Intelligent control of an automated adhesive dispensing cell.

Imperial Users onl

Investigation of Energy Efficient Retrofit HVAC Systems for a University: Case Study

We did energy efficient retrofits for the Indiana University Purdue University—Indianapolis Health Science Building using the eQuest energy software. The current dual-fan dual-duct (DFDD) system is 41 years old and has a higher energy utilization index (EUI) than the national average for similar building types. The baseline model with the DFDD system was compared with the actual electrical consumption. Then, two energy efficiency measures (EEMs) were applied to the model. The first EEM was ‘DFDD system with chilled water and steam heating,’ and the second EEM was ‘single-duct variable air volume (VAV) with chilled water and electric reheat.’ After comparative simulations and analyses, it was determined that the ‘single duct VAV with chilled water and electric reheat’ was the most energy efficient and saved 28% in utility costs. The recommendation given to the facility services was to change the current DFDD system to the single-duct VAV system. The single-duct VAV system will save energy and create additional space above the ceiling after the heating duct is removed

Analysis of magnetic flux in magneto-rheological damper

Magnetorheological materials are a class of smart substances whose rheological properties can rapidly be varied by application of a magnetic field. The proposed damper consists of an electromagnet and a piston immersed in MR fluid. When current is applied to the electromagnet, the MR fluid solidifies as its yield stress varies in response to the applied magnetic field. Hence, the generation of a magnetic field is an important phenomenon in MR damper. In this research, the magnetic field generated in the damper was analyzed by applying finite element method using COMSOL Multiphysics and was validated using magnetic circuit theory. A quasi-static, 2D—Axisymmetric model was developed using parametric study by varying current from 0–3 A and the magnetic flux density change generated in the fluid flow gap of MR fluid due to external applied current was evaluated. According to the analytical calculations magnetic flux density generated at MR fluid gap was 0.64 Tesla and when calculated using FEA magnetic flux density generated was 0.61 Tesla for 1A current. There is a difference of 4.8% in the simulated results and analytically calculated results of automotive MR damper due to non linear BH curve consideration in Finite element analysis over linear consideration of BH relation in magnetic circuit theory

Design and Implementation of Position Estimator Algorithm on Voice Coil Motor

Voice Coil Motors (VCMs) have been an inevitable element in the mechanisms that have been used for precise positioning in the applications like 3D printing., micro-stereolithography., etc. These voice coil motors translate in a linear direction and require a high accuracy position sensor that amounts for a major part in the budget. In this research work., an effort has been made to design and implement an algorithm that would predict the displacement of VCM and eliminate the need of high cost sensors. VCM was integrated with dSPACE DS1104 R&D controller via linear current amplifier (LCAM) which acts as a driver circuit for VCM. Sine input was given to VCM with various amplitude and frequency and the corresponding displacement is measured by using linear variable differential transformer (LVDT). The position estimator algorithm is also implemented at the same time on VCM and its output is compared with that of LVDT. It is observed that there is 97.8 % accuracy in between algorithm output and LVDT output. Further., PID controller is used in integration with the novel algorithm to minimize the error. The estimator algorithm is tested for various amplitudes and frequencies and it is found that it has a very good agreement of 99.2% with the actual displacement measured with the help of LVDT

Modelling of air handling unit subsystem in a commercial building

A real-time energy management system was developed to improve the energy efficiency of an Air Handling Unit (AHU). The system consists of models to analyse the performance of subsystems in an AHU, which was tested using actual data collected from AHU operation via wireless monitoring. The system detects control-related malfunctions such as simultaneously turning on the cooling coil and the pre-heating coil. The system estimated that this type of control malfunction wastes 63,455 kWh within the cooling coil and the pre-heating coil. Furthermore, the system helped identify other energy saving opportunities through set point changes. For the tested case, the opportunities identified had the potential of 77,141 kWh of energy saving during the same study period

Design, Development and Implementation of the Position Estimator Algorithm for Harmonic Motion on the XY Flexural Mechanism for High Precision Positioning

This article presents a novel concept of the position estimator algorithm for voice coil actuators used in precision scanning applications. Here, a voice coil motor was used as an actuator and a sensor using the position estimator algorithm, which was derived from an electro-mechanical model of a voice coil motor. According to the proposed algorithm, the position of coil relative to the fixed magnet position depends on the current drawn, voltage across coil and motor constant of the voice coil motor. This eliminates the use of a sensor that is an integral part of all feedback control systems. Proposed position estimator was experimentally validated for the voice coil actuator in integration with electro-mechanical modeling of the flexural mechanism. The experimental setup consisted of the flexural mechanism, voice coil actuator, current and voltage monitoring circuitry and its interfacing with PC via a dSPACE DS1104 R&D microcontroller board. Theoretical and experimental results revealed successful implementation of the proposed novel algorithm in the feedback control system with positioning resolution of less than ±5 microns at the scanning speed of more than 5 mm/s. Further, proportional-integral-derivative (PID) control strategy was implemented along with developed algorithm to minimize the error. The position determined by the position estimator algorithm has an accuracy of 99.4% for single direction motion with the experimentally observed position at those instantaneous states

Identification of Key Parameters Affecting Energy Consumption of an Air Handling Unit

Air handling unit system (AHU) is one of the series of mechanical systems that regulate and circulate the air through the ducts inside the buildings. In a commercial setting, air handling units accounted for more than 50% of the total energy cost of the building in 2013. The energy efficiency of the system depends on multiple factors. The set points of discharge air temperature and supply air static pressure are important ones. ASHRAE Standard 90.1-2010 requires multi-zone HVAC systems to implement supply air temperature reset. Energy is wasted if the set points are set constant. However, the waste has never been quantified. The objectives of this study were to (1) develop and validate a mathematical model, which can be used to predict the system performance in response to various controls, specifically the set-point control strategies, and associated energy consumption, and (2) to recommend measures for optimizing the AHU performance by optimizing the setting schedules. In this research, a gray box model was established to evaluate the performance of an AHU. Individual components were modeled using energy and mass balance governing equations that represent the inherent physical processes and interactions with other components. Engineering Equation Solver (EES) was selected for system simulation due to its capabilities of finding the solutions of a large set of complicated equations. The model was validated using two sets of sub hourly real time data. The model performance was evaluated employing Mean Absolute Percentage Error (MAPE) and Root Mean Square Deviation (RMSD). The model was used to create the baseline of energy consumption with constant set points and predict the energy savings using two different reset schedules. The AHU, which serves the entire basement of a campus building on IUPUI campus, was used for this study. It normally has constant set points of discharge air temperature and supply air static pressure. The AHU was monitored using sensors. The data were filtered and transferred to a Building Automation system. Operation information and design specifications of the AHU were collected. Two reset schedules were investigated to determine the better control strategy to minimize energy consumption of the AHU. Discharge air temperature was reset based on return air temperature (RA-T) with a linear reset schedule from March 4 to March 7. Static pressure of the supply air was reset based on the widest open Variable Air Volume (VAV) box damper position from March 20 to March 23. Additionally, uncertainty propagation method was used to identify the dominant parameters affecting the energy consumption. Results indicated that 17% energy savings was achieved using discharge air temperature reset while the energy consumption reduced by 7% using static pressure reset. The results also indicated that outside air temperature, supply airflow rate and return air temperature were the key parameters that impact the overall energy consumption

Learning-based CO2 concentration prediction: Application to indoor air quality control using demand-controlled ventilation

There have been increasing concerns over the air quality inside buildings as high levels of bio-effluents can cause nausea, dizziness, headaches, and fatigue to the people working in those spaces. First published in 2004 as Standard 62.1, ASHRAE Standard 62.2-2019 requires highly occupied spaces to implement heating, ventilation, and air conditioning (HVAC) that can dilute contaminants produced by occupants. In this regard, occupant-centric ventilation control has been regarded as an effective practice to maintain a satisfactory indoor air quality (IAQ) when dealing with highly variable occupancy environments. However, few established models in current literature and practice consider dynamic occupancy behavior and adaptive IAQ control. To address this gap, a dynamic indoor CO2 model is constructed using machine learning algorithms to forecast CO2 concentrations across a range of forecasting horizons. Herein, we tuned and compared six state-of-the-art learning algorithms—including Support Vector Machine, AdaBoost, Random Forest, Gradient Boosting, Logistic Regression, and Multilayer Perceptron. The algorithms’ performances are validated using CO2 and historical meteorological data collected from a campus classroom with a variable occupancy rate. Simulation results showed that Multilayer Perceptron can strongly predict the volatile CO2 behavior and also outperforms other algorithms in terms of accuracy. Furthermore, a control strategy capable of modeling and detecting dynamic patterns of CO2 level is utilized to modulate the ventilation rate in real-time and also reduce the energy consumption. The proposed controller reduced the HVAC fan’s energy consumption by 51.4% and provided ventilation as needed per the ASHRAE standards

Developing a PV and Energy Storage Sizing Methodology for Off-Grid Transactive Microgrids

A simulation tool was developed through MATLAB for comparing Centralized Energy Sharing (CES) and Interconnected Energy Sharing (IES) operating strategies with a standard Stand-Alone Photovoltaic System (SAPV). The tool can be used to investigate the effect of several variables on cost and trading behavior including: initial charge of Energy Storage System (ESS), amount of load variability, starting month, number of stand-alone systems, geographic location, and required reliability. It was found that the CES strategy improves initial cost by 7% to 10% compared to a standard SAPV in every simulation. The IES case consistently saved money compared to the baseline, just by a very small amount (less than 1%). The number of systems did not have a demonstrable effect, giving the same cost per system whether there were 2 systems or 50 involved in the trading strategies. Geographic locations studied (Indianapolis, Indiana; Phoenix, Arizona; Little Rock, Arkansas; and Erie, Pennsylvania) showed a large variation on the total installed cost with Phoenix being the least expensive and Erie being the most expensive location. Required reliability showed a consistent and predictable effect with cost going down as the requirement relaxed and more hours of outage were allowed

ARC algorithm: A novel approach to forecast and manage daily electrical maximum demand

This paper proposes an innovative algorithm for predicting short-term electrical maximum demand by using historical demand data. The ability to recognize in peak demand pattern for commercial or industrial customers would propose numerous direct and indirect benefits to the customers and utility providers in terms of demand reduction, cost control, and system stability. Prior works in electrical maximum demand forecasting have been mainly focused on seasonal effects, which is not a feasible approach for industrial manufacturing facilities in short-term load forecasting. The proposed algorithm, denoted as the Adaptive Rate of Change (ARC), determines the logarithmic rate-of-change in load profile prior to a peak by postulating the demand curve as a stochastic, mean-reverting process. The rationale behind this analysis, is that the energy efficient program requires not only demand estimation but also to warn the user of imminent maximum peak occurrence. This paper analyzes demand trend data and incorporates stochastic model and mean reverting half-life to develop an electrical maximum demand forecasting algorithm, which is statistically evaluated by cross-table and F-score for three different manufacturing facilities. The aggregate results show an overall accuracy of 0.91 and a F-score of 0.43, which indicates that the algorithm is effective predicting peak demand in predicting peak demand