Expert Systems Controlling the Iron Making Process in Closed Loop Operation (original) (raw)

New Business and Operating Models. Optimization of a Blast Furnace in the Steel Industry. Machine Learning as a Process Optimization

2021

The article takes the case of the adoption of machine learning in a steel manufacturing process through a platform provided by a novel Canadian startup, Canvass Analytics. This way the steel company could optimize the process in a blast furnace. The content of the paper includes a conceptual framework on key factors around steel manufacturing and machine learning. Method: The article takes the case of the adoption of machine learning in a steel manufacturing process through a platform provided by a novel Canadian startup, Canvass Analytics. This way the steel company could optimize the process in a blast furnace. The content of the paper includes a conceptual framework on key factors around steel manufacturing and machine learning. Results: This case is relevant for the authors by the way the business model proposed by the startup attempts to democratize Artificial Intelligence and Machine Learning in industrial environments. This way the startup delivers value to facilitate traditi...

New Business Models on Artificial Intelligence—The Case of the Optimization of a Blast Furnace in the Steel Industry by a Machine Learning Solution

Applied System Innovation, 2021

This article took the case of the adoption of a Machine Learning (ML) solution in a steel manufacturing process through a platform provided by a Canadian startup, Canvass Analytics. The content of the paper includes a study around the state of the art of AI/ML adoption in steel manufacturing industries to optimize processes. The work aimed to highlight the opportunities that bring new business models based on AI/ML to improve processes in traditional industries. Methodologically, bibliographic research in the Scopus database was performed to establish the conceptual framework and the state of the art in the steel industry, then the case was presented and analyzed, to finally evaluate the impact of the new business model on the operation of the steel mill. The results of the case highlighted the way the innovative business model, based on a No-Code/Low-Code solution, achieved results in less time than conventional approaches of analytics solutions, and the way it is possible to democ...

Application of Artificial Neural Network Modeling for Predicting the Ferro Alloys Furnace Output

2015

The major input to ferroalloys industry is the natural resources (ore). Availability of consistent quality of ore is very difficult. In order to maintain consistency in the quality of output, it is necessary to predict the output quality with respect to a set of input mix. The general practice in ferroalloys manufacturing is to prepare a material balance (charge mix) for a particular quality of output and keep on fine tuning the mix based on the tapping analysis and desired output. However, in this process generation of deviated quality product takes place resulting in increase in the cost of quality. Recent research activities in artificial neural networks (ANNs) have shown that ANNs have powerful pattern classification and pattern recognition capabilities. Inspired by biological systems, particularly by research into the human brain, ANNs are able to learn and generalize from experience. Currently, ANNs are being used for a wide variety of tasks in many different fields of busines...

Application of Neural Networks for Modeling Steelmaking Process

2012

One of the major causes of unconformity in the steelmaking process is the presence of impurities in the steel alloys. One of the main impurities found in the steel alloys is the phosphorus. The phosphorus causes several deleterious effects in the steel. In that way this research has as main objective to predict the percentage of phosphorus in the final composition of the material in the production process of one chemical industry. Our approached used one model of Artificial Neural Network in the data of the production process disposed. After apply the network it was compared with regression models developed with the same objective using statistical measures of error minimization. The results present that the network obtained the best results, an error minimization of 3, 68%.

Steelmaking Process Optimised through a Decision Support System Aided by Self-Learning Machine Learning

Processes, 2022

This paper presents the application of a reinforcement learning (RL) algorithm, concretely Q-Learning, as the core of a decision support system (DSS) for a steelmaking subprocess, the Composition Adjustment by Sealed Argon-bubbling with Oxygen Blowing (CAS-OB) from the SSAB Raahe steel plant. Since many CAS-OB actions are selected based on operator experience, this research aims to develop a DSS to assist the operator in taking the proper decisions during the process, especially less experienced operators. The DSS is intended to supports the operators in real-time during the process to facilitate their work and optimise the process, improving material and energy efficiency, thus increasing the operation’s sustainability. The objective is that the algorithm learns the process based only on raw data from the CAS-OB historical database, and on rewards set according to the objectives. Finally, the DSS was tested and validated by a developer engineer from the CAS-OB steelmaking plant. Th...