Quality Management in Chemical Processes Through Fuzzy Analysis: A Fuzzy C-Means and Predictive Models Approach
Ensuring high levels of quality and efficiency is essential for compliance with ISO standards in chemical manufacturing. Traditional methods, such as Statistical Process Control (SPC) and Six Sigma, often lack adaptability and fail to offer interpretable insights. This study proposes a hybrid qualit...
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| Format: | Article |
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MDPI AG
2025-04-01
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| Series: | ChemEngineering |
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| Online Access: | https://www.mdpi.com/2305-7084/9/3/45 |
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| author | Gabriel Marín Díaz |
| author_facet | Gabriel Marín Díaz |
| author_sort | Gabriel Marín Díaz |
| collection | DOAJ |
| description | Ensuring high levels of quality and efficiency is essential for compliance with ISO standards in chemical manufacturing. Traditional methods, such as Statistical Process Control (SPC) and Six Sigma, often lack adaptability and fail to offer interpretable insights. This study proposes a hybrid quality control model based on Explainable Artificial Intelligence (XAI), integrating fuzzy C-means clustering (FCM), machine learning (ML), and Fuzzy Inference Systems (FISs) to enhance defect prediction and interpretability in industrial environments. The approach uses fuzzy clusters to segment production batches, improving the understanding of process variability. A supervised ML model (XGBoost) is trained on historical data to predict defect probabilities, while an explainable FIS refines the final assessment using expert-defined rules. XAI techniques (SHAP and LIME) offer transparency and insight into the decision-making process. Experimental validation using a real-world white wine dataset, evaluated in terms of accuracy and interpretability, shows that the proposed model outperforms traditional approaches in both predictive performance and transparency. The results demonstrate the effectiveness of combining unsupervised clustering, predictive analytics, and fuzzy reasoning in an Industry 4.0 framework. This study provides a scalable and adaptable solution for real-time quality control in chemical manufacturing, improving decision support systems and enabling automated and explainable quality assessments. |
| format | Article |
| id | doaj-art-c35a6cffd4f74a4aa9bb1605e4f5dfb2 |
| institution | Matheson Library |
| issn | 2305-7084 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ChemEngineering |
| spelling | doaj-art-c35a6cffd4f74a4aa9bb1605e4f5dfb22025-06-25T13:37:01ZengMDPI AGChemEngineering2305-70842025-04-01934510.3390/chemengineering9030045Quality Management in Chemical Processes Through Fuzzy Analysis: A Fuzzy C-Means and Predictive Models ApproachGabriel Marín Díaz0Faculty of Statistics, Complutense University, Puerta de Hierro, 28040 Madrid, SpainEnsuring high levels of quality and efficiency is essential for compliance with ISO standards in chemical manufacturing. Traditional methods, such as Statistical Process Control (SPC) and Six Sigma, often lack adaptability and fail to offer interpretable insights. This study proposes a hybrid quality control model based on Explainable Artificial Intelligence (XAI), integrating fuzzy C-means clustering (FCM), machine learning (ML), and Fuzzy Inference Systems (FISs) to enhance defect prediction and interpretability in industrial environments. The approach uses fuzzy clusters to segment production batches, improving the understanding of process variability. A supervised ML model (XGBoost) is trained on historical data to predict defect probabilities, while an explainable FIS refines the final assessment using expert-defined rules. XAI techniques (SHAP and LIME) offer transparency and insight into the decision-making process. Experimental validation using a real-world white wine dataset, evaluated in terms of accuracy and interpretability, shows that the proposed model outperforms traditional approaches in both predictive performance and transparency. The results demonstrate the effectiveness of combining unsupervised clustering, predictive analytics, and fuzzy reasoning in an Industry 4.0 framework. This study provides a scalable and adaptable solution for real-time quality control in chemical manufacturing, improving decision support systems and enabling automated and explainable quality assessments.https://www.mdpi.com/2305-7084/9/3/45industrial quality controloperational decision-makingsmart manufacturing analyticsfuzzy c-means clusteringfuzzy inference system (FIS)explainable artificial intelligence (XAI) |
| spellingShingle | Gabriel Marín Díaz Quality Management in Chemical Processes Through Fuzzy Analysis: A Fuzzy C-Means and Predictive Models Approach ChemEngineering industrial quality control operational decision-making smart manufacturing analytics fuzzy c-means clustering fuzzy inference system (FIS) explainable artificial intelligence (XAI) |
| title | Quality Management in Chemical Processes Through Fuzzy Analysis: A Fuzzy C-Means and Predictive Models Approach |
| title_full | Quality Management in Chemical Processes Through Fuzzy Analysis: A Fuzzy C-Means and Predictive Models Approach |
| title_fullStr | Quality Management in Chemical Processes Through Fuzzy Analysis: A Fuzzy C-Means and Predictive Models Approach |
| title_full_unstemmed | Quality Management in Chemical Processes Through Fuzzy Analysis: A Fuzzy C-Means and Predictive Models Approach |
| title_short | Quality Management in Chemical Processes Through Fuzzy Analysis: A Fuzzy C-Means and Predictive Models Approach |
| title_sort | quality management in chemical processes through fuzzy analysis a fuzzy c means and predictive models approach |
| topic | industrial quality control operational decision-making smart manufacturing analytics fuzzy c-means clustering fuzzy inference system (FIS) explainable artificial intelligence (XAI) |
| url | https://www.mdpi.com/2305-7084/9/3/45 |
| work_keys_str_mv | AT gabrielmarindiaz qualitymanagementinchemicalprocessesthroughfuzzyanalysisafuzzycmeansandpredictivemodelsapproach |