智能藻类生物面板及其基于机器学习的生长预测研究进展

A. Husainy, Omkar S. Chougule, Prathamesh U. Jadhav, Samir N. Momin, Sanmesh S. Shinde
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摘要

世界正面临着与依赖化石燃料提供能源有关的重大问题,包括价格上涨、温室气体排放和枯竭风险。人们已经开发出各种技术来固定导致全球变暖的二氧化碳。利用大规模培养的光合微藻进行生物固定是一种很有前途的方法。在这种方法中,碳要么完全储存在藻类生物质中,要么替代化石燃料。藻类生物量可以降解为二氧化碳或甲烷,释放到大气中。近年来,微藻作为可再生能源和生物燃料的可持续来源引起了人们的极大关注。微藻的优点之一是它们能够积累高水平的脂质,使它们成为生物燃料生产的有前途的原料。此外,微藻可以在非耕地上种植,也可以利用海水等替代水源种植,这进一步增强了微藻作为可持续和环境友好型能源的潜力。光生物反应器(PBR)是微藻光合固定CO2的重要设备。在智能生物面板中实现的PBR系统利用藻类捕获太阳光并将其转化为电能,同时还产生生物质作为副产品并充当二氧化碳洗涤器。为使系统智能化,采用机器学习算法监测和预测藻类生长速率,并采用支持向量机(SVM)对微藻生长行为进行预测,结果表明,基于支持向量机的模型预测微藻生长速率的相关系数为90%。微藻生物量生产严重依赖光合作用,而光合作用仅利用太阳能的一小部分,主要是蓝色和红色波长。然而,在传统的微藻养殖中,未使用的太阳光谱部分加热了藻类池塘,导致水蒸发,导致盐度增加,特别是在炎热和半干旱地区。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Review on Smart Algae Bio Panel and its Growth Forecasting Using Machine Learning
The world is facing major issues associated with the reliance on fossil fuels for energy supply, including rising prices, greenhouse gas emissions, and the risk of depletion. Various technologies have been developed for fixing carbon dioxide, which contributes to global warming. Biological fixation using photosynthetic microalgae cultured on a large scale is a promising method. In this method, carbon should be either wholly stored in the algal biomass or substituted for fossil fuel. Algal biomass can be degraded to carbon dioxide or methane, which is released to the atmosphere. The use of microalgae as a sustainable source of renewable energy and biofuels has garnered significant attention in recent years. One of the advantages of microalgae is their ability to accumulate high levels of lipids, making them a promising feedstock for biofuel production. Moreover, microalgae can be cultivated on non-arable land and can be grown using alternative water sources such as seawater, which further enhances their potential as a sustainable and environmentally friendly energy source. A photo bioreactor (PBR) is essential equipment for microalgal photosynthetic fixation of CO2. A PBR system implemented in a smart bio panel utilizes algae to trap sunlight energy and convert it into electricity, while also generating biomass as a by-product and acting as a CO2 scrubber. To make the system smart, machine learning algorithms were implemented to monitor and predict the growth rate of the algae Support Vector Machines (SVM) were used to predict the growth behavior of the microalgae, and the results showed that the SVM-based model can predict the growth rate of microalgae with a correlation coefficient of 90 percent. Microalgae biomass production heavily relies on photosynthesis, which only utilizes a small portion of the solar energy, mainly in the blue and red wavelengths. However, in traditional microalgae cultivation, the unused portion of the solar spectrum heats up the algae ponds and causes water evaporation, leading to increased salinity, especially in hot and semi-arid locations.
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