CN117852432B - Biogas slurry drip irrigation pipeline system layout optimization method - Google Patents
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- 238000003973 irrigation Methods 0.000 title claims abstract description 143
- 230000002262 irrigation Effects 0.000 title claims abstract description 142
- 239000002002 slurry Substances 0.000 title claims abstract description 43
- 238000000034 method Methods 0.000 title claims abstract description 28
- 238000005457 optimization Methods 0.000 title claims abstract description 13
- 239000002689 soil Substances 0.000 claims abstract description 66
- 238000009736 wetting Methods 0.000 claims abstract description 55
- 230000008595 infiltration Effects 0.000 claims abstract description 38
- 238000001764 infiltration Methods 0.000 claims abstract description 38
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 37
- 238000012360 testing method Methods 0.000 claims abstract description 11
- 238000013461 design Methods 0.000 claims abstract description 7
- 238000004088 simulation Methods 0.000 claims description 15
- 230000008569 process Effects 0.000 claims description 9
- 238000009428 plumbing Methods 0.000 claims description 6
- 238000012795 verification Methods 0.000 claims description 5
- 230000035515 penetration Effects 0.000 claims description 4
- 238000002790 cross-validation Methods 0.000 claims description 2
- 230000001186 cumulative effect Effects 0.000 description 7
- 241000209149 Zea Species 0.000 description 2
- 235000005824 Zea mays ssp. parviglumis Nutrition 0.000 description 2
- 235000002017 Zea mays subsp mays Nutrition 0.000 description 2
- 235000005822 corn Nutrition 0.000 description 2
- 239000003337 fertilizer Substances 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 229910052799 carbon Inorganic materials 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 229940059082 douche Drugs 0.000 description 1
- 210000003608 fece Anatomy 0.000 description 1
- 230000004720 fertilization Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 239000003621 irrigation water Substances 0.000 description 1
- 239000010871 livestock manure Substances 0.000 description 1
- 235000012054 meals Nutrition 0.000 description 1
- 238000013508 migration Methods 0.000 description 1
- 230000005012 migration Effects 0.000 description 1
- 235000021049 nutrient content Nutrition 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 239000010902 straw Substances 0.000 description 1
- 238000010200 validation analysis Methods 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
- 239000002351 wastewater Substances 0.000 description 1
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Abstract
A biogas slurry drip irrigation pipeline system layout optimization method belongs to the field of system regulation and optimization. According to the invention, a biogas slurry drip irrigation Hydrus model based on a soil column test is constructed, a plurality of biogas slurry drip irrigation pipeline layout scenes are created based on the verified model, then a function model of a plurality of soil wetting parameters and accumulated infiltration amounts under the condition of creating the biogas slurry drip irrigation is analyzed, the accumulated infiltration amounts, namely irrigation quota during irrigation, are determined according to the design standard of irrigation uniformity, and then the drip irrigation belt distance is determined according to an irrigation quota deformation formula. On the premise of considering running and pipeline cost simultaneously, a regression model between the total cost and different initial soil water contents, emitter flow and drip tape intervals is established based on a Lasso regression algorithm, and the optimal solution of the minimum total cost is finally determined.
Description
Technical Field
The invention relates to the field of system regulation and optimization, in particular to a biogas slurry drip irrigation pipeline system layout optimization method.
Background
Biogas slurry belongs to wastewater containing carbon organic matters, and is usually prepared by fermenting animal manure, straw and meal waste. Biogas slurry is an important resource for irrigation and fertilization because of rich nutrient content and a large amount of water. The biogas slurry is applied to the field by means of a drip irrigation system, so that the damage of the biogas slurry to the environment can be relieved, and water resources can be saved. However, biogas slurry is different from traditional water sources. In recent researches, the inventor finds that the biogas slurry changes the soil moisture migration characteristics and rules of the traditional water sources, and the characteristic is that the wetting front of the two water sources has huge difference (shown in figure 1), and the wetting front is an important factor affecting the growth of crop root systems. Therefore, the distribution parameters of the pipelines (drip tapes) in the conventional drip irrigation system under the water source are required to be correspondingly adjusted so as to ensure the uniformity of water and fertilizer of the biogas slurry drip irrigation.
Hydrus software can simulate the movement process of moisture in soil, and has many applications in simulating traditional water source drip irrigation, but in terms of biogas slurry drip irrigation, only the university of lanzhou is used for Hydrus simulation in terms of indirect subsurface drip irrigation, and no arrangement of drip irrigation pipeline systems is involved, because the indirect subsurface drip irrigation is usually hole irrigation and is completely different from traditional surface drip irrigation. Therefore, in terms of biogas slurry surface drip irrigation, the inventor expects to optimally adjust the layout parameters of a biogas slurry drip irrigation pipeline system by means of a biogas slurry drip irrigation model established by Hydrus software so as to improve the uniformity of water and fertilizer under the biogas slurry drip irrigation condition.
Disclosure of Invention
In order to improve and even solve at least one problem in the prior art, the invention provides a biogas slurry drip irrigation pipeline system layout optimization method.
The biogas slurry drip irrigation system pipeline layout method comprises the following steps:
Firstly, establishing Hydrus a model based on a soil column test and calibrating and verifying hydraulic parameters, wherein the calibrating and verifying adopt a cross verification method, and the dynamic adjustment and the determination of the hydraulic parameters of the soil are carried out in a period of minutes according to the physical basic parameters of the soil and the actually measured water content data of the soil;
Secondly, constructing a plurality of irrigation scenario simulation schemes based on the initial water content HS, the flow Q and the interval d of the irrigators on the basis of the Hydrus model, wherein the irrigation scenario adopts a double-point source irrigation mode, and pipeline layout parameters of the irrigation mode comprise: emitter flow Q, emitter spacing d, and drip tape spacing b;
Thirdly, based on simulation results of the two-point source irrigation scene simulation schemes, constructing function models Y1, Y2, Y3 and Y4 of 4 soil wetting parameters and accumulated infiltration quantity I, wherein the 4 soil wetting parameters are respectively as follows: the intersection wetting depth H1, the maximum infiltration distance H2, the horizontal wetting distance W and the wetting uniformity WU are fitted according to a logarithmic function, a power function and an exponential function;
Substituting a WU value meeting the requirement of uniformity of irrigation design into the function model Y4 to determine the accumulated infiltration I under different situations, substituting the accumulated infiltration I into function equations of Y1, Y2 and Y3 respectively, and solving corresponding 3 soil wetting parameters (intersection wetting depth H1, maximum infiltration distance H2 and horizontal wetting distance W) under different situations, wherein the WU value meeting the requirement of uniformity of irrigation design is not lower than 80%;
And fifthly, distributing the solved accumulated infiltration quantity I to an irrigation quota M in an irrigation quota equation to determine the drip irrigation tape intervals b of a plurality of situations, wherein the irrigation quota equation adopts the following variant:
M＝γp d b h(θ1-θ2)×1000
In the method, in the process of the invention,
The irrigation rate is L, the volume weight of soil (g/cm 3), p is the drip irrigation wetting ratio, d is the spacing between drippers (cm), b is the capillary spacing (cm), h is the planned wetting depth (cm), theta 1 is the water content of soil (g/g) when irrigation is stopped, and theta 2 is the water content of soil (g/g) when irrigation is started;
Step six, determining the length L of the drip irrigation tape, the number n of the drip emitters and the total irrigation time TT according to the interval b of the drip irrigation tape, the interval d of the drip emitters and the flow Q of the drip emitters, and calculating the total cost TC of different situations;
Wherein the total cost TC comprises the running and plumbing costs, wherein the running cost is determined by the total irrigation time TT, and the plumbing costs are jointly determined by the length L of the drip tape and the number n of emitters, calculated according to the following formula:
L＝f1×f2/b×100；
n＝L/d×100；
TT＝TI/(Q×n/1000)；
TC＝UP1×TT+UP2×L+UP3×n；
Wherein L is the length (m) of the drip irrigation tape, f1 and f2 are the irrigation side length (m), b is the drip irrigation tape spacing (cm), n is the number (number) of the irrigators, d is the emitter spacing (cm), TT is the total irrigation time (h), Q is the emitter flow (L/h), TI is the total infiltration amount (m 3), TC is the total cost (civil monetary unit), UP1 is the electric charge per hour (yuan/hour), UP2 is the price per meter of drip irrigation tape (yuan/m), and UP3 is the price per emitter (yuan/number).
And seventh, establishing a regression model of the total cost and b among different initial soil water contents HS, the emitter flow Q and the drip interval based on the Lasso algorithm, and determining an optimal solution of the minimum total cost.
The biogas slurry drip irrigation pipeline system layout optimization method provided by the embodiment of the invention has the beneficial effects that:
1. Based on the measured data of the biogas slurry soil column test, a Hydrus model suitable for biogas slurry drip irrigation is constructed, in the process of debugging the hydraulic parameters of the check model, the inherent unchanged measurement of the hydraulic parameters of the model is changed by a cross verification error testing method, the dynamic adjustment of the hydraulic parameters by taking minutes as a period is innovatively provided, and finally, the model check and verification of the actual situation of the biogas slurry drip irrigation are passed.
2. And creating a plurality of biogas slurry drip irrigation pipeline layout scenes based on the verified model, analyzing and creating a function model of a plurality of soil wetting parameters and accumulated infiltration amount under the biogas slurry drip irrigation condition, and making up the blank of lacking a quantitative regression model of the biogas slurry drip irrigation soil wetting parameters and the accumulated infiltration amount.
3. According to the design standard of irrigation uniformity, the accumulated infiltration amount, namely the irrigation quota during irrigation, is determined by calling a quantitative regression model, and then the drip irrigation tape spacing is determined according to an irrigation quota deformation formula.
4. On the premise of considering the running and pipeline costs simultaneously, a regression model between the total cost and different initial soil water contents, emitter flow and drip tape intervals is established based on a Lasso regression algorithm, and the optimal solution of the minimum total cost is finally determined.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 shows a comparison of a conventional water and biogas slurry wetting zone;
FIG. 2 shows a schematic diagram of a two-point source irrigation in accordance with an embodiment of the present invention;
FIG. 3 shows a schematic view of a drip irrigation piping system layout in accordance with an embodiment of the present invention;
FIG. 4 shows a graph of soil wetting parameters as a function of cumulative infiltration for an embodiment of the present invention.
Icon: 1-a douche; 2-drip irrigation tape; 3-planning a wet zone; 4-actual wet zone.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.
In the present invention, all the embodiments, implementations and features of the invention may be combined with each other without contradiction or conflict. In the present invention, conventional equipment, devices, components, etc., are either commercially available or homemade in accordance with the present disclosure. In the present invention, some conventional operations and apparatuses, devices, components are omitted or only briefly described in order to highlight the gist of the present invention.
Embodiment case 1:
and firstly, establishing Hydrus a model based on a soil column test and calibrating and verifying hydraulic parameters.
In this example, the soil moisture content of the column test was used for model calibration and validation (cross validation). The Measured Relative Error (MRE) in equation (1) is used to evaluate the reliability of the HYDRUS model. In general, the closer the MRE is to zero, the more reliable the model is. When the MRE is less than 10%, the model is generally considered reliable. Referring to the study of the predecessor, based on the particle composition of the soil in the indoor test, the hydraulic parameters of the van Genuchten-Mualem soil hydraulic characteristic model are predicted by means of a neural network prediction module in Hydrus software and are used as initial values of model calibration, and then the calibration and verification of the model parameters are carried out according to the indoor soil column test data.
Wherein O i and P i are measured values and analog values, respectively, n is the number of observed values,Is the average of the measured values.
The specific calibration and parameter adjustment process comprises the following steps: parameters were adjusted by trial and error. In the parameter adjustment process, we find that when the hydraulic parameters alpha, n and Ks are simulated according to fixed values, the permeation rule of the biogas slurry is inconsistent with the measured value. Referring to the previous cases of error testing and calibration by changing the soil hydraulic parameters, and combining with the measured vertical infiltration law, we dynamically adjust and determine the soil hydraulic parameters by taking minutes as a period; firstly, within 60 minutes of biogas slurry infiltration, the infiltration law in different periods is greatly changed. The permeation rate gradually slows down and tends to stabilize. According to the rule and some measured soil water content data, the model adjustment process is divided into four time periods of 0-10, 10-20, 20-60 and 60-144 minutes, so that the simulated infiltration rule is more in line with the measured result. Subsequently, the model boundaries are dynamically adjusted based on the wetting ranges measured at the different stages. The parameter adjustment process for each cycle is performed by trial and error. By comparing the differences between the analog and measured values, the relative error (MRE) is considered to be less than 10% to obtain a calibrated parameter value. In addition, another portion of the measured soil moisture content data is used to validate the calibration model.
And finally, the hydraulic parameter value of Hydrus soil for biogas slurry drip irrigation is determined, namely, a Hydrus model suitable for biogas slurry drip irrigation is determined, and is shown in table 1.
Table 1 biogas slurry drip irrigation Hydrus model parameter table
And secondly, constructing 18 irrigation scenario simulation schemes based on the initial water content of soil, the flow rate of the irrigators and the spacing of the irrigators based on the determined Hydrus model.
The initial water content of the soil, the emitter flow rate and the emitter spacing are important factors affecting the penetration of the drip irrigation water, so the present embodiment determines a simulation scenario of 18 total factor combinations based on multiple levels of these three factors (table 2). Wherein, the initial water content of the soil is set to be 10%,15% and 20%, the field water holding capacity of the soil is 26.3% in the embodiment, and the three levels of the initial water content of the soil can be also interpreted as three levels of the lower limit of irrigation, namely 40% field holding, 60% field holding and 80% field holding; the flow specification of the water irrigator is set to be 1L/h and 2L/h; the emitter spacing was set at three levels of 20cm, 30cm and 40cm, and the specific simulation scenario is shown in table 2. And (3) simulating 18 scenes based on the biogas slurry drip irrigation Hydrus model in the step (1), and setting the maximum simulation duration to 1440min (24 h) according to the one-time irrigation quota duration of the drip irrigation engineering.
Table 2 scenario simulation scheme
Wherein the irrigation scenario adopts a double point source irrigation mode, as shown in fig. 2. The irrigation pipeline layout parameters comprise: emitter flow Q, emitter spacing d, and drip tape spacing b, as shown in fig. 3.
And constructing a two-point source infiltration model of 18 scenes based on the established biogas slurry drip irrigation Hydrus model, and solving values of soil wetting parameters (intersection wetting depth [ H1], maximum infiltration distance [ H2], horizontal wetting distance [ W ] and wetting uniformity [ WU=H2 ]) and accumulated infiltration amount (I).
Thirdly, based on simulation results of the 18 double-point source irrigation scene simulation schemes, constructing function models Y1, Y2, Y3 and Y4 of 4 soil wetting parameters and accumulated infiltration quantity I, wherein the 4 soil wetting parameters are respectively as follows: intersection wetting depth H1, maximum penetration distance H2, horizontal wetting distance W, and wetting uniformity WU. FIG. 4 shows a graph of soil wetting parameters as a function of cumulative infiltration for an embodiment of the present invention. Accordingly, regression fitting was performed on the relationship of wetting parameters to the cumulative infiltration (FIG. 4) using the self-contained fitting function of Origin (SIMPLEFIT), and the soil wetting parameters and cumulative infiltration model coefficients are shown in Table 3.
Table 3 shows that the intersection wetting depth, the maximum infiltration distance, the horizontal wetting distance, the wetting uniformity and the accumulated infiltration amount are respectively logarithmic, power and exponential functions, and the correlation coefficient R2 is close to 0.99, which illustrates that the function selected in this embodiment can be used for fitting the relationship between the soil wetting parameter and the accumulated infiltration amount in biogas slurry drip irrigation.
Namely, determining: fitting the four function models according to a logarithmic function, a power function and an exponential function;
TABLE 3 soil wetting parameters and cumulative infiltration model coefficients
Fourth, in drip irrigation engineering, it is considered reasonable to use the christmas-organ uniformity coefficient as the irrigation uniformity of the emitter flow in the irrigation system, and values of typically greater than 80%. The concept of introducing uniformity of wetting in this example characterizes the uniformity of the wetted area of the root zone of the soil. Similarly, when the wetting uniformity is set to be equal to or greater than 80%, the requirement of the drip irrigation engineering uniformity is considered to be met, and assuming that wu=80% of the drip irrigation engineering design in the embodiment is substituted into the function model Y4, the cumulative infiltration amount I under different situations can be determined, and then the cumulative infiltration amount I is substituted into the function equations of Y1, Y2 and Y3 respectively, so that 3 soil wetting parameters (the intersection wetting depth H1, the maximum infiltration distance H2 and the horizontal wetting distance W) corresponding to the different situations are solved.
And fifthly, distributing the solved accumulated infiltration quantity I to an irrigation quota M in an irrigation quota equation to determine the interval b of drip irrigation belts of 18 scenes, wherein the irrigation quota equation is obtained by conversion of an irrigation quota formula of GB/T50485-2020 micro-irrigation engineering technical Standard, and is shown in a formula (2). According to the actual measurement of the soil column test and Hydrus model, the soil volume weight is 1.4, the field water holding capacity is 26.3%, other parameters are assigned by taking corn as a case, the study of the predecessor is referred to, the corn drip irrigation is used for taking the planned wetting ratio to be 0.65, the planned wetting depth is used for taking 50cm, the upper limit of irrigation is used for taking 100% of the field water holding capacity (the upper limit of irrigation is 0.26 in the embodiment), and the lower limit of irrigation and the interval of the irrigator are assigned according to the specific scene simulation.
M＝γp d b h(θ1-θ2)×1000 (2)
In the method, in the process of the invention,
The irrigation rate is L, the volume weight of soil (g/cm 3), p is the drip irrigation wetting ratio, d is the spacing between drippers (cm), b is the capillary spacing (cm), h is the planned wetting depth (cm), theta 1 is the water content of soil (g/g) when irrigation is stopped, and theta 2 is the water content of soil (g/g) when irrigation is started;
And sixthly, determining the length L of the drip irrigation tape, the number n of the drip emitters and the total irrigation time TT according to the interval b of the drip irrigation tape, the interval d of the drip emitters and the flow rate Q of the drip emitters, and calculating the total cost TC of different situations. Table 4 shows the results of the fourth, fifth and sixth steps;
Wherein the total cost TC comprises the running and plumbing costs, wherein the running cost is determined by the total irrigation time TT, and the plumbing costs are jointly determined by the length L of the drip tape and the number n of emitters, calculated according to the following formula:
L＝f1×f2/b×100；
n＝L/d×100；
TT＝TI/(Q×n/1000)；
TC＝UP1×TT+UP2×L+UP3×n；
Wherein L is the length (m) of the drip tape, f1 and f2 are the irrigation side length (m), b is the drip tape spacing (cm), n is the number (number) of the irrigators, d is the emitter spacing (cm), TT is the total irrigation time (h), Q is the emitter flow (L/h), TI is the total infiltration amount (m 3), TC is the total cost (civil monetary element), UP1 is the electric charge per hour (yuan/hour), UP2 is the price per meter of drip tape (yuan/m), UP3 is the price per irrigator (yuan/number), and the units are calculated according to each hectare.
Table 4 wetting parameters and cost values for satisfying irrigation uniformity requirements
Seventh, according to the results in Table 4 and the Lasso regression algorithm, regression models of total cost and different initial soil water contents HS, emitter flow Q and drip interval d are established, TC= 9630.638-77.446HS-660.646Q-153.666d, and correlation coefficient=0.884, indicating that the regression equation is reliable. The coefficient of the regression equation in the model is negative, and when each parameter takes the maximum value, the cost of the biogas slurry drip irrigation system is the lowest. Thus, an optimal solution for the minimum total cost is obtained, which is the following scheme:
(1) If the lower limit of irrigation is 40%, namely when the initial water content of the soil is 10%, the flow rate of the irrigators is 2L/h, the spacing between the irrigators is 40cm, and the spacing between drip irrigation belts is 79cm (80 cm);
(2) If the lower limit of irrigation is 60% of the field, namely when the water content of the initial soil is 15%, the flow rate of the irrigators is 2L/h, the interval between the irrigators is 40cm, and the interval between drip irrigation belts is 86cm (90 cm);
(3) If the lower limit of irrigation is 80% of the field, namely when the initial water content of the soil is 20%, the flow rate of the irrigators is 2L/h, the spacing between the irrigators is 40cm, and the spacing between drip irrigation belts is 116cm (120 cm).
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (5)
1. The biogas slurry drip irrigation pipeline system layout optimization method is characterized by comprising the following steps:
S1, establishing Hydrus a model based on a soil column test, and calibrating and verifying hydraulic parameters by adopting a cross verification method;
s2, constructing a plurality of irrigation scenario simulation schemes based on the initial water content HS, the emitter flow Q and the emitter spacing d of soil based on the Hydrus model;
S3, based on simulation results of the irrigation scene simulation schemes, constructing function models Y1, Y2, Y3 and Y4 of 4 soil wetting parameters and accumulated infiltration quantity I, wherein the 4 soil wetting parameters are respectively as follows: intersection wetting depth H1, maximum penetration distance H2, horizontal wetting distance W, and wetting uniformity WU;
s4, substituting WU values meeting the requirement of uniformity of irrigation design into the function model Y4 to determine accumulated infiltration I under different situations, substituting the accumulated infiltration I into function equations of Y1, Y2 and Y3 respectively, and solving corresponding 3 soil wetting parameters under different situations: intersection wetting depth H1, maximum penetration distance H2, and horizontal wetting distance W;
S5, distributing the solved accumulated infiltration quantity I to an irrigation quota M in an irrigation quota equation to determine drip irrigation tape intervals b of a plurality of situations, wherein the irrigation quota equation M adopts the following formula:
；
In the method, in the process of the invention,
For the irrigation quota (L)/>Is soil volume weight (g/cm. Rate.)/>For drip irrigation wetting ratio,/>The spacing of the drippers (cm), b the capillary spacing (cm), h the planned wetting depth (cm),/>To stop the water content (g/g) of the soil when the irrigation is stopped,/>The water content (g/g) of the soil at the beginning of irrigation;
S6, determining the length L of the drip irrigation tape, the number n of the irrigators and the total irrigation time TT according to the interval b of the drip irrigation tape, the interval d of the irrigators and the flow Q of the irrigators, and calculating the total cost TC of different situations;
The total cost TC comprises operating and plumbing costs, wherein the operating cost is determined by the total irrigation time TT, and the plumbing cost is determined by both the length L of the drip tape and the number n of emitters, calculated according to the following formula:
L = f1 × f2/b × 100;
n = L/d × 100; TT = TI/(Q × n/1000); TC = UP1 × TT + UP2 × L + UP3 × n;
Wherein L is the length (m) of the drip irrigation tape, f1 and f2 are the irrigation side length (m), b is the drip irrigation tape spacing (cm), n is the number (number) of the irrigators, d is the emitter spacing (cm), TT is the total irrigation time (h), Q is the emitter flow (L/h), TI is the total infiltration amount (m 3), TC is the total cost (civil monetary unit), UP1 is the electric charge per hour (yuan/hour), UP2 is the price per meter (yuan/m) of the drip irrigation tape, and UP3 is the price per emitter (yuan/number);
S7, establishing a regression model between the total cost and three parameters of different initial soil water contents HS, the emitter flow Q and the drip tape interval b based on a Lasso algorithm, and determining an optimal solution of the minimum total cost.
2. The biogas slurry drip irrigation pipeline system layout optimization method according to claim 1, wherein the specific method of the cross-validation method in step S1 is as follows: and according to the soil physical basic parameters and the measured soil water content data, dynamically adjusting and determining the soil hydraulic parameters by taking minutes as a period.
3. The biogas slurry drip irrigation pipeline system layout optimization method according to claim 1, wherein the irrigation scenario in the step S2 adopts a two-point source irrigation mode, and pipeline layout parameters of the irrigation mode include: emitter flow Q, emitter spacing d, and drip tape spacing b.
4. The biogas slurry irrigation pipeline system layout optimization method according to claim 1, wherein the function models Y1, Y2, Y3 and Y4 in the step S3 are fitted according to a logarithmic function, a power function and an exponential function, respectively.
5. The biogas slurry drip irrigation pipeline system layout optimization method according to claim 1, wherein the WU value meeting the irrigation design uniformity requirement in the step S4 is greater than or equal to 80%.
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| CN100498807C (en) * | 2007-02-09 | 2009-06-10 | 中国农业大学 | Antiplugging drip irrigation irrigator design method |
| CN101606479B (en) * | 2009-07-28 | 2011-02-09 | 西北农林科技大学 | Micro irrigation method based on point source clinical infiltration rate |
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| WO2019144397A1 (en) * | 2018-01-29 | 2019-08-01 | 中国农业大学 | Flow channel design and forming method for tape-casted thin-wall drip irrigation hose |
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