New applications of industrial automation request great flexibility in the systems, supported by the increase in the interconnection between its components, allowing access to all the information of the system and its reconfiguration based on the changes that occur during its operations, with the purpose of reaching optimum points of operation. These aspects promote the Smart Factory paradigm, integrating physical and digital systems to create smarts products and processes capable of transforming conventional value chains, forming the Cyber-Physical Systems (CPSs). This flexibility opens a large gap that affects the security of control systems since the new communication links can be used by people to generate attacks that produce risk in these applications. This is a recent problem in the control systems, which originally were centralized and later were implemented as interconnected systems through isolated networks. To protect these systems, strategies that have presented acceptable results in other environments, such as office environments, have been chosen. However, the characteristics of these applications are not the same, and the results achieved are not as expected. This problem has motivated several efforts in order to contribute from different approaches to increase the security of control systems. Based on the above, this work proposes an architecture based on artificial neural networks for detection and isolation of cyber attacks Denial of Service (DoS) and integrity in CPS. Simulation results of two test benches, the Secure Water Treatment (SWaT) dataset, and a tanks system, show the effectiveness of the proposal. Regarding the SWaT dataset, the scores obtained from the recall and F1 score metrics was $0.95$ and was higher than other reported works, while, in terms of precision and accuracy, it obtained a score of $0.95$ which is close to other proposed methods. With respect to the interconnected tank system, scores of $0.96,0.83,0.81$, and $0.83$ were obtained for the accuracy, precision, F1 score, and recall metrics, respectively. The high true negatives rate in both cases is noteworthy. In general terms, the proposal has a high effectiveness in detecting and locating the proposed attacks.

Industry 4.0 involves the digital transformation of the industry with the integration and digitization of all industrial processes that make up the value chain, which is characterized by adaptability, flexibility, and efficiency to meet the needs of customers in today's market. Therefore, the adaptations of the new bioprocess industry require a lot of flexibility to react quickly and constantly to market changes and to be able to offer more specialized, customized products with high operational efficiency. This paper presents a flexible, scalable, and robust framework based on software components, container technology, microservice concepts, and the publish/subscribe paradigm. This framework allows new components to be added or removed online, without the need for system reconfiguration, while maintaining temporal and functional constraints in industrial automation systems. The main objective of the framework proposed is the use of components based on microservices, allowing easy implementation, scalability, and fast maintenance, without losing or degrading the robustness from previous developments. Finally, the effectiveness of the proposed framework was verified in two case studies (1) a soursop soda making process is presented, with a fuzzy controller implemented to keep the pasteurizer output flow constant (UHT) and (2) an automatic storage tank selection and filling process with actuated valves to direct the fluid to the corresponding tank at the time to start the process. The results showed that the platform provided a high-fidelity design, analysis, and testing environment for the flow of cyber information and its effect on the physical operation in a beverage processing plant with high demand for flexibility, scalability, and robustness of its processes, as they were experimentally verified in a real production process.

A hybrid AI and LLM-enabled architecture is presented for real-time decision support in industrial batch processes, where supervision still relies heavily on human operators and ad hoc SCADA logic. Unlike algorithmic contributions proposing novel AI methods, this work addresses the practical integration and deployment challenges arising when applying existing AI techniques to safety-critical industrial environments with legacy PLC/SCADA infrastructure and real-time constraints. The framework combines deterministic rule-based agents, fuzzy and statistical enrichment, and large language models (LLMs) to support monitoring, diagnostic interpretation, preventive maintenance planning, and operator interaction with minimal manual intervention. High-frequency sensor streams are collected into rolling buffers per active process instance; deterministic agents compute enriched variables, discrete supervisory states, and rule-based alarms, while an LLM-driven analytics agent answers free-form operator queries over the same enriched datasets through a conversational interface. The architecture is instantiated and deployed in the Clean-in-Place (CIP) system of an industrial beverage plant and evaluated following a case study design aimed at demonstrating architectural feasibility and diagnostic behavior under realistic operating regimes rather than statistical generalization. Three representative multi-stage CIP executions—purposively selected from 24 runs monitored during a six-month deployment—span nominal baseline, preventive-warning, and diagnostic-alert conditions. The study quantifies stage-specification compliance, state-to-specification consistency, and temporal stability of supervisory states, and performs spot-check audits of numerical consistency between language-based summaries and enriched logs. Results in the evaluated CIP deployment show high time within specification in sanitizing stages (100% compliance across the evaluated runs), coherent and mostly stable supervisory states in variable alkaline conditions (state-specification consistency ${\Gamma }_s\ge 0.98$), and data-grounded conversational diagnostics in real time (median numerical error below 3% in audited samples), without altering the existing CIP control logic. These findings suggest that the architecture can be transferred to other industrial cleaning and batch operations by reconfiguring process-specific rules and ontologies, though empirical validation in other process types remains future work. The contribution lies in demonstrating how to bridge the gap between AI theory and industrial practice through careful system architecture, data transformation pipelines, and integration patterns that enable reliable AI-enhanced decision support in production environments, offering a practical path toward AI-assisted process supervision with explainable conversational interfaces that support preventive maintenance decision-making and equipment health monitoring.

Modern industrial automation supported by Cyber–Physical Systems (CPSs) requires high flexibility, which is achieved through increased interconnection between modules. This interconnection introduces a layer of symmetry into the design and operation of CPSs, balancing the distribution of tasks and resources across the system and streamlining the flow of information. However, this adaptability also exposes control systems to security threats, particularly through novel communication links that are vulnerable to cyberattacks. Traditional strategies may have limitations in these applications. This research proposes a design approach for control applications supported by CPSs that incorporates cyberattack detection and tolerance strategies. Using a modular and adaptive approach, the system is partitioned into microservices for scalability and resilience, allowing structural symmetry to be maintained. Schedulability assessments ensure that critical timing constraints are met, improving overall system symmetry and performance. Advanced cyberattack detection and isolation systems generate alarms and facilitate rapid response with replicas of affected components. These replicas enable the system to recover from and tolerate cyberattacks, maintaining uninterrupted operation and preserving the balanced structure of the system. In conclusion, the proposed approach addresses the security challenges in CPS-based control applications and provides an integrated and robust approach to protect industrial automation systems from cyber threats. A case study conducted at a juice production facility in Colima, México, demonstrated how the architecture can be applied to complex processes such as pH control, from simulation to industrial implementation. The study highlighted a plug-and-play approach, starting with component definitions and relationships, and extending to technology integration, thereby reinforcing symmetry and efficiency within the system.

In microgrid projects, social ownership involves aspects beyond their operation that may compromise the sustainability of the system. For this reason, the development of analysis methods to assess the feasibility and impact during the design stages of these solutions is of growing interest. Recent studies have proposed methods that allow an individual analysis of technological components and social behaviors. However, a complete evaluation of the performance and the impact of these projects should allow the simultaneous evaluation of the behavior of these subsystems, allowing the analysis of their interactions and effects in a dynamic way. Accordingly, this paper presents simulation and emulation models to evaluate the impact of a microgrid in isolated communities. These models contemplate sublevels that consider the energetic, automation and computational aspects in the microgrids and a multi-agent system (MAS) that is used to study the environmental and economic impact of the microgrid through the evolution of certain indicators. The socio-technological interdependence in the operation of the isolated microgrid is analyzed through the integration of the microgrid emulation platform with the MAS. Our approach includes a comprehensive study of the performance of these projects in specific communities, in order to contribute to the design and implementation, considering the technological, economic, environmental, and social impacts.

A hybrid AI and LLM-enabled architecture is presented for real-time decision support in industrial batch processes, where supervision still relies heavily on human operators and ad-hoc SCADA logic. The framework combines deterministic rule-based agents, fuzzy and statistical enrichment, and large language models (LLMs) to support monitoring, diagnostic interpretation, preventive maintenance planning, and operator interaction with minimal manual intervention. High-frequency sensor streams are collected into rolling buffers per active process instance; deterministic agents compute enriched variables, discrete supervisory states, and rule-based alarms, while an LLM-driven analytics agent answers free-form operator queries over the same enriched datasets through a conversational interface. The architecture is instantiated and deployed in the Clean-in-Place (CIP) system of an industrial beverage plant and evaluated following a case-study design aimed at demonstrating architectural feasibility and diagnostic behavior under realistic operating regimes rather than statistical generalization. Three representative multi-stage CIP executions—purposively selected from 24 runs monitored during a six-month deployment—span nominal baseline, preventive-warning, and diagnostic-alert conditions. The study quantifies stage-specification compliance, state-to-specification consistency, and temporal stability of supervisory states, and performs spot-check audits of numerical consistency between language-based summaries and enriched logs. Results show high time within specification in sanitising stages (100% compliance across the evaluated runs), coherent and mostly stable supervisory states in variable alkaline conditions (state-specification consistency Γs≥0.98), and data-grounded conversational diagnostics in real time (median numerical error below 3% in audited samples), without altering the existing CIP control logic. These findings suggest that the architecture can be transferred to other industrial cleaning and batch operations by reconfiguring process-specific rules and ontologies, offering a practical path toward AI-assisted process supervision with explainable conversational interfaces that support preventive maintenance decision-making and equipment health monitoring.