A new process of smelting laterite by lowtemperature reduction and microwave irradiation

A new smelting process for the reduction of laterite with a relatively high yield of Ni has been presented in this paper; particularly, this process has been underwent industrialized experiment in China. This new process was based on the following fundamental research results. (1) Since nickel oxide can be reduced more easily than iron oxide, the nickel content of the produced ferronickel alloy increases significantly when the quantity of coal that is mixed with laterite ore is optimized, leading to a right oxygen potential of gas for reduction reaction. (2) When laterite is reduced at 1150°C, only 75% of the reduced ferronickel product can be magnetically separated from slag, although more than 95% Ni has been reduced from laterite. (3) The key technology is to realize a fast carburization at 1300°C by microwave irradiation, which causes ferronickel fines grow to a large size in the semi-melting state so that they can be separated magnetically with a high efficiency. The first demonstration production line with an annual capacity of 100 kt green balls has been established in China. The Ni content reaches 13.5%, moreover, both P and S contents are also within the expected range

Universal Multi-modal Entity Alignment via Iteratively Fusing Modality Similarity Paths

The objective of Entity Alignment (EA) is to identify equivalent entity pairs from multiple Knowledge Graphs (KGs) and create a more comprehensive and unified KG. The majority of EA methods have primarily focused on the structural modality of KGs, lacking exploration of multi-modal information. A few multi-modal EA methods have made good attempts in this field. Still, they have two shortcomings: (1) inconsistent and inefficient modality modeling that designs complex and distinct models for each modality; (2) ineffective modality fusion due to the heterogeneous nature of modalities in EA. To tackle these challenges, we propose PathFusion, consisting of two main components: (1) MSP, a unified modeling approach that simplifies the alignment process by constructing paths connecting entities and modality nodes to represent multiple modalities; (2) IRF, an iterative fusion method that effectively combines information from different modalities using the path as an information carrier. Experimental results on real-world datasets demonstrate the superiority of PathFusion over state-of-the-art methods, with 22.4%-28.9% absolute improvement on Hits@1, and 0.194-0.245 absolute improvement on MRR

MEAformer: Multi-modal Entity Alignment Transformer for Meta Modality Hybrid

As an important variant of entity alignment (EA), multi-modal entity alignment (MMEA) aims to discover identical entities across different knowledge graphs (KGs) with relevant images attached. We noticed that current MMEA algorithms all globally adopt the KG-level modality fusion strategies for multi-modal entity representation but ignore the variation in modality preferences for individual entities, hurting the robustness to potential noise involved in modalities (e.g., blurry images and relations). In this paper, we present MEAformer, a multi-modal entity alignment transformer approach for meta modality hybrid, which dynamically predicts the mutual correlation coefficients among modalities for entity-level feature aggregation. A modal-aware hard entity replay strategy is further proposed for addressing vague entity details. Experimental results show that our model not only achieves SOTA performance on multiple training scenarios including supervised, unsupervised, iterative, and low resource, but also has a comparable number of parameters, optimistic speed, and good interpretability. Our code and data are available at https://github.com/zjukg/MEAformer.Comment: Repository: https://github.com/zjukg/MEAforme