Kitchen waste treatment process carbon emission reduction evaluation method

Abstract

The invention relates to the technical field of kitchen waste anaerobic biological treatment, in particular to a kitchen waste treatment process carbon emission reduction evaluation method which comprises the following steps: constructing an anaerobic reactor operation balance model, and monitoring real-time load and energy consumption; a reverse correlation algorithm is used for comparing biogas output differences, and escape emission is reversely deduced to calibrate the biochar balance state of the reactor; and dynamically setting an operation risk critical value, and automatically triggering real-time regulation and control instructions aiming at the feeding rate, the pH value and the sludge reflux ratio. By quantifying the carbon flow characteristics in the biological metabolism process, diagnosing the reactor acid production or methanation decline risk in real time, and establishing a process parameter closed-loop feedback regulation mechanism based on the carbon balance state, the self-adaptive optimization control of the feed load and the environmental parameters is realized while the stability of the biochemical reaction of the anaerobic system is ensured, and the method is suitable for industrial production. The anaerobic digestion treatment efficiency of the kitchen waste is obviously improved.

Description

Technical Field

[0001] This invention relates to the field of anaerobic biological treatment technology for kitchen waste, specifically a method for evaluating carbon emission reduction in kitchen waste treatment processes. Background Technology

[0002] Traditional anaerobic digestion processes for food waste have been widely used for the resource recovery and stabilization of organic matter. However, existing technologies still have significant shortcomings in the operation management and environmental performance evaluation of anaerobic digestion processes: Existing biological process control mainly relies on offline or lagging indicators (such as volatile fatty acids and alkalinity), making it difficult to provide real-time and proactive regulation of anaerobic digestion systems when faced with feed load shocks and composition fluctuations. This can easily lead to reactor acidification or methanogenic degradation, affecting biogas production and system stability. Current methods typically use simple factor methods to estimate the carbon emission reduction benefits of food waste treatment, but they fail to effectively measure uncollected and unutilized escaped greenhouse gases (such as methane leakage) during anaerobic digestion. This results in severely distorted total carbon balance and emission reduction performance calculations, failing to accurately reflect the process's net environmental benefits.

[0003] Operation management and carbon emission reduction benefit assessment are disconnected, making it impossible to feed back the results of process parameter adjustments to the system's carbon performance status in real time, and making it difficult to achieve automated management and optimization based on environmental benefits.

[0004] Therefore, existing technologies lack an integrated method that can quantify escaped greenhouse gas emissions during the operation of anaerobic digestion systems in real time and accurately, and dynamically adjust key process parameters accordingly to achieve multi-level stable control and objective carbon emission reduction evaluation based on net environmental load.

[0005] Therefore, a carbon emission reduction evaluation method for food waste treatment processes is proposed. Summary of the Invention

[0006] The purpose of this invention is to provide a carbon emission reduction evaluation method for food waste treatment processes. By establishing a dynamic accounting model for biogas operation balance, the escape greenhouse gas emissions during anaerobic digestion can be quantified in real time and accurately. Based on the calibrated biocarbon balance index, a carbon performance risk threshold value can be dynamically set to achieve multi-level automated linkage control of core process parameters such as feed load and pH value of the anaerobic digester, thereby improving the system's operational stability and final carbon emission reduction performance.

[0007] To achieve the above objectives, the present invention provides the following technical solution: A method for evaluating carbon emission reduction in food waste treatment processes, comprising: Obtain biogas production and utilization data, establish a dynamic accounting model for biogas operation balance, and calculate the dynamic balance index of the reactor based on real-time load input, gas production efficiency and process energy consumption data. Based on the dynamic balance index, the reverse correlation real-time correction algorithm is used to compare the actual biogas utilization with the theoretical output, reversely calculate the escape greenhouse gas emissions, and include the escape greenhouse gas emissions in the emission item to obtain the calibrated biocarbon balance index; the biocarbon balance index indicates the reactor's operational stability and pollutant emission status in real time. Based on the aforementioned biological carbon balance index, a carbon performance risk threshold for the reactor is dynamically set. This threshold is adjusted in real time according to changes in treatment load, reaction temperature, and feed composition. Real-time control commands for feed rate, pH value, and / or sludge return ratio are automatically triggered based on whether the reactor's carbon balance exceeds the threshold.

[0008] Preferably, the biogas production and utilization data includes the actual biogas output, the electricity generated from biogas for power generation, and the heat generated from biogas for heating; the biogas operation balance dynamic accounting model is established based on the material balance and energy balance principles of the anaerobic digestion process. By calculating the carbon emissions generated from material consumption, electricity consumption, and avoided emissions generated from biogas resource utilization per unit mass of kitchen waste in the reactor and subsequent biogas utilization stages, the dynamic balance index of the reactor is calculated.

[0009] Preferably, the dynamic balance index of the reactor is obtained by calculating the net total carbon load generated by the reactor per unit mass of food waste processed; the net total carbon load is equal to the total carbon emissions calculated based on real-time load input and process energy consumption data in the pretreatment stage, core digestion stage and biogas utilization stage of the anaerobic digestion process, minus the difference between the avoided emissions calculated based on biogas production and utilization data; the avoided emissions are positive terms, which characterize the net environmental load of the anaerobic digestion process in real time.

[0010] Preferably, the reverse correlation real-time correction algorithm includes the following steps: Based on real-time load input and reactor reaction temperature, the theoretical biogas production of the reactor under the current operating state is predicted; actual biogas utilization data is obtained, and the theoretical biogas production is compared with the actual biogas utilization data. The difference between the two is used to calculate the amount of escaped greenhouse gas emissions that have not been collected and utilized in real time. The escaped greenhouse gas emissions are included as an emission item and added to the net total carbon load to obtain the biocarbon balance index.

[0011] Preferably, the determination of the carbon performance risk threshold includes the following steps: The carbon performance risk threshold is obtained through dynamic calculation using a multi-parameter correlation function; the input variables of the multi-parameter correlation function include the processing load, reaction temperature, and real-time fluctuation range of the feed composition. Based on the results calculated by the multi-parameter correlation function, a safe operating range is set that allows fluctuations in the biological carbon balance index; the carbon performance risk threshold is the upper and lower boundaries of the safe range, used to define whether the anaerobic digestion system has entered an unstable state of acid production accumulation and / or methanation decline. The carbon performance risk threshold is mapped to two risk levels: a warning level and a mandatory intervention level, each corresponding to different levels of adjustment of process parameters.

[0012] Preferably, the carbon performance risk threshold is calculated using the multi-parameter correlation function, including the following steps: mapping the real-time fluctuations of the processing load, reaction temperature, and feed composition to load risk factor, temperature risk factor, and composition fluctuation factor, respectively; and assigning preset weights to the load risk factor, temperature risk factor, and composition fluctuation factor based on the sensitivity of the anaerobic digestion process to each parameter under different operating conditions. The three risk factors, after being weighted, are nonlinearly transformed and then summed to obtain the comprehensive operational risk index; historical operational data is then introduced as a correction term to dynamically correct the comprehensive operational risk index in real time. The carbon performance risk threshold is obtained by mapping the modified comprehensive operational risk index to a predetermined biological carbon balance index change threshold table; the biological carbon balance index change threshold table defines the maximum safe range of allowable fluctuations in the biological carbon balance index under different risk indices.

[0013] Preferably, the automatically triggered real-time control command is: When the biocarbon balance index reaches the warning level risk, a feedforward flexible adjustment command is automatically triggered; the feed rate is adjusted and / or process personnel are advised to conduct on-site inspections. When the biological carbon balance index reaches the mandatory intervention level risk, a feedback rigid control command is automatically triggered; the reaction temperature, pH value and sludge return ratio are automatically adjusted. If the feedforward flexible adjustment command fails to remove the biocarbon balance index from the critical zone within the set time, the feedback rigid control command will be executed automatically.

[0014] Preferably, the average value of the biocarbon balance index within a set period is compared with a preset historical baseline; based on the comparison result of the biocarbon balance index and the historical baseline, the additional carbon emission reduction achieved by the real-time control algorithm in the current operating period is quantified; according to the value of the additional carbon emission reduction, the carbon emission reduction performance of the food waste treatment process is divided into Grade A, Grade B and Grade C.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention utilizes a dynamic accounting model for biogas operation balance and a reverse correlation real-time correction algorithm to compare the actual biogas utilization with the theoretical output in real time. This reverse calculation of escaped greenhouse gas emissions is then included in the emission items, resulting in a calibrated biocarbon balance index. This fundamentally improves the accuracy of the net environmental load assessment for food waste treatment processes, providing a reliable basis for subsequent control decisions.

[0016] 2. This invention uses the biocarbon balance index as the core real-time diagnostic basis and dynamically sets the carbon performance risk threshold based on a multi-parameter correlation function, overcoming the lag of traditional control indicators. When the index approaches the threshold, a feedforward flexible adjustment for the feed rate is automatically triggered; feedback rigid control is only triggered when the index crosses the threshold. This multi-level linkage intervention mechanism ensures that the system maintains optimal environmental performance while providing early and flexible warnings and interventions for risks such as acidification collapse, greatly improving the stability and robustness of system operation.

[0017] 3. This method directly maps the process control effect (system steady state) to carbon emission reduction evaluation. By quantifying the additional carbon emission reduction achieved through this real-time control algorithm, it completes the classification of the carbon emission reduction performance of the treatment process. This allows operators to intuitively understand the contribution of real-time control strategies to environmental benefits, realizing a closed-loop feedback between control technology and environmental performance assessment, and providing quantitative targets for continuous process optimization. Attached Figure Description

[0018] Figure 1 This is a flowchart of a carbon emission reduction evaluation method for a food waste treatment process proposed in this invention; Figure 2 This is a schematic diagram illustrating the steps of a carbon emission reduction evaluation method for a food waste treatment process proposed in this invention. Figure 3 This is a flowchart of the reverse correlation real-time correction algorithm proposed in this invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Example 1 Please see Figures 1 to 3 This invention provides a method for evaluating carbon emission reduction in food waste treatment processes, the technical solution of which is as follows: A method for evaluating carbon emission reduction in food waste treatment processes, such as Figures 1-2 As shown, it includes: Obtain biogas production and utilization data, establish a dynamic accounting model for biogas operation balance, and calculate the dynamic balance index of the reactor based on real-time load input, gas production efficiency and process energy consumption data. Based on the dynamic balance index, the reverse correlation real-time correction algorithm is used to compare the actual biogas utilization with the theoretical output, reversely calculate the escape greenhouse gas emissions, and include the escape greenhouse gas emissions in the emission item to obtain the calibrated biocarbon balance index; the biocarbon balance index indicates the reactor's operational stability and pollutant emission status in real time. Based on the aforementioned biological carbon balance index, a carbon performance risk threshold for the reactor is dynamically set. This threshold is adjusted in real time according to changes in treatment load, reaction temperature, and feed composition. Real-time control commands for feed rate, pH value, and / or sludge return ratio are automatically triggered based on whether the reactor's carbon balance exceeds the threshold.

[0021] Furthermore, the biogas production and utilization data includes the actual biogas output, the electricity generated from biogas for power generation, and the heat generated from biogas for heating; the biogas operation balance dynamic accounting model is established based on the material balance and energy balance principles of the anaerobic digestion process. By calculating the carbon emissions generated from material consumption, electricity consumption, and avoided emissions generated from biogas resource utilization per unit mass of kitchen waste in the reactor and subsequent biogas utilization stages, the dynamic balance index of the reactor is calculated.

[0022] The biogas operation balance dynamic accounting model is based on a modified anaerobic digestion model and is used to dynamically track and calculate the carbon flow during the food waste treatment process. The model's input data structure includes: real-time feed load, real-time reactor temperature, real-time process power consumption, and real-time process heat consumption. The model first converts the real-time load input into biodegradable carbon input. Biogas production efficiency is obtained through an empirical function corrected for real-time temperature. The dynamic balance index (i.e., net total carbon load) is calculated based on the difference between total carbon emissions and avoided emissions. The total carbon emissions calculation covers: carbon emissions resulting from power and heat consumption in the pretreatment and core digestion stages; and carbon emissions resulting from power and heat consumption in the subsequent biogas utilization stage. Avoided emissions are calculated based on the actual biogas utilization (electricity used for power generation and heat generated for heating) multiplied by the corresponding emission reduction factor.

[0023] This invention establishes a dynamic accounting model for biogas operation balance based on the principle of material and energy balance, achieving precise quantification of the net environmental load throughout the entire process of food waste treatment. This model integrates multi-dimensional data such as real-time feed load, reaction temperature, and process energy consumption, dynamically tracks carbon flow, and improves the accuracy of gas production prediction through a temperature correction empirical function. By offsetting the direct carbon emissions at each stage with the avoided emissions generated from biogas resource utilization, this method overcomes the errors of traditional extensive estimations and can objectively and in real-time reflect the true carbon reduction performance of the process.

[0024] Furthermore, the dynamic balance index of the reactor is obtained by calculating the net total carbon load generated by the reactor for each unit mass of food waste processed. The net total carbon load is equal to the total carbon emissions calculated based on real-time load input and process energy consumption data during the pretreatment, core digestion, and biogas utilization stages of the anaerobic digestion process, minus the amount of emissions avoided calculated based on biogas production and utilization data. The amount of emissions avoided is a positive term, which represents the net environmental load of the anaerobic digestion process in real time.

[0025] Specifically, the volatile solids (VSS) content of the food waste is first obtained through online monitoring instruments at the feed inlet. This VSS content value is then multiplied by a preset theoretical methanogenic potential coefficient, and combined with a reaction temperature correction coefficient, to calculate the theoretical biogas yield. The theoretical methanogenic potential coefficient is set at 0.45 to 0.55 standard cubic meters of methane per kilogram of VSS. The reaction temperature correction coefficient is set to 1 when the reaction temperature is within the optimal range of 35 degrees Celsius or 55 degrees Celsius; if the temperature deviates from the optimal range, the coefficient decreases by 0.05 for every degree Celsius deviation.

[0026] The temperature correction factor uses a piecewise linear approximation, which is reliable only within a range of ±5°C (i.e., 30-40°C for the 35°C region, and 50-60°C for the 55°C region). This linear approximation is based on the fact that within this temperature range, the activity of key anaerobic digestion enzymes (such as aldolases in methyltrophic bacteria) is approximately linear with temperature. Beyond this range, a more refined nonlinear model, such as the Arrhenius equation, should be used.

[0027] The power and heat consumption of the pretreatment system, anaerobic digester mixing system, and biogas desulfurization system are collected in real time. The power consumption is multiplied by the regional power grid average carbon emission factor (e.g., 0.58 kg CO2 equivalent per kilowatt-hour), and the heat consumption is multiplied by the natural gas heating carbon emission factor. The two are then added together to obtain the total carbon emissions of the process.

[0028] Based on the principle of energy substitution, the amount of emissions avoided is the sum of the carbon emission benchmark factor for coal-fired power generation multiplied by the carbon emission benchmark factor for coal-fired power generation, and the carbon emission benchmark factor for carbon emission for carbon emission from coal-fired boilers multiplied by the carbon emission benchmark factor for carbon emission from ...

[0029] The total carbon load is calculated by adding the escaped greenhouse gas emissions (converted to CO2 equivalent) derived from the reverse correction algorithm to the total carbon emissions of the process, and then subtracting the avoided emissions.

[0030] This invention significantly improves the accuracy and objectivity of carbon emission reduction assessment for food waste treatment by constructing a refined net total carbon load accounting system. The method covers the entire process from pretreatment to biogas utilization, dynamically calculating theoretical yield by real-time monitoring of volatile solids content and introducing a temperature correction coefficient, combined with reverse-engineered escape emissions, effectively overcoming the errors of traditional static estimation.

[0031] Furthermore, such as Figure 3 As shown, the reverse correlation real-time correction algorithm includes the following steps: Based on real-time load input and reactor reaction temperature, the theoretical biogas production of the reactor under the current operating state is predicted; actual biogas utilization data is obtained, and the theoretical biogas production is compared with the actual biogas utilization data. The difference between the two is used to calculate the amount of escaped greenhouse gas emissions that have not been collected and utilized in real time. The escaped greenhouse gas emissions are included as an emission item and added to the net total carbon load to obtain the biocarbon balance index.

[0032] The reverse correlation real-time correction algorithm is used to accurately estimate uncollected escaped greenhouse gas emissions. The "reverse correlation" in this algorithm refers to using the data discrepancy between biogas production and utilization to inversely calculate abnormal losses at the reactor front end. Specifically, it involves: first, obtaining the theoretical cumulative biogas production for the current cycle. Actual cumulative utilization of biogas All units are standard cubic meters. Then calculate the amount of uncollected biogas. In the real-time correction step, taking into account the inherent errors in system monitoring and losses, the algorithm introduces an empirical correction factor. Leakage conversion factor Escape greenhouse gas emissions The calculation is based on the following logic: First, the amount of uncollected and unused materials... Subtracting the system average error, i.e. Then This is converted into methane leakage and multiplied by the corresponding greenhouse gas potential value to obtain... Among them, the correction factor This refers to the system's average physical loss and metering deviation, calculated based on historical operational data. (The sentence is incomplete and requires further context.) Among them, the correction factor This refers to the system's average physical loss and metering deviation, calculated based on historical operational data. (The sentence is incomplete and requires further context.) This is added to the net total carbon load to complete the calibration of the biocarbon balance index.

[0033] This invention proposes a reverse correlation real-time correction algorithm, effectively solving the technical bottleneck of directly monitoring and quantifying escaped greenhouse gases in anaerobic digestion systems. By comparing the theoretical biogas production with the actual utilization in real time and using empirical correction factors to eliminate inherent system errors, this method can accurately calculate the methane leakage amount. Adding this hidden emission to the total carbon load achieves dynamic calibration of the biomass carbon balance index. This not only avoids evaluation distortion caused by ignoring escaped emissions and ensures the accuracy and comprehensiveness of carbon reduction data, but also sensitively indicates the system's sealing status, providing a reliable basis for timely detection of equipment hazards.

[0034] Furthermore, the determination of the carbon performance risk threshold includes the following steps: The carbon performance risk threshold is obtained through dynamic calculation using a multi-parameter correlation function; the input variables of the multi-parameter correlation function include the processing load, reaction temperature, and real-time fluctuation range of the feed composition. Based on the results calculated by the multi-parameter correlation function, a safe operating range is set that allows fluctuations in the biological carbon balance index; the carbon performance risk threshold is the upper and lower boundaries of the safe range, used to define whether the anaerobic digestion system has entered an unstable state of acid production accumulation and / or methanation decline. The carbon performance risk threshold is mapped to two risk levels: a warning level and a mandatory intervention level, each corresponding to different levels of adjustment of process parameters.

[0035] Specifically, the process for determining the carbon performance risk threshold is as follows: Real-time data acquisition and processing of load, reaction temperature, and feed composition. "Real-time fluctuation amplitude" is defined as: the absolute value of the difference between the current sampling value and the moving average over the past 24 hours, divided by the moving average, to obtain the normalized volatility.

[0036] The multi-parameter correlation function employs either a weighted Euclidean distance algorithm or an exponentially weighted summation algorithm. In this embodiment, the exponentially weighted logic is used: first, preset weights are assigned to the volatility of the processing load, reaction temperature, and feed composition (recommended weights are 0.4, 0.4, and 0.2, respectively); then, each volatility is multiplied by its corresponding weight and summed; finally, this weighted sum is used as the input to the exponential function to calculate a dimensionless comprehensive risk index.

[0037] In this process, the weighting ratios allocated to the load risk factor (0.4), temperature risk factor (0.4), and composition fluctuation factor (0.2) are primarily based on the following technical logic: The sensitivity of various parameters to the anaerobic digestion process under different operating conditions was weighted. The study showed that drastic fluctuations in the treatment load were the direct cause of acid accumulation, while reaction temperature directly affected the enzyme activity of methanogenic microorganisms. Both had the most significant and direct impact on the system's carbon balance, and therefore each was assigned a high weight of 0.4.

[0038] Considering that fluctuations in feed composition (such as oil and cellulose content) may affect the gas production rate, but their impact on the risk of immediate system collapse usually has a certain lag, a relatively low weight of 0.2 is assigned.

[0039] Historical operational data is incorporated as a correction factor to dynamically adjust the comprehensive operational risk index in real time. Statistical analysis of system acidification and collapse precursors during long-term operation is used to continuously calibrate the contribution rate of each factor, ensuring the accuracy of risk assessment.

[0040] A baseline safety threshold (e.g., 1) is preset. When the calculated comprehensive risk index is less than the baseline safety threshold, the system is considered to be in a stable state, and the biocarbon balance index is allowed to fluctuate within the standard range. When the comprehensive risk index is greater than the baseline safety threshold but less than 1.5 times that threshold, it is defined as a warning-level risk. At this time, the carbon performance risk threshold shrinks, and the system triggers a feedforward flexible adjustment for the feed rate. When the comprehensive risk index exceeds 1.5 times the baseline safety threshold, it is defined as a mandatory intervention-level risk. At this time, the system is considered to have entered an unstable state of acid accumulation or methanation decline, and the system automatically triggers a feedback rigid control of the reaction temperature and pH value.

[0041] This invention significantly improves the robustness and control precision of anaerobic digestion systems by constructing a dynamic risk assessment mechanism based on multi-parameter correlation functions. This method quantifies load, temperature, and composition fluctuations in real time, calculates a comprehensive risk index, and dynamically adjusts the safety range, effectively overcoming the lag of traditional static indicators. By establishing a graded response mechanism with early warning and mandatory intervention levels, it achieves intelligent switching from "feedforward flexible adjustment" to "feedback rigid control": it can buffer shocks by adjusting feed in the early stages of risk and forcibly regulate environmental parameters to curb acidification during instability. This multi-dimensional and precise intervention ensures that the system maintains efficient and stable carbon reduction performance even under complex operating conditions.

[0042] Furthermore, the carbon performance risk threshold is calculated using the multi-parameter correlation function, including the following steps: mapping the real-time fluctuations of the processing load, reaction temperature, and feed composition to load risk factor, temperature risk factor, and composition fluctuation factor, respectively; and assigning preset weights to the load risk factor, temperature risk factor, and composition fluctuation factor based on the sensitivity of the anaerobic digestion process to each parameter under different operating conditions. The three risk factors, after being weighted, are nonlinearly transformed and then summed to obtain the comprehensive operational risk index; historical operational data is then introduced as a correction term to dynamically correct the comprehensive operational risk index in real time. The carbon performance risk threshold is obtained by mapping the modified comprehensive operational risk index to a predetermined biological carbon balance index change threshold table; the biological carbon balance index change threshold table defines the maximum safe range of allowable fluctuations in the biological carbon balance index under different risk indices.

[0043] The nonlinear summation calculation is implemented using a polynomial function to accurately reflect the nonlinear impact of multi-factor coupling on the stability of the anaerobic digestion system. The correction term, based on the average and standard deviation of key biochemical indicators over the past 24 hours, is used to correct for the interference of short-term high-frequency fluctuations on the comprehensive operational risk index. The corrected comprehensive operational risk index... This was then used to map onto a predetermined threshold table for changes in biological carbon balance indicators. This threshold table defines the thresholds at different... Under this value, the maximum safe range of fluctuation allowed for the biological carbon balance index ( ),in This is the critical value for operational stability risk.

[0044] This invention significantly improves the accuracy and anti-interference capability of carbon performance risk assessment by constructing a multi-parameter correlation function based on nonlinear coupling and historical correction. The method utilizes a nonlinear summation algorithm to accurately capture the multi-factor coupled impact of load, temperature, and composition fluctuations on the stability of anaerobic systems, avoiding the biases of linear assessments. Simultaneously, the introduction of a correction term based on historical data effectively filters out short-term high-frequency noise interference, reducing the false alarm rate.

[0045] Furthermore, the automatically triggered real-time control command is as follows: When the biocarbon balance index reaches the warning level risk, a feedforward flexible adjustment command is automatically triggered; the feed rate is adjusted and / or process personnel are advised to conduct on-site inspections. When the biological carbon balance index reaches the mandatory intervention level risk, a feedback rigid control command is automatically triggered; the reaction temperature, pH value and sludge return ratio are automatically adjusted. If the feedforward flexible adjustment command fails to bring the biocarbon balance index out of the critical zone within the set time (20-40 minutes is recommended), the feedback rigid control command will be executed automatically.

[0046] When the biocarbon balance index exceeds the safe range but still fluctuates within 10% of the critical value, it is defined as a warning-level risk. At this time, the feedforward flexible adjustment command is activated: the system adjusts the feed rate proportionally according to the degree to which the biocarbon balance index deviates from the safe range. For example, for every 1% deviation of the index, the feed rate is automatically reduced by 2% to mitigate the impact on the reactor load.

[0047] When the biological carbon balance index crosses the critical upper and lower boundaries, it is defined as a risk requiring mandatory intervention. At this point, a feedback-based rigid control command is activated: the system automatically and irreversibly adjusts the reaction temperature, pH value, and sludge return ratio. For example, if an acidification trend is detected (a sharp deterioration in the index), the command will immediately raise the pH value to the target stable value (e.g., 7.0) and increase the sludge return ratio (e.g., by 50%) to enhance buffering capacity and microbial activity. This control logic ensures that the system can intervene in risks rapidly and at multiple levels while achieving optimal environmental benefits.

[0048] This invention constructs a multi-level linkage control mechanism based on the bio-carbon balance index, which significantly improves the system's adaptive steady-state regulation capability. By distinguishing between early warning level and mandatory intervention level risks, the system achieves an organic combination of "feedforward flexible buffering" and "feedback rigid correction": in the early stage of risk, the feed is adjusted proportionally to smooth fluctuations and avoid excessive intervention; at the instability critical point, the pH value and reflux ratio are forcibly adjusted to quickly curb acidification collapse.

[0049] Furthermore, the average value of the biocarbon balance index within a set period is compared with a preset historical baseline; based on the comparison result between the biocarbon balance index and the historical baseline, the additional carbon emission reduction achieved by the real-time control algorithm in the current operating period is quantified; according to the value of the additional carbon emission reduction, the carbon emission reduction performance of the food waste treatment process is divided into Grade A, Grade B and Grade C.

[0050] Specifically, the rules for classifying the carbon emission reduction performance are as follows: First, calculate the additional carbon reduction rate R using the following formula: .in, For additional carbon emission reductions in the current cycle, This represents the total carbon emissions corresponding to a pre-set historical baseline.

[0051] Based on the calculated additional carbon emission reduction rate R, the classification is determined according to the following criteria: when When the result is satisfactory, it is classified as Grade A. This grade indicates that the real-time control algorithm significantly optimizes anaerobic digestion efficiency, greatly reduces escape emissions, and increases biogas production.

[0052] when At that time, it was judged as Grade B. This grade indicates that the control algorithm has played a significant optimization role, and the system is performing better than the historical average.

[0053] when If the system is rated C, it indicates that the control algorithm maintains system stability, but provides limited additional environmental benefits.

[0054] like If the result is not satisfactory, it is considered an invalid performance, indicating a possible serious equipment malfunction or model distortion, requiring immediate manual intervention.

[0055] This invention constructs a graded evaluation system for carbon emission reduction performance based on historical baselines, enabling quantitative assessment and visualized management of environmental benefits. By calculating the additional carbon emission reduction rate and classifying performance into three levels (A, B, and C), this method intuitively presents the specific contribution of real-time control algorithms to process optimization. This graded mechanism establishes a closed loop of "monitoring-evaluation-feedback": it not only quickly identifies system faults but also provides clear quantitative targets for continuous process optimization.

[0056] Example 2 This embodiment selects a kitchen waste anaerobic digestion treatment project with a daily processing capacity of 200 tons as a specific application scenario.

[0057] First, the volatile solids content of this batch of kitchen waste raw materials was measured to be 180 grams per kilogram using online monitoring instruments. The system then used a preset theoretical methane production potential coefficient of 0.50 standard cubic meters per kilogram of volatile solids, while simultaneously monitoring the real-time temperature inside the reactor at 34 degrees Celsius. Since this temperature deviated from the optimal temperature range of 35 degrees Celsius by 1 degree Celsius, the system automatically adjusted the temperature correction coefficient from 1 to 0.95 according to preset rules. Based on these parameters, the system calculated the theoretical biogas production of this batch of waste to be 17,100 standard cubic meters.

[0058] Regarding process energy consumption monitoring, the system recorded the consumption of 3000 kWh of electricity and 5 tons of steam during this operating cycle. The system multiplied the electricity consumption by the regional power grid average carbon emission factor of 0.58 kg CO2 equivalent per kWh and the steam consumption by the natural gas heating carbon emission factor of 200 kg CO2 equivalent per ton, calculating the total process carbon emissions to be 2740 kg CO2 equivalent. Simultaneously, the system monitored the actual amount of biogas collected and used for power generation as 16800 standard cubic meters. Based on the principle of energy substitution, the system calculated that the emissions avoided through biogas resource utilization amounted to 15000 kg CO2 equivalent.

[0059] The reverse correlation real-time correction algorithm was then activated, comparing the theoretical biogas production of 17,100 standard cubic meters with the actual utilization of 16,800 standard cubic meters, resulting in a difference of 300 standard cubic meters. After deducting the inherent system measurement error of 50 standard cubic meters determined based on historical operating data, it was determined that the remaining 250 standard cubic meters of methane had escaped. In this embodiment, this value was multiplied by the global warming potential of methane, 25, to calculate the escaped greenhouse gas emissions as 6,250 kg CO2 equivalent. The system added the escaped emissions of 6,250 to the total process carbon emissions of 2,740, and then subtracted the avoided emissions of 15,000, calculating the net total carbon load for the current cycle as -6,010 kg CO2 equivalent, indicating that the process achieved a net carbon sink.

[0060] In the risk control phase, this embodiment collected real-time fluctuation rates of 0.05 for the processing load, 0.02 for the reaction temperature, and 0.03 for the feed component oil content. Following a preset weighted logic, the system multiplied the processing load fluctuation rate by 0.4, the reaction temperature fluctuation rate by 0.4, and the feed component fluctuation rate by 0.2, and then weighted the sum to obtain a comprehensive operational risk index of 0.034. Since this value is far less than the preset baseline safety threshold of 1, the system determines that the reactor is in a safe and stable zone, maintains the current feed rate and stirring speed unchanged, and does not trigger a forced intervention command.

[0061] In the final performance evaluation phase, the system aggregated the monthly operational data and calculated that the cumulative additional carbon emission reduction achieved during the current operating cycle was 60 tons of CO2 equivalent, while the total carbon emissions calculated based on historical baselines were 300 tons of CO2 equivalent. The system calculated the additional carbon emission reduction rate to be 20% using a formula. Since this 20% figure is greater than the 15% requirement of the Grade A standard, the system ultimately rated the carbon emission reduction performance of the food waste treatment process for this operating cycle as Grade A and generated a corresponding excellent performance operation report.

[0062] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for evaluating carbon emission reduction in food waste treatment processes. Includes, characterized in that: Obtain biogas production and utilization data, establish a dynamic accounting model for biogas operation balance, and calculate the dynamic balance index of the reactor based on real-time load input, gas production efficiency and process energy consumption data. Based on the dynamic balance index, the reverse correlation real-time correction algorithm is used to compare the actual biogas utilization with the theoretical output, reversely calculate the escape greenhouse gas emissions, and include the escape greenhouse gas emissions in the emission item to obtain the calibrated biocarbon balance index; the biocarbon balance index indicates the reactor's operational stability and pollutant emission status in real time. Based on the aforementioned biological carbon balance index, a carbon performance risk threshold for the reactor is dynamically set. This threshold is adjusted in real time according to changes in treatment load, reaction temperature, and feed composition. Real-time control commands for feed rate, pH value, and / or sludge return ratio are automatically triggered based on whether the reactor's carbon balance exceeds the threshold.

2. The carbon emission reduction evaluation method for a food waste treatment process according to claim 1, characterized in that: The biogas production and utilization data include the actual biogas output, the electricity generated from biogas for power generation, and the heat generated from biogas for heating. The biogas operation balance dynamic accounting model is established based on the material balance and energy balance principles of the anaerobic digestion process. It calculates the dynamic balance index of the reactor by accounting for the carbon emissions generated by material consumption, the carbon emissions generated by electricity consumption, and the amount of emissions avoided by biogas resource utilization per unit mass of kitchen waste in the reactor and subsequent biogas utilization stages.

3. The carbon emission reduction evaluation method for a food waste treatment process according to claim 2, characterized in that: The dynamic balance index of the reactor is obtained by calculating the net total carbon load generated by the reactor for each unit mass of food waste processed. The net total carbon load is the total carbon emissions calculated based on real-time load input and process energy consumption data during the pretreatment, core digestion and biogas utilization stages of the anaerobic digestion process, minus the amount of emissions avoided calculated based on biogas production and utilization data. The avoided emissions are positive terms, representing the net environmental load of the anaerobic digestion process in real time.

4. The carbon emission reduction evaluation method for a food waste treatment process according to claim 3, characterized in that, The reverse correlation real-time correction algorithm includes the following steps: Based on real-time load input and reactor reaction temperature, the theoretical biogas production of the reactor under the current operating state is predicted; actual biogas utilization data is obtained, and the theoretical biogas production is compared with the actual biogas utilization data. The difference between the two is used to calculate the amount of escaped greenhouse gas emissions that have not been collected and utilized in real time. The escaped greenhouse gas emissions are included as an emission item and added to the net total carbon load to obtain the biocarbon balance index.

5. The carbon emission reduction evaluation method for a food waste treatment process according to claim 1, characterized in that, The determination of the carbon performance risk threshold includes the following steps: The carbon performance risk threshold is obtained through dynamic calculation using a multi-parameter correlation function; the input variables of the multi-parameter correlation function include the processing load, reaction temperature, and real-time fluctuation range of the feed composition. Based on the results calculated by the multi-parameter correlation function, a safe operating range is set that allows fluctuations in the biological carbon balance index; the carbon performance risk threshold is the upper and lower boundaries of the safe range, used to define whether the anaerobic digestion system has entered an unstable state of acid production accumulation and / or methanation decline. The carbon performance risk threshold is mapped to two risk levels: a warning level and a mandatory intervention level, each corresponding to different levels of adjustment of process parameters.

6. The carbon emission reduction evaluation method for a food waste treatment process according to claim 5, characterized in that, The carbon performance risk threshold is calculated using the multi-parameter correlation function, including the following steps: The real-time fluctuations of processing load, reaction temperature, and feed composition are mapped to load risk factor, temperature risk factor, and composition fluctuation factor, respectively; and the load risk factor, temperature risk factor, and composition fluctuation factor are assigned a preset weight according to the sensitivity of the anaerobic digestion process to each parameter under different operating conditions. The three risk factors, after being weighted, are nonlinearly transformed and then summed to obtain the comprehensive operational risk index; historical operational data is then introduced as a correction term to dynamically correct the comprehensive operational risk index in real time. The carbon performance risk threshold is obtained by mapping the modified comprehensive operational risk index to a predetermined biological carbon balance index change threshold table; the biological carbon balance index change threshold table defines the maximum safe range of allowable fluctuations in the biological carbon balance index under different risk indices.

7. The carbon emission reduction evaluation method for a food waste treatment process according to claim 1, characterized in that, The automatically triggered real-time control command is: When the biocarbon balance index reaches the warning level risk, a feedforward flexible adjustment command is automatically triggered; the feed rate is adjusted and / or process personnel are advised to conduct on-site inspections. When the biological carbon balance index reaches the mandatory intervention level risk, a feedback rigid control command is automatically triggered; the reaction temperature, pH value and sludge return ratio are automatically adjusted. If the feedforward flexible adjustment command fails to remove the biocarbon balance index from the critical zone within the set time, the feedback rigid control command will be executed automatically.

8. The carbon emission reduction evaluation method for a food waste treatment process according to claim 1, characterized in that: The average value of the biocarbon balance index within a set period is compared with a preset historical baseline. Based on the comparison result between the biocarbon balance index and the historical baseline, the additional carbon emission reduction achieved by the real-time control algorithm in the current operating period is quantified. According to the value of the additional carbon emission reduction, the carbon emission reduction performance of the food waste treatment process is divided into Grade A, Grade B and Grade C.

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