Design of a Multi-Agent System for Process Monitoring and Supervision

New process monitoring and control strategies are developing every day together with process automation strategies to satisfy the needs of diverse industries. New automation systems are being developed with more capabilities for safety and reliability issues. Fault detection and diagnosis, and process monitoring and supervision are some of the new and promising growth areas in process control. With the help of the development of powerful computer systems, the extensive amount of process data from all over the plant can be put to use in an efficient manner by storing and manipulation. With this development, data-driven process monitoring approaches had the chance to emerge compared to model-based process monitoring approaches, where the quantitative model is known as a priori knowledge. Therefore, the objective of this research is to layout the basis for designing and implementing a multi-agent system for process monitoring and supervision. The agent-based programming approach adopted in our research provides a number of advantages, such as, flexibility, adaptation and ease of use. In its current status, the designed multi-agent system architecture has the three different functionalities ready for use for process monitoring and supervision. It allows: a) easy manipulation and preprocessing of plant data both for training and online application; b) detection of process faults; and c) diagnosis of the source of the fault. In addition, a number of alternative data driven techniques were implemented to perform monitoring and supervision tasks: Principal Component Analysis (PCA), Fisher Discriminant Analysis (FDA), and Self-Organizing Maps (SOM). The process system designed in this research project is generic in the sense that it can be used for multiple applications. The process monitoring system is successfully tested with Tennessee Eastman Process application. Fault detection rates and fault diagnosis rates are compared amongst PCA, FDA, and SOM for different faults using the proposed framework

Kinetic modeling of pyrolysis of vinyl polymers

Chemische recyclage van vast plastiek afval (SPW) biedt de maatschappij een uitgelezen kans om verdere vervuiling van oceanen en land tegen te gaan, en de circulaire economie te omarmen. Chemische recyclagetechnologieën op basis van pyrolyse zijn dominant door hun (economische) robuustheid. Desalniettemin zijn een beter inzicht in de achterliggende chemie en innovatievere reactorontwerpen vereist om de efficiëntie van de processen te verbeteren. Dit kan enkel verwezenlijkt worden door conversieprofielen en productdistributies te modelleren. Het hoofddoel van deze doctoraatsthesis is het ontwikkelen van geavanceerde, stochastische kinetische Monte Carlo simulatiemodellen voor de snelle pyrolyse van poly(styreenperoxide) en polystyreen. De ontwikkelde modellen verbeteren het inzicht in de kinetiek van pyrolyseprocessen. Ze zijn tevens cruciaal voor de verdere ontwikkeling en optimalisatie van pyrolysereactoren

Developing a new hybrid kinetic model for chemical recycling of vinyl polymers

Continuously increasing amounts of solid plastic waste (SPW) are being generated worldwide. Chemical recycling techniques, e.g. pyrolysis, show a great potential to reduce the environmental harm induced by SPW pollution. These techniques can be combined with innovative chemical processes driven by renewable energy, resulting in carbon circularity. Detailed kinetic modeling is required to understand and optimize these processes. The main aim of this work is to develop a detailed kinetic model for the thermal pyrolysis of vinyl polymers. The model combines a detailed explicit simulation of the chemistry of the light species with the Method of Moments to simulate heavy species. The model contains 9 reaction families, 45 species, and 66 moments. The explicit simulation and MoM are linked via a dedicated routine, based on the gamma distribution. The model simulations are performed with a new computational framework that implements an evaporation model, the exact simulation of the light species, the MoM-based simulation of the heavy species, and the links between these modeling regimes. The constructed preliminary model is validated against detailed pyrolysis product distributions. The preliminary model results yield a proof of concept for this new modeling approach

Primary thermal decomposition pathways of hydroxycinnamaldehydes

Hydroxycinnamaldehyde monomers are important intermediates in the lignin biosynthesis and can be incorporated into plants in large quantities via genetic modification. They are also important products formed during the pyrolysis or combustion of lignocellulose. In this work, the decomposition of three hydroxycinnamaldehyde model compounds, cinnamaldehyde, p-coumaraldehyde, and coniferaldehyde, was studied in a micropyrolysis reactor equipped with an online GC X GC-FID/TOF-MS coupled with a customized GC for the online analysis of all the pyrolysis vapors including permanent gases. The vaporization or sublimation process of these model compounds in the reactor was fitted well based on the experimental time-resolved data. The dominating initial decomposition pathways were elucidated from a first-principles based kinetic model and usage of a rate of production analysis. For cinnamaldehyde (at 773-1123 K) and p-coumaraldehyde (at 873-1123 K), the concerted decarbonylation reactions in the condensed phase determine the initial decomposition, with almost no contribution from radical chemistry. For coniferaldehyde (at 773-1023 K), the homolysis of the 0-methyl (O-CH3) bond initiates a radical chain mechanism at temperatures above 773 K, and the H atom abstraction on the aldehyde group is the most dominant consumption pathway at high temperatures. The presence of methoxy groups on the aromatic rings accelerates the decomposition of hydroxycinnamaldehydes. Models that do not account for these important structural differences will have difficulties in the prediction of the decomposition of real lignocellulosic biomass