Optimal Control Problem of Converter Steelmaking Production Process Based on Operation Optimization Method

Dynamic operation optimization has been utilized to realize optimal control problem for converter. The optimal control indicator is determined via current state of converter smelting production process, and the set values of operation variable would control converter production. Relationship between various operating variables, current temperature, and carbon content is constructed through operation analysis of a great deal of actual production data; then, the dynamic optimal control indicator is derived from historical excellent smelting data; finally, the dynamic operation optimization model is built by taking the minimum deviation between the current data—molten steel temperature and carbon content—and optimal data which are determined by the optimal control indicator as objective function. DE (differential evolution) with improved strategy is used to solve the proposed model for obtaining the set values of each operating variable, which is beneficial for further control. Simulation of actual production data shows the feasibility and efficiency of the proposed method. That proved that the proposed method solves the optimal control problem of converter steelmaking process as well

Challenges and Prospects of Steelmaking Towards the Year 2050

The world steel industry is strongly based on coal/coke in ironmaking, resulting in huge carbon dioxide emissions corresponding to approximately 7% of the total anthropogenic CO2 emissions. As the world is experiencing a period of imminent threat owing to climate change, the steel industry is also facing a tremendous challenge in next decades. This themed issue makes a survey on the current situation of steel production, energy consumption, and CO2 emissions, as well as cross-sections of the potential methods to decrease CO2 emissions in current processes via improved energy and materials efficiency, increasing recycling, utilizing alternative energy sources, and adopting CO2 capture and storage. The current state, problems and plans in the two biggest steel producing countries, China and India are introduced. Generally contemplating, incremental improvements in current processes play a key role in rapid mitigation of specific emissions, but finally they are insufficient when striving for carbon neutral production in the long run. Then hydrogen and electrification are the apparent solutions also to iron and steel production. The book gives a holistic overview of the current situation and challenges, and an inclusive compilation of the potential technologies and solutions for the global CO2 emissions problem

Book of abstracts of the 15th International Symposium of Croatian Metallurgical Society - SHMD \u272022, Materials and metallurgy

Book of abstracts of the 15th International Symposium of Croatian Metallurgical Society - SHMD \u272022, Materials and metallurgy, Zagreb, Croatia, March 22-23, 2022. Abstracts are organized in four sections: Materials - section A; Process metallurgy - Section B; Plastic processing - Section C and Metallurgy and related topics - Section D

Book of abstracts of the 15th International Symposium of Croatian Metallurgical Society - SHMD \u272022, Materials and metallurgy

Book of abstracts of the 15th International Symposium of Croatian Metallurgical Society - SHMD \u272022, Materials and metallurgy, Zagreb, Croatia, March 22-23, 2022. Abstracts are organized in four sections: Materials - section A; Process metallurgy - Section B; Plastic processing - Section C and Metallurgy and related topics - Section D

Intelligent Systems

This book is dedicated to intelligent systems of broad-spectrum application, such as personal and social biosafety or use of intelligent sensory micro-nanosystems such as "e-nose", "e-tongue" and "e-eye". In addition to that, effective acquiring information, knowledge management and improved knowledge transfer in any media, as well as modeling its information content using meta-and hyper heuristics and semantic reasoning all benefit from the systems covered in this book. Intelligent systems can also be applied in education and generating the intelligent distributed eLearning architecture, as well as in a large number of technical fields, such as industrial design, manufacturing and utilization, e.g., in precision agriculture, cartography, electric power distribution systems, intelligent building management systems, drilling operations etc. Furthermore, decision making using fuzzy logic models, computational recognition of comprehension uncertainty and the joint synthesis of goals and means of intelligent behavior biosystems, as well as diagnostic and human support in the healthcare environment have also been made easier

Enhancement of Industrial Energy Efficiency and Sustainability

Industrial energy efficiency has been recognized as a major contributor, in the broader set of industrial resources, to improved sustainability and circular economy. Nevertheless, the uptake of energy efficiency measures and practices is still quite low, due to the existence of several barriers. Research has broadly discussed them, together with their drivers. More recently, many researchers have highlighted the existence of several benefits, beyond mere energy savings, stemming from the adoption of such measures, for several stakeholders involved in the value chain of energy efficiency solutions. Nevertheless, a deep understanding of the relationships between the use of the energy resource and other resources in industry, together with the most important factors for the uptake of such measures—also in light of the implications on the industrial operations—is still lacking. However, such understanding could further stimulate the adoption of solutions for improved industrial energy efficiency and sustainability

Optimization of Operation Sequencing in CAPP Using Hybrid Genetic Algorithm and Simulated Annealing Approach

In any CAPP system, one of the most important process planning functions is selection of the operations and corresponding machines in order to generate the optimal operation sequence. In this paper, the hybrid GA-SA algorithm is used to solve this combinatorial optimization NP (Non-deterministic Polynomial) problem. The network representation is adopted to describe operation and sequencing flexibility in process planning and the mathematical model for process planning is described with the objective of minimizing the production time. Experimental results show effectiveness of the hybrid algorithm that, in comparison with the GA and SA standalone algorithms, gives optimal operation sequence with lesser computational time and lesser number of iterations