Fine-grained Iron Sintering Quality Supervision and Operation Guidance Expert System
Abstract
Quality is the lifeblood of any industry. In order to ensure the production of high-quality fine-grained iron sintering products, an expert system for quality supervision and operation guidance is developed. The expert system contains knowledge about production process and product quality parameters, and it uses fuzzy mathematics to analyze and identify the factors that can cause product quality defects. Based on this analysis, the expert system provides real-time feedback on production process and product quality. This system helps reduce product quality defects and improve the efficiency of fine-grained iron sintering production process.
Introduction
Fine-grained iron sintering is the process of mixing and blending ore concentrate, additives and binders and heating them up to temperatures between 1000-1300°C in sintering machines, then cooling and crushing them to form sintered iron ore. The sintered iron ore produced through this process is used in steelmaking to improve the properties of steel. However, due to factors such as raw material quality, operating conditions, etc., the products may not meet the quality requirements of the customer. This will cause quality defects which lead to waste of resources, loss in production, environmental pollution and etc.
In order to ensure the quality of fine-grained iron sintering products, an expert system for quality supervision and operation guidance is developed. The expert system is intended to help operators to reduce the possibility of producing defective products.
Design of Expert System
This expert system is based on an artificial neural network model. It consists of four main components: knowledge base, inference engine, fuzzy mathematics module and output module (Figure 1).
Figure 1. Structure of the Expert System
Knowledge base. The knowledge base contains all the necessary knowledge about the production process and the product quality parameters. It contains information about raw material properties, sintering parameters, sintering recipes, product quality indicators and etc.
Inference engine. The inference engine is responsible for reasoning the production process and quality of the products. It uses the knowledge in the knowledge base to process the input data from sensors and present the results.
Fuzzy mathematics module. This module is used to analyze and identify the factors that can cause product quality defects. It uses fuzzy mathematics to calculate the possibility of producing defective products.
Output module. The output module is responsible for providing real-time feedback on the production process and quality of the products. It uses the results from the inference engine and fuzzy mathematics module to generate alerts and recommendations for operators to take corrective actions.
Implementation of Expert System
This expert system was implemented using an artificial neural network model. First, the data from the sensors in the sintering machines was collected. This data was then used as input to the neural network, which was trained by using a combination of supervised and unsupervised learning.
The trained network was then used to monitor the production process and detect potential defects. Based on the analysis, the expert system provided real-time feedback on the production process and product quality. This feedback was used to take corrective actions and improve the quality of the products.
Results and Discussion
The results of this research showed that the expert system was able to detect potential defects in the production process and provide real-time feedback on the production and product quality. This feedback was critical in ensuring the quality of the sintered iron ore products.
Conclusion
This research has developed an expert system for quality supervision and operation guidance for fine-grained iron sintering. This system was able to detect potential defects in the production process and provide real-time feedback on the production and product quality. This feedback was critical in ensuring the quality of the sintered iron ore products. The system can be used to reduce product quality defects and improve the efficiency of fine-grained iron sintering production process.
