Kitchen waste anaerobic digestion treatment method and system based on big data evaluation

Abstract

The invention relates to a kitchen waste anaerobic digestion treatment method and system based on big data evaluation. The method comprises the following steps: respectively establishing kitchen waste anaerobic digestion treatment facilities without exogenous additives and kitchen waste anaerobic digestion treatment facilities with exogenous additives; state data of the kitchen waste are collected respectively; calculating the unit feeding VS biogas yield according to the biogas yield, the daily feeding amount of the kitchen waste, the feeding water content and the feeding VSin content, and calculating the methane yield by combining with the methane content; according to the consumption and methane yield under the anaerobic digestion treatment conditions without the exogenous additives and under the anaerobic digestion treatment conditions with the exogenous additives, evaluating kitchen waste anaerobic digestion treatment measures whether the exogenous additives are added or not, and determining a final treatment mode. Compared with the prior art, the kitchen waste anaerobic digestion treatment method based on big data is established, prediction of the unit feeding VS biogas yield and the methane yield is combined, and the subsequent cost analysis and comparison process can visually and objectively judge the kitchen waste anaerobic digestion treatment mode.

Description

Technical Field

[0001] This invention relates to the field of food waste treatment technology, and in particular to a method and system for anaerobic digestion treatment of food waste based on big data assessment. Background Technology

[0002] Food waste is a type of urban organic solid waste with high organic matter content and high resource potential. Especially with the implementation of waste sorting, the resource utilization of food waste has become easier to achieve. Anaerobic digestion can produce high-quality fuel methane and has a significant volume reduction and stabilization effect on food waste. It is currently the most commonly used method for realizing the resource utilization of food waste. However, anaerobic digestion currently suffers from poor stability and is easily inhibited by acids or ammonia.

[0003] To address these issues, researchers have improved the anaerobic digestion performance of food waste by adding exogenous additives, such as conductive materials (iron salts), adsorbent materials (activated carbon, diaspore), functional bacteria, cow dung, and sawdust. For example, CN111424056A discloses a method for improving biogas production efficiency in the anaerobic digestion of food waste. These methods have demonstrated stable anaerobic digestion and increased methane yield in the laboratory, but most have failed to be effectively implemented. The main reason is that food waste treatment companies are concerned that these exogenous additive enhancement measures would be too costly in practical applications, making them unacceptable. Based on existing literature, there is currently a lack of a direct and objective evaluation method to determine whether these exogenous additive enhancement measures have practical application prospects. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the existing technology and provide a method and system for anaerobic digestion treatment of kitchen waste based on big data evaluation, and to provide an intuitive and objective evaluation method to determine whether these exogenous additive enhancement measures have practical application prospects.

[0005] The objective of this invention can be achieved through the following technical solutions: A method for anaerobic digestion treatment of food waste based on big data assessment includes the following steps: Establish separate anaerobic digestion treatment facilities for kitchen waste without external additives and anaerobic digestion treatment facilities for kitchen waste with external additives. State data of food waste were collected under anaerobic digestion conditions with and without external additives. This state data included daily food waste inflow, inflow moisture content, and inflow VS... in Content, energy consumption, pharmaceutical consumption, output, output moisture content, biogas production, and methane content; Based on biogas production, daily intake of kitchen waste, moisture content of feed, and feed VS inThe content is calculated by comparing the unit feed to the biogas yield, and the methane yield is calculated by combining the methane content. Based on the consumption and methane yield under anaerobic digestion treatment conditions with and without exogenous additives, the evaluation of whether to add exogenous additives to the anaerobic digestion treatment measures for food waste was conducted to determine the final anaerobic digestion treatment method for food waste.

[0006] Furthermore, the calculation expression for the unit feed VS biogas yield is as follows: η g = Q g / [10Q in *(1-fw in )* C vs-in ] In the formula, η g Q represents the ratio of feed volume to biogas yield. in For the daily intake of food waste, fw in C represents the feed moisture content. vs-in For feeding VS in content.

[0007] Furthermore, the formula for calculating the methane yield is as follows: η CH4 =η g * C ch4 In the formula, η CH4 For methane yield, C ch4 This represents the methane content.

[0008] Furthermore, based on the consumption and methane yield under anaerobic digestion conditions with and without exogenous additives, an evaluation was conducted to determine whether to add exogenous additives to the anaerobic digestion treatment of food waste. Specifically: Calculate the VSS expenditure per unit mass under anaerobic digestion without external additives, based on the consumption generated. VSS expenditure per unit mass under anaerobic digestion treatment with exogenous additives ; Calculate the expected revenue W for anaerobic digestion without exogenous additives, based on the corresponding methane yield. 原预收 And the expected revenue W under anaerobic digestion treatment with exogenous additives. 后预收 ; The assessment of whether to add exogenous additives is based on the unit mass VSS expenditure and expected income under anaerobic digestion treatment with and without exogenous additives.

[0009] Furthermore, the corresponding VSS expenditure per unit mass under anaerobic digestion treatment without exogenous additives. The calculation expression is: In the formula, Cs 电 For electricity bills, For energy consumption, The unit price of the medicines currently used in the facility. For medicine consumption, For solid slag treatment costs, For output volume, The output moisture content, This refers to the daily intake of food waste. The feed moisture content, For feeding VS in content.

[0010] Furthermore, the corresponding VSS expenditure per unit mass under anaerobic digestion treatment with exogenous additives. The calculation expression is: In the formula, Cs 电 For electricity bills, For energy consumption, The unit price of the medicines currently used in the facility. For medicine consumption, The purchase price of exogenous additives, The dosage of exogenous additives, For solid slag treatment costs, For output volume, The output moisture content, This refers to the daily intake of food waste. The feed moisture content, For feeding VS in content.

[0011] Furthermore, the formula for calculating the expected income is as follows: In the formula, For expected revenue, The selling price of methane. The yield is denoted as methane.

[0012] Furthermore, an assessment of whether to add exogenous additives is conducted based on the unit mass VSS expenditure and expected revenue under anaerobic digestion treatments with and without exogenous additives, specifically: When W 后预收 -W 后支 >W 原预收 -W 原支When obtaining the total investment W required for engineering modifications and additional equipment after adding exogenous additives, 投 And the corresponding return period N 回报 and expected depreciation period N 折旧 This allows for an assessment of whether the expected outcome is met. If the expected outcome is met, then adding the exogenous additive is feasible; otherwise, it is not. When W 后预收 -W 后支 <W 原预收 -W 原支 When it is determined that adding exogenous additives is not feasible; When W 后预收 -W 后支 =W 原预收 -W 原支 At that time, there is no economic benefit after adding external additives to the output. It is necessary to make a comprehensive evaluation based on other factors, including the impact of adding external additives on system stability and the impact on the complexity of operation.

[0013] Furthermore, the evaluation process for determining whether the expectation is met specifically includes: The corresponding α and β values ​​are calculated using the following expression: α=0.1*Q in *(1-fw in )*C vs-in *(W) 后预收 -W 后支 >W 原预收 -W 原支 )*n 工作 -(W) 投 - W 残 ) / N 折旧 β=W 投 / N 回报 In the formula, Q in For the daily intake of food waste, fw in C represents the feed moisture content. vs-in For feeding VS in Content, n 工作 W represents the number of working days per year for the project. 残 The residual value after the investment period expires; If α > β, then the expected assessment is satisfied; otherwise, the expected assessment is not satisfied.

[0014] The present invention also provides an anaerobic digestion treatment system for kitchen waste based on big data assessment, including a memory and a processor. The memory stores a computer program, and the processor calls the computer program to execute the steps of the method described above.

[0015] Compared with the prior art, the present invention has the following advantages: (1) This invention provides an intuitive quantitative evaluation system for the application of new technologies. Before a new technology is put into actual operation, enterprises need to focus on evaluating two key indicators: first, operational efficiency, including technical parameters such as system stability, VS degradation rate, and biogas production. These efficiencies can be directly judged through testing and laboratory methods; second, economic benefits, which are often the core bottleneck restricting the industrialization of laboratory results. In traditional evaluations, even if the technical indicators meet the standards, it is still difficult to predict economic feasibility. This system innovatively establishes a prediction based on big data combined with the unit feed VS biogas production rate and methane production rate, as well as the subsequent cost analysis and comparison process. It can completely cover the decision-making chain from technology verification to commercialization through intuitive data comparison and analysis indicators, providing key evaluation criteria for the industrialization of new technologies.

[0016] (2) The evaluation process is relatively simple and practical. For actual operational anaerobic digestion projects of kitchen waste, the relevant parameters used in the evaluation process, such as Q... in 、fw in C vs-in N, M i Q out 、fw out Q g C ch4 These parameters, etc., all require testing during routine operation, meaning no new testing items need to be added. On-site verification and pilot-scale validation methods can be flexibly adjusted based on the external source addition method and experimental results. For some new technology promotion and application units, pilot-scale validation equipment is essentially essential. Therefore, the evaluation system proposed in this invention is relatively simple and highly operable.

[0017] (3) It is applicable to treatment projects of different scales and in different regions, and can also be extended to other similar projects. In the comparative evaluation process, this system is based on the VS per unit mass, so it can be applied to treatment projects of different scales and projects with different VS contents. It is also applicable to wet anaerobic and dry anaerobic processes. Since some of the data input into the system is based on local conditions, it can also be directly applied to relevant projects in different regions. It is also universally applicable to other similar sludge anaerobic and wastewater anaerobic projects. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of a method for anaerobic digestion treatment of kitchen waste based on big data evaluation provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the data processing process for an anaerobic digestion treatment method for kitchen waste based on big data evaluation, provided in an embodiment of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0020] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0021] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0022] Example 1 like Figure 1 As shown, this embodiment provides a method for anaerobic digestion treatment of food waste based on big data evaluation, including the following steps: S1: Establish anaerobic digestion treatment facilities for kitchen waste without external additives, and anaerobic digestion treatment facilities for kitchen waste with external additives; S2: Collect state data of food waste under anaerobic digestion treatment conditions with and without external additives. This state data includes daily food waste feed rate, feed moisture content, and feed VS. in Content, energy consumption, pharmaceutical consumption, output, output moisture content, biogas production, and methane content; S3: Based on biogas production, daily food waste input, feed moisture content, and feed VS in The content is calculated by comparing the unit feed to the biogas yield, and the methane yield is calculated by combining the methane content. S4: Based on the consumption and methane yield under anaerobic digestion treatment conditions with and without exogenous additives, evaluate whether to add exogenous additives to the anaerobic digestion treatment measures for food waste, and determine the final anaerobic digestion treatment method for food waste.

[0023] Step S4 specifically includes: Calculate the VSS expenditure per unit mass under anaerobic digestion without external additives, based on the consumption generated. VSS expenditure per unit mass under anaerobic digestion treatment with exogenous additives ; Calculate the expected revenue W for anaerobic digestion without exogenous additives, based on the corresponding methane yield. 原预收 And the expected revenue W under anaerobic digestion treatment with exogenous additives. 后预收 ; The assessment of whether to add exogenous additives is based on the unit mass VSS expenditure and expected income under anaerobic digestion treatment with and without exogenous additives.

[0024] The main processes in the implementation of this plan are as follows: 1) Collect and analyze the performance characteristics of existing anaerobic digestion facilities for food waste. Collect the daily feed volume Q of food waste from existing anaerobic digestion facilities in (t / d), feed moisture content fw in (%), Feed VS in Content C vs-in (%TS), Energy consumption (including electricity and heat consumption) N (kWh / d), Pharmaceutical consumption M i (kg / d), output Q out (t / d), discharge moisture content fw out (%), biogas production Q g (Nm) 3 / d), methane content C ch4 (%) etc. are transmitted to the data processing and evaluation system through the data collection system. The system automatically calculates the unit feed VS biogas yield η within the system (using Equations 1 and 2). g (Nm) 3 / kgVS) and methane yield η CH4 (Nm) 3 / kgVS): η g = Q g / [10Q in *(1-fw in )* C vs-in (1) η CH4 =η g * C ch4 (2) The local electricity price Cs 电 (RMB / kWh), Unit price of reagents used in the current facility Cs 药i (yuan / kg), methane sales price Cs CH4 (yuan / Nm) 3 ), Purchase price of exogenous additives Cs 外i (RMB / kg), Solid slag treatment cost Cs 固Inputting (yuan / tDS) and Formula 3 into the data processing center system automatically calculates the unit quality VSS expenditure and expected revenue: (yuan / kgVSS) (3) 2) Verification and analysis of the effects of exogenous additive enhancement measures For conditional effectiveness verification, at least one month of on-site actual verification is required; for unconditional verification, at least three months of on-site pilot verification is required (Note: the longer the verification period, the more objective and reliable the evaluation results).

[0025] On-site verification involves utilizing existing food waste treatment facilities and directly adding exogenous additives to the treatment system based on experimental results, including the types and amounts of additives determined in the research. The dosage Q is then fine-tuned according to actual conditions. 外i (kg / t) and dosing method, after the system stabilizes, run for more than one month. During this period, the Q of the enhanced anaerobic digestion facility will also be increased daily. in 、fw in C vs-in N, M, Q out 、fw out Q g C ch4 Once collected and transmitted to the data processing and evaluation system, η under these operating conditions will also be automatically calculated. g and η CH4 .

[0026] The on-site pilot-scale verification experiment required the operation of two sets of anaerobic digestion equipment of the same specifications, one as a control and the other as a system to be operated synchronously after the addition of exogenous substances. The type and dosage of the exogenous additive, Q, were specified. 外i The dosage (kg / t) and addition method can be determined based on the results of small-scale experiments, but can be fine-tuned during pilot-scale experiments. Apart from the addition of exogenous materials, other operating parameters of the two sets of experimental equipment (such as feed load, stirring intensity, residence time, operating temperature, etc.) should be the same as those of the actual operating equipment to facilitate data comparison. After the two sets of pilot-scale equipment systems stabilize, they should be run for more than 3 months. During this period, the Q of the two sets of pilot-scale equipment systems should be measured daily. in 、fw in C vs-in N, M, Q out 、fw out Q g C ch4 Once collected and transmitted to the data processing and evaluation system, the system will automatically calculate η for both sets of test equipment under their respective operating conditions. g and η CH4 .

[0027] For methods employing practical verification and enhancement measures in anaerobic systems, the same applies to the already input Cs. 电 Cs 药i Cs CH4 Cs 外i Cs 固 Formula 4 calculates the expenditure after adding exogenous additives: (yuan / kgVSS) (4) For methods that utilize on-site pilot-scale anaerobic systems for validation, based on the already input Cs 电 Cs 药i Cs CH4 Cs 外i Cs 固 The data were collected, and the expenditures of the anaerobic system and the anaerobic system with added exogenous additives were calculated and compared according to Equations 3 and 4.

[0028] 3) Comparative evaluation The data processing and evaluation system is based on the input Cs CH4 η derived from data and calculations CH4 Based on Equation 5, the expected project revenue W of the original anaerobic system, the actual verification enhanced anaerobic system, the pilot-scale comparison anaerobic system, and the anaerobic system with exogenous additives is automatically calculated. 预收 (RMB / kgVSS): (5) (1) When the results of the data processing and evaluation system are expressed as W 后预收 -W 后支 >W 原预收 -W 原支 When the system automatically determines that adding exogenous additives yields certain economic benefits, further evaluation is required based on the following two conditions: a. In engineering practice, meeting the requirements for adding exogenous additives necessitates relevant engineering modifications and the addition of dosing equipment and storage facilities, etc. Assume the total investment in this part is W. 投 (10,000 yuan).

[0029] b. Expected payback period and depreciation period. Different projects have different payback periods for the initial investment, and the depreciation periods for equipment, etc., also vary. Assume the expected payback period and depreciation period are N, respectively. 回报 (Year) and N 折旧 (Year).

[0030] Input the actual required W into the data processing and evaluation system 投 N 回报 N 折旧 n 工作 (Number of working days per year for the project (d / year)) and W 残(Residual value after the investment period expires (ten thousand yuan)), then the data processing and evaluation system can automatically calculate (Equations 6 and 7) the values ​​of α and β, and directly judge and output the evaluation results based on these values: α>β (economic benefits meet expectations), the project is feasible; otherwise, α<β (there are some direct benefits, but the overall economic benefits do not meet expectations), the project is not feasible.

[0031] α=0.1*Q in *(1-f win )*C vs-in *(W) 后预收 -W 后支 >W 原预收 -W 原支 )*n 工作 - (W) 投 - W 残 ) / N 折旧 (6) β=W 投 / N 回报 (7) (2) When the results of the data processing and evaluation system are expressed as W 后预收 -W 后支 <W 原预收 -W 原支 This indicates that there is no economic benefit after adding exogenous additives. From the perspective of economic benefits alone, the system automatically outputs the judgment result: the project is not feasible.

[0032] (3) When the results of the data processing and evaluation system are expressed as W 后预收 -W 后支 =W 原预收 -W 原支 If the statement indicates that there is no economic benefit after adding exogenous additives, the system will automatically output two options: "feasible" and "infeasible". The project operator can make a choice after comprehensive evaluation based on other factors, such as the impact of adding exogenous additives on the stability of the anaerobic system and the impact on the complexity of operation.

[0033] Example 2 This embodiment provides a food waste anaerobic digestion treatment system based on big data evaluation, including a memory and a processor. The memory stores a computer program, and the processor calls the computer program to execute the steps of the method as described in Embodiment 1.

[0034] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A method for anaerobic digestion treatment of kitchen waste based on big data evaluation, characterized in that, Includes the following steps: Establish separate anaerobic digestion treatment facilities for kitchen waste without external additives and anaerobic digestion treatment facilities for kitchen waste with external additives. State data of food waste were collected under anaerobic digestion conditions with and without external additives. This state data included daily food waste inflow, inflow moisture content, and inflow VS... in Content, energy consumption, pharmaceutical consumption, output, output moisture content, biogas production, and methane content; Based on biogas production, daily intake of kitchen waste, moisture content of feed, and feed VS in The content is calculated by comparing the unit feed to the biogas yield, and the methane yield is calculated by combining the methane content. Based on the consumption and methane yield under anaerobic digestion conditions with and without exogenous additives, an assessment was conducted to determine whether to add exogenous additives to the anaerobic digestion treatment of food waste, and the final anaerobic digestion treatment method for food waste was determined.

2. The method for anaerobic digestion treatment of kitchen waste based on big data evaluation according to claim 1, characterized in that, The formula for calculating the unit feed VS biogas yield is as follows: η g = Q g / [10Q in *(1-fw in )* C vs-in ] In the formula, η g Q represents the ratio of feed volume to biogas yield. in For the daily intake of food waste, fw in C represents the feed moisture content. vs-in For feeding VS in content.

3. The method for anaerobic digestion treatment of kitchen waste based on big data evaluation according to claim 2, characterized in that, The formula for calculating the methane yield is as follows: or CH4 =h g * C ch4 In the formula, η CH4 For methane yield, C ch4 This represents the methane content.

4. The method for anaerobic digestion treatment of kitchen waste based on big data evaluation according to claim 1, characterized in that, An evaluation of the feasibility of adding exogenous additives to anaerobic digestion treatment of food waste was conducted based on the consumption and methane yield under anaerobic digestion conditions with and without exogenous additives. Specifically: Calculate the VSS expenditure per unit mass under anaerobic digestion without external additives, based on the consumption generated. VSS expenditure per unit mass under anaerobic digestion treatment with exogenous additives ; Calculate the expected revenue W for anaerobic digestion without exogenous additives, based on the corresponding methane yield. 原预收 And the expected revenue W under anaerobic digestion treatment with exogenous additives. 后预收 ; The assessment of whether to add exogenous additives is based on the unit mass VSS expenditure and expected income under anaerobic digestion treatment with and without exogenous additives.

5. The method for anaerobic digestion treatment of kitchen waste based on big data evaluation according to claim 4, characterized in that, VSS expenditure per unit mass under anaerobic digestion treatment without exogenous additives The calculation expression is: In the formula, Cs 电 For electricity bills, For energy consumption, The unit price of the medicines currently used in the facility. For medicine consumption, For solid slag treatment costs, For output volume, The output moisture content, This refers to the daily intake of food waste. The feed moisture content, For feeding VS in content.

6. The method for anaerobic digestion treatment of kitchen waste based on big data evaluation according to claim 4, characterized in that, VSS expenditure per unit mass under anaerobic digestion treatment with exogenous additives The calculation expression is: In the formula, Cs 电 For electricity bills, For energy consumption, The unit price of the medicines currently used in the facility. For medicine consumption, The purchase price of exogenous additives, The dosage of exogenous additives, For solid slag treatment costs, For output volume, The output moisture content, This refers to the daily intake of food waste. The feed moisture content, For feeding VS in content.

7. The method for anaerobic digestion treatment of kitchen waste based on big data evaluation according to claim 4, characterized in that, The formula for calculating the expected income is as follows: In the formula, For expected revenue, The selling price of methane. The yield is denoted as methane.

8. The method for anaerobic digestion treatment of kitchen waste based on big data evaluation according to claim 4, characterized in that, The assessment of whether to add exogenous additives is based on the unit mass VSS expenditure and expected income under anaerobic digestion treatment with and without exogenous additives. Specifically: When W 后预收 -W 后支 >W 原预收 -W 原支 When obtaining the total investment W required for engineering modifications and additional equipment after adding exogenous additives, 投 And the corresponding return period N 回报 and expected depreciation period N 折旧 This allows for an assessment of whether the expected outcome is met. If the expected outcome is met, then adding the exogenous additive is feasible; otherwise, it is not. When W 后预收 -W 后支 <W 原预收 -W 原支 When it is determined that adding exogenous additives is not feasible; When W 后预收 -W 后支 =W 原预收 -W 原支 At that time, there is no economic benefit after adding external additives to the output. It is necessary to make a comprehensive evaluation based on other factors, including the impact of adding external additives on system stability and the impact on the complexity of operation.

9. A method for anaerobic digestion treatment of kitchen waste based on big data evaluation according to claim 8, characterized in that, The evaluation process for determining whether expectations are met is as follows: The corresponding α and β values ​​are calculated using the following expression: α=0.1*Q in *(1-fw in )*C vs-in *(W 后预收 -W 后支 >W 原预收 -W 原支 )*n 工作 -(W 投 - W 残 ) / N 折旧 β=W 投 / N 回报 In the formula, Q in For the daily intake of food waste, fw in C represents the feed moisture content. vs-in For feeding VS in Content, n 工作 W represents the number of working days per year for the project. 残 The residual value after the investment period expires; If α > β, then the expected assessment is satisfied; otherwise, the expected assessment is not satisfied.

10. A food waste anaerobic digestion treatment system based on big data evaluation, characterized in that, It includes a memory and a processor, the memory storing a computer program, and the processor calling the computer program to perform the steps of the method as described in any one of claims 1 to 9.

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