oxygen adaptation degree, represents the economic coefficient, represents the system reliability coefficient, represents the economic reliability penalty coefficient, represents the regulation index. Based on the aeration system electric consumption proportion and the electric consumption proportion regulation amount, an aeration electric consumption proportion optimization model is constructed to obtain a target aeration system electric consumption proportion, and the aeration electric consumption proportion optimization model is represented as: Among them, represents the target aeration system electric consumption proportion, represents the aeration system electric consumption proportion, represents the electric consumption proportion regulation amount. Adjust the aeration system electric consumption proportion to the target aeration system electric consumption proportion.

[0007] Further technical solutions: the working steps of the temperature-oxygen condition analysis module are: The absolute difference between the fermentation average temperature and the optimal temperature is processed by ratio with the allowed deviation from the optimal temperature value to obtain a temperature deviation index; The absolute difference between the fermentation average oxygen concentration and the optimal oxygen concentration is processed by ratio with the allowed deviation from the optimal oxygen concentration value to obtain an oxygen concentration deviation index.​​​ A performance state model is constructed based on the processing efficiency coefficient and the product quality coefficient, and a performance state coefficient is obtained, and the performance state model is represented as: ; Wherein, represents the performance state coefficient, represents the processing efficiency coefficient, represents the product quality coefficient, represents the weight coefficient, and The greater the value is, the better the device performance is. A temperature-oxygen adaptation model is constructed based on the temperature deviation index and the oxygen concentration index under the performance state coefficient, and a temperature-oxygen adaptation degree is obtained, and the temperature-oxygen adaptation model is represented as: ; Wherein, represents the temperature-oxygen adaptation degree, represents the performance state coefficient, represents the temperature deviation index, represents the oxygen concentration index, represents the temperature sensitivity coefficient, represents the oxygen concentration sensitivity coefficient, and The greater the value is, the higher the matching degree of the current temperature and oxygen conditions with the system target is.

[0008] Further technical solutions: the working content of the reliability analysis module includes: Obtain the full-automatic operation rate, the sensor data effective acquisition rate, and the average repair time of single failure; The average repair time of single failure is processed by ratio with the reference repair time, and a failure repair index is obtained; The full-automatic operation rate and the sensor data effective acquisition rate are processed by maximum-minimum normalization, and an automatic operation index and a sensor effective acquisition rate index are obtained; A system reliability model is constructed based on the automatic operation index, the sensor effective acquisition rate index, and the failure repair index, and a system reliability coefficient is obtained, and the system reliability model is represented as: ; Wherein, represents the system reliability coefficient, represents the automatic operation index, represents the sensor effective acquisition rate index, represents the failure repair index, represents the repair time decay coefficient, and The greater the value is, the higher the system reliability is.

[0009] Further technical solutions: the working content of the benefit analysis module includes:​ Obtain the power consumption, water consumption, and turning uniformity per ton of treatment; The turning uniformity is normalized by maximum-minimum to obtain a turning uniformity index; The power consumption and water consumption per ton of treatment are processed by ratio with reference values to obtain a power consumption index and a water consumption index per ton of treatment; An economic model is constructed based on the turning uniformity index, the power consumption index, and the water consumption index per ton of treatment to obtain an economic coefficient, and the economic model is expressed as: ; Among them, represents the economic coefficient, represents the turning uniformity index, represents the power consumption index per ton of treatment, represents the water consumption index, represents the turning uniformity sensitivity coefficient, represents the power consumption sensitivity coefficient per ton of treatment, represents the water consumption sensitivity coefficient, and the The greater the value, the better the economy.

[0010] Further technical solutions: the working content of the quality analysis module includes: Obtain the seed germination index, nutrient retention rate, and harmful organism inactivation rate; The seed germination index, nutrient retention rate, and harmful organism inactivation rate are normalized by maximum-minimum to obtain a seed germination factor, a nutrient retention rate index, and a harmful organism inactivation rate index; A product quality model is constructed based on the seed germination factor, the nutrient retention rate index, and the harmful organism inactivation rate index to obtain a product quality coefficient, and the product quality model is expressed as: ; Among them, represents the product quality coefficient, represents the seed germination factor, represents the nutrient retention rate index, represents the harmful organism inactivation rate index, represents the index weight coefficient and , the The greater the value, the better the product quality (completely decomposed and high nutrient retention rate).

[0011] Further technical solutions: the working content of the efficiency analysis module includes: Obtain the fermentation period, volatile solid degradation rate, and daily average treatment capacity; The fermentation period, volatile solid degradation rate, and daily average treatment capacity are normalized by maximum-minimum to obtain a period index, a degradation rate index, and a treatment capacity index; A processing efficiency model is constructed based on the cycle index, degradation rate index and processing capacity index, and a processing efficiency coefficient is obtained, and the processing efficiency model is expressed as: ; Wherein, represents the processing efficiency coefficient, represents the cycle index, represents the degradation rate index, represents the processing capacity index, represents the weight coefficient and , the and the greater the value, the higher the system performance processing efficiency.

[0012] The present application provides a kind of for compound fertilizer production fermentation device, compared with prior art has the following beneficial effects: 1, the efficiency analysis, quality analysis, benefit analysis and reliability analysis module are constructed in the present application, the multi-dimensional, comprehensive evaluation of fermentation device operating state is realized, and scientific basis is provided for precision control; 2, the present application associates processing efficiency with product quality and key fermentation environment parameters through temperature and oxygen condition analysis module, and establishes an effective bridge between process requirements and control targets; 3, the aeration power consumption adjustment module in the present application utilizes the output results of the aforementioned module, and dynamically adjusts the proportion of aeration system power consumption through the established optimization model, which realizes the optimization of device operation economy while ensuring the core fermentation process (efficiency and quality); 4, the control system of the present application has self-adaptive ability, and can intelligently adjust the control strength according to the current performance state and reliability level of the device, thereby enhancing the robustness of the system under different working conditions and the stability of long-term operation. BRIEF DESCRIPTION OF DRAWINGS

[0013] Figure 1 The flowchart of the control system of the present application is shown. DETAILED DESCRIPTION

[0014] In order to make the purpose, technical scheme and advantages of the present application more clear, the present application is further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and do not limit the present application.

[0015] The specific implementation of the present application is described in detail below in combination with specific examples.

[0016] Please refer to Figure 1 , an embodiment of the present application provides a fermentation device for compound fertilizer production, which comprises a device body and further comprises: A control system for regulating energy consumption of fermentation, comprising: An efficiency analysis module, which constructs a processing efficiency model based on fermentation cycle (total time required for completing a complete fermentation (from feeding to discharging)), volatile solid degradation rate (proportion of organic matter decomposed by microorganisms during fermentation), and daily average processing capacity (weight of raw materials that the device can process per day), and outputs a processing efficiency coefficient; A quality analysis module, which constructs a product quality model based on seed germination index, nutrient retention rate (total nitrogen (or core nutrients) after fermentation and before fermentation), and harmful organism inactivation rate (killing effect on pathogens such as E. coli and roundworm eggs), and outputs a product quality coefficient; A benefit analysis module, which constructs an economic model based on ton processing power consumption (electricity consumption for processing each ton of material), water consumption (amount of fresh water supplemented during fermentation to maintain humidity), and turning uniformity (uniformity of material (maximum moisture content gradient) when discharging), and outputs an economic coefficient; A reliability analysis module, which constructs a system reliability model based on full-automatic operation rate (ratio of automatic operation steps to total steps), sensor data effective collection rate (proportion of all sensor data successfully collected and recorded when the system is running), and average repair time for single failure (average time required from reporting a fault to restoring normal production after a fault occurs in the device), and outputs a system reliability coefficient; An oxygen-temperature condition analysis module, which constructs a temperature-oxygen adaptation model based on average fermentation temperature and average oxygen concentration under the processing efficiency coefficient and the product quality coefficient, and outputs a temperature-oxygen adaptation degree; An aeration power consumption adjustment module, which constructs an aeration power consumption proportion optimization model based on the temperature-oxygen adaptation degree, the economic coefficient, the system reliability coefficient, and the aeration system power consumption proportion, and outputs a target aeration system power consumption proportion, and adjusts the aeration system power consumption proportion to the target aeration system power consumption proportion.

[0017] Through the above technical solutions, the present application realizes dynamic balance and collaborative optimization of multi-dimensional indicators in the fermentation process. The quantitative evaluation of processing efficiency and product quality provides decision-making basis for process parameter adjustment, and the introduction of economic coefficient and reliability coefficient ensures that the optimization process takes into account cost control and system stability. The temperature-oxygen adaptation degree model effectively coordinates the relationship between environmental parameters and process goals, and the dynamic adjustment of aeration power consumption reduces energy consumption fluctuations while ensuring fermentation quality. This scheme solves the technical defects of single dimension and fragmented parameters in traditional control strategies, and improves the overall optimization level of the fermentation process.

[0018] Preferably, the working content of the efficiency analysis module includes: Obtaining based on fermentation cycle, volatile solid degradation rate, and daily average processing capacity; The fermentation cycle, volatile solid degradation rate, and daily processing capacity are maximum-minimum normalized to obtain cycle index, degradation rate index, and processing capacity index; A processing efficiency model is constructed based on the cycle index, degradation rate index, and processing capacity index to obtain a processing efficiency coefficient, and the processing efficiency model is represented as: ; wherein, represents the processing efficiency coefficient, represents the cycle index, represents the degradation rate index, represents the processing capacity index, represents a weight coefficient, and , the and the greater the value, the higher the system performance processing efficiency.

[0019] The fermentation cycle can be specifically realized by recording the time interval from feeding to discharging with a timer to measure the time efficiency of the device. The volatile solid degradation rate can be specifically realized by detecting the change in organic matter content in the material before and after fermentation and calculating the degradation percentage to evaluate the material decomposition effect. The daily processing capacity can be specifically realized by weighing sensor statistics daily processing capacity and calculating the average value to reflect the device productivity level. The processing efficiency model refers to a mathematical model that integrates multiple dimensions by weighted summation, which can be specifically realized by setting a weight coefficient and calculating a linear combination value to quantitatively evaluate the comprehensive processing efficiency of the device.

[0020] Specifically, first, the raw data of the fermentation cycle, volatile solid degradation rate, and daily processing capacity are obtained through sensors or detection equipment. Then, the maximum-minimum normalization method is used to convert each parameter into dimensionless cycle index, degradation rate index, and processing capacity index, eliminating the influence of dimensional differences on analysis. Finally, the three indexes are integrated into a processing efficiency coefficient through a weighted summation model, and the weight coefficient can be dynamically adjusted according to actual production needs. For example, when it is necessary to shorten the production cycle, the weight coefficient of the cycle index can be increased ; when it is necessary to improve the material decomposition effect, the weight coefficient of the degradation rate index can be increased . This processing method allows the device to flexibly adjust the evaluation focus according to different production targets while maintaining quantitative monitoring of the comprehensive efficiency.

[0021] Compared with the prior art, the existing fermentation device usually only monitors a single parameter for efficiency evaluation, for example, simply taking the fermentation period or processing capacity as the evaluation index. However, the present scheme constructs a multi-dimensional comprehensive evaluation system by fusing the indicators of time efficiency, material decomposition effect and production capacity level. The traditional method lacks a multi-parameter fusion mechanism, which easily leads to the problem of degradation of other indicators when optimizing a single indicator, for example, shortening the fermentation period may lead to a decrease in degradation rate. The present scheme realizes balanced optimization of multiple indicators through normalization processing and a weighting model, avoiding the limitations of single parameter control.

[0022] Through the above technical scheme, the present application can effectively solve the one-sidedness of evaluation caused by single parameter control in the prior art, and construct a multi-dimensional evaluation model by fusing time efficiency, decomposition effect and production capacity indicators, thereby providing a quantitative basis for the comprehensive efficiency optimization of the fermentation device. The dynamic calculation of the processing efficiency coefficient enables the system to reflect the running state of the device in real time, and the adjustable characteristics of the weight coefficient enable the evaluation model to adapt to different production demands, thereby providing reliable data support for the optimization decision of subsequent aeration power consumption and other control links.

[0023] Preferably, the working content of the quality analysis module includes: obtaining seed germination index, nutrient retention rate and harmful organism inactivation rate; performing maximum-minimum normalization processing on the seed germination index, the nutrient retention rate and the harmful organism inactivation rate to obtain a seed germination factor, a nutrient retention rate index and a harmful organism inactivation rate index; constructing a product quality model based on the seed germination factor, the nutrient retention rate index and the harmful organism inactivation rate index to obtain a product quality coefficient, the product quality model being expressed as: wherein, the product quality coefficient is represented by Q, the seed germination factor is represented by S, the nutrient retention rate index is represented by N, the harmful organism inactivation rate index is represented by H, the index weight coefficient is represented by W, and , the product quality is better (completely decomposed and high nutrient retention rate) when the value is larger.

[0024] ​​The seed germination index is an index for evaluating maturity, which is determined by detecting the degree of inhibition of seed germination by fermentation products. Specifically, the seed germination index can be determined by a standard germination test method, for example, by sowing specific plant seeds after mixing the fermentation products with the substrate, and then counting the germination rate and root length data. The nutrient retention rate is the ratio of the core nutrient content before and after fermentation, which can be determined by chemical analysis method to determine the retention rate of key elements such as total nitrogen, phosphorus, and potassium. The harmful organism inactivation rate is the killing effect of pathogenic microorganisms, which can be detected by microbial culture method to detect the survival rate of indicator bacteria such as Escherichia coli and roundworm eggs. The maximum-minimum normalization processing is a linear mapping of the original data to the [0, 1] interval, which eliminates the influence of different dimensions. For example, the seed germination index is divided by the historical maximum value of the index. The mathematical model in the form of product is that the contribution of each index is allocated by an exponential weight. Specifically, the weight coefficient can be adjusted based on production needs. For example, when the health and safety requirements are improved, the weight coefficient of the harmful organism inactivation rate index is increased.

[0025] Specifically, the seed germination index reflects the phytotoxicity of fermentation products through the germination test, and the higher the value, the more complete the maturity. The nutrient retention rate quantifies the degree of nutrient loss through chemical analysis, and the higher the value, the more effective the nutrient retention. The harmful organism inactivation rate assesses the health and safety level through microbial detection, and the higher the value, the better the pathogen inactivation effect. After converting the three indexes into dimensionless indexes through normalization processing, the product model is used to fuse the multi-dimensional data, and the exponential weight coefficient can be dynamically adjusted according to the needs of different production stages. For example, in the organic fertilizer production standard, if it is necessary to prioritize the harmless requirement, the weight coefficient of the harmful organism inactivation rate index can be set to 0.5, and the other two are 0.25, so that the model output more sensitively reflects the changes in health and safety level. The model fuses multi-dimensional quality indicators into a single coefficient through mathematical operations, realizing the quantitative evaluation of product quality.

[0026] Compared with the prior art, the traditional fermentation device usually only detects a single index such as temperature or pH value to indirectly infer the product quality, lacking direct quantitative evaluation of maturity, nutrient availability, and health and safety. Although some improved schemes in the prior art introduce germination index detection, they do not correlate it with nutrient retention rate and pathogen inactivation rate, and do not solve the problem of non-uniformity of multi-index dimensions. The present scheme eliminates the dimension difference through normalization processing, and realizes the fusion evaluation of multi-dimensional quality indicators by combining the product model with adjustable weight, making the product quality evaluation more comprehensive and objective.

[0027] By the technical solution, the maturity degree, the nutrient retention efficiency and the hygiene safety level of the fermentation product can be quantified synchronously, and the quality evaluation deviation caused by single index or non-uniform dimension in the traditional method can be solved. By dynamically adjusting the weight coefficient, the quality control demand under different production standards can be adapted, for example, the pest inactivation rate is preferentially ensured under the organic certification requirement, or the nutrient retention rate is focused on under the high-efficiency fertilizer production scene, so that the flexibility and accuracy of quality control are improved.

[0028] Preferably, the working content of the benefit analysis module includes: acquiring ton treatment power consumption, water consumption and turning uniformity; performing maximum-minimum normalization processing on the turning uniformity to obtain a turning uniformity index; performing ratio processing on the ton treatment power consumption and water consumption with reference values to obtain a ton treatment power consumption index and a water consumption index; constructing an economic model based on the turning uniformity index, the ton treatment power consumption index and the water consumption index to obtain an economic coefficient, the economic model being expressed as: wherein, represents the economic coefficient, represents the turning uniformity index, represents the ton treatment power consumption index, represents the water consumption index, represents a turning uniformity sensitivity coefficient, represents a ton treatment power consumption sensitivity coefficient, represents a water consumption sensitivity coefficient, and the greater the value, the better the economy.

[0029] wherein, the ton treatment power consumption can be realized by real-time acquisition by an electric energy metering device and divided by the total mass of the processed material, for quantifying energy utilization efficiency. The water consumption can be realized by monitoring the water supply amount per unit time by a flow meter and accumulated calculation, for evaluating water resource consumption level. The turning uniformity refers to the uniformity of the material when discharging, which can be realized by detecting the moisture content gradient by multi-point sampling and calculating the maximum gradient value, for representing material mixing quality. The reference value refers to a preset benchmark parameter, which can be set according to historical data or industry standards, for generating a standardized index. The sensitivity coefficient refers to the weight factor of each parameter in the model, which can be determined by multivariate regression analysis or expert experience assignment, for adjusting the influence degree of different indexes on economy.

[0030] ​Specifically, by collecting the ton processing power consumption, water consumption and pile uniformity data in real time, the standardized processing is performed to generate comparable indexes. The pile uniformity is converted into an index value in the interval of 0-1 after normalization, and the larger the value, the more uniform the material mixing. The ton processing power consumption and water consumption are compared with the preset reference value to generate a ratio index, reflecting the deviation degree of the actual consumption from the benchmark level. The three types of indexes are input into the economy model, and the sensitivity coefficient is weighted to calculate the comprehensive economy coefficient. When the power consumption or water consumption index increases, the denominator of the model increases, resulting in a decrease in the economy coefficient; when the pile uniformity improves, By adjusting the sensitivity coefficient, the correlation between energy consumption control, water saving demand and product quality can be dynamically balanced, providing a quantitative basis for production parameter optimization.

[0031] Compared with the prior art, the traditional method usually only monitors a single energy consumption indicator or uses a fixed threshold control, and cannot coordinate the contradictions between power consumption, water consumption and product quality. The present scheme builds a multi-parameter coupled economy model, which integrates energy efficiency, resource consumption and material uniformity into a unified evaluation system, and realizes the dynamic balance among the three. For example, when the pile uniformity is insufficient, the model automatically increases the weight of this indicator to guide the system to prioritize improving the mixing quality; when the water consumption exceeds the reference value, the model strengthens the water saving control through the index amplification effect, avoiding the problem of increasing the water replenishment frequency caused by simply reducing the power consumption.

[0032] Through the above technical scheme, the present application can evaluate the comprehensive influence of energy, resource consumption and product quality in the production process in real time, and identify the key factors restricting the economy. Based on the economy coefficient output by the model, the operating parameters of the aeration and pile turning devices can be dynamically adjusted to optimize the power consumption and water consumption ratio under the premise of ensuring material uniformity, thereby reducing the comprehensive cost per unit output. For example, when the pile uniformity meets the standard, the model automatically reduces the weight of this indicator to prioritize the water and power saving strategy; when the moisture content gradient increases, the pile turning operation frequency is increased to improve the uniformity index, avoiding the rework loss caused by substandard quality.

[0033] Preferably, the work content of the reliability analysis module includes: acquiring the full-automatic operation rate, the sensor data effective acquisition rate, and the average repair time of a single fault; performing ratio processing on the average repair time of a single fault and a reference repair time to obtain a fault repair index; performing maximum-minimum normalization processing on the full-automatic operation rate and the sensor data effective acquisition rate to obtain an automatic operation index and a sensor effective acquisition rate index; constructing a system reliability model based on the automatic operation index, the sensor effective acquisition rate index, and the fault repair index to obtain a system reliability coefficient, and the system reliability model is represented as ; wherein, represents a system reliability coefficient, represents an automatic operation index, represents a sensor effective collection rate index, represents a fault repair index, represents a repair time decay coefficient, the and the greater the value, the higher the system reliability.

[0034] wherein, the full automatic operation rate refers to the ratio of automatic operation steps to total steps, which can be specifically realized by counting the automatic execution proportion of the preset process flow in the control system, and is used to reflect the autonomous operation capability of the system. The sensor data effective collection rate can be specifically realized by using a data verification algorithm to count the ratio of the number of valid data packets to the total number of sent data packets, and is used to evaluate the stability of the monitoring system. The single fault average repair time can be specifically calculated by recording the sum of the fault response time and the recovery time in the maintenance log, and is used to quantify the fault tolerance capability of the system. The fault repair index refers to the ratio of the single fault average repair time to the reference repair time, which can be specifically calculated by dividing the actual repair time by the preset standard repair time, and is used to represent the influence degree of the fault handling efficiency on the reliability.

[0035] Specifically, when the system is running, the full automatic operation rate is counted in real time and converted into an automatic operation index, and the sensor data effective collection rate is calculated synchronously and converted into a sensor effective collection rate index, and the two are normalized to form standardized parameters. At the same time, the fault repair time recorded in the maintenance is extracted and compared with the preset reference value to generate a fault repair index. In the system reliability model, the automatic operation index and the sensor effective collection rate index are multiplied to reflect the synergistic enhancement effect of automatic operation and data collection stability, and the fault repair index is processed by an exponential decay function to reflect the nonlinear weakening effect of the extension of repair time on reliability. The model integrates the automation operation efficiency, data collection integrity and fault recovery capability to build a multi-dimensional reliability evaluation system.

[0036] Compared with the prior art, the traditional compound fertilizer fermentation device usually only evaluates the system reliability through the equipment failure rate or a single operation parameter, without considering the correlation influence of automation execution efficiency and data collection stability, and lacks dynamic quantification of fault repair timeliness. The present scheme realizes multi-dimensional dynamic monitoring of the system running state by constructing a composite model including an automatic operation index, a sensor collection index and a repair time index, which can more comprehensively reflect the equipment reliability level.

[0037] Through the above technical solution, this application solves the problem of inaccurate reliability analysis caused by the single dimension of traditional evaluation methods. By integrating three key indicators—automated operation efficiency, data acquisition integrity, and fault repair efficiency—a reliability evaluation model that can dynamically reflect the system's operating status is constructed, which effectively improves the accuracy of monitoring the stability of fermentation equipment and provides a decision-making basis for maintaining continuous and efficient production.

[0038] Preferably, the working steps of the temperature and oxygen condition analysis module are as follows: The temperature deviation index is obtained by comparing the absolute difference between the average fermentation temperature and the optimum temperature with the allowable deviation from the optimum temperature. The oxygen concentration deviation index is obtained by comparing the absolute difference between the average oxygen concentration during fermentation and the optimum oxygen concentration with the allowable deviation from the optimum oxygen concentration. A performance state model is constructed based on the processing efficiency coefficient and the product quality coefficient, and the performance state coefficient is obtained. The performance state model is expressed as follows: ; in, Represents the performance status coefficient. This represents the processing efficiency coefficient. Indicates the product quality coefficient. Represents the weight coefficient and The Furthermore, the higher the value, the better the device performance; A temperature-oxygen fit model is constructed based on the temperature deviation index and oxygen concentration index under the performance state coefficient, and the temperature-oxygen fit degree is obtained. The temperature-oxygen fit model is expressed as follows: ; in, Indicates temperature-oxygen compatibility. Represents the performance status coefficient. This indicates the temperature deviation index. Indicates the oxygen concentration index. Indicates the temperature sensitivity coefficient. The oxygen concentration sensitivity coefficient is represented by the following. Furthermore, the larger the value, the higher the degree of matching between the current temperature and oxygen conditions and the system objective.

[0039] The temperature deviation index is used to characterize the deviation of the current temperature condition from the ideal state, and real-time temperature data is collected by a temperature sensor. The oxygen concentration deviation index is used to reflect the deviation of the current oxygen condition from the target state, and the oxygen content in the fermentation bin can be monitored in real time by an oxygen concentration sensor. The performance state coefficient is used to characterize the comprehensive running state of the device in the efficiency and quality dimensions, and the weight coefficient can be determined by expert experience or analytic hierarchy process. The temperature-oxygen adaptation degree is used to quantify the matching degree of the current temperature-oxygen condition and the system target requirement, and the temperature sensitivity coefficient and the oxygen concentration sensitivity coefficient are determined by expert experience or experimental calibration.

[0040] Specifically, in obtaining the temperature deviation index and the oxygen concentration deviation index, the absolute difference between the actual measured value and the optimal value is divided by the allowable deviation threshold, so that the temperature and oxygen parameters of different dimensions are converted into dimensionless standardized indexes, which is convenient for subsequent model operation. In constructing the performance state coefficient, the processing efficiency coefficient and the product quality coefficient are weighted and fused, which can dynamically reflect the comprehensive performance level of the device under different working conditions of efficiency priority or quality priority. In generating the temperature-oxygen adaptation degree, the exponential function is used to nonlinearly map the influence of temperature deviation and oxygen concentration deviation on the adaptation degree, and the temperature sensitivity coefficient and the oxygen concentration sensitivity coefficient can be adjusted according to different material characteristics, for example, when processing volatile materials, the oxygen concentration sensitivity coefficient can be increased to strengthen the oxygen concentration control weight. The adaptation degree index formed can reflect the comprehensive influence of system running state and process parameter deviation, and provide dynamic adjustment basis for subsequent aeration power consumption optimization.

[0041] Compared with the prior art, the existing fermentation device usually only judges whether the temperature or oxygen concentration meets the standard by a fixed threshold, and does not establish a correlation model between temperature and oxygen parameters and the comprehensive performance of the system. The present scheme quantifies the real-time deviation of temperature and oxygen concentration into a calculable index parameter by constructing a temperature-oxygen adaptation degree index, and introduces a weighted coefficient of processing efficiency and product quality as a dynamic adjustment factor, so that the evaluation of temperature and oxygen conditions can be adaptively adjusted according to the running state of the device. For example, when the processing efficiency of the device decreases, the decrease of the performance state coefficient will cause the adaptation degree index to decrease synchronously, at this time the system can preferentially optimize the temperature and oxygen parameters to restore the processing efficiency, while the prior art cannot realize such dynamic correlation.

[0042] By the technical solution, the application can dynamically calculate the matching degree with the system target according to the real-time collected temperature and oxygen parameters, generate an adaptation index in combination with the balance state of processing efficiency and product quality, and provide a quantitative basis for energy consumption optimization of the aeration system. During the fermentation process, when the temperature deviation index or the oxygen concentration deviation index exceeds the threshold value, the system can automatically adjust the sensitivity coefficient according to the change trend of the performance state coefficient, thereby changing the adaptation calculation weight, and ensuring that the temperature and oxygen condition optimization direction matches the current operation demand of the device. For example, in the working condition of pursuing high yield, the system can increase the weight of the processing efficiency coefficient to increase the sensitivity of the adaptation degree to temperature change, and in the working condition of pursuing high quality, the weight of the product quality coefficient is increased to strengthen the priority of oxygen concentration control.

[0043] Preferably, the working content of the aeration power consumption adjustment module includes: A proportional gain model is constructed based on the temperature-oxygen adaptation degree, the economy coefficient and the system reliability coefficient, and an adaptive proportional gain coefficient is obtained, and the proportional gain model is represented as: Among them, represents the adaptive proportional gain coefficient, represents the basic proportional gain coefficient, represents the adaptive gain adjustment coefficient, represents the temperature-oxygen adaptation degree, represents the economy coefficient, represents the system reliability coefficient; A regulation amount model is constructed based on the proportional gain coefficient, the temperature-oxygen adaptation degree, the economy coefficient and the system reliability coefficient, and an electric consumption proportion regulation amount is obtained, and the regulation amount model is represented as: Among them, represents the electric consumption proportion regulation amount, represents the adaptive proportional gain coefficient, represents the target temperature-oxygen adaptation degree, represents the temperature-oxygen adaptation degree, represents the economy coefficient, represents the system reliability coefficient, represents the economy and reliability penalty coefficient, represents the regulation index; An aeration electric consumption proportion optimization model is constructed based on the aeration system electric consumption proportion and the electric consumption proportion regulation amount, and a target aeration system electric consumption proportion is obtained, and the aeration electric consumption proportion optimization model is represented as: Among them, represents the target aeration system electric consumption proportion, represents the aeration system electric consumption proportion, ​​​represents the power consumption proportion adjustment amount; Adjust the aeration system power consumption proportion to the target aeration system power consumption proportion.

[0044] Wherein, the basic proportional gain coefficient refers to the reference parameter for initial adjustment intensity, which is used to determine the basic amplitude of proportional adjustment. The adaptive gain adjustment coefficient refers to the parameter for dynamically adjusting the gain amplitude according to the system state, which can be realized by fuzzy logic or PID control algorithm, and is used to enhance the response sensitivity of the system to the temperature-oxygen adaptation degree deviation. The economic reliability penalty coefficient refers to the suppression parameter for the unbalanced state of economic and reliability indicators, which can be determined by expert experience assignment or historical data fitting analysis, and is used to avoid system imbalance caused by excessive single indicator. The adjustment index refers to the control parameter for nonlinear change of adjustment amount, which can be determined by expert experience assignment or historical data fitting analysis, and is used to balance the adjustment amplitude and system stability.

[0045] Specifically, the proportional gain model dynamically adjusts the proportional gain coefficient by weighting and fusing the temperature-oxygen adaptation degree deviation and economic and reliability indicators, so that the control system can adaptively change the adjustment intensity according to the current temperature-oxygen conditions and system running state. The adjustment amount model introduces the economic reliability penalty term to suppress the short board effect of economic and reliability coefficients, and realizes nonlinear adjustment by combining the adjustment index, preventing sudden changes in adjustment amount from affecting system stability. The aeration power consumption proportion optimization model gradually approaches the target value by progressively superimposing the current power consumption proportion and the adjustment amount, forming a closed-loop control. The whole process quantifies the temperature-oxygen adaptation degree deviation into the power consumption proportion adjustment amount through hierarchical calculation of mathematical models, and realizes the coordinated optimization of multi-dimensional parameters by combining economic constraints and reliability indicators.

[0046] Compared with the prior art, the traditional aeration control relies on fixed threshold or single parameter feedback, and cannot link economic cost and system reliability indicators when the temperature-oxygen adaptation degree changes. The present scheme builds a mathematical model coupled with multiple parameters to simultaneously consider economic cost constraints and equipment operation reliability when adjusting aeration power consumption, forming a dynamic balance mechanism. The prior art lacks a penalty mechanism for index short boards in adjustment amount calculation, which easily leads to one-sided deterioration of economic or reliability, while the present scheme effectively suppresses the risk of single indicator dominance masking system defects through nonlinear design of economic reliability penalty coefficient and adjustment index.

[0047] Through the above technical scheme, the present application realizes dynamic optimization and adjustment of aeration power consumption proportion, solves the problem of energy waste or system imbalance caused by fixed parameter control in traditional devices. Through multi-dimensional parameter fusion calculation, the operation economy and equipment reliability are ensured while maintaining the temperature-oxygen adaptation degree, avoiding negative effects caused by single indicator optimization. The progressive adjustment mechanism ensures the stability of the system adjustment process, preventing sudden interference from affecting the stability of the fermentation process.

[0048] It has to be noted that, in the present document, the terms "first", "second", etc. merely serve to identify different entities or actions from each other, without necessarily requiring or implying any actual relationship or order between these entities or actions. Moreover, the terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0049] While embodiments of the present application have been shown and described, it is to be understood that various modifications, substitutions, combinations, and variations of the embodiments can be undertaken without departing from the spirit and scope of the present application, which is defined by the appended claims and their equivalents.

Claims

1. A fermentation apparatus for the production of compound fertilizers, comprising an apparatus body, characterized in that, Also comprising: a control system for regulating the fermentation energy consumption, comprising: an efficiency analysis module, based on the fermentation cycle, volatile solid degradation rate and daily processing capacity, to construct a processing efficiency model to output a processing efficiency coefficient; a quality analysis module, based on the seed germination index, nutrient retention rate and harmful organism inactivation rate, to construct a product quality model to output a product quality coefficient; a benefit analysis module, based on the ton of processing electricity consumption, water consumption and turning uniformity, to construct an economic model to output an economic coefficient; a reliability analysis module, based on the full-automatic operation rate, sensor data effective acquisition rate and single fault average repair time, to construct a system reliability model to output a system reliability coefficient; a temperature and oxygen condition analysis module, based on the processing efficiency coefficient and the product quality coefficient, to construct a temperature-oxygen adaptation model to output a temperature-oxygen adaptation degree; an aeration electricity consumption adjustment module, based on the temperature-oxygen adaptation degree, economic coefficient, system reliability coefficient and aeration system electricity consumption proportion, to construct an aeration electricity consumption proportion optimization model to output a target aeration system electricity consumption proportion, and to adjust the aeration system electricity consumption proportion to the target aeration system electricity consumption proportion.

2. The fermentation device for compound fertilizer production according to claim 1, characterized in that, The working content of the aeration electricity consumption adjustment module comprises: A proportional gain model is constructed based on temperature-oxygen fitness, economic coefficient and system reliability coefficient to obtain an adaptive proportional gain coefficient, and the proportional gain model is expressed as: ; wherein, represents an adaptive proportional gain coefficient, represents a base proportional gain coefficient, represents an adaptive gain adjustment coefficient, represents a temperature-oxygen fit, represents an economy factor, represents a system reliability factor; The adjustment amount model is constructed based on a proportional gain coefficient, a temperature-oxygen adaptation degree, an economic coefficient and a system reliability coefficient, and the power consumption proportion adjustment amount is obtained, and the adjustment amount model is expressed as: ; wherein, represents an electricity consumption proportion adjustment amount, represents an adaptive proportional gain coefficient, represents a target temperature-oxygen fitness, represents a temperature-oxygen fitness, represents an economic coefficient, represents a system reliability coefficient, represents an economic reliability penalty coefficient, represents an adjustment index; An aeration power consumption proportion optimization model is constructed based on the aeration system power consumption proportion and the power consumption proportion adjustment amount, and a target aeration system power consumption proportion is obtained, and the aeration power consumption proportion optimization model is expressed as: ; wherein, represents the target proportion of power consumption of the aeration system, represents the proportion of power consumption of the aeration system, represents the adjustment amount of the proportion of power consumption; adjusting the aeration system electricity consumption proportion to the target aeration system electricity consumption proportion.

3. The fermentation device for compound fertilizer production according to claim 2, characterized in that, The working steps of the temperature and oxygen condition analysis module are: processing the absolute difference between the fermentation average temperature and the optimal temperature by the allowed deviation from the optimal temperature value to obtain a temperature deviation index; processing the absolute difference between the fermentation average oxygen concentration and the optimal oxygen concentration by the allowed deviation from the optimal oxygen concentration value to obtain an oxygen concentration deviation index; A performance state model is constructed based on the processing efficiency coefficient and the product quality coefficient, and a performance state coefficient is obtained, and the performance state model is expressed as: ; wherein, represents a performance state coefficient, represents a processing efficiency coefficient, represents a product quality coefficient, represents a weight coefficient and , said and the greater the value the better the device performance; A temperature-oxygen adaptation model is constructed based on a temperature deviation index under a performance state coefficient and an oxygen concentration index, and a temperature-oxygen adaptation degree is obtained, and the temperature-oxygen adaptation model is expressed as: ; wherein, represents a temperature-oxygen fitness, represents a performance state coefficient, represents a temperature deviation index, represents an oxygen concentration index, represents a temperature sensitivity coefficient, represents an oxygen concentration sensitivity coefficient, said and the greater the value, the higher the fitness of the current temperature-oxygen conditions to the system target.

4. The fermentation device for compound fertilizer production according to claim 3, characterized in that, The working content of the reliability analysis module comprises: obtaining the full-automatic operation rate, sensor data effective acquisition rate and single fault average repair time; processing the single fault average repair time by the reference repair time to obtain a fault repair index; performing maximum-minimum normalization processing on the full-automatic operation rate and sensor data effective acquisition rate to obtain an automatic operation index and a sensor effective acquisition rate index; A system reliability model is constructed based on an automatic operation index, a sensor effective collection rate index, and a fault repair index to obtain a system reliability coefficient, and the system reliability model is expressed as: ; wherein, represents a system reliability coefficient, represents an automatic operation index, represents a sensor effective collection rate index, represents a failure repair index, represents a repair time decay coefficient, said and the greater the value the higher the system reliability.

5. The fermentation device for compound fertilizer production according to claim 3, characterized in that, The working content of the benefit analysis module comprises: obtaining the ton of processing electricity consumption, water consumption and turning uniformity; performing maximum-minimum normalization processing on the turning uniformity to obtain a turning uniformity index; processing the ton of processing electricity consumption and water consumption by the reference value to obtain a ton of processing electricity consumption index and a water consumption index; An economic model is constructed based on the turning-over uniformity index, the power consumption index per ton of treatment, and the water consumption index, and an economic coefficient is obtained, and the economic model is expressed as: ; wherein, represents an economic coefficient, represents a turn-down uniformity index, represents a ton of treatment electricity consumption index, represents a water consumption index, represents a turn-down uniformity sensitivity coefficient, represents a ton of treatment electricity consumption sensitivity coefficient, represents a water consumption sensitivity coefficient, said and the greater the value the better the economy.

6. The fermentation device for compound fertilizer production according to claim 3, characterized in that, The working content of the quality analysis module comprises: obtaining the seed germination index, nutrient retention rate and harmful organism inactivation rate; performing maximum-minimum normalization processing on the seed germination index, nutrient retention rate and harmful organism inactivation rate to obtain a seed germination factor, nutrient retention rate index and harmful organism inactivation rate index; A product quality model is constructed based on a seed germination factor, a nutrient retention index, and a pest inactivation index, and a product quality coefficient is obtained, the product quality model being expressed as: ; wherein, represents a product quality coefficient, represents a seed germination factor, represents a nutrient retention index, represents a pest inactivation index, represents an index weight coefficient and , the and the greater the value the better the product quality.

7. The fermentation device for compound fertilizer production according to claim 3, characterized in that, The working content of the efficiency analysis module comprises: obtaining based on the fermentation cycle, volatile solid degradation rate and daily processing capacity; performing maximum-minimum normalization processing on the fermentation cycle, volatile solid degradation rate and daily processing capacity to obtain a cycle index, degradation rate index and processing capacity index; Based on the periodic index, degradation rate index and processing capacity index, a processing efficiency model is constructed to obtain a processing efficiency coefficient, and the processing efficiency model is expressed as: ; wherein, represents a processing efficiency coefficient, represents a periodicity index, represents a degradation rate index, represents a processing capacity index, represents a weight coefficient and , said and the greater the value the higher the system performance processing efficiency.

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