CN114093434A - Soft measurement method for components and concentrations of organic waste hydrolysis acidification volatile fatty acid - Google Patents
Soft measurement method for components and concentrations of organic waste hydrolysis acidification volatile fatty acid Download PDFInfo
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Abstract
The invention provides a soft measurement method for components and concentration of organic waste hydrolysis acidification volatile fatty acid, belonging to the field of organic waste two-phase anaerobic digestion, and determining experimental parameters related to yield of the volatile fatty acid beneficial to hydrolysis acidification by using an orthogonal optimization experiment; taking the yield of the volatile fatty acid of the hydrolytic acidification phase as a variable, and establishing a function regression equation by using a polynomial function fitting method; the method adopts a multiple linear regression model, has obvious advantages in processing the high-dimensional problem of small samples, provides an online continuous detection method for rapidly evaluating the running condition of the hydrolytic acidification phase, and has important significance in improving the monitoring level and the running effect of the hydrolytic acidification phase.
Description
Technical Field
The invention relates to the field of two-phase anaerobic digestion of organic waste, in particular to a soft measurement method for components and concentration of volatile fatty acid in hydrolytic acidification of organic waste.
Background
In recent years, with the continuous improvement of the agricultural economic level of China, the rural energy structure is gradually changed, the use population of commercial energy is increased, the utilization rate of straws is obviously in a descending trend, organic solid wastes such as straws, livestock and poultry manure and the like are excessively regional, seasonal and structural, most of the wastes are randomly burnt, discarded or directly discharged into the environment, so that the environmental pollution and the resource waste are caused, and the global warming is increased. Among the three renewable natural energy sources (solar energy, wind energy, biomass), biomass is the only physical energy source, with the greatest storability. Organic wastes such as crop straws, livestock and poultry manure and the like are subjected to anaerobic digestion to produce biogas, so that rural domestic energy can be replaced, the pressure on the environment is reduced, the biogas digester can be used for heat supply and power generation, the situation of shortage of energy supply and demand in China is relieved, and the way of solving the energy problem in China is expanded; meanwhile, the biogas residues can partially replace chemical fertilizers and pesticides, so that the using amount of the chemical fertilizers and the pesticides is reduced, the safety production of agricultural products is ensured, and the quality and the market competitiveness of the agricultural products are improved.
Volatile Fatty Acid (VFA) refers to fatty acid with 1-6 carbon atoms, including formic acid, acetic acid, propionic acid, butyric acid, valeric acid, caproic acid and isomers thereof, and is widely found in nature. They have the common feature of being readily soluble in water and highly volatile, and are therefore collectively referred to as volatile fatty acids. In the two-phase anaerobic fermentation process, whether the acid-producing phase can provide a proper substrate for the subsequent methane-producing phase is related to the stable operation of the whole system, and the volatile fatty acid is an important end product of the acid-producing phase, is an important link for connecting the acid-producing phase and the methane-producing phase and is an important evaluation index for the operation condition of the hydrolysis acidification phase, so that the sensitive and rapid analysis method of the VFAs is very necessary for controlling the operation of the anaerobic reactor. For the determination of VFAs, gas chromatography is mainly used at present, but the price of the gas chromatography is high, online measurement cannot be realized, and the cost is increased invisibly when the gas chromatography is used for an anaerobic digestion process. Bouvier et al propose an on-line titration detection method report, but the membrane filtration step is needed, and membrane pollution is easy to generate, so that the method has certain limitations; the connection of the gas chromatograph with autosampler and the reactor allows for accurate online measurement of VFAs, but is expensive; steyer et al propose a method for on-line measurement of parameters such as VFAs, COD, etc. in the anaerobic digestion process based on fourier transform infrared spectroscopy; munch et al estimate the VFAs concentration directly by measuring the pH, but under a number of assumptions. Therefore, an on-line measurement method for the concentration of the volatile fatty acid in the hydrolysis acidification phase is necessary.
Disclosure of Invention
In view of the problems in the background art, the present invention provides a soft measurement method for the components and concentrations of organic waste hydrolysis acidification volatile fatty acids, comprising:
step 1: determining experimental parameters related to the yield of the volatile fatty acid in the hydrolytic acidification phase by using an orthogonal optimization experiment;
step 2: performing a two-phase anaerobic digestion experiment on the organic waste, taking the yield of the volatile fatty acid of the hydrolytic acidification phase as a target amount, and drawing a change curve between the target amount and each experiment parameter at different moments by using an orthogonal optimization experiment;
and step 3: according to the obtained change curve, respectively using each experiment parameter xiUsing the yield y of the volatile fatty acid in the hydrolytic acidification phase as a dependent variable and a polynomial function fitting method to establish a function regression equation F1 (x)i,y)；
And 4, step 4: obtaining the yield y (x) of the volatile fatty acid of the hydrolytic acidification phase under different experimental conditions by changing experimental parametersiT) and regression with a function F1 (x)iY) calculating the yield value y' (x) of the volatile fatty acids of the hydrolyzed and acidified phaseiT) comparison, proof function regression equation F1 (x)iY) effectiveness;
and 5: in the experimental conditions set in step 4, the effective function was regressed to equation F1 (x)iY) corresponding experimental conditions as valid experimental conditions;
step 6: obtaining a component G (i) of the volatile fatty acid of the hydrolytic acidification phase by using the effective experimental conditions obtained in the step 5, taking the component G (i) of the volatile fatty acid of the hydrolytic acidification phase as a dependent variable and taking each experimental parameter xiBuilding a multiple linear regression equation F1 (x) for the independent variablesiG (i)), then equation F1 (x) is regressediG (i)) is an online soft measurement model of the concentration of the volatile fatty acid in the hydrolytic acidification phase.
The experimental parameters comprise temperature, pH value, oxidation-reduction potential ORP value, conductivity and total microorganism concentration.
The organic waste was subjected to two-phase anaerobic digestion experiments comprising:
step 2.1: crushing the air-dried corn straws into powder by a crusher, fully mixing the powder with pig manure to keep the water content between 60 and 70 percent, carrying out stack retting pretreatment for N days, and carrying out the whole stack retting process in a constant-temperature water bath box;
step 2.2: after pretreatment, adding water to obtain feed liquid with different concentrations and uniformly stirring;
step 2.3: discharging when the pH value begins to rise after the pH value is lowered to the lowest point, and then adding alkali liquor to adjust the pH value;
step 2.4: pumping the acid liquor into a UASB anaerobic digester, and controlling the temperature within a preset range;
step 2.5: methane is generated by the action of methanogens in the sludge and the feed liquid, and then gas is collected by a saturated salt water discharging method.
The step 4 is expressed as: when y (x)i,t)-y'(xiWhen t) is less than or equal to delta, the function regression equation F1 (x) is considerediY) has validity, δ is the set error value.
The invention has the beneficial effects that:
the invention provides a soft measurement method for components and concentration of organic waste hydrolysis acidification volatile fatty acid, 1) the adopted data is obtained based on dynamic simulation experiment, the dynamic experiment working medium and the operation working condition are completely the same as the working medium and the operation working condition of actual equipment, and the credibility of the experiment result applied to engineering equipment can be increased; 2) determining the influence degree of each factor on the volatile fatty acid of the hydrolytic acidification phase by optimizing experimental design; 3) a multiple linear regression model is adopted, and the model has obvious advantages in processing the high-dimensional problem of small samples; 4) the method realizes the prediction of the concentration of the volatile fatty acid in the hydrolytic acidification phase under the condition of a small sample, and is simple and easy to implement, and the result is reliable; 5) the method provides a new on-line continuous detection method for rapidly evaluating the running condition of the hydrolysis acidification phase, and has important significance for improving the monitoring level and the running effect of the hydrolysis acidification phase.
Drawings
FIG. 1 is a diagram of an experimental set-up of a two-phase anaerobic digestion system for organic waste in this example;
FIG. 2 is a polynomial fit curve of pH and acetic acid in this example;
FIG. 3 is a polynomial fit curve of pH and propionic acid in this example;
FIG. 4 is a polynomial fit curve of pH and butyric acid in the present example;
FIG. 5 is a polynomial fit curve of pH and isobutyric acid in this example;
FIG. 6 is a polynomial fit curve of pH and valeric acid in this example;
FIG. 7 is a polynomial fit curve of pH and isovaleric acid in this example;
FIG. 8 is a polynomial fit curve of the conductivity and acetic acid in the present example;
FIG. 9 is a polynomial fit curve of conductivity and propionic acid in this example;
FIG. 10 is a polynomial fit curve of the conductivity and butyric acid in the present example;
FIG. 11 is a polynomial fit curve of the conductivity and isobutyric acid in this example;
FIG. 12 is a polynomial fit curve of conductivity and valeric acid in this example;
FIG. 13 is a polynomial fit curve of conductivity and isovaleric acid in this example;
FIG. 14 is a polynomial fit curve of ORP and acetic acid in this example;
FIG. 15 is a polynomial fit curve of ORP and propionic acid in this example;
FIG. 16 is a polynomial fit curve of ORP and butyric acid in the present example;
FIG. 17 is a polynomial fit curve of ORP and isobutyric acid in this example;
FIG. 18 is a polynomial fit curve of ORP and pentanoic acid in this example;
FIG. 19 is a polynomial fit curve of ORP and isovaleric acid in this example;
FIG. 20 is a polynomial fit curve of temperature and acetic acid in the present example;
FIG. 21 is a polynomial fit curve of temperature and propionic acid in the present example;
FIG. 22 is a polynomial fit curve of temperature and butyric acid in the present example;
FIG. 23 is a polynomial fit curve of temperature and isobutyric acid in this example;
FIG. 24 is a polynomial fit curve of temperature and valeric acid in this example;
FIG. 25 is a polynomial fit curve of temperature and isovaleric acid in this example;
FIG. 26 is a polynomial fit curve of total microbial concentration and acetic acid in this example;
FIG. 27 is a polynomial fit curve of the total concentration of microorganisms and propionic acid in this example;
FIG. 28 is a polynomial fit curve of total microbial concentration and butyric acid in the present example;
FIG. 29 is a polynomial fit curve of total microbial concentration and isobutyric acid in this example;
FIG. 30 is a polynomial fit curve of total microbial concentration and valeric acid in this example;
FIG. 31 is a polynomial fit curve of total microbial concentration and isovaleric acid in this example;
in fig. 1: 1. the device comprises a magnetic stirrer, 2, a hydrolysis acidification tank, 3, a feed inlet, 4, a temperature sensor, 5, a pH sensor, 6, an ORP sensor, 7, a constant-temperature water bath tank, 8, a valve, 9-1, 9-2, 9-3, a feed liquid circulating pump, 10, a chemical analysis sampling port, 11, an acid liquid tank, 12, a regulating liquid inlet, 13, an acid liquid regulating tank, 14, a peristaltic pump, 15, a UASB anaerobic digester, 16, a heating coil, 17, a hot water circulating pump, 18, a hot water heater, 19, a gas storage tank, 20, a measuring cylinder, 21, a data acquisition card, 22 and an industrial control computer.
Detailed Description
The following is a detailed description of the technical solution of the present invention with reference to the accompanying drawings. The invention provides an on-line soft measurement method for the concentration of each component of hydrolyzed acidification phase volatile fatty acid, which is simple and feasible and has reliable results, and the method comprises the following steps: determining the temperature, pH value, oxidation-reduction potential ORP value, conductivity and total microbial concentration which are related to the yield of the volatile fatty acid in the hydrolytic acidification phase by utilizing an orthogonal optimization experiment; taking the yield of the volatile fatty acid in the hydrolytic acidification phase as a target quantity, and drawing a change curve of the target quantity and the temperature, the pH value, the oxidation-reduction potential ORP value, the conductivity and the total concentration of microorganisms at the corresponding moment by utilizing an orthogonal optimization experiment; based on the change curve obtained in the process, a function regression equation is established by using a polynomial function fitting method by respectively taking the temperature, the pH value, the oxidation-reduction potential ORP value, the conductivity and the total concentration of microorganisms as independent variables and the yield of the volatile fatty acid in the hydrolytic acidification phase as variables; through experiments of adjusting different temperatures, pH values, oxidation-reduction potential ORP values, conductivity and total microbial concentration, the yield of the volatile fatty acid in the hydrolytic acidification phase under corresponding experimental conditions is measured, and is compared with a polynomial function regression equation result obtained through fitting, and the rationality of the polynomial function regression equation of the yield of the volatile fatty acid in the hydrolytic acidification of the organic waste is verified; and measuring the components of the hydrolyzed acidification phase volatile fatty acid of the experimental results under different conditions, establishing a multiple linear regression equation by taking the components of the hydrolyzed acidification phase volatile fatty acid as variables and taking the temperature, the pH value, the oxidation-reduction potential ORP value, the conductivity and the total microbial concentration as independent variables, wherein the multiple linear regression equation is the online soft measurement model of the hydrolyzed acidification phase volatile fatty acid concentration.
As shown in figure 1 of the experimental apparatus of two-phase anaerobic digestion system of organic waste, the feed inlet 3 delivery end of the raw material is communicated with the input end of the hydrolysis acidification tank 2, the output end of the hydrolysis acidification tank 2 is communicated with the input end of the acid liquor tank 11 through the feed liquid circulating pump 9-1, the flow of the raw material input into the acid liquor tank 11 is controlled by the valve 8, the output end of the acid liquor tank 11 is communicated with the input end of the acid liquor adjusting tank 13 through the feed liquid circulating pump 9-3, the output end of the acid liquor adjusting tank 13 is communicated with the input end of the upflow anaerobic sludge bed (UASB anaerobic digester) 15 through the peristaltic pump 14, the output end of the UASB anaerobic digester 15 is communicated with the input end of the gas storage tank 19, the output end of the gas storage tank 19 is communicated with the input end of the measuring cylinder 20, the hot water heater 18 is communicated with the input end of the heating coil 16 through the hot water circulating pump 17, the output end of the heating coil 16 is connected with the hot water heater 18, the top of the UASB anaerobic digester 15 is communicated with the gas storage tank 19, the air reservoir 19 is connected to the measuring cylinder.
The hydrolysis acidification tank 2 is a hydrolysis acidification device provided with external heating and magnetic stirring, a constant temperature water bath tank 7 is arranged outside the hydrolysis acidification tank 2 to keep the temperature in the hydrolysis acidification tank 2 constant, a magnetic stirrer 1 is arranged in the hydrolysis acidification tank to fully stir so as to fully mix the feed liquid in the tank, a temperature sensor 4, a pH sensor 5 and an ORP sensor 6 are arranged on the hydrolysis acidification tank 2, and the experimental data acquired by the sensors are transmitted to an industrial control computer 22 through a data acquisition card 21 and are used for acquiring the experimental data.
The acid liquor adjusting tank 13 adjusts the pH value of the acid liquor through the adjusting liquor inlet 12, and the pH value is monitored through the pH sensor 5.
The UASB anaerobic digester 15 is an anaerobic digestion device provided with an external heating and heat insulation sleeve, and in order to improve the anaerobic digestion temperature in the methane anaerobic digester and improve the gas production rate, a heating coil 16 is arranged outside the UASB anaerobic digester 15 to keep the temperature in the UASB anaerobic digester 15 basically stable, and the temperature can be monitored by a temperature sensor 4.
The method adopts a medium-high temperature anaerobic digestion process, an acid-producing phase reactor is a completely mixed reactor (CSTR) and is connected with an acid liquor tank 11, the acid liquor tank 11 is connected with an acid liquor adjusting tank 13, the acid liquor adjusting tank 13 and the methane-producing phase reactor are Upflow Anaerobic Sludge Bed (UASB) through a peristaltic pump 14, and the two reactors are both made of organic glass.
A method for soft measurement of the components and concentrations of organic waste hydrolysis acidification volatile fatty acid, which is performed by using an organic waste two-phase anaerobic digestion system experimental device shown in figure 1, comprises the following steps:
step 1: determining experimental parameters related to the yield of the volatile fatty acid in the hydrolytic acidification phase by using an orthogonal optimization experiment; the experimental parameters comprise temperature, pH value, oxidation-reduction potential ORP value, conductivity and total microbial concentration;
the working condition which is favorable for the maximum output of the volatile fatty acid of the hydrolytic acidification phase is determined by utilizing an orthogonal optimization experiment, and the maximum output of the volatile fatty acid is determined as the optimal working condition by analyzing and determining through the orthogonal experiment under the working condition that the initial pH value is 7, the concentration of the feed liquid is 8 percent, and the carbon-nitrogen ratio is 25 in a dynamic simulation experiment.
Step 2: performing a two-phase anaerobic digestion experiment on the organic waste, taking the yield of the volatile fatty acid of the hydrolytic acidification phase as a target amount, and drawing a change curve between the target amount and each experiment parameter at different moments by using an orthogonal optimization experiment;
determining a two-phase anaerobic digestion dynamic simulation experiment according to the optimal working condition by taking the yield of the volatile fatty acid in the hydrolytic acidification phase as a target amount and taking the initial pH value, the conductivity and the total microbial concentration of the volatile fatty acid in the hydrolytic acidification phase as influence factors;
the experiment is carried out under the optimal working condition, eight groups of data in the experiment process are selected, the pH, the conductivity, the ORP, the temperature and the total concentration of microorganisms are selected as input quantities, the concentration of each component of the VFA is selected as output quantity, and the multiple linear regression analysis equation is
Yi＝A0+A1X1+A2X2+A3X3+A4X4+A5X5
Wherein, YiFor hydrolysis of the different components of the acidified phase of the volatile fatty acids, A0、A1、A2、A3、A4、A5Is a constant number, X0、X1、X2、X3、X4、X5Respectively pH, conductivity, ORP, temperature and total microbial concentration, and establishing a volatile fatty acid component concentration prediction model based on a multiple linear regression equation as follows:
Y1＝196.247-30.3746X1-10.7019X2+3.6638X3+5.6397×10-8X4+0.1958X5
Y2＝51.2838-15.4599X1-3.4371X2+3.1263X3+5.3514×10-8X4+0.1511X5
Y3＝-5.4413-9.2778X1+1.6280X2+2.3868X3+4.8191×10-8X4+0.1141X5
Y4＝-5.4413-9.2778X1+1.6280X2+2.3868X3+4.8191×10-8X4+0.1141X5
Y5＝331.2287-34.7053X1-8.3815X2-0.2111X3+1.3561×10-8X4+0.0049X5
Y6＝391.7939-43.6517X1-7.6136X2-0.2532X3+4.6375×10-8X4+0.0198X5
in the formula: y is1Is acetic acid; y is2Is propionic acid; y is3Is isobutyric acid; y is4Is butyric acid; y is5Is isovaleric acid; y is6Is valeric acid; x1Is pH; x2Is the electrical conductivity; x3Is the temperature; x4The total concentration of the microorganisms; x5Is ORP.
And step 3: according to the obtained change curve, respectively using each experiment parameter xiUsing the yield y of the volatile fatty acid in the hydrolytic acidification phase as a dependent variable and a polynomial function fitting method to establish a function regression equation F1 (x)i,y)；
Performing polynomial fitting analysis on experimental data obtained by the two-phase anaerobic digestion dynamic simulation experiment, and determining final variables, wherein the final variables comprise: volatile fatty acid component concentrations, pH, conductivity, total microbial concentration, ORP, and temperature, where the polynomial fit curve of pH and acetic acid is shown in fig. 2, the residual is 0.91654; the polynomial fit curves of pH and propionic acid are shown in fig. 3, with a residual of 0.24571; the polynomial fit curves of pH and butyric acid are shown in fig. 4, with a residual of 0.22311; a polynomial fit curve of pH and isobutyric acid is shown in fig. 5, with a residual of 0.22311; the polynomial fit curves for pH and valeric acid are shown in fig. 6, with a residual of 1.6754; the polynomial fit curves of pH and isovaleric acid are shown in fig. 7, with a residual of 0.96968; a polynomial fit curve of conductivity and acetic acid is shown in fig. 8, with a residual of 0.008735; the conductivity and propionic acid polynomial fit curves are shown in fig. 9, with a residual of 0.0026832; the conductivity and butyric acid polynomial fit curves are shown in fig. 10, and the residual is 0.0020851; the polynomial fit curve of conductivity and isobutyric acid is shown in FIG. 11, with a residual of 0.0020851; the polynomial fit curve for conductivity and pentanoic acid is shown in fig. 12, with a residual of 0.0063472; the polynomial fit curve of conductivity and isovaleric acid is shown in FIG. 13, with a residual of 0.0036273; polynomial fit curves of ORP and acetic acid are shown in FIG. 14, with residuals 6.1682 e-09; the polynomial fit curves of ORP and propionic acid are shown in FIG. 15, with residuals 4.7543 e-09; the polynomial fit curves of ORP and butyric acid are shown in FIG. 16, and the residual is 2.5304 e-09; a polynomial fit curve of ORP and isobutyric acid is shown in FIG. 17, with residuals 2.5304 e-09; the polynomial fit curves for ORP and valeric acid are shown in FIG. 18, with residuals 2.1404 e-09; the polynomial fitting curves of ORP and isovaleric acid are shown in FIG. 19, and the residual is 4.0354 e-09; the polynomial fit curves of temperature and acetic acid are shown in FIG. 20, with residuals 1.0396 e-07; the polynomial fit curves of temperature and propionic acid are shown in FIG. 21, with a residual of 2.467 e-06; the polynomial fit curves of temperature and butyric acid are shown in FIG. 22, and the residual is 1.5484 e-06; a polynomial fit curve of temperature and isobutyric acid is shown in FIG. 23, with residuals 1.5484 e-06; the polynomial fit curves of temperature and valeric acid are shown in FIG. 24, with residuals 1.5966 e-07; the polynomial fitting curve of temperature and isovaleric acid is shown in FIG. 25, and the residual error is 2.9624 e-06; the polynomial fitting curve of the total concentration of the microorganisms and the acetic acid is shown in FIG. 26, and the residual error is 4.0257 e-08; the polynomial fitting curve of the total concentration of the microorganisms and the propionic acid is shown in FIG. 27, and the residual error is 4.01 e-08; the polynomial fitting curve of the total concentration of the microorganisms and the butyric acid is shown in FIG. 28, and the residual error is 1.7981 e-08; the polynomial fit curve of total microbial concentration and isobutyric acid is shown in FIG. 29, with residuals 1.7981 e-08; the polynomial fit curve of total microbial concentration and valeric acid is shown in FIG. 30, and the residual is 1.1945 e-08; a polynomial fit curve of total microbial concentration and isovaleric acid is shown in FIG. 31, with residuals 2.5508 e-08.
And 4, step 4: obtaining the yield y (x) of the volatile fatty acid of the hydrolytic acidification phase under different experimental conditions by changing experimental parametersiT) and regression with a function F1 (x)iY) calculated hydrolysis acidification phaseYield value y' (x) of volatile fatty acidsiT) comparison, proof function regression equation F1 (x)iY) effectiveness; when y (x)i,t)-y'(xiWhen t) is less than or equal to delta, the function regression equation F1 (x) is considerediY) has validity, δ is a set error value;
and 5: in the experimental conditions set in step 4, the effective function was regressed to equation F1 (x)iY) corresponding experimental conditions as valid experimental conditions;
step 6: obtaining a component G (i) of the volatile fatty acid of the hydrolytic acidification phase by using the effective experimental conditions obtained in the step 5, taking the component G (i) of the volatile fatty acid of the hydrolytic acidification phase as a dependent variable and taking each experimental parameter xiBuilding a multiple linear regression equation F1 (x) for the independent variablesiG (i)), then equation F1 (x) is regressediG (i)) is an online soft measurement model of the concentration of the volatile fatty acid in the hydrolytic acidification phase.
Establishing an online soft measurement model of the concentration of the volatile fatty acid in the hydrolytic acidification phase based on a soft measurement method of multiple linear regression, checking a prediction result of the model, comparing the prediction result with an international method Q/YZJ10-03-02-2000 for determining the concentration of the volatile fatty acid, and verifying the rationality of the soft measurement method for the components and the concentration of the volatile fatty acid in the hydrolytic acidification of the organic waste.
The organic waste was subjected to two-phase anaerobic digestion experiments comprising:
step 2.1: crushing air-dried corn straws into powder by a crusher, fully mixing the powder with pig manure to keep the water content between 60 and 70 percent, carrying out stack retting pretreatment for N days, and carrying out the whole stack retting process in a constant-temperature water bath box at 30 ℃;
step 2.2: after pretreatment, adding water to obtain feed liquid with different concentrations and uniformly stirring;
step 2.3: discharging when the pH value begins to rise after the pH value is lowered to the lowest point, and then adding alkali liquor to adjust the pH value;
step 2.4: pumping the acid liquor into a UASB anaerobic digester, and controlling the temperature within 54 +/-1 ℃;
step 2.5: methane is generated by the action of methanogens in the sludge and the feed liquid, and then gas is collected by a saturated salt water discharging method.
The experimental device of the organic waste two-phase anaerobic digestion system shown in fig. 1 is used for carrying out experiments, and the specific experimental process for collecting experimental data is as follows: crushing the air-dried corn straws into powder by a crusher, fully mixing the powder with pig manure to keep the water content at 60-70%, and carrying out stack retting pretreatment for four days, wherein the whole stack retting process is carried out in a constant-temperature water bath box at 30 ℃. After pretreatment, feeding is carried out through a feeding port 3, a proper amount of water is added to realize different feed liquid concentrations, a monitoring system can monitor the change of the temperature, the pH value and the ORP in the hydrolysis acidification tank 2 at any time in the hydrolysis acidification process, namely the CSTR full-mixing reactor, and the temperature in the hydrolysis acidification tank 2 is kept at 35 +/-1 ℃ all the time. In addition, the material liquid can be uniformly mixed by stirring through a magnetic stirrer 1; when the pH value is lowered to the lowest point and then begins to rise, the materials can be discharged into an acid liquor tank 11 and pumped into an acid liquor adjusting tank 13 through a feed liquor circulating pump, and the materials are adjusted by alkali liquor to achieve the optimal living environment of acid-producing bacteria; acid liquid is pumped into a UASB anaerobic digester 15 by a peristaltic pump 14 with a set rotating speed, the temperature in the UASB anaerobic digester 15 is controlled within 55 +/-1 ℃ by a hot water heater 18 through a temperature controller, the feed liquid and methanogens in sludge act to generate methane, and gas is collected by a saturated salt water discharging method.
The advantages of the two-phase anaerobic digestion dynamic simulation experiment are established: the dynamic simulation experiment requires that the conditions which have decisive influence on the process meet the requirements of a similar principle, namely the proportion of the reactor used in a laboratory to the reactor used in engineering among structures, materials and scales is the same, the reactor not only ensures the geometric similarity of the shapes of the reactors, but also provides guarantee for the same flow field in the reactors, and the dynamic experiment working medium and the operation working condition are the same as the working medium and the operation working condition of actual equipment so as to increase the credibility of the application of the model experiment result to the engineering equipment.
Claims (4)
1. A soft measurement method for components and concentrations of organic waste hydrolysis acidification volatile fatty acids is characterized by comprising the following steps:
step 1: determining experimental parameters related to the yield of the volatile fatty acid in the hydrolytic acidification phase by using an orthogonal optimization experiment;
step 2: performing a two-phase anaerobic digestion experiment on the organic waste, taking the yield of the volatile fatty acid of the hydrolytic acidification phase as a target amount, and drawing a change curve between the target amount and each experiment parameter at different moments by using an orthogonal optimization experiment;
and step 3: according to the obtained change curve, respectively using each experiment parameter xiUsing the yield y of the volatile fatty acid in the hydrolytic acidification phase as a dependent variable and a polynomial function fitting method to establish a function regression equation F1 (x)i,y)；
And 4, step 4: obtaining the yield y (x) of the volatile fatty acid of the hydrolytic acidification phase under different experimental conditions by changing experimental parametersiT) and regression with a function F1 (x)iY) calculating the yield value y' (x) of the volatile fatty acids of the hydrolyzed and acidified phaseiT) comparison, proof function regression equation F1 (x)iY) effectiveness;
and 5: in the experimental conditions set in step 4, the effective function was regressed to equation F1 (x)iY) corresponding experimental conditions as valid experimental conditions;
step 6: obtaining a component G (i) of the volatile fatty acid of the hydrolytic acidification phase by using the effective experimental conditions obtained in the step 5, taking the component G (i) of the volatile fatty acid of the hydrolytic acidification phase as a dependent variable and taking each experimental parameter xiBuilding a multiple linear regression equation F1 (x) for the independent variablesiG (i)), then equation F1 (x) is regressediG (i)) is an online soft measurement model of the concentration of the volatile fatty acid in the hydrolytic acidification phase.
2. The method of claim 1, wherein the experimental parameters include temperature, pH, oxidation-reduction potential (ORP), conductivity, and total microbial concentration.
3. The method of claim 1, wherein the performing a two-phase anaerobic digestion experiment on the organic waste comprises:
step 2.1: crushing the air-dried corn straws into powder by a crusher, fully mixing the powder with pig manure to keep the water content within a certain range, carrying out stack retting pretreatment for N days, and carrying out the whole stack retting process in a constant-temperature water bath box;
step 2.2: after pretreatment, adding water to obtain feed liquid with different concentrations and uniformly stirring;
step 2.3: discharging when the pH value begins to rise after the pH value is lowered to the lowest point, and then adding alkali liquor to adjust the pH value;
step 2.4: pumping the acid liquor into a UASB anaerobic digester, and controlling the temperature within a preset range;
step 2.5: methane is generated by the action of methanogens in the sludge and the feed liquid, and then gas is collected by a saturated salt water discharging method.
4. The method of claim 1, wherein step 4 is represented by the following steps: when y (x)i,t)-y'(xiWhen t) is less than or equal to delta, the function regression equation F1 (x) is considerediY) has validity, δ is the set error value.
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| CN105045951A (en) * | 2015-05-27 | 2015-11-11 | 华南理工大学 | Soft-measurement method for volatile fatty acid in effluent of anaerobic wastewater treatment system |
| CN107132325A (en) * | 2017-04-14 | 2017-09-05 | 华南理工大学 | A kind of flexible measurement method of the Anaerobic Waste Treatment System water outlet volatile fatty acid based on particle cluster algorithm and SVMs |
| CN111855786A (en) * | 2020-07-13 | 2020-10-30 | 中国科学院重庆绿色智能技术研究院 | Microbial electrochemical coulomb method for determining volatile fatty acid in anaerobic digestion effluent |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN105045951A (en) * | 2015-05-27 | 2015-11-11 | 华南理工大学 | Soft-measurement method for volatile fatty acid in effluent of anaerobic wastewater treatment system |
| CN107132325A (en) * | 2017-04-14 | 2017-09-05 | 华南理工大学 | A kind of flexible measurement method of the Anaerobic Waste Treatment System water outlet volatile fatty acid based on particle cluster algorithm and SVMs |
| CN111855786A (en) * | 2020-07-13 | 2020-10-30 | 中国科学院重庆绿色智能技术研究院 | Microbial electrochemical coulomb method for determining volatile fatty acid in anaerobic digestion effluent |
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