Predicting Mechanical Properties of Galvanized Steels: Data Mining Approach

The purpose of this paper is to predict the mechanical properties of galvanized steel, using appropriate data mining techniques such as neural network, support vector machine, regression analysis and regression tree methods. It is found that by using the neural network technique one can get the best result for predicting the mechanical properties of galvanized steel according to the values of input parameters and also considering the effects of annealing temperature and line speed as the controlling parameters

Energy Conservation and Heat Transfer Enhancement for Mixed Convection on the Vertical Galvanizing Furnace

The alloying temperature is an important parameter that affects the properties of galvanized products. The objective of this study is to explore the mechanism of conjugate mixed convection in the vertical galvanizing furnace and propose a novel energy conservation method to improve the soaking zone temperature based on the flow pattern and heat transfer characteristics. Herein, the present study applied the k-ε two-equation turbulence model to enclose the Navier-Stokes fluid dynamic and energy conservation equations, and the temperature distributions of the steel plate and air-flow field in the furnace were obtained for six Richardson numbers between 1.91 ⋅ 105 and 6.30 ⋅ 105. In the industrial practice, the side baffles were installed at the lateral opening of the cooling tower to alter the height of vertical flow passage, which affected the Richardson number. The results indicate that the Richardson number of 2.4 ⋅ 105 generated the highest heat absorption and maximal temperature in the steel plate due to the balance between natural and forced convection. Furthermore, the results of the on-line experiments validated the simulation research. The method enhanced the steel plate temperature in the soaking zone without increasing the heat power, thereby characterizing it as energy conservation technology

Evaluating Decomposition Strategies to Enable Scalable Scheduling for a Real-World Multi-line Steel Scheduling Problem

Steel scheduling is recognised as one of the most difficult real-world scheduling problems. It is characterised by a wide range of operational constraints, variable dependencies and multiple objectives. This paper uses a divide and conquer method to reduce the combinatorial complexity of a real-world multi-line steel scheduling problem. The problem is first decomposed into sub-problems which are solved individually in parallel using parallel branch and bound, then sub-problems are combined to form a solution to the original problem. Three decomposition strategies are compared, specifically: a manual heuristic domain knowledge (DOM) intensive strategy, K-means++ (KM) clustering and Self-organising maps (SOM). Experimental results show that using SOM for decomposition is a promising approach. This paper demonstrates that despite being a highly complex and constrained problem, it is possible to use divide and conquer to achieve potentially good scalability characteristics without significant detriment to the solution quality

Evaluating decomposition strategies to enable scalable scheduling for a real-world multi-line steel scheduling problem

Steel scheduling is recognised as one of the most difficult real-world scheduling problems. It is characterised by a wide range of operational constraints, variable dependencies and multiple objectives. This paper uses a divide and conquer method to reduce the combinatorial complexity of a real-world multi-line steel scheduling problem. The problem is first decomposed into sub-problems which are solved individually in parallel using parallel branch and bound, then sub-problems are combined to form a solution to the original problem. Three decomposition strategies are compared, specifically: a manual heuristic domain knowledge (DOM) intensive strategy, K-means++ (KM) clustering and Self-organising maps (SOM). Experimental results show that using SOM for decomposition is a promising approach. This paper demonstrates that despite being a highly complex and constrained problem, it is possible to use divide and conquer to achieve potentially good scalability characteristics without significant detriment to the solution quality

The New York City Health and Hospitals Corporation: Transforming a Public Safety Net Delivery System to Achieve Higher Performance

Describes the results of the public benefit corporation's improvement initiatives -- a common clinical information system for continuity, coordination on chronic disease management, teamwork and continuous innovation, and access to appropriate care

Assessing the effectiveness of preventative maintenance for production systems : a case of a South African steel manufacturer

Abstract: Organisations nowadays are placing more emphasis on availability and reliability of systems, including safety in production plants. The uptime of production systems substantially contributes to an organisation’s productiveness, and the organisation's ability to become or remain competitive; for this reason, maintenance is now known to be compulsory and necessary. Companies are now changing their organisational maintenance policy from that of being reactive to more preventative strategies. This research was conducted at ArcelorMittal South Africa, which is situated in Vanderbijlpark, Gauteng province. The research aims to address the issues concerning maintenance that are occurring at ArcelorMittal South Africa, through assessing the maintenance planning policy which is being applied at the ArcerlorMittal plant so that the organisation can move toward a more preventative environment. Currently, the organisation has an operative preventative maintenance policy, though the failures and unplanned system stoppages are initiating maintenance to arise in a reactive approach. The research further aims to draw forward aspects that contribute to poor preventative maintenance planning. The study outlined three research objectives: (1) to determine the period in which preventative maintenance could have been conducted by reviewing the system's failure data; (2) to identify the requirements for enhancing machine reliability; and (3) to assess how often preventative maintenance is being performed at the organisation...M.Tech. (Operations Management

Advanced Methods of Power Load Forecasting

This reprint introduces advanced prediction models focused on power load forecasting. Models based on artificial intelligence and more traditional approaches are shown, demonstrating the real possibilities of use to improve prediction in this field. Models of LSTM neural networks, LSTM networks with a SESDA architecture, in even LSTM-CNN are used. On the other hand, multiple seasonal Holt-Winters models with discrete seasonality and the application of the Prophet method to demand forecasting are presented. These models are applied in different circumstances and show highly positive results. This reprint is intended for both researchers related to energy management and those related to forecasting, especially power load

Volume 64 - Issue 2 - November, 1952

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