Material Flow Analysis with Multiple Material Characteristics to Assess the Potential for Flat Steel Prompt Scrap Prevention and Diversion without Remelting

Coupled Dynamic Material Flow, Multimedia Environmental Model, and Ecological Risk Analysis for Chemical Management: A Di(2-ethylhexhyl) Phthalate Case in China

Di(2-ethylhexhyl) phthalate (DEHP) is a widely used plasticizer that has adverse effects on ecosystems and human health. However, data about its stocks, flows, emission rates, as well as ecological risks are generally unknown in China, one of the world's largest producers of chemicals including DEHP, limiting sound management of chemicals. Herein, dynamic material flow analysis, coupled with a multimedia environmental model and ecological risk analysis, was performed to fill the data gap about DEHP in China mainland from 1956 to 2020. Results indicate that the in-use stocks of DEHP increased from 6.54 × 10⁶ kg in 1956 to 8.40 × 10⁹ kg in 2020. With growth in the emission rates, DEHP concentrations in air, soil, water, and sediment kept increasing from 1956 to 2010, which declined after 2010 and regrew after 2015. Sediment was a main sink of DEHP with the highest ecological risk quotient of >10 after 1999, necessitating measures for controlling the risk, for example, technology innovation to reduce DEHP emission rates, and substitution of DEHP with low-toxic alternatives. The coupled models that connect socio-economic data with ecological risk output may provide a systematic methodology for verification of the data necessary for risk control of chemicals.

Multiscale Price Lead-Lag Relationship between Steel Materials and Industry Chain Products Based on Network Analysis

As two main steelmaking materials, iron ore and scrap steel have different price lead-lag relationships (PLRs) on midstream and downstream steel products in China. The relationships also differ as the time scale varies. In this study, we compare the price influences of two important steel materials on midstream and downstream steel products at different time scales. First, we utilize the maximal overlap discrete wavelet transform (MODWT) method to decompose the original steel materials and products price series into short-term, midterm, and long-term time scale series. Then, we introduce the cross-correlation and Podobnik test method to calculate and test the price lead-lag relationships (PLRs) between two steel materials and 16 steel products. Finally, we construct 12 price lead-lag relationship networks and choose network indicators to present the price influence of the two materials at different time scales. We find that first, most scrap steel and steel products prices fluctuate at the same time lag order, while iron ore leads most steel products price for one day. Second, products that exist in the downstream industry chain usually lead to iron ore. Third, as the time scale becomes longer, the lead relationships from steel materials to steel products become closer.

Applying probabilistic material flow analysis for quality control and management of waste recycling in steelmaking

In modern steelmaking, multiple processes comprise a continuous manufacturing system, but not all phosphorus content data are connected or integrated into a holistic and systematic database. Disconnected data hinder the improvement of material management and resource efficiency in the industry. The objective of this study was to establish a method to evaluate material flows, reduce uncertainty, and perform quality control for waste recycling in the steelmaking industry. The results indicate that 10% of the phosphorus input is present in the final products, 30% accumulates in the slags, and more than 60% of the total mass remains in the processes. Comparing the material flow analysis results obtained using static and probabilistic approaches, the partition ratio of the phosphorus content in slags changes from 24.07% to 40.78%, but that in processes changes from 49.10% to 68.05%. This indicates that the variations in phosphorus content in slags and processes might affect the effectiveness of slag recycling and might increase the resource consumption required to maintain the quality of final products. The probability of forming substandard products in the baseline scenario is 0.43. Adopting a 50% removal rate, the probabilities of forming substandard products are reduced to 0.36 (waste removal scenario), 0.38 (slag reduction scenario), and 0.31 (raw material treatment scenario). The performance of raw material treatment and waste removal is more efficient for quality control. The method used in this study can be applied to evaluate the possible outcomes of waste recycling and reduce the probability of forming substandard products.