Big Data Technologies are the key to optimizing and improving new processes. In the context of bioelectrochemical systems - BES, there are several possibilities. Thus, we developed a pipeline for real-time BES monitoring benefiting the maintenance and prediction of future operational failures.
Big Data Technologies are fundamentals for process improvements and optimization in the most diverse areas. In the context of bioelectrochemical systems - BES, which are devices that transform waste into electricity, there are several opportunities. Since the complexity of the physical and chemical phenomena associated with these systems generates large vulnerabilities which need to be mitigated.
At this point, it is possible to apply Big Data tools to optimize, monitor an prevent possible failures during the operation of bioelectrochemical cells. In this context, this study presents the development of a Big Data platform for real-time BES monitoring. The ability of integration with sensors, scalability for multiple cells monitoring, resilience, fault tolerance, high availability, and open source technologies were considered in during the development of this pipeline.
To achieve our goals, the following technologies have been integrated: analog sensors, Arduino, Kafka, Zookeeper, Spark, Elasticsearch, Kibana, Secor, Amazon S3 and DC/OS (“Platform as a Service”). Our results show the viability of integrating Big Data platforms for BES real-time monitoring in a scalable way, benefiting the maintenance and future prediction of failures during these systems operation. Therefore, the integration of the several knowledge areas, from software development to biotechnological processes, is fundamental for the advancement and future commercialization of BES based-technologies.
Engenheira Sanitarista e Doutora em Engenharia Química.
Atua no desenvolvimento de novas tecnologias para o tratamento de resíduos e reuso de água além da aplicação de ferramentas de Big Data para otimização desses processos.