Microplastic degradation process monitoring system based on microbiological treatment
By real-time monitoring and optimization of the microbial living environment, combined with degradation kinetic models, the problem of data bias in microplastic degradation analysis was solved, and a comprehensive analysis and optimization of microplastic degradation efficiency was achieved.
Patent Information
- Application Number
- CN202511251487.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-09-03
- Publication Date
- 2025-11-25
AI Technical Summary
In existing technologies, the impact of changes in the microbial living environment on concentration is ignored in the analysis of microplastic degradation, resulting in incomplete data collection and a rather one-sided analysis.
Through a microbial culture module, a data monitoring and processing module, and an intelligent analysis platform, multiple key indicators in the microplastic degradation process are monitored in real time, abnormal data is eliminated, degradation efficiency is predicted using a degradation kinetic model, and environmental reactors are adjusted to optimize reaction conditions.
A comprehensive analysis of microplastic degradation efficiency was achieved. By optimizing microbial activity through multiple parameters, degradation efficiency was improved, ensuring data accuracy and reliability.
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Figure CN121008033A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of monitoring and analysis technology, specifically to a monitoring system for the degradation process of microplastics based on microbial treatment. Background Technology
[0002] Microplastic degradation refers to the process by which microplastics are gradually broken down into smaller molecules in the natural environment through physical, chemical and biological processes. Physical degradation may be the fastest in changing the physical form of microplastics, while chemical degradation may be faster in breaking down microplastics. Furthermore, physical and chemical degradation require certain environmental conditions, while biodegradation, although slower, has the advantages of being environmentally friendly and sustainable.
[0003] When using microorganisms to degrade microplastics, the concentration of microorganisms directly affects the degradation efficiency. Therefore, most existing technologies are based on the first-order degradation kinetics principle, directly collecting the concentration of microorganisms to predict the degradation efficiency. Although this method can predict the degradation efficiency relatively intuitively, the data collection is not comprehensive enough and has a certain degree of singularity. It ignores the influence of changes in the microbial living environment on the microbial concentration, resulting in the microplastic degradation analysis focusing only on the influence of microbial concentration on the degradation efficiency, which is rather one-sided.
[0004] To address these shortcomings, this invention proposes a microplastic degradation process monitoring system based on microbial treatment. Summary of the Invention
[0005] The purpose of this invention is to provide a microplastic degradation process monitoring system based on microbial treatment, which solves the shortcomings of the prior art in that the data collection is not comprehensive enough, resulting in a one-sided analysis of microplastic degradation efficiency.
[0006] The objective of this invention can be achieved through the following technical solutions:
[0007] A microplastic degradation process monitoring system based on microbial treatment includes the following modules:
[0008] The microbial culture module is used to cultivate microorganisms during the degradation of microplastics and maintain the activity of the microorganisms by adjusting multiple parameters through an environmental reactor;
[0009] The data monitoring and processing module is used to monitor and obtain multiple key indicators of microplastics in the degradation liquid during the degradation process through multiple sensors, and to remove and supplement abnormal data. Finally, the data without abnormalities is synchronously uploaded to the cloud through IoT nodes.
[0010] The intelligent analysis platform includes a degradation prediction unit and a reporting unit;
[0011] The degradation prediction unit is used to predict degradation efficiency based on multiple monitored key indicators, combined with degradation kinetics models and historical data, and to automatically adjust the environmental reactor to optimize reaction conditions.
[0012] The reporting unit is used to generate degradation curves and interim reports based on the predicted degradation efficiency.
[0013] Preferably, the plurality of sensors includes a microbial concentration sensor, a temperature sensor, a pH sensor, and a dissolved oxygen sensor, and the abnormal data is removed and supplemented in the following ways:
[0014] The data collected by the sensors is divided into time series according to the collection timestamp. Data collected by different sensors is recorded according to the timestamp and data tags are generated.
[0015] Multiple data collected by the same type of sensor are compared individually to obtain suspicious abnormal data sequences. Then, the accurate abnormal data in the suspicious abnormal data sequences are checked by correlation comparison. Based on the comparison results and the check results, the accurate abnormal data are deleted.
[0016] The missing data is filled by linear interpolation based on the data collected before and after the missing data.
[0017] Preferably, the individual comparison is performed in the following ways:
[0018] Draw a data curve based on the currently collected data;
[0019] Based on the particle size and mass of the microplastics, approximate data are matched from historical data;
[0020] Multiple historical data curves were plotted based on data collected from multiple sensors in the historical data.
[0021] The historical data curve segments in the historical data curve chart are compared with multiple currently collected data curve segments to obtain the corresponding data differences, and the data fluctuation range is obtained based on the data differences.
[0022] Data differences that suddenly exceed the fluctuation range are marked and recorded as suspicious abnormal data.
[0023] Preferably, the correlation comparison and verification is performed in the following manner:
[0024] Retrieve data tags for suspicious anomalies from time series data and obtain data collected by other sensors;
[0025] Based on other data in the data tags and the corresponding impact coefficients, combined with the initial microbial concentration and degradation time, the estimated microbial concentration at the current timestamp is obtained;
[0026] Compare the error between the sensor monitoring data and the estimated value. If the error exceeds the safety threshold, the monitoring data is determined to be abnormal.
[0027] The safety threshold is the average error between the estimated value obtained from other data labels without suspicious anomalies and the actual monitored value.
[0028] Preferably, the plurality of parameters include degradation temperature, pH value, and dissolved oxygen content, and each of the plurality of parameters includes an initial value and an adjustment value;
[0029] The initial values are the initial values during microbial culture in the microplastic degradation process;
[0030] The adjustment value is the value that needs to be adjusted when the predicted degradation efficiency does not reach the degradation efficiency under similar degradation environments in historical data.
[0031] Preferably, the adjustment value is obtained in the following way:
[0032] Input the key indicators without abnormal data into the degradation kinetics model to calculate and obtain the predicted degradation efficiency at the corresponding time point;
[0033] The difference is obtained by subtracting the predicted degradation efficiency from the degradation efficiency under similar conditions in historical data;
[0034] The environmental reactor is numerically adjusted based on the difference between the predicted degradation efficiency and the historical degradation efficiency, combined with an adjustment coefficient.
[0035] The adjustment coefficient is obtained based on the influence coefficients of degradation temperature, pH value, and dissolved oxygen on microbial concentration.
[0036] Preferably, the automatically adjusting environmental reactor determines whether adjustment is needed in the following manner:
[0037] The predicted degradation efficiency is obtained based on degradation kinetics and multiple key indicators monitored by sensors. The difference between the predicted degradation efficiency and the degradation efficiency in historical data is then calculated.
[0038] The environmental reactor should be adjusted based on the fluctuation range and the sign of the difference.
[0039] If the difference fluctuates significantly and is negative, the environmental reactor will be automatically adjusted; otherwise, no adjustment will be made.
[0040] Preferably, the reporting unit operates as follows:
[0041] A degradation efficiency function was designed based on parameters obtained from multiple sensors and degradation kinetics.
[0042] Plot the degradation curve as a function of time, with time as the x-axis and degradation efficiency as the y-axis.
[0043] Based on the time series, the data collected at each time stamp and the calculated degradation efficiency are recorded to generate a phased report of the control time series.
[0044] The beneficial effects of this invention are:
[0045] 1. This invention maintains the activity of microorganisms by regulating their living environment through an environmental reactor. It utilizes multiple sensors to acquire various data related to the microbial living environment, and monitors and removes abnormal data through one-way and correlation comparisons to avoid affecting data recording. The invention predicts microplastic degradation efficiency by combining the microbial living environment, microbial concentration, and trained degradation kinetics. Based on the predicted degradation efficiency, the environmental reactor is adjusted to optimize the reaction conditions between microorganisms and microplastics. This allows for a comprehensive analysis of microplastic degradation efficiency by incorporating multiple parameters.
[0046] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description
[0047] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0048] Figure 1 This is a modular framework diagram of a microplastic degradation process monitoring system based on microbial treatment according to the present invention. Detailed Implementation
[0049] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0050] Please see Figure 1As shown, this invention is a microplastic degradation process monitoring system based on microbial treatment, mainly including the following modules: a microbial culture module, a data monitoring and processing module, and an intelligent analysis platform. Microplastic degradation mainly utilizes the ability of certain microorganisms to secrete enzymes to decompose microplastics into oligomers, dimers, or monomers, and even further mineralize them into inorganic substances such as carbon dioxide, water, and methane, thereby effectively controlling plastic pollution. During microplastic degradation, microorganisms first attach to the surface of microplastics to form a biofilm, and then contact the plastic by secreting extracellular polymers. For example, the intestinal flora of mealworms and wax moths can decompose polystyrene and polyethylene through the biofilm. The degradation solution can improve the plastic-free degradation efficiency by optimizing microbial activity or directly participating in chemical reactions. Special degradation containers are selected, such as aerobic or anaerobic culture containers, high-pressure reactors, transparent glass reactors, etc. Then, microplastics, degradation solution, and microbial strains that can produce microorganisms are put into the degradation container together. Then, through the continuous growth and cultivation of microorganisms, combined with the degradation solution, the degradation of microplastics can begin.
[0051] The degradation container requires a corresponding environmental reactor. Different microorganisms thrive better or have higher concentrations in specific environments. Higher microbial concentrations have a certain impact on the degradation efficiency of microplastics. Generally speaking, the factors that have a significant impact on the survival environment of microorganisms are temperature, pH, and dissolved oxygen. These three environmental factors directly affect the concentration of microorganisms. By setting up a corresponding environmental reactor and adjusting the temperature, pH, and dissolved oxygen, it is equivalent to adjusting the concentration of microorganisms in the degradation solution, which is also equivalent to adjusting the degradation efficiency of microplastics. Therefore, it is necessary to ensure the activity of microorganisms to ensure the degradation efficiency.
[0052] The data monitoring and processing module is mainly used to acquire various data during the microplastic degradation process. By setting up corresponding acquisition sensors, various parameters in the container are continuously recorded and observed. The main focus is on detecting and recording the survival temperature, pH value, and dissolved oxygen of microorganisms. Based on these acquired data, the microbial estimate can be calculated, and combined with the degradation kinetic model, the corresponding degradation efficiency can be predicted.
[0053] Microplastics typically degrade over a relatively long period, often measured in days. Consequently, the monitoring time for sensors is also correspondingly long. Data collected by sensors is divided into time series based on acquisition timestamps. Data from different sensors is recorded according to timestamps, generating data tags. Tag content includes acquisition time, acquisition temperature, pH value, and dissolved oxygen level. For example, tag 1 (time, temperature 26 degrees Celsius, pH 6, 0.2 mg / L). Many high-precision sensors on the market can capture extremely accurate parameter data. However, during monitoring, sensors may inevitably experience anomalies due to external factors. Therefore, the data acquired by the sensors needs to be calibrated and verified. Accurate and abnormal data is deleted and supplemented. Abnormal data is identified through single-item comparison and correlation comparison. Then, the missing data is supplemented by linear interpolation based on the data collected before and after the missing data. Finally, data without anomalies is uploaded in real time through network nodes to ensure timely retrieval of the corresponding data later.
[0054] The single-item comparison mainly uses historical data from similar sensors for comparison. Based on the particle size and mass of the microplastics during this microplastic degradation, similar historical data is queried. Then, the data collected by the current sensor is compared with the data recorded by similar sensors in the historical data and the time of collection to obtain abnormal data.
[0055] Based on the real-time acquisition of monitoring data from various sensors during the current degradation process by the data monitoring and processing module, including microbial concentration sensor, temperature sensor, pH sensor, and dissolved oxygen sensor, a current data curve reflecting the dynamic changes of each key indicator is plotted in time series, intuitively presenting the fluctuation trend of parameters such as temperature, pH, dissolved oxygen, and microbial concentration over time.
[0056] Based on the specific numerical range of particle size (e.g., micrometers) and mass (e.g., specific grams or milligrams) of the microplastics degraded in this study, case data of microplastic degradation with similar particle size and mass parameters are selected from the historical degradation data stored in the system to ensure that the selected historical data is highly comparable to the current degradation object.
[0057] For the selected historical data, we extracted the various index data collected by multiple sensors at the same degradation stage, and plotted multiple sets of historical data curves according to the time series. These curves correspond to the changing patterns of parameters such as temperature, pH value, dissolved oxygen, and microbial concentration in the historical cases.
[0058] The historical data curve segment corresponding to the current degradation stage in the historical data curve graph is compared point by point or segment by segment with the same stage in the multiple data curves that have been collected. The data difference between the historical data and the current data of the corresponding parameter in each time node or interval is calculated. The normal data fluctuation range of each parameter in this stage is determined by statistical analysis of these differences. This range reflects the reasonable range of change of microplastic degradation efficiency in historical data and degradation efficiency in the current degradation process.
[0059] If the difference of a data point or a continuous data segment suddenly exceeds the above-defined fluctuation range, and this exceedance is not based on a gradual change in the degradation process, such as a trend change caused by non-microbial natural proliferation or slow environmental adjustment, then the data point or data segment is marked as suspicious abnormal data.
[0060] Correlation comparison and verification is mainly used to verify suspicious and abnormal data that appear in single comparisons and to determine that the data is abnormal. First, the data labels marked as suspicious and abnormal data are accurately located from the data set organized by time series. The labels not only contain the specific timestamp of data collection, but also are associated with the complete environmental parameter data collected by temperature sensor, pH sensor and dissolved oxygen sensor at the corresponding time, so as to ensure that comprehensive environmental information in the same time dimension as the suspicious data can be obtained.
[0061] Based on these time-stamped temperature, pH, and dissolved oxygen values and their corresponding influence coefficients, which were obtained by fitting a large amount of historical degradation experimental data, these coefficients reflect the specific weight of the impact of each unit change in temperature, pH, and dissolved oxygen on the microbial concentration. For example, within the suitable temperature range, a 1-degree Celsius increase in temperature may increase the microbial concentration by 2%, while a 0.5-unit deviation of pH from the optimum value may decrease the concentration by 5%. Combining the microbial concentration recorded at the initial moment of this degradation process and the cumulative degradation time from the initial moment to the current time-stamp, the theoretical estimated microbial concentration at the current time-stamp is calculated using a preset microbial growth kinetic model.
[0062] Subsequently, the actual current microbial concentration data monitored by the sensor is compared with the estimated value calculated above. The error calculation adopts the absolute error, which is the absolute value of the monitored value minus the estimated value.
[0063] If the error exceeds the system's set safety threshold, the sensor's monitoring data is directly determined to be abnormal and must be removed. The safety threshold here is not a fixed value, but is obtained by selecting all other data labels without suspicious abnormal data from the dataset, calculating the error between the estimated microbial concentration and the actual monitored value under each label, and then taking the arithmetic mean of these errors. This average value can reflect the reasonable deviation range between the theoretical estimate and the actual monitored value under normal monitoring conditions.
[0064] The degradation kinetics principle is designed based on the first-order degradation kinetics principle, which states that the degradation efficiency is directly proportional to the substrate concentration in the degradation solution, expressed by the following formula:
[0065]
[0066] in, This indicates that the reactant concentration decreases over time, where k is the reaction rate constant and C is the reactant concentration.
[0067] The degradation kinetics model is based on the first-order degradation kinetics principle mentioned above. It is obtained by training an artificial neural network (ANN) using historical data. The historical data is decomposed into a training set, a validation set, and a test set. The training set is input into the ANN, and the output of the ANN is validated using the validation set. Based on the difference between the training result and the validation set, the parameters of the ANN are adjusted until the difference between the training result and the validation set is reduced to a reasonable range. Then, the ANN is tested using the test set to further evaluate the model's performance. The degradation kinetics model is optimized through network search, increasing or decreasing the model's complexity. Finally, data without anomalies are input into the degradation kinetics model, thereby predicting the degradation efficiency at the current stage based on the trained degradation kinetics model.
[0068] After obtaining the degradation efficiency at the current stage, if the degradation efficiency does not meet expectations, it is necessary to adjust various parameters of the environmental reactor. The expected values here include the degradation efficiency in the approximate historical data or the degradation efficiency that should theoretically increase or remain the same. If the current degradation efficiency predicted by the degradation kinetic model is lower than the degradation efficiency predicted in the previous stage, the parameters of the environmental reactor need to be adjusted. If the current degradation efficiency predicted by the degradation kinetic model differs too much from the degradation efficiency in the approximate historical data, the parameters of the environmental reactor also need to be adjusted.
[0069] The difference is obtained by subtracting the degradation efficiency from the approximate historical data or the previous predicted degradation efficiency. Based on the magnitude and sign of this difference, if the difference is positive, it means that the predicted efficiency is higher than the historical efficiency and no adjustment may be needed; if it is negative, the efficiency needs to be improved.
[0070] The specific adjustment values are calculated based on the current input data for predicted degradation efficiency: temperature, pH, and dissolved oxygen levels. The corresponding concentrations are then calculated and combined with the aforementioned influence coefficients to numerically correct the parameters of the environmental reactor. For example, experimental data shows that within the temperature range of 25–35 degrees Celsius, for every 1 degree Celsius increase, the microbial concentration increases by an average of 3%, so the influence coefficient for temperature is 0.03; within the pH range of 6.5–7.5, for every 0.1 unit deviation from the optimum value, the microbial concentration decreases by 2%, so the influence coefficient for pH is 0.02. / 0.1; For every 0.1 mg / L increase in dissolved oxygen, the microbial concentration increases by 1.5%, and the influence coefficient of dissolved oxygen is 0.015 / 0.1. The system will allocate the adjustment range of each parameter proportionally according to the absolute value of the difference. For example, if the difference is negative 5%, it may be necessary to adjust the temperature by 1 degree Celsius, correct the pH value to the optimum value by 0.2 units, and increase the dissolved oxygen by 0.3 mg / L to comprehensively increase the microbial concentration to compensate for the efficiency difference, and finally determine the specific adjustment values of temperature, pH value and dissolved oxygen in the environmental reactor.
[0071] After determining the specific adjustment values for the environmental reactor, the environmental reactor is adjusted, and the adjustment frequency and specific adjustment values are recorded. The recording format is: time - number of adjustments - temperature adjusted to the specified degree Celsius - pH value adjusted to the specified value - dissolved oxygen adjusted to the specified value.
[0072] After adjustment, the specific values of each data point are input into the degradation kinetic model again to obtain the adjusted predicted degradation efficiency. The current predicted degradation efficiency is then compared with the approximate degradation efficiency. If the adjusted degradation efficiency still differs too much from the approximate degradation efficiency, the degradation process or the physicochemical properties of the microplastic itself needs to be checked. If the difference between the adjusted degradation efficiency and the approximate degradation efficiency is within the fluctuation range, the system waits for the next data collection from the sensor to obtain the degradation efficiency at the next time point until the microplastic is degraded.
[0073] A two-dimensional image coordinate system was constructed, with the acquisition time as the horizontal axis. The data acquired by the sensor each time and the predicted degradation efficiency of the degradation kinetic model were used as the vertical axis of each two-dimensional image coordinate system to draw a degradation curve. The sensor data and degradation efficiency data acquired each time were recorded according to the time series to generate a phased report of the control time series. Finally, the degradation reaction was completed when the nanoplastics produced in the microplastic process decomposed into water, carbon dioxide and methane. The microorganisms generated in the degradation process were also inactivated.
[0074] The above description is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined in the claims, they should all fall within the protection scope of the present invention.
Claims
1. A monitoring system for the degradation process of microplastics based on microbial treatment, characterized in that, Includes the following modules: The microbial culture module is used to cultivate microorganisms during the degradation of microplastics and maintain the activity of the microorganisms by adjusting multiple parameters through an environmental reactor; The data monitoring and processing module is used to monitor and obtain multiple key indicators of microplastics in the degradation liquid during the degradation process through multiple sensors, and to remove and supplement abnormal data. Finally, the data without abnormalities is synchronously uploaded to the cloud through IoT nodes. The intelligent analysis platform includes a degradation prediction unit and a reporting unit; The degradation prediction unit is used to automatically adjust the environmental reactor to optimize reaction conditions based on the difference between the degradation efficiency predicted by the degradation kinetic model and historical data, according to multiple key indicators monitored. The reporting unit is used to generate degradation curves and periodic reports in real time based on the predicted degradation efficiency.
2. The microplastic degradation process monitoring system based on microbial treatment according to claim 1, characterized in that, The multiple sensors include a microbial concentration sensor, a temperature sensor, a pH sensor, and a dissolved oxygen sensor. Abnormal data is removed and supplemented through the following methods: The data collected by the sensors is divided into time series according to the collection timestamp. Data collected by different sensors is recorded according to the timestamp and data tags are generated. Multiple data collected by the same type of sensor are compared individually to obtain suspicious abnormal data sequences. Then, the accurate abnormal data in the suspicious abnormal data sequences are checked by correlation comparison. Based on the comparison results and the check results, the accurate abnormal data are deleted. The missing data is filled by linear interpolation based on the data collected before and after the missing data.
3. The microplastic degradation process monitoring system based on microbial treatment according to claim 2, characterized in that, The individual comparison is specifically performed in the following ways: Draw a data curve based on the currently collected data; Based on the particle size and mass of the microplastics, approximate data are matched from historical data; Multiple historical data curves were plotted based on data collected from multiple sensors in the historical data. The historical data curve segments in the historical data curve chart are compared with multiple currently collected data curve segments to obtain the corresponding data differences, and the data fluctuation range is obtained based on the data differences. Data differences that suddenly exceed the fluctuation range are marked and recorded as suspicious abnormal data.
4. The microplastic degradation process monitoring system based on microbial treatment according to claim 3, characterized in that, The correlation comparison and verification are performed in the following ways: Retrieve data tags for suspicious anomalies from time series data and obtain data collected by other sensors; Based on other data in the data tags and the corresponding impact coefficients, combined with the initial microbial concentration and degradation time, the estimated microbial concentration at the current timestamp is obtained; Compare the error between the sensor monitoring data and the estimated value. If the error exceeds the safety threshold, the monitoring data is determined to be abnormal. The safety threshold is the average error between the estimated value obtained from other data labels without suspicious anomalies and the actual monitored value.
5. The microplastic degradation process monitoring system based on microbial treatment according to claim 4, characterized in that, The parameters include degradation temperature, pH value, and dissolved oxygen content, and each parameter includes an initial value and an adjustment value. The initial values are the initial values during microbial culture in the microplastic degradation process; The adjustment value is the value that needs to be adjusted when the predicted degradation efficiency does not reach the degradation efficiency under similar degradation environments in historical data.
6. The microplastic degradation process monitoring system based on microbial treatment according to claim 5, characterized in that, The adjustment value is obtained in the following way: Input the key indicators without abnormal data into the degradation kinetics model to calculate and obtain the predicted degradation efficiency at the corresponding time point; The difference is obtained by subtracting the predicted degradation efficiency from the degradation efficiency under similar conditions in historical data; The environmental reactor is numerically adjusted based on the difference between the predicted degradation efficiency and the historical degradation efficiency, combined with an adjustment coefficient. The adjustment coefficient is obtained based on the influence coefficients of degradation temperature, pH value, and dissolved oxygen on microbial concentration.
7. The microplastic degradation process monitoring system based on microbial treatment according to claim 6, characterized in that, The automatically adjusting environmental reactor determines whether adjustment is needed using the following methods: The predicted degradation efficiency is obtained based on degradation kinetics and multiple key indicators monitored by sensors. The difference between the predicted degradation efficiency and the degradation efficiency in historical data is then calculated. The environmental reactor should be adjusted based on the fluctuation range and the sign of the difference. If the difference fluctuates significantly and is negative, the environmental reactor will be automatically adjusted; otherwise, no adjustment will be made.
8. The microplastic degradation process monitoring system based on microbial treatment according to claim 7, characterized in that, The reporting unit operates as follows: A degradation efficiency function was designed based on parameters obtained from multiple sensors and degradation kinetics. Plot the degradation curve as a function of time, with time as the x-axis and degradation efficiency as the y-axis. Based on the time series, the data collected at each time stamp and the calculated degradation efficiency are recorded to generate a phased report of the control time series.
Citation Information
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