Algorithm model for predicting thermal inactivation efficiency of microorganisms in biologically hazardous wastewater and application of algorithm model

Abstract

The invention belongs to the technical field of biology, and discloses an algorithm model for predicting the thermal inactivation efficiency of microorganisms in biohazard wastewater and application of the algorithm model, the model monitors the water temperature change through a water temperature probe, and calculates the real-time inactivation efficiency of the microorganisms in the sterilization process based on an integral algorithm. The model is not only used for optimizing the operation process of sterilization equipment and improving the efficiency and safety of the sterilization process, but also can be used for carrying out environmental risk assessment and judgment on wastewater discharge after thermal sterilization.

Description

Technical Field

[0001] The present invention belongs to the field of biotechnology and relates to a prediction model for the thermal inactivation efficiency (disinfecting rate) of microorganisms in wastewater, and in particular to a real-time quantitative prediction of the disinfecting rate of biohazardous wastewater in the fields of medical treatment, vaccine production and biosafety. Background Art

[0002] High-temperature hydrothermal method is an effective technology for inactivating microorganisms in wastewater, which is widely used in the treatment of biohazardous wastewater in the fields of medical treatment, vaccine production and biosafety. This technology mainly uses high temperature and the thermal properties of water to achieve the purpose of disinfecting microorganisms. Existing studies have shown that high-temperature treatment can effectively destroy the cell structure of microorganisms, thereby leading to their inactivation. In traditional thermal inactivation technology, a preset nonlinear time-temperature curve is often used for treatment. Although this method can adjust the sterilization conditions based on experience to a certain extent, it cannot accurately evaluate the efficiency and effect of sterilization, and it is difficult to achieve real-time monitoring and precise control of the sterilization effect. This method may not only lead to waste of energy and time due to excessive extension of sterilization time, but also make it difficult to ensure the comprehensive effectiveness of sterilization.

[0003] When dealing with the thermal inactivation efficiency of microorganisms in biohazardous wastewater, the commonly used quantitative method is based on the first-level thermal inactivation kinetic model or the more advanced Bigelow model. By integrating time, temperature and microbial survival curves to predict the thermal inactivation process of microorganisms, a sterilization time-temperature integral model is established, and the model parameters are calibrated based on rich experimental data. The thermal inactivation kinetics can be accurately predicted, and the time required to achieve the set sterilization rate at a specific temperature can be calculated, or the real-time sterilization rate. Through the application of this technology, the efficiency and safety of thermal inactivation technology can be greatly improved, the waste of energy and time can be reduced, and the comprehensiveness and reliability of the sterilization effect can be guaranteed. This is not only of great significance to the treatment of medical wastewater, but also has a positive role in promoting environmental protection and public health safety.

[0004] The spores of Geobacillus stearothermophilus have shown excellent stability and repeatability in the biological validation of high-temperature hydrothermal sterilization processes due to their remarkable high heat resistance. In the standard specifications for verifying the effectiveness of high-temperature inactivation processes in countries around the world, G. stearothermophilus spores are recognized as an accurate microbial indicator. Using it as a quantitative benchmark can reliably ensure the thorough elimination of highly tolerant microorganisms during the sterilization process. Summary of the invention

[0005] The present invention proposes a mathematical algorithm model that can instantly predict the thermal inactivation efficiency of biohazardous wastewater through the real-time temperature change curve during the thermal inactivation of wastewater. According to the algorithm model, the sterilization time and temperature of the system can be adjusted in time according to the sterilization efficiency to avoid excessive sterilization, effectively reducing the energy consumption and operating costs during the thermal inactivation of wastewater. In addition, the parameters of the algorithm model can be appropriately corrected according to the factors that may affect the inactivation efficiency (such as pH, organic matter, ion concentration, etc.) in the biohazardous wastewater, ensuring the reliability of the algorithm prediction results. The present invention can significantly improve the efficiency of the thermal sterilization process of biohazardous wastewater treatment, and promote the scientific improvement of wastewater thermal sterilization technology in the fields of medical treatment, biosafety and vaccine production. In addition, by combining with traditional biological indicators, the algorithm model can also perform environmental risk assessment and judgment on the wastewater discharge after thermal sterilization, providing a guarantee for the efficient treatment and safe discharge of biohazardous wastewater.

[0006] The present invention is achieved through the following technical solutions.

[0007] A prediction algorithm model for the thermal inactivation efficiency of microorganisms in biohazardous wastewater is characterized in that, based on the Bigelow empirical model, the dynamic temperature change is processed by integrating the temperature change with time during the inactivation process, so as to accurately predict the logarithmic reduction value (Log Reduction, LR) of specific microorganisms under different temperature and time conditions, which is used to compare and evaluate the efficiency and effectiveness of the thermal inactivation process of biohazardous wastewater.

[0008] Furthermore, the prediction algorithm model is:

[0009]

[0010] Including, the heat resistance parameter D of microorganisms at standard sterilization temperature ref (min), that is, the time required for the survival rate of microorganisms to drop to 1 / 10 of the original, the standard sterilization temperature T ref (℃), thermal inactivation time t (min), real-time temperature of the system T(t) (℃), temperature change Z (℃) required to reduce the microbial heat resistance parameter D value by one order of magnitude (i.e., a 90% reduction).

[0011] The algorithm model according to claim 1 or 2, characterized in that the parameter D ref The fitting of the Z values ​​was performed using standard thermotolerant microorganisms as reference.

[0012] The algorithm model according to claim 1 or 2 is characterized in that the heat-resistant model microorganism is Bacillus stearothermophilus ATCC7953.

[0013] Furthermore, the preparation steps of the Geobacillus stearothermophilus ATCC7953 include:

[0014] (1) Take the G. stearothermophilus ATCC7953 strain stored at -80°C and place it in nutrient broth. Incubate it at 56°C for 10-16 hours to activate the strain.

[0015] (2) The activated bacterial solution is diluted 10-10,000 times, spread on a solid bacterial enrichment medium, and cultured at 56°C for 24-48 hours until 20-100 colonies with a diameter of 0.5-1.5 mm appear.

[0016] (3) Pick a single colony with clear boundaries and disperse it in 1000 μL sterile water. Dilute it 10-10000 times and then spread it on the surface of solid spore-forming medium. Culture it at 60°C for 60-150 hours to produce spores. Use sterile water to collect the spores on the surface of the plate.

[0017] The algorithm model according to any one of claims 1 to 4 is characterized in that the algorithm model is established through four steps:

[0018] (1) heat-inactivating standard spores at different temperatures and times, and determining the change pattern of spore survival rate;

[0019] (2) According to formula 1, the data in step (1) are fitted to obtain the heat resistance parameter D of the spore at temperature T (℃) T (min);

[0020]

[0021] The original number of spores N0 (mL -1 ), the number of spores after heat treatment for t minutes N t (mL -1 ).

[0022] (3) According to formula 2, the spore heat resistance parameter D in step (2) is fitted T The relationship between the temperature (min) and the sterilization temperature T (℃), that is, the heat resistance parameter D T Reduce the change in T (℃) by one logarithmic level (i.e., reduce it by 90%);

[0023]

[0024] Among them, the heat resistance parameter D of spores at T℃ T (min), real-time sterilization temperature T (℃), reference sterilization temperature T ref (℃), referring to the heat resistance parameter D of spores at sterilization temperature ref(min), the temperature change Z (℃) required for the spore heat resistance parameter to decrease by one logarithmic level at the reference sterilization temperature;

[0025] (4) According to Formula 3, the LR of the spores at any time point during the dynamic heating process is predicted by integrating the actual residence time t (min) at the real-time sterilization temperature T (°C).

[0026]

[0027] Furthermore, the parameter D T Calibration can be performed based on the effect of wastewater pH on thermal inactivation efficiency.

[0028] Furthermore, the algorithm model performs real-time quantitative prediction of the thermal disinfection rate of biohazardous wastewater generated by hospitals and biosafety laboratories, and performs environmental risk assessment and determination on wastewater discharge after thermal disinfection.

[0029] A method for predicting the thermal inactivation efficiency of microorganisms in biohazardous wastewater, characterized in that the method comprises:

[0030] Step 1) Establishment of inactivation efficiency prediction algorithm model:

[0031] (1) The survival number of standard heat-resistant microorganisms inactivated at different temperatures and times was fitted using Formula 1 to obtain the spore heat resistance parameter D that depends on temperature T (°C): T (min);

[0032]

[0033] The original number of spores N0 (mL -1 ), the number of spores after heat treatment for t minutes N t (mL -1 );

[0034] (2) According to formula 2, the spore heat resistance parameter D in step (2) is fitted T The relationship between the temperature (min) and the sterilization temperature T (℃), that is, the heat resistance parameter D T The change in T (°C) required to reduce one log level (i.e., a 90% reduction);

[0035]

[0036] Among them, the heat resistance parameter D of spores at T℃ T (min), real-time sterilization temperature T (℃), reference sterilization temperature T ref (℃), referring to the heat resistance parameter D of spores at sterilization temperature ref(min), the temperature change Z (℃) required for the spore heat resistance parameter to decrease by one logarithmic level at the reference sterilization temperature;

[0037] (3) According to Formula 3, the actual residence time t (min) at the real-time sterilization temperature T (°C) is integrated to calculate the log reduction value (LR) at any time point during the dynamic temperature rise sterilization process to predict the real-time spore inactivation rate;

[0038]

[0039] Step 2) Preparation of standard spores of G. stearothermophilus ATCC7953:

[0040] Take the glycerol tube of G. stearothermophilus ATCC7953 spores stored at -80°C and activate it in nutrient broth, and culture it at 56°C and 180rpm for 12 hours to obtain a bacterial suspension; after dilution, the bacterial suspension is spread on the surface of a solid enrichment medium and cultured at 56°C for 24-48 hours until 20-100 colonies with a diameter of 1 mm appear. Pick a single colony with a clear boundary, mix it in 1000 μL sterile water, dilute it to a suitable multiple, inoculate and spread it on the surface of a solid spore-producing culture medium, and culture it at 60°C for 5 days. Collect the bacterial moss on the surface of the plate with sterile water to obtain a mixture of spores and vegetative cells; centrifuge the spore suspension at 4°C and 8000rpm for 10 minutes, discard the supernatant, add sterile water to resuspend the precipitate, and heat it in a 99.9°C water bath for 20 minutes to inactivate the vegetative cells, and immediately cool it in an ice bath for 5 minutes, repeat the centrifugation and resuspension operation twice, and separate the spore precipitation layer and the bacterial cell precipitation layer during the operation, and store the obtained spore suspension at 4°C;

[0041] Step 3) Model parameter fitting:

[0042] 50 μL of standard spore suspension was injected into the quartz capillary, and both ends of the capillary were sealed by hot melting to prepare five groups of capillaries, one of which was not sterilized, and the other four groups were exposed to specific sterilization conditions, at least one of which had a spore count drop of not less than 41 g after sterilization, and the sterilization conditions of the other three groups were between the above two groups; after the capillary was treated with an oil bath, it was immediately taken out and placed in a 4°C ice bath for 5 min, the residual oil on the outer wall of the capillary was cleaned with a detergent, and it was immersed in a sodium hypochlorite solution for 15 min, and the residual sodium hypochlorite on the outer wall was washed off with sterile water, the two ends of the capillary were opened, and the capillary was blown 20 times in 5 mL of sterile water to minimize the spore residue on the tube wall, and the spores were diluted to the optimal counting concentration and cultured at 56°C on a tryptic soy peptone agar medium for 24 h before counting;

[0043] Formula 1 is used to predict the inactivation of standard spores at different temperatures over time. The logarithmic values ​​of surviving spores at different temperatures show a linear downward trend with the extension of inactivation time, and within the same treatment time, the higher the treatment temperature, the fewer the number of surviving spores, that is, the heat resistance parameter D of the standard spores. T As the temperature increases, it decreases; the prediction model for the thermal inactivation of the standard spores of G. stearothermophilus ATCC7953 at a standard sterilization temperature of 121°C and a pH of 8.86 is established as follows:

[0044]

[0045] Step 4) Correction of model parameter D under the condition of the effect of pH change on the thermal inactivation efficiency of spores in wastewater:

[0046] The effect of pH of the sterilization environment on the D value of spores showed a trend of low-high-low, and the D value reached the highest around pH=8. 121 The correction equation is:

[0047] D T =-0.0065x 3 +0.0853x 2 -0.1695x+2.0295(R 2 =0.9907)

[0048] Wherein, x is the pH value, ranging from 4 to 10.

[0049] The beneficial effects achieved by the present invention mainly include but are not limited to the following aspects:

[0050] 1. Compared with the qualitative methods of biological indicators and chemical indicators, the present invention can quantitatively predict the sterilization efficiency in real time, making it possible to dynamically and intelligently adjust the sterilization parameters during the sterilization process to optimize the sterilization effect. This dynamic adjustment not only improves the effectiveness of sterilization, but also prevents over-sterilization, thereby protecting the quality of the sterilized items.

[0051] 2. The integral calculation algorithm calculates LR in real time. Compared with the D value of the reference microorganism, this method can more accurately reflect the inactivation efficiency of microorganisms under different operating temperatures and time accumulation.

[0052] 3. By integrating real-time temperature sensor data acquisition with real-time sterilization rate calculation based on model algorithm, it is easy to integrate and apply in the existing sterilization system. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1:Micrograph of spore and bacterial suspension of Bacillus stearothermophilus ATCC7953. The spores are blue and the bacterial bodies are red.

[0054] Figure 2 :Experimental value (log CFU / mL) and fitting curve of G. stearothermophilus ATCC7953 standard spore survival rate at different heat inactivation temperatures. Where · is the measured value, A, B, C, D represent heat inactivation temperatures of 119℃, 121℃, 123℃, 125℃, respectively.

[0055] Figure 3 :The logarithmic value of heat resistance parameter of G. stearothermophilus ATCC7953 standard spores at different heat inactivation temperatures lgD T The fitting result diagram with temperature T. Where · is the measured value.

[0056] Figure 4 :The pH value of heat inactivation and the heat resistance parameter D of G. stearothermophilus ATCC7953 spores 121 Value and fitting curve. Where · is the measured value. DETAILED DESCRIPTION

[0057] The embodiments of the present invention are described in detail below. It should be understood that the described embodiments are only some examples of the present invention. Based on the embodiments described herein, other embodiments that can be derived by those skilled in the art without creative work also belong to the protection scope of the present invention.

[0058] In actual use, the model is implemented through a system that combines the following hardware and software. The system includes:

[0059] (1) Temperature sensor: armored platinum resistance probe, in contact with the medium in the heating device, accuracy 0.1°C, equipped with a temperature transmitter, data acquisition frequency 10-15Hz

[0060] (2) Data acquisition and processing system: PC, serial port temperature data transmission method.

[0061] (3) Temperature collection, storage, model algorithm, and result output interface are implemented using Python or C++ programming.

[0062] Example 1

[0063] The present invention is implemented by an algorithm model that calculates the log reduction value (LR) of microorganisms in biohazardous wastewater during the thermal inactivation process in real time. The model collects temperature data within a specified interval, calculates the LR value in real time, and quantitatively reflects the thermal inactivation efficiency of the system. The specific steps include: establishing a model, preparing a standard model of heat-resistant spores, performing parameter fitting, and calibrating and verifying the model. The specific implementation steps include:

[0064] 1. Establishment of inactivation efficiency prediction algorithm model

[0065] (1) The survival number of standard heat-resistant microorganisms inactivated at different temperatures and times was fitted using Formula 1 to obtain the spore heat resistance parameter D that depends on temperature T (°C): T (min);

[0066]

[0067] The original number of spores N0 (mL -1 ), the number of spores after heat treatment for t minutes N t (mL -1 ).

[0068] (2) According to formula 2, the spore heat resistance parameter D in step (2) is fitted T The relationship between the temperature (min) and the sterilization temperature T (℃), that is, the heat resistance parameter D T The change in T (°C) required to reduce one log level (i.e., a 90% reduction);

[0069]

[0070] Among them, the heat resistance parameter D of spores at T℃ T (min), real-time sterilization temperature T (℃), reference sterilization temperature T ref (℃), referring to the heat resistance parameter D of spores at sterilization temperature ref (min), the temperature change Z (℃) required for the spore heat resistance parameter to decrease by one logarithmic level at the reference sterilization temperature;

[0071] (3) According to Formula 3, the actual residence time t (min) at the real-time sterilization temperature T (°C) is integrated to calculate the log reduction value (LR) at any time point during the dynamic temperature rise sterilization process to predict the real-time spore inactivation rate;

[0072]

[0073] 2. Preparation of standard spores of G. stearothermophilus ATCC7953

[0074] Take the glycerol tube of G. stearothermophilus ATCC7953 spores stored at -80℃ and activate it in nutrient broth, and culture it at 56℃ and 180rpm for 12h to obtain a bacterial suspension. After dilution, the bacterial suspension is spread on the surface of a solid enrichment medium and cultured at 56℃ for 24-48h until 20-100 colonies with a diameter of about 1mm appear. Pick a single colony with a clear boundary, mix it in 1000μL sterile water, dilute it to a suitable multiple, and inoculate it on the surface of a solid spore-producing medium. Culture it at 60℃ for 5 days, and collect the bacterial lawn on the surface of the plate with sterile water to obtain a mixture of spores and nutrient cells. The spore suspension was centrifuged at 4°C and 8000 rpm for 10 min, the supernatant was discarded, sterile water was added to resuspend the precipitate, and then heated in a 99.9°C water bath for 20 min to inactivate the vegetative cells, and immediately cooled in an ice bath for 5 min. The centrifugation and resuspension operation was repeated twice, and the spore precipitate layer and the bacterial cell precipitate layer were separated during the operation. The obtained spore suspension was stored at 4°C (Appendix Figure 1 ). Among them, the composition of the enrichment medium is soluble starch 3.0g / L, tryptone 10.0g / L, sodium chloride 10.0g / L, agar 15.0g / L, pH = 7.0±0.2. The composition of the spore production medium is yeast extract 3.0g / L, tryptone 10.0g / L, sodium chloride 10.0g / L, manganese sulfate monohydrate 0.022g / L, magnesium sulfate heptahydrate 0.246g / L, anhydrous calcium chloride 0.110g / L, potassium chloride 0.962g / L, agar 15.0g / L, pH = 7.0±0.2.

[0075] 3. Model parameter fitting

[0076] Towards 50 μL of standard spore suspension was injected into the quartz capillary, and both ends of the capillary were sealed by hot melt to prepare five groups of capillaries (no less than three in each group), one of which was not sterilized, and the other four groups were exposed to specific sterilization conditions (such as: 119±0.5℃ constant temperature oil bath for 6, 10, 14, 18 minutes), and the spore count of at least one group decreased by no less than 4lg after sterilization, and the sterilization conditions of the other three groups were between the above two groups. After the capillary has been treated with the oil bath, it is immediately taken out and placed in a 4°C ice bath to cool for 5 min. The residual oil on the outer wall of the capillary is cleaned with a detergent and immersed in a 525 ppm sodium hypochlorite solution for 15 min. The residual sodium hypochlorite on the outer wall is washed off with sterile water. The two ends of the capillary are opened and the capillary is blown about 20 times in 5 mL of sterile water to minimize the spore residue on the tube wall. After diluting to the optimal counting concentration, the spores are cultured in tryptic soy peptone agar (TSA) medium at 56°C for 24 h and then counted.

[0077] Formula 1 is used to predict the inactivation of standard spores over time at different temperatures (Appendix Figure 2). At different temperatures, the logarithmic value of the surviving spores showed a linear downward trend with the extension of the inactivation time, and within the same treatment time, the higher the treatment temperature, the fewer the number of surviving spores, that is, the standard spore heat resistance parameter D T Decreases as temperature increases.

[0078] Table 1 Heat resistance parameters D of G. stearothermophilus ATCC7953 standard spores at different sterilization temperatures T Value (pH = 8.86)

[0079]

[0080] The spore heat resistance parameter D in Table 1 was fitted by formula 2 T The relationship between the change of temperature T and the calculated standard spore Z value of G. stearothermophilus ATCC7953 is 7.13℃ (Appendix Figure 3 ). Based on this, the prediction model for thermal inactivation of G. stearothermophilus ATCC7953 standard spores at a standard sterilization temperature of 121°C and a pH of 8.86 was established as follows:

[0081]

[0082] 4. Correction of model parameter D under the condition of the influence of pH change on the efficiency of thermal inactivation of spores in wastewater

[0083] Dilute the standard spore suspension in aqueous solution at pH = 4, 6, 8, 10, or 12. Inject 50 μL of spore suspension with a specific pH value into the quartz capillary, and heat-melt the ends of the capillary. After the capillary is placed in an oil bath at 121±0.5℃ for a specific time, take it out immediately and put it in a 4℃ ice bath to cool for 5 minutes. Clean the residual silicone oil on the outer wall of the capillary with a detergent, and immerse it in a 525ppm sodium hypochlorite solution for 15 minutes. Wash the residual sodium hypochlorite on the outer wall with sterile water, open both ends of the capillary, blow the capillary about 20 times in 5mL sterile water to minimize the spore residue on the tube wall, and dilute it to the optimal counting concentration and culture it at 56℃ on tryptic soy peptone agar (TSA) medium for 24 hours before counting.

[0084] The effect of pH of the sterilization environment on the D value of spores showed a trend of low-high-low, and the D value reached the highest around pH=8 (see Appendix Figure 4 ). By fitting regression, D 121 The correction equation is:

[0085] D T =-0.0065x 3 +0.0853x 2-0.1695x+2.0295(R 2 =0.9907)

[0086] Wherein, x is the pH value, ranging from 4 to 10.

[0087] Example 2

[0088] The model of the present invention is applied and its reliability is determined. The model should be used to calculate the real-time inactivation efficiency LR of microorganisms at time point t (min) after the start of sterilization t , and compared and analyzed with the actual sampling data. It is known that when the pH of the sterilization environment is 8.86, the spore D 121 is 2.7021min. Substituting pH=8.86 into the pH correction equation, we get: D (121，pH＝8.86) =2.7030min, the two values ​​are very close, with only 0.03% deviation. Input the parameters of the sterilization conditions into the algorithm model and predict LR, calculate the error (ε) and error rate between the experimentally measured LR value

[0089] Table 2 Comparison of actual sterilization effect and model prediction effect under different sterilization conditions

[0090]

[0091] The results show that under different heat inactivation conditions, the error rate between the prediction results of the model of the present invention and the actual measurement results is controlled within 20%, and the error is controlled within 0.3 logarithmic values.

[0092] The smaller the sample size, the higher the error rate, and the calculated error rate may have greater randomness and instability. In addition, the calculation of the error rate only considers the correctness of the result, and does not consider the specific difference between the predicted value and the actual value. Therefore, in some cases, the error rate may be large but the error may be small, or the error rate may be small but the error may be large.

[0093] Since the model algorithm assumes that parameters such as sterilization temperature and time are constant, there are various uncertain factors in the actual application process, such as changes in the operating level of the experimenter, the accuracy of the experimental equipment, the water quality, the initial number of spores, etc., which may cause the actual value to deviate from the predicted value, that is, the error. If it is assumed that the experimental temperature fluctuates within the range of ±0.5℃ of the set sterilization temperature, the model sterilization temperature range is changed accordingly, and the predicted LR range obtained by re-analysis is shown in the table. From the comparison of the data in the table, it can be found that the experimentally determined LR is within the predicted LR range, and the prediction accuracy rate reaches 100%.

[0094] Although the above describes the specific implementation of the present invention in combination with the embodiments, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that it should be clear to those skilled in the art that these modifications or improvements made without departing from the spirit of the present invention all fall within the scope of protection of the present invention.

Claims

1. A prediction algorithm model for the thermal inactivation efficiency of microorganisms in biohazardous wastewater, characterized in that: Based on the Bigelow empirical model, the dynamic temperature change is handled by integrating the temperature change over time during the inactivation process, so as to accurately predict the log reduction value (LR) of specific microorganisms under different temperature and time conditions, which is used to compare and evaluate the efficiency and effectiveness of the thermal inactivation process of biohazardous wastewater.

2. The algorithm model according to claim 1, characterized in that: The prediction algorithm model is: Including, the heat resistance parameter D of microorganisms at standard sterilization temperature ref (min), that is, the time required for the survival rate of microorganisms to drop to 1 / 10 of the original, the standard sterilization temperature T ref (℃), thermal inactivation time t (min), real-time temperature of the system T(t) (℃), temperature change Z (℃) required to reduce the microbial heat resistance parameter D value by one order of magnitude (i.e., a 90% reduction).

3. The algorithm model according to claim 1 or 2, characterized in that: Parameter D ref The fitting of the Z values ​​was performed using standard thermotolerant microorganisms as reference.

4. The algorithm model according to claim 1 or 2, characterized in that: The thermotolerant model microorganism is Geobacillus stearothermophilus ATCC7953.

5. The algorithm model according to claim 4, characterized in that: The preparation steps of the thermophilic Bacillus ATCC7953 include: (1) Take the G. stearothermophilus ATCC7953 strain stored at -80°C and place it in nutrient broth. Incubate it at 56°C for 10-16 hours to activate the strain. (2) The activated bacterial solution is diluted 10-10,000 times, spread on a solid bacterial enrichment medium, and cultured at 56°C for 24-48 hours until 20-100 colonies with a diameter of 0.5-1.5 mm appear. (3) Pick a single colony with clear boundaries and disperse it in 1000 μL sterile water. Dilute it 10-10000 times and then spread it on the surface of solid spore-forming medium. Culture it at 60°C for 60-150 hours to produce spores. Use sterile water to collect the spores on the surface of the plate.

6. The algorithm model according to any one of claims 1 to 4, characterized in that: The algorithm model is established through four steps: (1) heat-inactivating standard spores at different temperatures and times, and determining the change pattern of spore survival rate; (2) According to formula 1, the data in step (1) are fitted to obtain the heat resistance parameter D of the spore at temperature T (℃) T (min); The original number of spores N0 (mL -1 ), the number of spores after heat treatment for t minutes N t (mL -1 ). (3) According to formula 2, the spore heat resistance parameter D in step (2) is fitted T The relationship between the temperature (min) and the sterilization temperature T (℃), that is, the heat resistance parameter D T Reduce the change in T (℃) by one logarithmic level (i.e., reduce it by 90%); Among them, the heat resistance parameter D of spores at T℃ T (min), real-time sterilization temperature T (℃), reference sterilization temperature T ref (℃), referring to the heat resistance parameter D of spores at sterilization temperature ref (min), the temperature change Z (℃) required for the spore heat resistance parameter to decrease by one logarithmic level at the reference sterilization temperature; (4) According to Formula 3, the LR of the spores at any time point during the dynamic heating process is predicted by integrating the actual residence time t (min) at the real-time sterilization temperature T (°C).

7. The algorithm model according to any one of claims 1 to 5, characterized in that: Parameter D T Calibration can be performed based on the effect of wastewater pH on thermal inactivation efficiency.

8. The algorithm model according to claim 1-6, characterized in that: The algorithm model performs real-time quantitative prediction of the thermal disinfection rate of biohazardous wastewater generated by hospitals and biosafety laboratories, and performs environmental risk assessment and determination on wastewater discharge after thermal disinfection.

9. A method for predicting the efficiency of thermal inactivation of microorganisms in biohazardous wastewater, characterized in that: The method comprises: step 1) establishing an inactivation efficiency prediction algorithm model: (1) The survival number of standard heat-resistant microorganisms inactivated at different temperatures and times was fitted using Formula 1 to obtain the spore heat resistance parameter D that depends on temperature T (°C): T (min); The original number of spores N0 (mL -1 ), the number of spores after heat treatment for t minutes N t (mL -1 ); (2) According to formula 2, the spore heat resistance parameter D in step (2) is fitted T The relationship between the temperature (min) and the sterilization temperature T (℃), that is, the heat resistance parameter D T The change in T (°C) required to reduce one log level (i.e., a 90% reduction); Among them, the heat resistance parameter D of spores at T℃ T (min), real-time sterilization temperature T (℃), reference sterilization temperature T ref (℃), referring to the heat resistance parameter D of spores at sterilization temperature ref (min), the temperature change Z (℃) required for the spore heat resistance parameter to decrease by one logarithmic level at the reference sterilization temperature; (3) According to Formula 3, the actual residence time t (min) at the real-time sterilization temperature T (°C) is integrated to calculate the log reduction value (LR) at any time point during the dynamic temperature rise sterilization process to predict the real-time spore inactivation rate; Step 2) Preparation of standard spores of G. stearothermophilus ATCC7953: Take the glycerol tube of G. stearothermophilus ATCC7953 spores stored at -80°C and activate it in nutrient broth, and culture it at 56°C and 180rpm for 12 hours to obtain a bacterial suspension; after dilution, the bacterial suspension is spread on the surface of a solid enrichment medium and cultured at 56°C for 24-48 hours until 20-100 colonies with a diameter of 1 mm appear. Pick a single colony with a clear boundary, mix it in 1000 μL sterile water, dilute it to a suitable multiple, inoculate and spread it on the surface of a solid spore-producing culture medium, and culture it at 60°C for 5 days. Collect the bacterial moss on the surface of the plate with sterile water to obtain a mixture of spores and vegetative cells; centrifuge the spore suspension at 4°C and 8000rpm for 10 minutes, discard the supernatant, add sterile water to resuspend the precipitate, and heat it in a 99.9°C water bath for 20 minutes to inactivate the vegetative cells, and immediately cool it in an ice bath for 5 minutes, repeat the centrifugation and resuspension operation twice, and separate the spore precipitation layer and the bacterial cell precipitation layer during the operation, and store the obtained spore suspension at 4°C; Step 3) Model parameter fitting: 50 μL of standard spore suspension was injected into the quartz capillary, and both ends of the capillary were sealed by hot melting to prepare five groups of capillaries, one of which was not sterilized, and the other four groups were exposed to specific sterilization conditions, at least one of which had a spore count drop of not less than 41 g after sterilization, and the sterilization conditions of the other three groups were between the above two groups; after the capillary was treated with an oil bath, it was immediately taken out and placed in a 4°C ice bath for 5 min, the residual oil on the outer wall of the capillary was cleaned with a detergent, and it was immersed in a sodium hypochlorite solution for 15 min, and the residual sodium hypochlorite on the outer wall was washed off with sterile water, the two ends of the capillary were opened, and the capillary was blown 20 times in 5 mL of sterile water to minimize the spore residue on the tube wall, and the spores were diluted to the optimal counting concentration and cultured at 56°C on a tryptic soy peptone agar medium for 24 h before counting; Formula 1 is used to predict the inactivation of standard spores at different temperatures over time. The logarithmic values ​​of surviving spores at different temperatures show a linear downward trend with the extension of inactivation time, and within the same treatment time, the higher the treatment temperature, the fewer the number of surviving spores, that is, the heat resistance parameter D of the standard spores. T As the temperature increases, it decreases; the prediction model for the thermal inactivation of the standard spores of G. stearothermophilus ATCC7953 at a standard sterilization temperature of 121°C and a pH of 8.86 is established as follows: Step 4) Correction of model parameter D under the condition of the effect of pH change on the thermal inactivation efficiency of spores in wastewater: The effect of pH of the sterilization environment on the D value of spores showed a trend of low-high-low, and the D value reached the highest around pH=8. 121 The correction equation is: D T =-0.0065x 3 +0.0853x 2 -0.1695x+2.0295(R 2 =0.9907) Wherein, x is the pH value, ranging from 4 to 10.

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