A simple and efficient feedback control strategy for wastewater denitrification

Due to severe mathematical modeling and calibration difficulties open-loop feedforward control is mainly employed today for wastewater denitrification, which is a key ecological issue. In order to improve the resulting poor performances a new model-free control setting and its corresponding "intelligent" controller are introduced. The pitfall of regulating two output variables via a single input variable is overcome by introducing also an open-loop knowledge-based control deduced from the plant behavior. Several convincing computer simulations are presented and discussed.Comment: IFAC 2017 World Congress, Toulouse, Franc

Decentralized Resource Allocation through Constrained Centroidal Voronoi Tessellations

The advancements in the fields of microelectronics facilitate incorporating team elements like coordination into engineering systems through advanced computing power. Such incorporation is useful since many engineering systems can be characterized as a collection of interacting subsystems each having access to local information, making local decisions, interacting with neighbors, and seeking to optimize local objectives that may well conflict with other subsystems, while also trying to optimize certain global objective. In this dissertation, we take advantage of such technological advancements to explore the problem of resource allocation through different aspects of the decentralized architecture like information structure in a team. Introduced in 1968 as a toy example in the field of team decision theory to demonstrate the significance of information structure within a team, the Witsenhausen counterexample remained unsolved until the analytical person-by-person optimal solution was developed within the past decade. We develop a numerical method to implement the optimal laws and show that our laws coincide with the optimal affine laws. For the region where the optimal laws are non-linear, we show that our laws result in the lowest costs when compared with previously reported costs. Recognizing that, in the framework of team decision theory, the difficulties arising from the non-classical information structure within a team currently limit its applicability in real-world applications, we move on to investigating Centroidal Voronoi Tessellations (CVTs) to solve the resource allocation problem. In one-dimensional spaces, a line communication network is sufficient to obtain CVTs in a decentralized manner, while being scalable to any number of agents in the team. We first solve the static resource allocation problem where the amount of resource is fixed. Using such static allocation solution as an initialization step, we solve the dynamic resource allocation problem in a truly decentralized manner. Furthermore, we allow for flexibility in agents\u27 embedding their local preferences through what we call a civility model. We end the dissertation by revisiting the application of Demand-response in smart grids and demonstrate the developed decentralized dynamic resource allocation method to solve the problem of power allocation in a group of building loads

Imaging Sensors and Applications

In past decades, various sensor technologies have been used in all areas of our lives, thus improving our quality of life. In particular, imaging sensors have been widely applied in the development of various imaging approaches such as optical imaging, ultrasound imaging, X-ray imaging, and nuclear imaging, and contributed to achieve high sensitivity, miniaturization, and real-time imaging. These advanced image sensing technologies play an important role not only in the medical field but also in the industrial field. This Special Issue covers broad topics on imaging sensors and applications. The scope range of imaging sensors can be extended to novel imaging sensors and diverse imaging systems, including hardware and software advancements. Additionally, biomedical and nondestructive sensing applications are welcome

SIMULATIONS-GUIDED DESIGN OF PROCESS ANALYTICAL SENSOR USING MOLECULAR FACTOR COMPUTING

Many areas of science now generate huge volumes of data that present visualization, modeling, and interpretation challenges. Methods for effectively representing the original data in a reduced coordinate space are therefore receiving much attention. The purpose of this research is to test the hypothesis that molecular computing of vectors for transformation matrices enables spectra to be represented in any arbitrary coordinate system. New coordinate systems are selected to reduce the dimensionality of the spectral hyperspace and simplify the mechanical/electrical/computational construction of a spectrometer. A novel integrated sensing and processing system, termed Molecular Factor Computing (MFC) based near infrared (NIR) spectrometer, is proposed in this dissertation. In an MFC -based NIR spectrometer, spectral features are encoded by the transmission spectrum of MFC filters which effectively compute the calibration function or the discriminant functions by weighing the signals received from a broad wavelength band. Compared with the conventional spectrometers, the novel NIR analyzer proposed in this work is orders of magnitude faster and more rugged than traditional spectroscopy instruments without sacrificing the accuracy that makes it an ideal analytical tool for process analysis. Two different MFC filter-generating algorithms are developed and tested for searching a near-infrared spectral library to select molecular filters for MFC-based spectroscopy. One using genetic algorithms coupled with predictive modeling methods to select MFC filters from a spectral library for quantitative prediction is firstly described. The second filter-generating algorithm designed to select MFC filters for qualitative classification purpose is then presented. The concept of molecular factor computing (MFC)-based predictive spectroscopy is demonstrated with quantitative analysis of ethanol-in-water mixtures in a MFC-based prototype instrument