Industry 4.0 has emerged as the perfect scenario for boosting the application of novel artificial intelligence (AI) and machine learning (ML) solutions to industrial process monitoring and optimization. One of the key elements on this new industrial revolution is the hatching of massive process monitoring data, enabled by the cyber-physical systems (CPS) distributed along the manufacturing processes, the proliferation of hybrid Internet of Things (IoT) architectures supported by polyglot data repositories, and big (small) data analytics capabilities. Industry 4.0 paradigm is data-driven, where the smart exploitation of data is providing a large set of competitive advantages impacting productivity, quality, and efficiency key performance indicators (KPIs). Overall equipment efficiency (OEE) has emerged as the target KPI for most manufacturing industries due to the fact that considers three key indicators: availability, quality, and performance. This chapter describes how different AI and ML solutions can enable a big step forward in industrial process control, focusing on OEE impact illustrated by means of real use cases and research project results.

The prediction of sediment levels in combined sewer system (CSS) would result in enormous savings in resources for their maintenance as a reduced number of inspections would be needed. In this paper, we benchmark different machine learning (ML) methodologies to improve the maintenance schedules of the sewerage and reduce the number of cleanings using historical sediment level and inspection data of the combined sewer system in the city of Barcelona. Two ML methodologies involve the use of spatial features for sediment prediction at critical sections of the sewer, where the cost of maintenance is high because of the dangerous access; one uses a regression model to predict the sediment level of a section, and the other one a binary classification model to identify whether or not a section needs cleaning. The last ML methodology is a short-term forecast of the possible sediment level in future days to improve the ability of operators to react and solve an imminent sediment level increase. Our study concludes with three different models. The spatial and short-term regression methodologies accomplished the best results with Artificial Neural Networks (ANN) with 0.76 and 0.61 R2 scores, respectively. The classification methodology resulted in a Gradient Boosting (GB) model with an accuracy score of 0.88 and an area under the curve (AUC) of 0.909.

This paper explores the application of offline reinforcement learning in batch manufacturing, with a specific focus on press hardening processes. Offline reinforcement learning presents a viable alternative to traditional control and reinforcement learning methods, which often rely on impractical real-world interactions or complex simulations and iterative adjustments to bridge the gap between simulated and real-world environments. We demonstrate how offline reinforcement learning can improve control policies by leveraging existing data, thereby streamlining the training pipeline and reducing reliance on high-fidelity simulators. Our study evaluates the impact of varying data exploration rates by creating five datasets with exploration rates ranging from ε=0 to ε=0.8. Using the conservative Q-learning algorithm, we train and assess policies against both a dynamic baseline and a static industry-standard policy. The results indicate that while offline reinforcement learning effectively refines behavior policies and enhances supervised learning methods, its effectiveness is heavily dependent on the quality and exploratory nature of the initial behavior policy.

Hot stamping is a hot metal forming technology increasingly in demand that produces ultra-high strength parts with complex shapes. A major concern in these systems is how to shorten production times to improve production Key Performance Indicators. In this work, we present a Reinforcement Learning approach that can obtain an optimal behavior strategy for dynamically managing the cycle time in hot stamping to optimize manufacturing production while maintaining the quality of the final product. Results are compared with the business-as-usual cycle time control approach and the optimal solution obtained by the execution of a dynamic programming algorithm. Reinforcement Learning control outperforms the business-as-usual behavior by reducing the cycle time and the total batch time in non-stable temperature phases.

A holistic Water-Energy-Food-Ecosystems (WEFE) nexus approach requires advanced decision-support systems capable of navigating complex interdependencies and optimizing trade-offs among multiple, often conflicting objectives. Moreover, the WEFE Nexus aims not only to promote efficiency in cross-sectoral interactions but also to ensure fairness by distributing resources in a fair and just manner. However, decision-making is further complicated by uncertainty in stakeholder preferences, as diverse actors often hold conflicting priorities and ambiguous visions of what constitutes an acceptable outcome. To address these challenges, this study presents a fairness-aware Multi-Objective Reinforcement Learning (MORL) framework based on Pareto Q-Learning, designed to support policy-making in high-dimensional objective spaces under preference uncertainty. The framework integrates a hybrid utility function combining efficiency and fairness with the real-time response of a learning approach, where a combination of a weighted prioritization mechanism with a targeted standard deviation regularization term enables dynamic balancing of competing objectives while preserving Pareto-optimality. We apply our methodology to the Inkomati-Usuthu river basin, a transboundary region shared by South Africa, Eswatini, and Mozambique. This basin plays a critical role in regional energy production, agricultural development, and water security, while also supporting ecologically sensitive ecosystems. Building on system dynamics modelling, the framework generates AI-driven recommendations for policy packages tailored to stakeholder-defined preferences, supports efficient many-objective optimization, and enables transparent identification of cross-sectoral impacts, synergies, and trade-offs across the WEFE Nexus. Our findings contribute to the growing body of research on AI-enabled governance, demonstrating how fairness-aware MORL can enhance real-time many-objective policy optimization, foster cross-sectoral coordination, improve resilience, and strengthen transboundary basin governance.