In the last decades, green and sustainable supply chain management practices have been developed, trying to integrate environmental concerns into organisations by reducing unintended negative consequences on the environment of production and consumption processes. In parallel to this, the circular economy discourse has been propagated in the industrial ecology literature and practice. Circular economy pushes the frontiers of environmental sustainability by emphasising the idea of transforming products in such a way that there are workable relationships between ecological systems and economic growth. Therefore, circular economy is not just concerned with the reduction of the use of the environment as a sink for residuals but rather with the creation of self-sustaining production systems in which materials are used over and over again.
Steel slag is the main waste product in the steelmaking process. Because of its chemical composition and technical properties, it can be reused as raw material in steel plants and can serve as a substitute for aggregates in civil engineering. In this paper, we reviewed steel slag treatment, recycling, and management in China. Although China's annual slag production reached more than 100 million tons, its utilization rate is only 29.5%. As of 2016, more than 300 million tons of steel slag have not been used effectively. Large steel slag emissions are causing environmental problems for China. China's steel slag utilization rate is low compared with that of industrial countries: the utilization rate is 98.4% in Japan, 87.0% in Europe, and 84.4% in the United States. Compared with other nations, China also has a gap in its usage of slag in road construction and agriculture. Although the Chinese government has been active in creating a legislative and institutional framework to realize effective steel slag treatment and recycling, these efforts are limited. Outdated treatment approaches is one of the reason for low utilization rate in China, most Chinese steel plants carry out the preliminary treatment (like family workshops) of steel slag, no one system can be used for all ferrous waste recovery, and 47% enterprises' steel slag stability after treatment do not meet requirements of follow-up product. Road construction issues caused by high costs and policy limited, legal restrictions and lack of standard on agricultural applications are other two reasons for low utilization rate of steel slag. New policies are needed to improve utilization rates. We propose the concept of gradual utilization to promote the effective utilization of steel slag.
Internet of Things (IoT) are being adopted for industrial and manufacturing applications such as manufacturing automation, remote machine diagnostics, prognostic health management of industrial machines and supply chain management. Cloud-Based Manufacturing is a recent on-demand model of manufacturing that is leveraging IoT technologies. While Cloud-Based Manufacturing enables on-demand access to manufacturing resources, a trusted intermediary is required for transactions between the users who wish to avail manufacturing services. We present a decentralized, peer-to-peer platform called BPIIoT for Industrial Internet of Things based on the Block chain technology. With the use of Blockchain technology, the BPIIoT platform enables peers in a decentralized, trustless, peer-to-peer network to interact with each other without the need for a trusted intermediary.
Abstract Deep learning applications have been thriving over the last decade in many different domains, including computer vision and natural language understanding. The drivers for the vibrant development of deep learning have been the availability of abundant data, breakthroughs of algorithms and the advancements in hardware. Despite the fact that complex industrial assets have been extensively monitored and large amounts of condition monitoring signals have been collected, the application of deep learning approaches for detecting, diagnosing and predicting faults of complex industrial assets has been limited. The current paper provides a thorough evaluation of the current developments, drivers, challenges, potential solutions and future research needs in the field of deep learning applied to Prognostics and Health Management (PHM) applications.
Abdulrahman M. Abomazid, Nader A. El-Taweel, H. Farag
The production of renewable hydrogen using water electrolysis has emerged with the increasing penetration of renewable energy sources. The energy management system (EMS) plays a key role in the production of renewable hydrogen by controlling electrolyzer’s operating point to achieve operational and economical benefits. In this regard, this article introduces the optimal scheduling for an EMS model for a hydrogen production system integrated with a photovoltaic (PV) system and a battery energy storage system (BESS) to satisfy electricity and hydrogen demands of an industrial hydrogen facility. The proposed EMS model aims to minimize the cost of hydrogen (CoH) production by minimizing the system net costs of industrial hydrogen facility while maintaining a reliable system operation. Furthermore, the proposed EMS model enables the application of seasonal hydrogen storage by incorporating the Z-score statistical measure of historical electricity prices, which follows seasonal electricity price trends. This allows the storage of hydrogen during periods of relatively low electricity prices. To demonstrate the validity of this model, it is tested for both intraseasonal and seasonal storage. Four case studies are used to prove the techno-economic benefits of the proposed EMS model. Furthermore, the impact of the electrolyzer’s capacity factor, the size of the hydrogen storage, and the PV share is investigated in terms of their techno-economic benefits to the system.
Industrial Internet of Things (IIoT) is considered as one of the most promising revolutionary technologies to prompt smart manufacturing and increase productivity. With manufacturing being more complicated and sophisticated, an entire manufacturing process usually involves several different administrative IoT domains (e.g., factories). Devices from different domains collaborate on the same task, which raises great security and privacy concerns about device-to-device communications. Existing authentication approaches may result in heavy key management overhead or rely on a trusted third party. Thus, security and privacy issues during communication remain unsolved but imperative. In this paper, we present an efficient blockchain-assisted secure device authentication mechanism $\textsf{BASA}$ for cross-domain IIoT. Specifically, consortium blockchain is introduced to construct trust among different domains. Identity-based signature (IBS) is exploited during the authentication process. To preserve the privacy of devices, we design an identity management mechanism, which can realize that devices being authenticated remain anonymous. Besides, session keys between two parties are negotiated, which can secure the subsequent communications. Extensive experiments have been conducted to show the effectiveness and efficiency of the proposed mechanism.
Industrial policy is back at the centre stage of policy debate, while the world is undergoing dramatic transformations. This article contributes to the debate by developing a new theory of industrial policy, incorporating some issues that have been neglected so far and taking into account the recent changes in economic reality. The authors explore how the incorporation of some of the neglected issues — commitments under uncertainty, learning in production, macroeconomic management (especially demand management), and conflict management — changes the theory. They then examine how the theory of industrial policy should be modified in light of recent changes in economic reality: the rise of the global value chain, financialization and new imperialism. This contribution aims at promoting a pragmatic approach to industrial policy and pointing to new areas for policy intervention in a changing world.
The extensive embrace of Android has amplified malware risks, resulting in a need for better detection methods. This article investigates the area of static analysis, which analyses applications without execution by examining code and manifest files. We focus on studies from 2022 to 2025, regarding the feature extraction, datasets, feature selection, and approaches based on Machine Learning (ML) and Deep Learning (DL). We conclude by defining the major limitations and research gaps presented in studies regarding static analysis, and many insights for potential development of detection models that are efficient, accurate, and lightweight to improve detection patterns of Android malware.
We study the effectiveness of textual information in predicting the returns of crude oil futures and understanding the behavior of market participants. Using a machine learning method to extract oil market sentiment from news articles, we find that the computed sentiment is significantly effective in explaining the crude oil futures returns, while existing textual analyses based on pre-defined dictionaries may mislead the contexts in the oil market. Consistent with previous findings that returns help explain the change in traders’ positions, the sentiment scores based on the machine learning method are also useful in explaining the behavior of different types of traders. Our empirical findings underscore the fact that accurately identifying textual information can increase the accuracy of oil price predictions and explain divergent behaviors of oil traders.