Ammonia gas real-time monitoring system in composting process

Abstract

The invention discloses a real-time monitoring system for ammonia gas in a composting process, which relates to the technical field of gas measurement and monitoring, and comprises an information acquisition module for acquiring data information in an ammonia ion concentration measurement process, including electrochemical characteristic information and dynamic response characteristic information, obtaining a microbial activity coefficient when the microorganism decomposes the fertilizer to generate ammonia gas in the composting process; the comprehensive analysis module is used for establishing a monitoring evaluation model based on a machine learning algorithm by combining the electrochemical characteristic information and the dynamic response characteristic information with the microbial activity coefficient, and generating a monitoring evaluation value; the hidden danger early warning module is used for comparing and analyzing the monitoring evaluation value with a preset monitoring evaluation threshold value, judging the ammonia gas measurement precision and sending out an early warning prompt for abnormal hidden dangers according to a comparison and analysis result; the problem of inaccurate ammonia monitoring in the composting process is solved, and the real-time ammonia monitoring efficiency and the ammonia measurement precision in the composting process are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of gas measurement and monitoring, and more specifically, to a real-time monitoring system for ammonia in a composting process. Background Art

[0002] The ammonia monitoring system in the composting process is an important tool to ensure the quality of compost and optimize compost management. In recent years, the technology and applications in this area have been continuously developed, and a variety of highly sensitive ammonia sensors have been developed, including electrochemical sensors, optical sensors, and metal oxide semiconductor sensors. These sensors can achieve real-time monitoring and have high accuracy and response speed; using advanced technologies such as gas chromatography (GC) and mass spectrometry (MS), the concentration and changes of ammonia in the composting process can be accurately analyzed. These technologies are usually used in laboratory research, but are gradually transformed into field applications. Many ammonia monitoring systems connect sensors to cloud platforms to achieve real-time data upload and remote monitoring. Users can view monitoring data at any time through mobile phones or computers; through machine learning and data analysis, the collected ammonia data can be deeply analyzed to predict the gas release trend in the composting process and optimize compost management.

[0003] Deficiencies of existing technologies:

[0004] Inaccurate measurement may lead to excessive ammonia emissions, causing pollution to the surrounding environment and affecting air quality; excessive ammonia will have a negative impact on plant growth, leading to soil acidification and reduced biodiversity; excessive ammonia concentration may pose a threat to human health, and long-term exposure may lead to respiratory problems, eye irritation and other health problems; in agriculture, ammonia emissions are closely related to the use of nitrogen fertilizers, and insufficient measurement accuracy may lead to improper fertilization, affecting crop growth and harvest; due to the inability to accurately assess ammonia release, excessive fertilizer use may occur, increasing agricultural costs. Therefore, the present invention relates to a real-time monitoring system for ammonia in a composting process to monitor and analyze ammonia measurement accuracy, solving the problem of inaccurate ammonia monitoring in a composting process.

[0005] In view of the above problems, the present invention proposes a solution. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a real-time monitoring system for ammonia in a composting process, which solves the problems raised in the above-mentioned background technology by monitoring and analyzing the measurement accuracy of ammonia.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A real-time monitoring system for ammonia in a composting process comprises the following steps: a concentration measurement module, used for obtaining a solution to be tested after ammonia is introduced into water, and measuring the ammonia ion concentration in the solution by an ion selective electrode method; an information acquisition module, used for obtaining data information in the process of measuring the ammonia ion concentration, including electrochemical characteristic information and dynamic response characteristic information, and obtaining the microbial activity coefficient when microorganisms decompose fertilizers to produce ammonia in the composting process; a comprehensive analysis module, used for combining the electrochemical characteristic information and the dynamic response characteristic information with the microbial activity coefficient to establish a monitoring and evaluation model based on a machine learning algorithm to generate a monitoring and evaluation value; a hidden danger warning module, used for comparing and analyzing the monitoring and evaluation value with a preset monitoring and evaluation threshold value, judging the ammonia measurement accuracy, and issuing a warning prompt for abnormal hidden dangers according to the comparison and analysis results; a feedback analysis module, used for maintaining and managing the accuracy anomalies existing in ammonia measurement, and performing comprehensive standard deviation and mean analysis on the monitoring and evaluation value output in real time after maintenance and management, and judging the maintenance and management results.

[0009] In a preferred embodiment, the specific process of measuring the concentration of ammonium ions in a solution using an ion-selective electrode method is as follows: obtain standard solutions of known different concentrations and immerse the electrode in the standard solutions; record the electrode potential value when the electrode potential is stable; repeat the potential measurement process for each standard solution and record the corresponding potential value; draw a standard curve based on the concentration and potential value of the standard solution based on linear regression fitting; obtain a solution to be tested and immerse the ion-selective electrode in the solution to be tested; record the electrode potential value when the electrode potential is stable; and obtain the concentration of the ammonium ions to be tested based on the drawn standard curve according to the electrode potential value.

[0010] In a preferred embodiment, the electrochemical characteristic information includes the coefficient of variation of the dissolved concentration; and the dynamic response characteristic information includes the activity coefficient of the ammonium ion.

[0011] In a preferred embodiment, the specific steps for obtaining the coefficient of variation of the dissolved concentration are as follows: the electrode potential of the solution to be tested is measured multiple times using an ion selective electrode method, and the corresponding ammonium ion concentration is found based on the drawn standard curve; the ammonium ion concentration value measured each time is recorded to obtain a data set, and the average value and standard deviation of this set of data are calculated; the coefficient of variation of the dissolved concentration is calculated based on the average value and standard deviation based on a preset coefficient of variation formula.

[0012] In a preferred embodiment, the specific steps for obtaining the ammonium ion activity coefficient are as follows: measuring the conductivity of the solution to be tested, and calculating the concentration of each ion in the solution based on the measured conductivity; calculating the ionic strength of the solution based on the concentration of each ion and the charge number of each ion; and calculating the ammonium ion activity coefficient based on the Debye-Hückel formula according to the ionic strength of the solution combined with the charge and radius of the ammonium ion.

[0013] In a preferred embodiment, the specific method for obtaining the microbial activity coefficient is as follows: collect compost solution, dilute the solution and inoculate it into a culture medium, and culture it at an appropriate temperature and time; obtain the initial cell concentration and the cell concentration at time t, calculate the growth rate based on the growth curve according to the initial cell concentration and the cell concentration at time t, and use the ratio of the growth rate to the known concentration of available nutrients in the matrix as the microbial activity coefficient.

[0014] In a preferred embodiment, the specific implementation process of the hidden danger warning module is as follows: the monitoring evaluation value is compared and analyzed with the preset monitoring evaluation threshold, and whether the ammonia measurement accuracy is abnormal is determined based on the comparison and analysis results; if the monitoring evaluation value is greater than the preset monitoring evaluation threshold, the ammonia measurement accuracy is normal and no early warning prompt is required; if the monitoring evaluation value is less than the preset monitoring evaluation threshold, the ammonia measurement accuracy is abnormal, and an early warning prompt needs to be issued, and maintenance management is required.

[0015] In a preferred embodiment, the specific implementation process of the feedback analysis module is as follows: after maintenance and management of the system with abnormal measurement accuracy, several monitoring and evaluation values ​​during ammonia measurement are obtained in real time, and an analysis set is formed to calculate the average value and standard deviation of the monitoring and evaluation values ​​in the analysis set; the average value and standard deviation of the monitoring and evaluation values ​​in the analysis set are compared and analyzed with a preset evaluation threshold and a standard deviation reference threshold; if the average value of the monitoring and evaluation values ​​in the set is less than the preset evaluation threshold, the maintenance management fails, the ammonia measurement accuracy is abnormal, and further maintenance management is required; if the average value of the monitoring and evaluation values ​​in the set is greater than the preset evaluation threshold, and the standard deviation of the monitoring and evaluation values ​​in the set is greater than the standard deviation reference threshold, the maintenance management effect fluctuates, and it is necessary to calculate the average value and standard deviation of the monitoring and evaluation values ​​again, and compare and analyze them with the preset evaluation threshold and standard deviation reference threshold; if the average value of the monitoring and evaluation values ​​in the set is greater than the preset evaluation threshold, and the standard deviation of the monitoring and evaluation values ​​in the set is less than the standard deviation reference threshold, the maintenance management is successful, and the ammonia measurement accuracy is normal.

[0016] The technical effects and advantages of the real-time monitoring system for ammonia in a composting process of the present invention are as follows:

[0017] 1. The present invention can monitor the concentration changes of ammonia in the composting process in real time, timely feedback the gas conditions of the composting environment, and help operators quickly identify potential problems. By monitoring the ammonia concentration, the ventilation and moisture management of the compost can be optimized, thereby improving the fermentation efficiency of the compost and the quality of the final product. Excessive emission of ammonia will cause environmental pollution. Real-time monitoring can effectively control the release of ammonia and reduce the impact on the surrounding environment, which meets the requirements of sustainable development.

[0018] 2. The present invention is linked with other composting equipment to achieve automated management, reduce manual intervention, improve operational efficiency, and be able to record ammonia concentration data to facilitate subsequent analysis and research, help improve composting processes and management strategies. It is usually equipped with a friendly user interface so that operators can easily view monitoring data and trends, enhance the operability of the system, and optimize the composting process, reduce unnecessary waste of resources, and reduce operating costs, thereby achieving improved economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 The present invention is a structural schematic diagram of a real-time monitoring system for ammonia in a composting process. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] Embodiment 1, Figure 1 The invention provides a real-time monitoring system for ammonia in a composting process.

[0022] A concentration measurement module is used to obtain a solution after ammonia gas is passed into water, and to measure the ammonia ion concentration in the solution using an ion selective electrode method;

[0023] Ion selective electrode method is an electrochemical analysis technique used to measure the concentration of specific ions. Its main principle is to use the sensitivity of selective electrodes to target ions and determine the concentration of ions in the solution by measuring the potential difference between the electrode and the reference electrode.

[0024] The specific process of measuring the concentration of ammonium ions in the solution by using the ion selective electrode method is as follows:

[0025] Select a series of standard solutions of known concentration to cover the target concentration range; for the ammonium ion selective electrode, different concentrations of ammonium chloride solutions can be used;

[0026] Immerse the ion-selective electrode and reference electrode separately in the first standard solution and stir gently to ensure homogeneity;

[0027] After waiting for the electrode potential to stabilize, record the electrode potential value;

[0028] Repeat the above process for each standard solution, measure and record its corresponding potential value;

[0029] The logarithm of the measured standard solution concentration is used as the horizontal coordinate, and the potential value is used as the vertical coordinate to draw a standard curve. The linear regression method is used to fit these data points to obtain a linear equation, which describes the relationship between ion concentration and electrode potential. The specific formula of the linear equation based on the Nystr equation is as follows: Where E is the electrode potential, E 0 is the standard electrode potential, R is the gas constant, T is the absolute temperature, n is the ion charge, F is the Faraday constant, [A n ] is the concentration of ammonium ions;

[0030] The solution after ammonia gas is passed into water is recorded as the test solution, and the ion selective electrode is immersed in the test solution, and the electrode potential is waited for stabilization;

[0031] Record the electrode potential value and use the calibration curve to calculate the concentration of the ammonia ion to be measured.

[0032] The information acquisition module obtains data information during the measurement of ammonia ion concentration, including electrochemical characteristic information and dynamic response characteristic information, and obtains the microbial activity coefficient when microorganisms decompose fertilizers to produce ammonia during the composting process;

[0033] The electrochemical characteristic information includes the coefficient of variation of dissolved concentration;

[0034] The specific steps for obtaining the coefficient of variation of the dissolved concentration are as follows:

[0035] The electrode potential of the solution to be tested is measured multiple times using the ion selective electrode method, and the corresponding ammonia ion concentration is found according to the drawn standard curve to ensure that the solution is measured under the same conditions (such as temperature, pH, etc.);

[0036] Record the ammonia ion concentration value measured each time to obtain a set of data, and calculate the average value and standard deviation of this set of data;

[0037] The coefficient of variation of the dissolved concentration is calculated based on the mean value and the standard deviation based on the preset coefficient of variation formula;

[0038] The dynamic response characteristic information includes the activity coefficient of ammonium ions;

[0039] The specific steps for obtaining the ammonium ion activity coefficient are as follows:

[0040] The conductivity of the solution to be tested is measured using a conductivity meter. The conductivity is related to the ion concentration in the solution and can reflect the ion activity. The concentration of each ion in the solution is calculated based on the measured conductivity. The ionic strength of the solution is calculated based on the concentration of each ion and the charge number of each ion. The activity coefficient of the ammonium ion is calculated based on the Debye-Hückel formula according to the ionic strength of the solution combined with the charge and radius of the ammonium ion.

[0041] The specific calculation formula of the ammonium ion activity coefficient is as follows: Where r is the activity coefficient of the ammonium ion, A and B are constants related to the characteristics of the solution, z is the charge of the ammonium ion, a is the radius of the ammonium ion, and I is the ionic strength of the solution;

[0042] The specific method for obtaining the microbial activity coefficient is as follows:

[0043] Collect the compost solution to ensure representativeness in order to analyze the microbial activity;

[0044] Determine the nitrogen content, carbon content and other nutrients in the solution to understand the basis of microbial growth;

[0045] Select a culture medium suitable for the target microorganism and provide the nutrients required for growth;

[0046] Dilute the solution and inoculate it into the culture medium, and culture it at a suitable temperature and time;

[0047] Use a spectrophotometer to monitor the turbidity of the culture solution to determine microbial growth;

[0048] The growth rate is calculated according to the growth curve, and the ratio of the growth rate to the concentration of available nutrients in the known matrix is ​​used as the microbial activity coefficient. The specific calculation formula is as follows: Where k is the microbial activity coefficient, N t is the cell concentration at time t, N0 is the initial cell concentration, and C is the concentration of nutrients available in the matrix;

[0049] Comprehensive analysis module, which is used to combine electrochemical characteristic information and dynamic response characteristic information with microbial activity coefficient to establish a monitoring and evaluation model based on machine learning algorithm to generate monitoring and evaluation values;

[0050] The specific calculation formula of the monitoring evaluation value is as follows: Wherein, P is the monitoring assessment value, b is the coefficient of variation of dissolved concentration, r is the activity coefficient of ammonium ion, k is the microbial activity coefficient, μ1 is the weight of the coefficient of variation of dissolved concentration, μ2 is the weight of the activity coefficient of ammonium ion, and μ3 is the weight of the microbial activity coefficient;

[0051] The coefficient of variation of dissolved concentration reflects the degree of dispersion of concentration measurement. A higher coefficient of variation indicates that the volatility of concentration measurement is greater, which means that the measurement result is unstable, thus reducing the measurement accuracy; the activity coefficient of ammonium ions is used to correct the relationship between the actual activity and concentration of ions in solution. A higher activity coefficient indicates that ammonium ions are more active in solution, which means that they have a stronger ability to participate in the reaction, thereby improving the accuracy and reliability of the measurement; the microbial activity coefficient reflects the ability of microorganisms to transform ammonia. A higher microbial activity means that microorganisms can effectively utilize ammonia, which may make the measurement results more representative. Therefore, when the microbial activity coefficient is higher, the measurement accuracy can usually be improved.

[0052] The hidden danger warning module compares and analyzes the monitoring and evaluation value with the preset monitoring and evaluation threshold value to determine the ammonia measurement accuracy, and issues early warning prompts for abnormal hidden dangers based on the comparative analysis results;

[0053] Compare and analyze the monitoring and evaluation value with the preset monitoring and evaluation threshold, and judge whether the ammonia measurement accuracy is abnormal according to the comparative analysis result. If the monitoring and evaluation value is greater than the preset monitoring and evaluation threshold, the ammonia measurement accuracy is normal and no early warning prompt is required;

[0054] If the monitoring evaluation value is less than the preset monitoring evaluation threshold, the ammonia measurement accuracy is abnormal, and an early warning prompt needs to be issued and maintenance management needs to be carried out.

[0055] The hidden danger warning module compares and analyzes the real-time monitoring evaluation value with the preset threshold value, judges the ammonia measurement accuracy, and issues early warning prompts for abnormal situations. The benefits of this system are mainly reflected in the following aspects:

[0056] 1. Improved security

[0057] Timely warning: When the ammonia concentration exceeds the safety threshold, the system can quickly issue an alarm to help take timely measures to avoid potential safety hazards;

[0058] Prevent accidents: Through early detection and early warning, the risk of accidents caused by ammonia leakage or excessive concentration, such as fire or explosion, can be effectively reduced;

[0059] 2. Optimize management

[0060] Real-time monitoring and feedback: Provide real-time data analysis to help managers understand ammonia levels at any time and adjust operating strategies in a timely manner;

[0061] Data-driven decision-making: Through the analysis results provided by the system, management can make more scientific decisions based on data, thereby improving management efficiency;

[0062] 3. Environmental protection

[0063] Reduce emissions: Through monitoring and early warning, timely measures can be taken to reduce ammonia emissions, reduce the impact on the environment, and comply with environmental regulations;

[0064] Protecting ecosystems: Effectively managing ammonia levels helps protect surrounding ecosystems and maintain biodiversity;

[0065] 4. Improve compliance

[0066] Ensure compliance with regulations: The system can help companies monitor ammonia emissions to ensure compliance with relevant environmental regulations and avoid fines or legal liabilities caused by violations;

[0067] Documentation: Automatically generated monitoring data and alarm records can serve as an important basis for compliance audits;

[0068] 5. Reduce operating costs

[0069] Reduce losses: Through timely warning and intervention, equipment damage and downtime caused by excessive ammonia can be reduced, thereby reducing operating costs;

[0070] Save resources: Optimize ammonia management and improve fertilizer utilization efficiency, which helps save costs and improve economic benefits;

[0071] 6. Enhance public trust

[0072] Improved transparency: Through effective ammonia monitoring and early warning, companies can demonstrate their environmental commitment to the public, enhance their corporate image and social responsibility;

[0073] Enhance employees' sense of security: provide a safe working environment, enhance employees' trust and satisfaction with the company, and promote employee enthusiasm;

[0074] 7. Technological progress and innovation

[0075] Promote technological development: The implementation of the hidden danger early warning module can promote the advancement of related technologies, encourage enterprises to invest in new technologies and methods, and improve overall monitoring capabilities;

[0076] Facilitate data sharing: Promote data sharing and collaboration among different departments and institutions through integration with other systems;

[0077] The hidden danger warning module not only improves the accuracy and real-time performance of ammonia monitoring, but also brings significant benefits to enterprises in terms of safety management, environmental protection, and compliance operations. The implementation of this system can effectively reduce risks, optimize resource allocation, and promote the sustainable development of enterprises.

[0078] The feedback analysis module performs maintenance management on the accuracy anomalies existing in ammonia measurement, and performs comprehensive standard deviation and mean analysis on the monitoring evaluation values ​​output in real time after maintenance management to determine the maintenance management results.

[0079] When performing maintenance management on the accuracy anomaly existing in ammonia measurement, several monitoring evaluation values ​​during ammonia measurement are obtained in real time and constitute an analysis set;

[0080] Calculate the mean and standard deviation of the monitoring and evaluation values ​​in the analysis set, and compare and analyze the mean and standard deviation of the monitoring and evaluation values ​​in the analysis set with the pre-set evaluation threshold and standard deviation reference threshold;

[0081] If the average value of the monitoring evaluation value in the set is less than the preset evaluation threshold, the maintenance management fails and the ammonia measurement accuracy is abnormal, and further maintenance management is required;

[0082] If the average value of the monitoring and evaluation values ​​within the set is greater than the pre-set evaluation threshold, and the standard deviation of the monitoring and evaluation values ​​within the set is greater than the standard deviation reference threshold, the maintenance management effect fluctuates, and it is necessary to calculate and analyze the average value and standard deviation of the monitoring and evaluation values ​​within the set again, and compare and analyze them with the pre-set evaluation threshold and standard deviation reference threshold;

[0083] If the average value of the monitoring evaluation value within the set is greater than the preset evaluation threshold, and the standard deviation of the monitoring evaluation value within the set is less than the standard deviation reference threshold, the maintenance management is successful and the ammonia measurement accuracy is normal.

[0084] The feedback analysis module plays an important role in the maintenance management of abnormal ammonia measurement accuracy. Its main benefits include:

[0085] 1. Improved accuracy

[0086] Continuous optimization of monitoring: By performing standard deviation and mean analysis on the monitoring evaluation values ​​output after maintenance, fluctuations in measurement data can be identified, helping to further optimize the calibration of sensors and measuring equipment;

[0087] Accurate data feedback: Quantitative analysis of maintenance results helps ensure the reliability of monitoring data and improve overall measurement accuracy;

[0088] 2. Real-time monitoring and response

[0089] Dynamic adjustment: The real-time monitoring and evaluation values ​​can quickly reflect the maintenance effect. Managers can adjust operations immediately based on the analysis results to ensure that the ammonia concentration remains at a safe level.

[0090] Quickly identify problems: By monitoring standard deviation and mean, potential problems can be quickly identified and necessary measures can be taken to reduce risks;

[0091] 3. Data-driven decision support

[0092] Scientific decision-making: Compare and analyze real-time monitoring data with historical data to help management make scientific decisions based on data;

[0093] Preventive maintenance: Identify potential weaknesses or failures in the system and perform maintenance in advance to reduce the occurrence of unexpected problems;

[0094] 4. Enhanced compliance and reporting capabilities

[0095] Proof of compliance: By recording and analyzing monitoring data after maintenance management, companies can provide compliance reports to prove their compliance in ammonia management;

[0096] Transparent records: Records of maintenance and analysis results provide a detailed basis for future audits and inspections, enhancing transparency;

[0097] 5. Reduce costs

[0098] Reduce downtime: Reduce operating costs by identifying and addressing accuracy anomalies in a timely manner, avoiding equipment failure or production stoppage caused by measurement errors;

[0099] Optimize resource allocation: After understanding the maintenance effect, you can allocate maintenance resources and funds more reasonably and avoid unnecessary expenses.

[0100] 6. Improve employees’ safety awareness

[0101] Safety training: Through the analysis results, we can provide employees with training and knowledge sharing on ammonia management to enhance their safety awareness;

[0102] Improve the working environment: More accurate ammonia monitoring can help improve the working environment and increase employees’ sense of safety and job satisfaction;

[0103] 7. Promote technological progress

[0104] Promote technology updates: The feedback results of the analysis module can provide a basis for equipment upgrades and technology improvements, and promote the application of new technologies;

[0105] Enhanced innovation capabilities: Through continuous feedback and improvement, the company can maintain technological leadership in ammonia monitoring and management;

[0106] 8. Support environmental protection

[0107] Reduce emission risks: Reduce ammonia emission risks and protect the surrounding environment through accurate monitoring and timely response;

[0108] Improve sustainability: Scientific management measures help achieve the company's sustainable development goals and comply with environmental protection policies;

[0109] The application of feedback analysis module in ammonia measurement not only improves measurement accuracy and data reliability, but also provides strong support for the company's safety management, decision support and environmental protection. The implementation of this system can not only reduce risks and costs, but also improve overall operational efficiency and promote sustainable development.

[0110] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0111] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0112] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0113] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0114] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0115] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A real-time monitoring system for ammonia in a composting process, characterized in that: The following steps are involved: A concentration measurement module is used to obtain a solution to be tested after ammonia gas is passed into water, and to measure the ammonia ion concentration in the solution using an ion selective electrode method; The information acquisition module is used to obtain data information during the measurement of ammonia ion concentration, including electrochemical characteristic information and dynamic response characteristic information, and to obtain the microbial activity coefficient when microorganisms decompose fertilizers to produce ammonia during the composting process; Comprehensive analysis module, which is used to combine electrochemical characteristic information and dynamic response characteristic information with microbial activity coefficient to establish a monitoring and evaluation model based on machine learning algorithm to generate monitoring and evaluation values; The hidden danger warning module is used to compare and analyze the monitoring and evaluation value with the preset monitoring and evaluation threshold value, judge the ammonia measurement accuracy, and issue a warning prompt for abnormal hidden dangers based on the comparison and analysis results; The feedback analysis module is used to perform maintenance management on the accuracy anomalies existing in ammonia measurement, and to perform comprehensive standard deviation and mean analysis on the monitoring evaluation values ​​output in real time after maintenance management to determine the maintenance management results.

2. A composting process ammonia real-time monitoring system according to claim 1, characterized in that: The specific process of measuring the concentration of ammonium ions in the solution by using the ion selective electrode method is as follows: Obtain standard solutions of known concentrations and immerse the electrodes in the standard solutions; When the electrode potential is stable, record the electrode potential value; Repeat the potential measurement process for each standard solution and record the corresponding potential value; The concentration and potential value of the standard solution are used to draw a standard curve based on linear regression fitting; Obtain a solution to be tested, and immerse an ion selective electrode in the solution to be tested; When the electrode potential is stable, record the electrode potential value; The concentration of the ammonia ion to be measured is obtained based on the electrode potential value and the standard curve drawn.

3. A composting process ammonia real-time monitoring system according to claim 2, characterized in that: The electrochemical characteristic information includes the coefficient of variation of dissolved concentration; and the dynamic response characteristic information includes the activity coefficient of ammonium ions.

4. A composting process ammonia real-time monitoring system according to claim 3, characterized in that: The specific steps for obtaining the coefficient of variation of the dissolved concentration are as follows: The electrode potential of the solution to be tested is measured multiple times using the ion selective electrode method, and the corresponding ammonium ion concentration is found based on the drawn standard curve; Record the ammonia ion concentration value measured each time to obtain a data set, and calculate the mean value and standard deviation of this set of data; The coefficient of variation of the dissolved concentration was calculated based on the mean value and standard deviation based on the preset coefficient of variation formula.

5. A composting process ammonia real-time monitoring system according to claim 4, characterized in that: The specific steps for obtaining the ammonium ion activity coefficient are as follows: Measuring the conductivity of the solution to be tested, and calculating the concentration of each ion in the solution based on the measured conductivity; The ionic strength of the solution is calculated based on the concentration of each ion and the charge number of each ion; The ammonium ion activity coefficient was calculated based on the Debye-Hückel formula according to the ionic strength of the solution combined with the charge and radius of the ammonium ion.

6. A composting process ammonia real-time monitoring system according to claim 5, characterized in that: The specific method for obtaining the microbial activity coefficient is as follows: Collect the compost solution, dilute the solution and inoculate it into the culture medium, and cultivate it at a suitable temperature and time; The initial cell concentration and the cell concentration at time t are obtained, the growth rate is calculated based on the growth curve according to the initial cell concentration and the cell concentration at time t, and the ratio of the growth rate to the concentration of available nutrients in the known matrix is ​​used as the microbial activity coefficient.

7. A composting process ammonia real-time monitoring system according to claim 6, characterized in that: The specific implementation process of the hidden danger warning module is as follows: Compare and analyze the monitoring and evaluation value with the preset monitoring and evaluation threshold value, and determine whether the ammonia measurement accuracy is abnormal based on the comparative analysis results; If the monitoring and evaluation value is greater than the preset monitoring and evaluation threshold, the ammonia measurement accuracy is normal and no early warning prompt is required; If the monitoring evaluation value is less than the preset monitoring evaluation threshold, the ammonia measurement accuracy is abnormal, and an early warning prompt needs to be issued and maintenance management needs to be carried out.

8. A composting process ammonia real-time monitoring system according to claim 7, characterized in that: The specific implementation process of the feedback analysis module is as follows: After the system with abnormal measurement accuracy is maintained and managed, several monitoring and evaluation values ​​during ammonia measurement are obtained in real time, and an analysis set is formed, and the average value and standard deviation of the monitoring and evaluation values ​​in the analysis set are calculated; Compare and analyze the mean and standard deviation of the monitoring assessment values ​​in the analysis set with the pre-set assessment threshold and standard deviation reference threshold; If the average value of the monitoring evaluation value in the set is less than the preset evaluation threshold, the maintenance management fails and the ammonia measurement accuracy is abnormal, and further maintenance management is required; If the average value of the monitoring and evaluation values ​​within the set is greater than the pre-set evaluation threshold, and the standard deviation of the monitoring and evaluation values ​​within the set is greater than the standard deviation reference threshold, the maintenance management effect fluctuates, and it is necessary to calculate the average value and standard deviation of the monitoring and evaluation values ​​again, and compare and analyze them with the pre-set evaluation threshold and standard deviation reference threshold; If the average value of the monitoring evaluation value within the set is greater than the preset evaluation threshold, and the standard deviation of the monitoring evaluation value within the set is less than the standard deviation reference threshold, the maintenance management is successful and the ammonia measurement accuracy is normal.

9. A composting process ammonia real-time monitoring system according to claim 8, characterized in that: The specific calculation formula of microbial activity coefficient is as follows: Where k is the microbial activity coefficient, N t is the cell concentration at time t, N0 is the initial cell concentration, and C is the concentration of nutrients available in the matrix.

10. A real-time monitoring system for ammonia in a composting process according to claim 9, characterized in that: The specific calculation formula of the monitoring evaluation value is as follows: Where P is the monitoring assessment value, b is the coefficient of variation of dissolved concentration, r is the activity coefficient of ammonium ion, k is the microbial activity coefficient, μ1 is the weight of the coefficient of variation of dissolved concentration, μ2 is the weight of the activity coefficient of ammonium ion, and μ3 is the weight of the microbial activity coefficient.

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