使用人工神经网络和支持向量机器优化操作参数,用于从大米皮中提取的生物油
Anas Ahmed1, Noorfidza Yub Harun2, Sharjeel Waqas3
1Department of Industrial and Systems Engineering, University of Jeddah, Jeddah 238090, Saudi Arabia.
ACS omega
|June 24, 2024
概括
人工神经网络 (ANN) 和支持矢量机器 (SVM) 优化了大米灰热解,以提高生物油生产. 这些模型准确地预测生物油的特性,促进从农业废物中获得可持续能源.
科学领域:
- 可再生能源工程可再生能源工程
- 生物质转化技术生物质转化技术
- 化学过程中的人工智能
背景情况:
- 米是一种丰富的农业残留物,具有可持续能源生产的潜力.
- 热解是一种将生物质转化为有价值的生物油的方法.
- 热解参数的优化对于最大限度地提高生物油产量和质量至关重要.
研究的目的:
- 使用人工神经网络 (ANN) 和支持矢量机器 (SVM) 建模,优化从大米灰 (RHA) 生产生物油的操作参数.
- 使用ANN. 开发生物油特性预测模型.
- 通过系统的过程优化,提高生物油的产量和质量.
主要方法:
- 采用ANN和SVM建模技术来分析热解工作条件 (温度,加热速率,颗粒大小).
- 训练和验证具有不同神经元配置和转移功能的ANN模型.
- 对比ANN和SVM模型对生物油性质的预测准确度.
主要成果:
- ANN和SVM模型有效地优化了用于从RHA.生物油生产的热解参数.
- 该ANN模型证明了生物油性质的高预测准确性,整体R值约为0.9960.0.
- 优化参数导致RHA有效转化为高质量的生物油.
结论:
- ANN和SVM是优化生物质热解过程的强大工具.
- 这项研究促进了从大米骨灰中可持续生产生物油,为实现可再生能源目标做出了贡献.
- 这些发现支持利用农业废物作为化石燃料的可行替代品.
相关概念视频
01:26Microbial Bioremediation of Hydrocarbons
162
Bioremediation is an environmentally sustainable process that employs living organisms—primarily microorganisms—to degrade or neutralize pollutants from contaminated environments. In oil spills and hydrocarbon pollution, bioremediation involves the use of hydrocarbon-degrading bacteria to transform toxic compounds into less harmful substances. This approach leverages natural microbial metabolic processes and is considered both cost-effective and ecologically favorable compared to...
162
01:29Bioreactor Design and Operational System
223
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...
223
01:28Methods of Medium Optimization
74
Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
74
01:25Biofuels
112
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...
112