2026· Materials Research Proceedings· Vol 71, pp. 161-168· 0 citations
TL;DR
A machine learning (ML)-based predictive control model is introduced to enhance energy efficiency in the contemporary manufacturing settings and combines predictive models based on data and Model Predictive Control (MPC) to optimize the performance of the systems in real time.
Abstract
Abstract. The fact that operational costs and the environmental impact are increasing is what has made energy consumption in manufacturing systems a serious issue. In this paper, a machine learning (ML)-based predictive control model is introduced to enhance energy efficiency in the contemporary manufacturing settings. The offered solution combines predictive models based on data and Model Predictive Control (MPC) to optimize the performance of the systems in real time. Machine learning algorithms are used to predict the energy demand, process dynamics, and disturbances, as well as to make decisions proactively. Industrial case studies confirm the validity of the framework, showing great progress in terms of energy efficiency, productivity, and stability of operations.
Comparative analysis shows that the RL-based approach outperforms the rule-based and heuristic strategies and reports remarkable energy efficiency and operational sustainability.
A. Jain· Materials Research Proceedin...· 0 citations
Abstract. The need to achieve sustainable production has become an urgent necessity in the conditions of stricter environmental requirements and the rise in the cost of energy worldwide. The classical proportionalintegralderivative controllers and linear Model Predictive Controllers are conventional model-based control...
Apoorva Verma· Materials Research Proceedin...· 0 citations
This paper introduces an extended outline of the AI-based decision-making in manufacturing facilities with the application of real-time sensor data, machine learning, and adaptive control and puts emphasis on the possibilities of AI-powered systems to reach Industry 4.0 goals.
Davinder Singh· Materials Research Proceedin...· 0 citations
This article explores the role of artificial intelligence (AI) in optimizing energy management systems for hybrid transport. The study examines machine learning methods, neural networks, and fuzzy logic systems used for the adaptive distribution of energy between internal combustion engines and electric powertrains. Th...
Pablo Emilio Iturralde Baquero, K. Karpukhin, O. A. Zhdanovich et al.· Tractors and Agricultural Ma...· 0 citations
The article proposes an integrated and reproducible methodology for developing mathematical models of technological objects, with a focus on their application to cyber-physical systems, industrial digital twins, and model predictive control (MPC/NMPC) algorithms in digital manufacturing environments. Unlike traditional...
Djalolitdin Mukhiddinov, Y. Kadirov, Vinera Shamsutdinova et al.· Geotechnology, Mining and Ra...· 0 citations
A nonlinear model predictive control framework using a data-driven prediction model is used to control an air separation unit (ASU) and is integrated into an industrial automation platform, providing real-time control irrespective of the base layer control system vendor.
Valentin Krespach, Nicolas Blum, M. Pottmann et al.· ACS Omega· 0 citations
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