Multi Agent System Based Approach for Industrial Process Simulation

Industrial systems become more and more complex. This complexity is due to the great number of elements that compose them and their interactions. This paper describes a multi-agent approach for modeling such systems. All of their parts are considered and are modeled by using adequate agents. The set of preoccupations were identified to find convenient multi agent models for their resolutions. Then, we implemented our application by using a MADKIT multi-agent platform. The main goal of this work is to build a simulator based on reactive agents able to translate this complex industrial system into a data processing programs that can represent its structure, its behavior, its interaction, its control loops and verify the integrity and its proper functioning. A concrete application of this approach was materialized by building an industrial gas process simulator.Industrial systems become more and more complex. This complexity is due to the great number of elements that compose them and their interactions. This paper describes a multi-agent approach for modeling such systems. All of their parts are considered and are modeled by using adequate agents. The set of preoccupations were identified to find convenient multi agent models for their resolutions. Then, we implemented our application by using a MADKIT multi-agent platform. The main goal of this work is to build a simulator based on reactive agents able to translate this complex industrial system into a data processing programs that can represent its structure, its behavior, its interaction, its control loops and verify the integrity and its proper functioning. A concrete application of this approach was materialized by building an industrial gas process simulator.Industrial systems become more and more complex. This complexity is due to the great number of elements that compose them and their interactions. This paper describes a multi-agent approach for modeling such systems. All of their parts are considered and are modeled by using adequate agents. The set of preoccupations were identified to find convenient multi agent models for their resolutions. Then, we implemented our application by using a MADKIT multi-agent platform. The main goal of this work is to build a simulator based on reactive agents able to translate this complex industrial system into a data processing programs that can represent its structure, its behavior, its interaction, its control loops and verify the integrity and its proper functioning. A concrete application of this approach was materialized by building an industrial gas process simulator.Industrial systems become more and more complex. This complexity is due to the great number of elements that compose them and their interactions. This paper describes a multi-agent approach for modeling such systems. All of their parts are considered and are modeled by using adequate agents. The set of preoccupations were identified to find convenient multi agent models for their resolutions. Then, we implemented our application by using a MADKIT multi-agent platform. The main goal of this work is to build a simulator based on reactive agents able to translate this complex industrial system into a data processing programs that can represent its structure, its behavior, its interaction, its control loops and verify the integrity and its proper functioning. A concrete application of this approach was materialized by building an industrial gas process simulator.

Study of the Compatibilization Effect of Different Reactive Agents in PHB/Natural Fiber-Based Composites

Fiber–matrix interfacial adhesion is one of the key factors governing the final properties of natural fiber-based polymer composites. In this work, four extrusion reactive agents were tested as potential compatibilizers in polyhydroxylbutyrate (PHB)/cellulose composites: dicumyl peroxide (DCP), hexamethylene diisocyanate (HMDI), resorcinol diglycidyl ether (RDGE), and triglycidyl isocyanurate (TGIC). The influence of the fibers and the different reactive agents on the mechanical properties, physical aging, and crystallization behavior were assessed. To evaluate the compatibilization effectiveness of each reactive agent, highly purified commercial cellulose fibers (TC90) were used as reference filler. Then, the influence of fiber purity on the compatibilization effect of the reactive agent HMDI was evaluated using untreated (U_RH) and chemically purified (T_RH) rice husk fibers, comparing the results with the ones using TC90 fibers. The results show that reactive agents interact with the polymer matrix at different levels, but all compositions showed a drastic embrittlement due to the aging of PHB. No clear compatibilization effect was found using DCP, RDGE, or TGIC reactive agents. On the other hand, the fiber–polymer interfacial adhesion was enhanced with HMDI. The purity of the fiber played an important role in the effectiveness of HMDI as a compatibilizer, since composites with highly purified fibers showed the greatest improvements in tensile strength and the most favorable morphology. None of the reactive agents negatively affected the compostability of PHB. Finally, thermoformed trays with good mold reproducibility were successfully obtained for PHB/T_RH/HMDI composition.

Applications of Photodynamic Therapy in Management of Periodontal Disease - A Review

A novel noninvasive photochemical approach for infection control, namely photodynamic therapy, has received much attention in the treatment of oral diseases which requires three nontoxic ingredients namely visible harmless light, a photosensitizer and oxygen are involved in this therapy. It is based on the principle that a photosensitizer binds to the target cells which when activated by light of a suitable wavelength results in the production of singlet oxygen and other very reactive agents that are extremely toxic to certain cells and bacteria. This article highlights the application of photo-dynamic therapy in management of periodontal disease and its current status.

Toughness Enhancement of PHBV/TPU/Cellulose Compounds with Reactive Additives for Compostable Injected Parts in Industrial Applications

Poly(3-hydroxybutyrate-co-3-valerate), PHBV, is a bacterial thermoplastic biopolyester that possesses interesting thermal and mechanical properties. As it is fully biodegradable, it could be an alternative to the use of commodities in single-use applications or in those intended for composting at their end of life. Two big drawbacks of PHBV are its low impact toughness and its high cost, which limit its potential applications. In this work, we proposed the use of a PHBV-based compound with purified α-cellulose fibres and a thermoplastic polyurethane (TPU), with the purpose of improving the performance of PHBV in terms of balanced heat resistance, stiffness, and toughness. Three reactive agents with different functionalities have been tested in these compounds: hexametylene diisocianate (HMDI), a commercial multi-epoxy-functionalized styrene-co-glycidyl methacrylate oligomer (Joncryl® ADR-4368), and triglycidyl isocyanurate (TGIC). The results indicate that the reactive agents play a main role of compatibilizers among the phases of the PHBV/TPU/cellulose compounds. HMDI showed the highest ability to compatibilize the cellulose and the PHBV in the compounds, with the topmost values of deformation at break, static toughness, and impact strength. Joncryl® and TGIC, on the other hand, seemed to enhance the compatibility between the fibres and the polymer matrix as well as the TPU within the PHBV.

Evolving reactive agents with SignalGP

We introduce SignalGP, a technique for creating digital organisms that harnesses the event-driven programming paradigm. These organisms can evolve to automatically react to signals from the environment or from other agents in a biologically-inspired manner. In addition to introducing SignalGP, we summarize previous results demonstrating the value of the event-driven paradigm in environments dominated by agent-agent and agent-environment interaction. Our full introduction to SignalGP will be published in the proceedings of the 2018 Genetic and Evolutionary Computation Conference (pre-print: https://arxiv.org/pdf/1804.05445.pdf).