A Model-Based Framework for the Smart Manufacturing of Polymers

It is hard to point a daily activity in which polymeric materials or plastics are not involved. The synthesis of polymers occurs by reacting small molecules together to form, under certain conditions, long molecules. In polymer synthesis, it is mandatory to assure uniformity between batches, high-quality of end-products, efficiency, minimum environmental impact, and safety. It remains as a major challenge the establishment of operational conditions capable of achieving all objectives together. In this dissertation, different model-centric strategies are combined, assessed, and tested for two polymerization systems. The first system is the synthesis of polyacrylamide in aqueous solution using potassium persulfate as initiator in a semi-batch reactor. In this system, the proposed framework integrates nonlinear modelling, dynamic optimization, advanced control, and nonlinear state estimation. The objectives include the achievement of desired polymer characteristics through feedback control and a complete motoring during the reaction. The estimated properties are close to experimental values, and there is a visible noise reduction. A 42% improvement of set point accomplishment in average is observed when comparing feedback control combined with a hybrid discrete-time extended Kalman filter (h-DEKF) and feedback control only. The 4-state geometric observer (GO) with passive structure, another state estimation strategy, shows the best performance. Besides achieving smooth signal processing, the observer improves 52% the estimation of the final molecular weight distribution when compared with the h-DEKF. The second system corresponds to the copolymerization of ethylene with 1,9-decadiene using a metallocene catalyst in a semi-batch reactor. The evaluated operating conditions consider different diene concentrations and reaction temperatures. Initially, the nonlinear model is validated followed by a global sensitivity analysis, which permits the selection of the important parameters. Afterwards, the most important kinetic parameters are estimated online using an extended Kalman filter (EKF), a variation of the GO that uses a preconditioner, and a data-driven strategy referred as the retrospective cost model refinement (RCMR) algorithm. The first two strategies improve the measured signal, but fail to predict other properties. The RCMR algorithm demonstrates an adequate estimation of the unknown parameters, and the estimates converge close to theoretical values without requiring prior knowledge

Approach of Passive Filters using NSGA II in industrial installations: Part I

The optimization of passive filters in industrial systems has been presented by different computational methods. The objective of this paper is to develop a computational algorithm with NSGA II to select the configuration and design parameters of a set of passive filters for industrial installations. As a methodology, the optimization problem was addressed using three independent objective functions of innovative character for compensation of harmonics through passive filters as a multiobjective problem. The results were the computational solution to this problem that determines a set of Pareto optimal solutions (Frontier). In addition, the computational tool has several new features such as: calculates the parameters that characterize the filters, but also selects the type of configuration and the number of branches of the filter in each candidate bar according to a set of pre-established configurations according to PRODIST-M8 (Brazilian Standard) and IEEE 519-2014. Also determine solutions with good power quality indicators (THD, TDD and NPV) for several characteristic and non-characteristic scenarios of the system that allow to represent: daily variations of the load, and variations of system parameters and filters. It evaluates the cost of energy bills in an industrial power grid that has different operating conditions (characteristic scenarios) and evaluates the economic effect of harmonic filters as reactive power compensators

Challenges and Opportunities for Multi-functional Oxide Thin Films for Voltage Tunable Radio Frequency/Microwave Components

There has been significant progress on the fundamental science and technological applications of complex oxides and multiferroics. Among complex oxide thin films, barium strontium titanate (BST) has become the material of choice for room-temperature-based voltage-tunable dielectric thin films, due to its large dielectric tunability and low microwave loss at room temperature. BST thin film varactor technology based reconfigurable radio frequency (RF)/microwave components have been demonstrated with the potential to lower the size, weight, and power needs of a future generation of communication and radar systems. Low-power multiferroic devices have also been recently demonstrated. Strong magneto-electric coupling has also been demonstrated in different multiferroic heterostructures, which show giant voltage control of the ferromagnetic resonance frequency of more than two octaves. This manuscript reviews recent advances in the processing, and application development for the complex oxides and multiferroics, with the focus on voltage tunable RF/microwave components. The over-arching goal of this review is to provide a synopsis of the current state-of the-art of complex oxide and multiferroic thin film materials and devices, identify technical issues and technical challenges that need to be overcome for successful insertion of the technology for both military and commercial applications, and provide mitigation strategies to address these technical challenges

A "poor man's" approach to topology optimization of natural convection problems

Topology optimization of natural convection problems is computationally expensive, due to the large number of degrees of freedom (DOFs) in the model and its two-way coupled nature. Herein, a method is presented to reduce the computational effort by use of a reduced-order model governed by simplified physics. The proposed method models the fluid flow using a potential flow model, which introduces an additional fluid property. This material property currently requires tuning of the model by comparison to numerical Navier-Stokes based solutions. Topology optimization based on the reduced-order model is shown to provide qualitatively similar designs, as those obtained using a full Navier-Stokes based model. The number of DOFs is reduced by 50% in two dimensions and the computational complexity is evaluated to be approximately 12.5% of the full model. We further compare to optimized designs obtained utilizing Newton's convection law.Comment: Preprint version. Please refer to final version in Structural Multidisciplinary Optimization https://doi.org/10.1007/s00158-019-02215-

Systems and methods for monitoring solids using mechanical resonator

Multi-phase system monitoringmethods, systems and apparatus aredisclosed. Preferred embodiments comprise one or more mechanical resonator sensing elements. In preferred embodiments a sensor or a sensor subassembly is ported to a fluidized bed vessel such as a fluidized bed polymerization reactor

Optimization study of high power static inverters and converters Final report

Optimization study and basic performance characteristics for conceptual designs for high power static inverter