Proportion and Performance Optimization of Biomass Seedling Trays Based on Response Surface Analysis
Abstract
:1. Introduction
2. Materials and Methods
2.1. Experimental Materials and Equipment
2.2. Experimental Design
2.2.1. Experimental Method
- (1)
- Biomass seedling tray forming experiment
- (2)
- Biomass seedling tray seedling cultivation experiment
2.2.2. Experimental Factors
2.2.3. Performance Evaluation Indexes
2.3. Data Analysis
3. Results
3.1. Orthogonal Experiment
3.1.1. Experiment Results
3.1.2. Variance Analysis and Optimization of Regression Models
3.2. Response Surface Analysis
3.2.1. The Impact of the Factor Interaction on the Bowl Hole Molding Rate
3.2.2. The Impact of the Factor Interaction on the Strong Seeding Index
3.3. Parameter Optimization and Experimental Validation
4. Discussion
4.1. The Impact of the Raw Material Ratio on the Bowl Hole Molding Rate
4.2. The Impact of the Raw Material Ratio on the Strong Seedling Index
4.3. The Advantage of Using Biomass Seedling Trays in Sustainable Development
- (1)
- Environmental protection: Biomass seedling trays are made of degradable organic materials that can naturally decay at the end of their life cycle, reducing environmental pollution. In contrast, traditional seedling trays are mostly made of plastic products that are difficult to degrade, causing serious plastic waste problems.
- (2)
- Resource conservation: Biomass seedling trays use organic waste such as crop straw as the main raw material, which achieves the reuse of waste and also saves limited natural resources. Traditional seedling trays, on the other hand, mainly rely on petrochemical materials such as plastic, which consumes a large amount of non-renewable energy.
- (3)
- Cost reduction: The production material cost of biomass seedling trays is relatively low, and their degradable nature also reduces the cost of disposal and recycling. Traditional seedling trays, on the other hand, have higher production and disposal costs.
- (4)
- Improving seedling efficiency: Biomass seedling trays have good air permeability and moisture retention, which can provide a better growth environment for seedlings, thereby improving the efficiency and quality of seedling production.
- (5)
- Promoting sustainable development of ecological agriculture: The use of biomass seedling trays can reduce the amounts of fertilizers and pesticides used in agricultural production, thereby lowering production costs and environmental pollution. This approach also helps improve the quality and safety of agricultural products and promotes the development of ecological agriculture.
- (6)
- Increasing farmers’ income: The use of biomass seedling trays can reduce farmers’ production costs, increase the added value of agricultural products, and also drive the development of related industries, thereby increasing farmers’ income.
5. Conclusions
- (1)
- This study conducted a multi-factor analysis and response surface analysis on the raw material ratio of biomass seedling trays, investigating the effects of slurry concentration, pulp content, adhesive content, and straw-to-cow manure mass ratio on the molding performance and seedling effect of biomass seedling trays. The primary and secondary order of influence of each factor on the bowl hole molding rate was obtained as follows: adhesive content x3, straw-to-cow manure mass ratio x4, slurry concentration x1, and pulp content x2. The primary and secondary order of influence on the strong seedling index was obtained as follows: slurry concentration x1, straw-to-cow manure mass ratio x4, adhesive content x3, and pulp content x2. The Design-Expert data analysis software was used to establish a regression model between the raw material ratio and the molding performance and seedling effect.
- (2)
- Through optimization and correction of the model, it is known that when the slurry concentration is 30%, the pulp content is 20%, the adhesive content is 530 g, and the straw-to-cow manure mass ratio is 2, the best forming effect and seedling quality of seedling trays can be achieved. At this time, the bowl hole forming rate is 91.03%, and the strong seedling index is 0.22. Through verification experiments, the relative errors between the measured values and the predicted values of the bowl hole molding rate and the strong seedling index are 1.7% and 13.6%, respectively, and these relative errors are small, indicating the high reliability of the model.
6. Forecast
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Materials | Total Nitrogen (g·kg−1) | Total Phosphorus (g·kg−1) | Total Potassium (g·kg−1) | PH | EC (us·cm−1) |
---|---|---|---|---|---|
Straw | 8.7 | 1.21 | 16.71 | 8.23 | 1344 |
Cow manure | 18.03 | 5.02 | 5.49 | 8.05 | 5952 |
Level | Slurry Concentration x1 (%) | Pulp Content x2 (%) | Adhesive Content x3 (g) | Straw-to-Cow Manure Mass Ratio x4 |
---|---|---|---|---|
+2 | 40 | 30 | 700 | 3 |
+1 | 35 | 25 | 600 | 2.5 |
0 | 30 | 20 | 500 | 2 |
−1 | 25 | 15 | 400 | 1.5 |
−2 | 20 | 10 | 300 | 1 |
No. | Experimental Factor | Experimental Index | ||||
---|---|---|---|---|---|---|
Slurry Concentration x1 (%) | Pulp Content x2 (%) | Adhesive Content x3 (g) | Straw-to-Cow Manure Mass Ratio x4 | Bowl Hole Molding Rate y1 (%) | Strong Seedling Index y2 | |
1 | −1 | −1 | −1 | −1 | 67.2 | 0.14 |
2 | 1 | −1 | −1 | −1 | 66.5 | 0.14 |
3 | −1 | 1 | −1 | −1 | 66.9 | 0.14 |
4 | 1 | 1 | −1 | −1 | 72.7 | 0.18 |
5 | −1 | −1 | 1 | −1 | 72.0 | 0.16 |
6 | 1 | −1 | 1 | −1 | 83.9 | 0.19 |
7 | −1 | 1 | 1 | −1 | 71.0 | 0.15 |
8 | 1 | 1 | 1 | −1 | 86.2 | 0.20 |
9 | −1 | −1 | −1 | 1 | 84.9 | 0.19 |
10 | 1 | −1 | −1 | 1 | 81.7 | 0.18 |
11 | −1 | 1 | −1 | 1 | 74.4 | 0.16 |
12 | 1 | 1 | −1 | 1 | 76.0 | 0.18 |
13 | −1 | −1 | 1 | 1 | 79.5 | 0.18 |
14 | 1 | −1 | 1 | 1 | 81.8 | 0.19 |
15 | −1 | 1 | 1 | 1 | 69.5 | 0.14 |
16 | 1 | 1 | 1 | 1 | 79.3 | 0.17 |
17 | −2 | 0 | 0 | 0 | 76.1 | 0.17 |
18 | 2 | 0 | 0 | 0 | 80.1 | 0.19 |
19 | 0 | −2 | 0 | 0 | 69.6 | 0.13 |
20 | 0 | 2 | 0 | 0 | 66.8 | 0.14 |
21 | 0 | 0 | −2 | 0 | 67.6 | 0.15 |
22 | 0 | 0 | 2 | 0 | 77.4 | 0.17 |
23 | 0 | 0 | 0 | −2 | 72.6 | 0.15 |
24 | 0 | 0 | 0 | 2 | 81.2 | 0.19 |
25 | 0 | 0 | 0 | 0 | 92.3 | 0.21 |
26 | 0 | 0 | 0 | 0 | 88.9 | 0.23 |
27 | 0 | 0 | 0 | 0 | 88.3 | 0.23 |
28 | 0 | 0 | 0 | 0 | 89.8 | 0.22 |
29 | 0 | 0 | 0 | 0 | 90.4 | 0.22 |
30 | 0 | 0 | 0 | 0 | 89.8 | 0.21 |
Source of Variation | Bowl Hole Molding Rate y1 | Strong Seedling Index y2 | ||||
---|---|---|---|---|---|---|
Sum of Squares | F Value | p-Value | Sum of Squares | F Value | p-Value | |
Model | 1906.24 | 33.99 | <0.0001 | 0.025 | 21.19 | <0.0001 |
x1 | 107.90 | 26.94 | 0.0001 ** | 2.090 × 10−3 | 24.77 | 0.0002 ** |
x2 | 30.79 | 7.69 | 0.0142 * | 9.769 × 10−5 | 1.16 | 0.2989 |
x3 | 114.02 | 28.46 | <0.0001 ** | 6.106 × 10−4 | 7.24 | 0.0168 * |
x4 | 137.89 | 34.42 | <0.0001 ** | 1.662 × 10−3 | 19.70 | 0.0005 ** |
x1x2 | 31.04 | 7.75 | 0.0139 * | 8.266 × 10−4 | 9.80 | 0.0069 ** |
x1x3 | 79.73 | 19.90 | 0.0005 ** | 5.152 × 10−4 | 6.11 | 0.0259 * |
x1x4 | 29.30 | 7.31 | 0.0163 * | 4.763 × 10−4 | 5.49 | 0.0212 * |
x2x3 | 0.076 | 0.019 | 0.8919 | 2.290 × 10−4 | 2.71 | 0.1202 |
x2x4 | 79.73 | 19.90 | 0.0005 ** | 1.108 × 10−3 | 13.14 | 0.0025 ** |
x3x4 | 137.70 | 34.37 | <0.0001 ** | 1.548 × 10−3 | 18.35 | 0.0007 ** |
x12 | 188.64 | 47.09 | <0.0001 ** | 2.356 × 10−3 | 27.93 | <0.0001 ** |
x22 | 713.90 | 178.21 | <0.0001 ** | 11.018 × 10−3 | 128.24 | <0.0001 ** |
x32 | 446.56 | 111.47 | <0.0001 ** | 5.229 × 10−3 | 61.99 | <0.0001 ** |
x42 | 235.26 | 58.73 | <0.0001 ** | 2.968 × 10−3 | 35.19 | <0.0001 ** |
Residual | 60.09 | 2.68 | 0.1440 | 1.265 × 10−3 | 1.57 | 0.3223 |
Lack of fit | 50.64 | 9.602 × 10−4 | ||||
Pure error | 9.45 | 3.053 × 10−4 | ||||
Sum total | 1966.33 | 0.026 |
Parameters | Bowl Hole Molding Rate/% | Strong Seedling Index/g·cm3 |
---|---|---|
predicted value | 91.03 | 0.22 |
Mean of tests | 89.47 | 0.19 |
Relative error/% | 1.7 | 13.6 |
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Li, H.; Wang, H.; Sun, W.; Wang, C.; Sun, H.; Yu, H. Proportion and Performance Optimization of Biomass Seedling Trays Based on Response Surface Analysis. Sustainability 2024, 16, 1103. https://doi.org/10.3390/su16031103
Li H, Wang H, Sun W, Wang C, Sun H, Yu H. Proportion and Performance Optimization of Biomass Seedling Trays Based on Response Surface Analysis. Sustainability. 2024; 16(3):1103. https://doi.org/10.3390/su16031103
Chicago/Turabian StyleLi, Hailiang, Hongxuan Wang, Weisheng Sun, Chun Wang, Haitian Sun, and Haiming Yu. 2024. "Proportion and Performance Optimization of Biomass Seedling Trays Based on Response Surface Analysis" Sustainability 16, no. 3: 1103. https://doi.org/10.3390/su16031103