通过观察-评估-决定-行动循环对食品废弃物堆肥进行智能控制:从技术景观到经济稳定性分析
Jufei Wang1, Xuesong Peng1, Wenbin Tang1
1College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
Journal of environmental management
|January 17, 2026
概括
使用机器学习的智能控制系统 (ICS) 显著降低了食品废弃物堆肥的成本和风险. 在试点规模上,与一般控制系统相比,ICS的净收入增长了67.79%.
科学领域:
- 环境工程 环境工程
- 废物管理 废物管理
- 人工智能的人工智能
背景情况:
- 传统的食品废弃物 (FW) 堆肥面临着效率低下和不稳定.
- 在FW堆肥中对智能控制系统 (ICS) 的定量技术经济评估是有限的.
- 机器学习 (ML) 和传感器技术提供了潜在的改进.
研究的目的:
- 分析FW堆肥中的ML模型性能.
- 对基于ANN的ICS与基于PID的GCS进行技术经济和风险分析.
- 评估用于FW堆肥的ICS的经济可行性和稳定性.
主要方法:
- 在观察-评估-决策-行动框架内审查和分析常见的ML模型 (决策树,人工神经网络 - ANN).
- 实验室和试点规模的比较技术经济分析.
- 基于蒙特卡洛的经济风险分析.
主要成果:
- 基于ANN的ICS显著降低了成本:64.31% (实验室) 和47.81% (试点) 的直接运营成本.
- 在ICS.下,资本强度减少了39.16% (实验室) 和32.11% (试点).
- 在试点规模上,ICS将平均净收入提高了67.79%,并在实验室规模上减少了22.35%的损失.
结论:
- ICS提高了FW堆肥的经济稳定性和利能力,特别是在试点规模.
- ICS缩小了实验室规模的损失幅度,并改善了整体经济表现.
- 该研究为FW堆肥中ICS的研究和实施提供了有价值的见解.
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