Optimization of coffee shell carbonization parameters based on BP neural network-genetic algorithm

doi:10.12398/j.issn.2096-7217.2025.01.005

Abstract

Abstract:

Biochar is a multifunctional material efficiently developed from biomass. It is combined with fertilizers to prepare biochar-based fertilizers， which has excellent slow-release performance and minimal soil burden. The carbonization temperature， carbonization time， and heating rate during biomass carbonization process affect the physical and chemical properties of biochar. The biochar under different carbonization temperatures， carbonization times and heating rates have a significant impact on the slow-release performance of biochar-based fertilizers. In this study， BP neural network coupled with genetic algorithm was used to predict and optimize key process parameters during the carbonization process of coffee shell biochar in order to improve the slow-release performance of biochar-based fertilizers. Results had shown that based on BP neural network-genetic algorithm， rapid prediction and optimization of the slow-release performance of coffee shell biochar-based fertilizer had been achieved through experiments. The optimal process parameters were： carbonization time of 2.8 h， carbonization temperature of 780.7 ℃， and the heating rate of 15.1 ℃/min. The seven-day cumulative nutrient release rate of the biochar-based fertilizer prepared under this process parameter was 45.9%， and the slow-release performance was improved. The study proposed a new method for optimizing the parameters of biochar carbonization process， which provided new ideas for the development of high-performance biochar preparation processes and had certain reference significance for improving the performance of biochar-based fertilizers.

Key words: biochar, BP neural network, genetic algorithm, biochar-based fertilizer, parameter optimization

CLC Number:

S225.5+2

ZHANG Xia, SU Panjie, ZHU Jingzhe, WANG Yiyang, HUANG Junwei. Optimization of coffee shell carbonization parameters based on BP neural network-genetic algorithm[J]. Journal of Intelligent Agricultural Mechanization, 2025, 6(1): 51-58.

Figures/Tables 8

Figure 1 Soil column eluvial device

Figure 2 BP neural network flow chart

Figure 3 BP-GA parameters optimization flow chart

Table 1 Design test scheme for biochar preparation

处理	炭化时间/h	炭化温度/℃	炭化速率/ (℃·min^-1)	养分累计释放率/%
1	2	600	10	60.3
2	3	600	10	59.6
3	4	600	10	63.6
4	1	600	10	68.4
5	1	600	5	76.8
6	1	600	10	68.4
7	1	600	15	60.5
8	1	600	20	65.4
9	2	700	10	56.5
10	3	700	20	54.7
11	4	700	15	55.6
12	1	700	10	59.7
13	2	800	10	48.7
14	3	800	15	46.5
15	4	800	20	48.6
16	1	800	5	50.4
17	2	900	15	52.7
18	3	900	10	52.9
19	4	900	15	50.8
20	1	900	10	54.7

Figure 4 Regression plot of actual and predicted values

Figure 5 Comparison of experimental data and model prediction results

Figure 6 Comparison of experimental data and model prediction results in the test set

Figure 7 Genetic algorithm iterative evolution fitness graph

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