English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Increasing the use and application of hydrogen as a renewable energy source or application in industrial processes, has increased a pressure to ensure the safety handling and that monitoring. The safety issues related to hydrogen gas are further exasperated by expensive instrumentation required to measure the percentage of explosive limits, flow rates and generated pressure. An innovative solution to address the safety issues of hydrogen and costly physical sensor is to use virtual sensors based on neural network which learn from the data to predict the parameters related to the safety of hydrogen. This study aimed to obtain the dataset that describe the entire behavior of the sintering process with a hydrogen atmosphere. This research developed a data acquisition system for the sintering process and testing using real sintering process to obtain the necessary data. The results of experiments on the data acquisition system for online data acquisition to measure process variables showed a good performance. The obtained data can be described from the overall behavior of hydrogen in the sintering process. Thus these data can be used as a dataset for modeling neural networks in predicting the safety parameters of hydrogen in future studies.

Sistem Akusisi Data Online Proses Sintering dengan Atmosfir Hidrogen untuk Prediksi Parameter Keselamatan