Bag Filter Design using Neural Network Algorithm and Structural Equation Modeling (Case Study: Cement Factories)

Subject Areas : Statistics

Masoomeh Zeinalnezhad ¹ , Towhid Pourrostam ²

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Received: 2021-10-04 Accepted : 2022-03-07 Published : 2024-01-18

Keywords: مدل‌سازی معادلات ساختاری, فیلترهای کیسه‌ای, داده‌کاوی, شبکه عصبی, صنعت سیمان,

Abstract :

Although data-driven modeling is now widely used to improve the processes of manufacturing organizations, it is less commonly used in the design of industrial machinery and equipment. In this study, according to the environmental pollution of the cement industry development, bag filters were designed to control and purify polluted air. Accurate calculation of the dimensions of the bag filter can play a significant role in reducing the pollution of cement factories and their economic justification. Therefore, the aim is to take into account the key parameters and provide an efficient method to calculate the optimal bag dimensions required for each filter using the analysis of available data. For this purpose, data mining techniques and structural equation modeling were used. Important parameters in the design of bag filters are temperature, pressure, percentage of inlet dust, dust granulation, dust moisture content, particle type and flammability of materials. Due to the stability of some parameters and non-flammability of particles in cement production, only three factors, dust flow rate, dust volume, and dust size were modeled. The results showed that the Ensemble neural network algorithm along with Boosting can predict the required area of the bag with 95.4% accuracy. Also, in each bag filter, the dust flow rate of 80% was effective in calculating the required surface area of the fabric, and the dust volume and size factors, with 15% and 6%, respectively, were of secondary and tertiary importance.

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