CN-122010300-A - Bean product wastewater treatment process optimization method and system based on digital twin

CN122010300ACN 122010300 ACN122010300 ACN 122010300ACN-122010300-A

Abstract

The invention discloses a digital twin-based bean product wastewater treatment process optimization method and system, particularly relates to the technical field of biological denitrification treatment of bean product processing wastewater, and is used for solving the secondary process problems that the existing carbon source addition control method only optimizes denitrification efficiency and neglects complex influence on microbial communities, foam, floating mud and the like, by acquiring real-time operation data and inputting the real-time operation data into a digital twin model comprising a water quality conversion and microbial community succession sub-model, synchronous prediction of denitrification efficiency values, denitrification foam formation risk values and inter-species competition imbalance risk values are realized on the basis of model simulation for different candidate carbon source addition strategies, further, the optimal strategy of comprehensive optimization of biological risks is selected from strategies meeting denitrification requirements to execute carbon source addition, the collaborative optimization of denitrification performance and system biological stability is realized, foam generation and microbial community imbalance are effectively inhibited, and the stable standard of effluent quality is ensured.

Inventors

CHENG JIANGHUA
XU YAYAN
WAN YAQIONG
WANG RONGPING
Zhang Sanchao
LI SHIRAN

Assignees

安徽省农业科学院农产品加工研究所

Dates

Publication Date: 20260512
Application Date: 20260415

Claims (10)

1. The optimizing method of the bean product wastewater treatment process based on digital twinning is characterized by comprising the following steps: S1, acquiring real-time operation data of a bean product wastewater treatment system; S2, inputting real-time operation data into a digital twin model; S3, setting a plurality of candidate carbon source adding strategies based on real-time operation data, wherein each candidate carbon source adding strategy comprises a candidate carbon source adding amount and a candidate carbon source adding rate mode, simulating a denitrification efficiency value and a denitrification foam formation risk value under each strategy by using a digital twin model, and establishing a mapping relation between the candidate carbon source adding strategy and the denitrification efficiency value and the denitrification foam formation risk value; S4, aiming at each candidate carbon source adding strategy, evaluating an inter-species competition imbalance risk value corresponding to the internal distribution structure change of the denitrifying bacteria caused by a candidate carbon source adding rate mode based on the simulation process of a digital twin model; S5, selecting a strategy which meets the preset denitrification efficiency requirement and is comprehensively optimal between the denitrification foam formation risk value and the inter-seed competition unbalance risk value from the candidate carbon source adding strategies based on the mapping relation and the inter-seed competition unbalance risk value as an optimized carbon source adding strategy; and S6, executing carbon source adding operation according to the optimized carbon source adding strategy.
2. The optimization method for a digital twin based bean product wastewater treatment process according to claim 1, wherein S1 comprises: Acquiring real-time water inlet parameters of a water inlet end of the bean product wastewater treatment system; acquiring real-time state parameters of a biological reaction tank in a bean product wastewater treatment system; And obtaining carbon source addition history data of the bean product wastewater treatment system.
3. The optimization method for a digital twin based bean product wastewater treatment process according to claim 1, wherein S2 comprises: inputting real-time water inlet parameters to a water inlet quality parameter interface of the digital twin model; inputting real-time state parameters to a biochemical reaction process parameter interface of the digital twin model; and inputting the carbon source addition history data to a carbon source addition recording interface of the digital twin model.
4. A method for optimizing a process for treating soy product wastewater based on digital twinning as claimed in claim 3, wherein the digital twinning model comprises: The water quality conversion dynamics sub-model is used for simulating ammonia nitrogen conversion, nitrification and denitrification reaction processes based on real-time water inlet parameters and carbon source addition historical data; The microbial community succession sub-model is used for simulating the growth, metabolism and population structure dynamics of the denitrification functional flora based on the real-time state parameters; the water quality conversion dynamics submodel is mutually coupled with the microbial community succession submodel, wherein the microbial metabolism activity parameter output by the microbial community succession submodel is used as a regulating factor of the denitrification reaction rate in the water quality conversion dynamics submodel.
5. The optimization method for a digital twin based bean product wastewater treatment process according to claim 1, wherein S3 comprises: Generating a plurality of candidate carbon source adding strategies comprising different candidate carbon source adding amounts and different candidate carbon source adding rate modes based on the real-time water inlet parameters and the carbon source adding historical data; inputting each candidate carbon source adding strategy into a digital twin model, and driving the digital twin model to simulate and execute a biochemical reaction process under the candidate carbon source adding strategy; obtaining the simulated total nitrogen concentration of the water output by the digital twin model to calculate a denitrification efficiency value, and obtaining a microorganism metabolism index generated by simulation to evaluate a denitrification foam formation risk value; and carrying out association record on each candidate carbon source adding strategy, the denitrification efficiency value and the denitrification foam formation risk value corresponding to the candidate carbon source adding strategy, and forming a mapping relation.
6. The method for optimizing a process for treating soybean product wastewater based on digital twinning according to claim 5, wherein the evaluation of the microorganism metabolic index on which the denitrification foam formation risk value depends comprises simulating the proportion of hydrophobic components and the secretion rate of the produced extracellular polymer.
7. The optimization method for a digital twin based bean product wastewater treatment process according to claim 1, wherein S4 comprises: aiming at each candidate carbon source adding strategy, obtaining denitrification functional flora simulation data output by the digital twin model in the biochemical reaction process of simulation execution of the candidate carbon source adding strategy; analyzing the relative abundance change trend and metabolic activity change trend of different denitrifying bacteria in a simulation period based on the denitrifying function flora simulation data; Calculating an index for representing the stability of the internal distribution structure of the denitrifying bacteria group as an inter-species competition imbalance risk value according to the relative abundance change trend and the metabolic activity change trend; the stability index of the internal distribution structure of the denitrifying bacteria group according to which the risk value of the competition unbalance among the species is calculated comprises the descending amplitude of the denitrifying bacteria genus diversity index in the simulation period and the dominant bacteria genus replacement frequency.
8. The optimization method for a digital twin based bean product wastewater treatment process according to claim 1, wherein S5 comprises: screening candidate carbon source adding strategies with denitrification efficiency values reaching preset denitrification efficiency requirements from all candidate carbon source adding strategies according to the mapping relation; Weighting calculation is carried out on the denitrification foam formation risk value and the inter-species competition unbalance risk value corresponding to each screened candidate carbon source adding strategy to obtain a comprehensive risk evaluation value; and selecting the candidate carbon source adding strategy with the minimum comprehensive risk evaluation value from the screened candidate carbon source adding strategies as an optimized carbon source adding strategy.
9. The optimization method for a digital twin based bean product wastewater treatment process according to claim 1, wherein S6 comprises: Extracting an optimized carbon source adding amount and an optimized carbon source adding rate mode from an optimized carbon source adding strategy; setting a total addition amount control parameter of carbon source addition equipment according to the optimized carbon source addition amount; Generating a rate control instruction of the carbon source adding equipment in an adding period according to the optimized carbon source adding rate mode; And driving a carbon source adding device to add a carbon source into the anoxic tank of the bean product wastewater treatment system according to the total adding amount control parameter and the speed control instruction.
10. A digital twin based bean product wastewater treatment process optimization system for implementing a digital twin based bean product wastewater treatment process optimization method as claimed in any one of claims 1-9, comprising the following modules: the data acquisition module is used for acquiring real-time operation data of the bean product wastewater treatment system; The model input module is used for inputting real-time operation data into the digital twin model; The strategy simulation module is used for setting a plurality of candidate carbon source adding strategies based on real-time operation data, wherein each candidate carbon source adding strategy comprises a candidate carbon source adding amount and a candidate carbon source adding rate mode, a digital twin model is utilized to simulate a denitrification efficiency value and a denitrification foam formation risk value under each strategy, and a mapping relation between the candidate carbon source adding strategy and the denitrification efficiency value and the denitrification foam formation risk value is established; the risk evaluation module is used for evaluating an inter-species competition imbalance risk value corresponding to the internal distribution structure change of the denitrifying bacteria caused by the candidate carbon source adding rate mode based on the simulation process of the digital twin model aiming at each candidate carbon source adding strategy; The strategy optimization module is used for selecting a strategy which meets the preset denitrification efficiency requirement and is comprehensively optimal in the denitrification foam formation risk value and the inter-seed competition unbalance risk value from the candidate carbon source adding strategies based on the mapping relation and the inter-seed competition unbalance risk value as an optimized carbon source adding strategy; and the carbon source adding module is used for executing carbon source adding operation according to the optimized carbon source adding strategy.

Description

Bean product wastewater treatment process optimization method and system based on digital twin Technical Field The invention relates to the technical field of biological denitrification treatment of bean product processing wastewater, in particular to a digital twin-based bean product wastewater treatment process optimization method and system. Background The waste water from bean product processing contains high concentration of organic matter and ammonia nitrogen, and is treated by anaerobic-anoxic-aerobic biological denitrification process, so as to meet strict total nitrogen discharge requirement of effluent, and the carbon nitrogen ratio required in denitrification process is generally required to be maintained by adding external carbon source in engineering practice. At present, the control of carbon source addition is mainly set according to real-time monitoring or experience of limited parameters such as influent ammonia nitrogen, nitrate nitrogen and the like, and aims to realize high-efficiency removal of nitrogen with the lowest possible carbon source consumption. However, the control mode only takes the stoichiometric efficiency of denitrification as a core optimization target, ignores the complex influence of a carbon source adding strategy on the metabolic behavior of a microbial community, and in a bean product wastewater treatment scene, the dynamic adding of a carbon source easily causes the excessive proliferation and metabolic conversion of denitrifying bacteria groups, so that a large amount of hydrophobic extracellular polymers are secreted to form intractable biochemical foam, the mud in a secondary sedimentation tank is caused to seriously damage the solid-liquid separation effect, and conversely, the suspended matters in the effluent and the total nitrogen concentration exceed the standard. Disclosure of Invention In order to overcome the defects in the prior art, the invention provides a bean product wastewater treatment process optimization method and system based on digital twinning, which are used for solving the problems in the background art. In order to achieve the above purpose, the present invention provides the following technical solutions: a bean product wastewater treatment process optimization method based on digital twinning comprises the following steps: S1, acquiring real-time operation data of a bean product wastewater treatment system; S2, inputting real-time operation data into a digital twin model; S3, setting a plurality of candidate carbon source adding strategies based on real-time operation data, wherein each candidate carbon source adding strategy comprises a candidate carbon source adding amount and a candidate carbon source adding rate mode, simulating a denitrification efficiency value and a denitrification foam formation risk value under each strategy by using a digital twin model, and establishing a mapping relation between the candidate carbon source adding strategy and the denitrification efficiency value and the denitrification foam formation risk value; S4, aiming at each candidate carbon source adding strategy, evaluating an inter-species competition imbalance risk value corresponding to the internal distribution structure change of the denitrifying bacteria caused by a candidate carbon source adding rate mode based on the simulation process of a digital twin model; S5, selecting a strategy which meets the preset denitrification efficiency requirement and is comprehensively optimal between the denitrification foam formation risk value and the inter-seed competition unbalance risk value from the candidate carbon source adding strategies based on the mapping relation and the inter-seed competition unbalance risk value as an optimized carbon source adding strategy; and S6, executing carbon source adding operation according to the optimized carbon source adding strategy. Further, S1 includes: Acquiring real-time water inlet parameters of a water inlet end of the bean product wastewater treatment system; acquiring real-time state parameters of a biological reaction tank in a bean product wastewater treatment system; And obtaining carbon source addition history data of the bean product wastewater treatment system. Further, S2 includes: inputting real-time water inlet parameters to a water inlet quality parameter interface of the digital twin model; inputting real-time state parameters to a biochemical reaction process parameter interface of the digital twin model; and inputting the carbon source addition history data to a carbon source addition recording interface of the digital twin model. Further, the digital twin model includes: The water quality conversion dynamics sub-model is used for simulating ammonia nitrogen conversion, nitrification and denitrification reaction processes based on real-time water inlet parameters and carbon source addition historical data; The microbial community succession sub-model is used for simulating the growth, metabolis