4.7 Review

How does the Internet of Things (IoT) help in microalgae biorefinery?

期刊

BIOTECHNOLOGY ADVANCES
卷 54, 期 -, 页码 -

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.biotechadv.2021.107819

关键词

Microalgae biorefinery; IoT technology; Biosensor; Automation; Machine learning

资金

  1. Fundamental Research Grant Scheme, Malaysia [FRGS/1/2019/STG05/UNIM/02/2]
  2. Taiwan's Ministry of Science and Technology (MOST) [110-2221-E-029 -004 -MY3, 110-2621-M-029 -001, 109-2622-E-110 -011]
  3. PAIRPHC-Hibiscus Grant [MyPAIR/1/2020/STG05/UNIM/1]

向作者/读者索取更多资源

This article introduces the innovative applications of Internet of Things (IoT) technologies in microalgae biorefinery and describes the latest advancements in upstream and downstream processing. It covers automation, sensors, lab-on-chip, and machine learning, among other advanced technologies. The future research directions highlighted include the development of sensors, application of microfluidic technology, robotized microalgae, and other innovative techniques.
Microalgae biorefinery is a platform for the conversion of microalgal biomass into a variety of value-added products, such as biofuels, bio-based chemicals, biomaterials, and bioactive substances. Commercialization and industrialization of microalgae biorefinery heavily rely on the capability and efficiency of large-scale cultivation of microalgae. Thus, there is an urgent need for novel technologies that can be used to monitor, automatically control, and precisely predict microalgae production. In light of this, innovative applications of the Internet of things (IoT) technologies in microalgae biorefinery have attracted tremendous research efforts. IoT has potential applications in a microalgae biorefinery for the automatic control of microalgae cultivation, monitoring and manipulation of microalgal cultivation parameters, optimization of microalgae productivity, identification of toxic algae species, screening of target microalgae species, classification of microalgae species, and viability detection of microalgal cells. In this critical review, cutting-edge IoT technologies that could be adopted to microalgae biorefinery in the upstream and downstream processing are described comprehensively. The current advances of the integration of IoT with microalgae biorefinery are presented. What this review discussed includes automation, sensors, lab-on-chip, and machine learning, which are the main constituent elements and advanced technologies of IoT. Specifically, future research directions are discussed with special emphasis on the development of sensors, the application of microfluidic technology, robotized microalgae, high throughput platforms, deep learning, and other innovative techniques. This review could contribute greatly to the novelty and relevance in the field of IoT-based microalgae biorefinery to develop smarter, safer, cleaner, greener, and economically efficient techniques for exhaustive energy recovery during the biorefinery process.

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