Fuzzy Logic in HEART and CREAM Methods to Assess Human Error and Find an Optimum Method Using a Hierarchical Fuzzy System: A Case Study in a Steel Factory

Numerous studies have been conducted to assess the role of human errors in accidents in different industries. Human reliability analysis (HRA) has drawn a great deal of attention among safety engineers and risk assessment analyzers. Despite all technical advances and the development of processes, damaging and catastrophic accidents still happen in many industries. Human Error Assessment and Reduction Technique (HEART) and Cognitive Reliability and Error Analysis Method (CREAM) methods were compared with the hierarchical fuzzy system in a steel industry to investigate the human error. This study was carried out in a rolling unit of the steel industry, which has four control rooms, three shifts, and a total of 46 technicians and operators. After observing the work process, reviewing the documents, and interviewing each of the operators, the worksheets of each research method were completed. CREAM and HEART methods were defined in the hierarchical fuzzy system and the necessary rules were analyzed. The findings of the study indicated that CREAM was more successful than HEART in showing a better capability to capture task interactions and dependencies as well as logical estimation of the HEP in the plant studied. Given the nature of the tasks in the studied plant and interactions and dependencies among tasks, it seems that CREAM is a better method in comparison with the HEART method to identify errors and calculate the HEP.

Denoising and Dehazing an Image in a Cascaded Pattern for Continuous Casting

Automatic vision systems have been widely used in the continuous casting of the steel industry, which improve efficiency and reduce labor. At present, high temperatures with evaporating fog cause images to be noisy and hazy, impeding the usage of advanced machine learning algorithms in this task. Instead of considering denoising and dehazing separately like previous papers, we established that by taking advantage of deep learning in a modeling complex formulation, our proposed algorithm, called Cascaded Denoising and Dehazing Net (CDDNet) reduces noise and hazy in a cascading pattern. Experimental results on both synthesized images and a pragmatic video from a continuous casting factory demonstrate our method’s superior performance in various metrics. Compared with existing methods, CDDNet achieved a 50% improvement in terms of peak signal-to-noise ratio on the validation dataset, and a nearly 5% improvement on a dataset that has never seen before. Besides, our model generalizes so well that processing a video from an operating continuous casting factory with CDDNet resulted in high visual quality.

Physicochemical properties of particulate matter (PM2.5) from the steel industry in Indonesia

Particulate matter (PM) as one of the pollutants in the atmosphere needs to be studied. PM has physical and chemical characteristics and is called physicochemical properties. These properties vary depending on the source of the PM. PM samplers are used for air sampling to characterize some fine particles (PM2.5). The PM2.5 samples have collected from four sampling sites in the steel industry in Cilegon, Indonesia. The sampling sites are the main gate, the hot strip mill, the billet post, and the hot blast plant. The sampling period was four months. The physicochemical properties analysed are morphology, elements content, heavy metals, and particle size. The instruments used to analyse were Scanning Electron Microscopy (SEM) and Energy Dispersive Spectrometry (EDS), Inductively Coupled Plasma Atomic Emission Spectroscopy (ICP-AES), and Particle Size Analyzer (PSA). The morphology of PM2.5 detected varied, but the elements and the most elements found were F and C particles. The metals concentration was below the Indonesia Regulation. While the average particle size analysed was below 2,500 nm. The physicochemical properties of PM2.5 are affected by the type of production process in the industry.

Unraveling the interactions of reductants and reaction path over Cu-zeolite for coal-gas- via transient reaction study

with coal gas can be a solution for NOx emission control in iron and steel industry, nevertheless the coal-gas- is not clearly understood and hard to study due to the...