About the Concept of the Environment Recycling—Energy (ERE) in the Romanian Steel Industry

This paper takes as its starting point an analysis of the ecological functioning of the electric arc furnace (EAF). Thus, we present a classification of emissions generated by EAF, including limits of variation in chemical composition of “dust” issued by EAF in various countries and limit values for permissible concentrations of these emissions.The paper presents and analyzes various abstraction and treatment-related emissions for hipo-polluting operation of EAF. In this chapter, the correlations between macro system represented by metallurgical environment and interacting systems: System-Energy-Recycling Environment (ERE), Ecological system (ECO), and Recycling, Reclamation System (REC-REV) are presented. These correlations are presented in the spirit of sustainable development concepts (DC) and total quality (TQ)

Research and Devolopment about Metallurgical Industry of Romania

Our chapter wishes to be a critical and reasoned radiography of the evolution/involution of metallurgical industry in Romania during the period 1990–2016. The importance of metallurgical industry, for any state, is obvious and overwhelming. In this context, paraphrasing a known dictum, we can strongly say that in the industrial environment and in life generally ‘if there is no metallurgy, nothing is!’ The structure and content of our material is logical one and evolutionary. So, we firstly present in order a description of the main metallurgical companies in Romania (companies in the steelmaking industry: COS Mechel Targoviste, ArcelorMittal Galati; companies from non‐ferrous metallurgy: Alro Slatina, CupruMin Abrud; metallurgical companies in the manufacturing and assembly industry: Metalurgica Aiud, Timken S.A Ploiesti). In Section 3, we describe critical components concerning the involution of steel industry in Romania. Thus, we analyse: benchmarks, restructuring of the steel industry in Romania, the impact of the global crisis on the steel sector in Romania, privatization, modernization/restructuring and monitoring between 2004 and 2008. In Section 4, we present the prospects of metallurgical industry in Romania, and in Section 5, we present technical‐economic components specific to the industry of metal materials

STOCHASTIC MODELING OF STRATEGIC SUPPLY CHAIN DESIGN

Supply chain risk management plays a critical role in the any business or industry environments, and it enables a good coordination of the input and outputs parameters that may affect the smooth processes development such as a manufacturing process for example. However, the supply chain risk management is often prone to the impact of various uncertainties associated with supply chain disruptions caused by meteorological, pandemic, resources shortage, etc. Therefore, one way to quantify these uncertainties are the stochastic modeling approaches of supply chain management. The stochastic modeling is a powerful tool that can predict with certain probability the events that may occur within the supply chain such as that associated with manufacturing processes. In the present research a stochastic model, based on probability theory, is developed and proposed for the analysis of supply chain risk management, for manufacturing processes. Therefore, the studies are performed to investigate the impact of the number of manufacturing processes on the supply chain proper evolution. The current study shows that the increase of the number of the manufacturing processes results in an increase of uncertainty in the supply chain management and thus, it increases the probability of supply chain disruption occurrences, within the supply chain. Therefore, it is recommended that a supply chain should contain a minimum number of manufacturing process, if the delivery time and final product allows

SUPPLY CHAIN MANAGEMENT OF MANUFACTURING PROCESSES USING MACHINE LEARNING TECHNIQUE

The expansion of the manufacturing processes network requires algorithms that can enable better planning and optimization of the manufacturing processes. Therefore, in the recent years the developments within the machine-learning (ML) and artificial intelligence (AI) have led to a new terminology, the so-called Industry 4.0. The fastest growth of Industry 4.0 has been encountered in the manufacturing, supply chain, services and products. The machine learning is prone to enable the development of smart supply-chains and manufacturing processes. The present research concerns the suitability and efficiency of the machine learning algorithm for the enhanced supply chain in manufacturing processes. The results show that the machine learning algorithm enables and enhances the efficiency of the manufacturing processes by clustering the machine-tools and increasing the number of manufactured components at the same tool location

Geopolymer Ceramic Application: A Review on Mix Design, Properties and Reinforcement Enhancement

Geopolymers have been intensively explored over the past several decades and consid‐ ered as green materials and may be synthesised from natural sources and wastes. Global attention has been generated by the use of kaolin and calcined kaolin in the production of ceramics, green cement, and concrete for the construction industry and composite materials. The previous findings on ceramic geopolymer mix design and factors affecting their suitability as green ceramics are re‐ viewed. It has been found that kaolin offers significant benefit for ceramic geopolymer applications, including excellent chemical resistance, good mechanical properties, and good thermal properties that allow it to sinter at a low temperature, 200 °C. The review showed that ceramic geopolymers can be made from kaolin with a low calcination temperature that have similar properties to those made from high calcined temperature. However, the choice of alkali activator and chemical com‐ position should be carefully investigated, especially under normal curing conditions, 27 °C. A com‐ prehensive review of the properties of kaolin ceramic geopolymers is also presented, including compressive strength, chemical composition, morphological, and phase analysis. This review also highlights recent findings on the range of sintering temperature in the ceramic geopolymer field which should be performed between 600 °C and 1200 °C. A brief understanding of kaolin geopolymers with a few types of reinforcement towards property enhancement were covered. To improve toughness, the role of zirconia was highlighted. The addition of zirconia between 10% and 40% in geopolymer materials promises better properties and the mechanism reaction is presented. Findings from the review should be used to identify potential strategies that could develop the performance of the kaolin ceramic geopolymers industry in the electronics industry, cement, and biomedical materials

Using Brain-Computer Interface to Control a Virtual Drone Using Non-Invasive Motor Imagery and Machine Learning

In recent years, the control of devices “by the power of the mind” has become a very controversial topic but has also been very well researched in the field of state-of-the-art gadgets, such as smartphones, laptops, tablets and even smart TVs, and also in medicine, to be used by people with disabilities for whom these technologies may be the only way to communicate with the outside world. It is well known that BCI control is a skill and can be improved through practice and training. This paper aims to improve and diversify signal processing methods for the implementation of a brain-computer interface (BCI) based on neurological phenomena recorded during motor tasks using motor imagery (MI). The aim of the research is to extract, select and classify the characteristics of electroencephalogram (EEG) signals, which are based on sensorimotor rhythms, for the implementation of BCI systems. This article investigates systems based on brain-computer interfaces, especially those that use the electroencephalogram as a method of acquisition of MI tasks. The purpose of this article is to allow users to manipulate quadcopter virtual structures (external, robotic objects) simply through brain activity, correlated with certain mental tasks using undecimal transformation (UWT) to reduce noise, Independent Component Analysis (ICA) together with determination coefficient (r2) and, for classification, a hybrid neural network consisting of Radial Basis Functions (RBF) and a multilayer perceptron–recurrent network (MLP–RNN), obtaining a classification accuracy of 95.5%. Following the tests performed, it can be stated that the use of biopotentials in human–computer interfaces is a viable method for applications in the field of BCI. The results presented show that BCI training can produce a rapid change in behavioral performance and cognitive properties. If more than one training session is used, the results may be beneficial for increasing poor cognitive performance. To achieve this goal, three steps were taken: understanding the functioning of BCI systems and the neurological phenomena involved; acquiring EEG signals based on sensorimotor rhythms recorded during MI tasks; applying and optimizing extraction methods, selecting and classifying characteristics using neuronal networks

Using Brain-Computer Interface to Control a Virtual Drone Using Non-Invasive Motor Imagery and Machine Learning

In recent years, the control of devices “by the power of the mind” has become a very controversial topic but has also been very well researched in the field of state-of-the-art gadgets, such as smartphones, laptops, tablets and even smart TVs, and also in medicine, to be used by people with disabilities for whom these technologies may be the only way to communicate with the outside world. It is well known that BCI control is a skill and can be improved through practice and training. This paper aims to improve and diversify signal processing methods for the implementation of a brain-computer interface (BCI) based on neurological phenomena recorded during motor tasks using motor imagery (MI). The aim of the research is to extract, select and classify the characteristics of electroencephalogram (EEG) signals, which are based on sensorimotor rhythms, for the implementation of BCI systems. This article investigates systems based on brain-computer interfaces, especially those that use the electroencephalogram as a method of acquisition of MI tasks. The purpose of this article is to allow users to manipulate quadcopter virtual structures (external, robotic objects) simply through brain activity, correlated with certain mental tasks using undecimal transformation (UWT) to reduce noise, Independent Component Analysis (ICA) together with determination coefficient (r2) and, for classification, a hybrid neural network consisting of Radial Basis Functions (RBF) and a multilayer perceptron–recurrent network (MLP–RNN), obtaining a classification accuracy of 95.5%. Following the tests performed, it can be stated that the use of biopotentials in human–computer interfaces is a viable method for applications in the field of BCI. The results presented show that BCI training can produce a rapid change in behavioral performance and cognitive properties. If more than one training session is used, the results may be beneficial for increasing poor cognitive performance. To achieve this goal, three steps were taken: understanding the functioning of BCI systems and the neurological phenomena involved; acquiring EEG signals based on sensorimotor rhythms recorded during MI tasks; applying and optimizing extraction methods, selecting and classifying characteristics using neuronal networks

Recent Advances in Stimuli-Responsive Doxorubicin Delivery Systems for Liver Cancer Therapy

Doxorubicin (DOX) is one of the most commonly used drugs in liver cancer. Unfortunately, the traditional chemotherapy with DOX presents many limitations, such as a systematic release of DOX, affecting both tumor tissue and healthy tissue, leading to the apparition of many side effects, multidrug resistance (MDR), and poor water solubility. Furthermore, drug delivery systems’ responsiveness has been intensively studied according to the influence of different internal and external stimuli on the efficiency of therapeutic drugs. In this review, we discuss both internal stimuli-responsive drug-delivery systems, such as redox, pH and temperature variation, and external stimuli-responsive drug-delivery systems, such as the application of magnetic, photo-thermal, and electrical stimuli, for the controlled release of Doxorubicin in liver cancer therapy, along with the future perspectives of these smart delivery systems in liver cancer therapy

Application possibilities of fuzzy networks concept in metallurgical industry

Our article presents possibilities of applying the concept Fuzzy Networks for an efficient metallurgical industry in Romania. We also present and analyze Fuzzy Networks complementary concepts, such as Expert Systems (ES), Enterprise Resource Planning (ERP), Analytics and Intelligent Strategies (SAI). The main results of our article are based on a case study of the possibilities of applying these concepts in metallurgy through Fuzzy Networks. There are presented the domains afferent to KIBS are defined complying with the standardized classification of industrial sectors according to European Monitoring Centre on Change (EMCC). It is also analyze important approaches in the specific scientific literature, the research methodology a study case. KIBS concept implementation to the Romanian metallurgical industry aims also to include performance of activities concerning the formulation and development of new specific concepts

Retail optimization in Romanian metallurgical industry by applying of fuzzy networks concept

Our article presents possibilities of applying the concept Fuzzy Networks for an efficient metallurgical industry in Romania. We also present and analyze Fuzzy Networks complementary concepts, such as Expert Systems (ES), Enterprise Resource Planning (ERP), Analytics and Intelligent Strategies (SAI). The main results of our article are based on a case study of the possibilities of applying these concepts in metallurgy through Fuzzy Networks. Also, it is presented a case study on the application of the FUZZY concept on the Romanian metallurgical industry