A method for obtaining optimal operating conditions for methanogenesis by anaerobic digestion of excess sludge

Abstract

The embodiment of the present invention provides a method for obtaining the optimal operating conditions for anaerobic digestion of excess sludge to produce methane. The method includes: obtaining the main environmental factors that affect the anaerobic digestion of excess sludge; establishing ADM1-FPM, and using the global sensitivity analysis algorithm to analyze the parameter factors specified in ADM1-FPM, and calculating the degree of influence on the methane production in the anaerobic digestion reactor, thereby screening out the key parameter factors; according to the main environmental factors and the key parameter factors, determining the component factors of the operating conditions for anaerobic digestion of excess sludge to produce methane; for any component factor, using ADM1-FPM to simulate the anaerobic digestion of excess sludge under different values ​​of the component factor to determine its optimal value range; combining and setting the values ​​of each component factor according to the optimal value range of each component factor, and using ADM1-FPM to simulate the anaerobic digestion of excess sludge under different value combinations of the methane production, thereby determining the optimal operating conditions for anaerobic digestion of excess sludge to produce methane.

Description

Technical Field

[0001] The invention relates to the technical field of biological treatment of sewage and organic solid waste, and in particular to a method for obtaining optimal operating conditions for methanogenesis by anaerobic digestion of excess sludge. Background Art

[0002] Waste activated sludge (WAS), as a by-product of the sewage treatment process, is considered a valuable potential resource because it contains a large amount of organic solid waste. Anaerobic digestion (AD) is one of the main ways to treat excess sludge in sewage treatment plants. It can not only reduce and stabilize sludge, but also realize the resource utilization of sludge. It should be noted that the current anaerobic digestion process of excess sludge has problems such as low conversion rate of resource products and high operating costs, which limits its large-scale industrial application. In response to this, researchers simulated the production of methane in anaerobic digestion systems through mathematical models to optimize operations and improve biological conversion efficiency. However, at present, the optimal operating conditions for methane production by anaerobic digestion of excess sludge cannot be effectively obtained based on mathematical models. Summary of the invention

[0003] In a first aspect, an embodiment of the present invention provides a method for obtaining optimal operating conditions for methanogenesis by anaerobic digestion of excess sludge, the method comprising:

[0004] Obtaining major environmental factors that affect anaerobic digestion of excess sludge, wherein the major environmental factors cannot be analyzed by a global sensitivity analysis algorithm;

[0005] The ADM1 full parameter correction model ADM1-FPM was established, and the parameter factors specified in ADM1-FPM were analyzed using the global sensitivity analysis algorithm to calculate the influence of the parameter factors on the methane production in the anaerobic digestion reactor, and then the parameter factors with an influence greater than the preset threshold were selected as the key parameter factors;

[0006] Determining the component factors of the operating conditions for methanogenesis by anaerobic digestion of excess sludge according to the main environmental factors and the key parameter factors;

[0007] For any component factor, ADM1-FPM is used to simulate the methane production of anaerobic digestion of residual sludge under different values, and then its optimal value range is determined;

[0008] The values ​​of each component factor are combined and set according to the optimal numerical range of each component factor, and ADM1-FPM is used to simulate the methane production of anaerobic digestion of residual sludge under different numerical combinations, so as to determine the optimal operating conditions for methane production of anaerobic digestion of residual sludge.

[0009] In some implementations of the first aspect, the main environmental factors include: alkalinity, solid retention time, organic load, and nutrients.

[0010] In some implementations of the first aspect, establishing the ADM1 full parameter correction model ADM1-FPM includes:

[0011] Based on instrumental analysis and 16S rRNA gene sequencing analysis, the stoichiometric and kinetic parameters of ADM1 were corrected and ADM1-FPM was established.

[0012] In some implementations of the first aspect, the specified parameter factors include: reaction kinetics parameter factors and operating condition parameter factors; reaction kinetics parameter factors include: decomposition coefficient, protein hydrolysis coefficient, carbohydrate hydrolysis coefficient, lipid hydrolysis coefficient, amino acid maximum specific absorption rate, acetic acid maximum specific absorption rate; operating condition parameter factors include: pH.

[0013] In some implementations of the first aspect, the methane production is represented by methane gas production and methane partial pressure.

[0014] In some implementations of the first aspect, the degree of influence is represented by a linear correlation coefficient.

[0015] In some possible implementations of the first aspect, for any component factor, using ADM1-FPM to simulate the methane production of excess sludge anaerobic digestion at different values, and then determining the optimal value range, includes:

[0016] For the component factors that are the main environmental factors, the corresponding parts of the influent file of ADM1-FPM are indirectly modified to simulate the methane production of anaerobic digestion of residual sludge under different values ​​of ADM1-FPM, so as to determine the optimal value range.

[0017] For the component factors, which are key parameter factors, ADM1-FPM is used to simulate the methane production of anaerobic digestion of residual sludge under different values ​​by directly changing the values ​​of the component factors in ADM1-FPM, so as to determine the optimal value range.

[0018] In a second aspect, an embodiment of the present invention provides a device for obtaining optimal operating conditions for methanogenesis by anaerobic digestion of excess sludge, the device comprising:

[0019] An acquisition module, used for acquiring main environmental factors affecting anaerobic digestion of excess sludge, wherein the main environmental factors cannot be analyzed by a global sensitivity analysis algorithm;

[0020] Establish a module for establishing the ADM1 full parameter correction model ADM1-FPM, and use the global sensitivity analysis algorithm to analyze the parameter factors specified in ADM1-FPM, calculate the influence of the parameter factors on the methane production in the anaerobic digestion reactor, and then screen out the parameter factors with an influence greater than a preset threshold as key parameter factors;

[0021] A determination module, used to determine the component factors of the operating conditions for methanogenesis by anaerobic digestion of excess sludge according to the main environmental factors and the key parameter factors;

[0022] The simulation module is used to simulate the methane production of anaerobic digestion of residual sludge under different values ​​of any component factor using ADM1-FPM, and then determine its optimal value range;

[0023] The simulation module is also used to combine and set the values ​​of each component factor according to the optimal value range of each component factor, and use ADM1-FPM to simulate the methane production of anaerobic digestion of residual sludge under different value combinations, so as to determine the optimal operating conditions for methane production of anaerobic digestion of residual sludge.

[0024] In a third aspect, an embodiment of the present invention provides an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method described above.

[0025] In a fourth aspect, an embodiment of the present invention provides a non-transitory computer-readable storage medium storing computer instructions, where the computer instructions are used to cause a computer to execute the method described above.

[0026] In an embodiment of the present invention, the main environmental factors affecting the anaerobic digestion of excess sludge are obtained; ADM1-FPM is established, and the parameter factors specified in ADM1-FPM are analyzed using a global sensitivity analysis algorithm to calculate the degree of influence of the parameter factors on the methane production in the anaerobic digestion reactor, thereby screening out the key parameter factors; the component factors of the operating conditions for the anaerobic digestion of excess sludge to produce methane are determined based on the main environmental factors and the key parameter factors; for any component factor, the anaerobic digestion of excess sludge under different values ​​of the component factor is simulated using ADM1-FPM to determine its optimal value range; the values ​​of each component factor are combined and set according to the optimal value range of each component factor, and the anaerobic digestion of excess sludge under different value combinations of the component factor is simulated using ADM1-FPM to determine the optimal operating conditions for the anaerobic digestion of excess sludge to produce methane.

[0027] In this way, the optimal operating conditions for rapid anaerobic digestion of excess sludge to produce methane can be optimized and regulated based on the global sensitivity analysis algorithm and ADM1-FPM, which saves actual workload and trial-and-error costs, significantly improves the efficiency of obtaining the optimal operating conditions for anaerobic digestion of excess sludge to produce methane, and provides a regulatory basis for the optimization of excess sludge conversion.

[0028] It should be understood that the contents described in the summary of the invention are not intended to limit the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The above and other features, advantages and aspects of the embodiments of the present invention will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. The accompanying drawings are provided for a better understanding of the present invention and do not constitute a limitation of the present invention. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, wherein:

[0030] Figure 1 A flow chart of a method for obtaining optimal operating conditions for methanogenesis by anaerobic digestion of excess sludge provided by an embodiment of the present invention;

[0031] Figure 2 Schematic diagram of the methanogenesis of the semi-continuous reaction of anaerobic digestion of excess sludge measured experimentally and the semi-continuous reaction of anaerobic digestion of excess sludge simulated by ADM1-FPM under different OLR conditions;

[0032] Figure 3 Schematic diagram of the influence of the specified parameter factors (Kdis, Khyd_pr, Khyd_ch, Khyd_li, Km_aa, Km_ac, pH_AD) on the gas production Gas_Q and methane partial pressure Gas_p_ch4 in the anaerobic digestion reactor;

[0033] Figure 4 Schematic diagram of methanogenesis in anaerobic digestion of excess sludge under different pH values ​​simulated by ADM1-FPM;

[0034] Figure 5 Schematic diagram of methanogenesis in anaerobic digestion of excess sludge under different SRT values ​​simulated by ADM1-FPM;

[0035] Figure 6 Schematic diagram of methanogenesis from anaerobic digestion of excess sludge under different OLR values ​​simulated by ADM1-FPM;

[0036] Figure 7 This is a schematic diagram of the methanogenesis of anaerobic digestion of excess sludge under the optimal pH value, SRT value and OLR value simulated by ADM1-FPM;

[0037] Figure 8 Schematic diagram of the methane production of the anaerobic digestion reactor of the measured excess sludge under the optimal operating conditions and non-optimized conditions (laboratory level, pH not regulated, SRT = 25d, OLR = 3.00g TS / L / d);

[0038] Fig. 9 Schematic diagram of the methane production of the anaerobic digestion reactor of the measured excess sludge under the optimal operating conditions and non-optimized conditions (pilot level, pH not regulated, SRT = 30d, OLR = 0.85g TS / L / d);

[0039] Fig.10 Schematic diagram of the methane production of the anaerobic digestion reactor of the measured excess sludge under the optimal operating conditions and non-optimized conditions (laboratory level, pH not regulated, SRT = 50d, OLR = 1.50g TS / L / d);

[0040] Fig.11 A structural diagram of a device for obtaining optimal operating conditions for methanogenesis by anaerobic digestion of excess sludge provided in an embodiment of the present invention;

[0041] Fig.12 is a structural diagram of an exemplary electronic device capable of implementing an embodiment of the present invention. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are 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.

[0043] In addition, the term "and / or" in the present invention is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects before and after are in an "or" relationship.

[0044] In response to the problems in the background technology, an embodiment of the present invention provides a method for obtaining the optimal operating conditions for anaerobic digestion of excess sludge to produce methane. Specifically, the main environmental factors affecting the anaerobic digestion of excess sludge are obtained; ADM1-FPM is established, and the parameter factors specified in ADM1-FPM are analyzed using a global sensitivity analysis algorithm to calculate the degree of influence on the methane production in the anaerobic digestion reactor, and then the key parameter factors are screened out; based on the main environmental factors and the key parameter factors, the component factors of the operating conditions for anaerobic digestion of excess sludge to produce methane are determined; for any component factor, ADM1-FPM is used to simulate the methane production of excess sludge under different numerical values ​​of the anaerobic digestion of excess sludge to determine its optimal numerical range; the values ​​of each component factor are combined and set according to the optimal numerical range of each component factor, and ADM1-FPM is used to simulate the methane production of excess sludge under different numerical combinations to determine the optimal operating conditions for anaerobic digestion of excess sludge to produce methane.

[0045] In this way, the optimal operating conditions for rapid anaerobic digestion of excess sludge to produce methane can be optimized and regulated based on the global sensitivity analysis algorithm and ADM1-FPM, which saves actual workload and trial-and-error costs, significantly improves the efficiency of obtaining the optimal operating conditions for anaerobic digestion of excess sludge to produce methane, and provides a regulatory basis for the optimization of excess sludge conversion.

[0046] In conjunction with the accompanying drawings, a method for obtaining optimal operating conditions for methanogenesis by anaerobic digestion of excess sludge provided by an embodiment of the present invention is described in detail through specific embodiments.

[0047] Figure 1 A flow chart of a method for obtaining optimal operating conditions for methanogenesis by anaerobic digestion of excess sludge provided by an embodiment of the present invention is shown in FIG. Figure 1 As shown, the method 100 may include the following steps:

[0048] S110, obtain the main environmental factors affecting anaerobic digestion of residual sludge.

[0049] Among them, the main environmental factors cannot be analyzed by the global sensitivity analysis algorithm, which may include: alkalinity, solid residence time (SRT), organic load (OLR), nutrients, etc.

[0050] S120, establishing ADM1-FPM, and using the global sensitivity analysis algorithm to analyze the parameter factors specified in ADM1-FPM, calculate the influence of the parameter factors on the methane production in the anaerobic digestion reactor, and then screen out the parameter factors with an influence greater than a preset threshold as key parameter factors.

[0051] In some embodiments, the stoichiometric parameters and kinetic parameters of ADM1 can be corrected based on instrumental analysis and 16S rRNA gene sequencing analysis to establish ADM1-FPM, and the parameter factors specified in ADM1-FPM can be analyzed using a global sensitivity analysis algorithm to calculate the degree of influence of the parameter factors on the methane production in the anaerobic digestion reactor, thereby screening out parameter factors with an influence greater than a preset threshold as key parameter factors.

[0052] Among them, the specified parameter factors may include: reaction kinetic parameter factors and operating condition parameter factors; reaction kinetic parameter factors include: decomposition coefficient (Kdis), protein hydrolysis coefficient (Khyd_pr), carbohydrate hydrolysis coefficient (Khyd_ch), lipid hydrolysis coefficient (Khyd_li), amino acid maximum specific absorption rate (Km_aa), acetic acid maximum specific absorption rate (Km_ac), etc.; operating condition parameter factors include: pH (pH_AD), etc.

[0053] Methane production is expressed in terms of methane gas production (Gas_Q) and methane partial pressure (Gas_p_ch4).

[0054] The degree of influence is expressed by the linear correlation coefficient (LCC).

[0055] S130, determining the component factors of the operating conditions for methanogenesis by anaerobic digestion of residual sludge based on the main environmental factors and key parameter factors.

[0056] In some embodiments, the component factors of the operating conditions for methanogenesis by anaerobic digestion of excess sludge can be determined based on major environmental factors, key parameter factors and actual needs.

[0057] S140, for any component factor, ADM1-FPM is used to simulate the methane production of anaerobic digestion of residual sludge under different values, and then its optimal value range is determined.

[0058] In some embodiments, for component factors that are themselves major environmental factors, ADM1-FPM can be used to simulate the methane production from anaerobic digestion of residual sludge under different values ​​by indirectly modifying the corresponding parts in the influent file of ADM1-FPM, thereby determining its optimal value range.

[0059] For the component factors that are themselves key parameter factors, ADM1-FPM can be used to simulate the methane production of anaerobic digestion of residual sludge under different values ​​by directly changing the value of the component factors in ADM1-FPM, and then determine its optimal value range.

[0060] As an example, it is assumed that the operating conditions are composed of PH, SRT, and OLR.

[0061] As for pH, it is the main environmental factor. By directly changing the pH value in ADM1-FPM, ADM1-FPM can be used to simulate the methane production of anaerobic digestion of residual sludge under different pH values, and the methane production of anaerobic digestion of residual sludge under different pH values ​​can be compared to determine the optimal pH value range.

[0062] As for SRT, it is the main environmental factor. By indirectly modifying the corresponding part (i.e., the amount of sludge inlet) in the influent file of ADM1-FPM, ADM1-FPM can be used to simulate the methane production of anaerobic digestion of residual sludge under different SRT values, and the methane production of anaerobic digestion of residual sludge under different SRT values ​​can be compared to determine the optimal value range of SRT.

[0063] As for OLR, it is itself a major environmental factor. By indirectly modifying the corresponding part (that is, the concentration of organic matter) in the influent file of ADM1-FPM, ADM1-FPM can be used to simulate the methane production of anaerobic digestion of residual sludge under different OLR values, and the methane production of anaerobic digestion of residual sludge under different OLR values ​​can be compared to determine the optimal value range of OLR.

[0064] S150, combining and setting the values ​​of each component factor according to the optimal value range of each component factor, and using ADM1-FPM to simulate the methane production of anaerobic digestion of residual sludge under different value combinations, and then determining the optimal operating conditions for methane production of anaerobic digestion of residual sludge.

[0065] It is worth noting that the methanogenesis of anaerobic digestion of residual sludge under optimal operating conditions can be verified at the laboratory level based on an anaerobic digestion reactor, and compared with the methanogenesis of anaerobic digestion of residual sludge under non-optimal conditions (laboratory or pilot level) to confirm the feasibility of this method 100%.

[0066] The method 100 provided by the present invention is described in detail below with reference to three specific embodiments:

[0067] Embodiment 1:

[0068] (1) Obtain the main environmental factors affecting anaerobic digestion of residual sludge, including alkalinity, SRT, OLR, nutrients, etc.

[0069] (2) Based on instrumental analysis and 16S rRNA gene sequencing analysis, the stoichiometric and kinetic parameters of ADM1 were corrected and ADM1-FPM was established. Figure 2As shown in the figure, ADM1-FPM can well simulate the methanogenesis process of anaerobic digestion of residual sludge under two working conditions.

[0070] (3) The global sensitivity analysis algorithm is used to analyze the parameter factors (Kdis, Khyd_pr, Khyd_ch, Khyd_li, Km_aa, Km_ac, pH_AD) specified in ADM1-FPM, and the influence degree (LCC) of each parameter factor on the methane production (Gas_Q, Gas_p_ch4) in the anaerobic digestion reactor is calculated. It is worth noting that medium-temperature anaerobic digestion (35℃) is the most commonly used and most applicable anaerobic digestion resource utilization of sludge in various sewage treatment plants, so temperature is not used as a specified parameter factor here.

[0071] (4) Calculate the impact of Figure 3 As shown in the figure, Khyd_pr, pH_AD, Km_ac, and Khyd_ch have a greater impact on Gas_Q and Gas_p_ch4. Here, Khyd_pr, pH_AD, Km_ac, and Khyd_ch can be used as key parameter factors. Since the regulation of hydrolysis kinetics is generally achieved through pretreatment, this article does not consider it. In summary, pH, SRT, and OLR are selected as the components of the operating conditions for methanogenesis in anaerobic digestion of residual sludge.

[0072] (5) By setting different pH values ​​in the parameter module of ADM1-FPM, the ADM1-FPM model was used to simulate the methane production of anaerobic digestion of residual sludge at different pH values. The results are shown in Figure 2. Figure 4 As shown in the figure, it can be seen that when pH = 7.1, 7.2, and 7.3, the methane production of the residual sludge is equal and significantly higher than that under pH = 7.0 and 7.5. This shows that the optimal pH value range for methane production by anaerobic digestion of residual sludge is 7.2±0.1. By changing the sludge inlet volume of the ADM1-FPM inlet file, ADM1-FPM is used to simulate the methane production of anaerobic digestion of residual sludge under different SRT values. The results are shown in Figure 5 As shown in the figure, when SRT = 27d, 30d and above, the methane production is significantly higher than that under SRT = 20d, 23d and 25d. Considering the actual operation of the sewage treatment plant and the methane production, the optimal SRT value range for anaerobic digestion of excess sludge to produce methane is 27d and 30d. By changing the concentration of organic matter (COD) in the ADM1-FPM influent file, ADM1-FPM was used to simulate the methane production of anaerobic digestion of excess sludge under different OLR values. The results are shown in the figure. Figure 6As shown, the methane production under the conditions of OLR of 1.15, 1.56, and 2.00 g TS / L / d was significantly higher than that of other groups. The methane production under the condition of OLR of 2.00 g TS / L / d increased slightly, and the higher the load during actual operation, the easier it was to collapse. Therefore, 1.15 and 1.56 g TS / L / d were preferentially selected as the optimal OLR value range for anaerobic digestion methanogenesis.

[0073] (6) The optimal value ranges of pH, SRT, and OLR obtained above are combined and set, and ADM1-FPM is used to simulate the methane production of anaerobic digestion of excess sludge under different value combinations, thereby determining the optimal operating conditions for methane production of anaerobic digestion of excess sludge. Figure 7 As shown in the figure, the methane production was high and stable when the pH was 7.2±0.1, the SRT was 30d, and the OLR was 1.56g TS / L / d, which was determined to be the optimal operating condition for methane production by anaerobic digestion of residual sludge.

[0074] (7) A semi-continuous anaerobic digestion reactor was operated at the laboratory level under the operating conditions of pH 7.2 ± 0.1, SRT = 30 days, and OLR = 1.56 g TS / L / d. The methane production was compared with that of anaerobic digestion of sludge at the laboratory level under non-optimal conditions (pH not controlled, SRT = 25 days, OLR = 3.00 g TS / L / d). The results are as follows: Figure 8 As shown, it can be seen that under the optimal conditions, the methane production by anaerobic digestion of sludge is significantly higher than that under non-optimized conditions, and the methane yield is increased by 93.4%, indicating that the method for obtaining the optimal operating conditions for methane production by anaerobic digestion of residual sludge in this embodiment significantly improves the methane production efficiency of anaerobic digestion of sludge, thereby promoting the actual industrial application of anaerobic digestion technology in the field of sludge resource utilization.

[0075] Embodiment 2:

[0076] (1) Obtain the main environmental factors affecting anaerobic digestion of residual sludge, including alkalinity, SRT, OLR, nutrients, etc.

[0077] (2) Based on instrumental analysis and 16S rRNA gene sequencing analysis, the stoichiometric and kinetic parameters of ADM1 were corrected and ADM1-FPM was established. Figure 2 As shown in the figure, ADM1-FPM can well simulate the methanogenesis process of anaerobic digestion of residual sludge under two working conditions.

[0078] (3) The global sensitivity analysis algorithm is used to analyze the parameter factors (Kdis, Khyd_pr, Khyd_ch, Khyd_li, Km_aa, Km_ac, pH_AD) specified in ADM1-FPM, and the influence degree (LCC) of each parameter factor on the methane production (Gas_Q, Gas_p_ch4) in the anaerobic digestion reactor is calculated. It is worth noting that medium-temperature anaerobic digestion (35℃) is the most commonly used and most applicable anaerobic digestion resource utilization of sludge in various sewage treatment plants, so temperature is not used as a specified parameter factor here.

[0079] (4) Calculate the impact of Figure 3 As shown in the figure, Khyd_pr, pH_AD, Km_ac, and Khyd_ch have a greater impact on Gas_Q and Gas_p_ch4. Here, Khyd_pr, pH_AD, Km_ac, and Khyd_ch can be used as key parameter factors. Since the regulation of hydrolysis kinetics is generally achieved through pretreatment, this article does not consider it. In summary, pH, SRT, and OLR are selected as the components of the operating conditions for methanogenesis in anaerobic digestion of residual sludge.

[0080] (5) By setting different pH values ​​in the parameter module of ADM1-FPM, the ADM1-FPM model was used to simulate the methane production of anaerobic digestion of residual sludge at different pH values. The results are shown in Figure 2. Figure 4 As shown in the figure, it can be seen that when pH = 7.1, 7.2, and 7.3, the methane production of the residual sludge is equal and significantly higher than that under pH = 7.0 and 7.5. This shows that the optimal pH value range for methane production by anaerobic digestion of residual sludge is 7.2±0.1. By changing the sludge inlet volume of the ADM1-FPM inlet file, ADM1-FPM is used to simulate the methane production of anaerobic digestion of residual sludge under different SRT values. The results are shown in Figure 5 As shown in the figure, when SRT = 27d, 30d and above, the methane production is significantly higher than that under SRT = 20d, 23d and 25d. Considering the actual operation of the sewage treatment plant and the methane production, the optimal SRT value range for anaerobic digestion of excess sludge to produce methane is 27d and 30d. By changing the concentration of organic matter (COD) in the ADM1-FPM influent file, ADM1-FPM was used to simulate the methane production of anaerobic digestion of excess sludge under different OLR values. The results are shown in the figure. Figure 6 As shown, the methane production under the conditions of OLR of 1.15, 1.56, and 2.00 g TS / L / d was significantly higher than that of other groups. The methane production under the condition of OLR of 2.00 g TS / L / d increased slightly, and the higher the load during actual operation, the easier it was to collapse. Therefore, 1.15 and 1.56 g TS / L / d were preferentially selected as the optimal OLR value range for anaerobic digestion methanogenesis.

[0081] (6) The optimal value ranges of pH, SRT, and OLR obtained above are combined and set, and ADM1-FPM is used to simulate the methane production of anaerobic digestion of excess sludge under different value combinations, thereby determining the optimal operating conditions for methane production of anaerobic digestion of excess sludge. Figure 7 As shown in the figure, the methane production was high and stable when the pH was 7.2±0.1, the SRT was 30d, and the OLR was 1.56g TS / L / d, which was determined to be the optimal operating condition for methane production by anaerobic digestion of residual sludge.

[0082] (7) A semi-continuous anaerobic digestion reactor was operated at the laboratory level under the operating conditions of pH 7.2 ± 0.1, SRT = 30 days, and OLR = 1.56 g TS / L / d. The methane production of sludge was compared with that of anaerobic digestion of sludge at the pilot level under non-optimal conditions (no pH control, SRT = 30 days, OLR = 0.85 g TS / L / d). The results are as follows: Fig. 9 As shown, it can be seen that under the optimal conditions, the methane production by anaerobic digestion of sludge is significantly higher than that under non-optimized conditions, and the methane yield is increased by 68.7%. This shows that the method for obtaining the optimal operating conditions for methane production by anaerobic digestion of residual sludge in this embodiment significantly improves the methane production efficiency of anaerobic digestion of sludge, thereby promoting the actual industrial application of anaerobic digestion technology in the field of sludge resource utilization.

[0083] Embodiment 3:

[0084] (1) Obtain the main environmental factors affecting anaerobic digestion of residual sludge, including alkalinity, SRT, OLR, nutrients, etc.

[0085] (2) Based on instrumental analysis and 16S rRNA gene sequencing analysis, the stoichiometric and kinetic parameters of ADM1 were corrected and ADM1-FPM was established. Figure 2 As shown in the figure, ADM1-FPM can well simulate the methanogenesis process of anaerobic digestion of residual sludge under two working conditions.

[0086] (3) The global sensitivity analysis algorithm is used to analyze the parameter factors (Kdis, Khyd_pr, Khyd_ch, Khyd_li, Km_aa, Km_ac, pH_AD) specified in ADM1-FPM, and the influence degree (LCC) of each parameter factor on the methane production (Gas_Q, Gas_p_ch4) in the anaerobic digestion reactor is calculated. It is worth noting that medium-temperature anaerobic digestion (35℃) is the most commonly used and most applicable anaerobic digestion resource utilization of sludge in various sewage treatment plants, so temperature is not used as a specified parameter factor here.

[0087] (4) Calculate the impact of Figure 3As shown in the figure, Khyd_pr, pH_AD, Km_ac, and Khyd_ch have a greater impact on Gas_Q and Gas_p_ch4. Here, Khyd_pr, pH_AD, Km_ac, and Khyd_ch can be used as key parameter factors. Since the regulation of hydrolysis kinetics is generally achieved through pretreatment, this article does not consider it. In summary, pH, SRT, and OLR are selected as the components of the operating conditions for methanogenesis in anaerobic digestion of residual sludge.

[0088] (5) By setting different pH values ​​in the parameter module of ADM1-FPM, the ADM1-FPM model was used to simulate the methane production of anaerobic digestion of residual sludge at different pH values. The results are shown in Figure 2. Figure 4 As shown in the figure, it can be seen that when pH = 7.1, 7.2, and 7.3, the methane production of the residual sludge is equal and significantly higher than that under pH = 7.0 and 7.5. This shows that the optimal pH value range for methane production by anaerobic digestion of residual sludge is 7.2±0.1. By changing the sludge inlet volume of the ADM1-FPM inlet file, ADM1-FPM is used to simulate the methane production of anaerobic digestion of residual sludge under different SRT values. The results are shown in Figure 5 As shown in the figure, when SRT = 27d, 30d and above, the methane production is significantly higher than that under SRT = 20d, 23d and 25d. Considering the actual operation of the sewage treatment plant and the methane production, the optimal SRT value range for anaerobic digestion of excess sludge to produce methane is 27d and 30d. By changing the concentration of organic matter (COD) in the ADM1-FPM influent file, ADM1-FPM was used to simulate the methane production of anaerobic digestion of excess sludge under different OLR values. The results are shown in the figure. Figure 6 As shown, the methane production under the conditions of OLR of 1.15, 1.56, and 2.00 g TS / L / d was significantly higher than that of other groups. The methane production under the condition of OLR of 2.00 g TS / L / d increased slightly, and the higher the load during actual operation, the easier it was to collapse. Therefore, 1.15 and 1.56 g TS / L / d were preferentially selected as the optimal OLR value range for anaerobic digestion methanogenesis.

[0089] (6) The optimal value ranges of pH, SRT, and OLR obtained above are combined and set, and ADM1-FPM is used to simulate the methane production of anaerobic digestion of excess sludge under different value combinations, thereby determining the optimal operating conditions for methane production of anaerobic digestion of excess sludge. Figure 7 As shown in the figure, the methane production was high and stable when the pH was 7.2±0.1, the SRT was 30d, and the OLR was 1.56g TS / L / d, which was determined to be the optimal operating condition for methane production by anaerobic digestion of residual sludge.

[0090] (7) A semi-continuous anaerobic digestion reactor was operated at the laboratory level under the operating conditions of pH 7.2 ± 0.1, SRT = 30 days, and OLR = 1.56 g TS / L / d. The methane production was compared with that of anaerobic digestion of sludge at the laboratory level under non-optimal conditions (pH not controlled, SRT = 50 days, OLR = 1.50 g TS / L / d). The results are as follows: Fig.10 As shown, it can be seen that under the optimal conditions, the methane production by anaerobic digestion of sludge is significantly higher than that under non-optimized conditions, and the methane yield is increased by 72.4%, indicating that the method for obtaining the optimal operating conditions for methane production by anaerobic digestion of residual sludge in this embodiment significantly improves the methane production efficiency of anaerobic digestion of sludge, thereby promoting the actual industrial application of anaerobic digestion technology in the field of sludge resource utilization.

[0091] According to the above embodiments, at least the following technical effects are achieved:

[0092] (1) By comprehensively considering the main environmental factors affecting anaerobic digestion methanogenesis and using global sensitivity analysis to analyze the key parameter factors affecting methanogenesis, the optimal operating conditions for anaerobic digestion of excess sludge can be quickly obtained, greatly improving the efficiency and reliability of obtaining the optimal operating parameters of the anaerobic digestion system;

[0093] (2) The improved and optimized ADM1-FPM can simulate the methane production of anaerobic digestion under different organic substrates and conditions, providing a reliable mathematical model for regulating the methane production of anaerobic digestion of residual sludge;

[0094] (3) Based on ADM1-FPM, global sensitivity analysis is performed to screen key parameter factors and simulate and predict methane production under different operating conditions. The optimal methane production range under the regulation of a single component factor can be quickly obtained, and the time and trial-and-error cost of actual operation can be reduced through digital simulation.

[0095] (4) Based on ADM1-FPM, the methanogenesis process of anaerobic digestion of residual sludge was regulated, which significantly improved the methane production efficiency of sludge and promoted the practical industrial application of anaerobic digestion technology in the field of organic solid waste resource utilization.

[0096] It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the present invention is not limited by the described order of actions, because according to the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0097] The above is an introduction to a method embodiment. The following is a further explanation of the solution of the present invention through an apparatus embodiment.

[0098] Fig.11 A structural diagram of a device for obtaining optimal operating conditions for anaerobic digestion of excess sludge to produce methane is provided in an embodiment of the present invention, such as Fig.11 As shown, the apparatus 1100 may include:

[0099] The acquisition module 1110 is used to acquire the main environmental factors that affect the anaerobic digestion of excess sludge, wherein the main environmental factors cannot be analyzed by the global sensitivity analysis algorithm.

[0100] Establish module 1120, which is used to establish the ADM1 full parameter correction model ADM1-FPM, and use the global sensitivity analysis algorithm to analyze the parameter factors specified in ADM1-FPM, calculate the influence of the parameter factors on the methane production in the anaerobic digestion reactor, and then screen out the parameter factors with an influence greater than a preset threshold as key parameter factors.

[0101] The determination module 1130 is used to determine the component factors of the operating conditions for methanogenesis by anaerobic digestion of excess sludge according to the main environmental factors and key parameter factors.

[0102] The simulation module 1140 is used to simulate the methane production of anaerobic digestion of residual sludge under different values ​​of any component factor using ADM1-FPM, so as to determine the optimal value range.

[0103] The simulation module 1140 is also used to combine and set the values ​​of each component factor according to the optimal value range of each component factor, and use ADM1-FPM to simulate the methane production of anaerobic digestion of residual sludge under different value combinations, so as to determine the optimal operating conditions for methane production of anaerobic digestion of residual sludge.

[0104] Understandably, Fig.11 The various modules / units in the device 1100 shown have the functions of implementing Figure 1 The functions of the various steps in the method 100 shown can achieve their corresponding technical effects, and for the sake of brevity, they will not be repeated here.

[0105] Fig.121 is a block diagram of an exemplary electronic device capable of implementing an embodiment of the present invention. Electronic device 1200 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic device 1200 may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown in the present invention, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required in the present invention.

[0106] like Fig.12 As shown, the electronic device 1200 may include a computing unit 1201, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 1202 or a computer program loaded from a storage unit 1208 into a random access memory (RAM) 1203. In the RAM 1203, various programs and data required for the operation of the electronic device 1200 may also be stored. The computing unit 1201, the ROM 1202, and the RAM 1203 are connected to each other via a bus 1204. An input / output (I / O) interface 1205 is also connected to the bus 1204.

[0107] Multiple components in the electronic device 1200 are connected to the I / O interface 1205, including: an input unit 1206, such as a keyboard, a mouse, etc.; an output unit 1207, such as various types of displays, speakers, etc.; a storage unit 1208, such as a disk, an optical disk, etc.; and a communication unit 1209, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 1209 allows the electronic device 1200 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0108] The computing unit 1201 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 1201 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 1201 performs the various methods and processes described above, such as method 100. For example, in some embodiments, the method 100 may be implemented as a computer program product, including a computer program, which is tangibly contained in a computer-readable medium, such as a storage unit 1208. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 1200 via the ROM 1202 and / or the communication unit 1209. When the computer program is loaded into the RAM 1203 and executed by the computing unit 1201, one or more steps of the method 100 described above may be performed. Alternatively, in other embodiments, the computing unit 1201 may be configured to execute the method 100 in any other appropriate manner (eg, by means of firmware).

[0109] The various embodiments described above in the present invention can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include: being implemented in one or more computer programs, which may be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general programmable processor, which may receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0110] The program code for implementing the method of the present invention can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0111] In the context of the present invention, a computer-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. A computer-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. A more specific example of a computer-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0112] It should be noted that the present invention also provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to execute method 100 and achieve the corresponding technical effect achieved by the embodiments of the present invention executing the method. For the sake of concise description, they are not repeated here.

[0113] In addition, the present invention also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the method 100 is implemented.

[0114] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution disclosed in the present invention can be achieved, and the present invention is not limited here.

[0115] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for obtaining the optimal operating conditions for methanogenesis by anaerobic digestion of excess sludge, characterized in that: The method comprises: Obtaining major environmental factors that affect anaerobic digestion of excess sludge, wherein the major environmental factors cannot be analyzed by a global sensitivity analysis algorithm; The ADM1 full parameter correction model ADM1-FPM was established, and the parameter factors specified in ADM1-FPM were analyzed using the global sensitivity analysis algorithm to calculate the influence of the parameter factors on the methane production in the anaerobic digestion reactor, and then the parameter factors with an influence greater than the preset threshold were selected as the key parameter factors; Determining the component factors of the operating conditions for methanogenesis by anaerobic digestion of excess sludge according to the main environmental factors and the key parameter factors; For any component factor, ADM1-FPM is used to simulate the methane production of anaerobic digestion of residual sludge under different values, and then its optimal value range is determined; The values ​​of each component factor are combined and set according to the optimal value range of each component factor, and ADM1-FPM is used to simulate the methane production of anaerobic digestion of excess sludge under different value combinations, so as to determine the optimal operating conditions for methane production of anaerobic digestion of excess sludge; The main environmental factors include: alkalinity, solid retention time, organic load and nutrients; The degree of influence is expressed as a linear correlation coefficient; For any component factor, ADM1-FPM is used to simulate the methane production of excess sludge anaerobic digestion under different values, and then the optimal value range is determined, including: For the component factors that are the main environmental factors, the corresponding parts of the influent file of ADM1-FPM are indirectly modified to simulate the methane production of anaerobic digestion of residual sludge under different values ​​of ADM1-FPM, so as to determine the optimal value range. For the component factors, which are key parameter factors, ADM1-FPM is used to simulate the methane production of anaerobic digestion of residual sludge under different values ​​by directly changing the values ​​of the component factors in ADM1-FPM, so as to determine the optimal value range.

2. The method according to claim 1, characterized in that The establishment of the ADM1 full parameter correction model ADM1-FPM comprises: Based on instrumental analysis and 16S rRNA gene sequencing analysis, the stoichiometric and kinetic parameters of ADM1 were corrected and ADM1-FPM was established.

3. The method according to claim 1, characterized in that The specified parameter factors include: reaction kinetics parameter factors and operating condition parameter factors; the reaction kinetics parameter factors include: decomposition coefficient, protein hydrolysis coefficient, carbohydrate hydrolysis coefficient, lipid hydrolysis coefficient, amino acid maximum specific absorption rate and acetic acid maximum specific absorption rate; the operating condition parameter factors include: pH.

4. The method according to claim 1, characterized in that: The methane production is expressed in terms of methane gas production and methane partial pressure.

5. A device for obtaining the optimal operating conditions for methanogenesis by anaerobic digestion of excess sludge, characterized in that: The device comprises: An acquisition module, used for acquiring main environmental factors affecting anaerobic digestion of excess sludge, wherein the main environmental factors cannot be analyzed by a global sensitivity analysis algorithm; Establish a module for establishing the ADM1 full parameter correction model ADM1-FPM, and use the global sensitivity analysis algorithm to analyze the parameter factors specified in ADM1-FPM, calculate the influence of the parameter factors on the methane production in the anaerobic digestion reactor, and then screen out the parameter factors with an influence greater than a preset threshold as key parameter factors; A determination module, used to determine the component factors of the operating conditions for methanogenesis by anaerobic digestion of excess sludge according to the main environmental factors and the key parameter factors; The simulation module is used to simulate the methane production of anaerobic digestion of residual sludge under different values ​​of any component factor using ADM1-FPM, and then determine its optimal value range; The simulation module is also used to combine and set the values ​​of each component factor according to the optimal value range of each component factor, and use ADM1-FPM to simulate the methane production of anaerobic digestion of excess sludge under different value combinations, so as to determine the optimal operating conditions for methane production of anaerobic digestion of excess sludge; The main environmental factors include: alkalinity, solid retention time, organic load and nutrients; The degree of influence is expressed as a linear correlation coefficient; The simulation module is specifically used for: For the component factors that are the main environmental factors, the corresponding parts of the influent file of ADM1-FPM are indirectly modified to simulate the methane production of anaerobic digestion of residual sludge under different values ​​of ADM1-FPM, so as to determine the optimal value range. For the component factors, which are key parameter factors, ADM1-FPM is used to simulate the methane production of anaerobic digestion of residual sludge under different values ​​by directly changing the values ​​of the component factors in ADM1-FPM, so as to determine the optimal value range.

6. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 4.

7. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to make a computer execute the method according to any one of claims 1 to 4.

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