Optimization of Compressors used in Air Conditioning Units

Most compressors in air conditioning (AC) units are designed by trying to minimize the initial cost. The effect of these designs on the operating cost of the system, for the life of the component, is often neglected. This project provides a solution to this issue. The objective of this project is to create an analytical model that simulates an actual AC unit and then use it to arrive at the optimized design variables based on the lowest total life-cycle cost. Performance of a compressor is affected by several factors including compressor speed, suction and discharge pressures, and component geometry and valve efficiencies. The effect of these factors on the compressor performance in terms of volumetric and isentropic efficiencies is modelled for a reciprocating compressor. Experimental data is collected from an AC unit to verify and validate the analytical model at different ambient conditions. Heat balances between the refrigerant and air side of the heat exchangers were used to confirm the accuracy of the instrumentation. A parameter optimization was conducted on the empirical coefficients used in the analytical model with an objective function of minimizing the RMS error between the model and experimental data. With this the heat balances were achieved within a 10% error over the entire range of operating conditions. This validated model can be used to optimize the compressor efficiencies to obtain the lowest total life-cycle cost for different working environments

Customer Engagement Plans for Peak Load Reduction in Residential Smart Grids

In this paper, we propose and study the effectiveness of customer engagement plans that clearly specify the amount of intervention in customer's load settings by the grid operator for peak load reduction. We suggest two different types of plans, including Constant Deviation Plans (CDPs) and Proportional Deviation Plans (PDPs). We define an adjustable reference temperature for both CDPs and PDPs to limit the output temperature of each thermostat load and to control the number of devices eligible to participate in Demand Response Program (DRP). We model thermostat loads as power throttling devices and design algorithms to evaluate the impact of power throttling states and plan parameters on peak load reduction. Based on the simulation results, we recommend PDPs to the customers of a residential community with variable thermostat set point preferences, while CDPs are suitable for customers with similar thermostat set point preferences. If thermostat loads have multiple power throttling states, customer engagement plans with less temperature deviations from thermostat set points are recommended. Contrary to classical ON/OFF control, higher temperature deviations are required to achieve similar amount of peak load reduction. Several other interesting tradeoffs and useful guidelines for designing mutually beneficial incentives for both the grid operator and customers can also be identified

Hierarchical and Distributed Architecture for Large-Scale Residential Demand Response Management

The implementation of smart grid brings several challenges to the power system. The ‘prosumer’ concept, proposed by the smart grid, allows small-scale ‘nano-grids’ to buy or sell electric power at their own discretion. One major problem in integrating prosumers is that they tend to follow the same pattern of generation and consumption, which is un-optimal for grid operations. One tool to optimize grid operations is demand response (DR). DR attempts to optimize by altering the power consumption patterns. DR is an integrated tool of the smart grid. FERC Order No. 2222 caters for distributed energy resources, including demand response resources, in participating in energy markets. However, DR contribution of an average residential energy consumer is insignificant. Most residential energy consumers pay a flat price for their energy usage and the established market for residential DR is quite small. In this dissertation, a survey is carried out on the current state-of-the-art in DR research and generalizations of the mathematical models are made. Additionally, a service provider model is developed along with an incentive program and user interfaces (UI). These UIs and incentive program are designed to be attractive and easily comprehended by a large customer base. Furthermore, customer behavior models are developed that characterize the potential customer base, allowing a demand response aggregator to understand and quantify the quality of the customer. Optimization methods for DR management with various characteristics are also explored in this dissertation. Moreover, A scalable demand response management framework that can incorporate millions of participants in the program is introduced. The framework is based on a hierarchical architecture. To improve DR management, hierarchical load forecasting method is studied. Specifically, optimal combination method for hierarchical forecast reconciliation is applied to the DR program. It is shown that the optimal combination for reconciliation of hierarchical predictions could reduce the stress levels of the consumer close to the ideal values for all scenarios

A New Constant Air Volume Flow Regulation Method for Blowers Used in HVAC&R Systems

Accurate constant air volume flow rate regulation is essential in HVAC&R systems to provide comfort and required system performance. In the motor, the torque is regulated to provide the required air volume flow rate by monitoring the motor rotational speed. Various correlations including exponential functions and high order of polynomials used to regulate the torque in terms of motor speed and air volume flow rate are presented in the literature. However, all these formulas have not addressed the issue of inaccuracy of the air volume flow control in high altitudes regions. Therefore, a new 5-coefficient formula is proposed in this paper to maintain the constant air volume flow rate even when the altitude changes significantly

The design of an indirect method for the human presence monitoring in the intelligent building

This article describes the design and verification of the indirect method of predicting the course of CO2 concentration (ppm) from the measured temperature variables Tindoor (degrees C) and the relative humidity rH(indoor) (%) and the temperature T-outdoor (degrees C) using the Artificial Neural Network (ANN) with the Bayesian Regulation Method (BRM) for monitoring the presence of people in the individual premises in the Intelligent Administrative Building (IAB) using the PI System SW Tool (PI-Plant Information enterprise information system). The CA (Correlation Analysis), the MSE (Root Mean Squared Error) and the DTW (Dynamic Time Warping) criteria were used to verify and classify the results obtained. Within the proposed method, the LMS adaptive filter algorithm was used to remove the noise of the resulting predicted course. In order to verify the method, two long-term experiments were performed, specifically from February 1 to February 28, 2015, from June 1 to June 28, 2015 and from February 8 to February 14, 2015. For the best results of the trained ANN BRM within the prediction of CO2, the correlation coefficient R for the proposed method was up to 92%. The verification of the proposed method confirmed the possibility to use the presence of people of the monitored IAB premises for monitoring. The designed indirect method of CO2 prediction has potential for reducing the investment and operating costs of the IAB in relation to the reduction of the number of implemented sensors in the IAB within the process of management of operational and technical functions in the IAB. The article also describes the design and implementation of the FEIVISUAL visualization application for mobile devices, which monitors the technological processes in the IAB. This application is optimized for Android devices and is platform independent. The application requires implementation of an application server that communicates with the data server and the application developed. The data of the application developed is obtained from the data storage of the PI System via a PI Web REST API (Application Programming Integration) client.Web of Science8art. no. 2

Design and Application of Distributed Economic Model Predictive Control for Large-Scale Building Temperature Regulation

Although recent research has suggested model predictive control as a promising solution for minimizing energy costs of commercial buildings, advanced control systems have not been widely deployed in practice. Large-scale implementations, including industrial complexes and university campuses, may contain thousands of air handler units each serving a multiplicity of zones. A single centralized control system for these applications is not desirable. In this paper, we propose a distributed control system to economically optimize temperature regulation for large-scale commercial building applications. The decomposition strategy considers the complexities of thermal energy storage, zone interactions, and chiller plant equipment while remaining computationally tractable. One of the primary benefits of the proposed formulation is that the low-level airside problem can be decoupled and solved in a distributed manner; hence, it can be easily extended to handle large applications. Peak demand charges, a major source of coupling, are included. The interactions of the airside system with the waterside system are also considered, including discrete decisions, such as turning chillers on and off. To deploy such a control scheme, a system model is required. Since using physical knowledge about building models can greatly reduce the number of parameters that must be identified, grey-box models are recommended to reduce the length of expensive identification testing. We demonstrate the effectiveness of this control system architecture and identification procedure via simulation studies

Spaces In, Outside Of, and Between

My practice involves leveraging analog and digital techniques from many disciplines, but especially graphic design, craft/material studies, and sculpture. I embrace reproduction and repetition as both tools and means to visualize what is often unseen, and to recognize not only what is made, but what supports making— from the straightforward and immediate to the complex and conceptual

Conflicts and Convergences of Preservation, Modernism and Sustainability in the Richards Medical Laboratories Renovation

Louis Kahn’s Richards Medical Laboratories underwent a two-phased renovation (2013-2015 & 2019), driven by University of Pennsylvania’s Century Bond program’s energy-saving objective. A national historic landmark, a Modernism masterpiece and a heavy equipment-bearing facility, the building had to meet satisfactory results for all the criteria as strict as possible: preservation, Modernism and sustainability. Every Mid-Century rehabilitation project accommodates similar requirements, Richards’ renovation provides a stringent example for others to reference upon. Thus a prudent review on what guidelines suggested, architects and engineers proposed and executed, post-renovation data and findings yielded, are crucial in forming a holistic apprehension. In Richards renovation, an upgrade on HVAC systems, an evaluation on historic monolithic single-pane glass, and eventually repurposing the building are the major renovation strategies. All of the strategies are for sustainability goals but also had to address specific design intention and significance Kahn left. In parallel to Richards, two comparable case studies, Penn’s Evans Dental Building renovation, also a Century Bond program project, and Yale University Art Gallery, also rehabilitated Kahn’s single-pane glass, provided comprehensive information for complementary purposes. The information induced could serve as an epitome for projects of similar context and restrictions