Evaluating the Efficiency of the Process Safety Management System through Analysis of Major Industrial Accidents in South Korea

Yoo, Byungtae; Shim, Woo Sub

doi:10.3390/pr11072022

Cited by 1 publication

(1 citation statement)

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“…Nevertheless, human and material casualties continue due to accidents involving human mistakes [4]. Oil refineries and petrochemical plants have a high rate of catastrophic accidents at their facilities, whereas only petrochemical and oil refining plants accounted for 67% of all significant accidents in Republic of Korea between 2005 and 2021 [5]. Since 1992, the oil refining industry has experienced many fatal and catastrophic incidents related to releasing highly hazardous chemicals in the U.S. [6].…”

Section: Introductionmentioning

confidence: 99%

A Smart Control System for the Oil Industry Using Text-to-Speech Synthesis Based on IIoT

et al. 2023

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Oil refineries have high operating expenses and are often exposed to increased asset integrity risks and functional failure. Real-time monitoring of their operations has always been critical to ensuring safety and efficiency. We proposed a novel Industrial Internet of Things (IIoT) design that employs a text-to-speech synthesizer (TTS) based on neural networks to build an intelligent extension control system. We enhanced a TTS model to achieve high inference speed by employing HiFi-GAN V3 vocoder in the acoustic model FastSpeech 2. We experimented with our system on a low resources-embedded system in a real-time environment. Moreover, we customized the TTS model to generate two target speakers (female and male) using a small dataset. We performed an ablation analysis by conducting experiments to evaluate the performance of our design (IoT connectivity, memory usage, inference speed, and output speech quality). The results demonstrated that our system Real-Time Factor (RTF) is 6.4 (without deploying the cache mechanism, which is a technique to call the previously synthesized speech sentences in our system memory). Using the cache mechanism, our proposed model successfully runs on a low-resource computational device with real-time speed (RTF equals 0.16, 0.19, and 0.29 when the memory has 250, 500, and 1000 WAV files, respectively). Additionally, applying the cache mechanism has reduced memory usage percentage from 16.3% (for synthesizing a sentence of ten seconds) to 6.3%. Furthermore, according to the objective speech quality evaluation, our TTS model is superior to the baseline TTS model.

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Section: Introductionmentioning

confidence: 99%