TY - GEN A1 - Dorneanu, Bogdan A1 - Heshmat, Mohamed A1 - Mohamed, Abdelrahim A1 - Arellano-García, Harvey T1 - Monitoring of smart chemical processes: A Sixth Sense approach T2 - Computer Aided Chemical Engineering N2 - This paper introduces the development of an intelligent monitoring and control framework for chemical processes, integrating the advantages of technologies such as Industry 4.0, cooperative control or fault detection via wireless sensor networks. The system described is able to detect faults using information on the process’ structure and behaviour, information on the equipment and expert knowledge. Its integration with the monitoring system facilitates the detection and optimisation of controller actions. The results indicate that the proposed approach achieves high fault detection accuracy based on plant measurements, while the cooperative controller improves the operation of the process. Y1 - 2022 UR - https://www.sciencedirect.com/science/article/abs/pii/B9780323851596502268?via%3Dihub U6 - https://doi.org/10.1016/B978-0-323-85159-6.50226-8 SN - 1570-7946 VL - 49 SP - 1357 EP - 1362 ER - TY - GEN A1 - Dorneanu, Bogdan A1 - Zhang, Sushen A1 - Ruan, Hang A1 - Heshmat, Mohamed A1 - Chen, Ruijuan A1 - Vassiliadis, Vassilios S. A1 - Arellano-García, Harvey T1 - Big data and machine learning: A roadmap towards smart plants T2 - Frontiers of Engineering Management N2 - Industry 4.0 aims to transform chemical and biochemical processes into intelligent systems via the integration of digital components with the actual physical units involved. This process can be thought of as the addition of a central nervous system with a sensing and control monitoring of components and regulating the performance of the individual physical assets (processes, units, etc.) involved. Established technologies central to the digital integrating components are smart sensing, mobile communication, Internet of Things, modelling and simulation, advanced data processing, storage and analysis, advanced process control, artificial intelligence and machine learning, cloud computing, and virtual and augmented reality. An essential element to this transformation is the exploitation of large amounts of historical process data and large volumes of data generated in real-time by smart sensors widely used in industry. Exploitation of the information contained in these data requires the use of advanced machine learning and artificial intelligence technologies integrated with more traditional modelling techniques. The purpose of this paper is twofold: a) to present the state-of-the-art of the aforementioned technologies, and b) to present a strategic plan for their integration toward the goal of an autonomous smart plant capable of self-adaption and self-regulation for short- and long-term production management. Y1 - 2022 UR - https://link.springer.com/article/10.1007/s42524-022-0218-0 U6 - https://doi.org/10.1007/s42524-022-0218-0 VL - 9 SP - 623 EP - 639 ER -