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 - 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 - CHAP A1 - Dorneanu, Bogdan A1 - Arellano-García, Harvey A1 - Ruan, Hang A1 - Mohamed, Abdelrahim A1 - Xiao, Pei A1 - Heshmat, Mohamed A1 - Gao, Yang T1 - Towards fault detection and self-healing of chemical processes over wireless sensor networks T2 - Industry 4.0 – Shaping The Future of The Digital World N2 - This contribution introduces a framework for the fault detection and healing of chemical processes over wireless sensor networks. The approach considers the development of a hybrid system which consists of a fault detection method based on machine learning, a wireless communication model and an ontology-based multi-agent system with a cooperative control for the process monitoring. Y1 - 2020 UR - https://www.taylorfrancis.com/chapters/edit/10.1201/9780367823085-02/towards-fault-detection-self-healing-chemical-processes-wireless-sensor-networks-dorneanu-arellano-garcia-ruan-mohamed-xiao-heshmat-gao SN - 9780367823085 U6 - https://doi.org/10.1201/9780367823085-02 SP - 9 EP - 14 PB - CRC Press CY - London, United Kingdom ET - 1st edition ER - TY - CHAP A1 - Dorneanu, Bogdan A1 - Arellano-García, Harvey A1 - Heshmat, Mohamed A1 - Gao, Yang T1 - A framework for intelligent monitoring and control of chemical processes with multi-agent systems T2 - Industry 4.0 – Shaping The Future of The Digital World N2 - Industry 4.0 is transforming chemical processes into complex, smart cyber-physical systems that require intelligent methods to support the operators in taking decisions for better and safer operation. In this paper, a multi-agent cooperative-based model predictive system for monitoring and control of a chemical process is proposed. This system uses ontology to formally represent the system knowledge. By integrating the cooperative-based model predictive controller with the multi-agent system, the control can be improved, and the process can be converted into a self-adaptive system. Y1 - 2020 UR - https://www.taylorfrancis.com/chapters/edit/10.1201/9780367823085-04/framework-intelligent-monitoring-control-chemical-processes-multi-agent-systems-dorneanu-arellano-garcia-heshmat-gao SN - 9780367823085 U6 - https://doi.org/10.1201/9780367823085-04 SP - 18 EP - 23 PB - CRC Press CY - London, United Kingdom ET - 1st edition ER - TY - GEN A1 - Dorneanu, Bogdan A1 - Arellano-García, Harvey A1 - Heshmat, Mohamed A1 - Gao, Yang T1 - Ontology Based Decision Making for Process Control T2 - 2019 AIChE Annual Meeting N2 - In the context of Industry 4.0, engineering systems and manufacturing processes are becoming increasingly complex, combining the physical world of the processing units with the cyber world of the wireless sensing and communication networks, big data analytics, ubiquitous computing and other elements that the Industrial Internet of Things technologies. The fields of ontology, knowledge management and decision-making systems have matured significantly in the recent years and their integration with the cyber-physical system (CPS) facilitates and improves the effectiveness of decision-support systems (DSS) [1]. Yet, this comes with an increase in the system’s complexity and a need for deployment of intelligent systems for process systems engineering (PSE) applications, that should adapt to the continuously new requirements of Industry 4.0. Considering the large number of devices existent in a CPS, distributed methods are required to transfer the computational load from centralised to local (decentralised) controllers. This has led to the motivation of applying multi-agent systems (MASs) methodologies as a solution to distributed control as a computational paradigm [2]. An agent can be defined as an entity placed in an environment that can sense different parameters used to make a decision based on the goals of the entity. A MAS is a computerised system composed of multiple interacting agents exploited to solve a problem. Their salient features, which include efficiency, low cost, flexibility, and reliability, make it an effective solution for solving tasks [3]. Usually, DSS adopt a rule-based or logic-based representation scheme [4]. For this reason, ontologies have attracted the attention of the PSE community as a convenient means for knowledge representation [1, 5]. An ontology is a formal representation of a set of concepts within a domain and the relationships between those concepts, and it serves as a library of knowledge to efficiently build intelligent systems and as a shared vocabulary for communication between interacting human and/or software agents [6]. In this paper, a multi-agent cooperative-based model predictive control (MPC) system for monitoring and control of a chemical process is proposed. The system uses ontology to formally represent the system knowledge at process, communication and decision-making level. The application of the proposed framework is discussed for a chemical process that produces iso-octane. A cooperative MPC is implemented to achieve the control of the plant. This protocol is defined using a simple algorithm to reach an agreement regarding the state of a number of N agents [7]. The monitoring feature is defined by means of a MAS, consisting of follower agents (FAs), a coordinator agent (CA) and a monitor agent (MoA), that is integrated with the MPC. The MAS has two main tasks: a) decide optimal connectivity between the distributed MPCs for safer and better operation; and b) monitor the system and detect any deviation in the behaviour. The addition of the MAS makes the cooperative MPC controller more efficient by taking advantage of the communication between the various elements of the CPS. Using the knowledge form the ontology and the agents’ sharing capabilities, the system can detect faster any deviation compared to standard operation. The framework can easily be adapted for other control approaches by very simple modifications in the structure and objectives. A practical demonstration in a pilot plant environment is envisaged for the future. Y1 - 2019 UR - https://www.aiche.org/conferences/aiche-annual-meeting/2019/proceeding/paper/145e-ontology-based-decision-making-process-control SN - 978-0-8169-1112-7 ER - TY - GEN A1 - Dorneanu, Bogdan A1 - Heshmat, Mohamed A1 - Mohamed, Abdelrahim A1 - Ruan, Hang A1 - Xiao, Pei A1 - Gao, Yang A1 - Arellano-García, Harvey T1 - Stepping Towards the Industrial Sixth Sense T2 - AIChE Annual Meeting, November 20, 2020 N2 - Industry 4.0 is transforming chemical processes into complex, smart cyber-physical systems, by the addition of elements such as smart sensors, Internet of Things, big data analytics or cloud computing. Modern engineering systems and manufacturing processes are operating in highly dynamic environments, and exhibiting scale, structure and behaviour complexity. Under these conditions, plant operators find it extremely difficult to manage all the information available, infer the desired conditions of the plant and take timely decisions to handle abnormal operation1. Human beings acquire information from the surroundings through sensory receptors for vision, sound, smell, touch, and taste, the Five Senses. The sensory stimulus is converted to electrical signals as nerve impulse data communicated with the brain. When one or more senses fail, the humans are able to re-establish communication and improve the other senses to protect from incoming dangers. Furthermore, a mechanism of ‘reasoning’ has been developed during evolution, which enable analysis of present data and generation of a vision of the future, which might be called the Sixth Sense. As industrial processes are already equipped with five senses: ‘hearing’ from acoustic sensors, ‘smelling’ from gas and liquid sensors, ‘seeing’ from camera, ‘touching’ from vibration sensors and ‘tasting’ from composition monitors, the Sixth Sense could be achieved by forming a sensing network which is self-adaptive and self-repairing, carrying out deep-thinking analysis with even limited data, and predicting the sequence of events via integrated system modelling. This contribution introduces the development of an intelligent monitoring and control framework for chemical process, integrating the advantages of Industry 4.0 technologies, cooperative control and fault detection via wireless sensor networks. Y1 - 2020 UR - https://www.aiche.org/academy/videos/conference-presentations/stepping-towards-industrial-sixth-sense SN - 978-0-8169-1114-1 ER -