Carbon emission reduction control method and system for anaerobic fermentation treatment of organic waste

Abstract

The present invention discloses a carbon emission reduction control method and system for anaerobic fermentation treatment of organic waste, including obtaining initial control parameters; monitoring specific indicators of raw materials and similarity analysis results; analyzing the initial control parameters, and selecting the minimum carbon emission control parameter as the final control parameter based on the carbon emission data of each control parameter; forming corresponding control commands; generating general control parameters based on the control parameters, and generating a control early warning report at the same time; generating a control strategy based on the general control parameters or generating a control strategy based on manually set control parameters. By analyzing the variability of raw materials and combining process control strategies with carbon emission data, the production process is controlled to the state with the lowest carbon emissions from different control strategy perspectives, thereby achieving carbon emission reduction control in the anaerobic fermentation treatment process of organic waste; it can adapt to different anaerobic fermentation states of organic waste, adapt to the differences in organic waste process details, and help enterprises reduce carbon emissions.

Description

Technical Field

[0001] The present invention belongs to the technical field of carbon emission reduction control, and specifically relates to a carbon emission reduction control method and system for anaerobic fermentation treatment of organic waste. Background Art

[0002] With the strategic goal of carbon neutrality, carbon emission reduction has become a key measure in achieving the dual carbon goals. According to relevant research, biogas currently has the potential to contribute approximately 1 billion tons of carbon dioxide equivalent to carbon reduction, accounting for 5%-10% of total emissions. For food waste and kitchen waste, anaerobic fermentation processes, through carbon accounting, have negative net carbon dioxide emissions, effectively achieving carbon sequestration.

[0003] Currently, anaerobic fermentation is the mainstream treatment process for organic waste disposal and is widely used in China. Regarding carbon emissions in the solid waste treatment industry, current research focuses on reducing carbon emissions from a process perspective, such as upgrading existing landfill processes to incineration. However, within a given process, varying operating conditions and control strategies can also have a certain impact on carbon emissions. However, effective methods for optimizing process control and reducing carbon emissions during production remain lacking. Summary of the Invention

[0004] The purpose of the present invention is to provide a carbon emission reduction control system for anaerobic fermentation treatment of organic waste to solve the above-mentioned problems existing in the prior art.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] In a first aspect, the present invention provides a method for controlling carbon emission reduction by anaerobic fermentation treatment of organic waste, comprising the following steps:

[0007] Based on the input operation of human-computer interaction, the initial control parameters are obtained;

[0008] Monitor specific indicators of the raw materials, obtain indicator monitoring data, perform similarity analysis between historical data and real-time data of the raw materials based on the indicator monitoring data, and obtain raw material similarity analysis results; the raw materials refer to the raw materials after pulping in the anaerobic fermentation process of organic waste;

[0009] According to the raw material similarity analysis results, the similarity of the raw materials is judged.

[0010] If yes, the initial control parameters are analyzed according to the raw material similarity analysis results to obtain control parameter analysis results;

[0011] Make similarity judgment on control parameters based on control parameter analysis results;

[0012] If so, the minimum carbon emission control parameter is selected as the final control parameter based on the carbon emission data of each control parameter;

[0013] Generate a control strategy based on the final control parameters; the control strategy refers to generating control indicators of various parameters based on the control parameters to form corresponding control commands;

[0014] If the raw material similarity is judged to be negative, general control parameters are generated according to the process control parameters, and a control early warning report is generated at the same time;

[0015] Manually review the general control parameters according to the control warning report;

[0016] Based on the human-computer interaction audit result input operation, the audit result is obtained;

[0017] Determine whether the audit result is passed based on the audit result;

[0018] If so, a control strategy is generated based on general control parameters;

[0019] If not, based on the parameter input operation of human-computer interaction, manually set control parameters are obtained, and a control strategy is generated according to the manually set control parameters.

[0020] According to the above technology, by analyzing the variability of raw materials and combining process control strategies with carbon emission data, the production process can be controlled to the state with the lowest carbon emissions from different control strategy perspectives, thereby achieving carbon emission reduction control in the anaerobic fermentation disposal process of organic waste; thereby, it can adapt to different anaerobic fermentation states of organic waste, and fully adapt to the regional differences and process details of organic waste, helping enterprises to reduce carbon emissions.

[0021] In one possible design, judging the similarity of the raw materials based on the raw material similarity analysis results includes the following steps:

[0022] Construct the raw material index vector:

[0023]

[0024]

[0025]

[0026]

[0027] ;

[0028] in, is the vector of the parameters of the current raw material, is the vector of parameters corresponding to the raw materials at a certain moment in the historical database; xa1, xa2, xa3...xan are the parameters of the current raw materials, xb1, xb2, xb3...xbn are the parameters corresponding to the raw materials at a certain moment in the current history, n is a positive integer, i is a positive integer;

[0029] Construct the following vector analysis formula:

[0030]

[0031] According to the above analysis formula, if the result d is less than the set threshold, and If the value is greater than the set threshold, the raw materials are judged to be similar and are selected as candidates for further analysis.

[0032] In one possible design, the method for analyzing the initial control parameters based on the raw material similarity analysis results to obtain the control parameter analysis results includes the following steps:

[0033] Based on human-computer interaction, the control parameters are divided into segments to obtain parameter segmentation information; the control parameters are divided into segments according to their characteristics and production control needs;

[0034] To determine whether the control parameters to be compared are in the same segment, the following judgment formula is constructed:

[0035]

[0036] Where: is the number of parameters Mi in the current parameter set M and the corresponding parameters Ni in the parameter set N at the comparison moment in the same partition, is the total number of control parameters compared;

[0037] Construct parameter importance coefficient vector and parameter comparison vector :

[0038]

[0039]

[0040] In the formula is the importance coefficient of the selected control parameter; M1, M2, M3...Mn are the control parameters of the system at each current moment, N1, N2, N3...Nn are the corresponding control parameters of the system at the comparison moment;

[0041] Construct a cosine similarity comparison algorithm for control parameters:

[0042]

[0043] Where: M is the current parameter set, Mi is the parameters in the parameter set M; N is the parameter set at the comparison moment, Ni is the parameters in the parameter set at the comparison moment;

[0044] Construct the following comprehensive analysis algorithm for control parameters:

[0045] Construct a new importance coefficient vector and the new parameter vector :

[0046]

[0047]

[0048] Comprehensive comparative analysis algorithm:

[0049]

[0050] According to the result of the comprehensive comparison and analysis algorithm, combined with the determination threshold, it is determined whether the comprehensive control requirements are met.

[0051] In one possible design, when selecting the minimum carbon emission control parameter as the final control parameter based on the carbon emission data of each candidate control parameter, the carbon emission data analysis method includes the following steps:

[0052] Calculate direct carbon emissions:

[0053] E methane =[Σ( M i × CC i )-Σ( M o × CC o )] ×α×(16 / 12)

[0054] in, E methane Represents the methane production from anaerobic digestion of organic waste, in tons; M i Represents the amount of input material into the organic waste anaerobic digestion system, in tons; M o Represents the amount of output material from the organic waste anaerobic digestion system, in tons; CC i Represents the carbon content of the input material; CC o Represents the carbon content of the output material; α represents the proportion of methane in the anaerobic digestion gas, and the default value can be 40%-65%;

[0055] Calculate the amount of greenhouse gas emissions caused by emissions during the production process:

[0056] E direct = Q HF ×C methane × GWP

[0057] Q HF = Σ( V i ×n i )

[0058] Where: E direct Represents the amount of greenhouse gas emissions during the anaerobic digestion of organic waste, in tons of carbon dioxide equivalent; Q HF Represents the air exchange volume, in cubic meters; C methane Represents the mass concentration of methane escape in the ventilation gas, in kilograms per cubic meter; V i Represents the facility space of the i-th section, in cubic meters; n i Represents the number of air changes in the i-th section, in times; GWP Represents the global warming potential coefficient; for specific values, please refer to the data provided by IPCC;

[0059] CO2 emissions embodied in net purchased electricity and heat:

[0060] The CO2 emissions implied by net purchased electricity and heat include the CO2 emissions implied by purchased electricity and heat consumption, minus the CO2 emissions from biogas generated by anaerobic digestion for power generation or external heat supply, and are calculated as follows:

[0061] E net = E 购入电 + E 购入热 - E 输出电 - E 输出热

[0062] E 购入电 = AD 购入电 × EF 电

[0063] E 购入热 = AD购入热 × EF 热

[0064] E 输出电 = AD 输出电 × EF 电

[0065] E 输出热 = AD 输出热 × EF 热

[0066] Where: E net Represents CO2 emissions from net purchased electricity and heat consumption, in tons of CO2; E 购入电 Represents the CO2 emissions caused by production activities corresponding to the consumption of purchased electricity, in tons of CO2; E 购入热 Represents the CO2 emissions caused by production activities corresponding to the consumption of purchased heat, in tons of CO2; E 输出电 Represents the CO2 emissions from biogas power generation, in tons of CO2; E 输出热 Represents the CO2 emissions generated by biogas incineration for external heat supply, in tons of carbon dioxide; AD 购入电 Represents the amount of electricity purchased, in megawatt-hours; EF 电 represents the emission factor for electricity production in tons of CO2 per megawatt-hour; AD 购入热 Represents the amount of thermal energy purchased, in GJ; EF 热 represents the emission factor for heat production, in tonnes of CO2 per gigajoules; AD 输出电 Represents the amount of electricity output in megawatt-hours; EF 电 represents the emission factor for electricity production in tons of CO2 per megawatt-hour; AD 输出热 Represents the output heat amount, in GJ; EF 热 represents the emission factor for heat production, in tonnes of CO2 per gigajoules;

[0067] Calculate the CO2 emission equivalent of the soil carbon sequestration effect, which refers to the carbon sequestration effect generated when biogas residue, a product of organic waste disposal, is used as a soil conditioner. This item is not calculated when biogas residue is not used as a soil conditioner;

[0068] E f = M 沼渣 ×C s ×γ×(44 / 12)

[0069] E f represents the carbon dioxide emission reduction equivalent formed by the soil carbon sequestration effect of biogas residue; γ represents the carbon sequestration rate of organic carbon in biogas residue, with a default value of 8% to 13%;

[0070] Calculate the output by-product CO2 emission equivalent:

[0071] E bp = Σ( M bpi × q × EF bpi )

[0072] E bp represents the carbon dioxide emission reduction equivalent of biomass by-products; M bpi represents the yield of the i-th biological by-product; q Represents the lower heating value of biological by-products; EF bpi represents the emission factor of the i-th biological by-product;

[0073] Calculate total carbon emissions:

[0074] E Total = E direct + E net - E f - E bp

[0075] Where: E Total Represents the total amount of greenhouse gas emissions in tons of carbon dioxide equivalent;

[0076] Calculate minimum carbon emissions:

[0077] E MinToutal =min(E Total1 , E Total2 , E Total3 ··· E Totaln )

[0078] E Totali The carbon emissions corresponding to the candidate items screened out through comprehensive analysis of the control parameters of the representative steps.

[0079] In one possible design, the analysis index parameters during the raw material similarity analysis include one or more of the following: material quantity, material moisture content, material pH value, material temperature, material alkalinity, material carbon content, material volatile fatty acids, and material sludge concentration.

[0080] The second invention provides a carbon emission reduction control system for anaerobic fermentation treatment of organic waste, including

[0081] A control parameter acquisition unit is used to acquire initial control parameters based on input operations of human-computer interaction; and transmit the initial control parameters to the carbon emission reduction strategy data analysis and processing unit;

[0082] The raw material data acquisition and monitoring unit is used to monitor specific indicators of the raw material, obtain monitoring data of various indicators of the raw material, and transmit the monitoring data of various indicators to the raw material data analysis and judgment unit; the raw material refers to the raw material after pulping in the anaerobic fermentation process of organic waste;

[0083] The raw material data analysis and judgment unit performs similarity analysis on the raw material historical data and the real-time data based on the monitoring data of each indicator transmitted by the raw material data acquisition and monitoring unit, obtains the raw material similarity analysis result, and transmits the raw material similarity analysis result to the carbon emission reduction strategy data analysis and processing unit; and performs similarity judgment on the raw materials based on the raw material similarity analysis result, and transmits the judgment result to the carbon emission reduction strategy data analysis and processing unit;

[0084] A carbon emission execution monitoring unit is used to monitor the carbon emission data corresponding to each control parameter and transmit the carbon emission data of each control parameter to the carbon emission reduction strategy data analysis and processing unit;

[0085] The carbon emission reduction strategy data analysis and processing unit is used to analyze the initial control parameters transmitted by the control parameter acquisition unit according to the raw material similarity analysis result transmitted by the raw material data analysis and judgment unit to obtain the control parameter analysis result; and perform similarity judgment on the control parameters according to the control parameter analysis result; if the raw material similarity is judged to be yes, then the minimum carbon emission control parameter is selected as the final control parameter according to the carbon emission data of each control parameter transmitted by the carbon emission execution monitoring unit; if the raw material similarity is judged to be no, then general control parameters are generated according to the process control parameters, and a control early warning report is generated at the same time; the general control parameters are manually reviewed according to the control early warning report; the review result is input based on the human-computer interaction operation to obtain the review result; based on the review result, it is judged whether the review result is passed; if so, a control strategy is generated according to the general control parameters; if not, the manually set control parameters are obtained based on the human-computer interaction parameter input operation, and a control strategy is generated according to the manually set control parameters; and the control parameters are transmitted to the strategy execution unit;

[0086] The strategy execution monitoring unit executes the strategy according to the control parameters transmitted by the carbon emission reduction strategy data analysis and processing unit.

[0087] In one possible design, the carbon emission monitoring unit monitors the carbon emission data of each control parameter, including one or more of the direct CH4 emissions from each emission source, CO2 emissions generated by net purchased electricity or heat, CO2 emissions generated by net external supply of power generation or heat, CO2 emission equivalents corresponding to the soil carbon sequestration effect generated by sludge as a soil conditioner, and CO2 emission equivalents corresponding to biodiesel as an output biological by-product.

[0088] In one possible design, it also includes a production control data acquisition and monitoring unit, which is used to collect and monitor production control data during the anaerobic fermentation process of organic waste; at the same time, the production control data acquisition and monitoring unit transmits the collected production control data to the carbon emission execution monitoring unit in real time; the data collected by the production control data acquisition and monitoring unit includes data used for carbon emission reduction calculations and formulating carbon emission reduction strategies, and is used to monitor the implementation of carbon emission reduction to avoid errors in the execution process or impact on the safety of the production system.

[0089] In one possible design, the production control data include fermentor pH value, fermentor alkalinity, fermentor temperature, fermentor stirring speed, fermentor volatile fatty acids, fermentor sludge concentration and ventilation volume.

[0090] In one possible design, a carbon emission factor database is further included, which is used to centrally manage the carbon emission-related data within the research boundary of the anaerobic fermentation disposal process of organic waste as the research object; the carbon emission-related data include the data for storing the initial control parameters obtained by the control parameter acquisition unit, the monitoring data of various raw material indicators obtained by the raw material data acquisition and monitoring unit, the carbon emission data corresponding to each control parameter obtained by the carbon emission execution monitoring unit, the production control data obtained by the production control data acquisition and monitoring unit, and the control strategy obtained by the carbon emission reduction strategy data analysis and processing unit.

[0091] Beneficial effects: The present invention provides a carbon emission reduction control method and system for the production process of anaerobic fermentation disposal of organic waste. By analyzing the variability of raw materials and combining process control strategies with carbon emission data, the production process is controlled to the state with the lowest carbon emissions from different control strategy perspectives, thereby achieving carbon emission reduction control in the process of anaerobic fermentation disposal of organic waste. In this way, it can adapt to different anaerobic fermentation states of organic waste and fully adapt to the regional differences and process details of organic waste, thereby helping enterprises to reduce carbon emissions. BRIEF DESCRIPTION OF THE DRAWINGS

[0092] Figure 1 A schematic flow chart of the method provided in the first aspect of the embodiment;

[0093] Figure 2 A schematic diagram of the system modules provided for the second aspect of the embodiment. DETAILED DESCRIPTION

[0094] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the present invention will be briefly introduced below in conjunction with the drawings and the description of the embodiments or the prior art. Obviously, the following description of the structure of the drawings is only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work. It should be noted that the description of these embodiments is used to help understand the present invention, but does not constitute a limitation of the present invention.

[0095] Example:

[0096] like Figure 1 As shown, this embodiment provides a method for controlling carbon emission reduction by anaerobic fermentation treatment of organic waste, comprising the following steps:

[0097] Based on the input operation of human-computer interaction, the initial control parameters are obtained;

[0098] Monitor specific indicators of the raw materials, obtain indicator monitoring data, perform similarity analysis between historical data and real-time data of the raw materials based on the indicator monitoring data, and obtain raw material similarity analysis results; the raw materials refer to the raw materials after pulping in the anaerobic fermentation process of organic waste;

[0099] According to the raw material similarity analysis results, the similarity of the raw materials is judged.

[0100] If yes, the initial control parameters are analyzed according to the raw material similarity analysis results to obtain control parameter analysis results;

[0101] Make similarity judgment on control parameters based on control parameter analysis results;

[0102] If so, the minimum carbon emission control parameter is selected as the final control parameter based on the carbon emission data of each control parameter;

[0103] Generate a control strategy based on the final control parameters; the control strategy refers to generating control indicators of various parameters based on the control parameters to form corresponding control commands;

[0104] If the raw material similarity is judged to be negative, general control parameters are generated according to the process control parameters, and a control early warning report is generated at the same time;

[0105] Manually review the general control parameters according to the control warning report;

[0106] Based on the human-computer interaction audit result input operation, the audit result is obtained;

[0107] Determine whether the audit result is passed based on the audit result;

[0108] If so, a control strategy is generated based on general control parameters;

[0109] If not, based on the parameter input operation of human-computer interaction, manually set control parameters are obtained, and a control strategy is generated according to the manually set control parameters.

[0110] In one possible embodiment, judging the similarity of the raw materials according to the raw material similarity analysis results includes the following steps:

[0111] Construct the raw material index vector:

[0112]

[0113]

[0114]

[0115]

[0116] ;

[0117] in, is the vector of the parameters of the current raw material, is the vector of parameters corresponding to the raw materials at a certain moment in the historical database; xa1, xa2, xa3...xan are the parameters of the current raw materials, xb1, xb2, xb3...xbn are the parameters corresponding to the raw materials at a certain moment in the current history, n is a positive integer, i is a positive integer;

[0118] Construct the following vector analysis formula:

[0119]

[0120] According to the above analysis formula, if the result d is less than the set threshold, and If the value is greater than the set threshold, the raw materials are judged to be similar and are selected as candidates for further analysis.

[0121] In one possible embodiment, the method of analyzing the initial control parameters according to the raw material similarity analysis results to obtain the control parameter analysis results includes the following steps:

[0122] Based on human-computer interaction, the control parameters are divided into segments to obtain parameter segmentation information; the control parameters are divided into segments according to their characteristics and production control needs;

[0123] Preferably, the control parameters selected in this embodiment are: fermentation tank temperature T, pH, alkalinity, VFA, MLSS, and COD.

[0124] Preferably, in this embodiment, the selected parameters are divided into segments as follows:

[0125]

[0126] To determine whether the control parameters to be compared are in the same segment, the following judgment formula is constructed:

[0127]

[0128] Where: is the number of parameters Mi in the current parameter set M and the corresponding parameters Ni in the parameter set N at the comparison moment in the same partition, is the total number of control parameters compared;

[0129] Construct parameter importance coefficient vector and parameter comparison vector :

[0130]

[0131]

[0132] In the formula is the importance coefficient of the selected control parameter; M1, M2, M3...Mn are the control parameters of the system at each current moment, N1, N2, N3...Nn are the corresponding control parameters of the system at the comparison moment;

[0133] Construct a cosine similarity comparison algorithm for control parameters:

[0134]

[0135] Where: M is the current parameter set, Mi is the parameters in the parameter set M; N is the parameter set at the comparison moment, Ni is the parameters in the parameter set at the comparison moment;

[0136] Construct the following comprehensive analysis algorithm for control parameters:

[0137] Construct a new importance coefficient vector and the new parameter vector :

[0138]

[0139]

[0140] Preferably, the control coefficient importance coefficient in this embodiment is as follows:

[0141]

[0142] Comprehensive comparative analysis algorithm:

[0143]

[0144] According to the result of the comprehensive comparison and analysis algorithm, combined with the determination threshold, it is determined whether the comprehensive control requirements are met.

[0145] In one possible implementation, when the minimum carbon emission control parameter is selected as the final control parameter based on the carbon emission data of each candidate control parameter, the carbon emission data analysis method includes the following steps:

[0146] Calculate direct carbon emissions:

[0147] E methane =[Σ( M i × CC i )-Σ( M o × CCo )] ×α×(16 / 12)

[0148] in, E methane Represents the methane production from anaerobic digestion of organic waste, in tons; M i Represents the amount of input material into the organic waste anaerobic digestion system, in tons; M o Represents the amount of output material from the organic waste anaerobic digestion system, in tons; CC i Represents the carbon content of the input material; CC o Represents the carbon content of the output material; α represents the proportion of methane in the anaerobic digestion gas, and the default value can be 40%-65%;

[0149] Calculate the amount of greenhouse gas emissions caused by emissions during the production process:

[0150] E direct = Q HF ×C methane × GWP

[0151] Q HF = Σ( V i ×n i )

[0152] Where: E direct Represents the amount of greenhouse gas emissions during the anaerobic digestion of organic waste, in tons of carbon dioxide equivalent; Q HF Represents the air exchange volume, in cubic meters; C methane Represents the mass concentration of methane escape in the ventilation gas, in kilograms per cubic meter; V i Represents the facility space of the i-th section, in cubic meters; n i Represents the number of air changes in the i-th section, in times; GWP Represents the global warming potential coefficient; for specific values, please refer to the data provided by IPCC;

[0153] CO2 emissions embodied in net purchased electricity and heat:

[0154] The CO2 emissions implied by net purchased electricity and heat include the CO2 emissions implied by purchased electricity and heat consumption, minus the CO2 emissions from biogas generated by anaerobic digestion for power generation or external heat supply, and are calculated as follows:

[0155] E net = E 购入电 + E 购入热 - E 输出电 - E 输出热

[0156] E 购入电 = AD 购入电 × EF 电

[0157] E 购入热 = AD 购入热 × EF 热

[0158] E 输出电 = AD 输出电 × EF 电

[0159] E 输出热 = AD 输出热 × EF 热

[0160] Where: CO2 emissions from electricity and heat consumption, in tons of carbon dioxide (tCO2);

[0161] E Purchased electricity – CO2 emissions caused by production activities corresponding to the consumption of purchased electricity, in tons of carbon dioxide (tCO2);

[0162] E Purchased heat - CO2 emissions caused by production activities corresponding to the consumption of purchased heat, in tons of carbon dioxide (tCO2);

[0163] E Output electricity - CO2 emissions from biogas power generation, measured in tons of carbon dioxide (tCO2);

[0164] E Output heat - CO2 emissions from biogas incineration for external heat supply, measured in tons of carbon dioxide (tCO2);

[0165] AD Purchased Electricity - the amount of electricity purchased, in megawatt-hours (MWh);

[0166] EFelectricity – emission factor for electricity production, expressed in tonnes of CO2 per megawatt-hour (tCO2 / MWh);

[0167] AD purchased heat - the amount of heat purchased, in gigajoules (GJ);

[0168] EFheat – emission factor for heat production, expressed in tonnes of carbon dioxide per gigajoules (tCO2 / GJ);

[0169] AD output power - the amount of electricity output, in megawatt-hours (MWh);

[0170] EFelectricity – emission factor for electricity production, expressed in tonnes of CO2 per megawatt-hour (tCO2 / MWh);

[0171] AD output heat - the amount of heat output, in gigajoules (GJ);

[0172] EFheat – Emission factor for heat production, expressed in tonnes of CO2 per gigajoules (tCO2 / GJ).

[0173] Calculate the CO2 emission equivalent of the soil carbon sequestration effect, which refers to the carbon sequestration effect generated when biogas residue, a product of organic waste disposal, is used as a soil conditioner. This item is not calculated when biogas residue is not used as a soil conditioner;

[0174] E f = M 沼渣 ×C s ×γ×(44 / 12)

[0175] E f represents the carbon dioxide emission reduction equivalent formed by the soil carbon sequestration effect of biogas residue; γ represents the carbon sequestration rate of organic carbon in biogas residue, with a default value of 8% to 13%;

[0176] Calculate the output by-product CO2 emission equivalent:

[0177] E bp = Σ( M bpi × q × EF bpi )

[0178] E bp represents the carbon dioxide emission reduction equivalent of biomass by-products; M bpi represents the yield of the i-th biological by-product; q Represents the lower heating value of biological by-products; EFbpi represents the emission factor of the i-th biological by-product;

[0179] Calculate total carbon emissions:

[0180] E Total = E direct + E net - E f - E bp

[0181] Where: E Total Represents the total amount of greenhouse gas emissions in tons of carbon dioxide equivalent;

[0182] Calculate minimum carbon emissions:

[0183] E MinToutal =min( E Total1 ,, E Total2 , E Total3 ··· E Totaln )

[0184] E Totali The carbon emissions corresponding to the candidate items screened out through comprehensive analysis of the control parameters of the representative steps.

[0185] Preferably, the biogas residue in this embodiment is incinerated, so there is no carbon sequestration effect as a soil conditioner. At the same time, the by-product in this embodiment is biodiesel, and its emission factor is as follows:

[0186]

[0187] Its total carbon emissions are calculated as follows: E Total = E direct + E net - E bp .

[0188] In one possible embodiment, the analysis index parameters during the raw material similarity analysis include one or more of the following: material quantity, material moisture content, material pH value, material temperature, material alkalinity, material carbon content, material volatile fatty acids, and material sludge concentration.

[0189] The second aspect of this embodiment provides a carbon emission reduction control system for anaerobic fermentation treatment of organic waste, including

[0190] A control parameter acquisition unit is used to acquire initial control parameters based on input operations of human-computer interaction; and transmit the initial control parameters to the carbon emission reduction strategy data analysis and processing unit;

[0191] The raw material data acquisition and monitoring unit is used to monitor specific indicators of the raw material, obtain monitoring data of various indicators of the raw material, and transmit the monitoring data of various indicators to the raw material data analysis and judgment unit; the raw material refers to the raw material after pulping in the anaerobic fermentation process of organic waste;

[0192] Specifically, the raw material data collection and monitoring unit includes manual input, automatic online monitoring, etc.

[0193] The raw material data analysis and judgment unit performs similarity analysis on the raw material historical data and the real-time data based on the monitoring data of each indicator transmitted by the raw material data acquisition and monitoring unit, obtains the raw material similarity analysis result, and transmits the raw material similarity analysis result to the carbon emission reduction strategy data analysis and processing unit; and performs similarity judgment on the raw materials based on the raw material similarity analysis result, and transmits the judgment result to the carbon emission reduction strategy data analysis and processing unit;

[0194] A carbon emission execution monitoring unit is used to monitor the carbon emission data corresponding to each control parameter and transmit the carbon emission data of each control parameter to the carbon emission reduction strategy data analysis and processing unit;

[0195] The carbon emission reduction strategy data analysis and processing unit is used to analyze the initial control parameters transmitted by the control parameter acquisition unit according to the raw material similarity analysis result transmitted by the raw material data analysis and judgment unit to obtain the control parameter analysis result; and perform similarity judgment on the control parameters according to the control parameter analysis result; if the raw material similarity is judged to be yes, then the minimum carbon emission control parameter is selected as the final control parameter according to the carbon emission data of each control parameter transmitted by the carbon emission execution monitoring unit; if the raw material similarity is judged to be no, then general control parameters are generated according to the process control parameters, and a control early warning report is generated at the same time; the general control parameters are manually reviewed according to the control early warning report; the review result is input based on the human-computer interaction operation to obtain the review result; whether the review result is passed or not is judged according to the review result; if so, a control strategy is generated according to the general control parameters; if not, the manually set control parameters are obtained based on the human-computer interaction parameter input operation, and a control strategy is generated according to the manually set control parameters; and the control parameters are transmitted to the strategy execution unit; the control strategy refers to generating each parameter control indicator according to the control parameters to form a corresponding control command. The control command refers to the conversion of control parameters into specific control commands such as speed, reflux ratio, heating switch control, material ratio switch, etc. through serial communication or other communication methods.

[0196] The strategy execution monitoring unit executes the policy according to the control parameters transmitted by the carbon emission reduction policy data analysis and processing unit. The strategy execution monitoring unit is also used to monitor deviations or errors in the execution process, timely identify and control potential risks, and ensure stable system operation.

[0197] Specifically, the historical raw material parameters, historical production control parameters and corresponding carbon emissions are associated and identified using timestamps.

[0198] In a possible embodiment, the carbon emission monitoring unit monitors the carbon emission data of each control parameter, including one or more of the direct CH4 emissions from each emission source, the CO2 emissions generated by the net purchase of electricity or heat, the CO2 emissions generated by the net external supply of power generation or heat, the CO2 emission equivalents corresponding to the soil carbon sequestration effect generated by sludge as a soil conditioner, and the CO2 emission equivalents corresponding to biodiesel as an output biological by-product.

[0199] In a possible embodiment, it also includes a production control data acquisition and monitoring unit, which is used to collect and monitor production control data during the anaerobic fermentation process of organic waste; at the same time, the production control data acquisition and monitoring unit transmits the collected production control data to the carbon emission execution monitoring unit in real time; the data collected by the production control data acquisition and monitoring unit includes data used for carbon emission reduction calculations and formulating carbon emission reduction strategies, and is used to monitor the implementation of carbon emission reduction to avoid errors in the execution process or affect the safety of the production system.

[0200] In one possible embodiment, the production control data includes but is not limited to fermentor pH, fermentor alkalinity, fermentor temperature (°C), fermentor stirring speed (r / min), fermentor volatile fatty acids (VFA), fermentor sludge concentration (MLSS), ventilation volume (m3 / min), etc.

[0201] In a possible embodiment, it also includes a carbon emission factor database for building a database and centrally managing the carbon emission related data within the research boundary of the anaerobic fermentation disposal process of organic waste as the research object; the carbon emission related data include the data for storing the initial control parameters obtained by the control parameter acquisition unit, the raw material index monitoring data obtained by the raw material data acquisition and monitoring unit, the carbon emission data corresponding to each control parameter obtained by the carbon emission execution monitoring unit, the production control data obtained by the production control data acquisition and monitoring unit, and the control strategy obtained by the carbon emission reduction strategy data analysis and processing unit.

[0202] For example, the carbon emission factor database includes a carbon emission index dataset, an emission factor dataset, and a historical carbon emission dataset.

[0203] Specifically, the carbon emission strategy data analysis and processing unit utilizes data aggregated from the carbon emission factor database, carbon emission calculation methods, and carbon emission monitoring execution units to perform methodological calculations to formulate effective carbon emission reduction operational strategies. Based on these operational strategies, these operational plans are then sent to the strategy execution and monitoring unit for production control. The carbon emission strategy data analysis and processing unit provides a service point for data processing and plan generation, and is the software and hardware configured to meet the logical control requirements of the carbon emission reduction control method. Specifically, a processing unit can refer to a networked or clustered collection of processors, related networks, and storage devices. Furthermore, a processing unit should also refer to application software that includes software developed in a variety of development languages, as well as one or more database systems and services provided by supporting processing units. From a hardware perspective, processing units can vary significantly in configuration, but generally include one or more central processing units and storage units. They may also include one or more large storage areas, one or more power supplies, one or more wired or wireless network components, one or more input devices, one or more output devices, and one or more operating systems. The above-mentioned development language can be C language, C# language, C++ language, VB language, Java language, Python language, Visual Basic.NET language, JavaScript language, SQL language, PHP language and / or R language, etc.; the operating system can be Windows Services operating system, MacOS X operating system, Linux operating system, Unix operating system or FreeBSD operating system, etc.

[0204] The background management unit may be implemented by, but is not limited to, a local database server and / or a cloud data server, and may include a single or multiple database servers or a combination of servers.

[0205] Carbon emission indicator datasets include but are not limited to: direct CO2 emissions and the CC required to calculate direct CO2 emissions. i Input material carbon content (%), CC o Output material carbon content (%), M i Input material quantity (t), M o Output material volume (t), methane ratio in α-digestion gas production (%);

[0206] The amount of CO2 emitted due to the production process, and the Q required to calculate the amount of CO2 emitted HF Air exchange volume (m 3 )、C methane Mass concentration of methane emission in ventilation gas (kg / m 3 )、V i The facility space of the i-th section (m 3 )、n i The number of air changes in the i-th section (times), and the global warming potential (GWP) (based on data provided by the IPCC);

[0207] CO2 emissions embodied in net purchased electricity and heat, and the AD required to calculate the CO2 embodied in net purchased electricity and heat 购入电 Purchased electricity (MWh), AD 购入热 Purchased thermal power, EF 输出电 Output power (MWh), EF 输出热 The output of heat.

[0208] CO2 emissions from exporting biological by-products, and the M required to calculate CO2 emissions from exporting biological by-products bpi The amount of the i-th biological by-product (t), the lower calorific value of the q-th biological by-product (GJ / t).

[0209] Table 1 below shows the types of by-products and their calorific values ​​in the embodiment:

[0210] Table 1 Types of by-products and their low calorific values

[0211]

[0212] Emission factor datasets include, but are not limited to:

[0213] EF 电 Electricity production emission factor (tCO2 / MWh), EF 热Heat production emission factor (tCO2 / GJ), EF bpi Emission factors of the i-th biological byproduct (tCO2 / GJ). Table 2 below shows the effective emission factors of the biological byproducts in the examples.

[0214] Table 2 Types of by-products and their emission factors

[0215]

[0216] The historical carbon emissions dataset refers to carbon emissions data calculated based on established carbon emission indicators and emission factors. This dataset includes historical raw material parameters, production control parameters, and the corresponding carbon emissions. Each data set is associated and identified using a timestamp.

[0217] Carbon emissions are calculated as: ETotal=Edirect + Enet - Ebp.

[0218] Where:

[0219] Edirect—the amount of greenhouse gas emissions caused by emissions during the production process (t);

[0220] Enet—CO2 emissions embodied in net purchased electricity and heat (t);

[0221] Ebp—CO2 emissions from output biological by-products (t).

[0222] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included in the scope of protection of the present invention.

Claims

1. A method for controlling carbon emission reduction by anaerobic fermentation of organic waste, characterized in that: The following steps are involved: Based on the input operation of human-computer interaction, the initial control parameters are obtained; Monitor specific indicators of the raw materials, obtain indicator monitoring data, perform similarity analysis between historical data and real-time data of the raw materials based on the indicator monitoring data, and obtain raw material similarity analysis results; the raw materials refer to the raw materials after pulping in the anaerobic fermentation process of organic waste; According to the raw material similarity analysis results, the similarity of the raw materials is judged. If yes, the initial control parameters are analyzed according to the raw material similarity analysis results to obtain control parameter analysis results; Make similarity judgment on control parameters based on control parameter analysis results; If so, the minimum carbon emission control parameter is selected as the final control parameter based on the carbon emission data of each control parameter; Generate a control strategy based on the final control parameters; the control strategy refers to generating control indicators of various parameters based on the control parameters to form corresponding control commands; If the raw material similarity is judged to be negative, general control parameters are generated according to the control parameters, and a control warning report is generated at the same time; Manually review the general control parameters according to the control warning report; Based on the human-computer interaction audit result input operation, the audit result is obtained; Determine whether the audit result is passed based on the audit result; If so, a control strategy is generated based on general control parameters; If not, based on the parameter input operation of human-computer interaction, manually set control parameters are obtained, and a control strategy is generated according to the manually set control parameters.

2. The method for controlling carbon emission reduction by anaerobic fermentation treatment of organic waste according to claim 1, characterized in that: The similarity judgment of the raw materials according to the raw material similarity analysis results includes the following steps: Construct the raw material index vector: in, is the vector of the parameters of the current raw material, is the vector of parameters corresponding to the raw materials at a certain moment in the historical database; a1 、x a2 、x a3 ···x an are the current raw material parameters, x b1 、x b2 、x b3· ··x bn are the parameters corresponding to the raw materials at a certain moment in the current history, n is a positive integer, i is a positive integer; Construct the following vector analysis formula: According to the above analysis formula, if the result d is less than the set threshold and cosθ is greater than the set threshold, the raw materials are judged to be similar and are selected as candidates for further analysis.

3. The method for controlling carbon emission reduction by anaerobic fermentation treatment of organic waste according to claim 1, characterized in that: The method of analyzing the initial control parameters according to the raw material similarity analysis results to obtain the control parameter analysis results comprises the following steps: Based on human-computer interaction, the control parameters are divided into segments to obtain parameter segmentation information; the control parameters are divided into segments according to their characteristics and production control needs; To determine whether the control parameters to be compared are in the same segment, the following judgment formula is constructed: Where: |M∩N| is the parameter M in the current parameter set M i The same parameter N corresponding to the parameter set N at the time of comparison i The number of parameters in the same partition segment, |M∪N| is the total number of control parameters for comparison; Construct parameter importance coefficient vector and parameter comparison vector In the formula is the importance coefficient of the selected control parameters; M1, M2, M3...M n N1, N2, N3...N are the control parameters of the system at each moment, n To compare the control parameters of the system at the moment; Construct a cosine similarity comparison algorithm for control parameters: Where: M is the current parameter set, M i are the parameters in the parameter set M; N is the parameter set at the comparison moment, N i To compare the parameters in the moment parameters; Construct the following comprehensive analysis algorithm for control parameters: Construct a new importance coefficient vector and the new parameter vector Comprehensive comparative analysis algorithm: According to the result of the comprehensive comparison and analysis algorithm, combined with the determination threshold, it is determined whether the comprehensive control requirements are met.

4. The method for controlling carbon emission reduction by anaerobic fermentation treatment of organic waste according to claim 1, characterized in that: When the minimum carbon emission control parameter is selected as the final control parameter based on the carbon emission data of each candidate control parameter, the carbon emission data analysis method includes the following steps: Calculate direct carbon emissions: E methane =[Σ(M i ×CC i )-Σ(M o ×CC o )]×α×(16 / 12) Among them, E methane Represents the methane production from anaerobic digestion of organic waste, in tons; M i Represents the amount of input material in the organic waste anaerobic digestion system, in tons; M o Represents the amount of output material from the organic waste anaerobic digestion system, in tons; CC i Represents the carbon content of the input material; CC o Represents the carbon content of the output material; α represents the proportion of methane in the anaerobic digestion gas, and the default value can be 40%-65%; Calculate the amount of greenhouse gas emissions caused by emissions during the production process: E direct =Q HF ×C methane ×GWP Q HF =Σ(V i ×n i ) Where: E direct represents the amount of greenhouse gas emissions during the anaerobic digestion of organic waste, in tons of carbon dioxide equivalent; Q HF Represents the air exchange volume, in cubic meters; C methane represents the mass concentration of methane in the ventilation gas, in kilograms per cubic meter; V i Represents the facility space of the i-th section, in cubic meters; n i represents the number of air changes in the i-th section, in times; GWP represents the global warming potential coefficient; CO2 emissions embodied in net purchased electricity and heat: The CO2 emissions implied by net purchased electricity and heat include the CO2 emissions implied by purchased electricity and heat consumption, minus the CO2 emissions from biogas generated by anaerobic digestion for power generation or external heat supply, and are calculated as follows: AND net =And 购入电 +E 购入热 -AND 输出电 -AND 输出热 AND 购入电 =AD 购入电 ×EF 电 AND 购入热 =AD 购入热 ×EF 热 AND 输出电 =AD 输出电 ×EF 电 AND 输出热 =AD 输出热 ×EF 热 Where: E net Represents the CO2 emissions from net purchased electricity and heat consumption, in tons of carbon dioxide; E 购入电 Represents the CO2 emissions caused by production activities corresponding to the consumption of purchased electricity, in tons of carbon dioxide; E 购入热 represents the CO2 emissions caused by production activities corresponding to the consumption of purchased heat, in tons of carbon dioxide; E 输出电 Represents the CO2 emissions from biogas power generation, in tons of carbon dioxide; E 输出热 Represents the CO2 emissions generated by biogas incineration for external heat supply, in tons of carbon dioxide; AD 购入电 Represents the amount of electricity purchased, in megawatt-hours; EF 电 represents the emission factor for electricity production, in tons of CO2 per megawatt-hour; AD 购入热 Represents the amount of heat purchased, in GJ; EF 热 represents the emission factor for heat production, in tons of CO2 per gigajoules; AD 输出电 Represents the output power in megawatt-hours; EF 电 represents the emission factor for electricity production, in tons of CO2 per megawatt-hour; AD 输出热 Represents the output thermal energy, in GJ; EF 热 represents the emission factor for heat production, in tonnes of CO2 per gigajoules; Calculate the CO2 emission equivalent of the soil carbon sequestration effect, which refers to the carbon sequestration effect generated when biogas residue, a product of organic waste disposal, is used as a soil conditioner. This item is not calculated when biogas residue is not used as a soil conditioner; HAVE BEEN f =M 沼渣 ×C s ×γ×(44 / 12) E f represents the carbon dioxide emission reduction equivalent formed by the soil carbon sequestration effect of biogas residue; γ represents the carbon sequestration rate of organic carbon in biogas residue, with a default value of 8% to 13%; Calculate the output by-product CO2 emission equivalent: E bp =Σ(M bpi ×q×EF bpi ) E bp represents the carbon dioxide emission reduction equivalent of biological by-products; M bpi represents the output of the i-th biological by-product; q represents the lower calorific value of the biological by-product; EF bpi represents the emission factor of the i-th biological by-product; Calculate total carbon emissions: AND Total =And direct +E net -AND f -AND bp Where: E Total Represents the total amount of greenhouse gas emissions in tons of carbon dioxide equivalent; Calculate minimum carbon emissions: The MinToutal =min(E Total1 ,E Total2 ,E Total3 ···E Totaln ) E Totali The carbon emissions corresponding to the candidate items screened out through comprehensive analysis of the control parameters of the representative steps.

5. The method for controlling carbon emission reduction by anaerobic fermentation treatment of organic waste according to claim 1, characterized in that: The analysis index parameters in the raw material similarity analysis include one or more of the following: material quantity, material moisture content, material pH value, material temperature, material alkalinity, material carbon content, material volatile fatty acids and material sludge concentration.

6. A carbon emission reduction control system for anaerobic fermentation treatment of organic waste, characterized in that: include A control parameter acquisition unit is used to acquire initial control parameters based on input operations of human-computer interaction; and transmit the initial control parameters to the carbon emission reduction strategy data analysis and processing unit; The raw material data acquisition and monitoring unit is used to monitor specific indicators of the raw material, obtain monitoring data of various indicators of the raw material, and transmit the monitoring data of various indicators to the raw material data analysis and judgment unit; the raw material refers to the raw material after pulping in the anaerobic fermentation process of organic waste; The raw material data analysis and judgment unit performs similarity analysis between the raw material historical data and the real-time data based on the monitoring data of each indicator transmitted by the raw material data acquisition and monitoring unit, obtains the raw material similarity analysis results, and transmits the raw material similarity analysis results to the carbon emission reduction strategy data analysis and processing unit; and performing similarity judgment on the raw materials according to the raw material similarity analysis results, and transmitting the judgment results to the carbon emission reduction strategy data analysis and processing unit; A carbon emission execution monitoring unit is used to monitor the carbon emission data corresponding to each control parameter and transmit the carbon emission data of each control parameter to the carbon emission reduction strategy data analysis and processing unit; a carbon emission reduction strategy data analysis and processing unit, configured to analyze the initial control parameters transmitted by the control parameter acquisition unit according to the raw material similarity analysis result transmitted by the raw material data analysis and judgment unit, and obtain a control parameter analysis result; And make similarity judgment on the control parameters based on the control parameter analysis results; If the raw material similarity is judged to be yes, then the minimum carbon emission control parameter is selected as the final control parameter based on the carbon emission data of each control parameter transmitted by the carbon emission execution monitoring unit; If the raw material similarity is judged to be negative, general control parameters are generated according to the control parameters, and a control warning report is generated at the same time; Manually review the general control parameters according to the control warning report; obtain the review results based on the human-computer interaction review result input operation; determine whether the review results are passed based on the review results; if so, generate a control strategy based on the general control parameters; if not, obtain the manually set control parameters based on the human-computer interaction parameter input operation, and generate a control strategy based on the manually set control parameters; and passing the control parameters to the strategy execution monitoring unit; The strategy execution monitoring unit executes and monitors according to the control parameters transmitted by the carbon emission reduction strategy data analysis and processing unit.

7. The carbon emission reduction control system for anaerobic fermentation treatment of organic waste according to claim 6, characterized in that: The carbon emission execution monitoring unit monitors the carbon emission data of each control parameter, including one or more of the direct CH4 emissions from each emission source, CO2 emissions generated by net purchased electricity or heat, CO2 emissions generated by net external supply of power generation or heat, CO2 emission equivalents corresponding to the soil carbon sequestration effect generated by sludge as a soil conditioner, and CO2 emission equivalents corresponding to biodiesel as an output biological by-product.

8. The carbon emission reduction control system for anaerobic fermentation treatment of organic waste according to claim 6, characterized in that: It also includes a production control data acquisition and monitoring unit, which is used to collect and monitor production control data during the anaerobic fermentation process of organic waste; at the same time, the production control data acquisition and monitoring unit transmits the collected production control data to the carbon emission execution monitoring unit in real time; the data collected by the production control data acquisition and monitoring unit includes data used for carbon emission reduction calculations and formulating carbon emission reduction strategies, and is used to monitor the implementation of carbon emission reduction to avoid errors in the execution process or impact on the safety of the production system.

9. The carbon emission reduction control system for anaerobic fermentation treatment of organic waste according to claim 8, characterized in that: The production control data include pH value of the fermentation tank, alkalinity of the fermentation tank, temperature of the fermentation tank, stirring speed of the fermentation tank, volatile fatty acids of the fermentation tank, sludge concentration of the fermentation tank and ventilation volume.

10. The carbon emission reduction control system for anaerobic fermentation treatment of organic waste according to claim 8, characterized in that: It also includes a carbon emission factor database, which is used to build a database and centrally manage the carbon emission related data within the research boundary of the anaerobic fermentation disposal process of organic waste as the research object; the carbon emission related data includes the data for storing the initial control parameters obtained by the control parameter acquisition unit, the raw material index monitoring data obtained by the raw material data acquisition and monitoring unit, the carbon emission data corresponding to each control parameter obtained by the carbon emission execution monitoring unit, the production control data obtained by the production control data acquisition and monitoring unit, and the control strategy obtained by the carbon emission reduction strategy data analysis and processing unit.

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