Machine Learning-Based Modeling for Industrial Process Optimization

Abstract

Industrial process optimization has become increasingly critical as manufacturing sectors strive to enhance productivity, reduce operational costs, and minimize energy consumption while maintaining product quality. Traditional modeling approaches, such as mechanistic and statistical models, often struggle to capture the complex, nonlinear, and dynamic relationships inherent in modern industrial processes. In response, machine learning (ML) has emerged as a powerful alternative, offering data-driven modeling capabilities that can adapt to intricate process behaviors without requiring exhaustive prior knowledge of underlying physical mechanisms. This paper presents a comprehensive review and framework for ML-based modeling in industrial process optimization. We examine commonly employed ML techniques including artificial neural networks, support vector machines, random forests, gradient boosting, and deep learning architectures and evaluate their applicability across key optimization tasks such as process parameter tuning, predictive maintenance, quality prediction, and energy efficiency improvement. A systematic analysis of recent literature reveals that ensemble and deep learning methods consistently outperform conventional approaches in predictive accuracy and robustness, particularly when large volumes of sensor data are available. However, challenges related to data quality, model interpretability, generalization across operating conditions, and integration with existing control systems remain significant barriers to widespread adoption. To address these gaps, we propose a practical modeling framework that combines data preprocessing, feature engineering, model selection, hyperparameter optimization, and validation strategies tailored to industrial settings. The framework is illustrated through representative case studies from the manufacturing and process industries, demonstrating measurable improvements in yield, throughput, and energy consumption. Finally, we discuss emerging directions, including physics-informed machine learning, transfer learning for cross-process adaptation, and digital twin integration, which hold promise for advancing the next generation of intelligent industrial optimization systems. The findings provide actionable insights for researchers and practitioners seeking to leverage ML modeling to achieve sustainable and efficient industrial operations.