Temperature Control Using an Air Handling Unit Installed with Carel pCO5+ Controller

This dissertation reports the project work developed in the Thesis/Dissertation course during the 2nd year of the Master of Electrical and Computer Engineering in the field of Automation and Systems, Department of Electrical Engineering (DEE) at Instituto Superior de Engenharia do Porto (ISEP). The installation of an Air Handling Unit (AHU) in a work place or a hospital plays an important role in the treatment and maintaining the purity of air. The temperature control is focused in this dissertation. The AHU maintains the temperature of the room or office at a set temperature. The heating and cooling function are done automatically by taking in the reference temperature of the room also depending on the outdoor climate. The main purpose of the AHU is to ensure comfort to the patients, staffs and the employees. In case of the hospitals, the main function of AHU is air cleanliness in hygiene applications. It also includes supplying a sufficient amount of oxygen and removing the carbon dioxide and maintaining a comfortable room climate. They help protect patients and staff from infections. This dissertation will focus on the study of wide range of technologies which will work on the AHU with the Carel electronic controller whose main function is to control the temperature of an office. The unit was installed at Farfetch, Barco, Portugal. The study includes the working of selection criteria of the supply and return fans, inverters, recovery unit, probes, dampers and the controller

Experimental Study on Extremum Seeking Control for Efficient Operation of Air-side Economizer

The air-side economizers are a major class of energy-saving devices for ventilation and air conditioning systems by taking advantage of outdoor air during cool or cold weather. Typical rule based control cannot justify energy optimal operation, while model based optimization of air-side economizer operation depends on the accurate knowledge of system model and enthalpy sensing of the ambient and return-air. Such optimal operation is hard to achieve in practice due to inaccurate model and degradation/failure of temperature and relative humidity (RH) sensors. As pointed out by Seem and House (2010), under certain indoor/outdoor air conditions, there exists a convex map between damper position and energy consumption of an air handling unit (AHU), which implies an optimal damper opening minimizing the cooling-coil load. Such convexity guarantees the use of gradient-search type of real-time optimization methods. An Extremum Seeking Control (ESC) was proposed by Li et al. (2009), where the chilled water flow rate of the cooling coil (equivalently the energy consumption) is minimized by tuning the damper opening. The proposed framework was validated with a Modelica based dynamic simulation model of an air-side economizer. This study is conducted to perform experimental evaluation of the ESC control of air-side economizer. The experimental setup is anchored on a Lennox XC25 variable-speed air conditioner. The Lennox, CBX40UHV indoor air handler unit is equipped with duct work to form an air-side economizer, connected to a foam based 16\u27X8\u27X8\u27 test chamber. The Lasko 751320 electrical heaters are used as heat source. The Honeywell HCM-890 humidifiers and Soleus Air SG-DEH-70EIP-6 dehumidifiers are used to regulate the indoor air humidity. A National Instruments CompactRIO-9024 platform is used for data acquisition and control. Major measurements include temperature, relative humidity (RH) and power consumption. A Watt Node Pulse WNB-3D-240-P electric power meter is used for power measurement. The Omega P-L-1/10-1/8-6-0-T-3 temperature sensors and Veris Industries HN3XVSX RH sensors are installed to monitor the indoor and outdoor air conditions. The Omega HHT13 speed sensors are used to measure fan speeds, while Fluke 80i-110s current sensors are used to measure the compressor motor current. The ESC controller is implemented with the damper opening as input and the total power consumption as feedback. Two experiments have been performed under different indoor/outdoor air conditions. The first experiment was performed under outdoor air temperature 23°C and RH 65%, a heat load of 6000 W and indoor temperature setpoint 28°C. The ESC turned on the outdoor damper 100% automatically to allow maximal outdoor air resulting in indoor RH 50%. The total power consumption was reduced from 540 W to 450 W with an energy saving of 16.67%. The second experiment was performed under same conditions with indoor RH regulated to 40%.The ESC turned off the outdoor damper to allow minimal outdoor air. The power consumption was reduced from 620 W to 600 W with an energy saving of 3.33%. More experiments will be performed in warmer weather in February and March to further validate the performance of the ESC controller

An agent-driven semantical identifier using radial basis neural networks and reinforcement learning

Due to the huge availability of documents in digital form, and the deception possibility raise bound to the essence of digital documents and the way they are spread, the authorship attribution problem has constantly increased its relevance. Nowadays, authorship attribution,for both information retrieval and analysis, has gained great importance in the context of security, trust and copyright preservation. This work proposes an innovative multi-agent driven machine learning technique that has been developed for authorship attribution. By means of a preprocessing for word-grouping and time-period related analysis of the common lexicon, we determine a bias reference level for the recurrence frequency of the words within analysed texts, and then train a Radial Basis Neural Networks (RBPNN)-based classifier to identify the correct author. The main advantage of the proposed approach lies in the generality of the semantic analysis, which can be applied to different contexts and lexical domains, without requiring any modification. Moreover, the proposed system is able to incorporate an external input, meant to tune the classifier, and then self-adjust by means of continuous learning reinforcement.Comment: Published on: Proceedings of the XV Workshop "Dagli Oggetti agli Agenti" (WOA 2014), Catania, Italy, Sepember. 25-26, 201

The Boston University Photonics Center annual report 2014-2015

This repository item contains an annual report that summarizes activities of the Boston University Photonics Center in the 2014-2015 academic year. The report provides quantitative and descriptive information regarding photonics programs in education, interdisciplinary research, business innovation, and technology development. The Boston University Photonics Center (BUPC) is an interdisciplinary hub for education, research, scholarship, innovation, and technology development associated with practical uses of light.This has been a good year for the Photonics Center. In the following pages, you will see that the center’s faculty received prodigious honors and awards, generated more than 100 notable scholarly publications in the leading journals in our field, and attracted $18.6M in new research grants/contracts. Faculty and staff also expanded their efforts in education and training, and were awarded two new National Science Foundation– sponsored sites for Research Experiences for Undergraduates and for Teachers. As a community, we hosted a compelling series of distinguished invited speakers, and emphasized the theme of Advanced Materials by Design for the 21st Century at our annual symposium. We continued to support the National Photonics Initiative, and are a part of a New York–based consortium that won the competition for a new photonics- themed node in the National Network of Manufacturing Institutes. Highlights of our research achievements for the year include an ambitious new DoD-sponsored grant for Multi-Scale Multi-Disciplinary Modeling of Electronic Materials led by Professor Enrico Bellotti, continued support of our NIH-sponsored Center for Innovation in Point of Care Technologies for the Future of Cancer Care led by Professor Catherine Klapperich, a new award for Personalized Chemotherapy Through Rapid Monitoring with Wearable Optics led by Assistant Professor Darren Roblyer, and a new award from DARPA to conduct research on Calligraphy to Build Tunable Optical Metamaterials led by Professor Dave Bishop. We were also honored to receive an award from the Massachusetts Life Sciences Center to develop a biophotonics laboratory in our Business Innovation Center

The Boston University Photonics Center annual report 2014-2015

This repository item contains an annual report that summarizes activities of the Boston University Photonics Center in the 2014-2015 academic year. The report provides quantitative and descriptive information regarding photonics programs in education, interdisciplinary research, business innovation, and technology development. The Boston University Photonics Center (BUPC) is an interdisciplinary hub for education, research, scholarship, innovation, and technology development associated with practical uses of light.This has been a good year for the Photonics Center. In the following pages, you will see that the center’s faculty received prodigious honors and awards, generated more than 100 notable scholarly publications in the leading journals in our field, and attracted $18.6M in new research grants/contracts. Faculty and staff also expanded their efforts in education and training, and were awarded two new National Science Foundation– sponsored sites for Research Experiences for Undergraduates and for Teachers. As a community, we hosted a compelling series of distinguished invited speakers, and emphasized the theme of Advanced Materials by Design for the 21st Century at our annual symposium. We continued to support the National Photonics Initiative, and are a part of a New York–based consortium that won the competition for a new photonics- themed node in the National Network of Manufacturing Institutes. Highlights of our research achievements for the year include an ambitious new DoD-sponsored grant for Multi-Scale Multi-Disciplinary Modeling of Electronic Materials led by Professor Enrico Bellotti, continued support of our NIH-sponsored Center for Innovation in Point of Care Technologies for the Future of Cancer Care led by Professor Catherine Klapperich, a new award for Personalized Chemotherapy Through Rapid Monitoring with Wearable Optics led by Assistant Professor Darren Roblyer, and a new award from DARPA to conduct research on Calligraphy to Build Tunable Optical Metamaterials led by Professor Dave Bishop. We were also honored to receive an award from the Massachusetts Life Sciences Center to develop a biophotonics laboratory in our Business Innovation Center

Adaptive neural network cascade control system with entropy-based design

A neural network (NN) based cascade control system is developed, in which the primary PID controller is constructed by NN. A new entropy-based measure, named the centred error entropy (CEE) index, which is a weighted combination of the error cross correntropy (ECC) criterion and the error entropy criterion (EEC), is proposed to tune the NN-PID controller. The purpose of introducing CEE in controller design is to ensure that the uncertainty in the tracking error is minimised and also the peak value of the error probability density function (PDF) being controlled towards zero. The NN-controller design based on this new performance function is developed and the convergent conditions are. During the control process, the CEE index is estimated by a Gaussian kernel function. Adaptive rules are developed to update the kernel size in order to achieve more accurate estimation of the CEE index. This NN cascade control approach is applied to superheated steam temperature control of a simulated power plant system, from which the effectiveness and strength of the proposed strategy are discussed by comparison with NN-PID controllers tuned with EEC and ECC criterions

FuXi: A cascade machine learning forecasting system for 15-day global weather forecast

Over the past few years, due to the rapid development of machine learning (ML) models for weather forecasting, state-of-the-art ML models have shown superior performance compared to the European Centre for Medium-Range Weather Forecasts (ECMWF)'s high-resolution forecast (HRES) in 10-day forecasts at a spatial resolution of 0.25 degree. However, the challenge remains to perform comparably to the ECMWF ensemble mean (EM) in 15-day forecasts. Previous studies have demonstrated the importance of mitigating the accumulation of forecast errors for effective long-term forecasts. Despite numerous efforts to reduce accumulation errors, including autoregressive multi-time step loss, using a single model is found to be insufficient to achieve optimal performance in both short and long lead times. Therefore, we present FuXi, a cascaded ML weather forecasting system that provides 15-day global forecasts with a temporal resolution of 6 hours and a spatial resolution of 0.25 degree. FuXi is developed using 39 years of the ECMWF ERA5 reanalysis dataset. The performance evaluation, based on latitude-weighted root mean square error (RMSE) and anomaly correlation coefficient (ACC), demonstrates that FuXi has comparable forecast performance to ECMWF EM in 15-day forecasts, making FuXi the first ML-based weather forecasting system to accomplish this achievement

Optimal design of cascaded control scheme for PV system using BFO algorithm

In this paper presents Bacteria Foraging Optimization (BFO) algorithm based approach to find the optimum design values for the Proportional-Integral (PI) Controllers in cascaded structure is presented. Tuning the values of four PI controllers is very complex when the system is difficult to express in terms of mathematical model due to system nonlinearity. Response surface methodology (RSM) is used to formulate a mathematical design which is required to apply optimization algorithm. To examine the performance of BFO algorithm in obtaining optimum values of multiple PI controllers, a grid connected Photovoltaic (PV) system is chosen. Transient performance of the PI controller with optimum design values is evaluated under grid fault conditions. The system is simulated using PSCAD/EMTDC. Simulation results have shown the validity of the optimal design values obtained from RSM-BFO approach under different disturbances and system parameter variations

Surrogate modelling and uncertainty quantification based on multi-fidelity deep neural network

To reduce training costs, several Deep neural networks (DNNs) that can learn from a small set of HF data and a sufficient number of low-fidelity (LF) data have been proposed. In these established neural networks, a parallel structure is commonly proposed to separately approximate the non-linear and linear correlation between the HF- and LF data. In this paper, a new architecture of multi-fidelity deep neural network (MF-DNN) was proposed where one subnetwork was built to approximate both the non-linear and linear correlation simultaneously. Rather than manually allocating the output weights for the paralleled linear and nonlinear correction networks, the proposed MF-DNN can autonomously learn arbitrary correlation. The prediction accuracy of the proposed MF-DNN was firstly demonstrated by approximating the 1-, 32- and 100-dimensional benchmark functions with either the linear or non-linear correlation. The surrogating modelling results revealed that MF-DNN exhibited excellent approximation capabilities for the test functions. Subsequently, the MF DNN was deployed to simulate the 1-, 32- and 100-dimensional aleatory uncertainty propagation progress with the influence of either the uniform or Gaussian distributions of input uncertainties. The uncertainty quantification (UQ) results validated that the MF-DNN efficiently predicted the probability density distributions of quantities of interest (QoI) as well as the statistical moments without significant compromise of accuracy. MF-DNN was also deployed to model the physical flow of turbine vane LS89. The distributions of isentropic Mach number were well-predicted by MF-DNN based on the 2D Euler flow field and few experimental measurement data points. The proposed MF-DNN should be promising in solving UQ and robust optimization problems in practical engineering applications with multi-fidelity data sources