Application of meta-synthesis in identifying methods of data-based algorithms for troubleshooting the polypropylene production process
Subject Areas : Industrial ManagementSoleiman Golpour Kandeh 1 , Reza Ramazani Khorshiddoost 2 , Mohammadreza kabarandadeh Ghadim 3
1 - PhD Candidate, Department of Industrial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran
2 - Assistant Professor, Department of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran, Iran
3 - Associate Professor, Department of Management, Center Tehran Branch, Islamic Azad University, Tehran, Iran
Keywords: Data-based algorithms, artificial intelligence, polypropylene production process troubleshooting, Meta-Synthesis.,
Abstract :
This study aimed to identify data-based algorithmic methods for troubleshooting the polypropylene production process. Employing a systematic review approach, we analyzed the findings of prior researchers and identified effective factors using the Seven-step method of Sandelowski and Barroso. Out of 500 articles, 34 were selected using the CASP method. To assess reliability and quality control, we utilized the Kappa index, which demonstrated excellent agreement for the identified indicators. Data analysis was conducted using ATLAS TI software, leading to the identification of 9 algorithms and their associated techniques, including ARIMA models, classification-based algorithms, decision trees, dynamic Bayesian networks, hybrid models, sample-based algorithms, hidden variable models, artificial intelligence networks, and rule-based models. Notably, 84% of the selected studies employed machine learning techniques falling into one of four categories: decision trees, artificial neural networks, hybrid models, and latent variable models. Furthermore, computational efficiency is recognized as a significant advantage for machine learning algorithms.
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