基于人工智能的建模生物气生产在一个结合微生物电解细胞-无氧消化系统使用人工神经网络和自适应神经模糊推理系统的合成神经网络和自适应神经模糊推理系统
Fatih Tufaner1,2, Kenan Dalkılıç3, Ayşenur Uğurlu3
1Environmental Engineering Department, Faculty of Engineering, Adiyaman University, 02040, Adıyaman, Türkiye. ftufaner@adiyaman.edu.tr.
概括
机器学习模型准确地预测微生物电解细胞和无氧消化 (MECAD) 系统中的沼气产量. 人工神经网络 (ANN) 和自适应神经模糊推理系统 (ANFIS) 显示了优化生物能源过程的高精度.
科学领域:
- 生物技术和生物工程 生物技术和生物工程
- 可再生能源系统可再生能源系统
- 环境科学 环境科学
背景情况:
- 准确的生物气生产预测对于优化生物能源过程和资源管理至关重要.
- 微生物电解细胞和无氧消化 (MECAD) 系统与传统的无氧消化 (AD) 相比,提供了增强的能量回收.
- 优化生物气产量对于MECAD技术的能源效率和可行性至关重要.
研究的目的:
- 使用机器学习 (ML) 模型预测MECAD系统的生物气体生产率.
- 评估人工神经网络 (ANN) 和自适应神经模糊推理系统 (ANFIS) 模型对生物气预测的性能.
- 确定影响MECAD反应堆生物气体生产的关键输入变量.
主要方法:
- 收集的输入变量包括pH,ORP,TS/VS去除,HRT,OLR,应用电压和实验室规模的MECAD反应堆的电流产量.
- 开发和训练ANN和ANFIS模型来预测生物气生产速度.
- 使用R平方,一致性指数 (IA),两个因子 (FA2) 和平均偏差 (MB) 度量来验证模型性能.
主要成果:
- 该ANN模型实现了高R平方值 (0.9844测试,整体0.9760),以及优秀的协议指标 (IA:0.9960,FA2:0.9962).
- 该ANFIS模型还表现出强大的预测性能,具有R平方值 (0.9811测试,整体0.9774) 和高一致性 (IA:0.9952,FA2:0.9962).
- 两种模型都准确地预测了沼气生产率,偏差和错误指标的差异很小.
结论:
- ANN和ANFIS模型都是有效的工具,可以准确预测MECAD反应堆中的生物气产量.
- 这些ML模型可以帮助优化MECAD系统性能并确保能源效率.
- 该研究验证了ML的使用,以提高生物能源应用中的资源管理.
相关概念视频
01:30Environmental Applications of Microorganisms
1.5K
Microorganisms play a pivotal role in maintaining ecosystem balance by recycling essential elements such as carbon, nitrogen, and phosphorus, as well as supporting processes like bioremediation, wastewater treatment, and biofuel production.Microbes in Elemental CyclesIn the carbon cycle, microorganisms decompose organic matter, releasing carbon dioxide via aerobic respiration. This carbon dioxide is subsequently used by photosynthetic organisms to synthesize organic compounds, closing the...
1.5K
01:26Microbes and Methanogenesis
105
Methanogenesis is a critical microbial process in anaerobic ecosystems responsible for the biological production of methane, a potent greenhouse gas and valuable biofuel. This metabolic pathway is primarily facilitated by methanogenic archaea, which thrive in anoxic environments such as wetlands, sediments, and animal gastrointestinal tracts. The absence of oxygen in these habitats prevents aerobic respiration, thereby favoring alternative biochemical pathways for organic matter degradation.In...
105
01:29Bioreactor Design and Operational System
220
Bioreactors are engineered vessels designed to cultivate microorganisms under controlled conditions for industrial bioprocessing. They maintain sterility and allow precise regulation of pH, temperature, oxygen, and nutrient levels to optimize microbial growth and metabolite production. Bioreactors range from small laboratory units of 1 liter to industrial systems holding up to 500,000 liters, though only about 75% of their volume is actively used for fermentation. The remaining headspace...
220
01:28Bioreactor Controls-I
103
Maintaining optimal conditions within fermenters is essential for maximizing microbial productivity and ensuring process efficiency. This lesson focuses on key parameters—temperature, foam, pH, carbon dioxide, oxygen, and pressure—and their precise measurement and control strategies in fermentation systems.Temperature ControlTemperature regulation is critical due to the exothermic nature of many fermentation processes. In small laboratory fermenters, temperature is commonly...
103
01:25Biofuels
108
The microbial conversion of organic matter into biofuels holds potential as a renewable energy source. Among biofuel sources, microalgae are recognized as a highly efficient and adaptable feedstock for biodiesel production, owing to their rapid biomass accumulation, elevated lipid productivity, and capacity to proliferate in diverse aquatic systems, including freshwater, marine, and wastewater habitats. Unlike terrestrial crops, microalgae do not compete for land and can achieve significantly...
108